From c437aad8f4a615476f9d10bb998bf2b285742921 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 25 Jul 2024 17:32:26 +0800
Subject: [PATCH 01/67] =?UTF-8?q?=E6=8F=90=E7=82=BC=E5=87=BD=E6=95=B0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../mindspore/dump/hook_cell/api_registry.py  | 93 +++++++++++++++++++
 .../msprobe/mindspore/service.py              |  2 +
 2 files changed, 95 insertions(+)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 5508416fde0..2f032d93d7c 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -13,12 +13,18 @@
 # limitations under the License.
 # ============================================================================
 
+import os
+import functools
 import mindspore as ms
+from mindspore.common.tensor import Tensor
+from msprobe.core.common.utils import Const
+from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs
 from msprobe.mindspore.dump.hook_cell.wrap_functional import get_functional_ops, setup_hooks, \
     HOOKFunctionalOP, HOOKMintOP, HOOKMintNNFunctionalOP
 from msprobe.mindspore.dump.hook_cell.wrap_tensor import get_tensor_ops, wrap_tensor_ops_and_bind, HOOKTensor
 from msprobe.core.common.utils import Const
 
+PRIMITIVE_PREFIX = "Primitive"
 
 class ApiRegistry:
     def __init__(self):
@@ -35,6 +41,7 @@ class ApiRegistry:
         self.norm_inner_ops_hook_attr = {}
 
         self.norm_inner_ops = ["norm", "square", "sqrt", "is_complex"]
+        self.primitive_counters = {}
 
     @staticmethod
     def store_ori_attr(ori_api_group, api_list, api_ori_attr):
@@ -100,5 +107,91 @@ class ApiRegistry:
             if attr_name.startswith(Const.ATTR_NAME_PREFIX):
                 self.mint_func_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintNNFunctionalOP, attr_name)    
 
+    def wrap_primitive(self, origin_func, primitive_name, service_instance):
+        primitive_instance = self
+        def func(self, *args, **kwargs):
+            if primitive_name not in primitive_instance.primitive_counters:
+                primitive_instance.primitive_counters[primitive_name] = 0
+            else:
+                primitive_instance.primitive_counters[primitive_name] += 1
+
+            current_count = primitive_instance.primitive_counters[primitive_name]
+            updated_primitive_name = f"{PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+            captured_grads_input = []
+            captured_grads_output = []
+
+            def input_backward_hook(grad):
+                print(f"Grad input length: {len(grad)}")
+                print("Captured input grad:", grad)
+                captured_grads_input.append(grad)
+                backward_primitive_name = updated_primitive_name + Const.BACKWARD
+                new_module_input_output = ModuleBackwardInputsOutputs(
+                    grad_input=tuple(captured_grads_input),
+                    grad_output=tuple(captured_grads_output) if captured_grads_output else None
+                )
+                service_instance.data_collector.backward_data_collect(
+                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
+                )
+#1未考虑多输出场景
+# 如果时多grad呢
+# 3 输出的序号问题
+            def output_backward_hook(grad):
+                captured_grads_output.append(grad)
+                backward_primitive_name = primitive_name + Const.BACKWARD
+                new_module_input_output = ModuleBackwardInputsOutputs(
+                    grad_input=None,
+                    grad_output=tuple(captured_grads_output)
+                )
+                service_instance.data_collector.backward_data_collect(
+                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
+                )
+
+            if not service_instance.switch:
+                return origin_func(*args, **kwargs)
+
+            print(f"Entering {updated_primitive_name} hook, number of args: {len(args)}, name: {self.name}")
+            hooked_inputs = []
+
+            # for idx, arg in enumerate(args):
+            #     if isinstance(arg, Tensor):
+            #         arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+            #         hooked_inputs.append(arg_hooked)
+            #     else:
+            #         hooked_inputs.append(arg)
+
+            out = origin_func(*arg, **kwargs)
+            forward_primitive_name = updated_primitive_name + Const.FORWARD
+
+            if service_instance.data_collector:
+                module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
+                service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
+                if service_instance.data_collector.if_return_forward_new_output():
+                    out = service_instance.data_collector.get_forward_new_output()
+
+            if isinstance(out, Tensor):
+                out = ops.HookBackward(output_backward_hook)(out)
+            elif isinstance(out, tuple):
+                hooked_outputs = []
+                for tensor in out:
+                    if isinstance(tensor, Tensor):
+                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+                    else:
+                        hooked_outputs.append(tensor)
+                out = tuple(hooked_outputs)
+
+            return out
+
+        return func
+
+    def register_hooks(self, service_instance):
+        primitive_set = set()
+        for name, cell in self.model.cells_and_names():
+            for pname, primitive in cell._primitives.items():
+                primitive_set.add((pname, primitive))
+
+        for pname, primitive in primitive_set:
+            print("primitive name is", pname)
+            NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname, service_instance)})
+            primitive.__class__ = NewPrimitive
 
 api_register = ApiRegistry()
diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index e8aa34dc4fe..8d802e14d06 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -136,3 +136,5 @@ class Service:
         if self.config.level == "L1":
             api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
             api_register.api_set_hook_func()
+            if self.model:
+                api_register.register_hooks(self)
-- 
Gitee


From e39e7175accd5ff882432572c889f1336a84cb8e Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 25 Jul 2024 20:28:17 +0800
Subject: [PATCH 02/67] =?UTF-8?q?=E8=B7=91=E9=80=9Aprimitive?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../mindspore/debugger/precision_debugger.py  |  4 +--
 .../mindspore/dump/hook_cell/api_registry.py  | 35 ++++---------------
 2 files changed, 8 insertions(+), 31 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py
index 30f7162ff5c..28161c66855 100644
--- a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py
+++ b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py
@@ -27,12 +27,12 @@ class PrecisionDebugger:
         self.service = Service(self.config)
 
     @classmethod
-    def start(cls):
+    def start(cls, target=None):
         instance = cls._instance
         if not instance:
             raise Exception("No instance of PrecisionDebugger found.")
         if ms.get_context("mode") == ms.PYNATIVE_MODE and instance.config.level_ori == "L1":
-            instance.service.start()
+            instance.service.start(target)
         else:
             handler = TaskHandlerFactory.create(instance.config)
             handler.handle()
diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 2f032d93d7c..03fd47e8fb1 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -16,6 +16,7 @@
 import os
 import functools
 import mindspore as ms
+from mindspore import ops
 from mindspore.common.tensor import Tensor
 from msprobe.core.common.utils import Const
 from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs
@@ -117,27 +118,11 @@ class ApiRegistry:
 
             current_count = primitive_instance.primitive_counters[primitive_name]
             updated_primitive_name = f"{PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
-            captured_grads_input = []
             captured_grads_output = []
 
-            def input_backward_hook(grad):
-                print(f"Grad input length: {len(grad)}")
-                print("Captured input grad:", grad)
-                captured_grads_input.append(grad)
-                backward_primitive_name = updated_primitive_name + Const.BACKWARD
-                new_module_input_output = ModuleBackwardInputsOutputs(
-                    grad_input=tuple(captured_grads_input),
-                    grad_output=tuple(captured_grads_output) if captured_grads_output else None
-                )
-                service_instance.data_collector.backward_data_collect(
-                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
-                )
-#1未考虑多输出场景
-# 如果时多grad呢
-# 3 输出的序号问题
             def output_backward_hook(grad):
                 captured_grads_output.append(grad)
-                backward_primitive_name = primitive_name + Const.BACKWARD
+                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                 new_module_input_output = ModuleBackwardInputsOutputs(
                     grad_input=None,
                     grad_output=tuple(captured_grads_output)
@@ -149,21 +134,13 @@ class ApiRegistry:
             if not service_instance.switch:
                 return origin_func(*args, **kwargs)
 
-            print(f"Entering {updated_primitive_name} hook, number of args: {len(args)}, name: {self.name}")
             hooked_inputs = []
 
-            # for idx, arg in enumerate(args):
-            #     if isinstance(arg, Tensor):
-            #         arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-            #         hooked_inputs.append(arg_hooked)
-            #     else:
-            #         hooked_inputs.append(arg)
-
-            out = origin_func(*arg, **kwargs)
-            forward_primitive_name = updated_primitive_name + Const.FORWARD
+            out = origin_func(*args, **kwargs)
+            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
 
             if service_instance.data_collector:
-                module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
+                module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
                 service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
                 if service_instance.data_collector.if_return_forward_new_output():
                     out = service_instance.data_collector.get_forward_new_output()
@@ -185,7 +162,7 @@ class ApiRegistry:
 
     def register_hooks(self, service_instance):
         primitive_set = set()
-        for name, cell in self.model.cells_and_names():
+        for name, cell in service_instance.model.cells_and_names():
             for pname, primitive in cell._primitives.items():
                 primitive_set.add((pname, primitive))
 
-- 
Gitee


From 6aaed48a6b8c22220b2a2e1e1878b8e2346aa185 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 11:11:06 +0800
Subject: [PATCH 03/67] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E8=BE=93=E5=85=A5?=
 =?UTF-8?q?=E6=A2=AF=E5=BA=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../core/data_dump/data_processor/base.py     | 24 +++++++++++++++++++
 .../mindspore/dump/hook_cell/api_registry.py  | 14 +++++++++++
 2 files changed, 38 insertions(+)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
index 5d901291973..4bcf6418197 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
@@ -237,6 +237,30 @@ class BaseDataProcessor:
 
         return api_info_struct
 
+    def analyze_backward_input(self, name, module, module_input_output: ModuleBackwardInputsOutputs):
+        """
+        Analyze and save backward input gradients.
+        """
+        api_info_struct = {}
+        if self.is_dump_for_data_mode(Const.BACKWARD, Const.INPUT):
+            api_info_struct[name] = {}
+            self.api_data_category = Const.INPUT
+            output_info_list = self.analyze_element(module_input_output.grad_output_tuple)
+            api_info_struct[name][Const.GRAD_OUTPUT] = output_info_list
+        return api_info_struct
+
+    def analyze_backward_output(self, name, module, module_input_output: ModuleBackwardInputsOutputs):
+        """
+        Analyze and save backward output gradients.
+        """
+        api_info_struct = {}
+        if self.is_dump_for_data_mode(Const.BACKWARD, Const.OUTPUT):
+            api_info_struct[name] = {}
+            self.api_data_category = Const.OUTPUT
+            input_info_list = self.analyze_element(module_input_output.grad_input_tuple)
+            api_info_struct[name][Const.GRAD_INPUT] = input_info_list
+        return api_info_struct
+
     def get_save_file_path(self, suffix):
         file_format = Const.PT_SUFFIX if self.config.framework == Const.PT_FRAMEWORK else Const.NUMPY_SUFFIX      
         dump_data_name = (self.current_api_or_module_name + Const.SEP + self.api_data_category + Const.SEP +
diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 03fd47e8fb1..77e740011f1 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -118,8 +118,22 @@ class ApiRegistry:
 
             current_count = primitive_instance.primitive_counters[primitive_name]
             updated_primitive_name = f"{PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+            captured_grads_input = []
             captured_grads_output = []
 
+            def input_backward_hook(grad):
+                print(f"Grad input length: {len(grad)}")
+                print("Captured input grad:", grad)
+                captured_grads_input.append(grad)
+                backward_primitive_name = updated_primitive_name + Const.BACKWARD
+                new_module_input_output = ModuleBackwardInputsOutputs(
+                    grad_input=tuple(captured_grads_input),
+                    grad_output=tuple(captured_grads_output) if captured_grads_output else None
+                )
+                service_instance.data_collector.backward_data_collect(
+                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
+                )
+
             def output_backward_hook(grad):
                 captured_grads_output.append(grad)
                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-- 
Gitee


From da8338f1f906f677fb6d79d0167300fdaef43593 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 11:31:03 +0800
Subject: [PATCH 04/67] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E8=BE=93=E5=85=A5hook?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py   | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 77e740011f1..b28a07d94c8 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -149,8 +149,14 @@ class ApiRegistry:
                 return origin_func(*args, **kwargs)
 
             hooked_inputs = []
-
-            out = origin_func(*args, **kwargs)
+            for idx, arg in enumerate(args):
+                if isinstance(arg, Tensor):
+                    arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+                    hooked_inputs.append(arg_hooked)
+                else:
+                    hooked_inputs.append(arg)
+
+            out = origin_func(*hooked_inputs, **kwargs)
             forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
 
             if service_instance.data_collector:
-- 
Gitee


From 085085d7ae18c8a0ce6060558754868cab0a404c Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 14:40:15 +0800
Subject: [PATCH 05/67] =?UTF-8?q?=E6=8B=86=E5=88=86=E5=87=BD=E6=95=B0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../msprobe/core/data_dump/data_collector.py     | 16 ++++++++++++++++
 .../mindspore/dump/hook_cell/api_registry.py     |  4 ++--
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py
index 800a2b81c2f..a537fa3d06a 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py
@@ -100,6 +100,22 @@ class DataCollector:
         data_info = self.data_processor.analyze_backward(name, module, module_input_output)
         self.handle_data(name, data_info)
 
+    def backward_input_data_collect(self, name, module, pid, module_input_output):
+        self.update_construct(name)
+        if not self.check_scope_and_pid(self.scope, name, pid):
+            return
+
+        data_info = self.data_processor.analyze_backward_input(name, module, module_input_output)
+        self.handle_data(name, data_info)
+
+    def backward_output_data_collect(self, name, module, pid, module_input_output):
+        self.update_construct(name)
+        if not self.check_scope_and_pid(self.scope, name, pid):
+            return 
+
+        data_info = self.data_processor.analyze_backward_output(name, module, module_input_output)
+        self.handle_data(name, data_info)
+
     def update_construct(self, name):
         if self.config.level not in DataCollector.level_without_construct:
             self.data_writer.update_construct({name: self.module_processor.api_parent_node})
diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index b28a07d94c8..4a790a5cbb8 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -130,7 +130,7 @@ class ApiRegistry:
                     grad_input=tuple(captured_grads_input),
                     grad_output=tuple(captured_grads_output) if captured_grads_output else None
                 )
-                service_instance.data_collector.backward_data_collect(
+                service_instance.data_collector.backward_input_data_collect(
                     backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
                 )
 
@@ -141,7 +141,7 @@ class ApiRegistry:
                     grad_input=None,
                     grad_output=tuple(captured_grads_output)
                 )
-                service_instance.data_collector.backward_data_collect(
+                service_instance.data_collector.backward_output_data_collect(
                     backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
                 )
 
-- 
Gitee


From 88ce243a0e483a165e42e010856a88afbbb15220 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 14:43:28 +0800
Subject: [PATCH 06/67] =?UTF-8?q?=E6=94=B9=E5=90=8D?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 4a790a5cbb8..e50502cc991 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -125,7 +125,7 @@ class ApiRegistry:
                 print(f"Grad input length: {len(grad)}")
                 print("Captured input grad:", grad)
                 captured_grads_input.append(grad)
-                backward_primitive_name = updated_primitive_name + Const.BACKWARD
+                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                 new_module_input_output = ModuleBackwardInputsOutputs(
                     grad_input=tuple(captured_grads_input),
                     grad_output=tuple(captured_grads_output) if captured_grads_output else None
-- 
Gitee


From b4ea092df299862c74880a870d432e028b56b4b0 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 14:58:46 +0800
Subject: [PATCH 07/67] =?UTF-8?q?=E5=B0=9D=E8=AF=95=E5=8F=AA=E8=BE=93?=
 =?UTF-8?q?=E5=85=A5/=E8=BE=93=E5=87=BA?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../core/data_dump/data_processor/base.py        | 15 +++++++++++++++
 .../mindspore/dump/hook_cell/api_registry.py     | 16 ++++++----------
 2 files changed, 21 insertions(+), 10 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
index 4bcf6418197..75238663f04 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
@@ -39,7 +39,22 @@ class ModuleBackwardInputsOutputs:
     @property
     def grad_output_tuple(self):
         return convert_tuple(self.grad_output)    
+
+@dataclass
+class ModuleBackwardInputs:
+    grad_input: Optional[Tuple]
+
+    @property
+    def grad_input_tuple(self):
+        return convert_tuple(self.grad_input)
+ 
+@dataclass
+class ModuleBackwardOutputs:
+    grad_output: Optional[Tuple]
         
+    @property
+    def grad_output_tuple(self):
+        return convert_tuple(self.grad_output)    
 
 class TensorStatInfo:
     def __init__(self, max_val=None, min_val=None, mean_val=None, norm_val=None):
diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index e50502cc991..a9f01ef5fac 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -122,27 +122,23 @@ class ApiRegistry:
             captured_grads_output = []
 
             def input_backward_hook(grad):
-                print(f"Grad input length: {len(grad)}")
-                print("Captured input grad:", grad)
+                # print(f"Grad input length: {len(grad)}")
+                # print("Captured input grad:", grad)
                 captured_grads_input.append(grad)
                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardInputsOutputs(
-                    grad_input=tuple(captured_grads_input),
-                    grad_output=tuple(captured_grads_output) if captured_grads_output else None
-                )
+                new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
                 service_instance.data_collector.backward_input_data_collect(
-                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
+                    backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
             def output_backward_hook(grad):
                 captured_grads_output.append(grad)
                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardInputsOutputs(
-                    grad_input=None,
+                new_module_input_output = ModuleBackwardOutputs(
                     grad_output=tuple(captured_grads_output)
                 )
                 service_instance.data_collector.backward_output_data_collect(
-                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
+                    backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
             if not service_instance.switch:
-- 
Gitee


From 477d07441c832f0cd376ba1e65ca54c83c86c5a7 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 15:00:26 +0800
Subject: [PATCH 08/67] Update api_registry.py

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index a9f01ef5fac..16528f76d70 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -19,7 +19,7 @@ import mindspore as ms
 from mindspore import ops
 from mindspore.common.tensor import Tensor
 from msprobe.core.common.utils import Const
-from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs
+from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs, ModuleForwardInputs, ModuleForwardOutputs
 from msprobe.mindspore.dump.hook_cell.wrap_functional import get_functional_ops, setup_hooks, \
     HOOKFunctionalOP, HOOKMintOP, HOOKMintNNFunctionalOP
 from msprobe.mindspore.dump.hook_cell.wrap_tensor import get_tensor_ops, wrap_tensor_ops_and_bind, HOOKTensor
-- 
Gitee


From 149f349cc60ad3caf096bbd1a2c297f458e8771a Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 15:01:49 +0800
Subject: [PATCH 09/67] Update api_registry.py

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 16528f76d70..a3f9ec35d76 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -19,7 +19,7 @@ import mindspore as ms
 from mindspore import ops
 from mindspore.common.tensor import Tensor
 from msprobe.core.common.utils import Const
-from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs, ModuleForwardInputs, ModuleForwardOutputs
+from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs, ModuleBackwardInputs, ModuleBackwardOutputs
 from msprobe.mindspore.dump.hook_cell.wrap_functional import get_functional_ops, setup_hooks, \
     HOOKFunctionalOP, HOOKMintOP, HOOKMintNNFunctionalOP
 from msprobe.mindspore.dump.hook_cell.wrap_tensor import get_tensor_ops, wrap_tensor_ops_and_bind, HOOKTensor
-- 
Gitee


From 4dead5fbefa7f68b436b0df1f00e4fd778ed971c Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 15:07:22 +0800
Subject: [PATCH 10/67] Update base.py

---
 .../msprobe/core/data_dump/data_processor/base.py         | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
index 75238663f04..d96d107ac5d 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
@@ -260,8 +260,8 @@ class BaseDataProcessor:
         if self.is_dump_for_data_mode(Const.BACKWARD, Const.INPUT):
             api_info_struct[name] = {}
             self.api_data_category = Const.INPUT
-            output_info_list = self.analyze_element(module_input_output.grad_output_tuple)
-            api_info_struct[name][Const.GRAD_OUTPUT] = output_info_list
+            output_info_list = self.analyze_element(module_input_output.grad_input_tuple)
+            api_info_struct[name][Const.GRAD_INPUT] = output_info_list
         return api_info_struct
 
     def analyze_backward_output(self, name, module, module_input_output: ModuleBackwardInputsOutputs):
@@ -272,8 +272,8 @@ class BaseDataProcessor:
         if self.is_dump_for_data_mode(Const.BACKWARD, Const.OUTPUT):
             api_info_struct[name] = {}
             self.api_data_category = Const.OUTPUT
-            input_info_list = self.analyze_element(module_input_output.grad_input_tuple)
-            api_info_struct[name][Const.GRAD_INPUT] = input_info_list
+            output_info_list = self.analyze_element(module_input_output.grad_output_tuple)
+            api_info_struct[name][Const.GRAD_OUTPUT] = output_info_list
         return api_info_struct
 
     def get_save_file_path(self, suffix):
-- 
Gitee


From d8fce96489f22adab19c9c7c93a5c15f3a05a40b Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 16:15:37 +0800
Subject: [PATCH 11/67] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E6=AD=A3=E5=90=91?=
 =?UTF-8?q?=E5=8F=8D=E5=90=91?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../msprobe/core/data_dump/data_processor/base.py           | 6 +++---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py        | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
index d96d107ac5d..e725d362e8c 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
@@ -261,7 +261,7 @@ class BaseDataProcessor:
             api_info_struct[name] = {}
             self.api_data_category = Const.INPUT
             output_info_list = self.analyze_element(module_input_output.grad_input_tuple)
-            api_info_struct[name][Const.GRAD_INPUT] = output_info_list
+            api_info_struct[name][Const.GRAD_OUTPUT] = output_info_list
         return api_info_struct
 
     def analyze_backward_output(self, name, module, module_input_output: ModuleBackwardInputsOutputs):
@@ -272,8 +272,8 @@ class BaseDataProcessor:
         if self.is_dump_for_data_mode(Const.BACKWARD, Const.OUTPUT):
             api_info_struct[name] = {}
             self.api_data_category = Const.OUTPUT
-            output_info_list = self.analyze_element(module_input_output.grad_output_tuple)
-            api_info_struct[name][Const.GRAD_OUTPUT] = output_info_list
+            input_info_list = self.analyze_element(module_input_output.grad_output_tuple)
+            api_info_struct[name][Const.GRAD_INPUT] = input_info_list
         return api_info_struct
 
     def get_save_file_path(self, suffix):
diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index a3f9ec35d76..97ab5e3285c 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -127,7 +127,7 @@ class ApiRegistry:
                 captured_grads_input.append(grad)
                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                 new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                service_instance.data_collector.backward_input_data_collect(
+                service_instance.data_collector.backward_output_data_collect(
                     backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
@@ -137,7 +137,7 @@ class ApiRegistry:
                 new_module_input_output = ModuleBackwardOutputs(
                     grad_output=tuple(captured_grads_output)
                 )
-                service_instance.data_collector.backward_output_data_collect(
+                service_instance.data_collector.backward_input_data_collect(
                     backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
-- 
Gitee


From c7cb33af18ed7c88a18d7dfc8905c0a4c9185c38 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 16:23:26 +0800
Subject: [PATCH 12/67] Update api_registry.py

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 97ab5e3285c..a3f9ec35d76 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -127,7 +127,7 @@ class ApiRegistry:
                 captured_grads_input.append(grad)
                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                 new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                service_instance.data_collector.backward_output_data_collect(
+                service_instance.data_collector.backward_input_data_collect(
                     backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
@@ -137,7 +137,7 @@ class ApiRegistry:
                 new_module_input_output = ModuleBackwardOutputs(
                     grad_output=tuple(captured_grads_output)
                 )
-                service_instance.data_collector.backward_input_data_collect(
+                service_instance.data_collector.backward_output_data_collect(
                     backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
-- 
Gitee


From f1a616bbb1c5f9ab892adfce80cb5961d37975c3 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 16:33:20 +0800
Subject: [PATCH 13/67] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E5=B8=B8=E9=87=8F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 debug/accuracy_tools/msprobe/core/common/const.py             | 1 +
 .../msprobe/mindspore/dump/hook_cell/api_registry.py          | 4 +---
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/common/const.py b/debug/accuracy_tools/msprobe/core/common/const.py
index df82455a676..81e21e3d2ac 100644
--- a/debug/accuracy_tools/msprobe/core/common/const.py
+++ b/debug/accuracy_tools/msprobe/core/common/const.py
@@ -16,6 +16,7 @@ class Const:
     OFF = 'OFF'
     BACKWARD = 'backward'
     FORWARD = 'forward'
+    PRIMITIVE_PREFIX = 'Primitive'
     DEFAULT_LIST = []
     DEFAULT_PATH = './'
     WHITE_LIST = 'white_list'
diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index a3f9ec35d76..01bce6b5259 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -117,13 +117,11 @@ class ApiRegistry:
                 primitive_instance.primitive_counters[primitive_name] += 1
 
             current_count = primitive_instance.primitive_counters[primitive_name]
-            updated_primitive_name = f"{PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
             captured_grads_input = []
             captured_grads_output = []
 
             def input_backward_hook(grad):
-                # print(f"Grad input length: {len(grad)}")
-                # print("Captured input grad:", grad)
                 captured_grads_input.append(grad)
                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                 new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-- 
Gitee


From 1bbc79438375ee7a53942a983a1434c9b6834962 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 16:37:34 +0800
Subject: [PATCH 14/67] Update api_registry.py

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py     | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 01bce6b5259..3ee7d9e0e46 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -109,14 +109,13 @@ class ApiRegistry:
                 self.mint_func_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintNNFunctionalOP, attr_name)    
 
     def wrap_primitive(self, origin_func, primitive_name, service_instance):
-        primitive_instance = self
         def func(self, *args, **kwargs):
-            if primitive_name not in primitive_instance.primitive_counters:
-                primitive_instance.primitive_counters[primitive_name] = 0
+            if primitive_name not in self.primitive_counters:
+                self.primitive_counters[primitive_name] = 0
             else:
-                primitive_instance.primitive_counters[primitive_name] += 1
+                self.primitive_counters[primitive_name] += 1
 
-            current_count = primitive_instance.primitive_counters[primitive_name]
+            current_count = self.primitive_counters[primitive_name]
             updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
             captured_grads_input = []
             captured_grads_output = []
-- 
Gitee


From 7df3f2edf624c06cb9f9ae1e8000347467aecb60 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 16:39:41 +0800
Subject: [PATCH 15/67] Update api_registry.py

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py     | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 3ee7d9e0e46..01bce6b5259 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -109,13 +109,14 @@ class ApiRegistry:
                 self.mint_func_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintNNFunctionalOP, attr_name)    
 
     def wrap_primitive(self, origin_func, primitive_name, service_instance):
+        primitive_instance = self
         def func(self, *args, **kwargs):
-            if primitive_name not in self.primitive_counters:
-                self.primitive_counters[primitive_name] = 0
+            if primitive_name not in primitive_instance.primitive_counters:
+                primitive_instance.primitive_counters[primitive_name] = 0
             else:
-                self.primitive_counters[primitive_name] += 1
+                primitive_instance.primitive_counters[primitive_name] += 1
 
-            current_count = self.primitive_counters[primitive_name]
+            current_count = primitive_instance.primitive_counters[primitive_name]
             updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
             captured_grads_input = []
             captured_grads_output = []
-- 
Gitee


From 95f07448ddd07ccbb18f724b104bc78b0e996e96 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 17:20:06 +0800
Subject: [PATCH 16/67] =?UTF-8?q?=E8=BF=81=E7=A7=BB=E5=87=BD=E6=95=B0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../mindspore/dump/hook_cell/api_registry.py  |  2 -
 .../msprobe/mindspore/service.py              | 78 ++++++++++++++++++-
 2 files changed, 77 insertions(+), 3 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 01bce6b5259..63a02cefef2 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -25,7 +25,6 @@ from msprobe.mindspore.dump.hook_cell.wrap_functional import get_functional_ops,
 from msprobe.mindspore.dump.hook_cell.wrap_tensor import get_tensor_ops, wrap_tensor_ops_and_bind, HOOKTensor
 from msprobe.core.common.utils import Const
 
-PRIMITIVE_PREFIX = "Primitive"
 
 class ApiRegistry:
     def __init__(self):
@@ -181,7 +180,6 @@ class ApiRegistry:
                 primitive_set.add((pname, primitive))
 
         for pname, primitive in primitive_set:
-            print("primitive name is", pname)
             NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname, service_instance)})
             primitive.__class__ = NewPrimitive
 
diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 8d802e14d06..e7039a3bdb3 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -39,6 +39,7 @@ class Service:
         self.first_start = True
         self.current_rank = None
         self.dump_iter_dir = None
+        self.primitive_counters = {}
 
     def build_hook(self, module_type, name):
         def forward_hook(api_or_module_name, module, input, output):
@@ -137,4 +138,79 @@ class Service:
             api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
             api_register.api_set_hook_func()
             if self.model:
-                api_register.register_hooks(self)
+                register_hooks(self)
+    
+    def register_hooks(self):
+        primitive_set = set()
+        for name, cell in self.model.cells_and_names():
+            for pname, primitive in cell._primitives.items():
+                primitive_set.add((pname, primitive))
+
+        for pname, primitive in primitive_set:
+            NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname)})
+            primitive.__class__ = NewPrimitive
+
+    def wrap_primitive(self, origin_func, primitive_name):
+        def func(self, *args, **kwargs):
+            if primitive_name not in self.primitive_counters:
+                self.primitive_counters[primitive_name] = 0
+            else:
+                self.primitive_counters[primitive_name] += 1
+
+            current_count = self.primitive_counters[primitive_name]
+            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+            captured_grads_input = []
+            captured_grads_output = []
+
+            def input_backward_hook(grad):
+                captured_grads_input.append(grad)
+                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
+                self.data_collector.backward_input_data_collect(
+                    backward_primitive_name, self, os.getpid(), new_module_input_output
+                )
+
+            def output_backward_hook(grad):
+                captured_grads_output.append(grad)
+                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                new_module_input_output = ModuleBackwardOutputs(
+                    grad_output=tuple(captured_grads_output)
+                )
+                self.data_collector.backward_output_data_collect(
+                    backward_primitive_name, self, os.getpid(), new_module_input_output
+                )
+
+            if not self.switch:
+                return origin_func(*args, **kwargs)
+
+            hooked_inputs = []
+            for idx, arg in enumerate(args):
+                if isinstance(arg, Tensor):
+                    arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+                    hooked_inputs.append(arg_hooked)
+                else:
+                    hooked_inputs.append(arg)
+
+            out = origin_func(*hooked_inputs, **kwargs)
+            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
+
+            if self.data_collector:
+                module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
+                self.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
+                if self.data_collector.if_return_forward_new_output():
+                    out = self.data_collector.get_forward_new_output()
+
+            if isinstance(out, Tensor):
+                out = ops.HookBackward(output_backward_hook)(out)
+            elif isinstance(out, tuple):
+                hooked_outputs = []
+                for tensor in out:
+                    if isinstance(tensor, Tensor):
+                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+                    else:
+                        hooked_outputs.append(tensor)
+                out = tuple(hooked_outputs)
+
+            return out
+
+        return func
-- 
Gitee


From fac1c0cc2fe68b7d40c67cf4a2305feffb5a3243 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 17:21:39 +0800
Subject: [PATCH 17/67] Update api_registry.py

---
 .../mindspore/dump/hook_cell/api_registry.py  | 150 +++++++++---------
 1 file changed, 75 insertions(+), 75 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 63a02cefef2..4c2f81cb905 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -107,80 +107,80 @@ class ApiRegistry:
             if attr_name.startswith(Const.ATTR_NAME_PREFIX):
                 self.mint_func_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintNNFunctionalOP, attr_name)    
 
-    def wrap_primitive(self, origin_func, primitive_name, service_instance):
-        primitive_instance = self
-        def func(self, *args, **kwargs):
-            if primitive_name not in primitive_instance.primitive_counters:
-                primitive_instance.primitive_counters[primitive_name] = 0
-            else:
-                primitive_instance.primitive_counters[primitive_name] += 1
-
-            current_count = primitive_instance.primitive_counters[primitive_name]
-            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
-            captured_grads_input = []
-            captured_grads_output = []
-
-            def input_backward_hook(grad):
-                captured_grads_input.append(grad)
-                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                service_instance.data_collector.backward_input_data_collect(
-                    backward_primitive_name, self, os.getpid(), new_module_input_output
-                )
-
-            def output_backward_hook(grad):
-                captured_grads_output.append(grad)
-                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardOutputs(
-                    grad_output=tuple(captured_grads_output)
-                )
-                service_instance.data_collector.backward_output_data_collect(
-                    backward_primitive_name, self, os.getpid(), new_module_input_output
-                )
-
-            if not service_instance.switch:
-                return origin_func(*args, **kwargs)
-
-            hooked_inputs = []
-            for idx, arg in enumerate(args):
-                if isinstance(arg, Tensor):
-                    arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-                    hooked_inputs.append(arg_hooked)
-                else:
-                    hooked_inputs.append(arg)
-
-            out = origin_func(*hooked_inputs, **kwargs)
-            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
-
-            if service_instance.data_collector:
-                module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
-                service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
-                if service_instance.data_collector.if_return_forward_new_output():
-                    out = service_instance.data_collector.get_forward_new_output()
-
-            if isinstance(out, Tensor):
-                out = ops.HookBackward(output_backward_hook)(out)
-            elif isinstance(out, tuple):
-                hooked_outputs = []
-                for tensor in out:
-                    if isinstance(tensor, Tensor):
-                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
-                    else:
-                        hooked_outputs.append(tensor)
-                out = tuple(hooked_outputs)
-
-            return out
-
-        return func
-
-    def register_hooks(self, service_instance):
-        primitive_set = set()
-        for name, cell in service_instance.model.cells_and_names():
-            for pname, primitive in cell._primitives.items():
-                primitive_set.add((pname, primitive))
-
-        for pname, primitive in primitive_set:
-            NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname, service_instance)})
-            primitive.__class__ = NewPrimitive
+    # def wrap_primitive(self, origin_func, primitive_name, service_instance):
+    #     primitive_instance = self
+    #     def func(self, *args, **kwargs):
+    #         if primitive_name not in primitive_instance.primitive_counters:
+    #             primitive_instance.primitive_counters[primitive_name] = 0
+    #         else:
+    #             primitive_instance.primitive_counters[primitive_name] += 1
+
+    #         current_count = primitive_instance.primitive_counters[primitive_name]
+    #         updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+    #         captured_grads_input = []
+    #         captured_grads_output = []
+
+    #         def input_backward_hook(grad):
+    #             captured_grads_input.append(grad)
+    #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+    #             new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
+    #             service_instance.data_collector.backward_input_data_collect(
+    #                 backward_primitive_name, self, os.getpid(), new_module_input_output
+    #             )
+
+    #         def output_backward_hook(grad):
+    #             captured_grads_output.append(grad)
+    #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+    #             new_module_input_output = ModuleBackwardOutputs(
+    #                 grad_output=tuple(captured_grads_output)
+    #             )
+    #             service_instance.data_collector.backward_output_data_collect(
+    #                 backward_primitive_name, self, os.getpid(), new_module_input_output
+    #             )
+
+    #         if not service_instance.switch:
+    #             return origin_func(*args, **kwargs)
+
+    #         hooked_inputs = []
+    #         for idx, arg in enumerate(args):
+    #             if isinstance(arg, Tensor):
+    #                 arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+    #                 hooked_inputs.append(arg_hooked)
+    #             else:
+    #                 hooked_inputs.append(arg)
+
+    #         out = origin_func(*hooked_inputs, **kwargs)
+    #         forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
+
+    #         if service_instance.data_collector:
+    #             module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
+    #             service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
+    #             if service_instance.data_collector.if_return_forward_new_output():
+    #                 out = service_instance.data_collector.get_forward_new_output()
+
+    #         if isinstance(out, Tensor):
+    #             out = ops.HookBackward(output_backward_hook)(out)
+    #         elif isinstance(out, tuple):
+    #             hooked_outputs = []
+    #             for tensor in out:
+    #                 if isinstance(tensor, Tensor):
+    #                     hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+    #                 else:
+    #                     hooked_outputs.append(tensor)
+    #             out = tuple(hooked_outputs)
+
+    #         return out
+
+    #     return func
+
+    # def register_hooks(self, service_instance):
+    #     primitive_set = set()
+    #     for name, cell in service_instance.model.cells_and_names():
+    #         for pname, primitive in cell._primitives.items():
+    #             primitive_set.add((pname, primitive))
+
+    #     for pname, primitive in primitive_set:
+    #         NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname, service_instance)})
+    #         primitive.__class__ = NewPrimitive
 
 api_register = ApiRegistry()
-- 
Gitee


From 36916dd494f20c97f2ec5f9926df833d7830ffb6 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 17:24:00 +0800
Subject: [PATCH 18/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 149 +++++++++---------
 1 file changed, 76 insertions(+), 73 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index e7039a3bdb3..b3f0f3885df 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -74,6 +74,81 @@ class Service:
             return backward_hook(*args, **kwargs)
         
         return wrap_forward_hook, wrap_backward_hook
+        
+    def wrap_primitive(self, origin_func, primitive_name):
+        def func(self, *args, **kwargs):
+            if primitive_name not in self.primitive_counters:
+                self.primitive_counters[primitive_name] = 0
+            else:
+                self.primitive_counters[primitive_name] += 1
+
+            current_count = self.primitive_counters[primitive_name]
+            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+            captured_grads_input = []
+            captured_grads_output = []
+
+            def input_backward_hook(grad):
+                captured_grads_input.append(grad)
+                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
+                self.data_collector.backward_input_data_collect(
+                    backward_primitive_name, self, os.getpid(), new_module_input_output
+                )
+
+            def output_backward_hook(grad):
+                captured_grads_output.append(grad)
+                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                new_module_input_output = ModuleBackwardOutputs(
+                    grad_output=tuple(captured_grads_output)
+                )
+                self.data_collector.backward_output_data_collect(
+                    backward_primitive_name, self, os.getpid(), new_module_input_output
+                )
+
+            if not self.switch:
+                return origin_func(*args, **kwargs)
+
+            hooked_inputs = []
+            for idx, arg in enumerate(args):
+                if isinstance(arg, Tensor):
+                    arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+                    hooked_inputs.append(arg_hooked)
+                else:
+                    hooked_inputs.append(arg)
+
+            out = origin_func(*hooked_inputs, **kwargs)
+            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
+
+            if self.data_collector:
+                module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
+                self.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
+                if self.data_collector.if_return_forward_new_output():
+                    out = self.data_collector.get_forward_new_output()
+
+            if isinstance(out, Tensor):
+                out = ops.HookBackward(output_backward_hook)(out)
+            elif isinstance(out, tuple):
+                hooked_outputs = []
+                for tensor in out:
+                    if isinstance(tensor, Tensor):
+                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+                    else:
+                        hooked_outputs.append(tensor)
+                out = tuple(hooked_outputs)
+
+            return out
+
+        return func
+
+    def register_hooks(self):
+            primitive_set = set()
+            for name, cell in self.model.cells_and_names():
+                for pname, primitive in cell._primitives.items():
+                    primitive_set.add((pname, primitive))
+
+            for pname, primitive in primitive_set:
+                NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname)})
+                primitive.__class__ = NewPrimitive
 
     def step(self):
         self.current_iter += 1
@@ -140,77 +215,5 @@ class Service:
             if self.model:
                 register_hooks(self)
     
-    def register_hooks(self):
-        primitive_set = set()
-        for name, cell in self.model.cells_and_names():
-            for pname, primitive in cell._primitives.items():
-                primitive_set.add((pname, primitive))
-
-        for pname, primitive in primitive_set:
-            NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname)})
-            primitive.__class__ = NewPrimitive
-
-    def wrap_primitive(self, origin_func, primitive_name):
-        def func(self, *args, **kwargs):
-            if primitive_name not in self.primitive_counters:
-                self.primitive_counters[primitive_name] = 0
-            else:
-                self.primitive_counters[primitive_name] += 1
-
-            current_count = self.primitive_counters[primitive_name]
-            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
-            captured_grads_input = []
-            captured_grads_output = []
-
-            def input_backward_hook(grad):
-                captured_grads_input.append(grad)
-                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                self.data_collector.backward_input_data_collect(
-                    backward_primitive_name, self, os.getpid(), new_module_input_output
-                )
-
-            def output_backward_hook(grad):
-                captured_grads_output.append(grad)
-                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardOutputs(
-                    grad_output=tuple(captured_grads_output)
-                )
-                self.data_collector.backward_output_data_collect(
-                    backward_primitive_name, self, os.getpid(), new_module_input_output
-                )
-
-            if not self.switch:
-                return origin_func(*args, **kwargs)
-
-            hooked_inputs = []
-            for idx, arg in enumerate(args):
-                if isinstance(arg, Tensor):
-                    arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-                    hooked_inputs.append(arg_hooked)
-                else:
-                    hooked_inputs.append(arg)
-
-            out = origin_func(*hooked_inputs, **kwargs)
-            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
-
-            if self.data_collector:
-                module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
-                self.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
-                if self.data_collector.if_return_forward_new_output():
-                    out = self.data_collector.get_forward_new_output()
-
-            if isinstance(out, Tensor):
-                out = ops.HookBackward(output_backward_hook)(out)
-            elif isinstance(out, tuple):
-                hooked_outputs = []
-                for tensor in out:
-                    if isinstance(tensor, Tensor):
-                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
-                    else:
-                        hooked_outputs.append(tensor)
-                out = tuple(hooked_outputs)
-
-            return out
+    
 
-        return func
-- 
Gitee


From da2f5ee3c3017c44b39e3133bc9d978c8d2a89f8 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 17:31:13 +0800
Subject: [PATCH 19/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 24 ++++++++++---------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index b3f0f3885df..24ca7d4609b 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -16,6 +16,7 @@
 import os
 from pathlib import Path
 import functools
+from mindspore.common.tensor import Tensor
 
 from msprobe.core.data_dump.data_collector import build_data_collector
 from msprobe.core.data_dump.scope import BaseScope
@@ -25,7 +26,7 @@ from msprobe.mindspore.common.log import logger
 from msprobe.core.common.utils import Const
 from msprobe.core.common.exceptions import DistributedNotInitializedError
 from msprobe.mindspore.dump.hook_cell.api_registry import api_register
-from msprobe.core.data_dump.data_processor.base import ModuleForwardInputsOutputs, ModuleBackwardInputsOutputs
+from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs, ModuleBackwardInputs, ModuleBackwardOutputs
 
 
 class Service:
@@ -74,15 +75,16 @@ class Service:
             return backward_hook(*args, **kwargs)
         
         return wrap_forward_hook, wrap_backward_hook
-        
+
     def wrap_primitive(self, origin_func, primitive_name):
+        service_instance = self
         def func(self, *args, **kwargs):
-            if primitive_name not in self.primitive_counters:
-                self.primitive_counters[primitive_name] = 0
+            if primitive_name not in service_instance.primitive_counters:
+                service_instance.primitive_counters[primitive_name] = 0
             else:
-                self.primitive_counters[primitive_name] += 1
+                service_instance.primitive_counters[primitive_name] += 1
 
-            current_count = self.primitive_counters[primitive_name]
+            current_count = service_instance.primitive_counters[primitive_name]
             updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
             captured_grads_input = []
             captured_grads_output = []
@@ -119,11 +121,11 @@ class Service:
             out = origin_func(*hooked_inputs, **kwargs)
             forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
 
-            if self.data_collector:
+            if service_instance.data_collector:
                 module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
-                self.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
-                if self.data_collector.if_return_forward_new_output():
-                    out = self.data_collector.get_forward_new_output()
+                service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
+                if service_instance.data_collector.if_return_forward_new_output():
+                    out = service_instance.data_collector.get_forward_new_output()
 
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
@@ -213,7 +215,7 @@ class Service:
             api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
             api_register.api_set_hook_func()
             if self.model:
-                register_hooks(self)
+                self.register_hooks()
     
     
 
-- 
Gitee


From f8735e2f3abb6321279cd019fe0d5d93a83cd7c0 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 17:32:14 +0800
Subject: [PATCH 20/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 24ca7d4609b..9435c48068d 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -17,6 +17,7 @@ import os
 from pathlib import Path
 import functools
 from mindspore.common.tensor import Tensor
+from mindspore import ops
 
 from msprobe.core.data_dump.data_collector import build_data_collector
 from msprobe.core.data_dump.scope import BaseScope
@@ -107,7 +108,7 @@ class Service:
                     backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
-            if not self.switch:
+            if not service_instance.switch:
                 return origin_func(*args, **kwargs)
 
             hooked_inputs = []
-- 
Gitee


From 71eea504ba61a5bb02e19bbdc97e5451f13e7c1e Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 26 Jul 2024 17:35:44 +0800
Subject: [PATCH 21/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 9435c48068d..fddd27718f4 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -94,7 +94,7 @@ class Service:
                 captured_grads_input.append(grad)
                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                 new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                self.data_collector.backward_input_data_collect(
+                service_instance.data_collector.backward_input_data_collect(
                     backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
@@ -104,7 +104,7 @@ class Service:
                 new_module_input_output = ModuleBackwardOutputs(
                     grad_output=tuple(captured_grads_output)
                 )
-                self.data_collector.backward_output_data_collect(
+                service_instance.data_collector.backward_output_data_collect(
                     backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
-- 
Gitee


From 41d45586ddd38c9ab246124e7c08eb3e7ce37103 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 27 Jul 2024 10:32:00 +0800
Subject: [PATCH 22/67] Update api_registry.py

---
 .../mindspore/dump/hook_cell/api_registry.py  | 76 -------------------
 1 file changed, 76 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 4c2f81cb905..3e9425c6fd8 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -107,80 +107,4 @@ class ApiRegistry:
             if attr_name.startswith(Const.ATTR_NAME_PREFIX):
                 self.mint_func_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintNNFunctionalOP, attr_name)    
 
-    # def wrap_primitive(self, origin_func, primitive_name, service_instance):
-    #     primitive_instance = self
-    #     def func(self, *args, **kwargs):
-    #         if primitive_name not in primitive_instance.primitive_counters:
-    #             primitive_instance.primitive_counters[primitive_name] = 0
-    #         else:
-    #             primitive_instance.primitive_counters[primitive_name] += 1
-
-    #         current_count = primitive_instance.primitive_counters[primitive_name]
-    #         updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
-    #         captured_grads_input = []
-    #         captured_grads_output = []
-
-    #         def input_backward_hook(grad):
-    #             captured_grads_input.append(grad)
-    #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-    #             new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-    #             service_instance.data_collector.backward_input_data_collect(
-    #                 backward_primitive_name, self, os.getpid(), new_module_input_output
-    #             )
-
-    #         def output_backward_hook(grad):
-    #             captured_grads_output.append(grad)
-    #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-    #             new_module_input_output = ModuleBackwardOutputs(
-    #                 grad_output=tuple(captured_grads_output)
-    #             )
-    #             service_instance.data_collector.backward_output_data_collect(
-    #                 backward_primitive_name, self, os.getpid(), new_module_input_output
-    #             )
-
-    #         if not service_instance.switch:
-    #             return origin_func(*args, **kwargs)
-
-    #         hooked_inputs = []
-    #         for idx, arg in enumerate(args):
-    #             if isinstance(arg, Tensor):
-    #                 arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-    #                 hooked_inputs.append(arg_hooked)
-    #             else:
-    #                 hooked_inputs.append(arg)
-
-    #         out = origin_func(*hooked_inputs, **kwargs)
-    #         forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
-
-    #         if service_instance.data_collector:
-    #             module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
-    #             service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
-    #             if service_instance.data_collector.if_return_forward_new_output():
-    #                 out = service_instance.data_collector.get_forward_new_output()
-
-    #         if isinstance(out, Tensor):
-    #             out = ops.HookBackward(output_backward_hook)(out)
-    #         elif isinstance(out, tuple):
-    #             hooked_outputs = []
-    #             for tensor in out:
-    #                 if isinstance(tensor, Tensor):
-    #                     hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
-    #                 else:
-    #                     hooked_outputs.append(tensor)
-    #             out = tuple(hooked_outputs)
-
-    #         return out
-
-    #     return func
-
-    # def register_hooks(self, service_instance):
-    #     primitive_set = set()
-    #     for name, cell in service_instance.model.cells_and_names():
-    #         for pname, primitive in cell._primitives.items():
-    #             primitive_set.add((pname, primitive))
-
-    #     for pname, primitive in primitive_set:
-    #         NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname, service_instance)})
-    #         primitive.__class__ = NewPrimitive
-
 api_register = ApiRegistry()
-- 
Gitee


From 5d69b20f2a7db6576522d38462f6d57b81933854 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 27 Jul 2024 15:51:41 +0800
Subject: [PATCH 23/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index fddd27718f4..e9ff2ab7520 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -40,8 +40,8 @@ class Service:
         self.current_iter = 0
         self.first_start = True
         self.current_rank = None
-        self.dump_iter_dir = None
         self.primitive_counters = {}
+        self.dump_iter_dir = None
 
     def build_hook(self, module_type, name):
         def forward_hook(api_or_module_name, module, input, output):
-- 
Gitee


From 95339b740327abfdb91d7df7928b473cde1b1e2c Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 27 Jul 2024 15:54:59 +0800
Subject: [PATCH 24/67] Update api_registry.py

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py        | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index 3e9425c6fd8..b30505f2d4f 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -13,13 +13,8 @@
 # limitations under the License.
 # ============================================================================
 
-import os
-import functools
 import mindspore as ms
 from mindspore import ops
-from mindspore.common.tensor import Tensor
-from msprobe.core.common.utils import Const
-from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs, ModuleBackwardInputs, ModuleBackwardOutputs
 from msprobe.mindspore.dump.hook_cell.wrap_functional import get_functional_ops, setup_hooks, \
     HOOKFunctionalOP, HOOKMintOP, HOOKMintNNFunctionalOP
 from msprobe.mindspore.dump.hook_cell.wrap_tensor import get_tensor_ops, wrap_tensor_ops_and_bind, HOOKTensor
@@ -41,7 +36,6 @@ class ApiRegistry:
         self.norm_inner_ops_hook_attr = {}
 
         self.norm_inner_ops = ["norm", "square", "sqrt", "is_complex"]
-        self.primitive_counters = {}
 
     @staticmethod
     def store_ori_attr(ori_api_group, api_list, api_ori_attr):
-- 
Gitee


From f06b73be7c40b5a7828e23c47459caa9ece5c30f Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 27 Jul 2024 15:55:36 +0800
Subject: [PATCH 25/67] Update api_registry.py

---
 .../msprobe/mindspore/dump/hook_cell/api_registry.py            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
index b30505f2d4f..5508416fde0 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry.py
@@ -14,7 +14,6 @@
 # ============================================================================
 
 import mindspore as ms
-from mindspore import ops
 from msprobe.mindspore.dump.hook_cell.wrap_functional import get_functional_ops, setup_hooks, \
     HOOKFunctionalOP, HOOKMintOP, HOOKMintNNFunctionalOP
 from msprobe.mindspore.dump.hook_cell.wrap_tensor import get_tensor_ops, wrap_tensor_ops_and_bind, HOOKTensor
@@ -101,4 +100,5 @@ class ApiRegistry:
             if attr_name.startswith(Const.ATTR_NAME_PREFIX):
                 self.mint_func_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintNNFunctionalOP, attr_name)    
 
+
 api_register = ApiRegistry()
-- 
Gitee


From b8f4d421fe9010a01163b8cfac20935569ff423f Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 29 Jul 2024 09:59:49 +0800
Subject: [PATCH 26/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index e9ff2ab7520..ba4eca957e2 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -80,6 +80,7 @@ class Service:
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
         def func(self, *args, **kwargs):
+            service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
             if primitive_name not in service_instance.primitive_counters:
                 service_instance.primitive_counters[primitive_name] = 0
             else:
-- 
Gitee


From 4ce94697456e2ffe08a5adf594c36c33e7c84fa3 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 29 Jul 2024 11:30:18 +0800
Subject: [PATCH 27/67] =?UTF-8?q?=E5=B0=9D=E8=AF=95=E6=97=A8=E5=9C=A8?=
 =?UTF-8?q?=E6=9C=80=E5=90=8E=E4=B8=80=E5=9D=97=E6=94=B6=E9=9B=86=E6=95=B0?=
 =?UTF-8?q?=E6=8D=AE?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../msprobe/mindspore/service.py              | 35 ++++++++++---------
 1 file changed, 19 insertions(+), 16 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index ba4eca957e2..f7dfa044e8e 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -80,24 +80,15 @@ class Service:
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
         def func(self, *args, **kwargs):
-            service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
-            if primitive_name not in service_instance.primitive_counters:
-                service_instance.primitive_counters[primitive_name] = 0
-            else:
-                service_instance.primitive_counters[primitive_name] += 1
-
-            current_count = service_instance.primitive_counters[primitive_name]
-            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
-            captured_grads_input = []
-            captured_grads_output = []
-
             def input_backward_hook(grad):
                 captured_grads_input.append(grad)
-                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                service_instance.data_collector.backward_input_data_collect(
-                    backward_primitive_name, self, os.getpid(), new_module_input_output
-                )
+                if len(captured_grads_input) == num_tensors:
+                    backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                    new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
+                    service_instance.data_collector.backward_input_data_collect(
+                        backward_primitive_name, self, os.getpid(), new_module_input_output
+                    )
+# 等所有加入后在收集
 
             def output_backward_hook(grad):
                 captured_grads_output.append(grad)
@@ -109,10 +100,22 @@ class Service:
                     backward_primitive_name, self, os.getpid(), new_module_input_output
                 )
 
+            service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
+            if primitive_name not in service_instance.primitive_counters:
+                service_instance.primitive_counters[primitive_name] = 0
+            else:
+                service_instance.primitive_counters[primitive_name] += 1
+
+            current_count = service_instance.primitive_counters[primitive_name]
+            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+            captured_grads_input = []
+            captured_grads_output = []
+
             if not service_instance.switch:
                 return origin_func(*args, **kwargs)
 
             hooked_inputs = []
+            num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             for idx, arg in enumerate(args):
                 if isinstance(arg, Tensor):
                     arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-- 
Gitee


From 9348c9a4df412c22050c2697279f5cd030474031 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 29 Jul 2024 11:31:14 +0800
Subject: [PATCH 28/67] =?UTF-8?q?=E6=89=93=E5=8D=B0tensor=E5=80=BC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index f7dfa044e8e..9ec251353df 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -116,6 +116,7 @@ class Service:
 
             hooked_inputs = []
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
+            print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
             for idx, arg in enumerate(args):
                 if isinstance(arg, Tensor):
                     arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-- 
Gitee


From 61659c62dfc2a3e7f8ecf2b9bfe2e78f34a82b23 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 29 Jul 2024 14:53:34 +0800
Subject: [PATCH 29/67] Update base.py

---
 .../msprobe/core/data_dump/data_processor/base.py          | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
index e725d362e8c..a18efccbc6a 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
@@ -240,13 +240,16 @@ class BaseDataProcessor:
         api_info_struct = {}
         if self.is_dump_for_data_mode(Const.BACKWARD, Const.OUTPUT):
             api_info_struct[name] = {}
-            self.api_data_category = Const.OUTPUT
+            # self.api_data_category = Const.OUTPUT
+            self.api_data_category = Const.INPUT
+
             input_info_list = self.analyze_element(module_input_output.grad_input_tuple)
             api_info_struct[name][Const.GRAD_INPUT] = input_info_list
 
         if self.is_dump_for_data_mode(Const.BACKWARD, Const.INPUT):
             api_info_struct[name] = api_info_struct.get(name, {})
-            self.api_data_category = Const.INPUT
+            self.api_data_category = Const.OUTPUT
+            # self.api_data_category = Const.INPUT
             output_info_list = self.analyze_element(module_input_output.grad_output_tuple)
             api_info_struct[name][Const.GRAD_OUTPUT] = output_info_list
 
-- 
Gitee


From f0818349a58c98888ca393b27d662488aef5ecd9 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 29 Jul 2024 15:03:17 +0800
Subject: [PATCH 30/67] Update base.py

---
 .../msprobe/core/data_dump/data_processor/base.py           | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
index a18efccbc6a..c55c5079e25 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
@@ -262,7 +262,8 @@ class BaseDataProcessor:
         api_info_struct = {}
         if self.is_dump_for_data_mode(Const.BACKWARD, Const.INPUT):
             api_info_struct[name] = {}
-            self.api_data_category = Const.INPUT
+            self.api_data_category = Const.OUTPUT
+            # self.api_data_category = Const.INPUT
             output_info_list = self.analyze_element(module_input_output.grad_input_tuple)
             api_info_struct[name][Const.GRAD_OUTPUT] = output_info_list
         return api_info_struct
@@ -274,7 +275,8 @@ class BaseDataProcessor:
         api_info_struct = {}
         if self.is_dump_for_data_mode(Const.BACKWARD, Const.OUTPUT):
             api_info_struct[name] = {}
-            self.api_data_category = Const.OUTPUT
+            self.api_data_category = Const.INPUT
+            # self.api_data_category = Const.OUTPUT
             input_info_list = self.analyze_element(module_input_output.grad_output_tuple)
             api_info_struct[name][Const.GRAD_INPUT] = input_info_list
         return api_info_struct
-- 
Gitee


From 7c2eec3adecd2a55791c0fd6a0adfba86fbf1f2c Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 29 Jul 2024 15:41:36 +0800
Subject: [PATCH 31/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 9ec251353df..2d4419b370c 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -117,7 +117,9 @@ class Service:
             hooked_inputs = []
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
+            print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
             for idx, arg in enumerate(args):
+                print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
                 if isinstance(arg, Tensor):
                     arg_hooked = ops.HookBackward(input_backward_hook)(arg)
                     hooked_inputs.append(arg_hooked)
@@ -133,6 +135,7 @@ class Service:
                 if service_instance.data_collector.if_return_forward_new_output():
                     out = service_instance.data_collector.get_forward_new_output()
 
+            # num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
             elif isinstance(out, tuple):
-- 
Gitee


From b0f4bfca7869145e067b3465e52ab351c8c8ee07 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 29 Jul 2024 15:54:18 +0800
Subject: [PATCH 32/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 2d4419b370c..06535109463 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -136,6 +136,7 @@ class Service:
                     out = service_instance.data_collector.get_forward_new_output()
 
             # num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
+            print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
             elif isinstance(out, tuple):
-- 
Gitee


From dc1f5a437661e8deb02d628f6b0a5b5cce4ff2ce Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 29 Jul 2024 16:01:48 +0800
Subject: [PATCH 33/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 06535109463..54d34861dc6 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -92,13 +92,14 @@ class Service:
 
             def output_backward_hook(grad):
                 captured_grads_output.append(grad)
-                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardOutputs(
-                    grad_output=tuple(captured_grads_output)
-                )
-                service_instance.data_collector.backward_output_data_collect(
-                    backward_primitive_name, self, os.getpid(), new_module_input_output
-                )
+                if len(captured_grads_output) == num_output_tensors:
+                    backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                    new_module_input_output = ModuleBackwardOutputs(
+                        grad_output=tuple(captured_grads_output)
+                    )
+                    service_instance.data_collector.backward_output_data_collect(
+                        backward_primitive_name, self, os.getpid(), new_module_input_output
+                    )
 
             service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
             if primitive_name not in service_instance.primitive_counters:
@@ -134,8 +135,8 @@ class Service:
                 service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
                 if service_instance.data_collector.if_return_forward_new_output():
                     out = service_instance.data_collector.get_forward_new_output()
-
-            # num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
+            
+            num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
             print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
-- 
Gitee


From 574e684341d5e16a2d3c8983faf507f7ee218233 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 30 Jul 2024 20:16:31 +0800
Subject: [PATCH 34/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 54d34861dc6..6c681a12422 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -223,8 +223,8 @@ class Service:
     def register_hook_new(self):
         logger.info_on_rank_0("The {} hook function is successfully mounted to the model.".format(self.config.task))
         if self.config.level == "L1":
-            api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
-            api_register.api_set_hook_func()
+            # api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
+            # api_register.api_set_hook_func()
             if self.model:
                 self.register_hooks()
     
-- 
Gitee


From 5b91d4839615c8572b6a478f42f31dd946cdce0c Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 1 Aug 2024 11:28:42 +0800
Subject: [PATCH 35/67] =?UTF-8?q?=E4=BF=AE=E5=A4=8Dbug?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../core/data_dump/data_processor/mindspore_processor.py     | 5 +++--
 debug/accuracy_tools/msprobe/mindspore/service.py            | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
index 7533e2ee0de..db02f26f607 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
@@ -57,8 +57,9 @@ class MindsporeDataProcessor(BaseDataProcessor):
         if data.numel() == 0:
             return tensor_stat
         elif data.dtype == ms.bool_:
-            tensor_stat.max = self.mint_ops_func["max"](data).item()
-            tensor_stat.min = self.mint_ops_func["min"](data).item()
+            data_np = data.asnumpy()
+            tensor_stat.max = bool(np.max(data_np))
+            tensor_stat.min = bool(np.min(data_np))
         elif not data.shape:
             tensor_stat.max = tensor_stat.min = tensor_stat.mean = tensor_stat.norm = data.item()
         elif data.dtype == ms.complex64 or data.dtype == ms.complex128:
diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 6c681a12422..46a054275b1 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -131,7 +131,7 @@ class Service:
             forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
 
             if service_instance.data_collector:
-                module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
+                module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
                 service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
                 if service_instance.data_collector.if_return_forward_new_output():
                     out = service_instance.data_collector.get_forward_new_output()
-- 
Gitee


From 74297ab27573157dc4050f162c30e8f846c987e2 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 1 Aug 2024 17:07:56 +0800
Subject: [PATCH 36/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 46a054275b1..42ab8b31aa8 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -77,19 +77,21 @@ class Service:
         
         return wrap_forward_hook, wrap_backward_hook
 
+
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
-        def func(self, *args, **kwargs):
+        def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
             def input_backward_hook(grad):
                 captured_grads_input.append(grad)
                 if len(captured_grads_input) == num_tensors:
                     backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                     new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
                     service_instance.data_collector.backward_input_data_collect(
-                        backward_primitive_name, self, os.getpid(), new_module_input_output
+                        backward_primitive_name, service_instance, os.getpid(), new_module_input_output
                     )
-# 等所有加入后在收集
+            return input_backward_hook
 
+        def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
             def output_backward_hook(grad):
                 captured_grads_output.append(grad)
                 if len(captured_grads_output) == num_output_tensors:
@@ -98,9 +100,11 @@ class Service:
                         grad_output=tuple(captured_grads_output)
                     )
                     service_instance.data_collector.backward_output_data_collect(
-                        backward_primitive_name, self, os.getpid(), new_module_input_output
+                        backward_primitive_name, service_instance, os.getpid(), new_module_input_output
                     )
+            return output_backward_hook
 
+        def func(self, *args, **kwargs):
             service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
             if primitive_name not in service_instance.primitive_counters:
                 service_instance.primitive_counters[primitive_name] = 0
@@ -119,6 +123,7 @@ class Service:
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
             print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
+            input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
             for idx, arg in enumerate(args):
                 print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
                 if isinstance(arg, Tensor):
@@ -138,6 +143,8 @@ class Service:
             
             num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
             print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
+            output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
+            
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
             elif isinstance(out, tuple):
-- 
Gitee


From 19330452a66810b9a7eacf23121ce1b406b47828 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 1 Aug 2024 17:16:59 +0800
Subject: [PATCH 37/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 64 ++++++++++++-------
 1 file changed, 41 insertions(+), 23 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 42ab8b31aa8..fea947dfd90 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -81,28 +81,46 @@ class Service:
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
         def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
-            def input_backward_hook(grad):
-                captured_grads_input.append(grad)
-                if len(captured_grads_input) == num_tensors:
-                    backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                    new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                    service_instance.data_collector.backward_input_data_collect(
-                        backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                    )
-            return input_backward_hook
-
-        def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
-            def output_backward_hook(grad):
-                captured_grads_output.append(grad)
-                if len(captured_grads_output) == num_output_tensors:
+        #     def input_backward_hook(grad):
+        #         captured_grads_input.append(grad)
+        #         if len(captured_grads_input) == num_tensors:
+        #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+        #             new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
+        #             service_instance.data_collector.backward_input_data_collect(
+        #                 backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+        #             )
+        #     return input_backward_hook
+
+        # def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
+        #     def output_backward_hook(grad):
+        #         captured_grads_output.append(grad)
+        #         if len(captured_grads_output) == num_output_tensors:
+        #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+        #             new_module_input_output = ModuleBackwardOutputs(
+        #                 grad_output=tuple(captured_grads_output)
+        #             )
+        #             service_instance.data_collector.backward_output_data_collect(
+        #                 backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+        #             )
+        #     return output_backward_hook
+        def create_backward_hook(captured_grads, num_grads, updated_primitive_name, is_input):
+            def backward_hook(grad):
+                captured_grads.append(grad)
+                if len(captured_grads) == num_grads:
                     backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                    new_module_input_output = ModuleBackwardOutputs(
-                        grad_output=tuple(captured_grads_output)
-                    )
-                    service_instance.data_collector.backward_output_data_collect(
-                        backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                    )
-            return output_backward_hook
+                    if is_input:
+                        new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
+                        service_instance.data_collector.backward_input_data_collect(
+                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                        )
+                    else:
+                        new_module_input_output = ModuleBackwardOutputs(
+                            grad_output=tuple(captured_grads)
+                        )
+                        service_instance.data_collector.backward_output_data_collect(
+                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                        )
+            return backward_hook
 
         def func(self, *args, **kwargs):
             service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
@@ -123,7 +141,7 @@ class Service:
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
             print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
-            input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
+            input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name, is_input=True)
             for idx, arg in enumerate(args):
                 print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
                 if isinstance(arg, Tensor):
@@ -143,7 +161,7 @@ class Service:
             
             num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
             print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
-            output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
+            output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name, is_input=False)
             
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
-- 
Gitee


From 9418a12bde6e93059a89adc2f2bde84df536731a Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 1 Aug 2024 17:20:49 +0800
Subject: [PATCH 38/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index fea947dfd90..326bc75d1b9 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -80,7 +80,7 @@ class Service:
 
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
-        def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
+        # def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
         #     def input_backward_hook(grad):
         #         captured_grads_input.append(grad)
         #         if len(captured_grads_input) == num_tensors:
@@ -141,7 +141,7 @@ class Service:
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
             print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
-            input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name, is_input=True)
+            input_backward_hook = create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name, is_input=True)
             for idx, arg in enumerate(args):
                 print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
                 if isinstance(arg, Tensor):
@@ -161,7 +161,7 @@ class Service:
             
             num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
             print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
-            output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name, is_input=False)
+            output_backward_hook = create_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name, is_input=False)
             
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
-- 
Gitee


From d44838b7fd782fd3441ccd8bdf0d47da94ae265d Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 1 Aug 2024 17:28:58 +0800
Subject: [PATCH 39/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 38 +++++++++----------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 326bc75d1b9..42b8df62695 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -77,7 +77,24 @@ class Service:
         
         return wrap_forward_hook, wrap_backward_hook
 
-
+    def create_backward_hook(captured_grads, num_grads, updated_primitive_name, is_input):
+        def backward_hook(grad):
+            captured_grads.append(grad)
+            if len(captured_grads) == num_grads:
+                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                if is_input:
+                    new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
+                    service_instance.data_collector.backward_input_data_collect(
+                        backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                    )
+                else:
+                    new_module_input_output = ModuleBackwardOutputs(
+                        grad_output=tuple(captured_grads)
+                    )
+                    service_instance.data_collector.backward_output_data_collect(
+                        backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                    )
+        return backward_hook
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
         # def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
@@ -103,24 +120,7 @@ class Service:
         #                 backward_primitive_name, service_instance, os.getpid(), new_module_input_output
         #             )
         #     return output_backward_hook
-        def create_backward_hook(captured_grads, num_grads, updated_primitive_name, is_input):
-            def backward_hook(grad):
-                captured_grads.append(grad)
-                if len(captured_grads) == num_grads:
-                    backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                    if is_input:
-                        new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
-                        service_instance.data_collector.backward_input_data_collect(
-                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                        )
-                    else:
-                        new_module_input_output = ModuleBackwardOutputs(
-                            grad_output=tuple(captured_grads)
-                        )
-                        service_instance.data_collector.backward_output_data_collect(
-                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                        )
-            return backward_hook
+
 
         def func(self, *args, **kwargs):
             service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
-- 
Gitee


From ae8a1f63bd9ef480a413d8443f3b9c33ce618967 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 1 Aug 2024 17:33:52 +0800
Subject: [PATCH 40/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 42b8df62695..108c4df94fc 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -141,7 +141,7 @@ class Service:
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
             print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
-            input_backward_hook = create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name, is_input=True)
+            input_backward_hook = self.create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name, is_input=True)
             for idx, arg in enumerate(args):
                 print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
                 if isinstance(arg, Tensor):
@@ -161,7 +161,7 @@ class Service:
             
             num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
             print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
-            output_backward_hook = create_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name, is_input=False)
+            output_backward_hook = self.create_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name, is_input=False)
             
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
-- 
Gitee


From 9a97bcf905b4686e746aff63dfff3cf1099f6645 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 1 Aug 2024 20:29:57 +0800
Subject: [PATCH 41/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 108c4df94fc..7868d0897cb 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -141,7 +141,7 @@ class Service:
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
             print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
-            input_backward_hook = self.create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name, is_input=True)
+            input_backward_hook = service_instance.create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name, is_input=True)
             for idx, arg in enumerate(args):
                 print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
                 if isinstance(arg, Tensor):
@@ -161,7 +161,7 @@ class Service:
             
             num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
             print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
-            output_backward_hook = self.create_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name, is_input=False)
+            output_backward_hook = service_instance.create_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name, is_input=False)
             
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
-- 
Gitee


From 2713c9d15fd71ccb4c51b9487a8f013ad69438d4 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Thu, 1 Aug 2024 20:37:07 +0800
Subject: [PATCH 42/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 74 +++++++++----------
 1 file changed, 37 insertions(+), 37 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 7868d0897cb..fed54b08d33 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -77,49 +77,49 @@ class Service:
         
         return wrap_forward_hook, wrap_backward_hook
 
-    def create_backward_hook(captured_grads, num_grads, updated_primitive_name, is_input):
-        def backward_hook(grad):
-            captured_grads.append(grad)
-            if len(captured_grads) == num_grads:
-                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                if is_input:
-                    new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
+    # def create_backward_hook(captured_grads, num_grads, updated_primitive_name, is_input):
+    #     def backward_hook(grad):
+    #         captured_grads.append(grad)
+    #         if len(captured_grads) == num_grads:
+    #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+    #             if is_input:
+    #                 new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
+    #                 service_instance.data_collector.backward_input_data_collect(
+    #                     backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+    #                 )
+    #             else:
+    #                 new_module_input_output = ModuleBackwardOutputs(
+    #                     grad_output=tuple(captured_grads)
+    #                 )
+    #                 service_instance.data_collector.backward_output_data_collect(
+    #                     backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+    #                 )
+    #     return backward_hook
+    def wrap_primitive(self, origin_func, primitive_name):
+        service_instance = self
+        def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
+            def input_backward_hook(grad):
+                captured_grads_input.append(grad)
+                if len(captured_grads_input) == num_tensors:
+                    backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                    new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
                     service_instance.data_collector.backward_input_data_collect(
                         backward_primitive_name, service_instance, os.getpid(), new_module_input_output
                     )
-                else:
+            return input_backward_hook
+
+        def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
+            def output_backward_hook(grad):
+                captured_grads_output.append(grad)
+                if len(captured_grads_output) == num_output_tensors:
+                    backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                     new_module_input_output = ModuleBackwardOutputs(
-                        grad_output=tuple(captured_grads)
+                        grad_output=tuple(captured_grads_output)
                     )
                     service_instance.data_collector.backward_output_data_collect(
                         backward_primitive_name, service_instance, os.getpid(), new_module_input_output
                     )
-        return backward_hook
-    def wrap_primitive(self, origin_func, primitive_name):
-        service_instance = self
-        # def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
-        #     def input_backward_hook(grad):
-        #         captured_grads_input.append(grad)
-        #         if len(captured_grads_input) == num_tensors:
-        #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-        #             new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-        #             service_instance.data_collector.backward_input_data_collect(
-        #                 backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-        #             )
-        #     return input_backward_hook
-
-        # def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
-        #     def output_backward_hook(grad):
-        #         captured_grads_output.append(grad)
-        #         if len(captured_grads_output) == num_output_tensors:
-        #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-        #             new_module_input_output = ModuleBackwardOutputs(
-        #                 grad_output=tuple(captured_grads_output)
-        #             )
-        #             service_instance.data_collector.backward_output_data_collect(
-        #                 backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-        #             )
-        #     return output_backward_hook
+            return output_backward_hook
 
 
         def func(self, *args, **kwargs):
@@ -141,7 +141,7 @@ class Service:
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
             print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
-            input_backward_hook = service_instance.create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name, is_input=True)
+            input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
             for idx, arg in enumerate(args):
                 print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
                 if isinstance(arg, Tensor):
@@ -161,7 +161,7 @@ class Service:
             
             num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
             print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
-            output_backward_hook = service_instance.create_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name, is_input=False)
+            output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
             
             if isinstance(out, Tensor):
                 out = ops.HookBackward(output_backward_hook)(out)
-- 
Gitee


From 65b228b154ca1fa14f679951a97a92fc62b81d2c Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 2 Aug 2024 11:16:58 +0800
Subject: [PATCH 43/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index fed54b08d33..1c4e98450c2 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -248,8 +248,8 @@ class Service:
     def register_hook_new(self):
         logger.info_on_rank_0("The {} hook function is successfully mounted to the model.".format(self.config.task))
         if self.config.level == "L1":
-            # api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
-            # api_register.api_set_hook_func()
+            api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
+            api_register.api_set_hook_func()
             if self.model:
                 self.register_hooks()
     
-- 
Gitee


From 980a7537b5e76418f77de886e1ca0443d6ce98c3 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Fri, 2 Aug 2024 15:52:20 +0800
Subject: [PATCH 44/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 150 ++++++++++--------
 1 file changed, 87 insertions(+), 63 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 1c4e98450c2..1d2439b14c2 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -99,83 +99,107 @@ class Service:
         service_instance = self
         def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
             def input_backward_hook(grad):
-                captured_grads_input.append(grad)
-                if len(captured_grads_input) == num_tensors:
-                    backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                    new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                    service_instance.data_collector.backward_input_data_collect(
-                        backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                    )
+                try:
+                    captured_grads_input.append(grad)
+                    if len(captured_grads_input) == num_tensors:
+                        backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                        new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
+                        service_instance.data_collector.backward_input_data_collect(
+                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                        )
+                        captured_grads_input.clear()
+
+                except Exception as e:
+                    print(f"Error occurred in input_backward_hook: {e}")
+                    print(f"Captured grads input: {captured_grads_input}")
+                    print(f"Num tensors: {num_tensors}")
+                    print(f"Updated primitive name: {updated_primitive_name}")
+                raise  # 重新引发异常
             return input_backward_hook
 
         def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
             def output_backward_hook(grad):
-                captured_grads_output.append(grad)
-                if len(captured_grads_output) == num_output_tensors:
-                    backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                    new_module_input_output = ModuleBackwardOutputs(
-                        grad_output=tuple(captured_grads_output)
-                    )
-                    service_instance.data_collector.backward_output_data_collect(
-                        backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                    )
+                try:
+                    captured_grads_output.append(grad)
+                    if len(captured_grads_output) == num_output_tensors:
+                        backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                        new_module_input_output = ModuleBackwardOutputs(
+                            grad_output=tuple(captured_grads_output)
+                        )
+                        service_instance.data_collector.backward_output_data_collect(
+                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                        )
+                        # 释放 captured_grads_output 列表
+                        captured_grads_output.clear()
+                except Exception as e:
+                    print(f"Error occurred in output_backward_hook: {e}")
+                    print(f"Captured grads output: {captured_grads_output}")
+                    print(f"Num output tensors: {num_output_tensors}")
+                    print(f"Updated primitive name: {updated_primitive_name}")
+                    raise  # 重新引发异常
             return output_backward_hook
 
 
         def func(self, *args, **kwargs):
-            service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
-            if primitive_name not in service_instance.primitive_counters:
-                service_instance.primitive_counters[primitive_name] = 0
-            else:
-                service_instance.primitive_counters[primitive_name] += 1
-
-            current_count = service_instance.primitive_counters[primitive_name]
-            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
-            captured_grads_input = []
-            captured_grads_output = []
-
-            if not service_instance.switch:
-                return origin_func(*args, **kwargs)
-
-            hooked_inputs = []
-            num_tensors = sum(isinstance(arg, Tensor) for arg in args)
-            print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
-            print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
-            input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
-            for idx, arg in enumerate(args):
-                print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
-                if isinstance(arg, Tensor):
-                    arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-                    hooked_inputs.append(arg_hooked)
+            try:
+                service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
+                if primitive_name not in service_instance.primitive_counters:
+                    service_instance.primitive_counters[primitive_name] = 0
                 else:
-                    hooked_inputs.append(arg)
+                    service_instance.primitive_counters[primitive_name] += 1
+
+                current_count = service_instance.primitive_counters[primitive_name]
+                updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+                captured_grads_input = []
+                captured_grads_output = []
 
-            out = origin_func(*hooked_inputs, **kwargs)
-            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
+                if not service_instance.switch:
+                    return origin_func(*args, **kwargs)
 
-            if service_instance.data_collector:
-                module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
-                service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
-                if service_instance.data_collector.if_return_forward_new_output():
-                    out = service_instance.data_collector.get_forward_new_output()
-            
-            num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
-            print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
-            output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
-            
-            if isinstance(out, Tensor):
-                out = ops.HookBackward(output_backward_hook)(out)
-            elif isinstance(out, tuple):
-                hooked_outputs = []
-                for tensor in out:
-                    if isinstance(tensor, Tensor):
-                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+                hooked_inputs = []
+                num_tensors = sum(isinstance(arg, Tensor) for arg in args)
+                print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
+                print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
+                input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
+                for idx, arg in enumerate(args):
+                    print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
+                    if isinstance(arg, Tensor):
+                        arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+                        hooked_inputs.append(arg_hooked)
                     else:
-                        hooked_outputs.append(tensor)
-                out = tuple(hooked_outputs)
+                        hooked_inputs.append(arg)
+
+                out = origin_func(*hooked_inputs, **kwargs)
+                forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
 
-            return out
+                if service_instance.data_collector:
+                    module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
+                    service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
+                    if service_instance.data_collector.if_return_forward_new_output():
+                        out = service_instance.data_collector.get_forward_new_output()
+                
+                num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
+                print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
+                output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
+                
+                if isinstance(out, Tensor):
+                    out = ops.HookBackward(output_backward_hook)(out)
+                elif isinstance(out, tuple):
+                    hooked_outputs = []
+                    for tensor in out:
+                        if isinstance(tensor, Tensor):
+                            hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+                        else:
+                            hooked_outputs.append(tensor)
+                    out = tuple(hooked_outputs)
 
+                return out
+            except Exception as e:
+                print(f"Error occurred in wrap_primitive: {e}")
+                print(f"Arguments(args): {args}")
+                print(f"Arguments(kwargs): {kwargs}")
+                print(f"Current primitive name: {primitive_name}")
+                raise Exception("This is a primitive op dump error")
         return func
 
     def register_hooks(self):
-- 
Gitee


From b3c234dedec9c4d63660461aabd1aaf96f99420f Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 3 Aug 2024 15:33:06 +0800
Subject: [PATCH 45/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 1d2439b14c2..2b27e84b7b9 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -114,7 +114,7 @@ class Service:
                     print(f"Captured grads input: {captured_grads_input}")
                     print(f"Num tensors: {num_tensors}")
                     print(f"Updated primitive name: {updated_primitive_name}")
-                raise  # 重新引发异常
+                    raise  # 重新引发异常
             return input_backward_hook
 
         def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
-- 
Gitee


From 02e759b5866555068f308c2de6d2445317a56adb Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 3 Aug 2024 15:33:55 +0800
Subject: [PATCH 46/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 2b27e84b7b9..66e5d71e664 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -114,7 +114,7 @@ class Service:
                     print(f"Captured grads input: {captured_grads_input}")
                     print(f"Num tensors: {num_tensors}")
                     print(f"Updated primitive name: {updated_primitive_name}")
-                    raise  # 重新引发异常
+                    raise Exception("This is a primitive op input_backward dump error")  # 重新引发异常
             return input_backward_hook
 
         def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
@@ -136,7 +136,7 @@ class Service:
                     print(f"Captured grads output: {captured_grads_output}")
                     print(f"Num output tensors: {num_output_tensors}")
                     print(f"Updated primitive name: {updated_primitive_name}")
-                    raise  # 重新引发异常
+                    raise Exception("This is a primitive op output_backward dump error")# 重新引发异常
             return output_backward_hook
 
 
-- 
Gitee


From 712981c40d3153a5ef7cf27f1ba4d013edea1eb7 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 3 Aug 2024 16:28:42 +0800
Subject: [PATCH 47/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 66e5d71e664..b8561a2d67a 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -272,8 +272,8 @@ class Service:
     def register_hook_new(self):
         logger.info_on_rank_0("The {} hook function is successfully mounted to the model.".format(self.config.task))
         if self.config.level == "L1":
-            api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
-            api_register.api_set_hook_func()
+            # api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
+            # api_register.api_set_hook_func()
             if self.model:
                 self.register_hooks()
     
-- 
Gitee


From de6b85dd48facbf8b44d5a9c79dff50f624a5e5e Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 3 Aug 2024 17:47:58 +0800
Subject: [PATCH 48/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index b8561a2d67a..7c26f54ec34 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -142,7 +142,7 @@ class Service:
 
         def func(self, *args, **kwargs):
             try:
-                service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
+                # service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
                 if primitive_name not in service_instance.primitive_counters:
                     service_instance.primitive_counters[primitive_name] = 0
                 else:
@@ -171,7 +171,7 @@ class Service:
 
                 out = origin_func(*hooked_inputs, **kwargs)
                 forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
-
+                service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
                 if service_instance.data_collector:
                     module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
                     service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
-- 
Gitee


From 9a781c25756b57a9ab325d86803b5b727253219a Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Sat, 3 Aug 2024 17:51:57 +0800
Subject: [PATCH 49/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 7c26f54ec34..ffdbc1e56ad 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -103,6 +103,7 @@ class Service:
                     captured_grads_input.append(grad)
                     if len(captured_grads_input) == num_tensors:
                         backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                        service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
                         new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
                         service_instance.data_collector.backward_input_data_collect(
                             backward_primitive_name, service_instance, os.getpid(), new_module_input_output
@@ -123,6 +124,7 @@ class Service:
                     captured_grads_output.append(grad)
                     if len(captured_grads_output) == num_output_tensors:
                         backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                        service_instance.data_collector.visit_and_clear_overflow_status(updated_primitive_name)
                         new_module_input_output = ModuleBackwardOutputs(
                             grad_output=tuple(captured_grads_output)
                         )
@@ -171,7 +173,7 @@ class Service:
 
                 out = origin_func(*hooked_inputs, **kwargs)
                 forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
-                service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
+                service_instance.data_collector.visit_and_clear_overflow_status(forward_primitive_name)
                 if service_instance.data_collector:
                     module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
                     service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
-- 
Gitee


From faf5b9f00c0f4661dc9695216ea890a8bd1f3826 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 5 Aug 2024 11:22:21 +0800
Subject: [PATCH 50/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 78 +++++++++++--------
 1 file changed, 46 insertions(+), 32 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index ffdbc1e56ad..12c5ea98c0e 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -97,6 +97,7 @@ class Service:
     #     return backward_hook
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
+
         def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
             def input_backward_hook(grad):
                 try:
@@ -110,12 +111,9 @@ class Service:
                         )
                         captured_grads_input.clear()
 
-                except Exception as e:
-                    print(f"Error occurred in input_backward_hook: {e}")
-                    print(f"Captured grads input: {captured_grads_input}")
-                    print(f"Num tensors: {num_tensors}")
-                    print(f"Updated primitive name: {updated_primitive_name}")
-                    raise Exception("This is a primitive op input_backward dump error")  # 重新引发异常
+                except Exception as exception:
+                    raise Exception(f"This is a primitive op input_backward dump error: {exception}"
+                                    f", updated_primitive_name: {updated_primitive_name}")
             return input_backward_hook
 
         def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
@@ -124,7 +122,7 @@ class Service:
                     captured_grads_output.append(grad)
                     if len(captured_grads_output) == num_output_tensors:
                         backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                        service_instance.data_collector.visit_and_clear_overflow_status(updated_primitive_name)
+                        service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
                         new_module_input_output = ModuleBackwardOutputs(
                             grad_output=tuple(captured_grads_output)
                         )
@@ -133,38 +131,51 @@ class Service:
                         )
                         # 释放 captured_grads_output 列表
                         captured_grads_output.clear()
-                except Exception as e:
-                    print(f"Error occurred in output_backward_hook: {e}")
-                    print(f"Captured grads output: {captured_grads_output}")
-                    print(f"Num output tensors: {num_output_tensors}")
-                    print(f"Updated primitive name: {updated_primitive_name}")
-                    raise Exception("This is a primitive op output_backward dump error")# 重新引发异常
+                except Exception as exception:
+                    raise Exception(f"This is a primitive op output_backward dump error: {exception}"
+                                    f", updated_primitive_name: {updated_primitive_name}")
             return output_backward_hook
 
+        def hook_primitive_inputs(args, captured_grads_input, updated_primitive_name):
+            hooked_inputs = []
+            num_tensors = sum(isinstance(arg, Tensor) for arg in args)
+            input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
+            for idx, arg in enumerate(args):
+                if isinstance(arg, Tensor):
+                    arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+                    hooked_inputs.append(arg_hooked)
+                else:
+                    hooked_inputs.append(arg)
+            return hooked_inputs
+
+
+        def hook_primitive_outputs(out, create_output_backward_hook, output_backward_hook):
+            if isinstance(out, Tensor):
+                return ops.HookBackward(create_output_backward_hook)(out)
+            elif isinstance(out, tuple):
+                hooked_outputs = []
+                for tensor in out:
+                    if isinstance(tensor, Tensor):
+                        hooked_outputs.append(ops.HookBackward(create_output_backward_hook)(tensor))
+                    else:
+                        hooked_outputs.append(tensor)
+                return tuple(hooked_outputs)
+            return out
 
         def func(self, *args, **kwargs):
             try:
-                # service_instance.data_collector.visit_and_clear_overflow_status(primitive_name)
-                if primitive_name not in service_instance.primitive_counters:
-                    service_instance.primitive_counters[primitive_name] = 0
-                else:
-                    service_instance.primitive_counters[primitive_name] += 1
-
+                service_instance._update_primitive_counters(primitive_name)
                 current_count = service_instance.primitive_counters[primitive_name]
                 updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
-                captured_grads_input = []
-                captured_grads_output = []
+                captured_grads_input, captured_grads_output = [], []
 
                 if not service_instance.switch:
                     return origin_func(*args, **kwargs)
 
                 hooked_inputs = []
                 num_tensors = sum(isinstance(arg, Tensor) for arg in args)
-                print(f"Number of tensor arguments: {num_tensors}")  # 打印 num_tensors 的值
-                print(f"Arguments(args): type={type(args)}")  # 打印每个 arg 的类型
                 input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
                 for idx, arg in enumerate(args):
-                    print(f"Argument {idx}: type={type(arg)}")  # 打印每个 arg 的类型
                     if isinstance(arg, Tensor):
                         arg_hooked = ops.HookBackward(input_backward_hook)(arg)
                         hooked_inputs.append(arg_hooked)
@@ -179,11 +190,11 @@ class Service:
                     service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
                     if service_instance.data_collector.if_return_forward_new_output():
                         out = service_instance.data_collector.get_forward_new_output()
-                
+
                 num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
                 print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
                 output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
-                
+
                 if isinstance(out, Tensor):
                     out = ops.HookBackward(output_backward_hook)(out)
                 elif isinstance(out, tuple):
@@ -196,14 +207,17 @@ class Service:
                     out = tuple(hooked_outputs)
 
                 return out
-            except Exception as e:
-                print(f"Error occurred in wrap_primitive: {e}")
-                print(f"Arguments(args): {args}")
-                print(f"Arguments(kwargs): {kwargs}")
-                print(f"Current primitive name: {primitive_name}")
-                raise Exception("This is a primitive op dump error")
+            except Exception as exception:
+                raise Exception(f"This is a primitive op dump error: {exception}"
+                                f", primitive_name: {primitive_name}")
         return func
 
+    def _update_primitive_counters(self, primitive_name):
+        if primitive_name not in self.primitive_counters:
+            self.primitive_counters[primitive_name] = 0
+        else:
+            self.primitive_counters[primitive_name] += 1
+
     def register_hooks(self):
             primitive_set = set()
             for name, cell in self.model.cells_and_names():
-- 
Gitee


From bbac45931076a68b1e87308862e359550140ddf4 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 5 Aug 2024 11:40:43 +0800
Subject: [PATCH 51/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 169 ++++++++++--------
 1 file changed, 92 insertions(+), 77 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 12c5ea98c0e..4cc60821606 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -77,69 +77,78 @@ class Service:
         
         return wrap_forward_hook, wrap_backward_hook
 
-    # def create_backward_hook(captured_grads, num_grads, updated_primitive_name, is_input):
-    #     def backward_hook(grad):
-    #         captured_grads.append(grad)
-    #         if len(captured_grads) == num_grads:
-    #             backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-    #             if is_input:
-    #                 new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
-    #                 service_instance.data_collector.backward_input_data_collect(
-    #                     backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-    #                 )
-    #             else:
-    #                 new_module_input_output = ModuleBackwardOutputs(
-    #                     grad_output=tuple(captured_grads)
-    #                 )
-    #                 service_instance.data_collector.backward_output_data_collect(
-    #                     backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-    #                 )
-    #     return backward_hook
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
 
-        def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
-            def input_backward_hook(grad):
+        # def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
+        #     def input_backward_hook(grad):
+        #         try:
+        #             captured_grads_input.append(grad)
+        #             if len(captured_grads_input) == num_tensors:
+        #                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+        #                 service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
+        #                 new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
+        #                 service_instance.data_collector.backward_input_data_collect(
+        #                     backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+        #                 )
+        #                 captured_grads_input.clear()
+        #
+        #         except Exception as exception:
+        #             raise Exception(f"This is a primitive op input_backward dump error: {exception}"
+        #                             f", updated_primitive_name: {updated_primitive_name}")
+        #     return input_backward_hook
+        #
+        # def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
+        #     def output_backward_hook(grad):
+        #         try:
+        #             captured_grads_output.append(grad)
+        #             if len(captured_grads_output) == num_output_tensors:
+        #                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+        #                 service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
+        #                 new_module_input_output = ModuleBackwardOutputs(
+        #                     grad_output=tuple(captured_grads_output)
+        #                 )
+        #                 service_instance.data_collector.backward_output_data_collect(
+        #                     backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+        #                 )
+        #                 # 释放 captured_grads_output 列表
+        #                 captured_grads_output.clear()
+        #         except Exception as exception:
+        #             raise Exception(f"This is a primitive op output_backward dump error: {exception}"
+        #                             f", updated_primitive_name: {updated_primitive_name}")
+        #     return output_backward_hook
+
+        def create_backward_hook(captured_grads, num_tensors, updated_primitive_name, hook_type):
+            def backward_hook(grad):
                 try:
-                    captured_grads_input.append(grad)
-                    if len(captured_grads_input) == num_tensors:
+                    captured_grads.append(grad)
+                    if len(captured_grads) == num_tensors:
                         backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                         service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
-                        new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-                        service_instance.data_collector.backward_input_data_collect(
-                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                        )
-                        captured_grads_input.clear()
 
+                        if hook_type == 'input':
+                            new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
+                            service_instance.data_collector.backward_input_data_collect(
+                                backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                            )
+                        elif hook_type == 'output':
+                            new_module_input_output = ModuleBackwardOutputs(grad_output=tuple(captured_grads))
+                            service_instance.data_collector.backward_output_data_collect(
+                                backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                            )
+
+                        captured_grads.clear()
                 except Exception as exception:
-                    raise Exception(f"This is a primitive op input_backward dump error: {exception}"
+                    raise Exception(f"This is a primitive op {hook_type}_backward dump error: {exception}"
                                     f", updated_primitive_name: {updated_primitive_name}")
-            return input_backward_hook
 
-        def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
-            def output_backward_hook(grad):
-                try:
-                    captured_grads_output.append(grad)
-                    if len(captured_grads_output) == num_output_tensors:
-                        backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                        service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
-                        new_module_input_output = ModuleBackwardOutputs(
-                            grad_output=tuple(captured_grads_output)
-                        )
-                        service_instance.data_collector.backward_output_data_collect(
-                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                        )
-                        # 释放 captured_grads_output 列表
-                        captured_grads_output.clear()
-                except Exception as exception:
-                    raise Exception(f"This is a primitive op output_backward dump error: {exception}"
-                                    f", updated_primitive_name: {updated_primitive_name}")
-            return output_backward_hook
+            return backward_hook
 
         def hook_primitive_inputs(args, captured_grads_input, updated_primitive_name):
             hooked_inputs = []
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
-            input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
+            input_backward_hook = create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name,
+                                                       'input')
             for idx, arg in enumerate(args):
                 if isinstance(arg, Tensor):
                     arg_hooked = ops.HookBackward(input_backward_hook)(arg)
@@ -148,15 +157,19 @@ class Service:
                     hooked_inputs.append(arg)
             return hooked_inputs
 
+        def hook_primitive_outputs(out, captured_grads_output, updated_primitive_name):
+            num_output_tensors = sum(
+                isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
+            output_backward_hook = create_backward_hook(captured_grads_output, num_output_tensors,
+                                                        updated_primitive_name, 'output')
 
-        def hook_primitive_outputs(out, create_output_backward_hook, output_backward_hook):
             if isinstance(out, Tensor):
-                return ops.HookBackward(create_output_backward_hook)(out)
+                return ops.HookBackward(output_backward_hook)(out)
             elif isinstance(out, tuple):
                 hooked_outputs = []
                 for tensor in out:
                     if isinstance(tensor, Tensor):
-                        hooked_outputs.append(ops.HookBackward(create_output_backward_hook)(tensor))
+                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
                     else:
                         hooked_outputs.append(tensor)
                 return tuple(hooked_outputs)
@@ -172,39 +185,41 @@ class Service:
                 if not service_instance.switch:
                     return origin_func(*args, **kwargs)
 
-                hooked_inputs = []
-                num_tensors = sum(isinstance(arg, Tensor) for arg in args)
-                input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
-                for idx, arg in enumerate(args):
-                    if isinstance(arg, Tensor):
-                        arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-                        hooked_inputs.append(arg_hooked)
-                    else:
-                        hooked_inputs.append(arg)
+                # hooked_inputs = []
+                # num_tensors = sum(isinstance(arg, Tensor) for arg in args)
+                # input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
+                # for idx, arg in enumerate(args):
+                #     if isinstance(arg, Tensor):
+                #         arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+                #         hooked_inputs.append(arg_hooked)
+                #     else:
+                #         hooked_inputs.append(arg)
+                hooked_inputs = hook_primitive_inputs(args, captured_grads_input, updated_primitive_name)
 
                 out = origin_func(*hooked_inputs, **kwargs)
                 forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
                 service_instance.data_collector.visit_and_clear_overflow_status(forward_primitive_name)
                 if service_instance.data_collector:
                     module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
-                    service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
+                    service_instance.data_collector.forward_data_collect(forward_primitive_name, self,
+                                                                         os.getpid(), module_input_output)
                     if service_instance.data_collector.if_return_forward_new_output():
                         out = service_instance.data_collector.get_forward_new_output()
-
-                num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
-                print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
-                output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
-
-                if isinstance(out, Tensor):
-                    out = ops.HookBackward(output_backward_hook)(out)
-                elif isinstance(out, tuple):
-                    hooked_outputs = []
-                    for tensor in out:
-                        if isinstance(tensor, Tensor):
-                            hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
-                        else:
-                            hooked_outputs.append(tensor)
-                    out = tuple(hooked_outputs)
+                out = hook_primitive_outputs(out, captured_grads_output, updated_primitive_name)
+                # num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
+                # print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
+                # output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
+                #
+                # if isinstance(out, Tensor):
+                #     out = ops.HookBackward(output_backward_hook)(out)
+                # elif isinstance(out, tuple):
+                #     hooked_outputs = []
+                #     for tensor in out:
+                #         if isinstance(tensor, Tensor):
+                #             hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+                #         else:
+                #             hooked_outputs.append(tensor)
+                #     out = tuple(hooked_outputs)
 
                 return out
             except Exception as exception:
-- 
Gitee


From 51a52c0117bb1b244c6c5c010638a3da2f33b883 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 5 Aug 2024 11:47:52 +0800
Subject: [PATCH 52/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 61 -------------------
 1 file changed, 61 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 4cc60821606..200f7759bf8 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -80,44 +80,6 @@ class Service:
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
 
-        # def create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name):
-        #     def input_backward_hook(grad):
-        #         try:
-        #             captured_grads_input.append(grad)
-        #             if len(captured_grads_input) == num_tensors:
-        #                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-        #                 service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
-        #                 new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads_input))
-        #                 service_instance.data_collector.backward_input_data_collect(
-        #                     backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-        #                 )
-        #                 captured_grads_input.clear()
-        #
-        #         except Exception as exception:
-        #             raise Exception(f"This is a primitive op input_backward dump error: {exception}"
-        #                             f", updated_primitive_name: {updated_primitive_name}")
-        #     return input_backward_hook
-        #
-        # def create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name):
-        #     def output_backward_hook(grad):
-        #         try:
-        #             captured_grads_output.append(grad)
-        #             if len(captured_grads_output) == num_output_tensors:
-        #                 backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-        #                 service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
-        #                 new_module_input_output = ModuleBackwardOutputs(
-        #                     grad_output=tuple(captured_grads_output)
-        #                 )
-        #                 service_instance.data_collector.backward_output_data_collect(
-        #                     backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-        #                 )
-        #                 # 释放 captured_grads_output 列表
-        #                 captured_grads_output.clear()
-        #         except Exception as exception:
-        #             raise Exception(f"This is a primitive op output_backward dump error: {exception}"
-        #                             f", updated_primitive_name: {updated_primitive_name}")
-        #     return output_backward_hook
-
         def create_backward_hook(captured_grads, num_tensors, updated_primitive_name, hook_type):
             def backward_hook(grad):
                 try:
@@ -185,15 +147,6 @@ class Service:
                 if not service_instance.switch:
                     return origin_func(*args, **kwargs)
 
-                # hooked_inputs = []
-                # num_tensors = sum(isinstance(arg, Tensor) for arg in args)
-                # input_backward_hook = create_input_backward_hook(captured_grads_input, num_tensors, updated_primitive_name)
-                # for idx, arg in enumerate(args):
-                #     if isinstance(arg, Tensor):
-                #         arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-                #         hooked_inputs.append(arg_hooked)
-                #     else:
-                #         hooked_inputs.append(arg)
                 hooked_inputs = hook_primitive_inputs(args, captured_grads_input, updated_primitive_name)
 
                 out = origin_func(*hooked_inputs, **kwargs)
@@ -206,20 +159,6 @@ class Service:
                     if service_instance.data_collector.if_return_forward_new_output():
                         out = service_instance.data_collector.get_forward_new_output()
                 out = hook_primitive_outputs(out, captured_grads_output, updated_primitive_name)
-                # num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
-                # print(f"Arguments(out): type={type(out)}")  # 打印每个 arg 的类型
-                # output_backward_hook = create_output_backward_hook(captured_grads_output, num_output_tensors, updated_primitive_name)
-                #
-                # if isinstance(out, Tensor):
-                #     out = ops.HookBackward(output_backward_hook)(out)
-                # elif isinstance(out, tuple):
-                #     hooked_outputs = []
-                #     for tensor in out:
-                #         if isinstance(tensor, Tensor):
-                #             hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
-                #         else:
-                #             hooked_outputs.append(tensor)
-                #     out = tuple(hooked_outputs)
 
                 return out
             except Exception as exception:
-- 
Gitee


From b62bcb43e3f7ea0a726187c947437413d5a2f2a4 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 5 Aug 2024 17:25:24 +0800
Subject: [PATCH 53/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 617f30ca1fa..a6c570f55f1 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -142,16 +142,16 @@ class Service:
                 return tuple(hooked_outputs)
             return out
 
-        def func(self, *args, **kwargs):
+        def func(instance_self, *args, **kwargs):
             try:
+                if not service_instance.switch:
+                    return origin_func(*args, **kwargs)
+
                 service_instance._update_primitive_counters(primitive_name)
                 current_count = service_instance.primitive_counters[primitive_name]
                 updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
                 captured_grads_input, captured_grads_output = [], []
 
-                if not service_instance.switch:
-                    return origin_func(*args, **kwargs)
-
                 hooked_inputs = hook_primitive_inputs(args, captured_grads_input, updated_primitive_name)
 
                 out = origin_func(*hooked_inputs, **kwargs)
@@ -159,7 +159,7 @@ class Service:
                 service_instance.data_collector.visit_and_clear_overflow_status(forward_primitive_name)
                 if service_instance.data_collector:
                     module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
-                    service_instance.data_collector.forward_data_collect(forward_primitive_name, self,
+                    service_instance.data_collector.forward_data_collect(forward_primitive_name, instance_self,
                                                                          os.getpid(), module_input_output)
                     if service_instance.data_collector.if_return_forward_new_output():
                         out = service_instance.data_collector.get_forward_new_output()
@@ -191,6 +191,8 @@ class Service:
         self.current_iter += 1
         self.data_collector.update_iter(self.current_iter)
         HOOKCell.cell_count = defaultdict(int)
+        self.primitive_counters.clear()
+
 
     def start(self, model=None):
         self.model = model
-- 
Gitee


From d3fdd9c3d7cc466dac0d3c42976a50bd2c7348f0 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 5 Aug 2024 20:27:10 +0800
Subject: [PATCH 54/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 50 +++++++++++--------
 1 file changed, 29 insertions(+), 21 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index a6c570f55f1..0829465bcc9 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -17,10 +17,10 @@ import os
 import copy
 from pathlib import Path
 import functools
-from mindspore.common.tensor import Tensor
-from mindspore import ops
 from collections import defaultdict
 
+from mindspore.common.tensor import Tensor
+from mindspore import ops
 from msprobe.core.data_dump.data_collector import build_data_collector
 from msprobe.core.data_dump.scope import BaseScope
 from msprobe.mindspore.common.utils import get_rank_if_initialized
@@ -29,7 +29,8 @@ from msprobe.mindspore.common.log import logger
 from msprobe.core.common.utils import Const
 from msprobe.core.common.exceptions import DistributedNotInitializedError
 from msprobe.mindspore.dump.hook_cell.api_registry import api_register
-from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs, ModuleBackwardInputs, ModuleBackwardOutputs
+from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs,\
+    ModuleBackwardInputs, ModuleBackwardOutputs
 from msprobe.mindspore.dump.hook_cell.hook_cell import HOOKCell
 
 
@@ -106,8 +107,13 @@ class Service:
 
                         captured_grads.clear()
                 except Exception as exception:
-                    raise Exception(f"This is a primitive op {hook_type}_backward dump error: {exception}"
-                                    f", updated_primitive_name: {updated_primitive_name}")
+                    raise Exception(
+                        "This is a primitive op {hook_type}_backward dump error: {exception},"
+                        " updated_primitive_name: {updated_primitive_name}".format(
+                            hook_type=hook_type, exception=exception, updated_primitive_name=updated_primitive_name
+                        )
+                    )
+#改为.format()
 
             return backward_hook
 
@@ -116,7 +122,7 @@ class Service:
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             input_backward_hook = create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name,
                                                        'input')
-            for idx, arg in enumerate(args):
+            for _, arg in enumerate(args):
                 if isinstance(arg, Tensor):
                     arg_hooked = ops.HookBackward(input_backward_hook)(arg)
                     hooked_inputs.append(arg_hooked)
@@ -144,14 +150,14 @@ class Service:
 
         def func(instance_self, *args, **kwargs):
             try:
+                service_instance.update_primitive_counters(primitive_name)
+                current_count = service_instance.primitive_counters[primitive_name]
+                updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+
                 if not service_instance.switch:
                     return origin_func(*args, **kwargs)
 
-                service_instance._update_primitive_counters(primitive_name)
-                current_count = service_instance.primitive_counters[primitive_name]
-                updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
                 captured_grads_input, captured_grads_output = [], []
-
                 hooked_inputs = hook_primitive_inputs(args, captured_grads_input, updated_primitive_name)
 
                 out = origin_func(*hooked_inputs, **kwargs)
@@ -167,25 +173,27 @@ class Service:
 
                 return out
             except Exception as exception:
-                raise Exception(f"This is a primitive op dump error: {exception}"
-                                f", primitive_name: {primitive_name}")
+                raise Exception("This is a primitive op dump error: {},"
+                                " primitive_name: {}".format(exception, primitive_name))
+
         return func
 
-    def _update_primitive_counters(self, primitive_name):
+    def update_primitive_counters(self, primitive_name):
         if primitive_name not in self.primitive_counters:
             self.primitive_counters[primitive_name] = 0
         else:
             self.primitive_counters[primitive_name] += 1
 
     def register_hooks(self):
-            primitive_set = set()
-            for name, cell in self.model.cells_and_names():
-                for pname, primitive in cell._primitives.items():
-                    primitive_set.add((pname, primitive))
-
-            for pname, primitive in primitive_set:
-                NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname)})
-                primitive.__class__ = NewPrimitive
+        primitive_set = set()
+        for _, cell in self.model.cells_and_names():
+            for pname, primitive in cell._primitives.items():
+                primitive_set.add((pname, primitive))
+
+        for pname, primitive in primitive_set:
+            NewPrimitive = type('NewPrimitive', (primitive.__class__,),
+                                {'__call__': self.wrap_primitive(primitive.__call__, pname)})
+            primitive.__class__ = NewPrimitive
 
     def step(self):
         self.current_iter += 1
-- 
Gitee


From 2e4e86c099cd49b30d4f8bd4b93ee2c86fb67d10 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 5 Aug 2024 20:33:18 +0800
Subject: [PATCH 55/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 0829465bcc9..8f3e4c3472e 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -112,8 +112,8 @@ class Service:
                         " updated_primitive_name: {updated_primitive_name}".format(
                             hook_type=hook_type, exception=exception, updated_primitive_name=updated_primitive_name
                         )
-                    )
-#改为.format()
+                    ) from exception
+
 
             return backward_hook
 
@@ -174,7 +174,7 @@ class Service:
                 return out
             except Exception as exception:
                 raise Exception("This is a primitive op dump error: {},"
-                                " primitive_name: {}".format(exception, primitive_name))
+                                " primitive_name: {}".format(exception, primitive_name)) from exception
 
         return func
 
-- 
Gitee


From 07bb2eb7e97b96f9c6c095b3bf1e44f3ee3b11fc Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Mon, 5 Aug 2024 20:35:57 +0800
Subject: [PATCH 56/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 8f3e4c3472e..bc81eb7bc56 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -266,8 +266,8 @@ class Service:
     def register_hook_new(self):
         logger.info("The {} hook function is successfully mounted to the model.".format(self.config.task))
         if self.config.level == "L1":
-            # api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
-            # api_register.api_set_hook_func()
+            api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
+            api_register.api_set_hook_func()
             if self.model:
                 self.register_hooks()
     
-- 
Gitee


From 66504478621bb1cf3396fb640983f60c83a91c3d Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 11:16:01 +0800
Subject: [PATCH 57/67] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E6=A3=80=E8=A7=86?=
 =?UTF-8?q?=E6=84=8F=E8=A7=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../data_processor/mindspore_processor.py     |  4 +-
 .../msprobe/mindspore/service.py              | 51 ++++++++++---------
 2 files changed, 28 insertions(+), 27 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
index e7718504791..b28817e4aa7 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
@@ -75,8 +75,8 @@ class MindsporeDataProcessor(BaseDataProcessor):
             return tensor_stat
         elif data.dtype == ms.bool_:
             data_np = data.asnumpy()
-            tensor_stat.max = bool(np.max(data_np))
-            tensor_stat.min = bool(np.min(data_np))
+            tensor_stat.max = np.max(data_np)
+            tensor_stat.min = np.min(data_np)
         elif not data.shape:
             tensor_stat.max = tensor_stat.min = tensor_stat.mean = tensor_stat.norm = data.item()
         elif data.dtype == ms.complex64 or data.dtype == ms.complex128:
diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index bc81eb7bc56..6ff31664156 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -88,24 +88,25 @@ class Service:
 
         def create_backward_hook(captured_grads, num_tensors, updated_primitive_name, hook_type):
             def backward_hook(grad):
+                captured_grads.append(grad)
                 try:
-                    captured_grads.append(grad)
-                    if len(captured_grads) == num_tensors:
+                    if len(captured_grads) == num_tensors and hook_type == Const.INPUT:
                         backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
                         service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
-
-                        if hook_type == 'input':
-                            new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
-                            service_instance.data_collector.backward_input_data_collect(
-                                backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                            )
-                        elif hook_type == 'output':
-                            new_module_input_output = ModuleBackwardOutputs(grad_output=tuple(captured_grads))
-                            service_instance.data_collector.backward_output_data_collect(
-                                backward_primitive_name, service_instance, os.getpid(), new_module_input_output
-                            )
-
+                        new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
+                        service_instance.data_collector.backward_input_data_collect(
+                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                        )
                         captured_grads.clear()
+                    elif len(captured_grads) == num_tensors and hook_type == Const.OUTPUT:
+                        backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                        service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
+                        new_module_input_output = ModuleBackwardOutputs(grad_output=tuple(captured_grads))
+                        service_instance.data_collector.backward_output_data_collect(
+                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                        )
+                        captured_grads.clear()
+
                 except Exception as exception:
                     raise Exception(
                         "This is a primitive op {hook_type}_backward dump error: {exception},"
@@ -114,14 +115,13 @@ class Service:
                         )
                     ) from exception
 
-
             return backward_hook
 
         def hook_primitive_inputs(args, captured_grads_input, updated_primitive_name):
             hooked_inputs = []
             num_tensors = sum(isinstance(arg, Tensor) for arg in args)
             input_backward_hook = create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name,
-                                                       'input')
+                                                       Const.INPUT)
             for _, arg in enumerate(args):
                 if isinstance(arg, Tensor):
                     arg_hooked = ops.HookBackward(input_backward_hook)(arg)
@@ -131,10 +131,12 @@ class Service:
             return hooked_inputs
 
         def hook_primitive_outputs(out, captured_grads_output, updated_primitive_name):
-            num_output_tensors = sum(
-                isinstance(tensor, Tensor) for tensor in out if isinstance(out, tuple)) if isinstance(out, tuple) else 1
+            if isinstance(out, tuple):
+                num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out)
+            else:
+                num_output_tensors = 1
             output_backward_hook = create_backward_hook(captured_grads_output, num_output_tensors,
-                                                        updated_primitive_name, 'output')
+                                                        updated_primitive_name, Const.OUTPUT)
 
             if isinstance(out, Tensor):
                 return ops.HookBackward(output_backward_hook)(out)
@@ -148,7 +150,7 @@ class Service:
                 return tuple(hooked_outputs)
             return out
 
-        def func(instance_self, *args, **kwargs):
+        def wrapped_primitive_call(instance_self, *args, **kwargs):
             try:
                 service_instance.update_primitive_counters(primitive_name)
                 current_count = service_instance.primitive_counters[primitive_name]
@@ -176,7 +178,7 @@ class Service:
                 raise Exception("This is a primitive op dump error: {},"
                                 " primitive_name: {}".format(exception, primitive_name)) from exception
 
-        return func
+        return wrapped_primitive_call
 
     def update_primitive_counters(self, primitive_name):
         if primitive_name not in self.primitive_counters:
@@ -201,7 +203,6 @@ class Service:
         HOOKCell.cell_count = defaultdict(int)
         self.primitive_counters.clear()
 
-
     def start(self, model=None):
         self.model = model
         self.start_call = True
@@ -269,7 +270,7 @@ class Service:
             api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
             api_register.api_set_hook_func()
             if self.model:
+                print(f"Type of self.model: {type(self.model)}")  # 使用 print
+                # 或者使用 logger
+                logger.info(f"Type of self.model: {type(self.model)}")
                 self.register_hooks()
-    
-    
-
-- 
Gitee


From 57b22c4e8a42630c224aeecfef3fc4e2e45075dd Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 11:24:16 +0800
Subject: [PATCH 58/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 6ff31664156..147dfde8b79 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -265,7 +265,7 @@ class Service:
             dump_file_path, stack_file_path, construct_file_path, dump_data_dir, None)
 
     def register_hook_new(self):
-        logger.info("The {} hook function is successfully mounted to the model.".format(self.config.task))
+        logger.info("The {} hook function1111 is successfully mounted to the model.".format(self.config.task))
         if self.config.level == "L1":
             api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
             api_register.api_set_hook_func()
-- 
Gitee


From e9eac8eab43cec60586f062b98669f6a7884a0a3 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 12:43:16 +0800
Subject: [PATCH 59/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 147dfde8b79..9b9da9ec3fe 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -21,6 +21,7 @@ from collections import defaultdict
 
 from mindspore.common.tensor import Tensor
 from mindspore import ops
+from mindspore import nn
 from msprobe.core.data_dump.data_collector import build_data_collector
 from msprobe.core.data_dump.scope import BaseScope
 from msprobe.mindspore.common.utils import get_rank_if_initialized
@@ -31,6 +32,7 @@ from msprobe.core.common.exceptions import DistributedNotInitializedError
 from msprobe.mindspore.dump.hook_cell.api_registry import api_register
 from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs,\
     ModuleBackwardInputs, ModuleBackwardOutputs
+from msprobe.core.common.exceptions import MsprobeException
 from msprobe.mindspore.dump.hook_cell.hook_cell import HOOKCell
 
 
@@ -203,8 +205,16 @@ class Service:
         HOOKCell.cell_count = defaultdict(int)
         self.primitive_counters.clear()
 
+    def check_model_valid(model):
+        if not model or isinstance(model, nn.Cell):
+            return model
+        raise MsprobeException(
+            MsprobeException.INVALID_PARAM_ERROR, "model 参数必须是 mindspore.nn.Cell 类型。"
+        )
+
+
     def start(self, model=None):
-        self.model = model
+        self.model = self.check_model_valid(model)
         self.start_call = True
         logger.info("msprobe: debugger.start() is set successfully")
         if self.config.step and self.current_iter > max(self.config.step):
-- 
Gitee


From deb692050d5252047d5a826dbc4bb2f8b1511bb2 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 12:47:34 +0800
Subject: [PATCH 60/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 9b9da9ec3fe..9f001fd8799 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -205,6 +205,7 @@ class Service:
         HOOKCell.cell_count = defaultdict(int)
         self.primitive_counters.clear()
 
+    @staticmethod
     def check_model_valid(model):
         if not model or isinstance(model, nn.Cell):
             return model
-- 
Gitee


From 9ea3eb706d0fb414ed6149c7016ed5dc5afe767b Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 12:57:13 +0800
Subject: [PATCH 61/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 9f001fd8799..912cb9608ef 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -87,7 +87,7 @@ class Service:
 
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
-
+        
         def create_backward_hook(captured_grads, num_tensors, updated_primitive_name, hook_type):
             def backward_hook(grad):
                 captured_grads.append(grad)
@@ -206,7 +206,7 @@ class Service:
         self.primitive_counters.clear()
 
     @staticmethod
-    def check_model_valid(model):
+    def check_model_valid(self, model):
         if not model or isinstance(model, nn.Cell):
             return model
         raise MsprobeException(
-- 
Gitee


From 89d57719f029081eaa936abeebfd1f0092706e78 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 13:01:59 +0800
Subject: [PATCH 62/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 912cb9608ef..9a240c379db 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -87,7 +87,7 @@ class Service:
 
     def wrap_primitive(self, origin_func, primitive_name):
         service_instance = self
-        
+
         def create_backward_hook(captured_grads, num_tensors, updated_primitive_name, hook_type):
             def backward_hook(grad):
                 captured_grads.append(grad)
@@ -206,7 +206,7 @@ class Service:
         self.primitive_counters.clear()
 
     @staticmethod
-    def check_model_valid(self, model):
+    def check_model_valid(model):
         if not model or isinstance(model, nn.Cell):
             return model
         raise MsprobeException(
@@ -215,7 +215,7 @@ class Service:
 
 
     def start(self, model=None):
-        self.model = self.check_model_valid(model)
+        self.model = Service.check_model_valid(model)
         self.start_call = True
         logger.info("msprobe: debugger.start() is set successfully")
         if self.config.step and self.current_iter > max(self.config.step):
-- 
Gitee


From 1525a443446b9de08412448887c9691948f20811 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 14:15:10 +0800
Subject: [PATCH 63/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 9a240c379db..8b71e988c8d 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -205,7 +205,7 @@ class Service:
         HOOKCell.cell_count = defaultdict(int)
         self.primitive_counters.clear()
 
-    @staticmethod
+#    @staticmethod
     def check_model_valid(model):
         if not model or isinstance(model, nn.Cell):
             return model
@@ -213,7 +213,6 @@ class Service:
             MsprobeException.INVALID_PARAM_ERROR, "model 参数必须是 mindspore.nn.Cell 类型。"
         )
 
-
     def start(self, model=None):
         self.model = Service.check_model_valid(model)
         self.start_call = True
-- 
Gitee


From f7ef581ea6ed7bba6dc7b01c4000004efa93e5a3 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 14:32:52 +0800
Subject: [PATCH 64/67] Update service.py

---
 .../msprobe/mindspore/service.py              | 52 +++++++++++++------
 1 file changed, 35 insertions(+), 17 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index 8b71e988c8d..af0bf466152 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -153,33 +153,51 @@ class Service:
             return out
 
         def wrapped_primitive_call(instance_self, *args, **kwargs):
-            try:
-                service_instance.update_primitive_counters(primitive_name)
-                current_count = service_instance.primitive_counters[primitive_name]
-                updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
 
-                if not service_instance.switch:
-                    return origin_func(*args, **kwargs)
+            service_instance.update_primitive_counters(primitive_name)
+            current_count = service_instance.primitive_counters[primitive_name]
+            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+
+            if not service_instance.switch:
+                return origin_func(*args, **kwargs)
+
+            captured_grads_input, captured_grads_output = [], []
 
-                captured_grads_input, captured_grads_output = [], []
+            try:
                 hooked_inputs = hook_primitive_inputs(args, captured_grads_input, updated_primitive_name)
+            except Exception as exception:
+                raise Exception("This is a primitive op dump error during input hooking: {},"
+                                " primitive_name: {}".format(exception, primitive_name)) from exception
 
+            try:
                 out = origin_func(*hooked_inputs, **kwargs)
-                forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
-                service_instance.data_collector.visit_and_clear_overflow_status(forward_primitive_name)
-                if service_instance.data_collector:
-                    module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
+            except Exception as exception:
+                raise Exception("This is a primitive op dump error during function call: {},"
+                                " primitive_name: {}".format(exception, primitive_name)) from exception
+
+            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
+            service_instance.data_collector.visit_and_clear_overflow_status(forward_primitive_name)
+            if service_instance.data_collector:
+                module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
+                try:
                     service_instance.data_collector.forward_data_collect(forward_primitive_name, instance_self,
                                                                          os.getpid(), module_input_output)
-                    if service_instance.data_collector.if_return_forward_new_output():
-                        out = service_instance.data_collector.get_forward_new_output()
-                out = hook_primitive_outputs(out, captured_grads_output, updated_primitive_name)
+                except Exception as exception:
+                    raise Exception("This is a primitive op dump error during forward data collection: {},"
+                                    " primitive_name: {}".format(exception, primitive_name)) from exception
 
-                return out
+                if service_instance.data_collector.if_return_forward_new_output():
+                    out = service_instance.data_collector.get_forward_new_output()
+
+            try:
+                out = hook_primitive_outputs(out, captured_grads_output, updated_primitive_name)
             except Exception as exception:
-                raise Exception("This is a primitive op dump error: {},"
+                raise Exception("This is a primitive op dump error during output hooking: {},"
                                 " primitive_name: {}".format(exception, primitive_name)) from exception
 
+            return out
+
+
         return wrapped_primitive_call
 
     def update_primitive_counters(self, primitive_name):
@@ -205,7 +223,7 @@ class Service:
         HOOKCell.cell_count = defaultdict(int)
         self.primitive_counters.clear()
 
-#    @staticmethod
+    @staticmethod
     def check_model_valid(model):
         if not model or isinstance(model, nn.Cell):
             return model
-- 
Gitee


From f32090296817bace40c8e971cae1c785866b6d9b Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 14:39:23 +0800
Subject: [PATCH 65/67] Update service.py

---
 debug/accuracy_tools/msprobe/mindspore/service.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index af0bf466152..b795ec10342 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -293,12 +293,9 @@ class Service:
             dump_file_path, stack_file_path, construct_file_path, dump_data_dir, None)
 
     def register_hook_new(self):
-        logger.info("The {} hook function1111 is successfully mounted to the model.".format(self.config.task))
+        logger.info("The {} hook function is successfully mounted to the model.".format(self.config.task))
         if self.config.level == "L1":
             api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
             api_register.api_set_hook_func()
             if self.model:
-                print(f"Type of self.model: {type(self.model)}")  # 使用 print
-                # 或者使用 logger
-                logger.info(f"Type of self.model: {type(self.model)}")
                 self.register_hooks()
-- 
Gitee


From 554232ab08dc914c322e11528019b664068f55c6 Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 15:04:40 +0800
Subject: [PATCH 66/67] primitive op dump

---
 .gitignore                                    |   9 +-
 .../bench_functions/npu_fusion_attention.py   |  56 +-
 .../api_accuracy_checker/common/utils.py      |   6 +-
 .../run_ut/data_generate.py                   |   8 +-
 .../run_ut/multi_run_ut.py                    |  11 +-
 .../tensor_transport_layer/attl.py            |   9 +-
 debug/accuracy_tools/grad_tool/README.md      |  34 +-
 .../grad_tool/common/base_comparator.py       |  20 +-
 .../grad_tool/common/constant.py              |   4 +-
 .../accuracy_tools/grad_tool/common/utils.py  |  11 +-
 .../grad_tool/grad_ms/global_context.py       |  30 +-
 .../grad_tool/grad_ms/grad_analyzer.py        |  26 +-
 .../grad_tool/grad_ms/grad_comparator.py      |  13 +-
 .../accuracy_tools/grad_tool/grad_ms/utils.py |  19 +-
 .../grad_tool/grad_pt/grad_comparator.py      |  15 +-
 .../grad_tool/grad_pt/grad_monitor.py         |   7 +-
 debug/accuracy_tools/msprobe/README.md        |  45 +-
 debug/accuracy_tools/msprobe/config/README.md |  60 +-
 .../msprobe/core/common/const.py              |  22 +-
 .../msprobe/core/common/utils.py              |  11 +-
 .../msprobe/core/common_config.py             |  41 +-
 .../msprobe/core/data_dump/data_collector.py  |  45 +-
 .../core/data_dump/data_processor/base.py     | 105 ++-
 .../core/data_dump/data_processor/factory.py  |  12 +-
 .../data_processor/mindspore_processor.py     |  90 +-
 .../data_processor/pytorch_processor.py       |  75 +-
 .../msprobe/core/data_dump/json_writer.py     |  22 +-
 .../msprobe/mindspore/common/utils.py         |  13 +
 .../mindspore/debugger/debugger_config.py     |  15 +-
 .../mindspore/debugger/precision_debugger.py  |  42 +-
 .../dump/hook_cell/api_registry copy.py       | 198 +++++
 .../mindspore/dump/hook_cell/hook_cell.py     |  18 +-
 .../msprobe/mindspore/ms_config.py            |  30 +-
 .../msprobe/mindspore/service.py              | 183 +++-
 .../run_ut/data_generate.py                   |  12 +-
 .../run_ut/multi_run_ut.py                    |  19 +-
 .../api_accuracy_checker/run_ut/run_ut.py     |  18 +-
 .../pytorch/bench_functions/__init__.py       |  15 +
 .../pytorch/bench_functions/apply_adam_w.py   |  28 +
 .../bench_functions/confusion_transpose.py    |  19 +
 .../pytorch/bench_functions/fast_gelu.py      |  55 ++
 .../bench_functions/layer_norm_eval.py        |   6 +
 .../msprobe/pytorch/bench_functions/linear.py |  12 +
 .../bench_functions/matmul_backward.py        |  48 +
 .../bench_functions/npu_fusion_attention.py   | 421 +++++++++
 .../pytorch/bench_functions/rms_norm.py       |  15 +
 .../pytorch/bench_functions/rotary_mul.py     |  52 ++
 .../bench_functions/scaled_mask_softmax.py    |  26 +
 .../msprobe/pytorch/bench_functions/swiglu.py |  55 ++
 .../msprobe/pytorch/common/parse_json.py      |   4 +-
 .../msprobe/pytorch/common/utils.py           |  35 +
 .../msprobe/pytorch/compare/acc_compare.py    |  29 +-
 .../pytorch/compare/distributed_compare.py    |  16 +-
 .../pytorch/debugger/debugger_config.py       |   2 +-
 .../pytorch/debugger/precision_debugger.py    |   1 +
 .../pytorch/doc/api_accuracy_checker.md       |  68 +-
 .../msprobe/pytorch/doc/dump.md               |  87 +-
 .../pytorch/free_benchmark/common/constant.py |   3 +
 .../pytorch/free_benchmark/common/utils.py    |   4 +
 .../result_handlers/base_handler.py           |  72 +-
 .../msprobe/pytorch/function_factory.py       |  75 ++
 .../pytorch/hook_module/hook_module.py        |   6 +
 .../pytorch/hook_module/support_wrap_ops.yaml |   3 +-
 .../msprobe/pytorch/hook_module/wrap_aten.py  |  21 +-
 .../pytorch/hook_module/wrap_npu_custom.py    |  20 +-
 .../msprobe/pytorch/module_processer.py       |  29 +-
 .../msprobe/pytorch/pt_config.py              |   4 +-
 .../accuracy_tools/msprobe/pytorch/service.py |  15 +-
 .../test/core_ut/test_common_config.py        |   2 +-
 .../test/mindspore_ut/test_ms_config.py       |   4 +-
 .../run_ut/test_multi_run_ut.py               |   4 +-
 .../msprobe/test/pytorch_ut/test_pt_config.py |   2 +-
 debug/accuracy_tools/setup.py                 |   2 +-
 .../.github/workflows/libkineto_ci.yml        |  56 --
 .../workflows/tb_plugin_build_pip_package.yml |  19 -
 .../.github/workflows/tb_plugin_ci.yml        |  57 --
 plugins/tensorboard-plugins/.gitignore        |   3 -
 plugins/tensorboard-plugins/.gitmodules       |   6 -
 .../tensorboard-plugins/CODE_OF_CONDUCT.md    |  77 --
 plugins/tensorboard-plugins/CONTRIBUTING.md   |  34 -
 plugins/tensorboard-plugins/LICENSE           |  33 -
 plugins/tensorboard-plugins/README.md         |  38 -
 .../libkineto/CMakeLists.txt                  | 198 -----
 .../tensorboard-plugins/libkineto/README.md   |  65 --
 .../libkineto/include/AbstractConfig.h        | 113 ---
 .../include/ActivityProfilerInterface.h       |  91 --
 .../include/ActivityTraceInterface.h          |  21 -
 .../libkineto/include/ActivityType.h          |  34 -
 .../libkineto/include/ClientInterface.h       |  16 -
 .../libkineto/include/Config.h                | 433 ---------
 .../libkineto/include/GenericTraceActivity.h  | 125 ---
 .../libkineto/include/IActivityProfiler.h     | 104 ---
 .../libkineto/include/ILoggerObserver.h       |  50 --
 .../libkineto/include/ITraceActivity.h        |  53 --
 .../libkineto/include/ThreadUtil.h            |  22 -
 .../libkineto/include/TraceSpan.h             |  36 -
 .../libkineto/include/libkineto.h             | 138 ---
 .../libkineto/include/time_since_epoch.h      |  16 -
 .../libkineto/libkineto_defs.bzl              |  77 --
 .../sample_programs/kineto_playground.cpp     |  38 -
 .../sample_programs/kineto_playground.cu      |  60 --
 .../sample_programs/kineto_playground.cuh     |  18 -
 .../libkineto/src/AbstractConfig.cpp          | 188 ----
 .../libkineto/src/ActivityBuffers.h           |  29 -
 .../libkineto/src/ActivityLoggerFactory.h     |  60 --
 .../src/ActivityProfilerController.cpp        | 246 -----
 .../src/ActivityProfilerController.h          |  84 --
 .../libkineto/src/ActivityProfilerProxy.cpp   | 119 ---
 .../libkineto/src/ActivityProfilerProxy.h     |  73 --
 .../libkineto/src/ActivityTrace.h             |  45 -
 .../libkineto/src/ActivityType.cpp            |  58 --
 .../libkineto/src/Config.cpp                  | 473 ----------
 .../libkineto/src/ConfigLoader.cpp            | 300 -------
 .../libkineto/src/ConfigLoader.h              | 147 ---
 .../libkineto/src/CudaDeviceProperties.cpp    | 130 ---
 .../libkineto/src/CudaDeviceProperties.h      |  31 -
 .../libkineto/src/CuptiActivity.h             | 114 ---
 .../libkineto/src/CuptiActivity.tpp           | 111 ---
 .../libkineto/src/CuptiActivityApi.cpp        | 343 -------
 .../libkineto/src/CuptiActivityApi.h          | 100 ---
 .../libkineto/src/CuptiActivityBuffer.h       |  51 --
 .../libkineto/src/CuptiActivityPlatform.cpp   |  31 -
 .../libkineto/src/CuptiActivityPlatform.h     |  12 -
 .../libkineto/src/CuptiActivityProfiler.cpp   | 841 ------------------
 .../libkineto/src/CuptiActivityProfiler.h     | 364 --------
 .../libkineto/src/CuptiCallbackApi.cpp        | 260 ------
 .../libkineto/src/CuptiCallbackApi.h          | 130 ---
 .../libkineto/src/CuptiCallbackApiMock.h      |  32 -
 .../libkineto/src/CuptiEventApi.cpp           | 112 ---
 .../libkineto/src/CuptiEventApi.h             |  49 -
 .../libkineto/src/CuptiMetricApi.cpp          | 107 ---
 .../libkineto/src/CuptiMetricApi.h            |  38 -
 .../libkineto/src/CuptiNvPerfMetric.cpp       | 504 -----------
 .../libkineto/src/CuptiNvPerfMetric.h         |  71 --
 .../libkineto/src/CuptiRangeProfilerApi.cpp   | 751 ----------------
 .../libkineto/src/CuptiRangeProfilerApi.h     | 220 -----
 .../src/CuptiRangeProfilerConfig.cpp          |  68 --
 .../libkineto/src/CuptiRangeProfilerConfig.h  |  86 --
 .../libkineto/src/DaemonConfigLoader.h        |  27 -
 .../libkineto/src/Demangle.cpp                |  49 -
 .../libkineto/src/Demangle.h                  |  12 -
 .../libkineto/src/EventProfiler.cpp           | 635 -------------
 .../libkineto/src/EventProfiler.h             | 341 -------
 .../libkineto/src/EventProfilerController.cpp | 423 ---------
 .../libkineto/src/EventProfilerController.h   |  63 --
 .../libkineto/src/GenericTraceActivity.cpp    |  10 -
 .../libkineto/src/ILoggerObserver.cpp         |  54 --
 .../libkineto/src/Logger.cpp                  | 136 ---
 .../libkineto/src/Logger.h                    | 244 -----
 .../libkineto/src/LoggerCollector.h           |  70 --
 .../libkineto/src/RoctracerActivityApi.cpp    | 569 ------------
 .../libkineto/src/RoctracerActivityApi.h      | 171 ----
 .../libkineto/src/RoctracerActivityBuffer.h   |  30 -
 .../libkineto/src/SampleListener.h            | 146 ---
 .../libkineto/src/ScopeExit.h                 |  29 -
 .../libkineto/src/ThreadUtil.cpp              | 203 -----
 .../libkineto/src/WeakSymbols.cpp             |  12 -
 .../libkineto/src/cupti_call.h                |  33 -
 .../libkineto/src/cupti_strings.cpp           | 502 -----------
 .../libkineto/src/cupti_strings.h             |  14 -
 .../libkineto/src/init.cpp                    | 139 ---
 .../libkineto/src/libkineto_api.cpp           |  41 -
 .../libkineto/src/output_base.h               | 104 ---
 .../libkineto/src/output_csv.cpp              |  88 --
 .../libkineto/src/output_csv.h                |  39 -
 .../libkineto/src/output_json.cpp             | 583 ------------
 .../libkineto/src/output_json.h               |  91 --
 .../libkineto/src/output_membuf.h             | 130 ---
 .../libkineto/test/CMakeLists.txt             |   3 -
 .../libkineto/test/ConfigTest.cpp             | 315 -------
 .../test/CuptiActivityProfilerTest.cpp        | 629 -------------
 .../libkineto/test/CuptiCallbackApiTest.cpp   | 239 -----
 .../libkineto/test/CuptiProfilerApiTest.cu    | 353 --------
 .../test/CuptiRangeProfilerApiTest.cpp        | 113 ---
 .../test/CuptiRangeProfilerConfigTest.cpp     |  67 --
 .../test/CuptiRangeProfilerTestUtil.h         |  96 --
 .../libkineto/test/CuptiStringsTest.cpp       |  29 -
 .../libkineto/test/EventProfilerTest.cpp      | 578 ------------
 .../libkineto/test/LoggerObserverTest.cpp     |  96 --
 .../test/MockActivitySubProfiler.cpp          |  49 -
 .../libkineto/test/MockActivitySubProfiler.h  |  72 --
 .../libkineto/test/PidInfoTest.cpp            |  27 -
 profiler/README.md                            |   1 +
 profiler/advisor/README.md                    |   5 +-
 profiler/advisor/common/profiling/ge_info.py  |   3 +-
 profiler/advisor/common/profiling/msprof.py   |   3 +-
 .../advisor/common/profiling/op_summary.py    |   4 +-
 profiler/advisor/common/profiling/tasktime.py |   4 +-
 .../config/profiling_data_version_config.yaml |  17 +-
 .../dataset/profiling/profiling_dataset.py    |  11 +-
 .../dataset/profiling/profiling_parser.py     |  27 +-
 profiler/advisor/img/overall.png              | Bin 64492 -> 49616 bytes
 profiler/advisor/img/overall_0.png            | Bin 0 -> 56377 bytes
 profiler/advisor/utils/utils.py               |  12 +-
 profiler/affinity_cpu_bind/README.md          |  40 -
 profiler/affinity_cpu_bind/bind_core.py       | 213 -----
 profiler/cli/cluster_cli.py                   |   4 +-
 profiler/cli/compare_cli.py                   |   2 +
 profiler/cluster_analyse/README.md            |  39 +-
 .../analysis/analysis_facade.py               |  12 -
 .../cluster_analyse/analysis/base_analysis.py | 153 ----
 .../analysis/cann_api_sum/__init__.py         |  14 -
 .../analysis/cann_api_sum/cann_api_sum.py     | 108 ---
 .../analysis/cann_api_sum/stats.ipynb         |  86 --
 .../analysis/cluster_display.py               | 239 -----
 .../analysis/compute_op_sum/__init__.py       |  14 -
 .../analysis/compute_op_sum/compute_op_sum.py | 103 ---
 .../analysis/compute_op_sum/stats.ipynb       | 164 ----
 .../analysis/hccl_sum/__init__.py             |  14 -
 .../analysis/hccl_sum/hccl_sum.py             | 133 ---
 .../analysis/hccl_sum/stats.ipynb             | 162 ----
 .../analysis/mstx_sum/__init__.py             |  14 -
 .../analysis/mstx_sum/mstx_sum.py             | 204 -----
 .../analysis/mstx_sum/stats.ipynb             | 180 ----
 profiler/cluster_analyse/cluster_analysis.py  |  75 +-
 .../cluster_statistics_export/__init__.py     |  14 -
 .../cann_api_sum_export.py                    |  65 --
 .../compute_op_sum_export.py                  |  49 -
 .../hccl_sum_export.py                        |  39 -
 .../mstx_mark_export.py                       |  57 --
 .../mstx_step_export.py                       |  35 -
 .../cluster_statistics_export/stats_export.py |  40 -
 .../common_func/analysis_loader.py            |  38 -
 .../cluster_analyse/common_func/constant.py   |  10 -
 .../cluster_analyse/common_func/context.py    |  85 --
 .../cluster_analyse/common_func/db_manager.py |   7 -
 .../common_func/sql_extention_func.py         |  73 --
 profiler/cluster_analyse/common_func/utils.py |  73 --
 profiler/compare_tools/README.md              |  82 +-
 .../comparator/api_compare_comparator.py      |  32 +
 .../comparator/kernel_compare_comparator.py   |  35 +
 .../compare_bean/api_compare_bean.py          |  47 +
 .../compare_bean/kernel_compare_bean.py       |  75 ++
 .../origin_data_bean/kernel_details_bean.py   |   6 +
 .../data_prepare/operator_data_prepare.py     |  17 +
 .../generator/detail_performance_generator.py |  22 +-
 .../profiling_parser/base_profiling_parser.py |  19 +-
 .../profiling_parser/gpu_profiling_parser.py  |   5 +
 .../profiling_parser/npu_profiling_parser.py  |  24 +
 .../compare_backend/utils/args_manager.py     |  13 +-
 .../compare_backend/utils/compare_args.py     |   4 +
 .../compare_backend/utils/constant.py         |   7 +-
 .../compare_backend/utils/excel_config.py     |  48 +-
 .../compare_backend/utils/torch_op_node.py    |   8 +
 .../compare_backend/utils/tree_builder.py     |   3 +-
 .../view/work_sheet_creator.py                |  12 +-
 profiler/compare_tools/img/OverallMetrics.png | Bin 0 -> 66941 bytes
 profiler/compare_tools/performance_compare.py |   2 +
 profiler/merge_profiling_timeline/README.md   | 115 ---
 profiler/merge_profiling_timeline/main.py     | 233 -----
 ...\345\257\274\346\210\252\345\233\2761.png" | Bin 53047 -> 0 bytes
 ...\345\257\274\346\210\252\345\233\2762.png" | Bin 64432 -> 0 bytes
 profiler/module_visualization/__init__.py     |   0
 .../module_visualization/graph/__init__.py    |   0
 .../module_visualization/graph/prof_node.py   |  90 --
 .../graph_build/__init__.py                   |   0
 .../graph_build/fwd_module_node.py            |  29 -
 .../graph_build/prof_graph_builder.py         | 115 ---
 .../module_visualization/prof_graph_export.py |  39 -
 .../prof_parse/__init__.py                    |   0
 .../prof_parse/prof_data_pre_process.py       | 102 ---
 .../test_base_profiling_parser.py             |   5 +
 262 files changed, 2720 insertions(+), 19791 deletions(-)
 create mode 100644 debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry copy.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/__init__.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/apply_adam_w.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/confusion_transpose.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/fast_gelu.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/layer_norm_eval.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/linear.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/matmul_backward.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/npu_fusion_attention.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/rms_norm.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/rotary_mul.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/scaled_mask_softmax.py
 create mode 100644 debug/accuracy_tools/msprobe/pytorch/bench_functions/swiglu.py
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diff --git a/.gitignore b/.gitignore
index c70c40e0f52..01a2222429c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -142,4 +142,11 @@ cython_debug/
 att_advisor*.html
 *.xlsx
 operator_tuning_file*.cfg
-.ipynb_checkpoints/
\ No newline at end of file
+.ipynb_checkpoints/
+.idea/vcs.xml
+.idea/inspectionProfiles/profiles_settings.xml
+.idea/misc.xml
+.idea/modules.xml
+.idea/mstt_primitive.iml
+.idea/.gitignore
+.gitignore
diff --git a/debug/accuracy_tools/api_accuracy_checker/bench_functions/npu_fusion_attention.py b/debug/accuracy_tools/api_accuracy_checker/bench_functions/npu_fusion_attention.py
index 4c230c17c04..d5a91ce3b5f 100644
--- a/debug/accuracy_tools/api_accuracy_checker/bench_functions/npu_fusion_attention.py
+++ b/debug/accuracy_tools/api_accuracy_checker/bench_functions/npu_fusion_attention.py
@@ -8,7 +8,6 @@ from api_accuracy_checker.common.utils import logger
 gtype = torch.float64  # arm host必须选择float64，x86环境选择float32即可，64也行。arm计算很慢，s=8k的场景建议使用x86
 softmax_build_mode = "QKV"  # "MAX_SUM"
 
-
 """
 # 前向函数声明对比
 标杆实现:fusion_attention_forward: q, k, v, drop_mask, atten_mask, pse, scale, keep_prob
@@ -45,6 +44,9 @@ def softmax_grad(dp, softmax_res):
 
 
 def broadcast_kv(num_heads, num_kv_heads, kv_tensor, dtype):
+    if num_kv_heads == 0 or num_kv_heads < num_heads:
+        raise ValueError(f"num_kv_heads must be non-zero and less than num_heads.")
+
     factor = num_heads // num_kv_heads
     kv_shape = kv_tensor.shape
     B = kv_shape[0]
@@ -102,28 +104,34 @@ def parse_bsnd_args(query, key, head_num, input_layout):
     if not isinstance(input_layout, str) or input_layout not in supported_input_layout:
         raise ValueError(f"Invalid input_layout arg which must be one of {supported_input_layout}.")
 
-    if input_layout == "BSH":
-        B, S1, H1 = query.shape
-        _, S2, H2 = key.shape
-        D = H1 // N1
-        N2 = H2 // D
-    elif input_layout == "SBH":
-        S1, B, H1 = query.shape
-        S2, _, H2 = key.shape
-        D = H1 // N1
-        N2 = H2 // D
-    elif input_layout == "BSND":
-        B, S1, N1, D = query.shape
-        _, S2, N2, _ = key.shape
-        H1 = N1 * D
-        H2 = N2 * D
-    elif input_layout == "BNSD":
-        B, N1, S1, D = query.shape
-        _, N2, S2, _ = key.shape
-        H1 = N1 * D
-        H2 = N2 * D
-    elif input_layout == "TND":
+    if input_layout == "TND":
         raise ValueError(f"input_layout {input_layout} does not supported for now.")
+    try:
+        if input_layout == "BSH":
+            B, S1, H1 = query.shape
+            _, S2, H2 = key.shape
+            D = H1 // N1
+            N2 = H2 // D
+        elif input_layout == "SBH":
+            S1, B, H1 = query.shape
+            S2, _, H2 = key.shape
+            D = H1 // N1
+            N2 = H2 // D
+        elif input_layout == "BSND":
+            B, S1, N1, D = query.shape
+            _, S2, N2, _ = key.shape
+            H1 = N1 * D
+            H2 = N2 * D
+        elif input_layout == "BNSD":
+            B, N1, S1, D = query.shape
+            _, N2, S2, _ = key.shape
+            H1 = N1 * D
+            H2 = N2 * D
+    except Exception as e:
+        raise ValueError(f"query.shape: {query.shape}, key.shape: {key.shape}, parse_bsnd_args error: {e}") from e
+
+    if D == 0:
+        raise ValueError(f"Value D must be non-zero.")
     DTYPE = query.dtype
     return B, S1, S2, N1, N2, D, H1, H2, DTYPE
 
@@ -251,6 +259,8 @@ def rebuild_softmax_by_max_sum(q, k, atten_mask, pse, scale, softmax_max, softma
     """
     print(f"Using softmax_max and softmax_sum to rebuild original softmax")
     qk = calculate_qk(q, k, atten_mask, pse, scale)
+    if softmax_max.shape[-1] == 0:
+        raise ValueError(f"softmax_max.shape[-1] must be non-zero, softmax_max.shape: {softmax_max.shape}")
     repeat_dim = qk.shape[-1] // softmax_max.shape[-1]
     softmax_res = torch.exp(qk.sub(softmax_max.repeat(1, 1, 1, repeat_dim))).div(
         softmax_sum.repeat(1, 1, 1, repeat_dim))
@@ -394,6 +404,8 @@ def npu_fusion_attention_grad(*args, **kwargs):
 
     # N不等长适配by cdy
     if not (N1 == N2):
+        if N2 == 0:
+            raise ValueError("dims_kwargs.N2 must be non-zero.")
         G = int(N1 / N2)
         dk = torch.sum(dk.reshape(B, N2, G, S2, D), dim=2, keepdim=True).reshape(B, N2, S2, D)
         dv = torch.sum(dv.reshape(B, N2, G, S2, D), dim=2, keepdim=True).reshape(B, N2, S2, D)
diff --git a/debug/accuracy_tools/api_accuracy_checker/common/utils.py b/debug/accuracy_tools/api_accuracy_checker/common/utils.py
index 76d117afb49..83b73e90f97 100644
--- a/debug/accuracy_tools/api_accuracy_checker/common/utils.py
+++ b/debug/accuracy_tools/api_accuracy_checker/common/utils.py
@@ -634,7 +634,11 @@ def initialize_save_path(save_path, dir_name):
 def write_pt(file_path, tensor):
     if os.path.exists(file_path):
         raise ValueError(f"File {file_path} already exists")
-    torch.save(tensor, file_path)
+    try:
+        torch.save(tensor, file_path)
+    except Exception as e:
+        error_message = "An unexpected error occurred: %s when saving tensor to %s" % (str(e), file_path)
+        print_error_log(error_message)
     full_path = os.path.realpath(file_path)
     file_check_util.change_mode(full_path, FileCheckConst.DATA_FILE_AUTHORITY)
     return full_path
diff --git a/debug/accuracy_tools/api_accuracy_checker/run_ut/data_generate.py b/debug/accuracy_tools/api_accuracy_checker/run_ut/data_generate.py
index 67dc5ad2532..57811648391 100644
--- a/debug/accuracy_tools/api_accuracy_checker/run_ut/data_generate.py
+++ b/debug/accuracy_tools/api_accuracy_checker/run_ut/data_generate.py
@@ -20,9 +20,10 @@ import math
 import torch
 import numpy
 
-from api_accuracy_checker.common.utils import Const, check_file_or_directory_path, check_object_type, print_warn_log, \
-    print_error_log, get_full_data_path, CompareException
+from api_accuracy_checker.common.utils import Const, check_object_type, print_warn_log, print_error_log, \
+    get_full_data_path, CompareException
 from api_accuracy_checker.run_ut.run_ut_utils import hf_32_standard_api
+from ptdbg_ascend.src.python.ptdbg_ascend.common.file_check_util import FileCheckConst, FileChecker
 
 TORCH_TYPE = ["torch.device", "torch.dtype"]
 TENSOR_DATA_LIST = ["torch.Tensor", "torch.nn.parameter.Parameter"]
@@ -83,7 +84,8 @@ def gen_real_tensor(data_path, convert_type):
         convert_type: convert ori_type to dist_type flag.
     """
     data_path = os.path.realpath(data_path)
-    check_file_or_directory_path(data_path)
+    data_path_checker = FileChecker(data_path, FileCheckConst.FILE, ability=FileCheckConst.READ_ABLE)
+    data_path = data_path_checker.common_check()
     if not data_path.endswith('.pt') and not data_path.endswith('.npy'):
         error_info = f"The file: {data_path} is not a pt or numpy file."
         raise CompareException(CompareException.INVALID_FILE_ERROR, error_info)
diff --git a/debug/accuracy_tools/api_accuracy_checker/run_ut/multi_run_ut.py b/debug/accuracy_tools/api_accuracy_checker/run_ut/multi_run_ut.py
index df6c99a567c..0ab8073937f 100644
--- a/debug/accuracy_tools/api_accuracy_checker/run_ut/multi_run_ut.py
+++ b/debug/accuracy_tools/api_accuracy_checker/run_ut/multi_run_ut.py
@@ -88,14 +88,9 @@ def run_parallel_ut(config):
     
     def update_progress_bar(progress_bar, result_csv_path):
         while any(process.poll() is None for process in processes):
-            try:
-                with open(result_csv_path, 'r') as result_file:
-                    completed_items = len(result_file.readlines()) - 1
-                    progress_bar.update(completed_items - progress_bar.n)
-            except FileNotFoundError:
-                print_warn_log(f"Result CSV file not found: {result_csv_path}.")
-            except Exception as e:
-                print_error_log(f"An unexpected error occurred while reading result CSV: {e}")
+            with FileOpen(result_csv_path, 'r') as result_file:
+                completed_items = len(result_file.readlines()) - 1
+                progress_bar.update(completed_items - progress_bar.n)
             time.sleep(1)
     
     for fwd, bwd in zip(config.forward_files, config.backward_files):
diff --git a/debug/accuracy_tools/api_accuracy_checker/tensor_transport_layer/attl.py b/debug/accuracy_tools/api_accuracy_checker/tensor_transport_layer/attl.py
index 0b91d2bbc82..5fb63779fbb 100644
--- a/debug/accuracy_tools/api_accuracy_checker/tensor_transport_layer/attl.py
+++ b/debug/accuracy_tools/api_accuracy_checker/tensor_transport_layer/attl.py
@@ -13,6 +13,7 @@ import torch
 from api_accuracy_checker.tensor_transport_layer.client import TCPClient
 from api_accuracy_checker.tensor_transport_layer.server import TCPServer
 from api_accuracy_checker.common.utils import logger
+from ptdbg_ascend.src.python.ptdbg_ascend.common.file_check_util import FileCheckConst, FileChecker
 from ptdbg_ascend.src.python.ptdbg_ascend.common.utils import remove_path
 
 
@@ -138,8 +139,10 @@ class ATTL:
             file_path = os.path.join(self.session_config.nfs_path, buffer.name + ".pt")
         else:
             file_path = os.path.join(self.session_config.nfs_path, buffer + f"_{int(time.time())}")
-
-        torch.save(buffer, file_path)
+        try:
+            torch.save(buffer, file_path)
+        except Exception as e:
+            self.logger.error("there is something error. please check it. %s", e)
 
     def download(self):
         for file_type in ("start*", "*.pt", "end*"):
@@ -150,6 +153,8 @@ class ATTL:
         if cur_file is None:
             return None
         else:
+            cur_file_checker = FileChecker(cur_file, FileCheckConst.FILE, ability=FileCheckConst.READ_ABLE)
+            cur_file = cur_file_checker.common_check()
             buffer = torch.load(cur_file)
             remove_path(cur_file)
             return buffer
diff --git a/debug/accuracy_tools/grad_tool/README.md b/debug/accuracy_tools/grad_tool/README.md
index a7929ca8187..1d35f03e479 100644
--- a/debug/accuracy_tools/grad_tool/README.md
+++ b/debug/accuracy_tools/grad_tool/README.md
@@ -28,7 +28,7 @@
 
 ### 梯度数据导出
 
-1. 创建配置文件config.yaml，PyTorch框架样例代码如下：
+1. 创建配置文件config.yaml，样例如下：
 
    ```python
    level: L1
@@ -38,40 +38,30 @@
    bounds: 
    output_path: your_output_dir
    ```
-   > 在MindSpore框架下，当前不支持rank和step配置，默认所有rank和所有step都进行采集，
-   > MindSpore中step指的是优化器被调用的次数（并非模型跑的step，某些step，例如loss为nan时，不会调用优化器）
+   > step指的是优化器被调用的次数（并非模型跑的step，某些step，例如loss为nan时，不会调用优化器）
 
    **参数说明**
 
-   | 参数                       | 说明                                               | 是否必选 |
-   |--------------------------------|----------------------------------------------------|----------|
-   | level                  | Level级别，PyTorch可取值：L0、L1、L2，MindSpore可取值：L0, L1, L2, L3。决定导出数据的详细程度，级别越大导出数据越详细。数据类型：str。 | PyTorch是（MindSpore否，默认为L0）       |
-   | param_list             | 填写需要监控的权重名称。不指定或列表为空就表示监控所有权重。数据类型：List[str]。 | 否       |
-   | rank                   | 在多卡场景下，填写需要导出梯度数据的卡的Rank ID，不指定或列表为空就表示导出所有Rank的数据。单卡场景无需关注该参数。数据类型：List[int]。（MindSpore当前不支持指定rank） | 否       |
-   | step                   | 指定需要导出数据的step。对于PyTorch不指定或列表为空就表示导出所有step的数据，对于MindSpore不指定表示导出所有step，指定时要求传入range列表，例如[1, 2]，否则无效。数据类型：List[int]。（MindSpore当前不支持指定step） | 否 |
-   | bounds                 | 用来划分区间以统计值分布。需要保证由数据小到大排列。不传则使用默认值[-10, -1, -0.1, -0.01, -0.001, 0, 0.001, 0.01, 0.1, 1, 10]（mindspore为[-0.1, 0., 1.0]），数据类型：List。 | 否  |
-   | output_path            | 输出目录。如果不存在就会创建一个新目录。数据类型：str。 | PyTorch是（MindSpore否，默认为./grad_stat |
+   | 参数                       | 说明                          | 输入类型                     | 是否必选 |
+   |--------------------------------|-----------------------------------|-----------------|----------|
+   | level                  | 输出级别。决定导出数据的详细程度，级别越大导出数据越详细。可取值：L0, L1, L2|str  | 是     |
+   | param_list             | 权重名称列表，表示需要监控的权重。不指定或列表为空就表示监控所有权重。 | List[str] | 否       |
+   | rank                   | rank id列表，在多卡场景下，表示需要导出梯度数据的进程的rank id。不指定或列表为空就表示导出所有rank的数据。单卡场景无需关注该参数。 （MindSpore静态图模式下，当前暂不支持指定rank功能） | List[int] | 否       |
+   | step                   | step列表，表示需要导出数据的step列表。不指定或列表为空就表示导出所有step的数据。（MindSpore静态图模式下，当前暂不支持指定step功能） | List[int] | 否 |
+   | bounds                 | 区间列表，用来划分区间以统计数值的分布。需要保证由数据小到大排列。不指定则使用默认值[-10, -1, -0.1, -0.01, -0.001, 0, 0.001, 0.01, 0.1, 1, 10] | List[float] | 否  |
+   | output_path            | 输出目录。如果不存在就会创建一个新目录。 |  str | 是 |
 
    **不同级别的level的导出数据**
 
-- PyTorch/MindSpore动态图不同level数据
 
    | 级别 | 特征数据表头                                                 | 是否有方向数据 |
    | ---- | ------------------------------------------------------------ | -------------- |
    | L0   | ("param_name", "MD5", "max", "min", "norm", "shape")         | 否             |
    | L1   | ("param_name", "max", "min", "norm", "shape")         | 是             |
    | L2   | ("param_name", *intervals, "=0", "max", "min", "norm", "shape") | 是             |
-
-- MindSpore静态图不同level数据
-
-   | 级别 | 特征数据表头                                                 | 是否有方向数据 |
-   | ---- | ------------------------------------------------------------ | -------------- |
-   | L0   | ("param_name", "max", "min", "norm", "shape")         | 否             |
-   | L1   | ("param_name", *intervals, "=0", "max", "min", "norm", "shape")         | 否             |
-   | L2   | ("param_name", "max", "min", "norm", "shape") | 是             |
-   | L3   | ("param_name", *intervals, "=0", "max", "min", "norm", "shape") | 是             |
    
    intervals就是根据值分布bounds划分出的区间。
+   MindSpore静态图模式下，L0级别中暂不支持"MD5"
    
    **方向数据解释**
    
@@ -98,7 +88,7 @@ gm = GradientMonitor("config_path", framework="MindSpore")
 gm.monitor(optimizer)
 ```
 
-3. 结束监控（MindSpore需要）
+3. 结束监控（MindSpore静态图模式下需要）
 
    在训练结束之后，调用stop接口
 
diff --git a/debug/accuracy_tools/grad_tool/common/base_comparator.py b/debug/accuracy_tools/grad_tool/common/base_comparator.py
index d3254ae71f9..03f74a21e47 100644
--- a/debug/accuracy_tools/grad_tool/common/base_comparator.py
+++ b/debug/accuracy_tools/grad_tool/common/base_comparator.py
@@ -7,7 +7,10 @@ import pandas as pd
 import matplotlib.pyplot as plt
 
 from grad_tool.common.constant import GradConst
-from grad_tool.common.utils import write_csv, check_file_or_directory_path, print_info_log, create_directory
+from grad_tool.common.utils import write_csv, check_file_or_directory_path, print_info_log, create_directory, print_error_log
+
+from ptdbg_ascend.src.python.ptdbg_ascend.common import file_check_util
+from ptdbg_ascend.src.python.ptdbg_ascend.common.file_check_util import FileCheckConst, check_path_pattern_valid, check_path_length
 
 
 class BaseComparator(ABC):
@@ -85,8 +88,19 @@ class BaseComparator(ABC):
             picture_dir = os.path.join(output_dir, "similarities_picture")
             if not os.path.isdir(picture_dir):
                 create_directory(picture_dir)
-            plt.savefig(os.path.join(picture_dir, f"{key}_similarities.png"))
-            plt.close()
+            file_path= os.path.join(picture_dir, f"{key}_similarities.png")
+            if os.path.exists(file_path):
+                raise ValueError(f"File {file_path} already exists")
+            check_path_length(file_path)
+            check_path_pattern_valid(file_path)
+            try:
+                plt.savefig(file_path)
+                plt.close()
+            except Exception as e:
+                error_message = "An unexpected error occurred: %s when savfig to %s" % (str(e), file_path)
+                print_error_log(error_message)
+            full_path = os.path.realpath(file_path)
+            file_check_util.change_mode(full_path, FileCheckConst.DATA_FILE_AUTHORITY)
             head_tuple = tuple(['step'] + [str(step) for step in steps])
             write_csv(os.path.join(output_dir, "similarities.csv"), [[key] + value], head_tuple)
 
diff --git a/debug/accuracy_tools/grad_tool/common/constant.py b/debug/accuracy_tools/grad_tool/common/constant.py
index d569d47c16d..38d33e98864 100644
--- a/debug/accuracy_tools/grad_tool/common/constant.py
+++ b/debug/accuracy_tools/grad_tool/common/constant.py
@@ -23,8 +23,7 @@ class GradConst:
     LEVEL0 = "L0"
     LEVEL1 = "L1"
     LEVEL2 = "L2"
-    LEVEL3 = "L3"
-    SUPPORTED_LEVEL = {"L0", "L1", "L2", "L3"}
+    SUPPORTED_LEVEL = {"L0", "L1", "L2"}
 
     # numpy coding
     STEP_IDX = 0
@@ -40,6 +39,7 @@ class GradConst:
     DIRECTORY_LENGTH = 4096
     FILE_NAME_LENGTH = 255
     FILE_VALID_PATTERN = r"^[a-zA-Z0-9_.:/-]+$"
+    PARAM_VALID_PATTERN = r"^[a-zA-Z0-9.]+$"
     DIR = "dir"
     FILE = "file"
 
diff --git a/debug/accuracy_tools/grad_tool/common/utils.py b/debug/accuracy_tools/grad_tool/common/utils.py
index cdce3fda7e3..fceda8ce0f2 100644
--- a/debug/accuracy_tools/grad_tool/common/utils.py
+++ b/debug/accuracy_tools/grad_tool/common/utils.py
@@ -7,6 +7,7 @@ import yaml
 import pandas as pd
 
 from grad_tool.common.constant import GradConst
+from ptdbg_ascend.src.python.ptdbg_ascend.common.file_check_util import FileOpen
 
 
 def _print_log(level, msg, end='\n'):
@@ -114,7 +115,7 @@ class ListCache(list):
 
 
 def get_config(filepath):
-    with open(filepath, 'r') as file:
+    with FileOpen(filepath, 'r') as file:
         config = yaml.safe_load(file)
     return config
 
@@ -220,3 +221,11 @@ def change_mode(path, mode):
     except PermissionError as ex:
         print_error_log(f'Failed to change {path} authority. {str(ex)}')
         raise ex
+
+def check_param(param_name):
+    if not re.match(GradConst.PARAM_VALID_PATTERN, param_name):
+        raise RuntimeError("The parameter name contains special characters.")
+    
+def check_str(string, variable_name):
+    if not isinstance(string, str):
+        raise ValueError(f'The variable: "{variable_name}" is not a string.')
\ No newline at end of file
diff --git a/debug/accuracy_tools/grad_tool/grad_ms/global_context.py b/debug/accuracy_tools/grad_tool/grad_ms/global_context.py
index d44bea52c78..424f16aedd3 100644
--- a/debug/accuracy_tools/grad_tool/grad_ms/global_context.py
+++ b/debug/accuracy_tools/grad_tool/grad_ms/global_context.py
@@ -4,7 +4,7 @@ from typing import Dict, List, Union
 
 from grad_tool.common.utils import print_warn_log
 from grad_tool.common.constant import GradConst
-from grad_tool.common.utils import path_valid_check, create_directory
+from grad_tool.common.utils import path_valid_check, create_directory, check_str
 
 
 class GlobalContext:
@@ -12,13 +12,13 @@ class GlobalContext:
     _instance = None
     _instance_lock = threading.Lock()
     _setting = {
-        GradConst.LEVEL: GradConst.LEVEL0,
+        GradConst.LEVEL: None,
         GradConst.PARAM_LIST: None,
         GradConst.STEP: None,
         GradConst.RANK: None,
         GradConst.CURRENT_STEP: 0,
-        GradConst.BOUNDS: [-1., 0., 1.],
-        GradConst.OUTPUT_PATH: "./grad_stat"
+        GradConst.BOUNDS: [-10, -1, -0.1, -0.01, -0.001, 0, 0.001, 0.01, 0.1, 1, 10],
+        GradConst.OUTPUT_PATH: None
     }
 
     def __new__(cls, *args, **kwargs):
@@ -29,23 +29,25 @@ class GlobalContext:
         return cls._instance
 
     def init_context(self, config_dict: Dict):
-        if config_dict.get(GradConst.LEVEL, None) in GradConst.SUPPORTED_LEVEL:
+        level = config_dict.get(GradConst.LEVEL)
+        check_str(level, variable_name = "level in yaml")
+        if level in GradConst.SUPPORTED_LEVEL:
             self._setting[GradConst.LEVEL] = config_dict.get(GradConst.LEVEL)
         else:
-            print_warn_log("Invalid level set in config yaml file, use L0 instead.")
+            raise ValueError("Invalid level set in config yaml file, level option: L0, L1, L2")
+
         self._set_input_list(config_dict, GradConst.PARAM_LIST, str)
         self._set_input_list(config_dict, GradConst.BOUNDS, float)
         self._set_input_list(config_dict, GradConst.STEP, int)
         self._set_input_list(config_dict, GradConst.RANK, int)
+
         output_path = config_dict.get(GradConst.OUTPUT_PATH)
-        if output_path:
-            try:
-                path_valid_check(output_path)
-            except RuntimeError as err:
-                print_warn_log(f"Invalid output_path, use default output_path. The error message is {err}.")
-                output_path = None
-        if output_path:
-            self._setting[GradConst.OUTPUT_PATH] = output_path
+        check_str(output_path, variable_name = "output_path in yaml")
+        try:
+            path_valid_check(output_path)
+        except RuntimeError as err:
+            raise ValueError(f"Invalid output_path: {output_path}. The error message is {err}.") from err
+        self._setting[GradConst.OUTPUT_PATH] = output_path
         if not os.path.isdir(self._setting.get(GradConst.OUTPUT_PATH)):
             create_directory(self._setting.get(GradConst.OUTPUT_PATH))
         else:
diff --git a/debug/accuracy_tools/grad_tool/grad_ms/grad_analyzer.py b/debug/accuracy_tools/grad_tool/grad_ms/grad_analyzer.py
index 75280b31944..c843df3884e 100644
--- a/debug/accuracy_tools/grad_tool/grad_ms/grad_analyzer.py
+++ b/debug/accuracy_tools/grad_tool/grad_ms/grad_analyzer.py
@@ -16,6 +16,7 @@ from grad_tool.common.utils import ListCache, print_warn_log
 from grad_tool.common.utils import create_directory, check_file_or_directory_path, write_csv
 from grad_tool.grad_ms.global_context import grad_context
 from grad_tool.grad_ms.global_context import GlobalContext
+from ptdbg_ascend.src.python.ptdbg_ascend.common.file_check_util import FileCheckConst, FileChecker
 
 
 def get_rank_id():
@@ -31,11 +32,10 @@ def grad_dump(dump_dir: str, g_name: str, dump_step: Parameter, grad: ms.Tensor,
     '''
     Dump gradient statistic data.
         level0: [step, max, min, norm, shape_dim, shape]
-        level1: [step, max, min, norm, shape_dim, shape, dist_dim, dist]
-        level2: [step, max, min, norm, shape_dim, shape] + grad_bool_data
-        level3: [step, max, min, norm, shape_dim, shape, dist_dim, dist] + grad_bool_data
+        level1: [step, max, min, norm, shape_dim, shape] + grad_bool_data
+        level2: [step, max, min, norm, shape_dim, shape, dist_dim, dist] + grad_bool_data
     '''
-    dump_path = dump_dir + g_name
+    dump_path = os.path.join(dump_dir, g_name)
     dump_dir_path = dump_path + "_dir"
     save_op = ms.ops.TensorDump()
 
@@ -51,7 +51,7 @@ def grad_dump(dump_dir: str, g_name: str, dump_step: Parameter, grad: ms.Tensor,
     level0_stat = ms.ops.concat((extrem_stat, shape_stat), axis=0)
     level_stat = level0_stat
 
-    if level == "L1" or level == "L3":
+    if level == GradConst.LEVEL2:
         zero_grad = (grad == 0).sum()
         dist_dim = ms.Tensor([len(bounds) + 2]).float()
         bucket_result = ms.ops.bucketize(grad.float(), bounds)
@@ -60,11 +60,11 @@ def grad_dump(dump_dir: str, g_name: str, dump_step: Parameter, grad: ms.Tensor,
         dist_stat.append(zero_grad)
         dist_stat.append(ms.Tensor(1, dtype=ms.int64))  # make sure dist_stat is not empty
         dist_stat = ms.ops.stack(dist_stat, axis=0).float()
-        level1_stat = ms.ops.concat((level0_stat, dist_dim, dist_stat), axis=0)
-        level_stat = level1_stat
+        level2_stat = ms.ops.concat((level0_stat, dist_dim, dist_stat), axis=0)
+        level_stat = level2_stat
 
     save_op(dump_path, level_stat)
-    if level == "L2" or level == "L3":
+    if level == GradConst.LEVEL1 or level == GradConst.LEVEL2:
         grad_direction = grad > 0
         save_op(dump_dir_path, grad_direction)
 
@@ -155,7 +155,7 @@ class CSVGenerator(Process):
         level = grad_context.get_context(GradConst.LEVEL)
         try:
             shape_dim = int(stat_data[GradConst.SHAPE_DIM_IDX])
-            if level in [GradConst.LEVEL1, GradConst.LEVEL3]:
+            if level == GradConst.LEVEL2:
                 dist_dim = int(stat_data[shape_dim + GradConst.SHAPE_DIM_IDX + 1])
                 length = shape_dim + dist_dim + 7
             else:
@@ -170,6 +170,8 @@ class CSVGenerator(Process):
         stat_data = None
         max_try = 10
         while max_try:
+            file_path_checker = FileChecker(file_path, FileCheckConst.DIR,FileCheckConst.READ_ABLE)
+            file_path = file_path_checker.common_check()
             try:
                 stat_data = np.load(file_path)
                 return stat_data
@@ -178,7 +180,7 @@ class CSVGenerator(Process):
                 max_try -= 1
                 time.sleep(0.1)
         return stat_data
-
+    
     def gen_csv_line(self, file_path: str, stat_data) -> None:
         shape_dim = int(stat_data[GradConst.SHAPE_DIM_IDX])
         file_name = os.path.basename(file_path)
@@ -187,7 +189,7 @@ class CSVGenerator(Process):
         if not param_name:
             raise RuntimeError("Invalid gradient statistic file name.")
         csv_line = [param_name]
-        if self.level == GradConst.LEVEL1 or self.level == GradConst.LEVEL3:
+        if self.level == GradConst.LEVEL2:
             csv_line.extend(self.get_dist_data(shape_dim, stat_data))
         csv_line.extend(self.get_extrem_data(shape_dim, stat_data))
         self.cache_list.append(csv_line)
@@ -208,7 +210,7 @@ class CSVGenerator(Process):
 
     def create_csv_file(self):
         headers = ["Param_name"]
-        if self.level == GradConst.LEVEL1 or self.level == GradConst.LEVEL3:
+        if self.level == GradConst.LEVEL2:
             headers.extend(self.get_dist_header())
         headers.extend(self.get_extrem_headers())
         output_path = f"{self.save_dir}/grad_summary_{self.current_step}.csv"
diff --git a/debug/accuracy_tools/grad_tool/grad_ms/grad_comparator.py b/debug/accuracy_tools/grad_tool/grad_ms/grad_comparator.py
index 2bfeda4387e..3b930d4e283 100644
--- a/debug/accuracy_tools/grad_tool/grad_ms/grad_comparator.py
+++ b/debug/accuracy_tools/grad_tool/grad_ms/grad_comparator.py
@@ -9,10 +9,19 @@ class MsGradComparator(BaseComparator):
 
     @classmethod
     def _load_grad_files(cls, grad_file1: str, grad_file2: str):
+        if not os.path.exists(grad_file1):
+            raise ValueError(f"file {grad_file1} not exists, please check the file path.")
+        if not os.path.exists(grad_file2):
+            raise ValueError(f"file {grad_file2} not exists, please check the file path.")
+
         grad1_suffix = grad_file1.split(".")[-1]
         grad2_suffix = grad_file2.split(".")[-1]
-        grad1 = torch.load(grad_file1).numpy() if grad1_suffix == "pt" else np.load(grad_file1)
-        grad2 = torch.load(grad_file2).numpy() if grad2_suffix == "pt" else np.load(grad_file2)
+
+        try:
+            grad1 = torch.load(grad_file1).numpy() if grad1_suffix == "pt" else np.load(grad_file1)
+            grad2 = torch.load(grad_file2).numpy() if grad2_suffix == "pt" else np.load(grad_file2)
+        except Exception as e:
+            raise RuntimeError(f"An unexpected error occurred: {e} when loading grad_file.") from e
 
         if grad1.shape != grad2.shape:
             raise RuntimeError(f"numpy shape is not equal: {grad_file1}, {grad_file2}")
diff --git a/debug/accuracy_tools/grad_tool/grad_ms/utils.py b/debug/accuracy_tools/grad_tool/grad_ms/utils.py
index 23703f28208..c8ee1fd1d45 100644
--- a/debug/accuracy_tools/grad_tool/grad_ms/utils.py
+++ b/debug/accuracy_tools/grad_tool/grad_ms/utils.py
@@ -3,7 +3,8 @@ import os
 import numpy as np
 import mindspore
 from grad_tool.common.constant import GradConst
-from grad_tool.common.utils import print_warn_log, create_directory, change_mode, check_file_or_directory_path
+from grad_tool.common.utils import (print_warn_log, create_directory, change_mode, check_file_or_directory_path,
+                                    path_valid_check, check_param)
 
 level_adp = {
         "L0": {
@@ -20,23 +21,27 @@ level_adp = {
         },
     }
 
+
 def save_grad_direction(param_name, grad, save_path):
     if not os.path.exists(save_path):
         create_directory(save_path)
+    check_file_or_directory_path(save_path, file_type=GradConst.DIR)
+    check_param(param_name)
     save_filepath = os.path.join(save_path, f"{param_name}.npy")
-    check_file_or_directory_path(save_filepath)
+    path_valid_check(save_filepath)
 
     if grad.dtype == mindspore.bfloat16:
         grad = grad.to(mindspore.float32)
     grad_direction_tensor = grad > 0
     grad_direction_ndarray = grad_direction_tensor.numpy()
 
-    np.save(save_filepath, grad_direction_ndarray)
+    try:
+        np.save(save_filepath, grad_direction_ndarray)
+    except Exception as e:
+        raise RuntimeError(f"An unexpected error occurred: {e} when saving numpy to {save_filepath}") from e
     change_mode(save_filepath, 0o640)
 
+
 def get_adapted_level(level: str):
-    if level == GradConst.LEVEL3:
-        print_warn_log(f"In mindpsore pynative mode, only 'L0', 'L1' and 'L2' are supported, use L0 instead")
-        level = GradConst.LEVEL0
     level_adapted = level_adp.get(level)
-    return level_adapted
\ No newline at end of file
+    return level_adapted
diff --git a/debug/accuracy_tools/grad_tool/grad_pt/grad_comparator.py b/debug/accuracy_tools/grad_tool/grad_pt/grad_comparator.py
index d1229b93de7..38f0e32153e 100644
--- a/debug/accuracy_tools/grad_tool/grad_pt/grad_comparator.py
+++ b/debug/accuracy_tools/grad_tool/grad_pt/grad_comparator.py
@@ -1,3 +1,5 @@
+import os
+
 import torch
 
 from grad_tool.common.base_comparator import BaseComparator
@@ -7,8 +9,17 @@ class PtGradComparator(BaseComparator):
 
     @classmethod
     def _load_grad_files(cls, grad_file1: str, grad_file2: str):
-        tensor1 = torch.load(grad_file1, map_location=torch.device("cpu"))
-        tensor2 = torch.load(grad_file2, map_location=torch.device("cpu"))
+        if not os.path.exists(grad_file1):
+            raise ValueError(f"file {grad_file1} not exists, please check the file path.")
+        if not os.path.exists(grad_file2):
+            raise ValueError(f"file {grad_file2} not exists, please check the file path.")
+
+        try:
+            tensor1 = torch.load(grad_file1, map_location=torch.device("cpu"))
+            tensor2 = torch.load(grad_file2, map_location=torch.device("cpu"))
+        except Exception as e:
+            raise RuntimeError(f"An unexpected error occurred: {e} when loading tensor.") from e
+
         if tensor1.shape != tensor2.shape:
             raise RuntimeError(f"tensor shape is not equal: {grad_file1}, {grad_file2}")
         if tensor1.dtype != torch.bool:
diff --git a/debug/accuracy_tools/grad_tool/grad_pt/grad_monitor.py b/debug/accuracy_tools/grad_tool/grad_pt/grad_monitor.py
index f3079e622c2..2e1abde0d1a 100644
--- a/debug/accuracy_tools/grad_tool/grad_pt/grad_monitor.py
+++ b/debug/accuracy_tools/grad_tool/grad_pt/grad_monitor.py
@@ -61,7 +61,10 @@ class PtGradientMonitor(BaseMonitor):
         param_grad = grad.clone().detach()
         is_positive = param_grad > 0
         save_filepath = os.path.join(save_path, f"{param_name}.pt")
-        torch.save(is_positive, save_filepath)
+        try:
+            torch.save(is_positive, save_filepath)
+        except Exception as e:
+            raise RuntimeError(f"An unexpected error occurred: {e} when saving tensor to {save_filepath}") from e
         change_mode(save_filepath, 0o640)
 
     def monitor(self, model):
@@ -96,7 +99,7 @@ class PtGradientMonitor(BaseMonitor):
                 output_lines.append(grad_info)
                 if self._level_adp["have_grad_direction"]:
                     PtGradientMonitor.save_grad_direction(param_name, grad,
-                                                    f'{self._output_path}/rank{self._rank}/step{self._step}')
+                                                          f'{self._output_path}/rank{self._rank}/step{self._step}')
             output_path = os.path.join(self._output_path, f"rank{getattr(self, '_rank')}",
                                        f"grad_summary_{self._step}.csv")
             write_csv(output_path, output_lines,
diff --git a/debug/accuracy_tools/msprobe/README.md b/debug/accuracy_tools/msprobe/README.md
index 1e8c1a1f08d..42743c50781 100644
--- a/debug/accuracy_tools/msprobe/README.md
+++ b/debug/accuracy_tools/msprobe/README.md
@@ -10,10 +10,15 @@ MindStudio精度调试工具（MindStudio Probe），简称msprobe，是MindStud
    ```shell
    pip install mindstudio-probe
    ```
-   说明
-   1. 使用`pip install mindstudio-probe==版本号`可安装指定版本的包
-   2. pip命令会自动安装包及其依赖
-   3. 安装成功后，日志会显示`Successfully installed mindstudio-probe-版本号`
+使用`pip install mindstudio-probe==版本号`可安装指定版本的包。
+
+pip命令会自动安装最新的包及其配套依赖。
+
+提示如下信息则表示安装成功。
+
+```bash
+Successfully installed mindstudio_probe-{version}
+```
 
 ### 下载whl包安装
 1. 使用pip命令安装numpy、openpyxl、pandas、PyYAML、rich、torch、tqdm依赖。
@@ -26,6 +31,7 @@ MindStudio精度调试工具（MindStudio Probe），简称msprobe，是MindStud
 
    | 版本  | 发布日期   | 支持PyTorch版本 | 下载链接                                                     | 校验码                                                       |
    | ----- | ---------- | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+   | 1.0.1 | 2024-07-25 | 2.0/2.1/2.2     | [mindstudio_probe-1.0.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/msprobe/1.0/mindstudio_probe-1.0.1-py3-none-any.whl) | b699e224e4d4e3bcf9412c54fa858a1ee370f0d7a2bc69cb3f1273ac14a6dc82 |
    | 1.0   | 2024-07-09 | 2.0/2.1/2.2     | [ascend_training_accuracy_tools-1.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/att/1.0/ascend_training_accuracy_tools-1.0-py3-none-any.whl) | 5016dfe886c5d340ec6f60a959673355855f313c91f100680da814efb49f8e81 |
    | 0.0.3 | 2024-06-11 | 2.0/2.1/2.2     | [ascend_training_accuracy_tools-0.0.3-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/att/0.0/ascend_training_accuracy_tools-0.0.3-py3-none-any.whl) | f46d9714704859e2d67861a65bbb3c76b0a250cf6e238b978b5b959ab1fe125a |
    | 0.0.2 | 2024-05-23 | 2.0/2.1/2.2     | [ascend_training_accuracy_tools-0.0.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/att/0.0/ascend_training_accuracy_tools-0.0.2-py3-none-any.whl) | 2e35809bde559e9c4d2f16a02ccde779ed9e436bb65fded0b7ebaf6ac2c88d93 |
@@ -92,6 +98,37 @@ MindStudio精度调试工具（MindStudio Probe），简称msprobe，是MindStud
    Finished processing dependencies for mindstudio-probe=={version}
    ```
 
+### 查看msprobe工具信息
+
+执行如下命令查看msprobe工具信息。
+
+```bash
+pip show mindstudio-probe
+```
+
+输出结果如下示例：
+
+```bash
+Name: mindstudio-probe
+Version: 1.0
+Summary: This is a pytorch precision comparison tools
+Home-page:
+Author:
+Author-email:
+License:
+Location: /home/xx/anaconda3/envs/pt21py38/lib/python3.8/site-packages
+Requires: numpy, openpyxl, pandas, pyyaml, rich, tqdm, wheel
+Required-by:
+```
+
+关键字段含义：
+
+- Name：工具名称。
+- Version：工具版本号。
+- Summary：工具概述。
+- Location：工具安装路径。
+- Requires：工具依赖。
+
 ## 工具使用
 
 安装msprobe工具后，可以按照如下思路选择合适的子工具进行精度调试：
diff --git a/debug/accuracy_tools/msprobe/config/README.md b/debug/accuracy_tools/msprobe/config/README.md
index 7b91bd26f16..7d11a365253 100644
--- a/debug/accuracy_tools/msprobe/config/README.md
+++ b/debug/accuracy_tools/msprobe/config/README.md
@@ -2,13 +2,38 @@
 
 当前配置文件主要为PrecisionDebugger接口执行dump或无标杆比对操作时调用的配置，当PrecisionDebugger接口未指定该配置文件时，使用该文件的默认配置。配置文件详见[config.json](./config.json)。
 
+当在环境上安装msprobe工具后，config.json文件位置可通过如下方式查找：
+
+查找msprobe工具安装路径。
+
+```
+pip show mindstudio-probe
+```
+
+输出结果如下示例：
+
+```
+Name: mindstudio-probe
+Version: 1.0
+Summary: This is a pytorch precision comparison tools
+Home-page:
+Author:
+Author-email:
+License:
+Location: /home/xx/anaconda3/envs/pt21py38/lib/python3.8/site-packages
+Requires: numpy, openpyxl, pandas, pyyaml, rich, tqdm, wheel
+Required-by:
+```
+
+Location字段为msprobe工具的安装路径，那么config.json文件位置为/home/xx/anaconda3/envs/pt21py38/lib/python3.8/site-packages/msprobe/config
+
 ## 参数说明
 
 ### **通用配置参数**
 
 | 参数名            | 说明                                                         | 是否必选 |
 | ----------------- | ------------------------------------------------------------ | -------- |
-| task              | dump的任务类型，str类型。可取值"free_benchmark"（无标杆比对，仅PyTorch场景支持）、"statistics"（仅dump API统计信息，默认值）、"tensor"（dump API统计信息和完全复刻整网的API运行情况的真实数据）、"overflow_check"（溢出检测）。配置示例："task": "tensor"。根据task参数取值的不同，可以配置不同场景参数，详见：“**task配置为free_benchmark**”，“**task配置为statistics**”，“**task配置为tensor**”，“**task配置为overflow_check**”。 | 否       |
+| task              | dump的任务类型，str类型。可取值：<br>        "free_benchmark"（无标杆比对，仅PyTorch场景支持）。<br/>        "statistics"（仅dump API统计信息，默认值）。<br/>        "tensor"（dump API统计信息和完全复刻整网的API运行情况的真实数据）。<br/>        "overflow_check"（溢出检测，仅PyTorch和MindSpore静态图场景支持）。<br/>        "run_ut"（精度预检配置，仅PyTorch场景支持）。<br/>配置示例："task": "tensor"。<br/>根据task参数取值的不同，可以配置不同场景参数，详见：“**task配置为free_benchmark**”，“**task配置为statistics**”，“**task配置为tensor**”，“**task配置为overflow_check**”，“**task配置为run_ut**”。 | 否       |
 | dump_path         | 设置dump数据目录路径，str类型。配置示例："dump_path": "./dump_path"。MindSpore场景仅支持绝对路径。 | 是       |
 | rank              | 指定对某张卡上的数据进行dump，list[int]类型，默认未配置（表示dump所有卡的数据），应配置为大于等于0的整数，且须配置实际可用的Rank ID。配置示例："rank": [1]。<br>        对于PyTorch场景，Rank ID从0开始计数，最大取值为所有节点可用卡总数-1，若所配置的值大于实际训练所运行的卡的Rank ID，则dump数据为空，比如当前环境Rank ID为0到7，实际训练运行0到3卡，此时若配置Rank ID为4或不存在的10等其他值，此时dump数据为空。<br/>        对于MindSpore场景，所有节点的Rank ID均从0开始计数，最大取值为每个节点可用卡总数-1，config.json配置一次rank参数对所有节点同时生效。 | 否       |
 | step              | 指定dump某个step的数据，list[int]类型。默认未配置，表示dump所有step数据。dump特定step时，须指定为训练脚本中存在的step。step为list格式，可配置逐个step，例如："step": [0,1,2]。 | 否       |
@@ -85,6 +110,18 @@ task配置为free_benchmark时，开启**无标杆比对**，在NPU环境下通
 | overflow_nums | 控制溢出次数，int类型，仅PyTorch场景支持，表示第N次溢出时，停止训练，过程中检测到溢出API对应kernel数据均dump。配置示例："overflow_nums": 3。默认为1，即检测到1次溢出，训练停止，配置为-1时，表示持续检测溢出直到训练结束。 | 否       |
 | check_mode    | MindSpore场景kernel级别的溢出检测，str类型，可取值"aicore"（开启AI Core的溢出检测）、"atomic"（开启Atomic的溢出检测）、"all"（开启AI Core和Atomic的溢出检测，默认值）。配置示例"check_mode": "aicore"。 | 否       |
 
+### task配置为run_ut
+
+仅PyTorch场景支持。
+
+| 参数名称        | 说明                                                         | 是否必选 |
+| --------------- | ------------------------------------------------------------ | -------- |
+| white_list      | API dump白名单，仅对指定的API进行dump。配置示例："white_list": ["conv1d", "conv2d"]。默认未配置白名单，即dump全量API数据。 | 否       |
+| black_list      | API dump黑名单，被指定的API不进行dump。配置示例："black_list": ["conv1d", "conv2d"]。默认未配置黑名单，即dump全量API数据。 | 否       |
+| error_data_path | 配置保存精度未达标的API输入输出数据路径，默认为当前路径。配置示例"error_data_path": "./"。 | 否       |
+
+说明：white_list和black_list同时配置时，二者配置的API名单若无交集，则白名单生效，若API名单存在交集，则白名单排除的部分以及交集的API不进行dump。
+
 ## 配置示例
 
 以下示例包含当前支持的所有场景可配置的完整参数。
@@ -180,6 +217,27 @@ task配置为free_benchmark时，开启**无标杆比对**，在NPU环境下通
 }
 ```
 
+### PyTorch场景task配置为run_ut
+
+```json
+{
+    "task": "run_ut",
+    "dump_path": "/home/data_dump",
+    "rank": [],
+    "step": [],
+    "level": "L1",
+    "seed": 1234,
+    "is_deterministic": false,
+    "enable_dataloader": false,
+
+    "run_ut": {
+        "white_list": [],
+        "black_list": [],
+        "error_data_path": "./"
+    }
+}
+```
+
 ### MindSpore场景task配置为statistics
 
 ```json
diff --git a/debug/accuracy_tools/msprobe/core/common/const.py b/debug/accuracy_tools/msprobe/core/common/const.py
index df82455a676..eff7b8be8ad 100644
--- a/debug/accuracy_tools/msprobe/core/common/const.py
+++ b/debug/accuracy_tools/msprobe/core/common/const.py
@@ -16,6 +16,7 @@ class Const:
     OFF = 'OFF'
     BACKWARD = 'backward'
     FORWARD = 'forward'
+    PRIMITIVE_PREFIX = 'Primitive'
     DEFAULT_LIST = []
     DEFAULT_PATH = './'
     WHITE_LIST = 'white_list'
@@ -45,6 +46,7 @@ class Const:
     PT_SUFFIX = ".pt"
     ONE_GB = 1073741824  # 1 * 1024 * 1024 * 1024
     TEN_GB = 10737418240  # 10 * 1024 * 1024 * 1024
+    ONE_MB = 1048576  # 1 * 1024 * 1024
     FILE_PATTERN = r'^[a-zA-Z0-9_./-]+$'
     DISTRIBUTED_PREFIX_LENGTH = 60
     # env dump path
@@ -80,12 +82,12 @@ class Const:
     INT_TYPE = [np.int32, np.int64]
     NPU = 'NPU'
     DISTRIBUTED = 'Distributed'
-    
+
     INPLACE_LIST = [
         "broadcast", "all_reduce", "reduce", "all_gather", "gather", "scatter", "reduce_scatter",
-        "_reduce_scatter_base", "_all_gather_base", "send", "recv", "irecv", "isend", "all_to_all_single"
+        "_reduce_scatter_base", "_all_gather_base", "send", "recv", "irecv", "isend", "all_to_all_single", "all_to_all"
     ]
-    
+
     CONVERT = {
         "int32_to_int64": ["torch.int32", "torch.int64"],
     }
@@ -252,3 +254,17 @@ class OverflowConst:
     OVERFLOW_DEBUG_MODE_ENABLE = "OVERFLOW_DEBUG_MODE_ENABLE"
     OVERFLOW_ORIGINAL_MODE = 0
     OVERFLOW_DEBUG_MODE = 1
+
+
+class MsConst:
+    CELL = "cell"
+    API = "api"
+    KERNEL = "kernel"
+    TOOL_LEVEL_DICT = {
+        "L0": CELL,
+        "L1": API,
+        "L2": KERNEL
+    }
+    PYNATIVE_MODE = "pynative"
+    GRAPH_GE_MODE = "graph_ge"
+    GRAPH_KBYK_MODE = "graph_kbyk"
diff --git a/debug/accuracy_tools/msprobe/core/common/utils.py b/debug/accuracy_tools/msprobe/core/common/utils.py
index 32aba8d8af4..6e901deb9eb 100644
--- a/debug/accuracy_tools/msprobe/core/common/utils.py
+++ b/debug/accuracy_tools/msprobe/core/common/utils.py
@@ -148,7 +148,7 @@ def check_summary_only_valid(summary_only):
     return summary_only
 
 
-def check_compare_param(input_parma, output_path, stack_mode=False, summary_compare=False, md5_compare=False):
+def check_compare_param(input_parma, output_path, summary_compare=False, md5_compare=False):
     if not (isinstance(input_parma, dict) and isinstance(output_path, str)):
         logger.error("Invalid input parameters")
         raise CompareException(CompareException.INVALID_PARAM_ERROR)
@@ -318,15 +318,6 @@ def execute_command(cmd):
         raise CompareException(CompareException.INVALID_DATA_ERROR)
 
 
-def save_numpy_data(file_path, data):
-    """
-    save_numpy_data
-    """
-    if not os.path.exists(os.path.dirname(file_path)):
-        os.makedirs(os.path.dirname(file_path))
-    np.save(file_path, data)
-
-
 def parse_value_by_comma(value):
     """
     parse value by comma, like '1,2,4,8'
diff --git a/debug/accuracy_tools/msprobe/core/common_config.py b/debug/accuracy_tools/msprobe/core/common_config.py
index ed38eba008b..d6c15e101e7 100644
--- a/debug/accuracy_tools/msprobe/core/common_config.py
+++ b/debug/accuracy_tools/msprobe/core/common_config.py
@@ -18,24 +18,27 @@ class CommonConfig:
 
     def _check_config(self):
         if self.task and self.task not in Const.TASK_LIST:
-            logger.error_log_with_exp(
-                "task is invalid, it should be one of {}".format(Const.TASK_LIST), MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+            logger.error_log_with_exp("task is invalid, it should be one of {}".format(Const.TASK_LIST),
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
         if self.rank is not None and not isinstance(self.rank, list):
-            logger.error_log_with_exp("rank is invalid, it should be a list", MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+            logger.error_log_with_exp("rank is invalid, it should be a list",
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
         if self.step is not None and not isinstance(self.step, list):
-            logger.error_log_with_exp("step is invalid, it should be a list", MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+            logger.error_log_with_exp("step is invalid, it should be a list",
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
         if self.level and self.level not in Const.LEVEL_LIST:
-            logger.error_log_with_exp(
-                "level is invalid, it should be one of {}".format(Const.LEVEL_LIST), MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+            logger.error_log_with_exp("level is invalid, it should be one of {}".format(Const.LEVEL_LIST),
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
         if self.seed is not None and not isinstance(self.seed, int):
-            logger.error_log_with_exp("seed is invalid, it should be an integer", MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+            logger.error_log_with_exp("seed is invalid, it should be an integer",
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
         if not isinstance(self.is_deterministic, bool):
-            logger.error_log_with_exp(
-                "is_deterministic is invalid, it should be a boolean", MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+            logger.error_log_with_exp("is_deterministic is invalid, it should be a boolean",
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
         if not isinstance(self.enable_dataloader, bool):
-            logger.error_log_with_exp(
-                "enable_dataloader is invalid, it should be a boolean", MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
-        
+            logger.error_log_with_exp("enable_dataloader is invalid, it should be a boolean",
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+
 
 class BaseConfig:
     def __init__(self, json_config):
@@ -44,15 +47,17 @@ class BaseConfig:
         self.data_mode = json_config.get('data_mode')
         self.backward_input = json_config.get("backward_input")
         self.file_format = json_config.get("file_format")
-        self.summary_mode =  json_config.get("summary_mode")
-        self.overflow_num = json_config.get("overflow_num")
+        self.summary_mode = json_config.get("summary_mode")
+        self.overflow_nums = json_config.get("overflow_nums")
         self.check_mode = json_config.get("check_mode")
 
     def check_config(self):
         if self.scope is not None and not isinstance(self.scope, list):
-            logger.error_log_with_exp("scope is invalid, it should be a list", MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+            logger.error_log_with_exp("scope is invalid, it should be a list",
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
         if self.list is not None and not isinstance(self.list, list):
-            logger.error_log_with_exp("list is invalid, it should be a list", MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
+            logger.error_log_with_exp("list is invalid, it should be a list",
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
         if self.data_mode is not None and not isinstance(self.data_mode, list):
-            logger.error_log_with_exp("data_mode is invalid, it should be a list", MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
-        
+            logger.error_log_with_exp("data_mode is invalid, it should be a list",
+                                      MsprobeException(MsprobeException.INVALID_PARAM_ERROR))
diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py
index 800a2b81c2f..de2b93c206d 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_collector.py
@@ -1,7 +1,6 @@
-
 import os
 
-from msprobe.core.data_dump.scope import  build_scope, ListScope
+from msprobe.core.data_dump.scope import build_scope, ListScope
 from msprobe.core.data_dump.json_writer import DataWriter
 from msprobe.core.common.log import logger
 from msprobe.core.common.const import Const
@@ -21,7 +20,8 @@ class DataCollector:
         self.config = config
         self.data_writer = DataWriter()
         self.data_processor = DataProcessorFactory.create_processor(self.config, self.data_writer)
-        self.module_processor = DataProcessorFactory.get_module_processor(self.config.framework) if self.config.framework == Const.PT_FRAMEWORK else None
+        self.module_processor = DataProcessorFactory.get_module_processor(self.config.framework) \
+            if self.config.framework == Const.PT_FRAMEWORK else None
         self.module_count = {}
         if self.config.task == Const.FREE_BENCHMARK:
             self.scope = build_scope(ListScope, self.config.scope, self.config.list)
@@ -35,7 +35,7 @@ class DataCollector:
     @property
     def dump_file_path(self):
         return self.data_writer.dump_file_path
-    
+
     @staticmethod
     def check_scope_and_pid(scope, name, pid):
         return (not scope or scope.check(name)) and pid == os.getpid()
@@ -43,10 +43,10 @@ class DataCollector:
     @staticmethod
     def is_inplace(module):
         return getattr(module, "op_is_inplace", False)
-    
+
     def if_return_forward_new_output(self):
         return self.data_processor.if_return_forward_new_output()
-    
+
     def get_forward_new_output(self):
         return self.data_processor.get_forward_new_output()
 
@@ -88,8 +88,11 @@ class DataCollector:
         else:
             data_info = self.data_processor.analyze_forward_inplace(name, module_input_output)
         if self.config.level == "L2":
-            return 
+            return
         self.data_writer.update_stack(self.data_processor.analyze_api_call_stack(name))
+        if self.data_processor.stop_run():
+            self.handle_data(name, data_info, use_buffer=False)
+            raise Exception("[msprobe] exit")
         self.handle_data(name, data_info)
 
     def backward_data_collect(self, name, module, pid, module_input_output):
@@ -98,6 +101,25 @@ class DataCollector:
             return
 
         data_info = self.data_processor.analyze_backward(name, module, module_input_output)
+        if self.data_processor.stop_run():
+            self.handle_data(name, data_info, use_buffer=False)
+            raise Exception("[msprobe] exit")
+        self.handle_data(name, data_info)
+
+    def backward_input_data_collect(self, name, module, pid, module_input_output):
+        self.update_construct(name)
+        if not self.check_scope_and_pid(self.scope, name, pid):
+            return
+
+        data_info = self.data_processor.analyze_backward_input(name, module, module_input_output)
+        self.handle_data(name, data_info)
+
+    def backward_output_data_collect(self, name, module, pid, module_input_output):
+        self.update_construct(name)
+        if not self.check_scope_and_pid(self.scope, name, pid):
+            return 
+
+        data_info = self.data_processor.analyze_backward_output(name, module, module_input_output)
         self.handle_data(name, data_info)
 
     def update_construct(self, name):
@@ -105,12 +127,15 @@ class DataCollector:
             self.data_writer.update_construct({name: self.module_processor.api_parent_node})
             self.data_writer.update_construct(self.module_processor.module_node)
 
-    def handle_data(self, name, data_info):
+    def handle_data(self, name, data_info, use_buffer=True):
         msg = f"msProbe is collecting data on {name}. "
         if data_info:
             msg = self.update_data(data_info, msg)
             logger.info(msg)
-        self.data_writer.flush_data_when_buffer_is_full()
+        if use_buffer:
+            self.data_writer.flush_data_when_buffer_is_full()
+        else:
+            self.write_json()
 
     def module_count_func(self, name, name_template):
         module_name = name.split(Const.SEP)[-3]
@@ -135,6 +160,6 @@ class DataCollector:
     def update_dump_paths(self, *args):
         self.data_writer.update_dump_paths(*args)
         self.data_writer.initialize_json_file(task=self.config.task, level=self.config.level)
-    
+
     def update_iter(self, current_iter):
         self.data_processor.update_iter(current_iter)
diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
index 5d901291973..13134d61980 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/base.py
@@ -35,11 +35,26 @@ class ModuleBackwardInputsOutputs:
     @property
     def grad_input_tuple(self):
         return convert_tuple(self.grad_input)
-        
+
     @property
     def grad_output_tuple(self):
-        return convert_tuple(self.grad_output)    
+        return convert_tuple(self.grad_output)
+
+@dataclass
+class ModuleBackwardInputs:
+    grad_input: Optional[Tuple]
+
+    @property
+    def grad_input_tuple(self):
+        return convert_tuple(self.grad_input)
+ 
+@dataclass
+class ModuleBackwardOutputs:
+    grad_output: Optional[Tuple]
         
+    @property
+    def grad_output_tuple(self):
+        return convert_tuple(self.grad_output)  
 
 class TensorStatInfo:
     def __init__(self, max_val=None, min_val=None, mean_val=None, norm_val=None):
@@ -53,7 +68,7 @@ class BaseDataProcessor:
     _recursive_key_stack = []
     special_type = (np.integer, np.floating, np.bool_, np.complexfloating, np.str_, np.byte, np.unicode_,
                     bool, int, float, str, slice)
-    
+
     def __init__(self, config, data_writer):
         self.data_writer = data_writer
         self.config = config
@@ -65,11 +80,11 @@ class BaseDataProcessor:
         self.current_iter = 0
         self._return_forward_new_output = False
         self._forward_new_output = None
-        
+
     @property
     def data_path(self):
         return self.data_writer.dump_tensor_data_dir
-        
+
     @staticmethod
     def analyze_api_call_stack(name):
         stack_str = []
@@ -87,7 +102,7 @@ class BaseDataProcessor:
             stack_str.append(stack_line)
         stack_info_struct = {name: stack_str}
         return stack_info_struct
-    
+
     @staticmethod
     def _convert_numpy_to_builtin(arg):
         type_mapping = {
@@ -103,26 +118,15 @@ class BaseDataProcessor:
             if isinstance(arg, numpy_type):
                 return builtin_type(arg), type(arg).__name__
         return arg, ''
-    
+
     @staticmethod
     def _analyze_numpy(value, numpy_type):
         return {"type": numpy_type, "value": value}
-    
-    @staticmethod
-    def _analyze_builtin(arg):
-        single_arg = {}
-        if isinstance(arg, slice):
-            single_arg.update({"type": "slice"})
-            single_arg.update({"value": [arg.start, arg.stop, arg.step]})
-        else:
-            single_arg.update({"type": type(arg).__name__})
-            single_arg.update({"value": arg})
-        return single_arg
-    
+
     @classmethod
     def get_special_types(cls):
         return cls.special_type
-    
+
     @classmethod
     def recursive_apply_transform(cls, args, transform):
         if isinstance(args, cls.get_special_types()):
@@ -177,13 +181,14 @@ class BaseDataProcessor:
         return (Const.ALL in self.config.data_mode or
                 forward_backward in self.config.data_mode or
                 input_output in self.config.data_mode)
-    
-    def analyze_pre_forward(self, name, module,module_input_output: ModuleForwardInputsOutputs):
+
+    def analyze_pre_forward(self, name, module, module_input_output: ModuleForwardInputsOutputs):
         pass
-    
+
     def analyze_forward(self, name, module, module_input_output: ModuleForwardInputsOutputs):
         api_info_struct = {}
-        if self.is_dump_for_data_mode(Const.FORWARD, Const.INPUT): # check whether data_mode contains forward or input
+        # check whether data_mode contains forward or input
+        if self.is_dump_for_data_mode(Const.FORWARD, Const.INPUT):
             api_info_struct[name] = {}
             self.api_data_category = Const.INPUT
             args_info_list = self.analyze_element(module_input_output.args_tuple)
@@ -192,13 +197,14 @@ class BaseDataProcessor:
             kwargs_info_list = self.analyze_element(module_input_output.kwargs)
             api_info_struct[name][Const.INPUT_KWARGS] = kwargs_info_list
 
-        if self.is_dump_for_data_mode(Const.FORWARD, Const.OUTPUT): # check whether data_mode contains forward or output
+        # check whether data_mode contains forward or output
+        if self.is_dump_for_data_mode(Const.FORWARD, Const.OUTPUT):
             api_info_struct[name] = api_info_struct.get(name, {})
             self.api_data_category = Const.OUTPUT
             output_info_list = self.analyze_element(module_input_output.output_tuple)
             api_info_struct[name][Const.OUTPUT] = output_info_list
         return api_info_struct
-    
+
     def analyze_pre_forward_inplace(self, name, module_input_output: ModuleForwardInputsOutputs):
         api_info_struct = {}
         if self.is_dump_for_data_mode(Const.FORWARD, Const.INPUT):
@@ -210,7 +216,7 @@ class BaseDataProcessor:
             kwargs_info_list = self.analyze_element(module_input_output.kwargs)
             api_info_struct[name][Const.INPUT_KWARGS] = kwargs_info_list
         return api_info_struct
-    
+
     def analyze_forward_inplace(self, name, module_input_output: ModuleForwardInputsOutputs):
         concat_args = module_input_output.concat_args_and_kwargs()
         api_info_struct = {}
@@ -220,26 +226,55 @@ class BaseDataProcessor:
             output_info_list = self.analyze_element(concat_args)
             api_info_struct[name][Const.OUTPUT] = output_info_list
         return api_info_struct
-    
+
     def analyze_backward(self, name, module, module_input_output: ModuleBackwardInputsOutputs):
         api_info_struct = {}
-        if self.is_dump_for_data_mode(Const.BACKWARD, Const.OUTPUT):
+        if self.is_dump_for_data_mode(Const.BACKWARD, Const.INPUT):
             api_info_struct[name] = {}
-            self.api_data_category = Const.OUTPUT
+            self.api_data_category = Const.INPUT
             input_info_list = self.analyze_element(module_input_output.grad_input_tuple)
-            api_info_struct[name][Const.GRAD_INPUT] = input_info_list
+            api_info_struct[name][Const.INPUT] = input_info_list
 
-        if self.is_dump_for_data_mode(Const.BACKWARD, Const.INPUT):
+        if self.is_dump_for_data_mode(Const.BACKWARD, Const.OUTPUT):
             api_info_struct[name] = api_info_struct.get(name, {})
-            self.api_data_category = Const.INPUT
+            self.api_data_category = Const.OUTPUT
             output_info_list = self.analyze_element(module_input_output.grad_output_tuple)
+            api_info_struct[name][Const.OUTPUT] = output_info_list
+
+        return api_info_struct
+
+    def analyze_backward_input(self, name, module, module_input_output: ModuleBackwardInputsOutputs):
+        """
+        Analyze and save backward input gradients.
+        """
+        api_info_struct = {}
+        if self.is_dump_for_data_mode(Const.BACKWARD, Const.INPUT):
+            api_info_struct[name] = {}
+            self.api_data_category = Const.OUTPUT
+            # self.api_data_category = Const.INPUT
+            output_info_list = self.analyze_element(module_input_output.grad_input_tuple)
             api_info_struct[name][Const.GRAD_OUTPUT] = output_info_list
+        return api_info_struct
 
+    def analyze_backward_output(self, name, module, module_input_output: ModuleBackwardInputsOutputs):
+        """
+        Analyze and save backward output gradients.
+        """
+        api_info_struct = {}
+        if self.is_dump_for_data_mode(Const.BACKWARD, Const.OUTPUT):
+            api_info_struct[name] = {}
+            self.api_data_category = Const.INPUT
+            # self.api_data_category = Const.OUTPUT
+            input_info_list = self.analyze_element(module_input_output.grad_output_tuple)
+            api_info_struct[name][Const.GRAD_INPUT] = input_info_list
         return api_info_struct
 
     def get_save_file_path(self, suffix):
-        file_format = Const.PT_SUFFIX if self.config.framework == Const.PT_FRAMEWORK else Const.NUMPY_SUFFIX      
+        file_format = Const.PT_SUFFIX if self.config.framework == Const.PT_FRAMEWORK else Const.NUMPY_SUFFIX
         dump_data_name = (self.current_api_or_module_name + Const.SEP + self.api_data_category + Const.SEP +
                           suffix + file_format)
         file_path = os.path.join(self.data_writer.dump_tensor_data_dir, dump_data_name)
-        return dump_data_name, file_path
\ No newline at end of file
+        return dump_data_name, file_path
+
+    def stop_run(self):
+        return False
diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/factory.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/factory.py
index 86ef2115fb2..ad74acdeeba 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/factory.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/factory.py
@@ -4,7 +4,7 @@ from msprobe.core.common.const import Const
 class DataProcessorFactory:
     _data_processor = {}
     _module_processor = {}
-    
+
     @classmethod
     def register_processor(cls, framework, task, processor_class):
         key = (framework, task)
@@ -13,7 +13,7 @@ class DataProcessorFactory:
     @classmethod
     def register_module_processor(cls, framework, processor_class):
         cls._module_processor[framework] = processor_class
-        
+
     @classmethod
     def get_module_processor(cls, framework):
         processor_class = cls._module_processor.get(framework)
@@ -39,7 +39,7 @@ class DataProcessorFactory:
                 TensorDataProcessor as PytorchTensorDataProcessor,
                 OverflowCheckDataProcessor as PytorchOverflowCheckDataProcessor,
                 FreeBenchmarkDataProcessor as PytorchFreeBenchmarkDataProcessor,
-                KernelDumpDataProcessor as PytorchKernelDumpDataProcessor 
+                KernelDumpDataProcessor as PytorchKernelDumpDataProcessor
             )
             from ....pytorch.module_processer import ModuleProcesser
             cls.register_processor(Const.PT_FRAMEWORK, Const.STATISTICS, PytorchStatisticsDataProcessor)
@@ -47,11 +47,13 @@ class DataProcessorFactory:
             cls.register_processor(Const.PT_FRAMEWORK, Const.OVERFLOW_CHECK, PytorchOverflowCheckDataProcessor)
             cls.register_processor(Const.PT_FRAMEWORK, Const.FREE_BENCHMARK, PytorchFreeBenchmarkDataProcessor)
             cls.register_processor(Const.PT_FRAMEWORK, Const.KERNEL_DUMP, PytorchKernelDumpDataProcessor)
-            cls.register_module_processor(Const.PT_FRAMEWORK, ModuleProcesser)                                              
+            cls.register_module_processor(Const.PT_FRAMEWORK, ModuleProcesser)
         elif framework == Const.MS_FRAMEWORK:
             from .mindspore_processor import (
                 StatisticsDataProcessor as MindsporeStatisticsDataProcessor,
-                TensorDataProcessor as MindsporeTensorDataProcessor
+                TensorDataProcessor as MindsporeTensorDataProcessor,
+                OverflowCheckDataProcessor as MindsporeOverflowCheckDataProcessor
             )
             cls.register_processor(Const.MS_FRAMEWORK, Const.STATISTICS, MindsporeStatisticsDataProcessor)
             cls.register_processor(Const.MS_FRAMEWORK, Const.TENSOR, MindsporeTensorDataProcessor)
+            cls.register_processor(Const.MS_FRAMEWORK, Const.OVERFLOW_CHECK, MindsporeOverflowCheckDataProcessor)
diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
index 7533e2ee0de..b28817e4aa7 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/mindspore_processor.py
@@ -19,7 +19,8 @@ from mindspore import ops
 import numpy as np
 
 from msprobe.core.common.const import Const
-from msprobe.core.data_dump.data_processor.base import BaseDataProcessor, TensorStatInfo
+from msprobe.core.data_dump.data_processor.base import (BaseDataProcessor, TensorStatInfo,
+                                                        ModuleForwardInputsOutputs, ModuleBackwardInputsOutputs)
 from msprobe.core.common.file_check import path_len_exceeds_limit, change_mode, FileCheckConst
 from msprobe.mindspore.dump.hook_cell.wrap_functional import load_ops_functions
 from msprobe.mindspore.common.utils import convert_bf16_to_fp32
@@ -30,7 +31,7 @@ from msprobe.mindspore.dump.hook_cell.api_registry import api_register
 class MindsporeDataProcessor(BaseDataProcessor):
     mindspore_special_type = tuple([ms.Tensor])
     ops_func, mint_ops_func, _ = load_ops_functions()
-    
+
     def __init__(self, config, data_writer):
         super().__init__(config, data_writer)
         self.mindspore_object_key = {
@@ -47,18 +48,35 @@ class MindsporeDataProcessor(BaseDataProcessor):
     @staticmethod
     def analyze_dtype_in_kwargs(element):
         return {"type": "mindspore.dtype", "value": str(element)}
-    
+
+    @staticmethod
+    def _analyze_builtin(arg):
+        single_arg = {}
+        if isinstance(arg, slice):
+            single_arg.update({"type": "slice"})
+            # slice参数中可能存在tensor类型，json序列化，需要转换为python数值类型
+            values = [
+                value if not isinstance(value, ms.Tensor) else value.item()
+                for value in [arg.start, arg.stop, arg.step]
+            ]
+            single_arg.update({"value": values})
+        else:
+            single_arg.update({"type": type(arg).__name__})
+            single_arg.update({"value": arg})
+        return single_arg
+
     @classmethod
     def get_special_types(cls):
         return super().get_special_types() + cls.mindspore_special_type
-    
+
     def get_stat_info(self, data):
         tensor_stat = TensorStatInfo()
         if data.numel() == 0:
             return tensor_stat
         elif data.dtype == ms.bool_:
-            tensor_stat.max = self.mint_ops_func["max"](data).item()
-            tensor_stat.min = self.mint_ops_func["min"](data).item()
+            data_np = data.asnumpy()
+            tensor_stat.max = np.max(data_np)
+            tensor_stat.min = np.min(data_np)
         elif not data.shape:
             tensor_stat.max = tensor_stat.min = tensor_stat.mean = tensor_stat.norm = data.item()
         elif data.dtype == ms.complex64 or data.dtype == ms.complex128:
@@ -90,7 +108,7 @@ class MindsporeDataProcessor(BaseDataProcessor):
 
         if isinstance(element, (bool, int, float, str, slice)):
             return self._analyze_builtin(element)
-        return None
+        return {}
 
     def analyze_element(self, element):
         return self.recursive_apply_transform(element, self.analyze_single_element)
@@ -129,3 +147,61 @@ class TensorDataProcessor(MindsporeDataProcessor):
         else:
             logger.warning(f'The file path {file_path} length exceeds limit.')
         return single_arg
+
+
+class OverflowCheckDataProcessor(MindsporeDataProcessor):
+    __slots__ = ["cached_tensors_and_file_paths"]
+
+    def __init__(self, config, data_writer):
+        super().__init__(config, data_writer)
+        self.cached_tensors_and_file_paths = {}
+        self.real_overflow_dump_times = 0
+        self.overflow_nums = config.overflow_nums
+
+    def analyze_forward(self, name, module, module_input_output: ModuleForwardInputsOutputs):
+        self.has_overflow = False
+        api_info_struct = super().analyze_forward(name, module, module_input_output)
+        self.maybe_save_overflow_data()
+        return api_info_struct if self.has_overflow else None
+
+    def analyze_backward(self, name, module, module_input_output: ModuleBackwardInputsOutputs):
+        self.has_overflow = False
+        api_info_struct = super().analyze_backward(name, module, module_input_output)
+        self.maybe_save_overflow_data()
+        return api_info_struct if self.has_overflow else None
+
+    def maybe_save_overflow_data(self):
+        if self.has_overflow:
+            for file_path, tensor in self.cached_tensors_and_file_paths.items():
+                tensor = convert_bf16_to_fp32(tensor)
+                np.save(file_path, tensor.asnumpy())
+                change_mode(file_path, FileCheckConst.DATA_FILE_AUTHORITY)
+            self.real_overflow_dump_times += 1
+        self.cached_tensors_and_file_paths = {}
+
+    def stop_run(self):
+        if self.overflow_nums == -1:
+            return False
+        if self.real_overflow_dump_times >= self.overflow_nums:
+            logger.warning(f"[msprobe] 超过预设溢出次数 当前溢出次数: {self.real_overflow_dump_times}")
+            return True
+        return False
+
+    def _analyze_maybe_overflow_tensor(self, tensor_json):
+        if tensor_json['Max'] is None:
+            return
+        if np.isinf(tensor_json['Max']) or np.isnan(tensor_json['Max']):
+            self.has_overflow = True
+        if np.isinf(tensor_json['Min']) or np.isnan(tensor_json['Min']):
+            self.has_overflow = True
+
+    def _analyze_tensor(self, tensor, suffix):
+        dump_data_name, file_path = self.get_save_file_path(suffix)
+        if not path_len_exceeds_limit(file_path):
+            self.cached_tensors_and_file_paths.update({file_path: tensor})
+        else:
+            logger.warning(f'The file path {file_path} length exceeds limit.')
+        single_arg = super()._analyze_tensor(tensor, suffix)
+        self._analyze_maybe_overflow_tensor(single_arg)
+        single_arg.update({"data_name": dump_data_name})
+        return single_arg
diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
index f307909a416..007fec80964 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
@@ -15,8 +15,9 @@ from msprobe.pytorch.free_benchmark import FreeBenchmarkCheck, UnequalRow
 
 try:
     import torch_npu
+    is_gpu = False
 except ImportError:
-    pass
+    is_gpu = True
 
 
 class PytorchDataProcessor(BaseDataProcessor):
@@ -77,6 +78,38 @@ class PytorchDataProcessor(BaseDataProcessor):
             tensor_stat.norm = torch._C._VariableFunctionsClass.norm(data_clone).item()
         return tensor_stat
     
+    @staticmethod
+    def handle_tensor_extremum_nan_inf(tensor, operator):
+        data_clone = tensor.detach()
+        data_nan = torch._C._VariableFunctionsClass.isnan(data_clone)
+        if int(torch._C._VariableFunctionsClass.sum(data_nan)) == data_clone.numel():
+            return float('nan')
+        finite_mask = torch._C._VariableFunctionsClass.isfinite(data_clone)
+        if int(torch._C._VariableFunctionsClass.sum(finite_mask)) > 0:
+            finite_values = data_clone[finite_mask]
+            return torch._C._VariableFunctionsClass.max(finite_values).item() if operator == 'max' else \
+                torch._C._VariableFunctionsClass.min(finite_values).item()
+        else:
+            data_no_nan = data_clone[~data_nan]
+            return torch._C._VariableFunctionsClass.max(data_no_nan).item() if operator == 'max' else \
+                torch._C._VariableFunctionsClass.min(data_no_nan).item()
+    
+    @staticmethod
+    def _analyze_builtin(arg):
+        single_arg = {}
+        if isinstance(arg, slice):
+            single_arg.update({"type": "slice"})
+            # slice参数中可能存在tensor类型，json序列化，需要转换为python数值类型
+            values = [
+                value if not isinstance(value, torch.Tensor) else value.item()
+                for value in [arg.start, arg.stop, arg.step]
+            ]
+            single_arg.update({"value": values})
+        else:
+            single_arg.update({"type": type(arg).__name__})
+            single_arg.update({"value": arg})
+        return single_arg
+    
     @staticmethod
     def _analyze_torch_size(arg):
         return {"type": "torch.Size", "value": list(arg)}
@@ -97,7 +130,7 @@ class PytorchDataProcessor(BaseDataProcessor):
             return self._analyze_tensor(element, Const.SEP.join(suffix_stack))
         if isinstance(element, (bool, int, float, str, slice)):
             return self._analyze_builtin(element)
-        return None
+        return {}
 
     def analyze_element(self, element):
         return self.recursive_apply_transform(element, self.analyze_single_element)
@@ -113,9 +146,17 @@ class PytorchDataProcessor(BaseDataProcessor):
         tensor_json.update({"Mean": tensor_stat.mean})
         tensor_json.update({"Norm": tensor_stat.norm})
         tensor_json.update({"requires_grad": tensor.requires_grad})
-        if self.config.summary_mode == "md5":
+
+        if tensor_stat.max is not None:
+            if np.isinf(tensor_stat.max) or np.isnan(tensor_stat.max):
+                tensor_json['Max_except_inf_nan'] = self.handle_tensor_extremum_nan_inf(tensor, "max")
+        if tensor_stat.min is not None:
+            if np.isinf(tensor_stat.min) or np.isnan(tensor_stat.min):
+                tensor_json['Min_except_inf_nan'] = self.handle_tensor_extremum_nan_inf(tensor, "min")
+
+        if self.config.summary_mode == Const.MD5:
             tensor_md5 = self.get_md5_for_tensor(tensor)
-            tensor_json.update({"md5": tensor_md5})
+            tensor_json.update({Const.MD5: tensor_md5})
         return tensor_json
 
 
@@ -143,7 +184,7 @@ class OverflowCheckDataProcessor(PytorchDataProcessor):
         super().__init__(config, data_writer)
         self.cached_tensors_and_file_paths = {}
         self.real_overflow_dump_times = 0
-        self.overflow_nums = config.overflow_num
+        self.overflow_nums = config.overflow_nums
         self.bits_for_overflow = 8
 
     @staticmethod
@@ -151,21 +192,6 @@ class OverflowCheckDataProcessor(PytorchDataProcessor):
         overflow_mode = os.getenv(OverflowConst.OVERFLOW_DEBUG_MODE_ENABLE, Const.ENV_DISABLE)
         return overflow_mode == Const.ENV_ENABLE
 
-    @staticmethod
-    def handle_tensor_extremum_nan_inf(data_clone, operator):
-        data_nan = torch._C._VariableFunctionsClass.isnan(data_clone)
-        if int(torch._C._VariableFunctionsClass.sum(data_nan)) == data_clone.numel():
-            return float('nan')
-        finite_mask = torch._C._VariableFunctionsClass.isfinite(data_clone)
-        if int(torch._C._VariableFunctionsClass.sum(finite_mask)) > 0:
-            finite_values = data_clone[finite_mask]
-            return torch._C._VariableFunctionsClass.max(finite_values).item() if operator == 'max' else \
-                torch._C._VariableFunctionsClass.min(finite_values).item()
-        else:
-            data_no_nan = data_clone[~data_nan]
-            return torch._C._VariableFunctionsClass.max(data_no_nan).item() if operator == 'max' else \
-                torch._C._VariableFunctionsClass.min(data_no_nan).item()
-
     def analyze_forward(self, name, module, module_input_output: ModuleForwardInputsOutputs):
         self.has_overflow = False
         api_info_struct = super().analyze_forward(name, module, module_input_output)
@@ -211,16 +237,13 @@ class OverflowCheckDataProcessor(PytorchDataProcessor):
         else:
             torch_npu._C._clear_overflow_npu()
 
-    def _analyze_maybe_overflow_tensor(self, tensor_json, tensor):
-        data_clone = tensor.detach()
-        if hasattr(torch_npu._C, '_npu_is_support_inf_nan') and torch_npu._C._npu_is_support_inf_nan():
+    def _analyze_maybe_overflow_tensor(self, tensor_json):
+        if is_gpu or (hasattr(torch_npu._C, '_npu_is_support_inf_nan') and torch_npu._C._npu_is_support_inf_nan()):
             if tensor_json['Max'] is None:
                 return
             if np.isinf(tensor_json['Max']) or np.isnan(tensor_json['Max']):
-                tensor_json['Max_except_inf_nan'] = self.handle_tensor_extremum_nan_inf(data_clone, "max")
                 self.has_overflow = True
             if np.isinf(tensor_json['Min']) or np.isnan(tensor_json['Min']):
-                tensor_json['Min_except_inf_nan'] = self.handle_tensor_extremum_nan_inf(data_clone, "min")
                 self.has_overflow = True
         else:
             self.has_overflow = self.check_overflow_npu()
@@ -234,7 +257,7 @@ class OverflowCheckDataProcessor(PytorchDataProcessor):
         else:
             logger.warning(f'The file path {file_path} length exceeds limit.')
         single_arg = super()._analyze_tensor(tensor, suffix)
-        self._analyze_maybe_overflow_tensor(single_arg, tensor)
+        self._analyze_maybe_overflow_tensor(single_arg)
         single_arg.update({"data_name": dump_data_name})
         return single_arg
 
diff --git a/debug/accuracy_tools/msprobe/core/data_dump/json_writer.py b/debug/accuracy_tools/msprobe/core/data_dump/json_writer.py
index c4b7fc11ec4..112e45171ef 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/json_writer.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/json_writer.py
@@ -4,7 +4,7 @@ import fcntl
 import json
 from pathlib import Path
 
-from msprobe.core.common.file_check import change_mode
+from msprobe.core.common.file_check import change_mode, FileOpen
 from msprobe.core.common.log import logger
 from msprobe.core.common.const import Const, FileCheckConst
 
@@ -30,20 +30,20 @@ class DataWriter:
             return
         is_exists = os.path.exists(file_path)
         append = "a+" if is_exists else "w+"
-        with os.fdopen(
-            os.open(file_path, Const.WRITE_FLAGS, FileCheckConst.DATA_FILE_AUTHORITY), append, newline=""
-        ) as csv_file:
+        with FileOpen(file_path, append) as csv_file:
             spawn_writer = csv.writer(csv_file)
             if not is_exists:
                 spawn_writer.writerow(result_header)
             spawn_writer.writerows([result,])
+        is_new_file = not is_exists
+        if is_new_file:
+            change_mode(file_path, FileCheckConst.DATA_FILE_AUTHORITY)
 
     def initialize_json_file(self, **kwargs):
         kwargs.update({"dump_data_dir": self.dump_tensor_data_dir, Const.DATA: {}})
-        with os.fdopen(
-            os.open(self.dump_file_path, Const.OVERWRITE_FLAGS, FileCheckConst.DATA_FILE_AUTHORITY), 'w'
-        ) as f:
+        with FileOpen(self.dump_file_path, 'w') as f:
             json.dump(kwargs, f)
+        change_mode(self.dump_file_path, FileCheckConst.DATA_FILE_AUTHORITY)
 
         if os.path.exists(self.stack_file_path):
             os.remove(self.stack_file_path)
@@ -83,7 +83,7 @@ class DataWriter:
     def write_data_json(self, file_path):
         logger.info(f"dump.json is at {os.path.dirname(os.path.dirname(file_path))}. ")
         if Path(file_path).exists() and os.path.getsize(file_path) > 0:
-            with open(file_path, "r+") as f:
+            with FileOpen(file_path, "r+") as f:
                 fcntl.flock(f, fcntl.LOCK_EX)
                 data_to_write = json.load(f)
                 fcntl.flock(f, fcntl.LOCK_UN)
@@ -91,7 +91,7 @@ class DataWriter:
             self.init_json['data_path'] = self.dump_tensor_data_dir
             data_to_write = self.init_json
         data_to_write[Const.DATA].update(self.cache_data[Const.DATA])
-        with open(file_path, 'w+') as f:
+        with FileOpen(file_path, 'w+') as f:
             fcntl.flock(f, fcntl.LOCK_EX)
             json.dump(data_to_write, f, indent=1)
             fcntl.flock(f, fcntl.LOCK_UN)
@@ -99,13 +99,13 @@ class DataWriter:
         self.cache_data[Const.DATA].clear()
 
     def write_stack_info_json(self, file_path):
-        with open(file_path, 'w+') as f:
+        with FileOpen(file_path, 'w+') as f:
             fcntl.flock(f, fcntl.LOCK_EX)
             json.dump(self.cache_stack, f, indent=1)
             fcntl.flock(f, fcntl.LOCK_UN)
 
     def write_construct_info_json(self, file_path):
-        with open(file_path, 'w+') as f:
+        with FileOpen(file_path, 'w+') as f:
             fcntl.flock(f, fcntl.LOCK_EX)
             json.dump(self.cache_construct, f, indent=1)
             fcntl.flock(f, fcntl.LOCK_UN)
diff --git a/debug/accuracy_tools/msprobe/mindspore/common/utils.py b/debug/accuracy_tools/msprobe/mindspore/common/utils.py
index d02f3819537..6abf0a1ee88 100644
--- a/debug/accuracy_tools/msprobe/mindspore/common/utils.py
+++ b/debug/accuracy_tools/msprobe/mindspore/common/utils.py
@@ -29,3 +29,16 @@ def convert_bf16_to_fp32(tensor):
         tensor = tensor.to(ms.float32)
     return tensor
 
+
+class MsprobeStep(ms.train.Callback):
+
+    def __init__(self, debugger):
+        super(MsprobeStep, self).__init__()
+        self.debugger = debugger
+
+    def on_train_step_begin(self, run_context):
+        self.debugger.start()
+
+    def on_train_step_end(self, run_context):
+        self.debugger.stop()
+        self.debugger.step()
diff --git a/debug/accuracy_tools/msprobe/mindspore/debugger/debugger_config.py b/debug/accuracy_tools/msprobe/mindspore/debugger/debugger_config.py
index 04d66d6a26e..23cb7294b8d 100644
--- a/debug/accuracy_tools/msprobe/mindspore/debugger/debugger_config.py
+++ b/debug/accuracy_tools/msprobe/mindspore/debugger/debugger_config.py
@@ -1,14 +1,10 @@
 import os
+
 from msprobe.core.common.utils import Const
+from msprobe.core.common.const import MsConst
 
 
 class DebuggerConfig:
-    convert_map = {
-        "L0": "cell",
-        "L1": "api",
-        "L2": 'kernel'
-    }
-
     def __init__(self, common_config, task_config):
         self.dump_path = common_config.dump_path
         self.task = common_config.task
@@ -16,12 +12,13 @@ class DebuggerConfig:
         self.step = [] if not common_config.step else common_config.step
         if not common_config.level:
             common_config.level = "L1"
-        self.level = DebuggerConfig.convert_map[common_config.level]
+        self.level = MsConst.TOOL_LEVEL_DICT.get(common_config.level, MsConst.API)
         self.level_ori = common_config.level
         self.list = [] if not task_config.list else task_config.list
-        self.scope =[] if not task_config.scope else task_config.scope
-        self.data_mode =  [] if not task_config.data_mode else task_config.data_mode
+        self.scope = [] if not task_config.scope else task_config.scope
+        self.data_mode = [] if not task_config.data_mode else task_config.data_mode
         self.file_format = task_config.file_format
+        self.overflow_nums = 1 if not task_config.overflow_nums else task_config.overflow_nums
         self.check_mode = task_config.check_mode
         self.framework = Const.MS_FRAMEWORK
         self.summary_mode = task_config.summary_mode
diff --git a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py
index 30f7162ff5c..40b44c57ec9 100644
--- a/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py
+++ b/debug/accuracy_tools/msprobe/mindspore/debugger/precision_debugger.py
@@ -1,9 +1,12 @@
 import os
+
 import mindspore as ms
+
 from msprobe.mindspore.service import Service
 from msprobe.mindspore.ms_config import parse_json_config
 from msprobe.mindspore.debugger.debugger_config import DebuggerConfig
 from msprobe.mindspore.task_handler_factory import TaskHandlerFactory
+from msprobe.core.common.const import MsConst
 
 
 class PrecisionDebugger:
@@ -14,6 +17,8 @@ class PrecisionDebugger:
             cls._instance = super().__new__(cls)
             cls._instance.initialized = False
             cls._instance.config = None
+            cls.service = None
+            cls.first_start = False
         return cls._instance
 
     def __init__(self, config_path=None):
@@ -24,28 +29,47 @@ class PrecisionDebugger:
         common_config, task_config = parse_json_config(config_path)
         self.config = DebuggerConfig(common_config, task_config)
         self.initialized = True
-        self.service = Service(self.config)
+
+    @staticmethod
+    def _get_execution_mode():
+        if ms.get_context("mode") == ms.GRAPH_MODE:
+            if ms.context.get_jit_config().get("jit_level") == "O2" or ms.get_context("jit_level") == "O2":
+                return MsConst.GRAPH_GE_MODE
+            else:
+                return MsConst.GRAPH_KBYK_MODE
+        else:
+            return MsConst.PYNATIVE_MODE
 
     @classmethod
-    def start(cls):
+    def start(cls, target=None):
         instance = cls._instance
         if not instance:
             raise Exception("No instance of PrecisionDebugger found.")
-        if ms.get_context("mode") == ms.PYNATIVE_MODE and instance.config.level_ori == "L1":
-            instance.service.start()
+
+        instance.config.execution_mode = instance._get_execution_mode()
+        if instance.config.execution_mode == MsConst.PYNATIVE_MODE and instance.config.level == MsConst.API:
+            if not instance.service:
+                instance.service = Service(instance.config)
+            instance.service.start(target)
         else:
-            handler = TaskHandlerFactory.create(instance.config)
-            handler.handle()
+            if not instance.first_start:
+                handler = TaskHandlerFactory.create(instance.config)
+                handler.handle()
+
+        instance.first_start = True
 
     @classmethod
     def stop(cls):
         instance = cls._instance
         if not instance:
             raise Exception("PrecisionDebugger instance is not created.")
-        instance.service.stop()
+        if instance.service:
+            instance.service.stop()
 
     @classmethod
     def step(cls):
-        if not cls._instance:
+        instance = cls._instance
+        if not instance:
             raise Exception("PrecisionDebugger instance is not created.")
-        cls._instance.service.step()
\ No newline at end of file
+        if instance.service:
+            instance.service.step()
diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry copy.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry copy.py
new file mode 100644
index 00000000000..ad73bcd9119
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry copy.py	
@@ -0,0 +1,198 @@
+# Copyright 2024 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import os
+import functools
+import mindspore as ms
+from mindspore import ops
+from mindspore.common.tensor import Tensor
+from msprobe.core.common.utils import Const
+from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs
+from msprobe.mindspore.dump.hook_cell.wrap_functional import get_functional_ops, setup_hooks, \
+    HOOKFunctionalOP, HOOKMintOP, HOOKMintNNFunctionalOP
+from msprobe.mindspore.dump.hook_cell.wrap_tensor import get_tensor_ops, wrap_tensor_ops_and_bind, HOOKTensor
+from msprobe.core.common.utils import Const
+
+PRIMITIVE_PREFIX = "Primitive"
+
+class ApiRegistry:
+    def __init__(self):
+        self.tensor_ori_attr = {}
+        self.functional_ori_attr = {}
+        self.mint_ops_ori_attr = {}
+        self.mint_func_ops_ori_attr = {}
+        self.norm_inner_ops_ori_attr = {}
+
+        self.tensor_hook_attr = {}
+        self.functional_hook_attr = {}
+        self.mint_ops_hook_attr = {}
+        self.mint_func_ops_hook_attr = {}
+        self.norm_inner_ops_hook_attr = {}
+
+        self.norm_inner_ops = ["norm", "square", "sqrt", "is_complex"]
+        self.primitive_counters = {}
+
+    @staticmethod
+    def store_ori_attr(ori_api_group, api_list, api_ori_attr):
+        for api in api_list:
+            if Const.SEP in api:
+                sub_module_name, sub_op = api.rsplit(Const.SEP, 1)
+                sub_module = getattr(ori_api_group, sub_module_name)
+                api_ori_attr[api] = getattr(sub_module, sub_op)
+            else:
+                api_ori_attr[api] = getattr(ori_api_group, api)
+
+    @staticmethod
+    def set_api_attr(api_group, attr_dict):
+        for api, api_attr in attr_dict.items():
+            if Const.SEP in api:
+                sub_module_name, sub_op = api.rsplit(Const.SEP, 1)
+                sub_module = getattr(api_group, sub_module_name, None)
+                if sub_module is not None:
+                    setattr(sub_module, sub_op, api_attr)
+            else:
+                setattr(api_group, api, api_attr)
+
+    def norm_inner_op_set_hook_func(self):
+        self.set_api_attr(ms.ops, self.norm_inner_ops_hook_attr)
+
+    def norm_inner_op_set_ori_func(self):
+        self.set_api_attr(ms.ops, self.norm_inner_ops_ori_attr)
+
+    def api_set_hook_func(self):
+        self.set_api_attr(ms.Tensor, self.tensor_hook_attr)
+        self.set_api_attr(ms.ops, self.functional_hook_attr)
+        self.set_api_attr(ms.mint, self.mint_ops_hook_attr)
+        self.set_api_attr(ms.mint.nn.functional, self.mint_func_ops_hook_attr)
+
+    def api_set_ori_func(self):
+        self.set_api_attr(ms.Tensor, self.tensor_ori_attr)
+        self.set_api_attr(ms.ops, self.functional_ori_attr)
+        self.set_api_attr(ms.mint, self.mint_ops_ori_attr)
+        self.set_api_attr(ms.mint.nn.functional, self.mint_func_ops_ori_attr)
+
+    def initialize_hook(self, hook):
+        self.store_ori_attr(ms.Tensor, get_tensor_ops(), self.tensor_ori_attr)
+        wrap_tensor_ops_and_bind(hook)
+        for attr_name in dir(HOOKTensor):
+            if attr_name.startswith(Const.ATTR_NAME_PREFIX):
+                self.tensor_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKTensor, attr_name)
+
+        functional_ops, mint_ops, mint_func_ops = get_functional_ops()
+        self.store_ori_attr(ms.ops, self.norm_inner_ops, self.norm_inner_ops_ori_attr)
+        self.store_ori_attr(ms.ops, functional_ops, self.functional_ori_attr)
+        self.store_ori_attr(ms.mint, mint_ops, self.mint_ops_ori_attr)
+        self.store_ori_attr(ms.mint.nn.functional, mint_func_ops, self.mint_func_ops_ori_attr)
+        setup_hooks(hook)
+        for attr_name in dir(HOOKFunctionalOP):
+            if attr_name.startswith(Const.ATTR_NAME_PREFIX):
+                self.functional_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKFunctionalOP, attr_name)
+                if attr_name[Const.ATTR_NAME_PREFIX_LEN:] in self.norm_inner_ops:
+                    self.norm_inner_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKFunctionalOP, attr_name)
+        for attr_name in dir(HOOKMintOP):
+            if attr_name.startswith(Const.ATTR_NAME_PREFIX):
+                self.mint_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintOP, attr_name)
+        for attr_name in dir(HOOKMintNNFunctionalOP):
+            if attr_name.startswith(Const.ATTR_NAME_PREFIX):
+                self.mint_func_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintNNFunctionalOP, attr_name)    
+
+    def wrap_primitive(self, origin_func, primitive_name, service_instance):
+        primitive_instance = self
+        def func(self, *args, **kwargs):
+            if primitive_name not in primitive_instance.primitive_counters:
+                primitive_instance.primitive_counters[primitive_name] = 0
+            else:
+                primitive_instance.primitive_counters[primitive_name] += 1
+
+            current_count = primitive_instance.primitive_counters[primitive_name]
+            updated_primitive_name = f"{PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+            captured_grads_input = []
+            captured_grads_output = []
+
+            def input_backward_hook(grad):
+                print(f"Grad input length: {len(grad)}")
+                print("Captured input grad:", grad)
+                captured_grads_input.append(grad)
+                backward_primitive_name = updated_primitive_name + Const.BACKWARD
+                new_module_input_output = ModuleBackwardInputsOutputs(
+                    grad_input=tuple(captured_grads_input),
+                    grad_output=tuple(captured_grads_output) if captured_grads_output else None
+                )
+                service_instance.data_collector.backward_data_collect(
+                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
+                )
+#1未考虑多输出场景
+# 如果时多grad呢
+# 3 输出的序号问题
+            def output_backward_hook(grad):
+                captured_grads_output.append(grad)
+                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                new_module_input_output = ModuleBackwardInputsOutputs(
+                    grad_input=None,
+                    grad_output=tuple(captured_grads_output)
+                )
+                service_instance.data_collector.backward_data_collect(
+                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
+                )
+
+            if not service_instance.switch:
+                return origin_func(*args, **kwargs)
+
+            print(f"Entering {updated_primitive_name} hook, number of args: {len(args)}, name: {self.name}")
+            hooked_inputs = []
+
+            # for idx, arg in enumerate(args):
+            #     if isinstance(arg, Tensor):
+            #         arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+            #         hooked_inputs.append(arg_hooked)
+            #     else:
+            #         hooked_inputs.append(arg)
+
+            out = origin_func(*args, **kwargs)
+            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
+
+            if service_instance.data_collector:
+                module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
+                service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
+                if service_instance.data_collector.if_return_forward_new_output():
+                    out = service_instance.data_collector.get_forward_new_output()
+
+            if isinstance(out, Tensor):
+                out = ops.HookBackward(output_backward_hook)(out)
+            elif isinstance(out, tuple):
+                hooked_outputs = []
+                for tensor in out:
+                    if isinstance(tensor, Tensor):
+                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+                    else:
+                        hooked_outputs.append(tensor)
+                out = tuple(hooked_outputs)
+
+            return out
+
+        return func
+
+    def register_hooks(self, service_instance):
+        primitive_set = set()
+        for name, cell in service_instance.model.cells_and_names():
+            for pname, primitive in cell._primitives.items():
+                primitive_set.add((pname, primitive))
+
+        for pname, primitive in primitive_set:
+            print("primitive name is", pname)
+            NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname, service_instance)})
+            primitive.__class__ = NewPrimitive
+
+api_register = ApiRegistry()
diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/hook_cell.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/hook_cell.py
index bcb80dd2266..57ed44111ca 100644
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/hook_cell.py
+++ b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/hook_cell.py
@@ -18,26 +18,23 @@ from mindspore import nn
 from msprobe.core.common.const import Const
 
 
-cell_count = defaultdict(int)
-g_stop_hook = False
-
-
 class HOOKCell(nn.Cell):
+    cell_count = defaultdict(int)
+    g_stop_hook = False
 
     def __init__(self, build_hook) -> None:
         super(HOOKCell, self).__init__()
         self.changed_status = False
         self.input_kwargs = {}
         self.prefix = ""
-        global g_stop_hook
-        if not g_stop_hook:
-            g_stop_hook = True
+        if not HOOKCell.g_stop_hook:
+            HOOKCell.g_stop_hook = True
             self.changed_status = True
             if hasattr(self, "prefix_op_name_"):
                 self.prefix = self.prefix_op_name_
 
-            cell_count[self.prefix] += 1
-            self.prefix = self.prefix + str(cell_count[self.prefix] - 1) + Const.SEP
+            HOOKCell.cell_count[self.prefix] += 1
+            self.prefix = self.prefix + str(HOOKCell.cell_count[self.prefix] - 1) + Const.SEP
             forward_hook, backward_hook = build_hook(self.prefix)
             self.register_forward_hook(forward_hook)
             self.register_backward_hook(backward_hook)
@@ -52,6 +49,5 @@ class HOOKCell(nn.Cell):
         finally:
             if self.changed_status:
                 self.changed_status = False
-                global g_stop_hook
-                g_stop_hook = False
+                HOOKCell.g_stop_hook = False
         return out
diff --git a/debug/accuracy_tools/msprobe/mindspore/ms_config.py b/debug/accuracy_tools/msprobe/mindspore/ms_config.py
index 49ce4cf2c09..c0ef6bb6c00 100644
--- a/debug/accuracy_tools/msprobe/mindspore/ms_config.py
+++ b/debug/accuracy_tools/msprobe/mindspore/ms_config.py
@@ -36,37 +36,39 @@ class StatisticsConfig(BaseConfig):
             raise Exception("summary_mode is invalid")
 
 
-class OverflowCheck(BaseConfig):
+class OverflowCheckConfig(BaseConfig):
     def __init__(self, json_config):
         super().__init__(json_config)
-        self.file_format = None
-        self.check_mode = json_config.get("check_mode")
+        self.data_mode = ["all"]
         self._check_config()
 
     def _check_config(self):
-        if self.data_mode is not None and len(self.data_mode) > 0:
-            if len(self.data_mode) > 1 or self.data_mode[0] not in ["all", "input", "output"]:
-                raise Exception("data_mode must be all, input or output")
+        if self.overflow_nums is not None and not isinstance(self.overflow_nums, int):
+            raise Exception("overflow_nums is invalid, it should be an integer")
+        if self.overflow_nums is not None and self.overflow_nums != -1 and self.overflow_nums <= 0:
+            raise Exception("overflow_nums should be -1 or positive integer")
         if self.check_mode and self.check_mode not in ["all", "aicore", "atomic"]:
             raise Exception("check_mode is invalid")
 
 
+TaskDict = {
+    Const.TENSOR: TensorConfig,
+    Const.STATISTICS: StatisticsConfig,
+    Const.OVERFLOW_CHECK: OverflowCheckConfig,
+}
+
+
 def parse_common_config(json_config):
     return CommonConfig(json_config)
 
 
 def parse_task_config(task, json_config):
-    task_map = json_config[task]
+    task_map = json_config.get(task)
     if not task_map:
         task_map = dict()
-    if task == Const.TENSOR:
-        return TensorConfig(task_map)
-    elif task == Const.STATISTICS:
-        return StatisticsConfig(task_map)
-    elif task == Const.OVERFLOW_CHECK:
-        return OverflowCheck(task_map)
-    else:
+    if task not in TaskDict:
         raise Exception("task is invalid.")
+    return TaskDict.get(task)(task_map)
 
 
 def parse_json_config(json_file_path):
diff --git a/debug/accuracy_tools/msprobe/mindspore/service.py b/debug/accuracy_tools/msprobe/mindspore/service.py
index e8aa34dc4fe..b795ec10342 100644
--- a/debug/accuracy_tools/msprobe/mindspore/service.py
+++ b/debug/accuracy_tools/msprobe/mindspore/service.py
@@ -14,9 +14,14 @@
 # ============================================================================
 
 import os
+import copy
 from pathlib import Path
 import functools
+from collections import defaultdict
 
+from mindspore.common.tensor import Tensor
+from mindspore import ops
+from mindspore import nn
 from msprobe.core.data_dump.data_collector import build_data_collector
 from msprobe.core.data_dump.scope import BaseScope
 from msprobe.mindspore.common.utils import get_rank_if_initialized
@@ -25,20 +30,25 @@ from msprobe.mindspore.common.log import logger
 from msprobe.core.common.utils import Const
 from msprobe.core.common.exceptions import DistributedNotInitializedError
 from msprobe.mindspore.dump.hook_cell.api_registry import api_register
-from msprobe.core.data_dump.data_processor.base import ModuleForwardInputsOutputs, ModuleBackwardInputsOutputs
+from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs,\
+    ModuleBackwardInputs, ModuleBackwardOutputs
+from msprobe.core.common.exceptions import MsprobeException
+from msprobe.mindspore.dump.hook_cell.hook_cell import HOOKCell
 
 
 class Service:
     def __init__(self, config):
         self.model = None
-        self.config = config
+        self.config = copy.deepcopy(config)
         self.config.level = self.config.level_ori
-        self.data_collector = build_data_collector(config)
+        self.data_collector = build_data_collector(self.config)
         self.switch = False
         self.current_iter = 0
         self.first_start = True
         self.current_rank = None
+        self.primitive_counters = {}
         self.dump_iter_dir = None
+        self.start_call = False
 
     def build_hook(self, module_type, name):
         def forward_hook(api_or_module_name, module, input, output):
@@ -50,6 +60,7 @@ class Service:
                 self.data_collector.forward_data_collect(api_or_module_name, module, pid, module_input_output)
                 if self.data_collector.if_return_forward_new_output():
                     return self.data_collector.get_forward_new_output()
+                del module.input_kwargs
             return output
 
         def backward_hook(api_or_module_name, module, grad_input, grad_output):
@@ -68,18 +79,162 @@ class Service:
 
         def wrap_forward_hook(*args, **kwargs):
             return forward_hook(*args, **kwargs)
-        
+
         def wrap_backward_hook(*args, **kwargs):
             return backward_hook(*args, **kwargs)
-        
+
         return wrap_forward_hook, wrap_backward_hook
 
+    def wrap_primitive(self, origin_func, primitive_name):
+        service_instance = self
+
+        def create_backward_hook(captured_grads, num_tensors, updated_primitive_name, hook_type):
+            def backward_hook(grad):
+                captured_grads.append(grad)
+                try:
+                    if len(captured_grads) == num_tensors and hook_type == Const.INPUT:
+                        backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                        service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
+                        new_module_input_output = ModuleBackwardInputs(grad_input=tuple(captured_grads))
+                        service_instance.data_collector.backward_input_data_collect(
+                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                        )
+                        captured_grads.clear()
+                    elif len(captured_grads) == num_tensors and hook_type == Const.OUTPUT:
+                        backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
+                        service_instance.data_collector.visit_and_clear_overflow_status(backward_primitive_name)
+                        new_module_input_output = ModuleBackwardOutputs(grad_output=tuple(captured_grads))
+                        service_instance.data_collector.backward_output_data_collect(
+                            backward_primitive_name, service_instance, os.getpid(), new_module_input_output
+                        )
+                        captured_grads.clear()
+
+                except Exception as exception:
+                    raise Exception(
+                        "This is a primitive op {hook_type}_backward dump error: {exception},"
+                        " updated_primitive_name: {updated_primitive_name}".format(
+                            hook_type=hook_type, exception=exception, updated_primitive_name=updated_primitive_name
+                        )
+                    ) from exception
+
+            return backward_hook
+
+        def hook_primitive_inputs(args, captured_grads_input, updated_primitive_name):
+            hooked_inputs = []
+            num_tensors = sum(isinstance(arg, Tensor) for arg in args)
+            input_backward_hook = create_backward_hook(captured_grads_input, num_tensors, updated_primitive_name,
+                                                       Const.INPUT)
+            for _, arg in enumerate(args):
+                if isinstance(arg, Tensor):
+                    arg_hooked = ops.HookBackward(input_backward_hook)(arg)
+                    hooked_inputs.append(arg_hooked)
+                else:
+                    hooked_inputs.append(arg)
+            return hooked_inputs
+
+        def hook_primitive_outputs(out, captured_grads_output, updated_primitive_name):
+            if isinstance(out, tuple):
+                num_output_tensors = sum(isinstance(tensor, Tensor) for tensor in out)
+            else:
+                num_output_tensors = 1
+            output_backward_hook = create_backward_hook(captured_grads_output, num_output_tensors,
+                                                        updated_primitive_name, Const.OUTPUT)
+
+            if isinstance(out, Tensor):
+                return ops.HookBackward(output_backward_hook)(out)
+            elif isinstance(out, tuple):
+                hooked_outputs = []
+                for tensor in out:
+                    if isinstance(tensor, Tensor):
+                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
+                    else:
+                        hooked_outputs.append(tensor)
+                return tuple(hooked_outputs)
+            return out
+
+        def wrapped_primitive_call(instance_self, *args, **kwargs):
+
+            service_instance.update_primitive_counters(primitive_name)
+            current_count = service_instance.primitive_counters[primitive_name]
+            updated_primitive_name = f"{Const.PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
+
+            if not service_instance.switch:
+                return origin_func(*args, **kwargs)
+
+            captured_grads_input, captured_grads_output = [], []
+
+            try:
+                hooked_inputs = hook_primitive_inputs(args, captured_grads_input, updated_primitive_name)
+            except Exception as exception:
+                raise Exception("This is a primitive op dump error during input hooking: {},"
+                                " primitive_name: {}".format(exception, primitive_name)) from exception
+
+            try:
+                out = origin_func(*hooked_inputs, **kwargs)
+            except Exception as exception:
+                raise Exception("This is a primitive op dump error during function call: {},"
+                                " primitive_name: {}".format(exception, primitive_name)) from exception
+
+            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
+            service_instance.data_collector.visit_and_clear_overflow_status(forward_primitive_name)
+            if service_instance.data_collector:
+                module_input_output = ModuleForwardInputsOutputs(args=hooked_inputs, kwargs=kwargs, output=out)
+                try:
+                    service_instance.data_collector.forward_data_collect(forward_primitive_name, instance_self,
+                                                                         os.getpid(), module_input_output)
+                except Exception as exception:
+                    raise Exception("This is a primitive op dump error during forward data collection: {},"
+                                    " primitive_name: {}".format(exception, primitive_name)) from exception
+
+                if service_instance.data_collector.if_return_forward_new_output():
+                    out = service_instance.data_collector.get_forward_new_output()
+
+            try:
+                out = hook_primitive_outputs(out, captured_grads_output, updated_primitive_name)
+            except Exception as exception:
+                raise Exception("This is a primitive op dump error during output hooking: {},"
+                                " primitive_name: {}".format(exception, primitive_name)) from exception
+
+            return out
+
+
+        return wrapped_primitive_call
+
+    def update_primitive_counters(self, primitive_name):
+        if primitive_name not in self.primitive_counters:
+            self.primitive_counters[primitive_name] = 0
+        else:
+            self.primitive_counters[primitive_name] += 1
+
+    def register_hooks(self):
+        primitive_set = set()
+        for _, cell in self.model.cells_and_names():
+            for pname, primitive in cell._primitives.items():
+                primitive_set.add((pname, primitive))
+
+        for pname, primitive in primitive_set:
+            NewPrimitive = type('NewPrimitive', (primitive.__class__,),
+                                {'__call__': self.wrap_primitive(primitive.__call__, pname)})
+            primitive.__class__ = NewPrimitive
+
     def step(self):
         self.current_iter += 1
         self.data_collector.update_iter(self.current_iter)
+        HOOKCell.cell_count = defaultdict(int)
+        self.primitive_counters.clear()
+
+    @staticmethod
+    def check_model_valid(model):
+        if not model or isinstance(model, nn.Cell):
+            return model
+        raise MsprobeException(
+            MsprobeException.INVALID_PARAM_ERROR, "model 参数必须是 mindspore.nn.Cell 类型。"
+        )
 
     def start(self, model=None):
-        self.model = model
+        self.model = Service.check_model_valid(model)
+        self.start_call = True
+        logger.info("msprobe: debugger.start() is set successfully")
         if self.config.step and self.current_iter > max(self.config.step):
             self.stop()
             raise Exception("msprobe: exit after iteration {}".format(max(self.config.step)))
@@ -96,16 +251,22 @@ class Service:
             self.register_hook_new()
             self.first_start = False
         self.switch = True
-        logger.info_on_rank_0(f"Dump switch is turned on at step {self.current_iter}. ")
+        logger.info(f"Dump switch is turned on at step {self.current_iter}. ")
         self.create_dirs()
-        logger.info_on_rank_0(f"Dump data will be saved in {self.dump_iter_dir}.")
+        logger.info(f"Dump data will be saved in {self.dump_iter_dir}.")
 
     def stop(self):
+        logger.info("msprobe: debugger.stop() is set successfully. "
+                    "Please set debugger.start() to turn on the dump switch again. ")
+        if not self.start_call:
+            logger.error("msprobe: debugger.start() is not set in the current scope.")
+            raise Exception("debugger.start() is not set in the current scope.")
         if self.config.step and self.current_iter not in self.config.step:
             return
         if self.config.rank and self.current_rank not in self.config.rank:
             return
         self.switch = False
+        self.start_call = False
         self.data_collector.write_json()
 
     def create_dirs(self):
@@ -130,9 +291,11 @@ class Service:
         construct_file_path = os.path.join(dump_dir, "construct.json")
         self.data_collector.update_dump_paths(
             dump_file_path, stack_file_path, construct_file_path, dump_data_dir, None)
-        
+
     def register_hook_new(self):
-        logger.info_on_rank_0("The {} hook function is successfully mounted to the model.".format(self.config.task))
+        logger.info("The {} hook function is successfully mounted to the model.".format(self.config.task))
         if self.config.level == "L1":
             api_register.initialize_hook(functools.partial(self.build_hook, BaseScope.Module_Type_API))
             api_register.api_set_hook_func()
+            if self.model:
+                self.register_hooks()
diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/data_generate.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/data_generate.py
index f495cd673d7..b2eec691af0 100644
--- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/data_generate.py
+++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/data_generate.py
@@ -21,10 +21,11 @@ import torch
 import numpy
 
 from msprobe.pytorch.api_accuracy_checker.run_ut.run_ut_utils import hf_32_standard_api
-from msprobe.pytorch.api_accuracy_checker.common.utils import check_file_or_directory_path, check_object_type, \
-    get_full_data_path, CompareException
+from msprobe.pytorch.api_accuracy_checker.common.utils import check_object_type, get_full_data_path, \
+    CompareException
+from msprobe.core.common.file_check import FileChecker
 from msprobe.pytorch.common.log import logger
-from msprobe.core.common.const import Const
+from msprobe.core.common.const import Const, FileCheckConst
 
 TORCH_TYPE = ["torch.device", "torch.dtype"]
 TENSOR_DATA_LIST = ["torch.Tensor", "torch.nn.parameter.Parameter"]
@@ -87,12 +88,13 @@ def gen_real_tensor(data_path, convert_type):
         convert_type: convert ori_type to dist_type flag.
     """
     data_path = os.path.realpath(data_path)
-    check_file_or_directory_path(data_path)
+    data_path_checker = FileChecker(data_path, FileCheckConst.FILE, ability=FileCheckConst.READ_ABLE)
+    data_path = data_path_checker.common_check()
     if not data_path.endswith('.pt') and not data_path.endswith('.npy'):
         error_info = f"The file: {data_path} is not a pt or numpy file."
         raise CompareException(CompareException.INVALID_FILE_ERROR, error_info)
     if data_path.endswith('.pt'):
-        data = torch.load(data_path).cpu()
+        data = torch.load(data_path, map_location=torch.device('cpu'))
     else:
         data_np = numpy.load(data_path)
         data = torch.from_numpy(data_np)
diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py
index 9c96a52d8bd..9acb5ee6498 100644
--- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py
+++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/multi_run_ut.py
@@ -68,7 +68,7 @@ signal.signal(signal.SIGTERM, signal_handler)
 
 ParallelUTConfig = namedtuple('ParallelUTConfig', ['api_files', 'out_path', 'num_splits',
                                                    'save_error_data_flag', 'jit_compile_flag', 'device_id',
-                                                   'result_csv_path', 'total_items', 'real_data_path'])
+                                                   'result_csv_path', 'total_items', 'config_path'])
 
 
 def run_parallel_ut(config):
@@ -90,7 +90,7 @@ def run_parallel_ut(config):
             *(['-j'] if config.jit_compile_flag else []),
             *(['-save_error_data'] if config.save_error_data_flag else []),
             '-csv_path', config.result_csv_path,
-            *(['-real_data_path', config.real_data_path] if config.real_data_path else [])
+            *(['-config', config.config_path] if config.config_path else [])
         ]
         return cmd
 
@@ -110,14 +110,9 @@ def run_parallel_ut(config):
 
     def update_progress_bar(progress_bar, result_csv_path):
         while any(process.poll() is None for process in processes):
-            try:
-                with open(result_csv_path, 'r') as result_file:
-                    completed_items = len(result_file.readlines()) - 1
-                    progress_bar.update(completed_items - progress_bar.n)
-            except FileNotFoundError:
-                logger.warning(f"Result CSV file not found: {result_csv_path}.")
-            except Exception as e:
-                logger.error(f"An unexpected error occurred while reading result CSV: {e}")
+            with FileOpen(result_csv_path, 'r') as result_file:
+                completed_items = len(result_file.readlines()) - 1
+                progress_bar.update(completed_items - progress_bar.n)
             time.sleep(1)
 
     for api_info in config.api_files:
@@ -175,7 +170,7 @@ def prepare_config(args):
     out_path_checker = FileChecker(out_path, FileCheckConst.DIR, ability=FileCheckConst.WRITE_ABLE)
     out_path = out_path_checker.common_check()
     split_files, total_items = split_json_file(api_info, args.num_splits, args.filter_api)
-
+    config_path = os.path.realpath(args.config_path) if args.config_path else None
     result_csv_path = args.result_csv_path or os.path.join(out_path, f"accuracy_checking_result_{time.strftime('%Y%m%d%H%M%S')}.csv")
     if not args.result_csv_path:
         details_csv_path = os.path.join(out_path, f"accuracy_checking_details_{time.strftime('%Y%m%d%H%M%S')}.csv")
@@ -187,7 +182,7 @@ def prepare_config(args):
     logger.info(f"UT task details will be saved in {details_csv_path}")
     return ParallelUTConfig(split_files, out_path, args.num_splits, args.save_error_data,
                             args.jit_compile, args.device_id, result_csv_path,
-                            total_items, args.real_data_path)
+                            total_items, config_path)
 
 
 def main():
diff --git a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py
index 30994f70944..559dfdc0f14 100644
--- a/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py
+++ b/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/run_ut/run_ut.py
@@ -27,6 +27,8 @@ from msprobe.pytorch.api_accuracy_checker.compare.compare_column import CompareC
 from msprobe.pytorch.hook_module.wrap_tensor import TensorOPTemplate
 from msprobe.pytorch.hook_module.wrap_functional import FunctionalOPTemplate
 from msprobe.pytorch.hook_module.wrap_torch import TorchOPTemplate
+from msprobe.pytorch.hook_module.wrap_npu_custom import NpuOPTemplate
+from msprobe.pytorch.hook_module.wrap_aten import AtenOPTemplate
 from msprobe.pytorch.api_accuracy_checker.common.config import msCheckerConfig
 from msprobe.pytorch.common.parse_json import parse_json_info_forward_backward
 from msprobe.core.common.file_check import FileOpen, FileChecker, \
@@ -78,6 +80,12 @@ def exec_api(api_type, api_name, args, kwargs):
     if api_type == "Torch":
         torch_api = TorchOPTemplate(api_name, str, False)
         out = torch_api.forward(*args, **kwargs)
+    if api_type == "Aten":
+        torch_api = AtenOPTemplate(api_name, None, False)
+        out = torch_api.forward(*args, **kwargs)
+    if api_type == "NPU":
+        torch_api = NpuOPTemplate(api_name, None, False)
+        out = torch_api.forward(*args, **kwargs)
     return out
 
 
@@ -274,7 +282,7 @@ def run_torch_api(api_full_name, real_data_path, backward_content, api_info_dict
 
     if need_backward:
         if need_to_backward(grad_index, out):
-            backward_args = backward_content[api_full_name].get("grad_output")
+            backward_args = backward_content[api_full_name].get("input")
             grad = gen_args(backward_args, api_name, real_data_path=real_data_path)[0]
             bench_grad, _ = generate_cpu_params(grad, {}, False, api_name)
             bench_grad_out = run_backward(cpu_args, bench_grad, grad_index, out)
@@ -379,10 +387,6 @@ def _run_ut_parser(parser):
                         help="<optional> The path of accuracy_checking_result_{timestamp}.csv, "
                              "when run ut is interrupted, enter the file path to continue run ut.",
                         required=False)
-    parser.add_argument("-real_data_path", dest="real_data_path", nargs="?", const="", default="", type=str,
-                        help="<optional> In real data mode, the root directory for storing real data "
-                             "must be configured.",
-                        required=False)
     parser.add_argument("-f", "--filter_api", dest="filter_api", action="store_true",
                         help="<optional> Whether to filter the api in the api_info_file.", required=False)
     parser.add_argument("-config", "--config_path", dest="config_path", default="", type=str,
@@ -400,9 +404,9 @@ def preprocess_forward_content(forward_content):
         if key not in arg_cache:
             filtered_new_args = [
                 {k: v for k, v in arg.items() if k not in ['Max', 'Min']}
-                for arg in value['args'] if isinstance(arg, dict)
+                for arg in value['input_args'] if isinstance(arg, dict)
             ]
-            arg_cache[key] = (filtered_new_args, value['kwargs'])
+            arg_cache[key] = (filtered_new_args, value['input_kwargs'])
 
         filtered_new_args, new_kwargs = arg_cache[key]
 
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/__init__.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/__init__.py
new file mode 100644
index 00000000000..eb06867371c
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/__init__.py
@@ -0,0 +1,15 @@
+import os
+from pkgutil import iter_modules
+from importlib import import_module
+
+"""
+gpu and cpu not implement benchmark function, supplementary benchmarking function implementation
+"""
+
+package_path = os.path.dirname(os.path.realpath(__file__))
+for _, module_name, _ in iter_modules([package_path]):
+    module = import_module(f"{__name__}.{module_name}")
+    for attr_name in dir(module):
+        attr = getattr(module, attr_name)
+        if callable(attr) and "npu_custom" not in attr_name:
+            globals()[attr_name] = attr
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/apply_adam_w.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/apply_adam_w.py
new file mode 100644
index 00000000000..caf21a604c6
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/apply_adam_w.py
@@ -0,0 +1,28 @@
+import torch
+
+
+def npu_apply_adam_w(beta1_power, beta2_power, lr, weight_decay,
+                     beta1, beta2, eps, grad, max_grad_norm, amsgrad, maximize, out):
+    var, m, v = out
+    if amsgrad:
+        max_grad_norm = (torch.rand(var.shape) * 10.0 - 5.0).to(var.dtype)
+    beta1_power_out = beta1_power * beta1
+    beta2_power_out = beta2_power * beta2
+    var_t = var * (1 + (-lr * weight_decay))
+    gt = -grad if maximize else grad
+    m_out = m * beta1 - (beta1 + (-1)) * gt
+    v_out = v * beta2 - (beta2 + (-1)) * gt * gt
+
+    if amsgrad:
+        max_grad_norm_out = torch.max(max_grad_norm, v_out)
+        if (1 - beta2_power_out) == 0:
+            beta2_power_out -= eps
+        denom = torch.sqrt(torch.div(max_grad_norm_out, (1 - beta2_power_out))) + eps
+    else:
+        vraintain = torch.div(v_out, (1 - beta2_power_out))
+        denom = torch.sqrt(vraintain) + eps
+
+    if (1 - beta1_power_out) == 0:
+        beta1_power_out -= eps
+    var_out = var_t + torch.div(-lr * m_out, (1 - beta1_power_out)).div(denom)
+    return var_out.cpu(), m_out.cpu(), v_out.cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/confusion_transpose.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/confusion_transpose.py
new file mode 100644
index 00000000000..627bf11b64f
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/confusion_transpose.py
@@ -0,0 +1,19 @@
+def npu_confusion_transpose(data, perm, shape, transpose_first):
+    if transpose_first:
+        output = data.permute(*perm).contiguous().view(shape)
+    else:
+        output = data.view(shape).permute(*perm)
+    return output.cpu()
+
+
+def npu_confusion_transpose_backward(grad, perm, shape, transpose_first):
+    shape_cal = shape if transpose_first else [shape[perm_dim] for perm_dim in perm]
+    perm_cal = [0] * len(perm)
+    for i, perm_dim in enumerate(perm):
+        perm_cal[perm_dim] = i
+
+    if transpose_first:
+        result = grad.permute(*perm_cal).reshape(shape_cal)
+    else:
+        result = grad.reshape(shape_cal).permute(*perm_cal)
+    return result.cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/fast_gelu.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/fast_gelu.py
new file mode 100644
index 00000000000..a1a9ca08085
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/fast_gelu.py
@@ -0,0 +1,55 @@
+import torch
+
+
+def fast_gelu(input0):
+    attr = 1.702
+    const_0 = 0 - attr
+    const_1 = 1
+    const_2 = attr / 2
+
+    abs_x = torch.abs(input0)
+    mul_abs_x = abs_x * const_0
+    exp_abs_x = torch.exp(mul_abs_x)
+    div_down = exp_abs_x + const_1
+
+    pn_x = input0 - abs_x
+    mul_pn_x = pn_x * const_2
+    exp_pn_x = torch.exp(mul_pn_x)
+    div_up = input0 * exp_pn_x
+    div_down_rec = torch.reciprocal(div_down)
+    result = div_up * div_down_rec
+
+    return result.cpu()
+
+
+def npu_fast_gelu_backward(grad, input_x):
+    const_2 = 1.702
+    const_3 = 1.0
+    const_1 = 0.0 - const_2
+
+    # e^(-1.702x)
+    abs_x = torch.abs(input_x)
+    mul_abs_x = abs_x * const_1
+    exp_x = torch.exp(mul_abs_x)
+
+    # 1.702xe^(-1.702x)
+    add_2 = input_x * exp_x
+    add_2 = add_2 * const_2
+
+    # e^(1.702(x-|x|))
+    pn_x = input_x - abs_x
+    mul_pn_x = pn_x * const_2
+    exp_pn_x = torch.exp(mul_pn_x)
+
+    #  e^(-1.702x) + 1.702xe^(-1.702x) + e^(1.702(x-|x|))
+    div_up = exp_x + add_2
+    div_up = div_up + exp_pn_x
+
+    # (e^(-1.702x)+1)^2
+    div_down_i = exp_x + const_3
+    div_down = div_down_i * div_down_i
+    div_down_rec = torch.reciprocal(div_down)
+    result_temp = div_up * div_down_rec
+    result = grad * result_temp
+
+    return result.cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/layer_norm_eval.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/layer_norm_eval.py
new file mode 100644
index 00000000000..f6949c079e2
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/layer_norm_eval.py
@@ -0,0 +1,6 @@
+import torch
+
+
+def npu_layer_norm_eval(data, normalized_shape):
+    result = torch.nn.functional.layer_norm(data, normalized_shape)
+    return result.cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/linear.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/linear.py
new file mode 100644
index 00000000000..95db875edf6
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/linear.py
@@ -0,0 +1,12 @@
+import torch
+
+
+def npu_linear(x, weight, bias):
+    output = torch.nn.functional.linear(x, weight, bias)
+    return output.cpu()
+
+
+def npu_linear_backward(grad, input_data, weight):
+    input_grad = torch.matmul(grad, weight)
+    weight_grad = torch.matmul(grad.t(), input_data)
+    return input_grad.cpu(), weight_grad.cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/matmul_backward.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/matmul_backward.py
new file mode 100644
index 00000000000..ed1c746ec16
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/matmul_backward.py
@@ -0,0 +1,48 @@
+import torch
+
+
+def matmul_backward(grad, self, other, mask):
+    grad_self, grad_other = None, None
+    dim_self = self.dim()
+    dim_other = other.dim()
+
+    size_grad = list(grad.size())
+    size_self = list(self.size())
+    size_other = list(other.size())
+    if dim_self == 1 and dim_other == 1:
+        grad_self = other.mul(grad) if mask[0] else grad_self
+        grad_other = self.mul(grad) if mask[1] else grad_other
+    elif dim_self == 2 and dim_other == 1:
+        grad_self = grad.unsqueeze(1).mm(other.unsqueeze(0)) if mask[0] else grad_self
+        grad_other = self.transpose(-1, -2).mm(grad.unsqueeze(1)).squeeze_(1) if mask[1] else grad_other
+    elif dim_self == 1 and dim_other == 2:
+        grad_self = grad.unsqueeze(0).mm(other.transpose(-1, -2)).squeeze_(0) if mask[0] else grad_self
+        grad_other = self.unsqueeze(1).mm(grad.unsqueeze(0)) if mask[1] else grad_other
+    elif dim_self >= 3 and (dim_other == 1 or dim_other == 2):
+        view_size = 1 if dim_other == 1 else size_grad[-1]
+        unfolded_grad = (grad.unsqueeze(-1) if dim_other == 1 else grad).contiguous().view(-1, view_size)
+        if mask[0]:
+            grad_self = unfolded_grad.mm(other.unsqueeze(0) if dim_other == 1 else other.transpose(-1, -2)) \
+                .view(size_self)
+        if mask[1]:
+            unfolded_self = self.contiguous().view([-1, size_self[-1]])
+            grad_other = unfolded_self.transpose(-1, -2).mm(unfolded_grad).view(size_other)
+    elif (dim_self == 1 or dim_self == 2) and dim_other >= 3:
+        view_size = 1 if dim_self == 1 else size_grad[-2]
+        unfolded_grad_T = grad.view([-1, view_size]) \
+            if dim_self == 1 else grad.transpose(-1, -2).contiguous().view([-1, view_size])
+        if mask[0]:
+            # create a 2D-matrix from other
+            unfolded_other_T = \
+                other.transpose(-1, -2).contiguous().view([-1, size_other[-2]]).transpose(-1, -2)
+            grad_self = unfolded_other_T.mm(unfolded_grad_T).transpose(-1, -2).view(size_self)
+        if mask[1]:
+            size_other_T = size_other[:-2]
+            size_other_T.extend(size_other[::-1][:2])
+            grad_other = \
+                unfolded_grad_T.mm(self.unsqueeze(0) if dim_self == 1 else self).view(size_other_T).transpose(-1, -2)
+    else:
+        grad_self = torch.matmul(grad, other.transpose(-1, -2)) if mask[0] else grad_self
+        grad_other = torch.matmul(self.transpose(-1, -2), grad) if mask[1] else grad_other
+
+    return grad_self.cpu(), grad_other.cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/npu_fusion_attention.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/npu_fusion_attention.py
new file mode 100644
index 00000000000..63f1fa2a3b6
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/npu_fusion_attention.py
@@ -0,0 +1,421 @@
+import torch
+import numpy as np
+from einops import rearrange
+
+from msprobe.pytorch.common.utils import logger
+
+gtype = torch.float64  # arm host必须选择float64，x86环境选择float32即可，64也行。arm计算很慢，s=8k的场景建议使用x86
+softmax_build_mode = "QKV"  # "MAX_SUM"
+
+"""
+# 前向函数声明对比
+标杆实现:fusion_attention_forward: q, k, v, drop_mask, atten_mask, pse, scale, keep_prob
+融合算子:npu_fusion_attention_forward: query, key, value, head_num, input_layout, *, pse=None, padding_mask=None,
+                                      atten_mask=None, scale=1.0, keep_prob=1.0, pre_tockens=2147483647,
+                                      next_tockens=2147483647, inner_precise=0, prefix=None, sparse_mode=0,
+                                      gen_mask_parallel=True, sync=False
+
+# 反向函数声明对比
+标杆实现:fusion_attention_backward: dx, q, k, v, softmax_res, drop_mask, pse, scale, keep_prob
+融合算子:npu_fusion_attention_backward: query, key, value, dy, head_num, input_layout, *, pse=None, padding_mask=None,
+                                       atten_mask=None, softmax_max=None, softmax_sum=None, softmax_in=None,
+                                       attention_in=None, scale_value=1.0, keep_prob=1.0, pre_tockens=2147483647,
+                                       next_tockens=2147483647, inner_precise=0, seed=0, offset=0,
+                                       numels=0, prefix=None, sparse_mode=0, gen_mask_parallel=True, sync=False
+"""
+
+
+def softmax_forward(x):
+    x_max = torch.max(x, dim=-1, keepdims=True)[0]
+    x_sub = x.sub(x_max)
+    y = torch.exp(x_sub)
+    x_sum = y.sum(dim=-1, keepdims=True)
+    res = y.div(x_sum)
+    return res, x_max, x_sum
+
+
+def softmax_grad(dp, softmax_res):
+    muls = dp * softmax_res
+    muls_r = muls.sum(dim=-1, keepdims=True)
+    sub_r = dp - muls_r
+    res = sub_r * softmax_res
+    return res
+
+
+def broadcast_kv(num_heads, num_kv_heads, kv_tensor, dtype):
+    if num_kv_heads == 0 or num_kv_heads < num_heads:
+        raise ValueError(f"num_kv_heads must be non-zero and less than num_heads.")
+
+    factor = num_heads // num_kv_heads
+    kv_shape = kv_tensor.shape
+    B = kv_shape[0]
+    S = kv_shape[2]
+    D = kv_shape[3]
+    kv_res = torch.zeros([B, num_heads, S, D]).to(dtype)
+    for i in range(num_heads):
+        j = i // factor
+        kv_res[:, i:i + 1, :, :] = kv_tensor[:, j:j + 1, :, :]
+    return kv_res
+
+
+def calculate_qk(q, k, atten_mask, pse, scale):
+    if pse is None or len(pse.shape) == 0:
+        qk = torch.matmul(q, k.permute(0, 1, 3, 2)).mul(scale)
+    else:
+        qk = (torch.matmul(q, k.permute(0, 1, 3, 2)) + pse).mul(scale)
+    if atten_mask is None or len(atten_mask.shape) == 0:
+        return qk
+    else:
+        qk = qk + atten_mask.bool() * (-40000.0)  # -10000
+    return qk
+
+
+def fusion_attention_forward(q, k, v, drop_mask, atten_mask, pse, scale, keep_prob):
+    qk = calculate_qk(q, k, atten_mask, pse, scale)
+    softmax_res, softmax_max, softmax_sum = softmax_forward(qk)
+    if drop_mask is None or len(drop_mask.shape) == 0:
+        drop_res = softmax_res
+    else:
+        drop_res = softmax_res * drop_mask * (1.0 / keep_prob)
+    y = torch.matmul(drop_res, v)
+    return y, softmax_max, softmax_sum
+
+
+def fusion_attention_backward(dx, q, k, v, softmax_res, drop_mask, pse, scale, keep_prob):
+    dp = torch.matmul(dx, v.permute(0, 1, 3, 2))
+    if drop_mask is None or len(drop_mask.shape) == 0:
+        drop_res = softmax_res.permute(0, 1, 3, 2)
+        dp_drop = dp
+    else:
+        drop_res = softmax_res.mul(drop_mask).mul(1.0 / keep_prob).permute(0, 1, 3, 2)
+        dp_drop = dp * drop_mask * (1.0 / keep_prob)
+    dv = torch.matmul(drop_res, dx)
+    softmax_grad_res = (softmax_grad(dp_drop, softmax_res) * scale)
+    dq = torch.matmul(softmax_grad_res, k)
+    dk = torch.matmul(softmax_grad_res.permute(0, 1, 3, 2), q)
+    return dq, dk, dv
+
+
+def parse_bsnd_args(query, key, head_num, input_layout):
+    supported_input_layout = ["BSH", "SBH", "BSND", "BNSD", "TND"]
+    B, S1, S2, N1, N2, D, H1, H2 = None, None, None, head_num, None, None, None, None
+
+    if not isinstance(input_layout, str) or input_layout not in supported_input_layout:
+        raise ValueError(f"Invalid input_layout arg which must be one of {supported_input_layout}.")
+
+    if input_layout == "TND":
+        raise ValueError(f"input_layout {input_layout} does not supported for now.")
+    try:
+        if input_layout == "BSH":
+            B, S1, H1 = query.shape
+            _, S2, H2 = key.shape
+            D = H1 // N1
+            N2 = H2 // D
+        elif input_layout == "SBH":
+            S1, B, H1 = query.shape
+            S2, _, H2 = key.shape
+            D = H1 // N1
+            N2 = H2 // D
+        elif input_layout == "BSND":
+            B, S1, N1, D = query.shape
+            _, S2, N2, _ = key.shape
+            H1 = N1 * D
+            H2 = N2 * D
+        elif input_layout == "BNSD":
+            B, N1, S1, D = query.shape
+            _, N2, S2, _ = key.shape
+            H1 = N1 * D
+            H2 = N2 * D
+    except Exception as e:
+        raise ValueError(f"query.shape: {query.shape}, key.shape: {key.shape}, parse_bsnd_args error: {e}") from e
+
+    if D == 0:
+        raise ValueError(f"Value D must be non-zero.")
+    DTYPE = query.dtype
+    return B, S1, S2, N1, N2, D, H1, H2, DTYPE
+
+
+def convert_from_bnsd(_input, input_layout):
+    if input_layout == "BSH":
+        # (B,N,S,D)=>(B,S,N*D)
+        out = rearrange(_input, 'b n s d -> b s (n d)').contiguous()
+    elif input_layout == "SBH":
+        # (B,N,S,D)=>(S,B,N*D)
+        out = rearrange(_input, 'b n s d -> s b (n d)').contiguous()
+    elif input_layout == "BSND":
+        # (B,N,S,D)=>(B,S,N,D)
+        out = rearrange(_input, 'b n s d -> b s n d').contiguous()
+    elif input_layout == "TND":
+        raise ValueError(f"input_layout {input_layout} does not supported for now.")
+    else:
+        out = _input
+    return out
+
+
+def convert_to_bnsd(_input, n, input_layout):
+    # 默认"BNSD"无需处理
+    if input_layout == "BSH":
+        # (B,S,N*D)=>(B,N,S,D)
+        out = rearrange(_input, 'b s (n d) -> b n s d', n=n)
+    elif input_layout == "SBH":
+        # (S,B,N*D)=>(B,N,S,D)
+        out = rearrange(_input, 's b (n d) -> b n s d', n=n)
+    elif input_layout == "BSND":
+        # (B,S,N,D)=>(B,N,S,D)
+        out = rearrange(_input, 'b s n d -> b n s d', n=n)
+    elif input_layout == "TND":
+        raise ValueError(f"input_layout {input_layout} does not supported for now.")
+    else:
+        out = _input
+    if out.dim() != 4:
+        raise ValueError(f"convert qkv format failed with input_layout {input_layout}.")
+    return out.to(gtype)
+
+
+def generate_atten_mask(sparse_mode, atten_mask, B, N1, S1, S2, pre_tocken, next_tocken, dtype):
+    """
+    # 当sparse_mode=2、3、4时小算子到融合算子会走这个优化，反过来看就要拆解回原来的基本实现
+    ===> atten_mask = torch.from_numpy(np.triu(np.ones([2048, 2048]), k=1)).to(dtype)
+    """
+    shape = [S1, S2]
+
+    if atten_mask is not None:
+        # 当FA的输入已经包含atten_mask时，可以认为已经是转换之后的mask矩阵了，有三种特殊场景，即稀疏矩阵场景，需要进行逆向还原
+        if sparse_mode == 2 or sparse_mode == 3 or sparse_mode == 4:
+            logger.info(f"S1: {S1}, S2:{S2}, atten_mask.shape:{atten_mask.shape}, atten_mask.dtype:{atten_mask.dtype}")
+
+            if atten_mask.dim() == 2 and atten_mask.shape[0] == 2048 and atten_mask.shape[1] == 2048:
+                if atten_mask.equal(torch.from_numpy(np.triu(np.ones([2048, 2048]), k=1)).to(atten_mask.dtype)):
+                    if sparse_mode == 2:
+                        atten_mask = torch.from_numpy(np.triu(np.ones(shape), k=1))
+                    elif sparse_mode == 3:
+                        atten_mask = torch.from_numpy(np.triu(np.ones(shape), k=S2 - S1 + 1))
+                    elif sparse_mode == 4:
+                        atten_mask_u = torch.from_numpy(np.triu(np.ones(shape), k=next_tocken + 1))
+                        atten_mask_l = torch.from_numpy(np.tril(np.ones(shape), k=-pre_tocken - 1))
+                        atten_mask = atten_mask_u + atten_mask_l
+                    logger.debug(f"反向转换atten_mask {atten_mask.shape}")
+                    return atten_mask.to(dtype)
+
+        return atten_mask.to(dtype)
+
+    if atten_mask is not None:
+        if atten_mask.dim() == 2:
+            if atten_mask.shape[0] != S1 or atten_mask.shape[1] != S2:
+                raise ValueError(f"Invalid atten_mask shape `SS` {atten_mask.shape}")
+            shape = [S1, S2]
+        elif atten_mask.dim() == 4:
+            if atten_mask.shape[1] == 1:
+                shape = [B, 1, S1, S2] if B != 1 else [1, 1, S1, S2]
+            else:
+                shape = [B, N1, S1, S2] if B != 1 else [1, N1, S1, S2]
+
+    if sparse_mode == 0:
+        atten_mask_u = torch.from_numpy(np.triu(np.ones(shape), k=next_tocken + 1))
+        atten_mask_l = torch.from_numpy(np.tril(np.ones(shape), k=-pre_tocken - 1))
+        atten_mask = atten_mask_u + atten_mask_l
+    elif sparse_mode == 1:  # no sparse
+        atten_mask = torch.from_numpy(np.zeros(shape))
+    elif sparse_mode == 2:
+        atten_mask = torch.from_numpy(np.triu(np.ones(shape), k=1))
+    elif sparse_mode == 3:
+        atten_mask = torch.from_numpy(np.triu(np.ones(shape), k=S2 - S1 + 1))
+    elif sparse_mode == 4:
+        atten_mask_u = torch.from_numpy(np.triu(np.ones(shape), k=next_tocken + 1))
+        atten_mask_l = torch.from_numpy(np.tril(np.ones(shape), k=-pre_tocken - 1))
+        atten_mask = atten_mask_u + atten_mask_l
+    # 注:不会出现sparse_mode=5的情况，该情况要求必须要传入atten_mask，且atten_mask矩阵数据格式须为BNSS或B1SS，
+    # 因此可以认为FA的输入已经是正确的atten_mask了
+    return atten_mask.to(dtype)
+
+
+def generate_kv(key, value, N1, N2):
+    # N不等长适配by cdy
+    if not (N1 == N2):
+        k_new = broadcast_kv(N1, N2, key, key.dtype)
+        v_new = broadcast_kv(N1, N2, value, value.dtype)
+    else:
+        k_new = key
+        v_new = value
+    return k_new, v_new
+
+
+def rebuid_softmax_by_qkv(q, k, atten_mask, pse, scale):
+    """
+    attention = softmax(QK^T/sqrt(d))V
+    softmax(x_i) = e^(x_i - x_max) / sum(e^(x_i - x_max))
+    """
+    logger.info("Using QKV to rebuild original softmax")
+    qk = calculate_qk(q, k, atten_mask, pse, scale)
+    softmax_res, x_max, x_sum = softmax_forward(qk)
+    return softmax_res
+
+
+def rebuild_softmax_by_max_sum(q, k, atten_mask, pse, scale, softmax_max, softmax_sum):
+    """
+    attention = softmax(QK^T/sqrt(d))V
+    softmax(x_i) = e^(x_i - x_max_i) / x_sum_i)
+    """
+    logger.info("Using softmax_max and softmax_sum to rebuild original softmax")
+    qk = calculate_qk(q, k, atten_mask, pse, scale)
+    if softmax_max.shape[-1] == 0:
+        raise ValueError(f"softmax_max.shape[-1] must be non-zero, softmax_max.shape: {softmax_max.shape}")
+    repeat_dim = qk.shape[-1] // softmax_max.shape[-1]
+    softmax_res = torch.exp(qk.sub(softmax_max.repeat(1, 1, 1, repeat_dim))).div(
+        softmax_sum.repeat(1, 1, 1, repeat_dim))
+    return softmax_res
+
+
+def npu_fusion_attention_forward_patch(*args, **kwargs):
+    # query, key, value, head_num, input_layout
+    if len(args) != 5:
+        raise ValueError(f"Unsupported npu_fusion_attention args {args}.")
+
+    B, S1, S2, N1, N2, D, H1, H2, DTYPE = parse_bsnd_args(args[0], args[1], args[3], args[4])
+    if N1 == N2 and S1 == S2:
+        logger.debug(f"running case : BNSD = {B}_{N1}_{S1}_{D}, sparse = {kwargs.get('sparse_mode', 0)}")
+    else:
+        logger.debug(f"running case: BNSD = {B}_{N1}({N2})_{S1}({S2})_{D}, sparse = {kwargs.get('sparse_mode', 0)}")
+    if not (N1 % N2 == 0 and N1 >= N2):
+        raise ValueError(f"N1与N2不匹配,请检查: N1 = {N1}, N2 = {N2}.")
+
+    dims_kwargs = {"B": B, "S1": S1, "S2": S2, "N1": N1, "N2": N2,
+                   "D": D, "H1": H1, "H2": H2, "DTYPE": DTYPE}
+
+    new_kwargs = {"keep_prob": 1,
+                  "scale": kwargs.get("scale", 1 / (D ** 0.5)),
+                  "sparse_mode": kwargs.get("sparse_mode", 0),
+                  "prefix": kwargs.get("prefix"),
+                  "pre_tockens": kwargs.get("pre_tockens", 2147483647),
+                  "next_tockens": kwargs.get("next_tockens", 2147483647),
+                  "pse": kwargs.get("pse"),
+                  "padding_mask": kwargs.get("padding_mask"),
+                  "atten_mask": kwargs.get("atten_mask")}
+
+    return args, dims_kwargs, new_kwargs
+
+
+def npu_fusion_attention_backward_patch(*args, **kwargs):
+    if len(args) != 6:
+        raise ValueError(f"Unsupported npu_fusion_attention_grad args {args}.")
+
+    B, S1, S2, N1, N2, D, H1, H2, DTYPE = parse_bsnd_args(args[0], args[1], args[4], args[5])
+    if N1 == N2 and S1 == S2:
+        logger.info(f"running case : BNSD = {B}_{N1}_{S1}_{D}, sparse = {kwargs.get('sparse_mode', 0)}")
+    else:
+        logger.info(f"running case: BNSD = {B}_{N1}({N2})_{S1}({S2})_{D}, sparse = {kwargs.get('sparse_mode', 0)}")
+    if not (N1 % N2 == 0 and N1 >= N2):
+        raise ValueError(f"N1与N2不匹配,请检查: N1 = {N1}, N2 = {N2}.")
+
+    dims_kwargs = {"B": B, "S1": S1, "S2": S2, "N1": N1, "N2": N2,
+                   "D": D, "H1": H1, "H2": H2, "DTYPE": DTYPE}
+
+    new_kwargs = {"keep_prob": 1,
+                  "scale_value": kwargs.get("scale_value", 1 / (D ** 0.5)),
+                  "sparse_mode": kwargs.get("sparse_mode", 0),
+                  "prefix": kwargs.get("prefix"),
+                  "pre_tockens": kwargs.get("pre_tockens", 2147483647),
+                  "next_tockens": kwargs.get("next_tockens", 2147483647),
+                  "pse": kwargs.get("pse"),
+                  "padding_mask": kwargs.get("padding_mask"),
+                  "softmax_max": kwargs.get("softmax_max"),
+                  "softmax_sum": kwargs.get("softmax_sum"),
+                  "softmax_in": kwargs.get("softmax_in"),
+                  "attention_in": kwargs.get("attention_in"),
+                  "seed": kwargs.get("seed", 0),
+                  "offset": kwargs.get("offset", 0),
+                  "numels": kwargs.get("numels", 0),
+                  "atten_mask": kwargs.get("atten_mask")}
+
+    return args, dims_kwargs, new_kwargs
+
+
+def npu_fusion_attention(*args, **kwargs):
+    new_args, dims_kwargs, new_kwargs = npu_fusion_attention_forward_patch(*args, **kwargs)
+    query, key, value, input_layout = new_args[0], new_args[1], new_args[2], new_args[4]
+    N1 = dims_kwargs.get("N1")
+    N2 = dims_kwargs.get("N2")
+    S1 = dims_kwargs.get("S1")
+    S2 = dims_kwargs.get("S2")
+    B = dims_kwargs.get("B")
+    DTYPE = dims_kwargs.get("DTYPE")
+    atten_mask = new_kwargs.get("atten_mask")
+    keep_prob = new_kwargs.get("keep_prob")
+    sparse_mode = new_kwargs.get("sparse_mode")
+    pre_tockens = new_kwargs.get("pre_tockens")
+    next_tockens = new_kwargs.get("next_tockens")
+    pse = new_kwargs.get("pse")
+    scale = new_kwargs.get("scale")
+
+    atten_mask = generate_atten_mask(sparse_mode, atten_mask, B, N1, S1, S2, pre_tockens, next_tockens, DTYPE)
+    query = convert_to_bnsd(query, N1, input_layout)
+    key = convert_to_bnsd(key, N2, input_layout)
+    value = convert_to_bnsd(value, N2, input_layout)
+    k_new, v_new = generate_kv(key, value, N1, N2)
+    out_golden, softmax_max, softmax_sum = fusion_attention_forward(q=query, k=k_new, v=v_new,
+                                                                    drop_mask=None, atten_mask=atten_mask,
+                                                                    pse=pse, scale=scale,
+                                                                    keep_prob=keep_prob)
+    if out_golden.dim() == 5:
+        out_golden = out_golden.reshape(out_golden.size(0), out_golden.size(1) * out_golden.size(2), out_golden.size(3),
+                                        out_golden.size(4))
+    out_golden = convert_from_bnsd(out_golden, input_layout)
+
+    return out_golden.cpu(), softmax_max.repeat(1, 1, 1, 8).cpu(), softmax_sum.repeat(1, 1, 1, 8).cpu()
+
+
+def npu_fusion_attention_grad(*args, **kwargs):
+    # dx, q, k, v, softmax_res, drop_mask, pse, scale, keep_prob
+    new_args, dims_kwargs, new_kwargs = npu_fusion_attention_backward_patch(*args, **kwargs)
+    query, key, value, dx, input_layout = new_args[0], new_args[1], new_args[2], new_args[3], new_args[5]
+    N1 = dims_kwargs.get("N1")
+    N2 = dims_kwargs.get("N2")
+    S1 = dims_kwargs.get("S1")
+    S2 = dims_kwargs.get("S2")
+    B = dims_kwargs.get("B")
+    D = dims_kwargs.get("D")
+    DTYPE = dims_kwargs.get("DTYPE")
+    atten_mask = new_kwargs.get("atten_mask")
+    keep_prob = new_kwargs.get("keep_prob")
+    sparse_mode = new_kwargs.get("sparse_mode")
+    pre_tockens = new_kwargs.get("pre_tockens")
+    next_tockens = new_kwargs.get("next_tockens")
+    pse = new_kwargs.get("pse")
+    softmax_max = new_kwargs.get("softmax_max")
+    softmax_sum = new_kwargs.get("softmax_sum")
+    scale_value = new_kwargs.get("scale_value")
+
+    atten_mask = generate_atten_mask(sparse_mode, atten_mask, B, N1, S1, S2, pre_tockens, next_tockens, DTYPE)
+    query = convert_to_bnsd(query, N1, input_layout)
+    dx = convert_to_bnsd(dx, N1, input_layout)
+    key = convert_to_bnsd(key, N2, input_layout)
+    value = convert_to_bnsd(value, N2, input_layout)
+    k_new, v_new = generate_kv(key, value, N1, N2)
+
+    if softmax_build_mode == "QKV":
+        softmax_res = rebuid_softmax_by_qkv(query, k_new, atten_mask, pse, scale_value)
+    else:
+        softmax_res = rebuild_softmax_by_max_sum(query, k_new, atten_mask, pse, scale_value, softmax_max, softmax_sum)
+
+    dq, dk, dv = fusion_attention_backward(dx, query, k_new, v_new, softmax_res, None, pse, scale_value, keep_prob)
+
+    # N不等长适配by cdy
+    if not (N1 == N2):
+        if N2 == 0:
+            raise ValueError("dims_kwargs.N2 must be non-zero.")
+        G = int(N1 / N2)
+        dk = torch.sum(dk.reshape(B, N2, G, S2, D), dim=2, keepdim=True).reshape(B, N2, S2, D)
+        dv = torch.sum(dv.reshape(B, N2, G, S2, D), dim=2, keepdim=True).reshape(B, N2, S2, D)
+
+    if dq.dim() == 5:
+        dq = dq.reshape(dq.size(0), dq.size(1) * dq.size(2), dq.size(3), dq.size(4))
+    if dk.dim() == 5:
+        dk = dk.reshape(dk.size(0), dk.size(1) * dk.size(2), dk.size(3), dk.size(4))
+    if dv.dim() == 5:
+        dv = dv.reshape(dv.size(0), dv.size(1) * dv.size(2), dv.size(3), dv.size(4))
+
+    dq = convert_from_bnsd(dq, input_layout)
+    dk = convert_from_bnsd(dk, input_layout)
+    dv = convert_from_bnsd(dv, input_layout)
+
+    return dq.cpu(), dk.cpu(), dv.cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/rms_norm.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/rms_norm.py
new file mode 100644
index 00000000000..e647312fdb2
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/rms_norm.py
@@ -0,0 +1,15 @@
+import torch
+
+
+def npu_rms_norm(x, gamma, epsilon=1e-5):
+    rstd = torch.rsqrt(torch.mean(torch.pow(x, 2), axis=-1, keepdim=True) + epsilon)
+    res = x * rstd * gamma
+    return res.cpu(), rstd.float().cpu()
+
+
+def npu_rms_norm_backward(grad, x, gamma, rstd):
+    mean_gy = (grad * x * gamma * rstd).mean(dim=-1, keepdim=True)
+    grad_x = (grad * gamma - x * rstd * mean_gy) * rstd
+    grad_gamma = x * grad * rstd
+    return grad_x.cpu(), grad_gamma.cpu()
+
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/rotary_mul.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/rotary_mul.py
new file mode 100644
index 00000000000..0e0fda5f73f
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/rotary_mul.py
@@ -0,0 +1,52 @@
+import torch
+
+
+def npu_rotary_mul(x, r1, r2):
+    x1, x2 = torch.chunk(x, 2, -1)
+    x_new = torch.cat((-x2, x1), dim=-1)
+    output = r1 * x + r2 * x_new
+    return output.cpu()
+
+
+def npu_rotary_mul_backward(dy_tensor, x, r1, r2):
+    x.requires_grad = True
+    r1.requires_grad = True
+    r2.requires_grad = True
+    # golden
+    x1, x2 = torch.chunk(x, 2, -1)
+    x_new = torch.cat((-x2, x1), dim=-1)
+    golden_tensor = r1 * x + r2 * x_new
+    golden_tensor.backward(dy_tensor)
+    r1_shape = r1.shape
+    r1_grad = torch.zeros(r1_shape).type(torch.float32)
+    r2_grad = torch.zeros(r1_shape).type(torch.float32)
+    x1, x2 = torch.chunk(x.float(), 2, -1)
+    x_new2 = torch.cat((-x2, x1), dim=-1)
+    x_shape = x.shape
+    h = x.float()
+    grad = dy_tensor.float()
+    condition_1 = (((r1_shape[0] == 1 and x_shape[0] != 1) or (r1_shape[0] == 1 and x_shape[0] == 1)) and
+                   ((r1_shape[2] == 1 and x_shape[2] != 1) or (r1_shape[2] == 1 and x_shape[2] == 1)) and
+                   (r1_shape[1] == x_shape[1]) and (r1_shape[3] == x_shape[3]))
+    condition_2 = (((r1_shape[0] == 1 and x_shape[0] != 1) or (r1_shape[0] == 1 and x_shape[0] == 1)) and
+                   ((r1_shape[1] == 1 and x_shape[1] != 1) or (r1_shape[1] == 1 and x_shape[1] == 1)) and
+                   (r1_shape[2] == x_shape[2]) and (r1_shape[3] == x_shape[3]))
+    condition_3 = (((r1_shape[2] == 1 and x_shape[2] != 1) or (r1_shape[2] == 1 and x_shape[2] == 1)) and
+                   ((r1_shape[1] == 1 and x_shape[1] != 1) or (r1_shape[1] == 1 and x_shape[1] == 1)) and
+                   (r1_shape[0] == x_shape[0]) and (r1_shape[3] == x_shape[3]))
+    if condition_1:
+        for i in range(x_shape[0]):
+            for j in range(x_shape[2]):
+                r2_grad[0, :, 0, :] += (x_new2[i, :, j, :] * grad[i, :, j, :])
+                r1_grad[0, :, 0, :] += (h[i, :, j, :] * grad[i, :, j, :])
+    elif condition_2:
+        for i in range(x_shape[0]):
+            for j in range(x_shape[1]):
+                r2_grad[0, 0, :, :] += (x_new2[i, j, :, :] * grad[i, j, :, :])
+                r1_grad[0, 0, :, :] += (h[i, j, :, :] * grad[i, j, :, :])
+    elif condition_3:
+        for i in range(x_shape[1]):
+            for j in range(x_shape[2]):
+                r2_grad[:, 0, 0, :] += (x_new2[:, i, j, :] * grad[:, i, j, :])
+                r1_grad[:, 0, 0, :] += (h[:, i, j, :] * grad[:, i, j, :])
+    return x.grad.cpu(), r1_grad.cpu(), r2_grad.cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/scaled_mask_softmax.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/scaled_mask_softmax.py
new file mode 100644
index 00000000000..8717aebaf90
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/scaled_mask_softmax.py
@@ -0,0 +1,26 @@
+import torch
+
+
+def npu_scaled_masked_softmax(x, mask, scale, fixed_triu_mask):
+    if fixed_triu_mask:
+        mask = (torch.triu(torch.ones(mask.shape), k=1)).bool().to(mask.device)
+    dtype = x.dtype
+    x = (x * scale).masked_fill(mask, value=-10000)
+    x = x - torch.max(x, dim=-1, keepdims=True)[0]
+    x = torch.exp(x.float())
+    y = torch.div(x, torch.sum(x, dim=-1, keepdims=True))
+    return y.to(dtype).cpu()
+
+
+def npu_scaled_masked_softmax_backward(y_grad, y, mask, scale, fixed_triu_mask):
+    if fixed_triu_mask:
+        mask = (torch.triu(torch.ones(mask.shape), k=1)).bool().to(mask.device)
+    dtype = y_grad.dtype
+    y_grad = y_grad.float()
+    y = y.float()
+    x_grad = y_grad * y
+    x_grad = y_grad - torch.sum(x_grad, dim=-1, keepdims=True)
+    x_grad = x_grad * y
+    x_grad = x_grad * scale
+    x_grad = x_grad.masked_fill(mask, value=0)
+    return x_grad.to(dtype).cpu()
diff --git a/debug/accuracy_tools/msprobe/pytorch/bench_functions/swiglu.py b/debug/accuracy_tools/msprobe/pytorch/bench_functions/swiglu.py
new file mode 100644
index 00000000000..e03c975a50a
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/bench_functions/swiglu.py
@@ -0,0 +1,55 @@
+import torch
+
+
+def npu_swiglu(x, dim=-1):
+    tensor_dtype = x.dtype
+
+    inTensors = torch.chunk(x, 2, dim=dim)
+    if tensor_dtype == torch.float32:
+        tensor_scalar = torch.sigmoid(torch.mul(inTensors[0], 1.0))
+        output_data = torch.mul(torch.mul(tensor_scalar, inTensors[0]), inTensors[1])
+    else:
+        tensor_self_float = inTensors[0].type(torch.float)
+        tensor_other_float = inTensors[1].type(torch.float)
+        tensor_out_float = torch.nn.functional.silu(tensor_self_float).type(tensor_dtype).type(
+            torch.float32) * tensor_other_float
+        output_data = tensor_out_float.type(tensor_dtype)
+    return output_data.cpu()
+
+
+def npu_swiglu_backward(grad, x, dim=-1):
+    tensor_dtype = grad.dtype
+    in_tensors = torch.chunk(x, 2, dim=dim)
+    tensor_grad_out = grad
+
+    if tensor_dtype == torch.float16:
+        tensor_out1 = torch.mul(
+            torch.mul(in_tensors[1].type(torch.float32), swish_grad(1, in_tensors[0].type(torch.float32))),
+            tensor_grad_out.type(torch.float32)).type(torch.float16)
+        tensor_out2 = torch.mul(tensor_grad_out.type(torch.float32),
+                                swish(1, in_tensors[0].type(torch.float32))).type(torch.float16)
+        output = torch.cat((tensor_out1, tensor_out2), dim)
+    elif tensor_dtype == torch.bfloat16:
+        tensor_self_float = in_tensors[0].type(torch.float)
+        tensor_other_float = in_tensors[1].type(torch.float)
+        tensor_gradout_float = tensor_grad_out.type(torch.float)
+
+        tensor_out1 = torch.mul(tensor_gradout_float, swish_grad(1.0, tensor_self_float)).type(torch.bfloat16).type(
+            torch.float32) * tensor_other_float
+        tensor_out2 = swish(1.0, tensor_self_float).type(torch.bfloat16).type(torch.float32) * tensor_gradout_float
+        tensor_out_float = torch.cat((tensor_out1, tensor_out2), dim=dim)
+        output = tensor_out_float.type(torch.bfloat16)
+    else:
+        tensor_out1 = torch.mul(torch.mul(in_tensors[1], swish_grad(1.0, in_tensors[0])), tensor_grad_out)
+        tensor_out2 = torch.mul(tensor_grad_out, swish(1.0, in_tensors[0]))
+        output = torch.cat((tensor_out1, tensor_out2), dim)
+    return output.cpu()
+
+
+def swish_grad(beta, x):
+    return torch.sigmoid(beta * x) + x * (1 - torch.sigmoid(beta * x)) * torch.sigmoid(beta * x) * beta
+
+
+def swish(beta, x):
+    return x * torch.sigmoid(beta * x)
+
diff --git a/debug/accuracy_tools/msprobe/pytorch/common/parse_json.py b/debug/accuracy_tools/msprobe/pytorch/common/parse_json.py
index 22f79879867..ccad903724c 100644
--- a/debug/accuracy_tools/msprobe/pytorch/common/parse_json.py
+++ b/debug/accuracy_tools/msprobe/pytorch/common/parse_json.py
@@ -1,5 +1,7 @@
 import json
+
 from msprobe.core.common.exceptions import ParseJsonException
+from msprobe.core.common.file_check import FileOpen
 
 
 def parse_json_info_forward_backward(json_path):
@@ -11,7 +13,7 @@ def parse_json_info_forward_backward(json_path):
         api_name = '.'.join(name_struct[:-1])
         return api_name
 
-    with open(json_path, 'r') as f:
+    with FileOpen(json_path, 'r') as f:
         dump_json = json.load(f)
 
     real_data_path = dump_json.get("dump_data_dir")
diff --git a/debug/accuracy_tools/msprobe/pytorch/common/utils.py b/debug/accuracy_tools/msprobe/pytorch/common/utils.py
index acc1de10514..181491488f9 100644
--- a/debug/accuracy_tools/msprobe/pytorch/common/utils.py
+++ b/debug/accuracy_tools/msprobe/pytorch/common/utils.py
@@ -14,10 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
+import logging
 import os
 import random
 import stat
 import torch
+import torch.distributed as dist
 import numpy as np
 from functools import wraps
 from msprobe.core.common.exceptions import DistributedNotInitializedError
@@ -221,3 +223,36 @@ class Const:
     CONVERT_API = {
         "int32_to_int64": ["cross_entropy"]
     }
+
+
+def get_tensor_rank(in_feat, out_feat):
+    if dist.is_initialized():
+        return dist.get_rank()
+
+    def get_tensor_rank_single(x):
+        if isinstance(x, (list, tuple)):
+            if len(x) > 0:
+                return get_tensor_rank_single(x[0])
+        elif isinstance(x, torch.Tensor):
+            device = x.device
+            if device.type != 'cpu':
+                return device.index
+        return None
+
+    in_rank = get_tensor_rank_single(in_feat)
+    out_rank = get_tensor_rank_single(out_feat)
+    tensor_rank = in_rank if in_rank else out_rank
+    return tensor_rank
+
+
+def _create_logger(level=logging.INFO):
+    logger_ = logging.getLogger()
+    logger_.setLevel(level)
+    ch = logging.StreamHandler()
+    ch.setLevel(level)
+    logger_.addHandler(ch)
+    return logger_
+
+
+log_level = logging.DEBUG if os.environ.get("API_ACCURACY_CHECK_LOG_LEVEL") == "1" else logging.INFO
+logger = _create_logger(log_level)
diff --git a/debug/accuracy_tools/msprobe/pytorch/compare/acc_compare.py b/debug/accuracy_tools/msprobe/pytorch/compare/acc_compare.py
index e214910566e..2a68c756ed3 100644
--- a/debug/accuracy_tools/msprobe/pytorch/compare/acc_compare.py
+++ b/debug/accuracy_tools/msprobe/pytorch/compare/acc_compare.py
@@ -492,7 +492,7 @@ def compare_by_op(op_name, op_name_mapping_dict, input_parma):
             error_file = error.filename
             n_value, b_value = CompareConst.READ_NONE, CompareConst.READ_NONE
             error_flag = True
-        except FileCheckerException:
+        except FileCheckException:
             error_file = data_name
             n_value, b_value = CompareConst.READ_NONE, CompareConst.READ_NONE
             error_flag = True
@@ -645,7 +645,11 @@ def highlight_rows_xlsx(result_df, highlight_dict, file_path):
             elif (i - 2) in highlight_dict['yellow_rows']:
                 ws.cell(row=i, column=j).fill = PatternFill(start_color=CompareConst.YELLOW,
                                                             end_color=CompareConst.YELLOW, fill_type="solid")
-    wb.save(file_path)
+    try:
+        wb.save(file_path)
+    except Exception as e:
+        logger.error('Save result file failed')
+        raise CompareException(CompareException.WRITE_FILE_ERROR) from e
     change_mode(file_path, FileCheckConst.DATA_FILE_AUTHORITY)
 
 
@@ -655,8 +659,8 @@ def compare(input_parma, output_path, stack_mode=False, auto_analyze=True,
         summary_compare, md5_compare = task_dumppath_get(input_parma)
         check_configuration_param(stack_mode, auto_analyze, fuzzy_match)
         create_directory(output_path)
-        check_compare_param(input_parma, output_path, stack_mode, summary_compare, md5_compare)
-    except CompareException as error:
+        check_compare_param(input_parma, output_path, summary_compare, md5_compare)
+    except (CompareException, FileCheckException) as error:
         logger.error('Compare failed. Please check the arguments and do it again!')
         sys.exit(error.code)
     compare_core(input_parma, output_path, stack_mode=stack_mode,
@@ -764,9 +768,14 @@ def op_item_parse(item, op_name, index, item_list=None, top_bool=True):
         else:
             full_op_name = op_name
     else:
-        full_op_name = op_name + '.' + str(index)
+        full_op_name = op_name + Const.SEP + str(index)
     if isinstance(item, dict):
-        if 'dtype' in item:
+        if 'type' not in item:
+            for kwarg in item:
+                kwarg_parsed_list = op_item_parse(item[kwarg], op_name + Const.SEP + kwarg, None)
+                item_list += kwarg_parsed_list
+                kwarg_parsed_list.clear()
+        elif 'dtype' in item:
             parsed_item = item
             parsed_item['full_op_name'] = full_op_name
             item_list.append(parsed_item)
@@ -869,13 +878,13 @@ def read_op(op_data, op_name):
             op_parsed_list += output_parsed_list
             output_parsed_list.clear()
     if 'backward' in op_name:
-        if 'grad_input' in op_data:
-            input_item = op_data['grad_input']
+        if 'input' in op_data:
+            input_item = op_data['input']
             input_parsed_list = op_item_parse(input_item, op_name + '_input', None)
             op_parsed_list = input_parsed_list.copy()
             input_parsed_list.clear()
-        if 'grad_output' in op_data:
-            output_item = op_data['grad_output']
+        if 'output' in op_data:
+            output_item = op_data['output']
             output_parsed_list = op_item_parse(output_item, op_name + '_output', None)
             op_parsed_list += output_parsed_list
             output_parsed_list.clear()
diff --git a/debug/accuracy_tools/msprobe/pytorch/compare/distributed_compare.py b/debug/accuracy_tools/msprobe/pytorch/compare/distributed_compare.py
index 0298eca9e7e..caac1395807 100644
--- a/debug/accuracy_tools/msprobe/pytorch/compare/distributed_compare.py
+++ b/debug/accuracy_tools/msprobe/pytorch/compare/distributed_compare.py
@@ -21,6 +21,7 @@ from msprobe.core.common.utils import CompareException, check_compare_param, \
     check_configuration_param, task_dumppath_get, check_file_or_directory_path, check_regex_prefix_format_valid
 from msprobe.pytorch.compare.acc_compare import compare_core
 from msprobe.core.common.file_check import create_directory
+from msprobe.core.common.exceptions import FileCheckException
 from msprobe.pytorch.common.log import logger
 
 
@@ -86,12 +87,11 @@ def compare_distributed(npu_dump_dir, bench_dump_dir, output_path, **kwargs):
                         'or use compare() api and manually match the ranks.')
         raise CompareException(CompareException.INVALID_PATH_ERROR)
     for nr, br in zip(npu_ranks, bench_ranks):
-        n_dir = os.path.join(npu_dump_dir, nr)
-        b_dir = os.path.join(bench_dump_dir, br)
-        s_dir = b_dir
-        npu_json_path = extract_json(n_dir, stack_json=False)
-        bench_json_path = extract_json(b_dir, stack_json=False)
-        stack_json_path = extract_json(s_dir, stack_json=True)
+        npu_data_dir = os.path.join(npu_dump_dir, nr)
+        bench_data_dir = os.path.join(bench_dump_dir, br)
+        npu_json_path = extract_json(npu_data_dir, stack_json=False)
+        bench_json_path = extract_json(bench_data_dir, stack_json=False)
+        stack_json_path = extract_json(npu_data_dir, stack_json=True)
 
         dump_result_param = {
             'npu_json_path': npu_json_path,
@@ -103,8 +103,8 @@ def compare_distributed(npu_dump_dir, bench_dump_dir, output_path, **kwargs):
             summary_compare, md5_compare = task_dumppath_get(dump_result_param)
             check_configuration_param(stack_mode, auto_analyze, fuzzy_match)
             create_directory(output_path)
-            check_compare_param(dump_result_param, output_path, stack_mode=stack_mode, summary_compare=summary_compare)
-        except CompareException as error:
+            check_compare_param(dump_result_param, output_path, summary_compare=summary_compare, md5_compare=md5_compare)
+        except (CompareException, FileCheckException) as error:
             logger.error('Compare failed. Please check the arguments and do it again!')
             sys.exit(error.code)
         compare_core(dump_result_param, output_path, suffix=f'_{nr}-{br}', summary_compare=summary_compare,
diff --git a/debug/accuracy_tools/msprobe/pytorch/debugger/debugger_config.py b/debug/accuracy_tools/msprobe/pytorch/debugger/debugger_config.py
index cfc588e1e97..f1289e9b013 100644
--- a/debug/accuracy_tools/msprobe/pytorch/debugger/debugger_config.py
+++ b/debug/accuracy_tools/msprobe/pytorch/debugger/debugger_config.py
@@ -21,7 +21,7 @@ class DebuggerConfig:
         self.acl_config = common_config.acl_config if common_config.acl_config else ""
         self.is_forward_acl_dump = True
         self.summary_mode = task_config.summary_mode if task_config.summary_mode else Const.STATISTICS
-        self.overflow_num = task_config.overflow_num if task_config.overflow_num else 1
+        self.overflow_nums = task_config.overflow_nums if task_config.overflow_nums else 1
         self.framework = Const.PT_FRAMEWORK
 
         if self.task == Const.FREE_BENCHMARK:
diff --git a/debug/accuracy_tools/msprobe/pytorch/debugger/precision_debugger.py b/debug/accuracy_tools/msprobe/pytorch/debugger/precision_debugger.py
index 137c51895d0..6119bbd1d4f 100644
--- a/debug/accuracy_tools/msprobe/pytorch/debugger/precision_debugger.py
+++ b/debug/accuracy_tools/msprobe/pytorch/debugger/precision_debugger.py
@@ -27,6 +27,7 @@ class PrecisionDebugger:
         step=None,
     ):
         if not hasattr(self, "initialized"):
+            self.api_origin = False
             self.initialized = True
             self.model = self.check_model_valid(model)
             common_config, task_config = parse_json_config(config_path, task)
diff --git a/debug/accuracy_tools/msprobe/pytorch/doc/api_accuracy_checker.md b/debug/accuracy_tools/msprobe/pytorch/doc/api_accuracy_checker.md
index b3ed4a9e24c..41b97098ae9 100644
--- a/debug/accuracy_tools/msprobe/pytorch/doc/api_accuracy_checker.md
+++ b/debug/accuracy_tools/msprobe/pytorch/doc/api_accuracy_checker.md
@@ -21,7 +21,7 @@
 精度预检操作流程如下：
 
 1. 在NPU和GPU环境下分别安装msprobe工具。详见《[MindStudio精度调试工具](../../README.md)》的“工具安装”章节。
-2. 在NPU训练脚本内添加msprobe工具dump接口PrecisionDebugger采集待预检数据。详见《[精度数据采集](./dump.md)》。
+2. 在NPU训练脚本内添加msprobe工具dump接口PrecisionDebugger，采集待预检数据。详见《[精度数据采集](./dump.md)》，注意需要配置level="L1"。
 3. 将NPU环境下dump的预检数据拷贝至GPU环境。
 4. 在NPU和GPU环境下分别执行run_ut，生成结果用于最终api_precision_compare操作的输入。详见“**run_ut预检操作**”。
 5. 将NPU和GPU执行run_ut生成的`accuracy_checking_details_{timestamp}.csv`结果文件拷贝至同一环境下。
@@ -51,10 +51,12 @@ run_ut预检操作包括如下场景：
    | -api_info或--api_info_file   | 指定API信息文件dump.json。                                   | 是                                 |
    | -save_error_data             | 保存精度未达标的API输入输出数据。                            | 否                                 |
    | -o或--out_path               | 指定run_ut执行结果存盘路径，默认“./”（相对于run_ut的路径）。 | 否                                 |
+   |                              |                                                              |                                    |
    | -j或--jit_compile            | 开启jit编译。                                                | 否                                 |
    | -d或--device                 | 指定Device ID，选择UT代码运行所在的卡，默认值为0。           | 否                                 |
    | -csv_path或--result_csv_path | 指定本次运行中断时生成的`accuracy_checking_result_{timestamp}.csv`文件路径，执行run_ut中断时，若想从中断处继续执行，配置此参数即可。需要指定为上次中断的`accuracy_checking_result_{timestamp}.csv`文件。详见“**断点续检**”。 | run_ut操作中断后继续执行场景下必选 |
    | -f或--filter_api             | 过滤模型中除最大值和最小值以外其他参数和结构相同的API。适用于模型较大且重复API较多的场景。 | 否                                 |
+   | -config或--config_path       | 指定预检操作过程中的额外配置（包括黑名单、白名单等）的[config.json](https://gitee.com/ascend/mstt/tree/master/debug/accuracy_tools/msprobe/config)文件，默认未配置。config.json文件的配置可参考《[配置文件说明](https://gitee.com/ascend/mstt/blob/master/debug/accuracy_tools/msprobe/config/README.md#pytorch场景task配置为run_ut)》。 | 否                                 |
    
    run_ut执行结果包括`accuracy_checking_result_{timestamp}.csv`和`accuracy_checking_details_{timestamp}.csv`两个文件。`accuracy_checking_result_{timestamp}.csv`是API粒度的，标明每个API是否通过测试。建议用户先查看`accuracy_checking_result_{timestamp}.csv`文件，对于其中没有通过测试的或者特定感兴趣的API，根据其API name字段在`accuracy_checking_details_{timestamp}.csv`中查询其各个输出的达标情况以及比较指标。详细介绍请参见“**预检结果**”。
    
@@ -64,7 +66,7 @@ run_ut预检操作包括如下场景：
    msprobe -f pytorch run_ut -api_info ./dump.json -save_error_data
    ```
 
-   数据默认会存盘到'./ut_error_data{timestamp}'路径下（相对于启动run_ut的路径），有需要的话，用户可以通过修改mstt/debug/accuracy_tools/api_accuracy_checker目录下，config.yaml文件的error_data_path参数来配置保存路径，详见“config.yaml文件说明”。
+   数据默认会存盘到'./ut_error_data{timestamp}'路径下（相对于启动run_ut的路径），有需要的话，用户可以通过error_data_path参数来配置保存路径，error_data_path参数在[config.json](https://gitee.com/ascend/mstt/tree/master/debug/accuracy_tools/msprobe/config)文件或config.yaml文件配置，config.json文件需要在run_ut操作时通过-config参数指定，config.yaml文件详见“**config.yaml文件说明**”。
 
 #### 使用multi_run_ut.py执行多线程预检
 
@@ -99,23 +101,65 @@ msprobe -f pytorch multi_run_ut -api_info ./dump.json -n 32 -d 0 1 2 3
 msprobe -f pytorch run_ut -api_info ./dump.json -csv_path /home/xxx/ut/accuracy_checking_result_{timestamp}.csv
 ```
 
-#### API预检白名单
+#### API预检黑名单和白名单
 
-run_ut过程支持API预检白名单，操作方式如下：
+run_ut过程支持API预检黑名单和白名单，通过如下文件配置black_list（黑名单）或white_list（白名单）参数来指定不需要或需要预检的API名称：
 
-修改mstt/debug/accuracy_tools/api_accuracy_checker目录下config.yaml文件的white_list参数，配置需要预检的API名称，详见“config.yaml文件说明”。
+- 配置[config.json](https://gitee.com/ascend/mstt/tree/master/debug/accuracy_tools/msprobe/config)文件，config.json文件需要在run_ut操作时通过-config参数指定。
+- 配置config.yaml文件，详见“**config.yaml文件说明**”。
+
+config.json文件的优先级高于config.yaml文件，即执行config.json文件时，config.yaml文件的配置不生效。
 
 ### config.yaml文件说明
 
-config.yaml文件可以通过配置参数来控制dump和run_ut操作的白名单等功能。
+config.yaml文件可以通过配置参数来控制dump和run_ut操作的白名单、黑名单等功能。操作步骤如下：
+
+1. 查找msprobe工具安装路径。
+
+   ```bash
+   pip show mindstudio-probe
+   ```
+
+   输出结果如下示例：
+
+   ```bash
+   Name: mindstudio-probe
+   Version: 1.0
+   Summary: This is a pytorch precision comparison tools
+   Home-page:
+   Author:
+   Author-email:
+   License:
+   Location: /home/xx/anaconda3/envs/pt21py38/lib/python3.8/site-packages
+   Requires: numpy, openpyxl, pandas, pyyaml, rich, tqdm, wheel
+   Required-by:
+   ```
+
+   Location字段为msprobe工具的安装路径，那么config.yaml文件位置为/home/xx/anaconda3/envs/pt21py38/lib/python3.8/site-packages/msprobe/pytorch/api_accuracy_checker/config.yaml
+
+2. 进入config.yaml文件
+
+   ```bash
+   vi /home/xx/anaconda3/envs/pt21py38/lib/python3.8/site-packages/msprobe/pytorch/api_accuracy_checker/config.yaml
+   ```
+
+3. 修改config.yaml文件参数。
+
+   ```yaml
+   white_list: []
+   black_list: []
+   error_data_path: './'
+   precision: 14
+   ```
 
-文件路径为：mstt/debug/accuracy_tools/msprobe/pytorch/api_accuracy_checker/config.yaml 
+   | 参数名称        | 说明                                                         | 是否必选 |
+   | --------------- | ------------------------------------------------------------ | -------- |
+   | white_list      | API dump白名单，仅对指定的API进行dump。参数示例：white_list=["conv1d", "conv2d"]。默认未配置白名单，即dump全量API数据。 | 否       |
+   | black_list      | API dump黑名单，被指定的API不进行dump。参数示例：black_list=["conv1d", "conv2d"]。默认未配置黑名单，即dump全量API数据。 | 否       |
+   | error_data_path | 配置保存精度未达标的API输入输出数据路径。参数示例"error_data_path": "./"。默认为当前路径。 | 否       |
+   | precision       | 浮点数表示位数，默认取小数点后14位。                         | 否       |
 
-| 参数名称        | 说明                                                         | 是否必选 |
-| --------------- | ------------------------------------------------------------ | -------- |
-| white_list      | API dump白名单，指定dump具体API数据，也可以直接配置预检的API白名单，详细请参见“**API预检白名单**”。参数示例：white_list=["conv1d", "conv2d"]。默认未配置白名单，即dump全量API数据。 | 否       |
-| error_data_path | 配置保存精度未达标的API输入输出数据路径。                    | 否       |
-| precision       | 浮点数表示位数，默认取小数点后14位。                         | 否       |
+   说明：white_list和black_list同时配置时，二者配置的API名单若无交集，则白名单生效，若API名单存在交集，则白名单排除的部分以及交集的API不进行dump。
 
 ## 预检结果
 
diff --git a/debug/accuracy_tools/msprobe/pytorch/doc/dump.md b/debug/accuracy_tools/msprobe/pytorch/doc/dump.md
index 7d0763b6848..7e393cd1026 100644
--- a/debug/accuracy_tools/msprobe/pytorch/doc/dump.md
+++ b/debug/accuracy_tools/msprobe/pytorch/doc/dump.md
@@ -12,7 +12,7 @@ msprobe工具主要通过在训练脚本内添加dump接口并启动训练的方
 
 通过加载dump配置文件的方式来确定dump操作的详细配置。
 
-可以在from msprobe.pytorch import PrecisionDebugger和模型初始化之间的任意位置添加该接口。
+PrecisionDebugger接口可以在from msprobe.pytorch import PrecisionDebugger之后的位置添加。详细使用可参考“**示例代码**”或“**model配置代码示例**”。
 
 **原型**
 
@@ -20,7 +20,7 @@ msprobe工具主要通过在训练脚本内添加dump接口并启动训练的方
 PrecisionDebugger(config_path=None, task=None, dump_path=None, level=None, model=None, step=None)
 ```
 
-说明：上述参数除config_path和model外，其他参数均在[config.json](../../config)文件中可配，此处的参数优先级高于[config.json](../../config)文件中的配置，而config.json文件可以配置更多参数，若需要进行更多场景的精度数据dump，建议配置[config.json](../../config)文件。
+说明：上述参数除config_path和model外，其他参数均在[config.json](../../config)文件中可配，此处的参数优先级高于[config.json](../../config)文件中的配置，而config.json文件可以配置更多参数，若需要进行更多场景的精度数据dump，建议配置[config.json](../../config)文件。config.json文件的配置可参考《[配置文件说明](https://gitee.com/ascend/mstt/blob/master/debug/accuracy_tools/msprobe/config/README.md)》。
 
 **参数说明**
 
@@ -77,9 +77,9 @@ if __name__ == "__main__"
 
 **功能说明**
 
-启动函数。
+dump启动函数。
 
-在模型初始化之后的任意位置添加。
+在模型初始化之后的位置添加。需要与stop函数一起添加在for循环内。
 
 **原型**
 
@@ -93,9 +93,9 @@ debugger.start()
 
 **功能说明**
 
-停止函数。
+dump停止函数。
 
-在**start**函数之后的任意位置添加。
+在**start**函数之后的任意位置添加。若需要dump反向数据，则需要添加在反向计算代码（如loss.backward）之后。
 
 **原型**
 
@@ -105,13 +105,33 @@ debugger.stop()
 
 该函数为类函数，可以使用debugger.stop()也可以使用PrecisionDebugger.stop()。
 
+### forward_backward_dump_end函数
+
+**功能说明**
+
+dump停止函数。用于dump指定代码的前反向数据。
+
+在**start**函数之后，反向计算代码（如loss.backward）之前的任意位置添加，可以dump **start**函数和该函数之间的前反向数据，可以通过调整**start**函数与该函数的位置，来指定需要dump的代码块。
+
+要求**stop**函数添加在反向计算代码（如loss.backward）之后，此时该函数与**stop**函数之间的代码不会被dump。
+
+使用示例参见“**示例代码 > 扩展示例**”。
+
+**原型**
+
+```Python
+forward_backward_dump_end()
+```
+
+该函数为类函数，可以使用debugger.forward_backward_dump_end()也可以使用PrecisionDebugger.forward_backward_dump_end()。
+
 ### step函数
 
 **功能说明**
 
 结束标识。
 
-在最后一个**stop**函数后或一个step结束的位置添加。
+在最后一个**stop**函数后或一个step结束的位置添加。需要与start函数一起添加在for循环内。
 
 **原型**
 
@@ -123,24 +143,57 @@ debugger.step()
 
 ## 示例代码
 
+### 基础操作
+
+如下示例可dump完整代码的前反向数据。
+
 ```Python
 from msprobe.pytorch import PrecisionDebugger
+
+# 请勿将PrecisionDebugger的初始化流程插入到循环代码中
 debugger = PrecisionDebugger(config_path="./config.json", dump_path="./dump_path")
-# 请勿将以上初始化流程插入到循环代码中
 
-# 模型初始化
-# 下面代码也可以用PrecisionDebugger.start()和PrecisionDebugger.stop()
-debugger.start()
+# 模型、损失函数的定义及初始化等操作
+# ...
 
-# 需要dump的代码片段1
+# 数据集迭代的位置一般为模型训练开始的位置
+for data, label in data_loader:
+	debugger.start() # 开启数据dump
 
-debugger.stop()
-debugger.start()
+	# 如下是模型每个step执行的逻辑
+    output = model(data)
+    #...
+    loss.backward()
+
+	debugger.stop() # 关闭数据dump
+	debugger.step() # 结束一个step的dump
+```
 
-# 需要dump的代码片段2
+### 扩展示例
 
-debugger.stop()
-debugger.step()
+如下示例dump指定代码块前反向数据。
+
+```Python
+from msprobe.pytorch import PrecisionDebugger
+
+# 请勿将PrecisionDebugger的初始化流程插入到循环代码中
+debugger = PrecisionDebugger(config_path="./config.json", dump_path="./dump_path")
+
+# 模型、损失函数的定义及初始化等操作
+# ...
+
+# 数据集迭代的位置一般为模型训练开始的位置
+for data, label in data_loader:
+	debugger.start() # 开启数据dump
+
+	# 如下是模型每个step执行的逻辑
+    output = model(data)
+    debugger.forward_backward_dump_end() # 插入该函数到start函数之后，只dump start函数到该函数之间代码的前反向数据，本函数到stop函数之间的数据则不dump
+    #...
+    loss.backward()
+
+	debugger.stop() # 关闭数据dump
+	debugger.step() # 结束一个step的dump
 ```
 
 ## dump结果文件介绍
diff --git a/debug/accuracy_tools/msprobe/pytorch/free_benchmark/common/constant.py b/debug/accuracy_tools/msprobe/pytorch/free_benchmark/common/constant.py
index e737e7b2179..c5e93be138d 100644
--- a/debug/accuracy_tools/msprobe/pytorch/free_benchmark/common/constant.py
+++ b/debug/accuracy_tools/msprobe/pytorch/free_benchmark/common/constant.py
@@ -52,6 +52,7 @@ class ThresholdConfig:
 
     DTYPE_PER_THD = {
         torch.float16: 1.002,
+        torch.bfloat16: 1.004,
         torch.float32: 1.0002,
     }
     BENCHMARK_THD_DICT = {
@@ -60,6 +61,8 @@ class ThresholdConfig:
         torch.bfloat16: BenchmarkThd(2**-8, 1.0, 2**-8, 1e-4),
     }
 
+    TENSOR_SPLIT_MAX_CHUNK = 128
+
 
 class PreheatConfig:
     IF_PREHEAT = "if_preheat"
diff --git a/debug/accuracy_tools/msprobe/pytorch/free_benchmark/common/utils.py b/debug/accuracy_tools/msprobe/pytorch/free_benchmark/common/utils.py
index ddcbd9d0f5c..631beeb85cb 100644
--- a/debug/accuracy_tools/msprobe/pytorch/free_benchmark/common/utils.py
+++ b/debug/accuracy_tools/msprobe/pytorch/free_benchmark/common/utils.py
@@ -96,3 +96,7 @@ class TorchC:
     add = torch._C._VariableFunctionsClass.add
     bitwise_xor = torch._C._VariableFunctionsClass.bitwise_xor
     clone = torch._C._VariableFunctionsClass.clone
+    clamp = torch._C._VariableFunctionsClass.clamp
+    tensor_split = torch._C._VariableFunctionsClass.tensor_split
+    stack = torch._C._VariableFunctionsClass.stack
+    reshape = torch._C._VariableFunctionsClass.reshape
diff --git a/debug/accuracy_tools/msprobe/pytorch/free_benchmark/result_handlers/base_handler.py b/debug/accuracy_tools/msprobe/pytorch/free_benchmark/result_handlers/base_handler.py
index 1728b096f5b..e36f5867355 100644
--- a/debug/accuracy_tools/msprobe/pytorch/free_benchmark/result_handlers/base_handler.py
+++ b/debug/accuracy_tools/msprobe/pytorch/free_benchmark/result_handlers/base_handler.py
@@ -1,6 +1,7 @@
 import math
 from abc import ABC, abstractmethod
 from typing import Any, Optional, Tuple
+import numpy as np
 
 import torch
 from msprobe.core.common.const import Const
@@ -34,15 +35,36 @@ class FuzzHandler(ABC):
             origin_ouput = origin_ouput.values
             perturbed_output = perturbed_output.values
         if hasattr(perturbed_output, "dtype"):
-            abs_tol = ThresholdConfig.ABS_TOL_VALUE_DICT.get(perturbed_output.dtype)
+            abs_tol = ThresholdConfig.ABS_TOL_VALUE_DICT.get(perturbed_output.dtype, FuzzThreshold.F32_THD)
         else:
-            abs_tol = FuzzThreshold.F32_THD.value
+            abs_tol = FuzzThreshold.F32_THD
         return (
             origin_ouput.to(perturbed_output.dtype).to(perturbed_output.device),
             perturbed_output,
             abs_tol,
         )
 
+    @staticmethod
+    def tensor_split_for_error_calculate(origin_output, perturbed_output):
+        """
+        对将投入误差值计算的扰动前后输出张量进行分块
+        :param origin_output: 原始输出
+        :param perturbed_output: 扰动后输出
+        :return origin_output_chunks: 切块后原始输出列表
+        :return perturbed_output_chunks: 切块后扰动后输出列表
+        """
+        single_output_mem = origin_output.element_size() * origin_output.nelement() / Const.ONE_MB
+        if single_output_mem == 0 or origin_output.ndim == 0:
+            return [origin_output], [perturbed_output]
+        # 张量大小和批数之间的关系：chunks_exp=math.log(M,2)-4, chunks=2**chunks_exp (M为对比张量数据大小[Mb])
+        chunks_exp = int(math.log(single_output_mem, 2)) - 4
+        chunks = 2 ** chunks_exp
+        chunks = max(chunks, 1)
+        chunks = min(chunks, ThresholdConfig.TENSOR_SPLIT_MAX_CHUNK)
+        origin_output_chunks = TorchC.tensor_split(TorchC.reshape(origin_output, (-1,)), chunks)
+        perturbed_output_chunks = TorchC.tensor_split(TorchC.reshape(perturbed_output, (-1,)), chunks)
+        return origin_output_chunks, perturbed_output_chunks
+
     @staticmethod
     def convert_overflow_ratio_to_consistent(ratio):
         if math.isnan(ratio) or math.isinf(ratio):
@@ -61,36 +83,28 @@ class FuzzHandler(ABC):
             self, origin_output, perturbed_output, norm_type, abs_tol
     ):
         if norm_type == NormType.ENDLESS_NORM:
-            return self.get_endless_norm(origin_output, perturbed_output, abs_tol)
+            return self.calculate_error(origin_output, perturbed_output, abs_tol)
         return ThresholdConfig.COMP_CONSISTENT
 
-    def get_endless_norm(self, origin_output, perturbed_output, abs_tol):
-        ratio_tensor1 = TorchC.where(
-            TorchC.gt(TorchC.abs(perturbed_output), abs_tol),
-            TorchC.div(
-                TorchC.abs(origin_output),
-                TorchC.add(TorchC.abs(perturbed_output), abs_tol),
-            ),
-            1,
-        )
-        ratio_tensor2 = TorchC.where(
-            TorchC.gt(TorchC.abs(origin_output), abs_tol),
-            TorchC.div(
-                TorchC.abs(perturbed_output),
-                TorchC.add(TorchC.abs(origin_output), abs_tol),
-            ),
-            1,
-        )
+    def calculate_error(self, origin_output, perturbed_output, abs_tol):
+        origin_output_chunks, perturbed_output_chunks = self.tensor_split_for_error_calculate(origin_output, perturbed_output)
+        norm1 = -np.inf
+        norm2 = -np.inf
+        norm3 = np.inf
+        for i, chunk_origin in enumerate(origin_output_chunks):
+            if chunk_origin.nelement() == 0:
+                break
+            chunk_perturbed = perturbed_output_chunks[i]
+            ratio_tensor1 = TorchC.where(TorchC.abs(chunk_perturbed) > abs_tol,
+                                         TorchC.div(TorchC.clamp(chunk_origin, min=abs_tol), TorchC.clamp(chunk_perturbed, min=abs_tol)), 1)
+            ratio_tensor2 = TorchC.where(TorchC.abs(chunk_origin) > abs_tol,
+                                         TorchC.div(TorchC.clamp(chunk_perturbed, min=abs_tol), TorchC.clamp(chunk_origin, min=abs_tol)), 1)
+            norm_values = TorchC.stack([TorchC.max(ratio_tensor1), TorchC.max(ratio_tensor2)])
+            max_ratio1, max_ratio2 = norm_values.tolist()
+            norm1 = max(norm1, self.convert_overflow_ratio_to_consistent(max_ratio1))
+            norm2 = max(norm2, self.convert_overflow_ratio_to_consistent(max_ratio2))
+            norm3 = min(norm3, self.convert_overflow_ratio_to_consistent(max_ratio1))
 
-        norm1 = self.convert_overflow_ratio_to_consistent(
-            TorchC.max(ratio_tensor1).item()
-        )
-        norm2 = self.convert_overflow_ratio_to_consistent(
-            TorchC.max(ratio_tensor2).item()
-        )
-        norm3 = self.convert_overflow_ratio_to_consistent(
-            TorchC.min(ratio_tensor1).item()
-        )
         if norm3 < 0:
             ratio = ThresholdConfig.SYMBOL_FLIPPING
         else:
diff --git a/debug/accuracy_tools/msprobe/pytorch/function_factory.py b/debug/accuracy_tools/msprobe/pytorch/function_factory.py
new file mode 100644
index 00000000000..c2fd8bfd0cb
--- /dev/null
+++ b/debug/accuracy_tools/msprobe/pytorch/function_factory.py
@@ -0,0 +1,75 @@
+from msprobe.pytorch.common.utils import logger
+from msprobe.pytorch.bench_functions.apply_adam_w import npu_apply_adam_w
+from msprobe.pytorch.bench_functions.confusion_transpose import npu_confusion_transpose, \
+    npu_confusion_transpose_backward
+from msprobe.pytorch.bench_functions.fast_gelu import fast_gelu, npu_fast_gelu_backward
+from msprobe.pytorch.bench_functions.layer_norm_eval import npu_layer_norm_eval
+from msprobe.pytorch.bench_functions.linear import npu_linear, npu_linear_backward
+from msprobe.pytorch.bench_functions.matmul_backward import matmul_backward
+from msprobe.pytorch.bench_functions.npu_fusion_attention import npu_fusion_attention, npu_fusion_attention_grad
+from msprobe.pytorch.bench_functions.rms_norm import npu_rms_norm, npu_rms_norm_backward
+from msprobe.pytorch.bench_functions.rotary_mul import npu_rotary_mul, npu_rotary_mul_backward
+from msprobe.pytorch.bench_functions.scaled_mask_softmax import npu_scaled_masked_softmax, \
+    npu_scaled_masked_softmax_backward
+from msprobe.pytorch.bench_functions.swiglu import npu_swiglu, npu_swiglu_backward, swish_grad, swish
+
+
+class Register(dict):
+    def __init__(self, *args, **kwargs):
+        super(Register, self).__init__(*args, **kwargs)
+        self._dict = {}
+
+    def __call__(self, target_func_list):
+        for target in target_func_list:
+            self.register(target)
+        return
+
+    def __setitem__(self, key, value):
+        self._dict[key] = value
+
+    def __getitem__(self, key):
+        return self._dict[key]
+
+    def __contains__(self, key):
+        return key in self._dict
+
+    def __str__(self):
+        return str(self._dict)
+
+    def keys(self):
+        return self._dict.keys()
+
+    def values(self):
+        return self._dict.values()
+
+    def items(self):
+        return self._dict.items()
+
+    def register(self, target):
+
+        def add_register_item(key, value):
+            if key in self._dict:
+                logger.warning(f"{value.__name__} has been registered before, so we will overriden it.")
+            self[key] = value
+            return value
+
+        if callable(target):
+            return add_register_item(target.__name__, target)
+        else:
+            raise Exception(f"The func {target} is not callable.")
+
+
+# register for npu custom bench functions
+npu_custom_functions = Register()
+npu_custom_functions([
+    npu_apply_adam_w, npu_confusion_transpose, fast_gelu, npu_layer_norm_eval, npu_linear, npu_fusion_attention,
+    npu_rms_norm, npu_rotary_mul, npu_scaled_masked_softmax, npu_swiglu
+])
+
+# register for npu custom backward bench functions
+npu_custom_grad_functions = Register()
+npu_custom_grad_functions([
+    npu_confusion_transpose_backward, npu_fast_gelu_backward, npu_linear_backward, matmul_backward,
+    npu_fusion_attention_grad, npu_rms_norm_backward, npu_rotary_mul_backward, npu_scaled_masked_softmax_backward,
+    npu_swiglu_backward
+])
diff --git a/debug/accuracy_tools/msprobe/pytorch/hook_module/hook_module.py b/debug/accuracy_tools/msprobe/pytorch/hook_module/hook_module.py
index 6693a09d028..ff6427e51e5 100644
--- a/debug/accuracy_tools/msprobe/pytorch/hook_module/hook_module.py
+++ b/debug/accuracy_tools/msprobe/pytorch/hook_module/hook_module.py
@@ -17,9 +17,11 @@
 
 import functools
 import threading
+
 import torch
 import torch.nn as nn
 import torch.utils.hooks as full_hooks
+
 from msprobe.core.common.const import Const
 
 
@@ -61,6 +63,10 @@ class HOOKModule(nn.Module):
             HOOKModule.inner_stop_hook[self.current_thread] = False
         return result
 
+    @classmethod
+    def reset_module_stats(cls):
+        cls.module_count = {}
+
     def _call_func(self, *input, **kwargs):
         full_backward_hooks, non_full_backward_hooks = [], []
         if len(self._backward_hooks) > 0:
diff --git a/debug/accuracy_tools/msprobe/pytorch/hook_module/support_wrap_ops.yaml b/debug/accuracy_tools/msprobe/pytorch/hook_module/support_wrap_ops.yaml
index d64c577ff38..f68708e945e 100644
--- a/debug/accuracy_tools/msprobe/pytorch/hook_module/support_wrap_ops.yaml
+++ b/debug/accuracy_tools/msprobe/pytorch/hook_module/support_wrap_ops.yaml
@@ -1873,4 +1873,5 @@ distributed:
   - reduce_scatter
   - _reduce_scatter_base
   - _all_gather_base
-  - all_to_all_single
\ No newline at end of file
+  - all_to_all_single
+  - all_to_all
\ No newline at end of file
diff --git a/debug/accuracy_tools/msprobe/pytorch/hook_module/wrap_aten.py b/debug/accuracy_tools/msprobe/pytorch/hook_module/wrap_aten.py
index 4617e4854fc..a02abbe5f4b 100644
--- a/debug/accuracy_tools/msprobe/pytorch/hook_module/wrap_aten.py
+++ b/debug/accuracy_tools/msprobe/pytorch/hook_module/wrap_aten.py
@@ -24,12 +24,14 @@ from msprobe.pytorch.hook_module.hook_module import HOOKModule
 from msprobe.pytorch.common.utils import torch_device_guard
 from msprobe.core.common.const import Const
 from msprobe.core.common.file_check import FileOpen
-
+from msprobe.pytorch.function_factory import npu_custom_grad_functions
 
 cur_path = os.path.dirname(os.path.realpath(__file__))
 yaml_path = os.path.join(cur_path, "support_wrap_ops.yaml")
 with FileOpen(yaml_path, 'r') as f:
-    WrapAtenOps = yaml.safe_load(f).get('aten')
+    Ops = yaml.safe_load(f)
+    WrapAtenOps = Ops.get('aten')
+    WhiteAtenOps = Ops.get('white_aten_ops', [])
 
 
 aten_func = {}
@@ -48,7 +50,7 @@ class HOOKAtenOP(object):
 
 
 class AtenOPTemplate(HOOKModule):
-    def __init__(self, op, hook):
+    def __init__(self, op, hook, need_hook=True):
         if isinstance(op, torch._ops.OpOverloadPacket):
             op_name_ = op._qualified_op_name.split("::")[-1]
         else:
@@ -58,10 +60,21 @@ class AtenOPTemplate(HOOKModule):
                 op_name_ = op_name_ + '.' + overload_name
         self.op = op
         self.prefix_op_name_ = "Aten" + Const.SEP + str(op_name_) + Const.SEP
-        super().__init__(hook)
+        self.need_hook = need_hook
+        if self.need_hook:
+            super().__init__(hook)
 
     @torch_device_guard
     def forward(self, *args, **kwargs):
+        if isinstance(self.op, str):
+            if self.op in npu_custom_grad_functions:
+                return npu_custom_grad_functions[self.op](*args, **kwargs)
+            if self.op in WhiteAtenOps:
+                return eval(f"torch.ops.aten.{self.op}")(*args, **kwargs)
+            if self.op not in aten_func:
+                raise Exception(f"Skip op[{self.op}] accuracy check, because the op is not "
+                                f"in dir(torch.ops.aten) and support yaml.")
+            return aten_func[self.op](*args, **kwargs)
         return self.op(*args, **kwargs)
 
 
diff --git a/debug/accuracy_tools/msprobe/pytorch/hook_module/wrap_npu_custom.py b/debug/accuracy_tools/msprobe/pytorch/hook_module/wrap_npu_custom.py
index 992713bce57..8a67ed94290 100644
--- a/debug/accuracy_tools/msprobe/pytorch/hook_module/wrap_npu_custom.py
+++ b/debug/accuracy_tools/msprobe/pytorch/hook_module/wrap_npu_custom.py
@@ -17,19 +17,26 @@
 
 import os
 import torch
-import torch_npu
 import yaml
 
 from msprobe.pytorch.hook_module.hook_module import HOOKModule
 from msprobe.pytorch.common.utils import torch_device_guard, torch_without_guard_version
 from msprobe.core.common.const import Const
 from msprobe.core.common.file_check import FileOpen
+from msprobe.pytorch.function_factory import npu_custom_functions
 
 cur_path = os.path.dirname(os.path.realpath(__file__))
 yaml_path = os.path.join(cur_path, "support_wrap_ops.yaml")
 with FileOpen(yaml_path, 'r') as f:
     WrapNpuOps = yaml.safe_load(f).get('torch_npu')
 
+try:
+    import torch_npu
+except ImportError:
+    is_gpu = True
+else:
+    is_gpu = False
+
 
 def get_npu_ops():
     global WrapNpuOps
@@ -46,13 +53,19 @@ class HOOKNpuOP(object):
 
 class NpuOPTemplate(HOOKModule):
 
-    def __init__(self, op_name, hook):
+    def __init__(self, op_name, hook, need_hook=True):
         self.op_name_ = op_name
         self.prefix_op_name_ = "NPU" + Const.SEP + str(op_name) + Const.SEP
-        super().__init__(hook)
+        self.need_hook = need_hook
+        if need_hook:
+            super().__init__(hook)
 
     @torch_device_guard
     def forward(self, *args, **kwargs):
+        if not self.need_hook:
+            if self.op_name_ not in npu_custom_functions:
+                raise Exception(f'There is not bench function {self.op_name_}')
+            return npu_custom_functions[self.op_name_](*args, **kwargs)
         if torch_without_guard_version:
             return getattr(torch.ops.npu, str(self.op_name_))(*args, **kwargs)
         else:
@@ -60,7 +73,6 @@ class NpuOPTemplate(HOOKModule):
 
 
 def wrap_npu_op(op_name, hook):
-
     def npu_op_template(*args, **kwargs):
         return NpuOPTemplate(op_name, hook)(*args, **kwargs)
 
diff --git a/debug/accuracy_tools/msprobe/pytorch/module_processer.py b/debug/accuracy_tools/msprobe/pytorch/module_processer.py
index 422d36d6ac7..f9368a08745 100644
--- a/debug/accuracy_tools/msprobe/pytorch/module_processer.py
+++ b/debug/accuracy_tools/msprobe/pytorch/module_processer.py
@@ -1,15 +1,17 @@
 from functools import wraps
+
 import torch
 from torch.utils.hooks import BackwardHook
+
 from msprobe.core.common.const import Const
 from msprobe.core.data_dump.scope import ModuleRangeScope
 
 
 class ModuleProcesser:
+    module_count = {}
     module_stack = []
     api_parent_node = ""
     module_node = {}
-    current_module_name = ""
 
     def __init__(self, scope):
         if isinstance(scope, ModuleRangeScope):
@@ -19,7 +21,6 @@ class ModuleProcesser:
         BackwardHook.setup_input_hook = ModuleProcesser.clone_return_value(BackwardHook.setup_input_hook)
         BackwardHook.setup_output_hook = ModuleProcesser.clone_return_value(BackwardHook.setup_output_hook)
         BackwardHook.setup_output_hook = ModuleProcesser.filter_tensor_and_tuple(BackwardHook.setup_output_hook)
-        self.module_count = {}
 
     @staticmethod
     def filter_tensor_and_tuple(func):
@@ -55,11 +56,26 @@ class ModuleProcesser:
         else:
             return result
 
+    @staticmethod
+    def module_count_func(module_name):
+        if module_name not in ModuleProcesser.module_count:
+            ModuleProcesser.module_count[module_name] = 0
+        else:
+            ModuleProcesser.module_count[module_name] += 1
+        return ModuleProcesser.module_count[module_name]
+
+    @classmethod
+    def reset_module_stats(cls):
+        cls.module_count = {}
+        cls.module_stack = []
+        cls.api_parent_node = ""
+        cls.module_node = {}
+
     def node_hook(self, name_prefix, start_or_stop, **kwargs):
 
         def pre_hook(module, input, output=None):
             try:
-                index = self.module_count_func(name_prefix)
+                index = ModuleProcesser.module_count_func(name_prefix)
             except IndexError as e:
                 index = None
                 pass
@@ -89,10 +105,3 @@ class ModuleProcesser:
             return pre_hook
         else:
             return end_hook
-
-    def module_count_func(self, module_name):
-        if module_name not in self.module_count:
-            self.module_count[module_name] = 0
-        else:
-            self.module_count[module_name] += 1
-        return self.module_count[module_name]
diff --git a/debug/accuracy_tools/msprobe/pytorch/pt_config.py b/debug/accuracy_tools/msprobe/pytorch/pt_config.py
index a3d765f3a4d..ceec92a633a 100644
--- a/debug/accuracy_tools/msprobe/pytorch/pt_config.py
+++ b/debug/accuracy_tools/msprobe/pytorch/pt_config.py
@@ -32,12 +32,12 @@ class StatisticsConfig(BaseConfig):
 class OverflowCheckConfig(BaseConfig):
     def __init__(self, json_config):
         super().__init__(json_config)
-        self.overflow_num = json_config.get("overflow_nums")
+        self.overflow_nums = json_config.get("overflow_nums")
         self.check_mode = json_config.get("check_mode")
         self.check_overflow_config()
 
     def check_overflow_config(self):
-        if self.overflow_num is not None and not isinstance(self.overflow_num, int):
+        if self.overflow_nums is not None and not isinstance(self.overflow_nums, int):
             raise Exception("overflow_num is invalid")
         if self.check_mode is not None and self.check_mode not in ["all", "aicore", "atomic"]:
             raise Exception("check_mode is invalid")
diff --git a/debug/accuracy_tools/msprobe/pytorch/service.py b/debug/accuracy_tools/msprobe/pytorch/service.py
index daeda889879..6b8d67abc9f 100644
--- a/debug/accuracy_tools/msprobe/pytorch/service.py
+++ b/debug/accuracy_tools/msprobe/pytorch/service.py
@@ -2,17 +2,18 @@ import functools
 import os
 from pathlib import Path
 
-from msprobe.pytorch.common.log import logger
-from msprobe.core.common.file_check import FileChecker, check_path_before_create
 from msprobe.core.common.const import Const, FileCheckConst
 from msprobe.core.common.exceptions import DistributedNotInitializedError, MsprobeException
+from msprobe.core.common.file_check import FileChecker, check_path_before_create
 from msprobe.core.data_dump.data_collector import build_data_collector
-from msprobe.core.data_dump.scope import BaseScope
 from msprobe.core.data_dump.data_processor.base import ModuleForwardInputsOutputs, ModuleBackwardInputsOutputs
+from msprobe.core.data_dump.scope import BaseScope
+from msprobe.pytorch.common.log import logger
 from msprobe.pytorch.common.utils import get_rank_if_initialized
-from msprobe.pytorch.module_processer import ModuleProcesser
 from msprobe.pytorch.hook_module import remove_dropout
 from msprobe.pytorch.hook_module.api_registry import api_register
+from msprobe.pytorch.hook_module.hook_module import HOOKModule
+from msprobe.pytorch.module_processer import ModuleProcesser
 
 
 class Service:
@@ -67,7 +68,8 @@ class Service:
             if not self.switch:
                 return
             if self.data_collector:
-                module_input_output = ModuleBackwardInputsOutputs(grad_input=grad_input, grad_output=grad_output)
+                # 此处获取到的grad_input实际为反向过程的输出数据，grad_output为反向过程的输入数据，因此传入时调换顺序
+                module_input_output = ModuleBackwardInputsOutputs(grad_input=grad_output, grad_output=grad_input)
                 self.data_collector.backward_data_collect(api_or_module_name, module, pid, module_input_output)
 
         pid = os.getpid()
@@ -82,6 +84,9 @@ class Service:
         self.current_iter += 1
         self.data_collector.update_iter(self.current_iter)
 
+        ModuleProcesser.reset_module_stats()
+        HOOKModule.reset_module_stats()
+
     def start(self, model, api_origin=False):
         self.model = model
         if self.config.step and self.current_iter > max(self.config.step):
diff --git a/debug/accuracy_tools/msprobe/test/core_ut/test_common_config.py b/debug/accuracy_tools/msprobe/test/core_ut/test_common_config.py
index 06c7378ed36..8b2138a485b 100644
--- a/debug/accuracy_tools/msprobe/test/core_ut/test_common_config.py
+++ b/debug/accuracy_tools/msprobe/test/core_ut/test_common_config.py
@@ -121,7 +121,7 @@ class TestCommonConfig(TestCase):
         self.assertIsNone(base_config.backward_input)
         self.assertIsNone(base_config.file_format)
         self.assertIsNone(base_config.summary_mode)
-        self.assertIsNone(base_config.overflow_num)
+        self.assertIsNone(base_config.overflow_nums)
         self.assertIsNone(base_config.check_mode)
 
         json_config.update({"scope": "Tensor_Add"})
diff --git a/debug/accuracy_tools/msprobe/test/mindspore_ut/test_ms_config.py b/debug/accuracy_tools/msprobe/test/mindspore_ut/test_ms_config.py
index 673386afb5d..30212d95e62 100644
--- a/debug/accuracy_tools/msprobe/test/mindspore_ut/test_ms_config.py
+++ b/debug/accuracy_tools/msprobe/test/mindspore_ut/test_ms_config.py
@@ -19,7 +19,7 @@ from unittest.mock import patch, mock_open
 
 from msprobe.core.common.const import Const
 from msprobe.mindspore.ms_config import (parse_json_config, parse_task_config,
-                                      TensorConfig, StatisticsConfig, OverflowCheck)
+                                         TensorConfig, StatisticsConfig, OverflowCheckConfig)
 
 
 class TestMsConfig(TestCase):
@@ -62,7 +62,7 @@ class TestMsConfig(TestCase):
         self.assertTrue(isinstance(task_config, StatisticsConfig))
 
         task_config = parse_task_config("overflow_check", mock_json_config)
-        self.assertTrue(isinstance(task_config, OverflowCheck))
+        self.assertTrue(isinstance(task_config, OverflowCheckConfig))
 
         with self.assertRaises(Exception) as context:
             parse_task_config("free_benchmark", mock_json_config)
diff --git a/debug/accuracy_tools/msprobe/test/pytorch_ut/api_accuracy_checker/run_ut/test_multi_run_ut.py b/debug/accuracy_tools/msprobe/test/pytorch_ut/api_accuracy_checker/run_ut/test_multi_run_ut.py
index 771e0423804..27126cdddda 100644
--- a/debug/accuracy_tools/msprobe/test/pytorch_ut/api_accuracy_checker/run_ut/test_multi_run_ut.py
+++ b/debug/accuracy_tools/msprobe/test/pytorch_ut/api_accuracy_checker/run_ut/test_multi_run_ut.py
@@ -48,7 +48,7 @@ class TestMultiRunUT(unittest.TestCase):
             device_id=[0, 1],
             result_csv_path='result.csv',
             total_items=2,
-            real_data_path=None
+            config_path=None
         )
 
         mock_file.side_effect = [
@@ -81,7 +81,7 @@ class TestMultiRunUT(unittest.TestCase):
         args.jit_compile = False
         args.device_id = [0, 1]
         args.result_csv_path = None
-        args.real_data_path = None
+        args.config_path = None
 
         config = prepare_config(args)
 
diff --git a/debug/accuracy_tools/msprobe/test/pytorch_ut/test_pt_config.py b/debug/accuracy_tools/msprobe/test/pytorch_ut/test_pt_config.py
index c344f0b66b0..470390d77b2 100644
--- a/debug/accuracy_tools/msprobe/test/pytorch_ut/test_pt_config.py
+++ b/debug/accuracy_tools/msprobe/test/pytorch_ut/test_pt_config.py
@@ -45,7 +45,7 @@ class TestPtConfig(TestCase):
             }
         }
         result = parse_task_config(Const.OVERFLOW_CHECK, overflow_check_config)
-        self.assertEqual(result.overflow_num, 1)
+        self.assertEqual(result.overflow_nums, 1)
         self.assertEqual(result.check_mode, "all")
 
         free_benchmark_config = {
diff --git a/debug/accuracy_tools/setup.py b/debug/accuracy_tools/setup.py
index 4e0eaa1f375..afbf8feb3a0 100644
--- a/debug/accuracy_tools/setup.py
+++ b/debug/accuracy_tools/setup.py
@@ -14,7 +14,7 @@
 import setuptools
 
 
-__version__ = '1.0.0'
+__version__ = '1.0.1'
 
 INSTALL_REQUIRED = [
     "wheel",
diff --git a/plugins/tensorboard-plugins/.github/workflows/libkineto_ci.yml b/plugins/tensorboard-plugins/.github/workflows/libkineto_ci.yml
deleted file mode 100644
index 3133d6400fb..00000000000
--- a/plugins/tensorboard-plugins/.github/workflows/libkineto_ci.yml
+++ /dev/null
@@ -1,56 +0,0 @@
-name: LIBKINETOCI
-
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-
-jobs:
-  build:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [ubuntu-latest]
-
-    steps:
-    - uses: actions/checkout@v2
-    - name: Checkout submodules
-      shell: bash
-      run: |
-        auth_header="$(git config --local --get http.https://github.com/.extraheader)"
-        git submodule sync --recursive
-        git -c "http.extraheader=$auth_header" -c protocol.version=2 submodule update --init --force --recursive --depth=1
-
-    - name: Get env vars
-      run: |
-        echo GITHUB_WORKFLOW   = $GITHUB_WORKFLOW
-        echo HOME              = $HOME
-        echo GITHUB_ACTION     = $GITHUB_ACTION
-        echo GITHUB_ACTIONS    = $GITHUB_ACTIONS
-        echo GITHUB_REPOSITORY = $GITHUB_REPOSITORY
-        echo GITHUB_EVENT_NAME = $GITHUB_EVENT_NAME
-        echo GITHUB_EVENT_PATH = $GITHUB_EVENT_PATH
-        echo GITHUB_WORKSPACE  = $GITHUB_WORKSPACE
-        echo GITHUB_SHA        = $GITHUB_SHA
-        echo GITHUB_REF        = $GITHUB_REF
-        c++ --verbose
-
-    # TODO: Figure out how to install cupti headers T84637671
-    - name: Build static lib
-      run: |
-        set -e
-        mkdir build_static
-        cd build_static
-        cmake -DKINETO_LIBRARY_TYPE=static ../libkineto/
-        make -j
-
-    - name: Build shared lib
-      run: |
-        set -e
-        mkdir build_shared
-        cd build_shared
-        cmake -DKINETO_LIBRARY_TYPE=shared ../libkineto/
-        make -j
diff --git a/plugins/tensorboard-plugins/.github/workflows/tb_plugin_build_pip_package.yml b/plugins/tensorboard-plugins/.github/workflows/tb_plugin_build_pip_package.yml
deleted file mode 100644
index 9bdafcc4426..00000000000
--- a/plugins/tensorboard-plugins/.github/workflows/tb_plugin_build_pip_package.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-name: Build torch-tb-profiler Pip Package
-
-on:
-  # TODO: Add an on_release trigger to build on tags
-  workflow_dispatch:
-
-jobs:
-  build-package:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v2
-      - name: build pip package
-        run: |
-          set -e
-          cd tb_plugin
-          python setup.py sdist bdist_wheel
-          cd dist/
-          pip install *.whl
-          python -c "import torch_tb_profiler;print(torch_tb_profiler.__version__)"
diff --git a/plugins/tensorboard-plugins/.github/workflows/tb_plugin_ci.yml b/plugins/tensorboard-plugins/.github/workflows/tb_plugin_ci.yml
deleted file mode 100644
index 1b59a7bf90a..00000000000
--- a/plugins/tensorboard-plugins/.github/workflows/tb_plugin_ci.yml
+++ /dev/null
@@ -1,57 +0,0 @@
-name: TB_Plugin_CI
-
-on:
-  push:
-    branches:
-      - main
-      - release/**
-      - plugin/**
-
-  pull_request:
-    branches:
-      - main
-      - release/**
-      - plugin/**
-
-jobs:
-  generate-matrix:
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    steps:
-      - id: set-matrix
-        run: |
-          echo $GITHUB_BASE_REF
-          if [ $GITHUB_BASE_REF == "plugin/vnext" ]
-          then
-            echo "::set-output name=matrix::{\"python-version\":[3.7, 3.8, 3.9], \"cuda-version\":[\"cpu\"], \"pytorch-version\":[\"nightly\"]}"
-          else
-            echo "::set-output name=matrix::{\"python-version\":[3.7, 3.8, 3.9], \"cuda-version\":[\"cpu\"], \"pytorch-version\":[\"nightly\", \"1.11rc\", \"stable\"]}"
-          fi
-
-  build:
-    needs: generate-matrix
-    runs-on: ubuntu-latest
-    strategy:
-      matrix: ${{fromJSON(needs.generate-matrix.outputs.matrix)}}
-    steps:
-      - uses: actions/checkout@v2
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: 'x64'
-      - name: Test
-        env:
-          CUDA_VERSION: ${{ matrix.cuda-version }}
-          PYTORCH_VERSION: ${{ matrix.pytorch-version }}
-          TORCH_PROFILER_LOG_LEVEL: DEBUG
-          GRPC_VERBOSITY: DEBUG
-          GRPC_ENABLE_FORK_SUPPORT: 'False'
-        run: |
-          set -e
-          cd tb_plugin
-          sh ./ci_scripts/install_env.sh
-          pip install .[gs]
-          cd test
-          pytest
diff --git a/plugins/tensorboard-plugins/.gitignore b/plugins/tensorboard-plugins/.gitignore
deleted file mode 100644
index ce186381c0b..00000000000
--- a/plugins/tensorboard-plugins/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-# ignore common items
-.idea
-.vscode
diff --git a/plugins/tensorboard-plugins/.gitmodules b/plugins/tensorboard-plugins/.gitmodules
deleted file mode 100644
index 4660ee8bc9e..00000000000
--- a/plugins/tensorboard-plugins/.gitmodules
+++ /dev/null
@@ -1,6 +0,0 @@
-[submodule "libkineto/third_party/googletest"]
-	path = libkineto/third_party/googletest
-	url = https://github.com/google/googletest.git
-[submodule "libkineto/third_party/fmt"]
-	path = libkineto/third_party/fmt
-	url = https://github.com/fmtlib/fmt.git
diff --git a/plugins/tensorboard-plugins/CODE_OF_CONDUCT.md b/plugins/tensorboard-plugins/CODE_OF_CONDUCT.md
deleted file mode 100644
index a0cbeaab765..00000000000
--- a/plugins/tensorboard-plugins/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-  advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-  address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <conduct@pytorch.org>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
-
diff --git a/plugins/tensorboard-plugins/CONTRIBUTING.md b/plugins/tensorboard-plugins/CONTRIBUTING.md
deleted file mode 100644
index a2e931bb6f0..00000000000
--- a/plugins/tensorboard-plugins/CONTRIBUTING.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Contributing to Kineto
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Code of Conduct
-The code of conduct is described in [`CODE_OF_CONDUCT.md`](CODE_OF_CONDUCT.md).
-
-## Pull Requests
-We actively welcome your pull requests.
-
-1. Fork the repo and create your branch from `main`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Facebook's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## License
-By contributing to Kineto, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
diff --git a/plugins/tensorboard-plugins/LICENSE b/plugins/tensorboard-plugins/LICENSE
deleted file mode 100644
index edb179715b5..00000000000
--- a/plugins/tensorboard-plugins/LICENSE
+++ /dev/null
@@ -1,33 +0,0 @@
-BSD License
-
-For Kineto software
-
-Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
-
-All contributions by Microsoft:
-Copyright (c) Microsoft Corporation. (The Azure AI Platform team)
-
-Redistribution and use in source and binary forms, with or without modification,
-are permitted provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
-
- * Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
-
- * Neither the name Facebook nor the names of its contributors may be used to
-   endorse or promote products derived from this software without specific
-   prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/plugins/tensorboard-plugins/README.md b/plugins/tensorboard-plugins/README.md
deleted file mode 100644
index 3a18f4c6239..00000000000
--- a/plugins/tensorboard-plugins/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Kineto
-
-Kineto is part of the PyTorch Profiler.
-
-The Kineto project was started to help enable
-- **performance observability and diagnostics** across common ML bottleneck components
-- **actionable recommendations** for common issues
-- integration of external system-level profiling tools
-- integration with popular visualization platforms and analysis pipelines
-
-A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.
-
-The PyTorch Profiler TensorBoard plugin provides powerful and intuitive visualizations of profiling results, as well as actionable recommendations, and is the best way to experience the new PyTorch Profiler.
-
-## Libkineto
-Libkineto is an in-process profiling library integrated with the PyTorch Profiler. Please refer to the [README](libkineto/README.md) file in the `libkineto` folder as well as documentation on the [new PyTorch Profiler API](https://pytorch.org/docs/master/profiler.html).
-
-## PyTorch TensorBoard Profiler NPU Plugin
-The goal of the PyTorch TensorBoard Profiler is to provide a seamless and intuitive end-to-end profiling experience, including straightforward collection from PyTorch and insightful visualizations and recommendations in the TensorBoard UI.
-Please refer to the [README](tb_plugin/README.md) file in the `tb_plugin` folder.
-
-## Future Development Direction:
-Some areas we're currently working on:
-- Support for tracing distributed workloads
-- Trace processing, analysis and recommendation engine
-- System-level activities, multiple tracing sources
-- Profiling and monitoring daemon for larger scale deployments
-
-## Releases and Contributing
-We will follow the PyTorch release schedule which roughly happens on a 3 month basis.
-
-We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.
-
-If you plan to contribute new features, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR because we might be taking the infrastructure in a different direction than you might be aware of. We expect the architecture to keep evolving.
-
-## License
-Kineto has a BSD-style license, as found in the [LICENSE](LICENSE) file.
-
diff --git a/plugins/tensorboard-plugins/libkineto/CMakeLists.txt b/plugins/tensorboard-plugins/libkineto/CMakeLists.txt
deleted file mode 100644
index 63966de803a..00000000000
--- a/plugins/tensorboard-plugins/libkineto/CMakeLists.txt
+++ /dev/null
@@ -1,198 +0,0 @@
-cmake_minimum_required(VERSION 3.5 FATAL_ERROR)
-
-list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules")
-
-#install libraries into correct locations on all platforms
-include(GNUInstallDirs)
-
-# function to extract filelists from libkineto_defs.bzl file
-find_package(PythonInterp)
-function(get_filelist name outputvar)
-  execute_process(
-    COMMAND "${PYTHON_EXECUTABLE}" -c
-            "exec(open('libkineto_defs.bzl').read());print(';'.join(${name}))"
-    WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
-    OUTPUT_VARIABLE _tempvar)
-  string(REPLACE "\n" "" _tempvar "${_tempvar}")
-  set(${outputvar} ${_tempvar} PARENT_SCOPE)
-endfunction()
-
-project(kineto VERSION 0.1 LANGUAGES CXX C)
-
-set(KINETO_LIBRARY_TYPE "default" CACHE STRING
-  "Type of library (default, static or shared) to build")
-set_property(CACHE KINETO_LIBRARY_TYPE PROPERTY STRINGS default shared)
-option(KINETO_BUILD_TESTS "Build kineto unit tests" ON)
-
-set(LIBKINETO_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/src")
-set(LIBKINETO_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
-set(LIBKINETO_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
-set(LIBKINETO_THIRDPARTY_DIR "${CMAKE_CURRENT_SOURCE_DIR}/third_party")
-set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
-
-#We should default to a Release build
-if (NOT CMAKE_BUILD_TYPE OR CMAKE_BUILD_TYPE STREQUAL "")
-    set(CMAKE_BUILD_TYPE "Release" CACHE STRING "" FORCE)
-endif()
-
-if (NOT CUDA_SOURCE_DIR)
-    set(CUDA_SOURCE_DIR "$ENV{CUDA_SOURCE_DIR}")
-    message(INFO " CUDA_SOURCE_DIR = ${CUDA_SOURCE_DIR}")
-endif()
-
-if (NOT ROCM_SOURCE_DIR)
-    set(ROCM_SOURCE_DIR "$ENV{ROCM_SOURCE_DIR}")
-    message(INFO " ROCM_SOURCE_DIR = ${ROCM_SOURCE_DIR}")
-endif()
-
-# Set LIBKINETO_NOCUPTI to explicitly disable CUPTI
-# Otherwise, CUPTI is disabled if not found
-IF (NOT CUDA_SOURCE_DIR OR NOT CUPTI_INCLUDE_DIR OR NOT CUDA_cupti_LIBRARY)
-    set(LIBKINETO_NOCUPTI ON CACHE BOOL "" FORCE)
-endif()
-
-IF (NOT ROCM_SOURCE_DIR AND NOT ROCTRACER_INCLUDE_DIR)
-    set(LIBKINETO_NOROCTRACER ON CACHE BOOL "" FORCE)
-endif()
-
-# Define file lists
-if (LIBKINETO_NOCUPTI AND LIBKINETO_NOROCTRACER)
-    get_filelist("get_libkineto_cpu_only_srcs(with_api=False)" LIBKINETO_SRCS)
-    message(INFO " CUPTI unavailable or disabled - not building GPU profilers")
-elseif(NOT LIBKINETO_NOROCTRACER)
-    get_filelist("get_libkineto_roctracer_srcs()" LIBKINETO_SRCS)
-    message(INFO " Building with roctracer")
-else()
-    get_filelist("get_libkineto_cupti_srcs(with_api=False)" LIBKINETO_SRCS)
-endif()
-get_filelist("get_libkineto_public_headers()" LIBKINETO_PUBLIC_HEADERS)
-get_filelist("get_libkineto_api_srcs()" LIBKINETO_API_SRCS)
-
-add_library(kineto_base OBJECT ${LIBKINETO_SRCS})
-add_library(kineto_api OBJECT ${LIBKINETO_API_SRCS})
-
-# Make libraries depend on libkineto_defs.bzl
-add_custom_target(libkineto_defs.bzl DEPENDS libkineto_defs.bzl)
-add_dependencies(kineto_base libkineto_defs.bzl)
-
-set_target_properties(kineto_base kineto_api PROPERTIES
-      CXX_STANDARD 14
-      CXX_STANDARD_REQUIRED YES
-      CXX_EXTENSIONS NO
-      CXX_VISIBILITY_PRESET hidden)
-
-set(KINETO_COMPILE_OPTIONS "-DKINETO_NAMESPACE=libkineto")
-list(APPEND KINETO_COMPILE_OPTIONS "-DFMT_HEADER_ONLY")
-if(NOT MSVC)
-  list(APPEND KINETO_COMPILE_OPTIONS "-std=c++14")
-else()
-  list(APPEND KINETO_COMPILE_OPTIONS "/std:c++14")
-  list(APPEND KINETO_COMPILE_OPTIONS "-DWIN32_LEAN_AND_MEAN")
-  list(APPEND KINETO_COMPILE_OPTIONS "-DNOGDI")
-endif()
-if (NOT LIBKINETO_NOCUPTI)
-    list(APPEND KINETO_COMPILE_OPTIONS "-DHAS_CUPTI")
-endif()
-if (NOT LIBKINETO_NOROCTRACER)
-    target_compile_options(kineto_base PRIVATE "-DHAS_ROCTRACER")
-    target_compile_options(kineto_base PRIVATE "-D__HIP_PLATFORM_HCC__")
-    target_compile_options(kineto_base PRIVATE "-D__HIP_PLATFORM_AMD__")
-endif()
-
-target_compile_options(kineto_base PRIVATE "${KINETO_COMPILE_OPTIONS}")
-target_compile_options(kineto_api PRIVATE "${KINETO_COMPILE_OPTIONS}")
-
-if(NOT TARGET fmt)
-  if(NOT FMT_SOURCE_DIR)
-    set(FMT_SOURCE_DIR "${LIBKINETO_THIRDPARTY_DIR}/fmt"
-      CACHE STRING "fmt source directory from submodules")
-  endif()
-
-  # Build FMT.
-  # FMT and some other libraries use BUILD_SHARED_LIBS to control
-  # the library type.
-  # Save and restore the value after configuring FMT
-  set(TEMP_BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS})
-  set(BUILD_SHARED_LIBS OFF CACHE BOOL "Build shared libs" FORCE)
-  set(FMT_LIBRARY_TYPE static CACHE STRING "Set lib type to static")
-  add_subdirectory("${FMT_SOURCE_DIR}" "${LIBKINETO_BINARY_DIR}/fmt")
-  set_property(TARGET fmt PROPERTY POSITION_INDEPENDENT_CODE ON)
-  set(BUILD_SHARED_LIBS ${TEMP_BUILD_SHARED_LIBS} CACHE BOOL "Build shared libs" FORCE)
-endif()
-
-set(FMT_INCLUDE_DIR "${FMT_SOURCE_DIR}/include")
-message(STATUS "Kineto: FMT_SOURCE_DIR = ${FMT_SOURCE_DIR}")
-message(STATUS "Kineto: FMT_INCLUDE_DIR = ${FMT_INCLUDE_DIR}")
-if (NOT CUPTI_INCLUDE_DIR)
-    set(CUPTI_INCLUDE_DIR "${CUDA_SOURCE_DIR}/extras/CUPTI/include")
-endif()
-if (NOT CUDA_INCLUDE_DIRS)
-    set(CUDA_INCLUDE_DIRS "${CUDA_SOURCE_DIR}/include")
-endif()
-if (NOT ROCTRACER_INCLUDE_DIR)
-    set(ROCTRACER_INCLUDE_DIR "${ROCM_SOURCE_DIR}/roctracer/include")
-endif()
-if (NOT ROCM_INCLUDE_DIRS)
-    set(ROCM_INCLUDE_DIRS "${ROCM_SOURCE_DIR}/include")
-endif()
-
-message(INFO " CUPTI_INCLUDE_DIR = ${CUPTI_INCLUDE_DIR}")
-message(INFO " ROCTRACER_INCLUDE_DIR = ${ROCTRACER_INCLUDE_DIR}")
-
-target_include_directories(kineto_base PUBLIC
-      $<BUILD_INTERFACE:${LIBKINETO_INCLUDE_DIR}>
-      $<BUILD_INTERFACE:${LIBKINETO_SOURCE_DIR}>
-      $<BUILD_INTERFACE:${FMT_INCLUDE_DIR}>
-      $<BUILD_INTERFACE:${CUPTI_INCLUDE_DIR}>
-      $<BUILD_INTERFACE:${CUDA_INCLUDE_DIRS}>
-      $<BUILD_INTERFACE:${ROCTRACER_INCLUDE_DIR}>
-      $<BUILD_INTERFACE:${ROCM_INCLUDE_DIRS}>)
-
-target_include_directories(kineto_api PUBLIC
-      $<BUILD_INTERFACE:${FMT_INCLUDE_DIR}>
-      $<BUILD_INTERFACE:${LIBKINETO_INCLUDE_DIR}>)
-
-if(KINETO_LIBRARY_TYPE STREQUAL "default")
-  add_library(kineto
-    $<TARGET_OBJECTS:kineto_base>
-    $<TARGET_OBJECTS:kineto_api>)
-elseif(KINETO_LIBRARY_TYPE STREQUAL "static")
-  add_library(kineto STATIC
-    $<TARGET_OBJECTS:kineto_base>
-    $<TARGET_OBJECTS:kineto_api>)
-elseif(KINETO_LIBRARY_TYPE STREQUAL "shared")
-  add_library(kineto SHARED
-    $<TARGET_OBJECTS:kineto_base>)
-  set_property(TARGET kineto_base PROPERTY POSITION_INDEPENDENT_CODE ON)
-  set_target_properties(kineto PROPERTIES
-    CXX_VISIBILITY_PRESET hidden)
-else()
-  message(FATAL_ERROR "Unsupported library type ${KINETO_LIBRARY_TYPE}")
-endif()
-
-if(NOT LIBKINETO_NOROCTRACER)
-  find_library(ROCTRACER_LIBRARY NAMES libroctracer64.so HINTS /opt/rocm/roctracer/lib)
-  target_link_libraries(kineto "${ROCTRACER_LIBRARY}")
-  find_library(KINETO_HIP_LIBRARY NAMES libamdhip64.so HINTS /opt/rocm/lib)
-  target_link_libraries(kineto "${KINETO_HIP_LIBRARY}")
-endif()
-
-if(NOT LIBKINETO_NOCUPTI)
-  target_link_libraries(kineto "${CUDA_cupti_LIBRARY}")
-endif()
-target_link_libraries(kineto $<BUILD_INTERFACE:fmt::fmt-header-only>)
-add_dependencies(kineto fmt::fmt-header-only)
-
-install(TARGETS kineto EXPORT kinetoLibraryConfig
-  ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR})
-
-install(FILES ${LIBKINETO_PUBLIC_HEADERS}
-  DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}/kineto")
-
-install(EXPORT kinetoLibraryConfig DESTINATION share/cmake/kineto
-  FILE kinetoLibraryConfig.cmake)
-
-if(KINETO_BUILD_TESTS)
-  add_subdirectory(test)
-endif()
diff --git a/plugins/tensorboard-plugins/libkineto/README.md b/plugins/tensorboard-plugins/libkineto/README.md
deleted file mode 100644
index 37127ca5aa8..00000000000
--- a/plugins/tensorboard-plugins/libkineto/README.md
+++ /dev/null
@@ -1,65 +0,0 @@
-# Libkineto
-
-Libkineto is an in-process profiling library, part of the Kineto performance
-tools project.
-
-The library provides a way to collect GPU traces and metrics from the host
-process, either via the library public API or by sending a signal, if enabled.
-
-Currently only NVIDIA GPUs are supported.
-
-## Build Notes
-Libkineto uses the standard CMAKE-based build flow.
-
-### Dependencies
-Libkineto requires gcc 5+ and:
-
-- NVIDIA CUPTI: used to collect traces and metrics from NVIDIA GPUs.
-- fmt: used for its convenient and lightweight string formatting functionality.
-- googletest: required to build and run Kineto's tests.
-  - **googletest is not required** if you don't want to run Kineto tests.
-By default, building of tests is **on**. Turn it off by setting `KINETO_BUILD_TESTS` to **off**.
-
-You can download [NVIDIA CUPTI][1], [fmt][2], [googletest][3] and set
-`CUDA_SOURCE_DIR`, `FMT_SOURCE_DIR`, `GOOGLETEST_SOURCE_DIR` respectively for
-cmake to find these libraries. If the fmt and googletest variables are not set, cmake will
-build the git submodules found in the `third_party` directory.
-If `CUDA_SOURCE_DIR` is not set, libkineto will fail to build.
-
-### Building Libkineto
-
-```
-# Check out repo and sub modules
-git clone --recursive https://github.com/pytorch/kineto.git
-# Build libkineto with cmake
-cd kineto/libkineto
-mkdir build && cd build
-cmake ..
-make
-```
-
-To run the tests after building libkineto (if tests are built), use the following
-command:
-```
-make test
-```
-
-### Installing Libkineto
-```
-make install
-```
-
-## How Libkineto works
-We will provide a high-level overview, design philosophy and brief descriptions of various
-parts of Libkineto in upcoming blogs.
-
-## Full documentation
-We strive to keep our source files readable. The best and up-to-date
-documentation is available in the source files.
-
-## License
-Libkineto is BSD licensed, as detailed in the [LICENSE](../LICENSE) file.
-
-[1]:https://developer.nvidia.com/CUPTI-CTK10_2
-[2]:https://github.com/fmt
-[3]:https://github.com/google/googletest
diff --git a/plugins/tensorboard-plugins/libkineto/include/AbstractConfig.h b/plugins/tensorboard-plugins/libkineto/include/AbstractConfig.h
deleted file mode 100644
index 1cadf4906c1..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/AbstractConfig.h
+++ /dev/null
@@ -1,113 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <chrono>
-#include <map>
-#include <memory>
-#include <string>
-#include <vector>
-
-namespace KINETO_NAMESPACE {
-
-class AbstractConfig {
- public:
-  AbstractConfig& operator=(const AbstractConfig&) = delete;
-  AbstractConfig(AbstractConfig&&) = delete;
-  AbstractConfig& operator=(AbstractConfig&&) = delete;
-
-  virtual ~AbstractConfig() {
-    for (const auto& p : featureConfigs_) {
-      delete p.second;
-    }
-  }
-
-  // Return a copy of the full derived class
-  virtual AbstractConfig* cloneDerived(AbstractConfig& parent) const = 0;
-
-  // Returns true if successfully parsed the config string
-  bool parse(const std::string& conf);
-
-  // Default setup for signal-triggered profiling
-  virtual void setSignalDefaults() {
-    for (auto& p : featureConfigs_) {
-      p.second->setSignalDefaults();
-    }
-  }
-
-  // Default setup for client-triggered profiling
-  virtual void setClientDefaults() {
-    for (auto& p : featureConfigs_) {
-      p.second->setClientDefaults();
-    }
-  }
-
-  // Time config was created / updated
-  std::chrono::time_point<std::chrono::system_clock> timestamp() const {
-    return timestamp_;
-  }
-
-  // Source config string that this was parsed from
-  const std::string& source() const {
-    return source_;
-  }
-
-  AbstractConfig& feature(std::string name) const {
-    const auto& pos = featureConfigs_.find(name);
-    return *pos->second;
-  }
-
-  // Transfers ownership of cfg arg
-  void addFeature(const std::string& name, AbstractConfig* cfg) {
-    featureConfigs_[name] = cfg;
-  }
-
- protected:
-  AbstractConfig() {}
-  AbstractConfig(const AbstractConfig& other) = default;
-
-  // Return true if the option was recognized and successfully parsed.
-  // Throw std::invalid_argument if val is invalid.
-  virtual bool handleOption(const std::string& name, std::string& val);
-
-  // Perform post-validation checks, typically conditons involving
-  // multiple options.
-  // Throw std::invalid_argument if automatic correction can not be made.
-  //
-  // @param fallbackProfileStartTime Specify a fallback profile start timestamp in case it was never specified by the client
-  virtual void validate(const std::chrono::time_point<std::chrono::system_clock>& fallbackProfileStartTime) = 0;
-
-  // TODO: Separate out each profiler type into features?
-  virtual void printActivityProfilerConfig(std::ostream& s) const;
-
-  // Helpers for use in handleOption
-  // Split a string by delimiter and remove external white space
-  std::vector<std::string> splitAndTrim(const std::string& s, char delim) const;
-  // Lowercase for case-insensitive comparisons
-  std::string toLower(std::string& s) const;
-  // Does string end with suffix
-  bool endsWith(const std::string& s, const std::string& suffix) const;
-  // Conversions
-  int64_t toIntRange(const std::string& val, int64_t min, int64_t max) const;
-  int32_t toInt32(const std::string& val) const;
-  int64_t toInt64(const std::string& val) const;
-  bool toBool(std::string& val) const;
-
-  void cloneFeaturesInto(AbstractConfig& cfg) const {
-    for (const auto& feature : featureConfigs_) {
-      cfg.featureConfigs_[feature.first] = feature.second->cloneDerived(cfg);
-    }
-  }
-
- private:
-  // Time config was created / updated
-  std::chrono::time_point<std::chrono::system_clock> timestamp_{};
-
-  // Original configuration string, used for comparison
-  std::string source_{""};
-
-  // Configuration objects for optional features
-  std::map<std::string, AbstractConfig*> featureConfigs_{};
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/include/ActivityProfilerInterface.h b/plugins/tensorboard-plugins/libkineto/include/ActivityProfilerInterface.h
deleted file mode 100644
index 29871e47ab8..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/ActivityProfilerInterface.h
+++ /dev/null
@@ -1,91 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <memory>
-#include <set>
-#include <thread>
-#include <vector>
-
-#include "ActivityType.h"
-#include "ActivityTraceInterface.h"
-#include "IActivityProfiler.h"
-
-namespace libkineto {
-
-class ActivityProfilerController;
-struct CpuTraceBuffer;
-class Config;
-
-class ActivityProfilerInterface {
-
- public:
-  virtual ~ActivityProfilerInterface() {};
-
-  virtual void init() {}
-  virtual bool isInitialized() {
-    return false;
-  }
-  virtual bool isActive(){
-    return false;
-  }
-
-  // *** Asynchronous API ***
-  // Instead of starting and stopping the trace manually, provide a start time
-  // and duration and / or iteration stop criterion.
-  // Tracing terminates when either condition is met.
-  virtual void scheduleTrace(const std::string& configStr) {}
-
-  // *** Synchronous API ***
-  // These must be called in order:
-  // prepareTrace -> startTrace -> stopTrace.
-
-  // Many tracing structures are lazily initialized during trace collection,
-  // with potentially high overhead.
-  // Call prepareTrace to enable tracing, then run the region to trace
-  // at least once (and ideally run the same code that is to be traced) to
-  // allow tracing structures to be initialized.
-  virtual void prepareTrace(
-      const std::set<ActivityType>& activityTypes,
-      const std::string& configStr = "") {}
-
-  // Start recording, potentially reusing any buffers allocated since
-  // prepareTrace was called.
-  virtual void startTrace() {}
-
-  // Stop and process trace, producing an in-memory list of trace records.
-  // The processing will be done synchronously (using the calling thread.)
-  virtual std::unique_ptr<ActivityTraceInterface> stopTrace() {
-    return nullptr;
-  }
-
-  // Re-evaluate internal state to allow for triggering operations based
-  // on number of iteration. each implicitly increments the iteration count
-  virtual void step() {}
-
-  // *** TraceActivity API ***
-  // FIXME: Pass activityProfiler interface into clientInterface?
-  virtual void pushCorrelationId(uint64_t id){}
-  virtual void popCorrelationId(){}
-  virtual void transferCpuTrace(
-      std::unique_ptr<CpuTraceBuffer> traceBuffer){}
-
-  // Correlation ids for user defined spans
-  virtual void pushUserCorrelationId(uint64_t){}
-  virtual void popUserCorrelationId(){}
-
-  // Saves information for the current thread to be used in profiler output
-  // Client must record any new kernel thread where the activity has occured.
-  virtual void recordThreadInfo() {}
-
-  // Record trace metadata, currently supporting only string key and values,
-  // values with the same key are overwritten
-  virtual void addMetadata(const std::string& key, const std::string& value) = 0;
-
-  // Add a child activity profiler, this enables frameworks in the application
-  // to enable custom framework events.
-  virtual void addChildActivityProfiler(
-      std::unique_ptr<IActivityProfiler> profiler) {}
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/ActivityTraceInterface.h b/plugins/tensorboard-plugins/libkineto/include/ActivityTraceInterface.h
deleted file mode 100644
index 23d4edab00c..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/ActivityTraceInterface.h
+++ /dev/null
@@ -1,21 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <memory>
-#include <string>
-
-namespace libkineto {
-
-struct ITraceActivity;
-
-class ActivityTraceInterface {
- public:
-  virtual ~ActivityTraceInterface() {}
-  virtual const std::vector<const ITraceActivity*>* activities() {
-    return nullptr;
-  }
-  virtual void save(const std::string& path) {}
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/ActivityType.h b/plugins/tensorboard-plugins/libkineto/include/ActivityType.h
deleted file mode 100644
index 74c6a2531d6..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/ActivityType.h
+++ /dev/null
@@ -1,34 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <array>
-#include <string>
-
-namespace libkineto {
-
-enum class ActivityType {
-    CPU_OP = 0, // cpu side ops
-    USER_ANNOTATION,
-    GPU_USER_ANNOTATION,
-    GPU_MEMCPY,
-    GPU_MEMSET,
-    CONCURRENT_KERNEL, // on-device kernels
-    EXTERNAL_CORRELATION,
-    CUDA_RUNTIME, // host side cuda runtime events
-    CUDA_PROFILER_RANGE, // CUPTI Profiler range for performance metrics
-    GLOW_RUNTIME, // host side glow runtime events
-    CPU_INSTANT_EVENT, // host side point-like events
-    PYTHON_FUNCTION,
-    OVERHEAD, // CUPTI induced overhead events sampled from its overhead API.
-    ENUM_COUNT // This is to add buffer and not used for any profiling logic. Add your new type before it.
-};
-
-const char* toString(ActivityType t);
-ActivityType toActivityType(const std::string& str);
-
-// Return an array of all activity types except COUNT
-constexpr int activityTypeCount = (int)ActivityType::ENUM_COUNT;
-const std::array<ActivityType, activityTypeCount> activityTypes();
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/ClientInterface.h b/plugins/tensorboard-plugins/libkineto/include/ClientInterface.h
deleted file mode 100644
index 06dc0758381..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/ClientInterface.h
+++ /dev/null
@@ -1,16 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-namespace libkineto {
-
-class ClientInterface {
- public:
-  virtual ~ClientInterface() {}
-  virtual void init() = 0;
-  virtual void warmup(bool setupOpInputsCollection) = 0;
-  virtual void start() = 0;
-  virtual void stop() = 0;
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/Config.h b/plugins/tensorboard-plugins/libkineto/include/Config.h
deleted file mode 100644
index 040e96c9f75..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/Config.h
+++ /dev/null
@@ -1,433 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include "AbstractConfig.h"
-#include "ActivityType.h"
-
-#include <assert.h>
-#include <chrono>
-#include <functional>
-#include <set>
-#include <string>
-#include <vector>
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-
-class Config : public AbstractConfig {
- public:
-  Config();
-  Config& operator=(const Config&) = delete;
-  Config(Config&&) = delete;
-  Config& operator=(Config&&) = delete;
-
-  // Return a full copy including feature config object
-  std::unique_ptr<Config> clone() const {
-    auto cfg = std::unique_ptr<Config>(new Config(*this));
-    cloneFeaturesInto(*cfg);
-    return cfg;
-  }
-
-  bool handleOption(const std::string& name, std::string& val) override;
-
-  void setClientDefaults() override;
-
-  // Log events to this file
-  const std::string& eventLogFile() const {
-    return eventLogFile_;
-  }
-
-  bool activityProfilerEnabled() const {
-    return activityProfilerEnabled_ ||
-      activitiesOnDemandTimestamp_.time_since_epoch().count() > 0;
-  }
-
-  // Log activitiy trace to this file
-  const std::string& activitiesLogFile() const {
-    return activitiesLogFile_;
-  }
-
-  // Log activitiy trace to this url
-  const std::string& activitiesLogUrl() const {
-    return activitiesLogUrl_;
-  }
-
-  void setActivitiesLogUrl(const std::string& url) {
-    activitiesLogUrl_ = url;
-  }
-
-  bool activitiesLogToMemory() const {
-    return activitiesLogToMemory_;
-  }
-
-  // Is profiling enabled for the given device?
-  bool eventProfilerEnabledForDevice(uint32_t dev) const {
-    return 0 != (eventProfilerDeviceMask_ & (1 << dev));
-  }
-
-  // Take a sample (read hardware counters) at this frequency.
-  // This controls how often counters are read - if all counters cannot
-  // be collected simultaneously then multiple samples are needed to
-  // collect all requested counters - see multiplex period.
-  std::chrono::milliseconds samplePeriod() const {
-    return samplePeriod_;
-  }
-
-  void setSamplePeriod(std::chrono::milliseconds period) {
-    samplePeriod_ = period;
-  }
-
-  // When all requested counters cannot be collected simultaneously,
-  // counters will be multiplexed at this frequency.
-  // Multiplexing can have a large performance impact if done frequently.
-  // To avoid a perf impact, keep this at 1s or above.
-  std::chrono::milliseconds multiplexPeriod() const {
-    return multiplexPeriod_;
-  }
-
-  void setMultiplexPeriod(std::chrono::milliseconds period) {
-    multiplexPeriod_ = period;
-  }
-
-  // Report counters at this frequency. Note that several samples can
-  // be reported each time, see samplesPerReport.
-  std::chrono::milliseconds reportPeriod() const {
-    return reportPeriod_;
-  }
-
-  void setReportPeriod(std::chrono::milliseconds msecs);
-
-  // Number of samples dispatched each report period.
-  // Must be in the range [1, report period / sample period].
-  // In other words, aggregation is supported but not interpolation.
-  int samplesPerReport() const {
-    return samplesPerReport_;
-  }
-
-  void setSamplesPerReport(int count) {
-    samplesPerReport_ = count;
-  }
-
-  // The names of events to collect
-  const std::set<std::string>& eventNames() const {
-    return eventNames_;
-  }
-
-  // Add additional events to be profiled
-  void addEvents(const std::set<std::string>& names) {
-    eventNames_.insert(names.begin(), names.end());
-  }
-
-  // The names of metrics to collect
-  const std::set<std::string>& metricNames() const {
-    return metricNames_;
-  }
-
-  // Add additional metrics to be profiled
-  void addMetrics(const std::set<std::string>& names) {
-    metricNames_.insert(names.begin(), names.end());
-  }
-
-  const std::vector<int>& percentiles() const {
-    return eventReportPercentiles_;
-  }
-
-  // Profile for this long, then revert to base config
-  std::chrono::seconds eventProfilerOnDemandDuration() const {
-    return eventProfilerOnDemandDuration_;
-  }
-
-  void setEventProfilerOnDemandDuration(std::chrono::seconds duration) {
-    eventProfilerOnDemandDuration_ = duration;
-  }
-
-  // Too many event profilers on a single system can overload the driver.
-  // At some point, latencies shoot through the roof and collection of samples
-  // becomes impossible. To avoid this situation we have a limit of profilers
-  // per GPU.
-  // NOTE: Communication with a daemon is needed for this feature.
-  // Library must be built with an active DaemonConfigLoader.
-  int maxEventProfilersPerGpu() const {
-    return eventProfilerMaxInstancesPerGpu_;
-  }
-
-  // On Cuda11 we've seen occasional hangs when reprogramming counters
-  // Monitor profiling threads and report when a thread is not responding
-  // for a given number of seconds.
-  // A period of 0 means disable.
-  std::chrono::seconds eventProfilerHeartbeatMonitorPeriod() const {
-    return eventProfilerHeartbeatMonitorPeriod_;
-  }
-
-  // The types of activities selected in the configuration file
-  const std::set<ActivityType>& selectedActivityTypes() const {
-    return selectedActivityTypes_;
-  }
-
-  void setSelectedActivityTypes(const std::set<ActivityType>& types) {
-    selectedActivityTypes_ = types;
-  }
-
-  bool isOpInputsCollectionEnabled() const {
-    return enableOpInputsCollection_;
-  }
-
-  // Trace for this long
-  std::chrono::milliseconds activitiesDuration() const {
-    return activitiesDuration_;
-  }
-
-  // Trace for this many iterations, determined by external API
-  int activitiesRunIterations() const {
-    return activitiesRunIterations_;
-  }
-
-  std::chrono::milliseconds activitiesDurationDefault() const;
-
-  void setActivitiesDuration(std::chrono::milliseconds duration) {
-    activitiesDuration_ = duration;
-  }
-
-  int activitiesMaxGpuBufferSize() const {
-    return activitiesMaxGpuBufferSize_;
-  }
-
-  std::chrono::seconds activitiesWarmupDuration() const {
-    return activitiesWarmupDuration_;
-  }
-
-  int activitiesWarmupIterations() const {
-    return activitiesWarmupIterations_;
-  }
-
-  // Timestamp at which the profiling to start, requested by the user.
-  const std::chrono::time_point<std::chrono::system_clock> requestTimestamp()
-      const {
-    if  (profileStartTime_.time_since_epoch().count()) {
-      return profileStartTime_;
-    }
-
-    // TODO(T94634890): Deperecate requestTimestamp
-    return requestTimestamp_ + maxRequestAge() + activitiesWarmupDuration();
-  }
-
-  bool hasProfileStartTime() const {
-    return requestTimestamp_.time_since_epoch().count() > 0 ||
-        profileStartTime_.time_since_epoch().count() > 0;
-  }
-
-  int profileStartIteration() const {
-    return profileStartIteration_;
-  }
-
-  bool hasProfileStartIteration() const {
-    return profileStartIteration_ >= 0 && activitiesRunIterations_ > 0;
-  }
-
-  void setProfileStartIteration(int iter) {
-    profileStartIteration_ = iter;
-  }
-
-  int profileStartIterationRoundUp() const {
-    return profileStartIterationRoundUp_;
-  }
-
-  // calculate the start iteration accounting for warmup
-  int startIterationIncludingWarmup() const {
-    if (!hasProfileStartIteration()) {
-      return -1;
-    }
-    return profileStartIteration_ - activitiesWarmupIterations_;
-  }
-
-  const std::chrono::seconds maxRequestAge() const;
-
-  // All VLOG* macros will log if the verbose log level is >=
-  // the verbosity specified for the verbose log message.
-  // Default value is -1, so messages with log level 0 will log by default.
-  int verboseLogLevel() const {
-    return verboseLogLevel_;
-  }
-
-  // Modules for which verbose logging is enabled.
-  // If empty, logging is enabled for all modules.
-  const std::vector<std::string>& verboseLogModules() const {
-    return verboseLogModules_;
-  }
-
-  bool sigUsr2Enabled() const {
-    return enableSigUsr2_;
-  }
-
-  bool ipcFabricEnabled() const {
-    return enableIpcFabric_;
-  }
-
-  static std::chrono::milliseconds alignUp(
-      std::chrono::milliseconds duration,
-      std::chrono::milliseconds alignment) {
-    duration += alignment;
-    return duration - (duration % alignment);
-  }
-
-  std::chrono::time_point<std::chrono::system_clock>
-  eventProfilerOnDemandStartTime() const {
-    return eventProfilerOnDemandTimestamp_;
-  }
-
-  std::chrono::time_point<std::chrono::system_clock>
-  eventProfilerOnDemandEndTime() const {
-    return eventProfilerOnDemandTimestamp_ + eventProfilerOnDemandDuration_;
-  }
-
-  std::chrono::time_point<std::chrono::system_clock>
-  activityProfilerRequestReceivedTime() const {
-    return activitiesOnDemandTimestamp_;
-  }
-
-  // Users may request and set trace id and group trace id.
-  const std::string& requestTraceID() const {
-    return requestTraceID_;
-  }
-
-  void setRequestTraceID(const std::string& tid) {
-    requestTraceID_ = tid;
-  }
-
-  const std::string& requestGroupTraceID() const {
-    return requestGroupTraceID_;
-  }
-
-  void setRequestGroupTraceID(const std::string& gtid) {
-    requestGroupTraceID_ = gtid;
-  }
-
-  void updateActivityProfilerRequestReceivedTime();
-
-  void printActivityProfilerConfig(std::ostream& s) const override;
-
-  void validate(
-      const std::chrono::time_point<std::chrono::system_clock>& fallbackProfileStartTime) override;
-
-  static void addConfigFactory(
-      std::string name,
-      std::function<AbstractConfig*(Config&)> factory);
-
-  void print(std::ostream& s) const;
-
- private:
-  explicit Config(const Config& other) = default;
-
-  AbstractConfig* cloneDerived(AbstractConfig& parent) const override {
-    // Clone from AbstractConfig not supported
-    assert(false);
-    return nullptr;
-  }
-
-  uint8_t createDeviceMask(const std::string& val);
-
-  // Adds valid activity types from the user defined string list in the
-  // configuration file
-  void setActivityTypes(const std::vector<std::string>& selected_activities);
-
-  // Sets the default activity types to be traced
-  void selectDefaultActivityTypes() {
-    // If the user has not specified an activity list, add all types
-    for (ActivityType t : activityTypes()) {
-      // Do no enable this by default
-      // TODO: introduce optional types
-      if (t != ActivityType::OVERHEAD) {
-        selectedActivityTypes_.insert(t);
-      }
-    }
-  }
-
-  int verboseLogLevel_;
-  std::vector<std::string> verboseLogModules_;
-
-  // Event profiler
-  // These settings are also supported in on-demand mode
-  std::chrono::milliseconds samplePeriod_;
-  std::chrono::milliseconds reportPeriod_;
-  int samplesPerReport_;
-  std::set<std::string> eventNames_;
-  std::set<std::string> metricNames_;
-
-  // On-demand duration
-  std::chrono::seconds eventProfilerOnDemandDuration_;
-  // Last on-demand request
-  std::chrono::time_point<std::chrono::system_clock>
-      eventProfilerOnDemandTimestamp_;
-
-  int eventProfilerMaxInstancesPerGpu_;
-
-  // Monitor whether event profiler threads are stuck
-  // at this frequency
-  std::chrono::seconds eventProfilerHeartbeatMonitorPeriod_;
-
-  // These settings can not be changed on-demand
-  std::string eventLogFile_;
-  std::vector<int> eventReportPercentiles_ = {5, 25, 50, 75, 95};
-  uint8_t eventProfilerDeviceMask_ = ~0;
-  std::chrono::milliseconds multiplexPeriod_;
-
-  // Activity profiler
-  bool activityProfilerEnabled_;
-  std::set<ActivityType> selectedActivityTypes_;
-
-  // The activity profiler settings are all on-demand
-  std::string activitiesLogFile_;
-
-  std::string activitiesLogUrl_;
-
-  // Log activities to memory buffer
-  bool activitiesLogToMemory_{false};
-
-  int activitiesMaxGpuBufferSize_;
-  std::chrono::seconds activitiesWarmupDuration_;
-  int activitiesWarmupIterations_;
-
-  // Client Interface
-  // Enable inputs collection when tracing ops
-  bool enableOpInputsCollection_{true};
-
-  // Profile for specified iterations and duration
-  std::chrono::milliseconds activitiesDuration_;
-  int activitiesRunIterations_;
-
-  // Below are not used
-  // Use this net name for iteration count
-  std::string activitiesExternalAPIIterationsTarget_;
-  // Only profile nets that includes this in the name
-  std::vector<std::string> activitiesExternalAPIFilter_;
-  // Only profile nets with at least this many operators
-  int activitiesExternalAPINetSizeThreshold_;
-  // Only profile nets with at least this many GPU operators
-  int activitiesExternalAPIGpuOpCountThreshold_;
-  // Last activity profiler request
-  std::chrono::time_point<std::chrono::system_clock>
-      activitiesOnDemandTimestamp_;
-
-  // Synchronized start timestamp
-  std::chrono::time_point<std::chrono::system_clock> profileStartTime_;
-  // or start iteration
-  int profileStartIteration_;
-  int profileStartIterationRoundUp_;
-
-  // DEPRECATED
-  std::chrono::time_point<std::chrono::system_clock> requestTimestamp_;
-
-  // Enable profiling via SIGUSR2
-  bool enableSigUsr2_;
-
-  // Enable IPC Fabric instead of thrift communication
-  bool enableIpcFabric_;
-
-  // Logger Metadata
-  std::string requestTraceID_;
-  std::string requestGroupTraceID_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/include/GenericTraceActivity.h b/plugins/tensorboard-plugins/libkineto/include/GenericTraceActivity.h
deleted file mode 100644
index 4272cf1efa4..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/GenericTraceActivity.h
+++ /dev/null
@@ -1,125 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <fmt/format.h>
-#include <string>
-#include <thread>
-#include <vector>
-
-#include "ThreadUtil.h"
-#include "ITraceActivity.h"
-#include "TraceSpan.h"
-
-namespace libkineto {
-
-// Link type, used in GenericTraceActivity.flow.type
-constexpr unsigned int kLinkFwdBwd = 1;
-constexpr unsigned int kLinkAsyncCpuGpu = 2;
-
-// @lint-ignore-every CLANGTIDY cppcoreguidelines-non-private-member-variables-in-classes
-// @lint-ignore-every CLANGTIDY cppcoreguidelines-pro-type-member-init
-class GenericTraceActivity : public ITraceActivity {
-
- public:
-  GenericTraceActivity() : activityType(ActivityType::ENUM_COUNT), traceSpan_(NULL) {}
-
-  GenericTraceActivity(
-      const TraceSpan& trace, ActivityType type, const std::string& name)
-      : activityType(type), activityName(name), traceSpan_(&trace) {
-  }
-
-  int64_t deviceId() const override {
-    return device;
-  }
-
-  int64_t resourceId() const override {
-    return resource;
-  }
-
-  int32_t getThreadId() const override {
-    return threadId;
-  }
-
-  int64_t timestamp() const override {
-    return startTime;
-  }
-
-  int64_t duration() const override {
-    return endTime - startTime;
-  }
-
-  int64_t correlationId() const override {
-    return id;
-  }
-
-  ActivityType type() const override {
-    return activityType;
-  }
-
-  const ITraceActivity* linkedActivity() const override {
-    return nullptr;
-  }
-
-  int flowType() const override {
-    return flow.type;
-  }
-
-  int flowId() const override {
-    return flow.id;
-  }
-
-  bool flowStart() const override {
-    return flow.start;
-  }
-
-  const std::string name() const override {
-    return activityName;
-  }
-
-  const TraceSpan* traceSpan() const override {
-    return traceSpan_;
-  }
-
-  void log(ActivityLogger& logger) const override;
-
-  //Encode client side metadata as a key/value
-  template <typename ValType>
-  void addMetadata(const std::string& key, const ValType& value) {
-    metadata_.push_back(fmt::format("\"{}\": {}", key, value));
-  }
-
-  void addMetadataQuoted(const std::string& key, const std::string& value) {
-    metadata_.push_back(fmt::format("\"{}\": \"{}\"", key, value));
-  }
-
-  const std::string metadataJson() const override {
-    return fmt::format("{}", fmt::join(metadata_, ", "));
-  }
-
-  virtual ~GenericTraceActivity() {};
-
-  int64_t startTime{0};
-  int64_t endTime{0};
-  int32_t id{0};
-  int32_t device{0};
-  int32_t resource{0};
-  int32_t threadId{0};
-  ActivityType activityType;
-  std::string activityName;
-  struct Flow {
-    Flow(): id(0), type(0), start(0) {}
-    // Ids must be unique within each type
-    uint32_t id : 27;
-    // Type will be used to connect flows between profilers, as
-    // well as look up flow information (name etc)
-    uint32_t type : 4;
-    uint32_t start : 1;
-  } flow;
-
- private:
-  const TraceSpan* traceSpan_;
-  std::vector<std::string> metadata_;
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/IActivityProfiler.h b/plugins/tensorboard-plugins/libkineto/include/IActivityProfiler.h
deleted file mode 100644
index f5d4b3fb828..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/IActivityProfiler.h
+++ /dev/null
@@ -1,104 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <memory>
-#include <set>
-#include <vector>
-
-#include "Config.h"
-#include "GenericTraceActivity.h"
-
-/* This file includes an abstract base class for an activity profiler
- * that can be implemented by multiple tracing agents in the application.
- * The high level Kineto profiler can co-ordinate start and end of tracing
- * and combine together events from multiple such activity profilers.
- */
-
-namespace libkineto {
-
-using namespace KINETO_NAMESPACE;
-
-#ifdef _MSC_VER
-// workaround for the predefined ERROR macro on Windows
-#undef ERROR
-#endif // _MSC_VER
-
-enum class TraceStatus {
-  READY, // Accepting trace requests
-  WARMUP, // Performing trace warmup
-  RECORDING, // Actively collecting activities
-  PROCESSING, // Recording is complete, preparing results
-  ERROR, // One or more errors (and possibly also warnings) occurred.
-  WARNING, // One or more warnings occurred.
-};
-
-/* IActivityProfilerSession:
- *   an opaque object that can be used by a high level profiler to
- *   start/stop and return trace events.
- */
-class IActivityProfilerSession {
-
- public:
-  virtual ~IActivityProfilerSession() {}
-
-  // start the trace collection synchronously
-  virtual void start() = 0;
-
-  // stop the trace collection synchronously
-  virtual void stop() = 0;
-
-  TraceStatus status() {
-    return status_;
-  }
-
-  // returns list of Trace Activities
-  virtual std::vector<GenericTraceActivity>& activities() = 0;
-
-  // returns errors with this trace
-  virtual std::vector<std::string> errors() = 0;
-
-  // processes trace activities using logger
-  virtual void processTrace(ActivityLogger& logger) = 0;
-
-  // XXX define trace formats
-  // virtual save(string name, TraceFormat format)
-
- protected:
-  TraceStatus status_ = TraceStatus::READY;
-};
-
-
-/* Activity Profiler Plugins:
- *   These allow other frameworks to integrate into Kineto's primariy
- *   activity profiler. While the primary activity profiler handles
- *   timing the trace collections and correlating events the plugins
- *   can become source of new trace activity types.
- */
-class IActivityProfiler {
-
- public:
-
-  virtual ~IActivityProfiler() {}
-
-  // name of profiler
-  virtual const std::string& name() const = 0;
-
-  // returns activity types this profiler supports
-  virtual const std::set<ActivityType>& availableActivities() const = 0;
-
-  // Calls prepare() on registered tracer providers passing in the relevant
-  // activity types. Returns a profiler session handle
-  virtual std::unique_ptr<IActivityProfilerSession> configure(
-      const std::set<ActivityType>& activity_types,
-      const Config& config) = 0;
-
-  // asynchronous version of the above with future timestamp and duration.
-  virtual std::unique_ptr<IActivityProfilerSession> configure(
-      int64_t ts_ms,
-      int64_t duration_ms,
-      const std::set<ActivityType>& activity_types,
-      const Config& config) = 0;
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/ILoggerObserver.h b/plugins/tensorboard-plugins/libkineto/include/ILoggerObserver.h
deleted file mode 100644
index 4fce7851b96..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/ILoggerObserver.h
+++ /dev/null
@@ -1,50 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <string>
-
-// Stages in libkineto used when pushing logs to UST Logger.
-constexpr char kWarmUpStage[] = "Warm Up";
-constexpr char kCollectionStage[] = "Collection";
-constexpr char kPostProcessingStage[] = "Post Processing";
-
-#if !USE_GOOGLE_LOG
-
-#include <map>
-#include <vector>
-
-namespace libkineto {
-
-enum LoggerOutputType {
-  VERBOSE = 0,
-  INFO = 1,
-  WARNING = 2,
-  ERROR = 3,
-  STAGE = 4,
-  ENUM_COUNT = 5
-};
-
-const char* toString(LoggerOutputType t);
-LoggerOutputType toLoggerOutputType(const std::string& str);
-
-constexpr int LoggerTypeCount = (int) LoggerOutputType::ENUM_COUNT;
-
-class ILoggerObserver {
- public:
-  virtual ~ILoggerObserver() = default;
-  virtual void write(const std::string& message, LoggerOutputType ot) = 0;
-  virtual const std::map<LoggerOutputType, std::vector<std::string>> extractCollectorMetadata() = 0;
-  virtual void reset() = 0;
-  virtual void addDevice(const int64_t device) = 0;
-  virtual void setTraceDurationMS(const int64_t duration) = 0;
-  virtual void addEventCount(const int64_t count) = 0;
-  virtual void setTraceID(const std::string&) {}
-  virtual void setGroupTraceID(const std::string&) {}
-  virtual void addDestination(const std::string& dest) = 0;
-
-};
-
-} // namespace libkineto
-
-#endif // !USE_GOOGLE_LOG
diff --git a/plugins/tensorboard-plugins/libkineto/include/ITraceActivity.h b/plugins/tensorboard-plugins/libkineto/include/ITraceActivity.h
deleted file mode 100644
index a477ed81466..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/ITraceActivity.h
+++ /dev/null
@@ -1,53 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <string>
-
-#include "ActivityType.h"
-
-namespace libkineto {
-
-class ActivityLogger;
-struct TraceSpan;
-
-// Generic activity interface is borrowed from tensorboard protobuf format.
-struct ITraceActivity {
-  virtual ~ITraceActivity() {}
-  // Device is a physical or logical entity, e.g. CPU, GPU or process
-  virtual int64_t deviceId() const = 0;
-  // A resource is something on the device, h/w thread,
-  // functional units etc.
-  virtual int64_t resourceId() const = 0;
-  // s/w thread
-  virtual int32_t getThreadId() const = 0;
-  // Start timestamp in mucrosecond
-  virtual int64_t timestamp() const = 0;
-  // Duration in microseconds
-  virtual int64_t duration() const = 0;
-  // Used to link up async activities
-  virtual int64_t correlationId() const = 0;
-  // Part of a flow, identified by flow id and type
-  virtual int flowType() const = 0;
-  virtual int flowId() const = 0;
-  virtual bool flowStart() const = 0;
-  virtual ActivityType type() const = 0;
-  virtual const std::string name() const = 0;
-  // Optional linked activity
-  virtual const ITraceActivity* linkedActivity() const = 0;
-  // Optional containing trace object
-  virtual const TraceSpan* traceSpan() const = 0;
-  // Log activity
-  virtual void log(ActivityLogger& logger) const = 0;
-  // Return json formatted metadata
-  // FIXME: Return iterator to dynamic type map here instead
-  virtual const std::string metadataJson() const = 0;
-
-  static int64_t nsToUs(int64_t ns) {
-    // It's important that this conversion is the same everywhere.
-    // No rounding!
-    return ns / 1000;
-  }
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/ThreadUtil.h b/plugins/tensorboard-plugins/libkineto/include/ThreadUtil.h
deleted file mode 100644
index d1dc80ad2ab..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/ThreadUtil.h
+++ /dev/null
@@ -1,22 +0,0 @@
-#pragma once
-
-#include <cstdint>
-#include <string>
-#include <utility>
-#include <vector>
-
-namespace libkineto {
-
-int32_t systemThreadId();
-int32_t threadId();
-bool setThreadName(const std::string& name);
-std::string getThreadName();
-
-int32_t processId();
-std::string processName(int32_t pid);
-
-// Return a list of pids and process names for the current process
-// and its parents.
-std::vector<std::pair<int32_t, std::string>> pidCommandPairsOfAncestors();
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/TraceSpan.h b/plugins/tensorboard-plugins/libkineto/include/TraceSpan.h
deleted file mode 100644
index af9a9d5ee55..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/TraceSpan.h
+++ /dev/null
@@ -1,36 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <atomic>
-#include <string>
-#include <thread>
-
-namespace libkineto {
-
-struct TraceSpan {
-  TraceSpan() = delete;
-  TraceSpan(
-      int64_t startTime, int64_t endTime, std::string name)
-      : startTime(startTime), endTime(endTime), name(std::move(name)) {
-  }
-  TraceSpan(
-      int opCount, int it, std::string name, std::string prefix)
-      : opCount(opCount),
-        iteration(it),
-        name(std::move(name)),
-        prefix(std::move(prefix)) {
-  }
-
-  // FIXME: change to duration?
-  int64_t startTime{0};
-  int64_t endTime{0};
-  int opCount{0};
-  int iteration{-1};
-  // Name is used to identify timeline
-  std::string name;
-  // Prefix used to distinguish trace spans on the same timeline
-  std::string prefix;
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/libkineto.h b/plugins/tensorboard-plugins/libkineto/include/libkineto.h
deleted file mode 100644
index 87c3d64f638..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/libkineto.h
+++ /dev/null
@@ -1,138 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-// Mediator for initialization and profiler control
-
-#pragma once
-
-#include <atomic>
-#include <chrono>
-#include <functional>
-#include <memory>
-#include <mutex>
-#include <string>
-#include <set>
-#include <thread>
-#include <vector>
-#include <deque>
-
-#include "ActivityProfilerInterface.h"
-#include "ActivityType.h"
-#include "ClientInterface.h"
-#include "GenericTraceActivity.h"
-#include "TraceSpan.h"
-#include "IActivityProfiler.h"
-#include "ActivityTraceInterface.h"
-
-#include "ThreadUtil.h"
-
-extern "C" {
-  void suppressLibkinetoLogMessages();
-  int InitializeInjection(void);
-  bool libkineto_init(bool cpuOnly, bool logOnError);
-}
-
-namespace libkineto {
-
-class Config;
-class ConfigLoader;
-
-struct CpuTraceBuffer {
-  TraceSpan span{0, 0, "none"};
-  int gpuOpCount;
-  std::deque<GenericTraceActivity> activities;
-};
-
-using ChildActivityProfilerFactory =
-  std::function<std::unique_ptr<IActivityProfiler>()>;
-
-class LibkinetoApi {
- public:
-
-  explicit LibkinetoApi(ConfigLoader& configLoader)
-      : configLoader_(configLoader) {
-  }
-
-  // Called by client that supports tracing API.
-  // libkineto can still function without this.
-  void registerClient(ClientInterface* client);
-
-  // Called by libkineto on init
-  void registerProfiler(std::unique_ptr<ActivityProfilerInterface> profiler) {
-    activityProfiler_ = std::move(profiler);
-    initClientIfRegistered();
-  }
-
-  ActivityProfilerInterface& activityProfiler() {
-    return *activityProfiler_;
-  }
-
-  ClientInterface* client() {
-    return client_;
-  }
-
-  void initProfilerIfRegistered() {
-    static std::once_flag once;
-    if (activityProfiler_) {
-      std::call_once(once, [this] {
-        if (!activityProfiler_->isInitialized()) {
-          activityProfiler_->init();
-          initChildActivityProfilers();
-        }
-      });
-    }
-  }
-
-  bool isProfilerInitialized() const {
-    return activityProfiler_ && activityProfiler_->isInitialized();
-  }
-
-  bool isProfilerRegistered() const {
-    return activityProfiler_ != nullptr;
-  }
-
-  void suppressLogMessages() {
-    suppressLibkinetoLogMessages();
-  }
-
-  // Provides access to profier configuration manaegement
-  ConfigLoader& configLoader() {
-    return configLoader_;
-  }
-
-  void registerProfilerFactory(
-      ChildActivityProfilerFactory factory) {
-    if (isProfilerInitialized()) {
-      activityProfiler_->addChildActivityProfiler(factory());
-    } else {
-      childProfilerFactories_.push_back(factory);
-    }
-  }
-
- private:
-
-  void initChildActivityProfilers() {
-    if (!isProfilerInitialized()) {
-      return;
-    }
-    for (const auto& factory : childProfilerFactories_) {
-      activityProfiler_->addChildActivityProfiler(factory());
-    }
-    childProfilerFactories_.clear();
-  }
-
-  // Client is initialized once both it and libkineto has registered
-  void initClientIfRegistered();
-
-  ConfigLoader& configLoader_;
-  std::unique_ptr<ActivityProfilerInterface> activityProfiler_{};
-  ClientInterface* client_{};
-  int32_t clientRegisterThread_{0};
-
-  bool isLoaded_{false};
-  std::vector<ChildActivityProfilerFactory> childProfilerFactories_;
-};
-
-// Singleton
-LibkinetoApi& api();
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/include/time_since_epoch.h b/plugins/tensorboard-plugins/libkineto/include/time_since_epoch.h
deleted file mode 100644
index caa6b4d9276..00000000000
--- a/plugins/tensorboard-plugins/libkineto/include/time_since_epoch.h
+++ /dev/null
@@ -1,16 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <chrono>
-
-namespace libkineto {
-
-inline int64_t timeSinceEpoch(
-      const std::chrono::time_point<std::chrono::system_clock>& t) {
-    return std::chrono::duration_cast<std::chrono::microseconds>(
-               t.time_since_epoch())
-        .count();
-}
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/libkineto_defs.bzl b/plugins/tensorboard-plugins/libkineto/libkineto_defs.bzl
deleted file mode 100644
index 330c54a22df..00000000000
--- a/plugins/tensorboard-plugins/libkineto/libkineto_defs.bzl
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-def get_libkineto_api_srcs():
-    return [
-        "src/ThreadUtil.cpp",
-        "src/libkineto_api.cpp",
-    ]
-
-def get_libkineto_cupti_srcs(with_api = True):
-    return [
-        "src/CudaDeviceProperties.cpp",
-        "src/CuptiActivityApi.cpp",
-        "src/CuptiActivityPlatform.cpp",
-        "src/CuptiCallbackApi.cpp",
-        "src/CuptiEventApi.cpp",
-        "src/CuptiMetricApi.cpp",
-        "src/CuptiRangeProfilerApi.cpp",
-        "src/Demangle.cpp",
-        "src/EventProfiler.cpp",
-        "src/EventProfilerController.cpp",
-        "src/WeakSymbols.cpp",
-        "src/cupti_strings.cpp",
-    ] + (get_libkineto_cpu_only_srcs(with_api))
-
-def get_libkineto_roctracer_srcs(with_api = True):
-    return [
-        "src/RoctracerActivityApi.cpp",
-    ] + (get_libkineto_cpu_only_srcs(with_api))
-
-def get_libkineto_cpu_only_srcs(with_api = True):
-    return [
-        "src/AbstractConfig.cpp",
-        "src/CuptiActivityProfiler.cpp",
-        "src/ActivityProfilerController.cpp",
-        "src/ActivityProfilerProxy.cpp",
-        "src/ActivityType.cpp",
-        "src/Config.cpp",
-        "src/ConfigLoader.cpp",
-        "src/CuptiActivityApi.cpp",
-        "src/Demangle.cpp",
-        "src/GenericTraceActivity.cpp",
-        "src/ILoggerObserver.cpp",
-        "src/Logger.cpp",
-        "src/init.cpp",
-        "src/output_csv.cpp",
-        "src/output_json.cpp",
-    ] + (get_libkineto_api_srcs() if with_api else [])
-
-def get_libkineto_public_headers():
-    return [
-        "include/AbstractConfig.h",
-        "include/ActivityProfilerInterface.h",
-        "include/ActivityType.h",
-        "include/Config.h",
-        "include/ClientInterface.h",
-        "include/GenericTraceActivity.h",
-        "include/GenericTraceActivity.h",
-        "include/IActivityProfiler.h",
-        "include/ILoggerObserver.h",
-        "include/ITraceActivity.h",
-        "include/TraceSpan.h",
-        "include/ThreadUtil.h",
-        "include/libkineto.h",
-        "include/time_since_epoch.h",
-    ]
-
-# kineto code should be updated to not have to
-# suppress these warnings.
-KINETO_COMPILER_FLAGS = [
-    "-fexceptions",
-    "-Wno-deprecated-declarations",
-    "-Wno-unused-function",
-    "-Wno-unused-private-field",
-]
diff --git a/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cpp b/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cpp
deleted file mode 100644
index 780047912ed..00000000000
--- a/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cpp
+++ /dev/null
@@ -1,38 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <string>
-
-#include <common/logging/logging.h>
-#include <libkineto.h>
-
-#include "kineto/libkineto/sample_programs/kineto_playground.cuh"
-
-using namespace kineto;
-
-static const std::string kFileName = "/tmp/kineto_playground_trace.json";
-
-int main() {
-  warmup();
-
-  // Kineto config
-
-  // Empty types set defaults to all types
-  std::set<libkineto::ActivityType> types;
-
-  auto& profiler = libkineto::api().activityProfiler();
-  libkineto::api().initProfilerIfRegistered();
-  profiler.prepareTrace(types);
-
-  // Good to warm up after prepareTrace to get cupti initialization to settle
-  warmup();
-  profiler.startTrace();
-  playground();
-
-  auto trace = profiler.stopTrace();
-  LOG(INFO) << "Stopped and processed trace. Got " << trace->activities()->size() << " activities.";
-  trace->save(kFileName);
-  return 0;
-}
-
diff --git a/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cu b/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cu
deleted file mode 100644
index 54c6f82ff4b..00000000000
--- a/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cu
+++ /dev/null
@@ -1,60 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <stdio.h>
-
-#include "kineto_playground.cuh"
-
-
-namespace kineto {
-
-void warmup(void) {
-  // Inititalizing CUDA can take a while which we normally do not want to see in Kineto traces.
-  // This is done in various ways that take Kineto as dependency. This is our way of doing warmup
-  // for kineto_playground
-	size_t bytes = 1000;
-	float* mem = NULL;
-	auto error = cudaMalloc(&mem, bytes);
-  if (error != cudaSuccess) {
-    printf("cudaMalloc failed during kineto_playground warmup. error code: %d", error);
-    return;
-  }
-
-  cudaFree(mem); 
-}
-
-void basicMemcpyMemset(void) {
-  size_t size = (1 << 8) * sizeof(float);
-  float *hostMemSrc, *deviceMem, *hostMemDst;
-  cudaError_t err;
-
-  hostMemSrc = (float*)malloc(size);
-  hostMemDst = (float*)malloc(size);
-  err = cudaMalloc(&deviceMem, size);
-  if (err != cudaSuccess) {
-    printf("cudaMalloc failed during %s", __func__);
-    return;
-  }
-
-  memset(hostMemSrc, 1, size);
-  cudaMemcpy(deviceMem, hostMemSrc, size, cudaMemcpyHostToDevice);
-  if (err != cudaSuccess) {
-    printf("cudaMemcpy failed during %s", __func__);
-    return;
-  }
-
-  cudaMemcpy(hostMemDst, deviceMem, size, cudaMemcpyDeviceToHost);
-  if (err != cudaSuccess) {
-    printf("cudaMemcpy failed during %s", __func__);
-    return;
-  }
-
-  free(hostMemSrc);
-  free(hostMemDst);
-  cudaFree(deviceMem);
-}
-
-void playground(void) {
-  // Add your experimental CUDA implementation here. 
-}
-
-}
diff --git a/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cuh b/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cuh
deleted file mode 100644
index 54e1ee59ada..00000000000
--- a/plugins/tensorboard-plugins/libkineto/sample_programs/kineto_playground.cuh
+++ /dev/null
@@ -1,18 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <cuda.h>
-
-namespace kineto {
-
-// Warms up CUDA before the tracing starts
-void warmup(void);
-
-// Basic usage of cudaMemcpy and cudaMemset
-void basicMemcpyMemset(void);
-
-// Your experimental code goes in here!
-void playground(void);
-
-}
diff --git a/plugins/tensorboard-plugins/libkineto/src/AbstractConfig.cpp b/plugins/tensorboard-plugins/libkineto/src/AbstractConfig.cpp
deleted file mode 100644
index d60ab43c9a3..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/AbstractConfig.cpp
+++ /dev/null
@@ -1,188 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "AbstractConfig.h"
-
-#include <array>
-#include <fmt/format.h>
-#include <sstream>
-
-#include "Logger.h"
-
-using namespace std::chrono;
-
-using std::string;
-using std::vector;
-
-namespace KINETO_NAMESPACE {
-
-constexpr char kWhitespace[] = "\t\n ";
-
-static bool isWhitespace(string& s) {
-  return s.find_first_not_of(kWhitespace) == string::npos;
-}
-
-// Remove whitespace from both end of string
-static inline string trim(string& s) {
-  if (s.empty()) {
-    return s;
-  } else if (isWhitespace(s)) {
-    return "";
-  }
-  auto start = s.find_first_not_of(kWhitespace);
-  auto end = s.find_last_not_of(kWhitespace);
-  return s.substr(start, end - start + 1);
-}
-
-// Helper function for split.
-// Return the index of char d in string s.
-// If not found, returns the length of the string.
-static int find(const char* s, char delim) {
-  int i;
-  for (i = 0; s[i]; i++) {
-    if (s[i] == delim) {
-      break;
-    }
-  }
-  return i;
-}
-
-// Split a string by delimiter
-static vector<string> split(const string& s, char delim) {
-  vector<string> res;
-  const char* cs = s.c_str();
-  for (int i = find(cs, delim); cs[i]; cs += i + 1, i = find(cs, delim)) {
-    res.emplace_back(cs, i);
-  }
-  res.emplace_back(cs);
-  return res;
-}
-
-// Remove a trailing comment.
-static inline string stripComment(const string& s) {
-  std::size_t pos = s.find("#");
-  return s.substr(0, pos);
-}
-
-string AbstractConfig::toLower(string& s) const {
-  string res = s;
-  for (int i = 0; i < res.size(); i++) {
-    if (res[i] >= 'A' && res[i] <= 'Z') {
-      res[i] += ('a' - 'A');
-    }
-  }
-  return res;
-}
-
-bool AbstractConfig::endsWith(const string& s, const string& suffix) const {
-  if (suffix.size() > s.size()) {
-    return false;
-  }
-  return s.compare(s.size() - suffix.size(), suffix.size(), suffix) == 0;
-}
-
-vector<string> AbstractConfig::splitAndTrim(const string& s, char delim) const {
-  auto res = split(s, delim);
-  for (string& x : res) {
-    x = trim(x);
-  }
-  return res;
-}
-
-int64_t AbstractConfig::toIntRange(const string& val, int64_t min, int64_t max)
-    const {
-  char* invalid;
-  int64_t res = strtoll(val.c_str(), &invalid, 10);
-  if (val.empty() || *invalid) {
-    throw std::invalid_argument(fmt::format("Invalid integer: {}", val));
-  } else if (res < min || res > max) {
-    throw std::invalid_argument(fmt::format(
-        "Invalid argument: {} - expected range [{}, {}]", res, min, max));
-  }
-  return res;
-}
-
-int32_t AbstractConfig::toInt32(const string& val) const {
-  return toIntRange(val, 0, ~0u / 2);
-}
-
-int64_t AbstractConfig::toInt64(const string& val) const {
-  return toIntRange(val, 0, ~0ul / 2);
-}
-
-bool AbstractConfig::toBool(string& val) const {
-  const std::array<string, 8> bool_vals{
-      "n", "y", "no", "yes", "f", "t", "false", "true"};
-  const string lower_val = toLower(val);
-  for (int i = 0; i < bool_vals.size(); i++) {
-    if (lower_val == bool_vals[i]) {
-      return i % 2;
-    }
-  }
-  throw std::invalid_argument(fmt::format("Invalid bool argument: {}", val));
-  return false;
-}
-
-bool AbstractConfig::parse(const string& conf) {
-  std::istringstream iss(conf);
-  string line;
-
-  timestamp_ = system_clock::now();
-
-  // Read the string stream 1 line at a time to parse.
-  while (std::getline(iss, line)) {
-    line = stripComment(line);
-    if (isWhitespace(line)) {
-      continue;
-    }
-    vector<string> key_val = splitAndTrim(line, '=');
-    if (key_val.size() != 2) {
-      LOG(ERROR) << "Invalid config line: " << line;
-      return false;
-    } else {
-      bool handled = false;
-      try {
-        handled = handleOption(key_val[0], key_val[1]);
-        if (!handled) {
-          for (auto& feature_cfg : featureConfigs_) {
-            if (feature_cfg.second->handleOption(key_val[0], key_val[1])) {
-              handled = true;
-              break;
-            }
-          }
-        }
-      } catch (const std::exception& e) {
-        LOG(ERROR) << "Failed to parse config line: " << line;
-        LOG(ERROR) << e.what();
-        return false;
-      }
-      if (!handled) {
-        // This might be due to using a newer config option on an
-        // older binary where it is not supported. In this case,
-        // print a warning message - but it is expected to work!
-        LOG(WARNING) << "Unrecognized config line: " << line;
-      }
-    }
-  }
-
-  validate(timestamp_);
-
-  // Store original text, used to detect updates
-  source_ = conf;
-  timestamp_ = system_clock::now();
-  return true;
-}
-
-bool AbstractConfig::handleOption(
-    const std::string& /* unused */,
-    std::string& /* unused */) {
-  LOG(ERROR) << "handleOption unimplemented";
-  return false;
-}
-
-void AbstractConfig::printActivityProfilerConfig(std::ostream& s) const {
-  for (const auto& feature_cfg : featureConfigs_) {
-    feature_cfg.second->printActivityProfilerConfig(s);
-  }
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/ActivityBuffers.h b/plugins/tensorboard-plugins/libkineto/src/ActivityBuffers.h
deleted file mode 100644
index 157af879379..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ActivityBuffers.h
+++ /dev/null
@@ -1,29 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-
-#include <list>
-#include <memory>
-
-#include "libkineto.h"
-#include "CuptiActivityBuffer.h"
-
-namespace KINETO_NAMESPACE {
-
-struct ActivityBuffers {
-  std::list<std::unique_ptr<libkineto::CpuTraceBuffer>> cpu;
-  std::unique_ptr<CuptiActivityBufferMap> gpu;
-
-  // Add a wrapper object to the underlying struct stored in the buffer
-  template<class T>
-  const ITraceActivity& addActivityWrapper(const T& act) {
-    wrappers_.push_back(std::make_unique<T>(act));
-    return *wrappers_.back().get();
-  }
-
- private:
-  std::vector<std::unique_ptr<const ITraceActivity>> wrappers_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/ActivityLoggerFactory.h b/plugins/tensorboard-plugins/libkineto/src/ActivityLoggerFactory.h
deleted file mode 100644
index 0d1bf642cd6..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ActivityLoggerFactory.h
+++ /dev/null
@@ -1,60 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <algorithm>
-#include <cctype>
-#include <fmt/format.h>
-#include <functional>
-#include <map>
-#include <string>
-
-namespace KINETO_NAMESPACE {
-
-class ActivityLogger;
-
-class ActivityLoggerFactory {
-
- public:
-  using FactoryFunc =
-    std::function<std::unique_ptr<ActivityLogger>(const std::string& url)>;
-
-  // Add logger factory for a protocol prefix
-  void addProtocol(const std::string& protocol, FactoryFunc f) {
-    factories_[tolower(protocol)] = f;
-  }
-
-  // Create a logger, invoking the factory for the protocol specified in url
-  std::unique_ptr<ActivityLogger> makeLogger(const std::string& url) const {
-    std::string protocol = extractProtocol(url);
-    auto it = factories_.find(tolower(protocol));
-    if (it != factories_.end()) {
-      return it->second(stripProtocol(url));
-    }
-    throw std::invalid_argument(fmt::format(
-        "No logger registered for the {} protocol prefix",
-        protocol));
-    return nullptr;
-  }
-
- private:
-  static std::string tolower(std::string s) {
-    std::transform(s.begin(), s.end(), s.begin(),
-        [](unsigned char c) { return std::tolower(c); }
-    );
-    return s;
-  }
-
-  static std::string extractProtocol(std::string url) {
-    return url.substr(0, url.find("://"));
-  }
-
-  static std::string stripProtocol(std::string url) {
-    size_t pos = url.find("://");
-    return pos == url.npos ? url : url.substr(pos + 3);
-  }
-
-  std::map<std::string, FactoryFunc> factories_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerController.cpp b/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerController.cpp
deleted file mode 100644
index c85d41ed73f..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerController.cpp
+++ /dev/null
@@ -1,246 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "ActivityProfilerController.h"
-
-#include <chrono>
-#include <thread>
-
-#include "ActivityLoggerFactory.h"
-#include "ActivityTrace.h"
-#include "CuptiActivityApi.h"
-#ifdef HAS_ROCTRACER
-#include "RoctracerActivityApi.h"
-#endif
-#include "ThreadUtil.h"
-#include "output_json.h"
-#include "output_membuf.h"
-
-#include "Logger.h"
-
-using namespace std::chrono;
-
-namespace KINETO_NAMESPACE {
-
-constexpr milliseconds kProfilerIntervalMsecs(1000);
-
-ActivityProfilerController::ActivityProfilerController(
-    ConfigLoader& configLoader, bool cpuOnly)
-    : configLoader_(configLoader) {
-#ifdef HAS_ROCTRACER
-  profiler_ = std::make_unique<CuptiActivityProfiler>(
-      RoctracerActivityApi::singleton(), cpuOnly);
-#else
-  profiler_ = std::make_unique<CuptiActivityProfiler>(
-      CuptiActivityApi::singleton(), cpuOnly);
-#endif
-  configLoader_.addHandler(ConfigLoader::ConfigKind::ActivityProfiler, this);
-}
-
-ActivityProfilerController::~ActivityProfilerController() {
-  configLoader_.removeHandler(
-      ConfigLoader::ConfigKind::ActivityProfiler, this);
-  if (profilerThread_) {
-    // signaling termination of the profiler loop
-    stopRunloop_ = true;
-    profilerThread_->join();
-    delete profilerThread_;
-    profilerThread_ = nullptr;
-  }
-}
-
-static ActivityLoggerFactory initLoggerFactory() {
-  ActivityLoggerFactory factory;
-  factory.addProtocol("file", [](const std::string& url) {
-      return std::unique_ptr<ActivityLogger>(new ChromeTraceLogger(url));
-  });
-  return factory;
-}
-
-static ActivityLoggerFactory& loggerFactory() {
-  static ActivityLoggerFactory factory = initLoggerFactory();
-  return factory;
-}
-
-void ActivityProfilerController::addLoggerFactory(
-    const std::string& protocol, ActivityLoggerFactory::FactoryFunc factory) {
-  loggerFactory().addProtocol(protocol, factory);
-}
-
-static std::unique_ptr<ActivityLogger> makeLogger(const Config& config) {
-  if (config.activitiesLogToMemory()) {
-    return std::make_unique<MemoryTraceLogger>(config);
-  }
-  return loggerFactory().makeLogger(config.activitiesLogUrl());
-}
-
-bool ActivityProfilerController::canAcceptConfig() {
-  return !profiler_->isActive();
-}
-
-void ActivityProfilerController::acceptConfig(const Config& config) {
-  VLOG(1) << "acceptConfig";
-  if (config.activityProfilerEnabled()) {
-    scheduleTrace(config);
-  }
-}
-
-void ActivityProfilerController::profilerLoop() {
-  setThreadName("Kineto Activity Profiler");
-  VLOG(0) << "Entering activity profiler loop";
-
-  auto now = system_clock::now();
-  auto next_wakeup_time = now + kProfilerIntervalMsecs;
-
-  while (!stopRunloop_) {
-    now = system_clock::now();
-
-    while (now < next_wakeup_time) {
-      /* sleep override */
-      std::this_thread::sleep_for(next_wakeup_time - now);
-      now = system_clock::now();
-    }
-
-    if (!profiler_->isActive()) {
-      std::lock_guard<std::mutex> lock(asyncConfigLock_);
-      if (asyncRequestConfig_
-          && !asyncRequestConfig_->hasProfileStartIteration()) {
-        // Note on now + kProfilerIntervalMsecs
-        // Profiler interval does not align perfectly upto startTime - warmup. Waiting until the next tick
-        // won't allow sufficient time for the profiler to warm up. So check if we are very close to the warmup time and trigger warmup
-        if (now + kProfilerIntervalMsecs
-            >= (asyncRequestConfig_->requestTimestamp() - asyncRequestConfig_->activitiesWarmupDuration())) {
-          LOG(INFO) << "Received on-demand activity trace request by "
-                    << " profile timestamp = "
-                    << asyncRequestConfig_->
-                    requestTimestamp().time_since_epoch().count();
-          activateConfig(now);
-        }
-      }
-    }
-
-    while (next_wakeup_time < now) {
-      next_wakeup_time += kProfilerIntervalMsecs;
-    }
-
-    if (profiler_->isActive()) {
-      next_wakeup_time = profiler_->performRunLoopStep(now, next_wakeup_time);
-      VLOG(1) << "Profiler loop: "
-          << duration_cast<milliseconds>(system_clock::now() - now).count()
-          << "ms";
-    }
-  }
-
-  VLOG(0) << "Exited activity profiling loop";
-}
-
-void ActivityProfilerController::step() {
-  int64_t currentIter = ++iterationCount_;
-  VLOG(0) << "Step called , iteration  = " << currentIter;
-
-  // optimization to not take the lock unless necessary
-  if (asyncRequestConfig_ && !profiler_->isActive()) {
-    std::lock_guard<std::mutex> lock(asyncConfigLock_);
-    auto startIter = asyncRequestConfig_->startIterationIncludingWarmup();
-
-    if (asyncRequestConfig_->hasProfileStartIteration()
-        && currentIter >= startIter) {
-      LOG(INFO) << "Received on-demand activity trace request by profile"
-                << " start iteration = "
-                << asyncRequestConfig_->profileStartIteration()
-                << " current iteration = " << currentIter;
-
-      if (currentIter > startIter) {
-        // adjust the start iteration if it is in the past
-        auto newProfileStart = currentIter +
-          asyncRequestConfig_->activitiesWarmupIterations();
-        LOG(INFO) << "Start iteration updated to " << newProfileStart;
-        asyncRequestConfig_->setProfileStartIteration(newProfileStart);
-      }
-      activateConfig(system_clock::now());
-    }
-  }
-
-  if (profiler_->isActive()) {
-    auto now = system_clock::now();
-    auto next_wakeup_time = now + kProfilerIntervalMsecs;
-    profiler_->performRunLoopStep(now, next_wakeup_time, currentIter);
-  }
-}
-
-void ActivityProfilerController::activateConfig(
-    std::chrono::time_point<std::chrono::system_clock> now) {
-  logger_ = makeLogger(*asyncRequestConfig_);
-  profiler_->setLogger(logger_.get());
-  profiler_->configure(*asyncRequestConfig_, now);
-  asyncRequestConfig_ = nullptr;
-}
-
-void ActivityProfilerController::scheduleTrace(const Config& config) {
-  VLOG(1) << "scheduleTrace";
-  if (profiler_->isActive()) {
-    LOG(ERROR) << "Ignored request - profiler busy";
-    return;
-  }
-  int64_t currentIter = iterationCount_;
-  if (config.hasProfileStartIteration() && currentIter < 0) {
-    LOG(ERROR) << "Ignored profile iteration count based request as "
-               << "application is not updating iteration count";
-    return;
-  }
-  std::lock_guard<std::mutex> lock(asyncConfigLock_);
-  asyncRequestConfig_ = config.clone();
-
-  auto startIter = asyncRequestConfig_->startIterationIncludingWarmup();
-
-  if (asyncRequestConfig_->hasProfileStartIteration()
-      && (currentIter > startIter)
-      && asyncRequestConfig_->profileStartIterationRoundUp() > 0) {
-    auto newProfileStart
-      = currentIter + asyncRequestConfig_->activitiesWarmupIterations();
-    // round up to nearest multiple
-    auto divisor = asyncRequestConfig_->profileStartIterationRoundUp();
-    auto rem = newProfileStart % divisor;
-    newProfileStart += ((rem == 0) ? 0 : divisor - rem);
-    LOG(INFO) << "Rounding up profiler start iteration to : " << newProfileStart;
-    asyncRequestConfig_->setProfileStartIteration(newProfileStart);
-  }
-
-  // start a profilerLoop() thread to handle request
-  if (!profilerThread_) {
-    profilerThread_ =
-        new std::thread(&ActivityProfilerController::profilerLoop, this);
-  }
-}
-
-void ActivityProfilerController::prepareTrace(const Config& config) {
-  // Requests from ActivityProfilerApi have higher priority than
-  // requests from other sources (signal, daemon).
-  // Cancel any ongoing request and refuse new ones.
-  auto now = system_clock::now();
-  if (profiler_->isActive()) {
-    LOG(WARNING) << "Cancelling current trace request in order to start "
-                 << "higher priority synchronous request";
-    if (libkineto::api().client()) {
-      libkineto::api().client()->stop();
-    }
-    profiler_->stopTrace(now);
-    profiler_->reset();
-  }
-
-  profiler_->configure(config, now);
-}
-
-std::unique_ptr<ActivityTraceInterface> ActivityProfilerController::stopTrace() {
-  profiler_->stopTrace(std::chrono::system_clock::now());
-  auto logger = std::make_unique<MemoryTraceLogger>(profiler_->config());
-  profiler_->processTrace(*logger);
-  profiler_->reset();
-  return std::make_unique<ActivityTrace>(std::move(logger), loggerFactory());
-}
-
-void ActivityProfilerController::addMetadata(
-    const std::string& key, const std::string& value) {
-  profiler_->addMetadata(key, value);
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerController.h b/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerController.h
deleted file mode 100644
index 415f107cbed..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerController.h
+++ /dev/null
@@ -1,84 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <atomic>
-#include <cstdint>
-#include <memory>
-#include <mutex>
-#include <thread>
-
-#include "ActivityLoggerFactory.h"
-#include "CuptiActivityProfiler.h"
-#include "ActivityProfilerInterface.h"
-#include "ActivityTraceInterface.h"
-#include "ConfigLoader.h"
-#include "CuptiActivityApi.h"
-
-namespace KINETO_NAMESPACE {
-
-class Config;
-
-class ActivityProfilerController : public ConfigLoader::ConfigHandler {
- public:
-  explicit ActivityProfilerController(ConfigLoader& configLoader, bool cpuOnly);
-  ActivityProfilerController(const ActivityProfilerController&) = delete;
-  ActivityProfilerController& operator=(const ActivityProfilerController&) =
-      delete;
-
-  ~ActivityProfilerController();
-
-  static void addLoggerFactory(
-      const std::string& protocol,
-      ActivityLoggerFactory::FactoryFunc factory);
-
-  bool canAcceptConfig() override;
-  void acceptConfig(const Config& config) override;
-
-  void scheduleTrace(const Config& config);
-
-  void prepareTrace(const Config& config);
-
-  void startTrace() {
-    profiler_->startTrace(std::chrono::system_clock::now());
-  }
-
-  void step();
-
-  std::unique_ptr<ActivityTraceInterface> stopTrace();
-
-  bool isActive() {
-    return profiler_->isActive();
-  }
-
-  void transferCpuTrace(
-      std::unique_ptr<libkineto::CpuTraceBuffer> cpuTrace) {
-    return profiler_->transferCpuTrace(std::move(cpuTrace));
-  }
-
-  void recordThreadInfo() {
-    profiler_->recordThreadInfo();
-  }
-
-  void addChildActivityProfiler(
-      std::unique_ptr<IActivityProfiler> profiler) {
-    profiler_->addChildActivityProfiler(std::move(profiler));
-  }
-
-  void addMetadata(const std::string& key, const std::string& value);
-
- private:
-  void profilerLoop();
-  void activateConfig(std::chrono::time_point<std::chrono::system_clock> now);
-
-  std::unique_ptr<Config> asyncRequestConfig_;
-  std::mutex asyncConfigLock_;
-  std::unique_ptr<CuptiActivityProfiler> profiler_;
-  std::unique_ptr<ActivityLogger> logger_;
-  std::thread* profilerThread_{nullptr};
-  std::atomic_bool stopRunloop_{false};
-  std::atomic<std::int64_t> iterationCount_{-1};
-  ConfigLoader& configLoader_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerProxy.cpp b/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerProxy.cpp
deleted file mode 100644
index b2d36b7b3ab..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerProxy.cpp
+++ /dev/null
@@ -1,119 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "ActivityProfilerProxy.h"
-
-#include "ActivityProfilerController.h"
-#include "Config.h"
-#include "CuptiActivityApi.h"
-#include "Logger.h"
-#include <chrono>
-
-namespace KINETO_NAMESPACE {
-
-ActivityProfilerProxy::ActivityProfilerProxy(
-    bool cpuOnly, ConfigLoader& configLoader)
-  : cpuOnly_(cpuOnly), configLoader_(configLoader) {
-}
-
-ActivityProfilerProxy::~ActivityProfilerProxy() {
-  delete controller_;
-};
-
-void ActivityProfilerProxy::init() {
-  if (!controller_) {
-    controller_ = new ActivityProfilerController(configLoader_, cpuOnly_);
-  }
-}
-
-void ActivityProfilerProxy::scheduleTrace(const std::string& configStr) {
-  Config config;
-  config.parse(configStr);
-  controller_->scheduleTrace(config);
-}
-
-void ActivityProfilerProxy::scheduleTrace(const Config& config) {
-  controller_->scheduleTrace(config);
-}
-
-void ActivityProfilerProxy::prepareTrace(
-    const std::set<ActivityType>& activityTypes,
-    const std::string& configStr) {
-  Config config;
-  bool validate_required = true;
-
-  // allow user provided config to override default options
-  if (!configStr.empty()) {
-    if (!config.parse(configStr)) {
-      LOG(WARNING) << "Failed to parse config : " << configStr;
-    }
-    // parse also runs validate
-    validate_required = false;
-  }
-
-  config.setClientDefaults();
-  config.setSelectedActivityTypes(activityTypes);
-
-  if (validate_required) {
-    config.validate(std::chrono::system_clock::now());
-  }
-
-  controller_->prepareTrace(config);
-}
-
-void ActivityProfilerProxy::startTrace() {
-  controller_->startTrace();
-}
-
-std::unique_ptr<ActivityTraceInterface>
-ActivityProfilerProxy::stopTrace() {
-  return controller_->stopTrace();
-}
-
-void ActivityProfilerProxy::step() {
-  controller_->step();
-}
-
-bool ActivityProfilerProxy::isActive() {
-  return controller_->isActive();
-}
-
-void ActivityProfilerProxy::pushCorrelationId(uint64_t id) {
-  CuptiActivityApi::pushCorrelationID(id,
-    CuptiActivityApi::CorrelationFlowType::Default);
-}
-
-void ActivityProfilerProxy::popCorrelationId() {
-  CuptiActivityApi::popCorrelationID(
-    CuptiActivityApi::CorrelationFlowType::Default);
-}
-
-void ActivityProfilerProxy::pushUserCorrelationId(uint64_t id) {
-  CuptiActivityApi::pushCorrelationID(id,
-    CuptiActivityApi::CorrelationFlowType::User);
-}
-
-void ActivityProfilerProxy::popUserCorrelationId() {
-  CuptiActivityApi::popCorrelationID(
-    CuptiActivityApi::CorrelationFlowType::User);
-}
-
-void ActivityProfilerProxy::transferCpuTrace(
-   std::unique_ptr<CpuTraceBuffer> traceBuffer) {
-  controller_->transferCpuTrace(std::move(traceBuffer));
-}
-
-void ActivityProfilerProxy::addMetadata(
-    const std::string& key, const std::string& value) {
-  controller_->addMetadata(key, value);
-}
-
-void ActivityProfilerProxy::recordThreadInfo() {
-  controller_->recordThreadInfo();
-}
-
-void ActivityProfilerProxy::addChildActivityProfiler(
-    std::unique_ptr<IActivityProfiler> profiler) {
-  controller_->addChildActivityProfiler(std::move(profiler));
-}
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerProxy.h b/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerProxy.h
deleted file mode 100644
index b5cf84b2f1d..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ActivityProfilerProxy.h
+++ /dev/null
@@ -1,73 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include "ActivityProfilerInterface.h"
-
-#include <memory>
-#include <set>
-#include <vector>
-
-#include "ActivityType.h"
-#include "ITraceActivity.h"
-
-namespace libkineto {
-  // previous declaration is struct so this one must be too.
-  struct CpuTraceBuffer;
-}
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-
-class ActivityProfilerController;
-class Config;
-class ConfigLoader;
-
-class ActivityProfilerProxy : public ActivityProfilerInterface {
-
- public:
-  ActivityProfilerProxy(bool cpuOnly, ConfigLoader& configLoader);
-  ~ActivityProfilerProxy() override;
-
-  void init() override;
-  bool isInitialized() override {
-    return controller_ != nullptr;
-  }
-
-  bool isActive() override;
-
-  void recordThreadInfo() override;
-
-  void scheduleTrace(const std::string& configStr) override;
-  void scheduleTrace(const Config& config);
-
-  void prepareTrace(
-      const std::set<ActivityType>& activityTypes,
-      const std::string& configStr = "") override;
-
-  void startTrace() override;
-  void step() override;
-  std::unique_ptr<ActivityTraceInterface> stopTrace() override;
-
-  void pushCorrelationId(uint64_t id) override;
-  void popCorrelationId() override;
-
-  void pushUserCorrelationId(uint64_t id) override;
-  void popUserCorrelationId() override;
-
-  void transferCpuTrace(
-     std::unique_ptr<CpuTraceBuffer> traceBuffer) override;
-
-  void addMetadata(const std::string& key, const std::string& value) override;
-
-  virtual void addChildActivityProfiler(
-      std::unique_ptr<IActivityProfiler> profiler) override;
-
- private:
-  bool cpuOnly_{true};
-  ConfigLoader& configLoader_;
-  ActivityProfilerController* controller_{nullptr};
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/ActivityTrace.h b/plugins/tensorboard-plugins/libkineto/src/ActivityTrace.h
deleted file mode 100644
index 0be76af08e4..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ActivityTrace.h
+++ /dev/null
@@ -1,45 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <memory>
-#include <string>
-
-#include "ActivityLoggerFactory.h"
-#include "ActivityTraceInterface.h"
-#include "output_json.h"
-#include "output_membuf.h"
-
-namespace libkineto {
-
-class ActivityTrace : public ActivityTraceInterface {
- public:
-  ActivityTrace(
-      std::unique_ptr<MemoryTraceLogger> tmpLogger,
-      const ActivityLoggerFactory& factory)
-    : memLogger_(std::move(tmpLogger)),
-      loggerFactory_(factory) {
-  }
-
-  const std::vector<const ITraceActivity*>* activities() override {
-    return memLogger_->traceActivities();
-  };
-
-  void save(const std::string& url) override {
-    std::string prefix;
-    // if no protocol is specified, default to file
-    if (url.find("://") == url.npos) {
-      prefix = "file://";
-    }
-    memLogger_->log(*loggerFactory_.makeLogger(prefix + url));
-  };
-
- private:
-  // Activities are logged into a buffer
-  std::unique_ptr<MemoryTraceLogger> memLogger_;
-
-  // Alternative logger used by save() if protocol prefix is specified
-  const ActivityLoggerFactory& loggerFactory_;
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/ActivityType.cpp b/plugins/tensorboard-plugins/libkineto/src/ActivityType.cpp
deleted file mode 100644
index 18856b72370..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ActivityType.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "ActivityType.h"
-
-#include <fmt/format.h>
-
-namespace libkineto {
-
-struct ActivityTypeName {
-  const char* name;
-  ActivityType type;
-};
-
-static constexpr std::array<ActivityTypeName, activityTypeCount + 1> map{{
-    {"cpu_op", ActivityType::CPU_OP},
-    {"user_annotation", ActivityType::USER_ANNOTATION},
-    {"gpu_user_Annotation", ActivityType::GPU_USER_ANNOTATION},
-    {"gpu_memcpy", ActivityType::GPU_MEMCPY},
-    {"gpu_memset", ActivityType::GPU_MEMSET},
-    {"kernel", ActivityType::CONCURRENT_KERNEL},
-    {"external_correlation", ActivityType::EXTERNAL_CORRELATION},
-    {"cuda_runtime", ActivityType::CUDA_RUNTIME},
-    {"cuda_profiler_range", ActivityType::CUDA_PROFILER_RANGE},
-    {"glow_runtime", ActivityType::GLOW_RUNTIME},
-    {"cpu_instant_event", ActivityType::CPU_INSTANT_EVENT},
-    {"python_function", ActivityType::PYTHON_FUNCTION},
-    {"overhead", ActivityType::OVERHEAD},
-    {"ENUM_COUNT", ActivityType::ENUM_COUNT}
-}};
-
-static constexpr bool matchingOrder(int idx = 0) {
-  return map[idx].type == ActivityType::ENUM_COUNT ||
-    ((idx == (int) map[idx].type) && matchingOrder(idx + 1));
-}
-static_assert(matchingOrder(), "ActivityTypeName map is out of order");
-
-const char* toString(ActivityType t) {
-  return map[(int)t].name;
-}
-
-ActivityType toActivityType(const std::string& str) {
-  for (int i = 0; i < activityTypeCount; i++) {
-    if (str == map[i].name) {
-      return map[i].type;
-    }
-  }
-  throw std::invalid_argument(fmt::format("Invalid activity type: {}", str));
-}
-
-const std::array<ActivityType, activityTypeCount> activityTypes() {
-  std::array<ActivityType, activityTypeCount> res;
-  for (int i = 0; i < activityTypeCount; i++) {
-    res[i] = map[i].type;
-  }
-  return res;
-}
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/Config.cpp b/plugins/tensorboard-plugins/libkineto/src/Config.cpp
deleted file mode 100644
index 95538840f37..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/Config.cpp
+++ /dev/null
@@ -1,473 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "Config.h"
-
-#include <stdlib.h>
-
-#include <fmt/chrono.h>
-#include <fmt/format.h>
-#include <chrono>
-#include <fstream>
-#include <iomanip>
-#include <istream>
-#include <mutex>
-#include <ostream>
-#include <sstream>
-#include <time.h>
-
-#include "Logger.h"
-#include "ThreadUtil.h"
-
-using namespace std::chrono;
-
-using std::string;
-using std::vector;
-
-namespace KINETO_NAMESPACE {
-
-constexpr milliseconds kDefaultSamplePeriodMsecs(1000);
-constexpr milliseconds kDefaultMultiplexPeriodMsecs(1000);
-constexpr milliseconds kDefaultActivitiesProfileDurationMSecs(500);
-constexpr int kDefaultActivitiesMaxGpuBufferSize(128 * 1024 * 1024);
-constexpr seconds kDefaultActivitiesWarmupDurationSecs(5);
-constexpr seconds kDefaultBufferUntilWarmup(10);
-constexpr seconds kDefaultReportPeriodSecs(1);
-constexpr int kDefaultSamplesPerReport(1);
-constexpr int kDefaultMaxEventProfilersPerGpu(1);
-constexpr int kDefaultEventProfilerHearbeatMonitorPeriod(0);
-constexpr seconds kMaxRequestAge(10);
-
-// Event Profiler
-constexpr char kEventsKey[] = "EVENTS";
-constexpr char kMetricsKey[] = "METRICS";
-constexpr char kSamplePeriodKey[] = "SAMPLE_PERIOD_MSECS";
-constexpr char kMultiplexPeriodKey[] = "MULTIPLEX_PERIOD_MSECS";
-constexpr char kReportPeriodKey[] = "REPORT_PERIOD_SECS";
-constexpr char kSamplesPerReportKey[] = "SAMPLES_PER_REPORT";
-constexpr char kEventsLogFileKey[] = "EVENTS_LOG_FILE";
-constexpr char kEventsEnabledDevicesKey[] = "EVENTS_ENABLED_DEVICES";
-constexpr char kOnDemandDurationKey[] = "EVENTS_DURATION_SECS";
-constexpr char kMaxEventProfilersPerGpuKey[] = "MAX_EVENT_PROFILERS_PER_GPU";
-constexpr char kHeartbeatMonitorPeriodKey[] =
-    "EVENTS_HEARTBEAT_MONITOR_PERIOD_SECS";
-
-// Activity Profiler
-constexpr char kActivitiesEnabledKey[] = "ACTIVITIES_ENABLED";
-constexpr char kActivityTypesKey[] = "ACTIVITY_TYPES";
-constexpr char kActivitiesLogFileKey[] = "ACTIVITIES_LOG_FILE";
-constexpr char kActivitiesDurationKey[] = "ACTIVITIES_DURATION_SECS";
-constexpr char kActivitiesDurationMsecsKey[] = "ACTIVITIES_DURATION_MSECS";
-constexpr char kActivitiesWarmupDurationSecsKey[] = "ACTIVITIES_WARMUP_PERIOD_SECS";
-constexpr char kActivitiesMaxGpuBufferSizeKey[] =
-    "ACTIVITIES_MAX_GPU_BUFFER_SIZE_MB";
-
-// Client Interface
-constexpr char kClientInterfaceEnableOpInputsCollection[] = "CLIENT_INTERFACE_ENABLE_OP_INPUTS_COLLECTION";
-
-constexpr char kActivitiesWarmupIterationsKey[] = "ACTIVITIES_WARMUP_ITERATIONS";
-constexpr char kActivitiesIterationsKey[] = "ACTIVITIES_ITERATIONS";
-// Common
-
-// Client-side timestamp used for synchronized start across hosts for
-// distributed workloads.
-// Specified in milliseconds Unix time (milliseconds since epoch).
-// To use, compute a future timestamp as follows:
-//    * C++: <delay_ms> + duration_cast<milliseconds>(
-//               system_clock::now().time_since_epoch()).count()
-//    * Python: <delay_ms> + int(time.time() * 1000)
-//    * Bash: $((<delay_ms> + $(date +%s%3N)))
-// If used for a tracing request, timestamp must be far enough in the future
-// to accommodate ACTIVITIES_WARMUP_PERIOD_SECS as well as any delays in
-// propagating the request to the profiler.
-// If the request can not be honored, it is up to the profilers to report
-// an error somehow - no checks are done at config parse time.
-// Note PROFILE_START_ITERATION has higher precedence
-constexpr char kProfileStartTimeKey[] = "PROFILE_START_TIME";
-// DEPRECATED - USE PROFILE_START_TIME instead
-constexpr char kRequestTimestampKey[] = "REQUEST_TIMESTAMP";
-
-// Alternatively if the application supports reporting iterations
-// start the profile at specific iteration. If the iteration count
-// is >= this value the profile is started immediately.
-// A value >= 0 is valid for this config option to take effect.
-// Note PROFILE_START_ITERATION will take precedence over PROFILE_START_TIME.
-constexpr char kProfileStartIterationKey[] = "PROFILE_START_ITERATION";
-
-// Users can also start the profile on an integer multiple of the config
-// value PROFILE_START_ITERATION_ROUNDUP. This knob behaves similar to
-// PROFILE_START_ITERATION but instead of saying : "start collection trace on
-// iteration 500", one can configure it to "start collecting trace on the next
-// 100th iteration".
-//
-// For example,
-//   PROFILE_START_ITERATION_ROUNDUP = 1000, and the current iteration is 2010
-//   The profile will then be collected on the next multiple of 1000 ie. 3000
-// Note PROFILE_START_ITERATION_ROUNDUP will also take precedence over
-// PROFILE_START_TIME.
-constexpr char kProfileStartIterationRoundUpKey[]
-  = "PROFILE_START_ITERATION_ROUNDUP";
-
-// Enable on-demand trigger via kill -USR2 <pid>
-// When triggered in this way, /tmp/libkineto.conf will be used as config.
-constexpr char kEnableSigUsr2Key[] = "ENABLE_SIGUSR2";
-
-// Enable communication through IPC Fabric
-// and disable thrift communication with dynolog daemon
-constexpr char kEnableIpcFabricKey[] = "ENABLE_IPC_FABRIC";
-
-// Verbose log level
-// The actual glog is not used and --v and --vmodule has no effect.
-// Instead set the verbose level and modules in the config file.
-constexpr char kLogVerboseLevelKey[] = "VERBOSE_LOG_LEVEL";
-// By default, all modules will log verbose messages >= verboseLogLevel.
-// But to reduce noise we can specify one or more modules of interest.
-// A module is a C/C++ object file (source file name),
-// Example argument: ActivityProfiler.cpp,output_json.cpp
-constexpr char kLogVerboseModulesKey[] = "VERBOSE_LOG_MODULES";
-
-// Max devices supported on any system
-constexpr uint8_t kMaxDevices = 8;
-
-namespace {
-
-struct FactoryMap {
-
-  void addFactory(
-      std::string name,
-      std::function<AbstractConfig*(Config&)> factory) {
-    std::lock_guard<std::mutex> lock(lock_);
-    factories_[name] = factory;
-  }
-
-  void addFeatureConfigs(Config& cfg) {
-    std::lock_guard<std::mutex> lock(lock_);
-    for (const auto& p : factories_) {
-      cfg.addFeature(p.first, p.second(cfg));
-    }
-  }
-
-// Config factories are shared between objects and since
-// config objects can be created by multiple threads, we need a lock.
-  std::mutex lock_;
-  std::map<std::string, std::function<AbstractConfig*(Config&)>> factories_;
-};
-
-std::shared_ptr<FactoryMap> configFactories() {
-  // Ensure this is safe to call during shutdown, even as static
-  // destructors are invoked. Once factories destructor has been
-  // invoked, weak_ptr.lock() will return nullptr.
-  // But calls before that point will have a valid shared_ptr,
-  // delaying destruction of the underlying FactoryMap.
-  static auto factories = std::make_shared<FactoryMap>();
-  static std::weak_ptr<FactoryMap> weak_ptr = factories;
-  return weak_ptr.lock();
-}
-
-} // namespace
-
-void Config::addConfigFactory(
-    std::string name,
-    std::function<AbstractConfig*(Config&)> factory) {
-  auto factories = configFactories();
-  if (factories) {
-    factories->addFactory(name, factory);
-  }
-}
-
-static string defaultTraceFileName() {
-  return fmt::format("/tmp/libkineto_activities_{}.json", processId());
-}
-
-Config::Config()
-    : verboseLogLevel_(-1),
-      samplePeriod_(kDefaultSamplePeriodMsecs),
-      reportPeriod_(duration_cast<milliseconds>(kDefaultReportPeriodSecs)),
-      samplesPerReport_(kDefaultSamplesPerReport),
-      eventProfilerOnDemandDuration_(seconds(0)),
-      eventProfilerMaxInstancesPerGpu_(kDefaultMaxEventProfilersPerGpu),
-      eventProfilerHeartbeatMonitorPeriod_(
-          kDefaultEventProfilerHearbeatMonitorPeriod),
-      multiplexPeriod_(kDefaultMultiplexPeriodMsecs),
-      activityProfilerEnabled_(true),
-      activitiesLogFile_(defaultTraceFileName()),
-      activitiesLogUrl_(fmt::format("file://{}", activitiesLogFile_)),
-      activitiesMaxGpuBufferSize_(kDefaultActivitiesMaxGpuBufferSize),
-      activitiesWarmupDuration_(kDefaultActivitiesWarmupDurationSecs),
-      activitiesWarmupIterations_(0),
-      activitiesDuration_(kDefaultActivitiesProfileDurationMSecs),
-      activitiesRunIterations_(0),
-      activitiesOnDemandTimestamp_(milliseconds(0)),
-      profileStartTime_(milliseconds(0)),
-      profileStartIteration_(-1),
-      profileStartIterationRoundUp_(-1),
-      requestTimestamp_(milliseconds(0)),
-      enableSigUsr2_(false),
-      enableIpcFabric_(false) {
-  auto factories = configFactories();
-  if (factories) {
-    factories->addFeatureConfigs(*this);
-  }
-}
-
-uint8_t Config::createDeviceMask(const string& val) {
-  uint8_t res = 0;
-  for (const auto& d : splitAndTrim(val, ',')) {
-    res |= 1 << toIntRange(d, 0, kMaxDevices - 1);
-  }
-  return res;
-}
-
-const seconds Config::maxRequestAge() const {
-  return kMaxRequestAge;
-}
-
-static std::string getTimeStr(time_point<system_clock> t) {
-  std::time_t t_c = system_clock::to_time_t(t);
-  return fmt::format("{:%H:%M:%S}", fmt::localtime(t_c));
-}
-
-static time_point<system_clock> handleRequestTimestamp(int64_t ms) {
-  auto t = time_point<system_clock>(milliseconds(ms));
-  auto now = system_clock::now();
-  if (t > now) {
-    throw std::invalid_argument(fmt::format(
-        "Invalid {}: {} - time is in future",
-        kRequestTimestampKey,
-        getTimeStr(t)));
-  } else if ((now - t) > kMaxRequestAge) {
-    throw std::invalid_argument(fmt::format(
-        "Invalid {}: {} - time is more than {}s in the past",
-        kRequestTimestampKey,
-        getTimeStr(t),
-        kMaxRequestAge.count()));
-  }
-  return t;
-}
-
-void Config::setActivityTypes(
-  const std::vector<std::string>& selected_activities) {
-  selectedActivityTypes_.clear();
-  if (selected_activities.size() > 0) {
-    for (const auto& activity : selected_activities) {
-      if (activity == "") {
-        continue;
-      }
-      selectedActivityTypes_.insert(toActivityType(activity));
-    }
-  }
-}
-
-bool Config::handleOption(const std::string& name, std::string& val) {
-  // Event Profiler
-  if (!name.compare(kEventsKey)) {
-    vector<string> event_names = splitAndTrim(val, ',');
-    eventNames_.insert(event_names.begin(), event_names.end());
-  } else if (!name.compare(kMetricsKey)) {
-    vector<string> metric_names = splitAndTrim(val, ',');
-    metricNames_.insert(metric_names.begin(), metric_names.end());
-  } else if (!name.compare(kSamplePeriodKey)) {
-    samplePeriod_ = milliseconds(toInt32(val));
-  } else if (!name.compare(kMultiplexPeriodKey)) {
-    multiplexPeriod_ = milliseconds(toInt32(val));
-  } else if (!name.compare(kReportPeriodKey)) {
-    setReportPeriod(seconds(toInt32(val)));
-  } else if (!name.compare(kSamplesPerReportKey)) {
-    samplesPerReport_ = toInt32(val);
-  } else if (!name.compare(kEventsLogFileKey)) {
-    eventLogFile_ = val;
-  } else if (!name.compare(kEventsEnabledDevicesKey)) {
-    eventProfilerDeviceMask_ = createDeviceMask(val);
-  } else if (!name.compare(kOnDemandDurationKey)) {
-    eventProfilerOnDemandDuration_ = seconds(toInt32(val));
-    eventProfilerOnDemandTimestamp_ = timestamp();
-  } else if (!name.compare(kMaxEventProfilersPerGpuKey)) {
-    eventProfilerMaxInstancesPerGpu_ = toInt32(val);
-  } else if (!name.compare(kHeartbeatMonitorPeriodKey)) {
-    eventProfilerHeartbeatMonitorPeriod_ = seconds(toInt32(val));
-  }
-
-  // Activity Profiler
-  else if (!name.compare(kActivitiesDurationKey)) {
-    activitiesDuration_ =
-        duration_cast<milliseconds>(seconds(toInt32(val)));
-    activitiesOnDemandTimestamp_ = timestamp();
-  } else if (!name.compare(kActivityTypesKey)) {
-    vector<string> activity_types = splitAndTrim(toLower(val), ',');
-    setActivityTypes(activity_types);
-  } else if (!name.compare(kActivitiesDurationMsecsKey)) {
-    activitiesDuration_ = milliseconds(toInt32(val));
-    activitiesOnDemandTimestamp_ = timestamp();
-  } else if (!name.compare(kActivitiesIterationsKey)) {
-    activitiesRunIterations_ = toInt32(val);
-    activitiesOnDemandTimestamp_ = timestamp();
-  } else if (!name.compare(kLogVerboseLevelKey)) {
-    verboseLogLevel_ = toInt32(val);
-  } else if (!name.compare(kLogVerboseModulesKey)) {
-    verboseLogModules_ = splitAndTrim(val, ',');
-  } else if (!name.compare(kActivitiesEnabledKey)) {
-    activityProfilerEnabled_ = toBool(val);
-  } else if (!name.compare(kActivitiesLogFileKey)) {
-    activitiesLogFile_ = val;
-    activitiesLogUrl_ = fmt::format("file://{}", val);
-    activitiesOnDemandTimestamp_ = timestamp();
-  } else if (!name.compare(kActivitiesMaxGpuBufferSizeKey)) {
-    activitiesMaxGpuBufferSize_ = toInt32(val) * 1024 * 1024;
-  } else if (!name.compare(kActivitiesWarmupDurationSecsKey)) {
-    activitiesWarmupDuration_ = seconds(toInt32(val));
-  } else if (!name.compare(kActivitiesWarmupIterationsKey)) {
-    activitiesWarmupIterations_ = toInt32(val);
-  }
-
-  // Client Interface
-  else if (!name.compare(kClientInterfaceEnableOpInputsCollection)) {
-    enableOpInputsCollection_ = toBool(val);
-  }
-
-  // Common
-  else if (!name.compare(kRequestTimestampKey)) {
-    VLOG(0) << kRequestTimestampKey
-            << " has been deprecated - please use "
-            << kProfileStartTimeKey;
-    requestTimestamp_ = handleRequestTimestamp(toInt64(val));
-  } else if (!name.compare(kProfileStartTimeKey)) {
-    profileStartTime_ =
-      time_point<system_clock>(milliseconds(toInt64(val)));
-  } else if (!name.compare(kProfileStartIterationKey)) {
-    profileStartIteration_ = toInt32(val);
-  } else if (!name.compare(kProfileStartIterationRoundUpKey)) {
-    profileStartIterationRoundUp_ = toInt32(val);
-  } else if (!name.compare(kEnableSigUsr2Key)) {
-    enableSigUsr2_ = toBool(val);
-  } else if (!name.compare(kEnableIpcFabricKey)) {
-    enableIpcFabric_ = toBool(val);
-  } else {
-    return false;
-  }
-  return true;
-}
-
-std::chrono::milliseconds Config::activitiesDurationDefault() const {
-  return kDefaultActivitiesProfileDurationMSecs;
-};
-
-void Config::updateActivityProfilerRequestReceivedTime() {
-  activitiesOnDemandTimestamp_ = system_clock::now();
-}
-
-void Config::setClientDefaults() {
-  AbstractConfig::setClientDefaults();
-  activitiesLogToMemory_ = true;
-}
-
-void Config::validate(
-    const time_point<system_clock>& fallbackProfileStartTime) {
-  if (samplePeriod_.count() == 0) {
-    LOG(WARNING) << "Sample period must be greater than 0, setting to 1ms";
-    samplePeriod_ = milliseconds(1);
-  }
-
-  if (multiplexPeriod_ < samplePeriod_) {
-    LOG(WARNING) << "Multiplex period can not be smaller "
-                 << "than sample period";
-    LOG(WARNING) << "Setting multiplex period to " << samplePeriod_.count()
-                 << "ms";
-    multiplexPeriod_ = samplePeriod_;
-  }
-
-  if ((multiplexPeriod_ % samplePeriod_).count() != 0) {
-    LOG(WARNING) << "Multiplex period must be a "
-                 << "multiple of sample period";
-    multiplexPeriod_ = alignUp(multiplexPeriod_, samplePeriod_);
-    LOG(WARNING) << "Setting multiplex period to " << multiplexPeriod_.count()
-                 << "ms";
-  }
-
-  if ((reportPeriod_ % multiplexPeriod_).count() != 0 ||
-      reportPeriod_.count() == 0) {
-    LOG(WARNING) << "Report period must be a "
-                 << "multiple of multiplex period";
-    reportPeriod_ = alignUp(reportPeriod_, multiplexPeriod_);
-    LOG(WARNING) << "Setting report period to " << reportPeriod_.count()
-                 << "ms";
-  }
-
-  if (samplesPerReport_ < 1) {
-    LOG(WARNING) << "Samples per report must be in the range "
-                 << "[1, report period / sample period]";
-    LOG(WARNING) << "Setting samples per report to 1";
-    samplesPerReport_ = 1;
-  }
-
-  int max_samples_per_report = reportPeriod_ / samplePeriod_;
-  if (samplesPerReport_ > max_samples_per_report) {
-    LOG(WARNING) << "Samples per report must be in the range "
-                 << "[1, report period / sample period] ([1, "
-                 << reportPeriod_.count() << "ms / " << samplePeriod_.count()
-                 << "ms = " << max_samples_per_report << "])";
-    LOG(WARNING) << "Setting samples per report to " << max_samples_per_report;
-    samplesPerReport_ = max_samples_per_report;
-  }
-
-  if (!hasProfileStartTime()) {
-    VLOG(0)
-        << "No explicit timestamp has been set. "
-        << "Defaulting it to now + activitiesWarmupDuration with buffer.";
-    profileStartTime_ = fallbackProfileStartTime +
-        activitiesWarmupDuration() + kDefaultBufferUntilWarmup;
-  }
-
-  if (profileStartIterationRoundUp_ == 0) {
-    // setting to 0 will mess up modulo arithmetic, set it to -1 so it has no effect
-    LOG(WARNING) << "Profiler start iteration round up should be >= 1.";
-    profileStartIterationRoundUp_ = -1;
-  }
-
-  if (profileStartIterationRoundUp_ > 0 && !hasProfileStartIteration()) {
-    VLOG(0) << "Setting profiler start iteration to 0 so this config is "
-            << "triggered via iteration count.";
-    profileStartIteration_ = 0;
-  }
-
-  if (selectedActivityTypes_.size() == 0) {
-    selectDefaultActivityTypes();
-  }
-}
-
-void Config::setReportPeriod(milliseconds msecs) {
-  reportPeriod_ = msecs;
-}
-
-void Config::printActivityProfilerConfig(std::ostream& s) const {
-  s << "Log file: " << activitiesLogFile() << std::endl;
-  if (hasProfileStartIteration()) {
-    s << "Trace start Iteration: " << profileStartIteration() << std::endl;
-    s << "Trace warmup Iterations: " << activitiesWarmupIterations() << std::endl;
-    s << "Trace profile Iterations: " << activitiesRunIterations() << std::endl;
-    if (profileStartIterationRoundUp() > 0) {
-      s << "Trace start iteration roundup : " << profileStartIterationRoundUp()
-        << std::endl;
-    }
-  } else if (hasProfileStartTime()) {
-    std::time_t t_c = system_clock::to_time_t(requestTimestamp());
-    LOG(INFO) << "Trace start time: "
-              << fmt::format("{:%Y-%m-%d %H:%M:%S}", fmt::localtime(t_c));
-    s << "Trace duration: " << activitiesDuration().count() << "ms"
-      << std::endl;
-    s << "Warmup duration: " << activitiesWarmupDuration().count() << "s"
-      << std::endl;
-  }
-
-  s << "Max GPU buffer size: " << activitiesMaxGpuBufferSize() / 1024 / 1024
-    << "MB" << std::endl;
-
-  std::vector<const char*> activities;
-  for (const auto& activity : selectedActivityTypes_) {
-    activities.push_back(toString(activity));
-  }
-  s << "Enabled activities: "
-    << fmt::format("{}", fmt::join(activities, ",")) << std::endl;
-
-  AbstractConfig::printActivityProfilerConfig(s);
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/ConfigLoader.cpp b/plugins/tensorboard-plugins/libkineto/src/ConfigLoader.cpp
deleted file mode 100644
index 4080b678d37..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ConfigLoader.cpp
+++ /dev/null
@@ -1,300 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "ConfigLoader.h"
-
-#ifdef __linux__
-#include <signal.h>
-#endif
-
-#include <stdlib.h>
-#include <chrono>
-#include <fmt/format.h>
-#include <fstream>
-#include <memory>
-
-#include "DaemonConfigLoader.h"
-
-#include "Logger.h"
-
-using namespace std::chrono;
-using std::string;
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-
-constexpr char kConfigFileEnvVar[] = "KINETO_CONFIG";
-#ifdef __linux__
-constexpr char kConfigFile[] = "/etc/libkineto.conf";
-constexpr char kOnDemandConfigFile[] = "/tmp/libkineto.conf";
-#else
-constexpr char kConfigFile[] = "libkineto.conf";
-constexpr char kOnDemandConfigFile[] = "libkineto.conf";
-#endif
-
-constexpr std::chrono::seconds kConfigUpdateIntervalSecs(300);
-constexpr std::chrono::seconds kOnDemandConfigUpdateIntervalSecs(5);
-
-#ifdef __linux__
-static struct sigaction originalUsr2Handler = {};
-#endif
-
-// Use SIGUSR2 to initiate profiling.
-// Look for an on-demand config file.
-// If none is found, default to base config.
-// Try to not affect existing handlers
-static bool hasOriginalSignalHandler() {
-#ifdef __linux__
-  return originalUsr2Handler.sa_handler != nullptr ||
-      originalUsr2Handler.sa_sigaction != nullptr;
-#else
-  return false;
-#endif
-}
-
-static void handle_signal(int signal) {
-#ifdef __linux__
-  if (signal == SIGUSR2) {
-    ConfigLoader::instance().handleOnDemandSignal();
-    if (hasOriginalSignalHandler()) {
-      // Invoke original handler and reinstate ours
-      struct sigaction act;
-      sigaction(SIGUSR2, &originalUsr2Handler, &act);
-      raise(SIGUSR2);
-      sigaction(SIGUSR2, &act, &originalUsr2Handler);
-    }
-  }
-#endif
-}
-
-static void setupSignalHandler(bool enableSigUsr2) {
-#ifdef __linux__
-  if (enableSigUsr2) {
-    struct sigaction act = {};
-    act.sa_handler = &handle_signal;
-    act.sa_flags = SA_NODEFER;
-    if (sigaction(SIGUSR2, &act, &originalUsr2Handler) < 0) {
-      PLOG(ERROR) << "Failed to register SIGUSR2 handler";
-    }
-    if (originalUsr2Handler.sa_handler == &handle_signal) {
-      originalUsr2Handler = {};
-    }
-  } else if (hasOriginalSignalHandler()) {
-    sigaction(SIGUSR2, &originalUsr2Handler, nullptr);
-    originalUsr2Handler = {};
-  }
-#endif
-}
-
-// return an empty string if reading gets any errors. Otherwise a config string.
-static std::string readConfigFromConfigFile(const char* filename) {
-  // Read whole file into a string.
-  std::ifstream file(filename);
-  std::string conf;
-  try {
-    conf.assign(
-        std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>());
-  } catch (std::exception& e) {
-    VLOG(0) << "Error reading " << filename << ": "
-            << e.what();
-    conf = "";
-  }
-  return conf;
-}
-
-static std::function<std::unique_ptr<DaemonConfigLoader>()>&
-daemonConfigLoaderFactory() {
-  static std::function<std::unique_ptr<DaemonConfigLoader>()> factory = nullptr;
-  return factory;
-}
-
-void ConfigLoader::setDaemonConfigLoaderFactory(
-    std::function<std::unique_ptr<DaemonConfigLoader>()> factory) {
-  daemonConfigLoaderFactory() = factory;
-}
-
-ConfigLoader& ConfigLoader::instance() {
-  static ConfigLoader config_loader;
-  return config_loader;
-}
-
-// return an empty string if polling gets any errors. Otherwise a config string.
-std::string ConfigLoader::readOnDemandConfigFromDaemon(
-    time_point<system_clock> now) {
-  if (!daemonConfigLoader_) {
-    return "";
-  }
-  bool events = canHandlerAcceptConfig(ConfigKind::EventProfiler);
-  bool activities = canHandlerAcceptConfig(ConfigKind::ActivityProfiler);
-  return daemonConfigLoader_->readOnDemandConfig(events, activities);
-}
-
-int ConfigLoader::contextCountForGpu(uint32_t device) {
-  if (!daemonConfigLoader_) {
-    // FIXME: Throw error?
-    return 0;
-  }
-  return daemonConfigLoader_->gpuContextCount(device);
-}
-
-ConfigLoader::ConfigLoader()
-    : configUpdateIntervalSecs_(kConfigUpdateIntervalSecs),
-      onDemandConfigUpdateIntervalSecs_(kOnDemandConfigUpdateIntervalSecs),
-      stopFlag_(false),
-      onDemandSignal_(false) {
-}
-
-void ConfigLoader::startThread() {
-  if (!updateThread_) {
-    // Create default base config here - at this point static initializers
-    // of extensions should have run and registered all config feature factories
-    std::lock_guard<std::mutex> lock(configLock_);
-    if (!config_) {
-      config_ = std::make_unique<Config>();
-    }
-    updateThread_ =
-        std::make_unique<std::thread>(&ConfigLoader::updateConfigThread, this);
-  }
-}
-
-ConfigLoader::~ConfigLoader() {
-  if (updateThread_) {
-    stopFlag_ = true;
-    {
-      std::lock_guard<std::mutex> lock(updateThreadMutex_);
-      updateThreadCondVar_.notify_one();
-    }
-    updateThread_->join();
-  }
-#if !USE_GOOGLE_LOG
-  Logger::clearLoggerObservers();
-#endif // !USE_GOOGLE_LOG
-}
-
-void ConfigLoader::handleOnDemandSignal() {
-  onDemandSignal_ = true;
-  {
-    std::lock_guard<std::mutex> lock(updateThreadMutex_);
-    updateThreadCondVar_.notify_one();
-  }
-}
-
-const char* ConfigLoader::configFileName() {
-  if (!configFileName_) {
-    configFileName_ = getenv(kConfigFileEnvVar);
-    if (configFileName_ == nullptr) {
-      configFileName_ = kConfigFile;
-    }
-  }
-  return configFileName_;
-}
-
-DaemonConfigLoader* ConfigLoader::daemonConfigLoader() {
-  if (!daemonConfigLoader_ && daemonConfigLoaderFactory()) {
-    daemonConfigLoader_ = daemonConfigLoaderFactory()();
-    daemonConfigLoader_->setCommunicationFabric(config_->ipcFabricEnabled());
-  }
-  return daemonConfigLoader_.get();
-}
-
-void ConfigLoader::updateBaseConfig() {
-  // First try reading local config file
-  // If that fails, read from daemon
-  // TODO: Invert these once daemon path fully rolled out
-  std::string config_str = readConfigFromConfigFile(configFileName());
-  if (config_str.empty() && daemonConfigLoader()) {
-    // If local config file was not successfully loaded (e.g. not found)
-    // then try the daemon
-    config_str = daemonConfigLoader()->readBaseConfig();
-  }
-  if (config_str != config_->source()) {
-    std::lock_guard<std::mutex> lock(configLock_);
-    config_ = std::make_unique<Config>();
-    config_->parse(config_str);
-    if (daemonConfigLoader()) {
-      daemonConfigLoader()->setCommunicationFabric(config_->ipcFabricEnabled());
-    }
-    setupSignalHandler(config_->sigUsr2Enabled());
-    SET_LOG_VERBOSITY_LEVEL(
-        config_->verboseLogLevel(),
-        config_->verboseLogModules());
-    VLOG(0) << "Detected base config change";
-  }
-}
-
-void ConfigLoader::configureFromSignal(
-    time_point<system_clock> now,
-    Config& config) {
-  LOG(INFO) << "Received on-demand profiling signal, "
-            << "reading config from " << kOnDemandConfigFile;
-  // Reset start time to 0 in order to compute new default start time
-  const std::string config_str = "PROFILE_START_TIME=0\n"
-      + readConfigFromConfigFile(kOnDemandConfigFile);
-  config.parse(config_str);
-  config.setSignalDefaults();
-  notifyHandlers(config);
-}
-
-void ConfigLoader::configureFromDaemon(
-    time_point<system_clock> now,
-    Config& config) {
-  const std::string config_str = readOnDemandConfigFromDaemon(now);
-  if (config_str.empty()) {
-    return;
-  }
-
-  LOG(INFO) << "Received config from dyno:\n" << config_str;
-  config.parse(config_str);
-  notifyHandlers(config);
-}
-
-void ConfigLoader::updateConfigThread() {
-  auto now = system_clock::now();
-  auto next_config_load_time = now;
-  auto next_on_demand_load_time = now + onDemandConfigUpdateIntervalSecs_;
-  seconds interval = configUpdateIntervalSecs_;
-  if (interval > onDemandConfigUpdateIntervalSecs_) {
-    interval = onDemandConfigUpdateIntervalSecs_;
-  }
-  auto onDemandConfig = std::make_unique<Config>();
-
-  // This can potentially sleep for long periods of time, so allow
-  // the desctructor to wake it to avoid a 5-minute long destruct period.
-  for (;;) {
-    {
-      std::unique_lock<std::mutex> lock(updateThreadMutex_);
-      updateThreadCondVar_.wait_for(lock, interval);
-    }
-    if (stopFlag_) {
-      break;
-    }
-    now = system_clock::now();
-    if (now > next_config_load_time) {
-      updateBaseConfig();
-      next_config_load_time = now + configUpdateIntervalSecs_;
-    }
-    if (onDemandSignal_.exchange(false)) {
-      onDemandConfig = config_->clone();
-      configureFromSignal(now, *onDemandConfig);
-    } else if (now > next_on_demand_load_time) {
-      onDemandConfig = std::make_unique<Config>();
-      configureFromDaemon(now, *onDemandConfig);
-      next_on_demand_load_time = now + onDemandConfigUpdateIntervalSecs_;
-    }
-    if (onDemandConfig->verboseLogLevel() >= 0) {
-      LOG(INFO) << "Setting verbose level to "
-                << onDemandConfig->verboseLogLevel()
-                << " from on-demand config";
-      SET_LOG_VERBOSITY_LEVEL(
-          onDemandConfig->verboseLogLevel(),
-          onDemandConfig->verboseLogModules());
-    }
-  }
-}
-
-bool ConfigLoader::hasNewConfig(const Config& oldConfig) {
-  std::lock_guard<std::mutex> lock(configLock_);
-  return config_->timestamp() > oldConfig.timestamp();
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/ConfigLoader.h b/plugins/tensorboard-plugins/libkineto/src/ConfigLoader.h
deleted file mode 100644
index 4ce3468e48d..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ConfigLoader.h
+++ /dev/null
@@ -1,147 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <algorithm>
-#include <atomic>
-#include <chrono>
-#include <condition_variable>
-#include <mutex>
-#include <string>
-#include <thread>
-
-#include "Config.h"
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "ILoggerObserver.h"
-
-namespace libkineto {
-  class LibkinetoApi;
-}
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-class DaemonConfigLoader;
-
-class ConfigLoader {
- public:
-
-  static ConfigLoader& instance();
-
-  enum ConfigKind {
-    ActivityProfiler = 0,
-    EventProfiler,
-    NumConfigKinds
-  };
-
-  struct ConfigHandler {
-    virtual ~ConfigHandler() {}
-    virtual bool canAcceptConfig() = 0;
-    virtual void acceptConfig(const Config& cfg) = 0;
-  };
-
-  void addHandler(ConfigKind kind, ConfigHandler* handler) {
-    std::lock_guard<std::mutex> lock(updateThreadMutex_);
-    handlers_[kind].push_back(handler);
-    startThread();
-  }
-
-  void removeHandler(ConfigKind kind, ConfigHandler* handler) {
-    std::lock_guard<std::mutex> lock(updateThreadMutex_);
-    auto it = std::find(
-        handlers_[kind].begin(), handlers_[kind].end(), handler);
-    if (it != handlers_[kind].end()) {
-      handlers_[kind].erase(it);
-    }
-  }
-
-  void notifyHandlers(const Config& cfg) {
-    std::lock_guard<std::mutex> lock(updateThreadMutex_);
-    for (auto& key_val : handlers_) {
-      for (ConfigHandler* handler : key_val.second) {
-        handler->acceptConfig(cfg);
-      }
-    }
-  }
-
-  bool canHandlerAcceptConfig(ConfigKind kind) {
-    std::lock_guard<std::mutex> lock(updateThreadMutex_);
-    for (ConfigHandler* handler : handlers_[kind]) {
-      if (!handler->canAcceptConfig()) {
-        return false;
-      }
-    }
-    return true;
-  }
-
-  void initBaseConfig() {
-    bool init = false;
-    {
-      std::lock_guard<std::mutex> lock(configLock_);
-      init = !config_ || config_->source().empty();
-    }
-    if (init) {
-      updateBaseConfig();
-    }
-  }
-
-  inline std::unique_ptr<Config> getConfigCopy() {
-    std::lock_guard<std::mutex> lock(configLock_);
-    return config_->clone();
-  }
-
-  bool hasNewConfig(const Config& oldConfig);
-  int contextCountForGpu(uint32_t gpu);
-
-  void handleOnDemandSignal();
-
-  static void setDaemonConfigLoaderFactory(
-      std::function<std::unique_ptr<DaemonConfigLoader>()> factory);
-
- private:
-  ConfigLoader();
-  ~ConfigLoader();
-
-  const char* configFileName();
-  DaemonConfigLoader* daemonConfigLoader();
-
-  void startThread();
-  void updateConfigThread();
-  void updateBaseConfig();
-
-  // Create configuration when receiving SIGUSR2
-  void configureFromSignal(
-      std::chrono::time_point<std::chrono::system_clock> now,
-      Config& config);
-
-  // Create configuration when receiving request from a daemon
-  void configureFromDaemon(
-      std::chrono::time_point<std::chrono::system_clock> now,
-      Config& config);
-
-  std::string readOnDemandConfigFromDaemon(
-      std::chrono::time_point<std::chrono::system_clock> now);
-
-  std::mutex configLock_;
-  std::atomic<const char*> configFileName_{nullptr};
-  std::unique_ptr<Config> config_;
-  std::unique_ptr<DaemonConfigLoader> daemonConfigLoader_;
-  std::map<ConfigKind, std::vector<ConfigHandler*>> handlers_;
-
-  std::chrono::seconds configUpdateIntervalSecs_;
-  std::chrono::seconds onDemandConfigUpdateIntervalSecs_;
-  std::unique_ptr<std::thread> updateThread_;
-  std::condition_variable updateThreadCondVar_;
-  std::mutex updateThreadMutex_;
-  std::atomic_bool stopFlag_{false};
-  std::atomic_bool onDemandSignal_{false};
-
-#if !USE_GOOGLE_LOG
-  std::unique_ptr<std::set<ILoggerObserver*>> loggerObservers_;
-  std::mutex loggerObserversMutex_;
-#endif // !USE_GOOGLE_LOG
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CudaDeviceProperties.cpp b/plugins/tensorboard-plugins/libkineto/src/CudaDeviceProperties.cpp
deleted file mode 100644
index 1e909d5f9cf..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CudaDeviceProperties.cpp
+++ /dev/null
@@ -1,130 +0,0 @@
-/*
- * Copyright (c) Kineto Contributors
- * All rights reserved.
- * This source code is licensed under the BSD-style license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#include "CudaDeviceProperties.h"
-
-#include <fmt/format.h>
-#include <vector>
-
-#include <cuda_runtime.h>
-#include <cuda_occupancy.h>
-
-#include "Logger.h"
-
-namespace KINETO_NAMESPACE {
-
-static const std::vector<cudaDeviceProp> createDeviceProps() {
-  std::vector<cudaDeviceProp> props;
-  int device_count;
-  cudaError_t error_id = cudaGetDeviceCount(&device_count);
-  // Return empty vector if error.
-  if (error_id != cudaSuccess) {
-    LOG(ERROR) << "cudaGetDeviceCount failed with code " << error_id;
-    return {};
-  }
-  VLOG(0) << "Device count is " << device_count;
-  for (size_t i = 0; i < device_count; ++i) {
-    cudaDeviceProp prop;
-    error_id = cudaGetDeviceProperties(&prop, i);
-    // Return empty vector if any device property fail to get.
-    if (error_id != cudaSuccess) {
-      LOG(ERROR) << "cudaGetDeviceProperties failed with " << error_id;
-      return {};
-    }
-    props.push_back(prop);
-    LOGGER_OBSERVER_ADD_DEVICE(i);
-  }
-  return props;
-}
-
-static const std::vector<cudaDeviceProp>& deviceProps() {
-  static const std::vector<cudaDeviceProp> props = createDeviceProps();
-  return props;
-}
-
-static const std::string createDevicePropertiesJson(
-    size_t id, const cudaDeviceProp& props) {
-  return fmt::format(R"JSON(
-    {{
-      "id": {}, "name": "{}", "totalGlobalMem": {},
-      "computeMajor": {}, "computeMinor": {},
-      "maxThreadsPerBlock": {}, "maxThreadsPerMultiprocessor": {},
-      "regsPerBlock": {}, "regsPerMultiprocessor": {}, "warpSize": {},
-      "sharedMemPerBlock": {}, "sharedMemPerMultiprocessor": {},
-      "numSms": {}, "sharedMemPerBlockOptin": {}
-    }})JSON",
-      id, props.name, props.totalGlobalMem,
-      props.major, props.minor,
-      props.maxThreadsPerBlock, props.maxThreadsPerMultiProcessor,
-      props.regsPerBlock, props.regsPerMultiprocessor, props.warpSize,
-      props.sharedMemPerBlock, props.sharedMemPerMultiprocessor,
-      props.multiProcessorCount, props.sharedMemPerBlockOptin);
-}
-
-static const std::string createDevicePropertiesJson() {
-  std::vector<std::string> jsonProps;
-  const auto& props = deviceProps();
-  for (size_t i = 0; i < props.size(); i++) {
-    jsonProps.push_back(createDevicePropertiesJson(i, props[i]));
-  }
-  return fmt::format("{}", fmt::join(jsonProps, ","));
-}
-
-const std::string& devicePropertiesJson() {
-  static std::string devicePropsJson = createDevicePropertiesJson();
-  return devicePropsJson;
-}
-
-int smCount(uint32_t deviceId) {
-  const std::vector<cudaDeviceProp> &props = deviceProps();
-  return deviceId >= props.size() ? 0 :
-     props[deviceId].multiProcessorCount;
-}
-
-float kernelOccupancy(
-    uint32_t deviceId,
-    uint16_t registersPerThread,
-    int32_t staticSharedMemory,
-    int32_t dynamicSharedMemory,
-    int32_t blockX,
-    int32_t blockY,
-    int32_t blockZ,
-    float blocksPerSm) {
-  // Calculate occupancy
-  float occupancy = -1.0;
-  const std::vector<cudaDeviceProp> &props = deviceProps();
-  if (deviceId < props.size()) {
-    cudaOccFuncAttributes occFuncAttr;
-    occFuncAttr.maxThreadsPerBlock = INT_MAX;
-    occFuncAttr.numRegs = registersPerThread;
-    occFuncAttr.sharedSizeBytes = staticSharedMemory;
-    occFuncAttr.partitionedGCConfig = PARTITIONED_GC_OFF;
-    occFuncAttr.shmemLimitConfig = FUNC_SHMEM_LIMIT_DEFAULT;
-    occFuncAttr.maxDynamicSharedSizeBytes = 0;
-    const cudaOccDeviceState occDeviceState = {};
-    int blockSize = blockX * blockY * blockZ;
-    size_t dynamicSmemSize = dynamicSharedMemory;
-    cudaOccResult occ_result;
-    cudaOccDeviceProp prop(props[deviceId]);
-    cudaOccError status = cudaOccMaxActiveBlocksPerMultiprocessor(
-          &occ_result, &prop, &occFuncAttr, &occDeviceState,
-          blockSize, dynamicSmemSize);
-    if (status == CUDA_OCC_SUCCESS) {
-      if (occ_result.activeBlocksPerMultiprocessor < blocksPerSm) {
-        blocksPerSm = occ_result.activeBlocksPerMultiprocessor;
-      }
-      occupancy = blocksPerSm * blockSize /
-          (float) props[deviceId].maxThreadsPerMultiProcessor;
-    } else {
-      LOG_EVERY_N(ERROR, 1000) << "Failed to calculate occupancy, status = "
-                               << status;
-    }
-  }
-  return occupancy;
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CudaDeviceProperties.h b/plugins/tensorboard-plugins/libkineto/src/CudaDeviceProperties.h
deleted file mode 100644
index b731fde0c2a..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CudaDeviceProperties.h
+++ /dev/null
@@ -1,31 +0,0 @@
-/*
- * Copyright (c) Kineto Contributors
- * All rights reserved.
- * This source code is licensed under the BSD-style license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#pragma once
-
-#include <stdint.h>
-#include <string>
-
-namespace KINETO_NAMESPACE {
-
-int smCount(uint32_t deviceId);
-
-// Return estimated achieved occupancy for a kernel
-float kernelOccupancy(
-    uint32_t deviceId,
-    uint16_t registersPerThread,
-    int32_t staticSharedMemory,
-    int32_t dynamicSharedMemory,
-    int32_t blockX,
-    int32_t blockY,
-    int32_t blockZ,
-    float blocks_per_sm);
-
-// Return compute properties for each device as a json string
-const std::string& devicePropertiesJson();
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivity.h b/plugins/tensorboard-plugins/libkineto/src/CuptiActivity.h
deleted file mode 100644
index 09c29504060..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivity.h
+++ /dev/null
@@ -1,114 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <cupti.h>
-
-#include "ITraceActivity.h"
-#include "CuptiActivityPlatform.h"
-#include "ThreadUtil.h"
-#include "cupti_strings.h"
-
-namespace libkineto {
-  class ActivityLogger;
-}
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-struct TraceSpan;
-
-// These classes wrap the various CUPTI activity types
-// into subclasses of ITraceActivity so that they can all be accessed
-// using the ITraceActivity interface and logged via ActivityLogger.
-
-// Abstract base class, templated on Cupti activity type
-template<class T>
-struct CuptiActivity : public ITraceActivity {
-  explicit CuptiActivity(const T* activity, const ITraceActivity* linked)
-      : activity_(*activity), linked_(linked) {}
-  int64_t timestamp() const override {
-    return nsToUs(unixEpochTimestamp(activity_.start));
-  }
-  int64_t duration() const override {
-    return nsToUs(activity_.end - activity_.start);
-  }
-  // TODO(T107507796): Deprecate ITraceActivity
-  int64_t correlationId() const override {return 0;}
-  int32_t getThreadId() const override {return 0;}
-  const ITraceActivity* linkedActivity() const override {return linked_;}
-  int flowType() const override {return kLinkAsyncCpuGpu;}
-  int flowId() const override {return correlationId();}
-  const T& raw() const {return activity_;}
-  const TraceSpan* traceSpan() const override {return nullptr;}
-
- protected:
-  const T& activity_;
-  const ITraceActivity* linked_{nullptr};
-};
-
-// CUpti_ActivityAPI - CUDA runtime activities
-struct RuntimeActivity : public CuptiActivity<CUpti_ActivityAPI> {
-  explicit RuntimeActivity(
-      const CUpti_ActivityAPI* activity,
-      const ITraceActivity* linked,
-      int32_t threadId)
-      : CuptiActivity(activity, linked), threadId_(threadId) {}
-  int64_t correlationId() const override {return activity_.correlationId;}
-  int64_t deviceId() const override {return processId();}
-  int64_t resourceId() const override {return threadId_;}
-  ActivityType type() const override {return ActivityType::CUDA_RUNTIME;}
-  bool flowStart() const override;
-  const std::string name() const override {return runtimeCbidName(activity_.cbid);}
-  void log(ActivityLogger& logger) const override;
-  const std::string metadataJson() const override;
-
- private:
-  const int32_t threadId_;
-};
-
-// CUpti_ActivityAPI - CUDA runtime activities
-struct OverheadActivity : public CuptiActivity<CUpti_ActivityOverhead> {
-  explicit OverheadActivity(
-      const CUpti_ActivityOverhead* activity,
-      const ITraceActivity* linked,
-      int32_t threadId=0)
-      : CuptiActivity(activity, linked), threadId_(threadId) {}
-
-  int64_t timestamp() const override {
-    return nsToUs(unixEpochTimestamp(activity_.start));
-  }
-  int64_t duration() const override {
-    return nsToUs(activity_.end - activity_.start);
-  }
-  // TODO: Update this with PID ordering
-  int64_t deviceId() const override {return -1;}
-  int64_t resourceId() const override {return threadId_;}
-  ActivityType type() const override {return ActivityType::OVERHEAD;}
-  bool flowStart() const override;
-  const std::string name() const override {return overheadKindString(activity_.overheadKind);}
-  void log(ActivityLogger& logger) const override;
-  const std::string metadataJson() const override;
-
- private:
-  const int32_t threadId_;
-};
-
-// Base class for GPU activities.
-// Can also be instantiated directly.
-template<class T>
-struct GpuActivity : public CuptiActivity<T> {
-  explicit GpuActivity(const T* activity, const ITraceActivity* linked)
-      : CuptiActivity<T>(activity, linked) {}
-  int64_t correlationId() const override {return raw().correlationId;}
-  int64_t deviceId() const override {return raw().deviceId;}
-  int64_t resourceId() const override {return raw().streamId;}
-  ActivityType type() const override;
-  bool flowStart() const override {return false;}
-  const std::string name() const override;
-  void log(ActivityLogger& logger) const override;
-  const std::string metadataJson() const override;
-  const T& raw() const {return CuptiActivity<T>::raw();}
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivity.tpp b/plugins/tensorboard-plugins/libkineto/src/CuptiActivity.tpp
deleted file mode 100644
index 1ff2dafe06b..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivity.tpp
+++ /dev/null
@@ -1,111 +0,0 @@
- /*
- * Copyright (c) Facebook, Inc. and its affiliates.
- * All rights reserved.
- * This source code is licensed under the BSD-style license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#include "CuptiActivity.h"
-
-#include <fmt/format.h>
-
-#include "Demangle.h"
-#include "output_base.h"
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-
-template<>
-inline const std::string GpuActivity<CUpti_ActivityKernel4>::name() const {
-  return demangle(raw().name);
-}
-
-template<>
-inline ActivityType GpuActivity<CUpti_ActivityKernel4>::type() const {
-  return ActivityType::CONCURRENT_KERNEL;
-}
-
-static inline std::string memcpyName(uint8_t kind, uint8_t src, uint8_t dst) {
-  return fmt::format(
-      "Memcpy {} ({} -> {})",
-      memcpyKindString((CUpti_ActivityMemcpyKind)kind),
-      memoryKindString((CUpti_ActivityMemoryKind)src),
-      memoryKindString((CUpti_ActivityMemoryKind)dst));
-}
-
-template<>
-inline ActivityType GpuActivity<CUpti_ActivityMemcpy>::type() const {
-  return ActivityType::GPU_MEMCPY;
-}
-
-template<>
-inline const std::string GpuActivity<CUpti_ActivityMemcpy>::name() const {
-  return memcpyName(raw().copyKind, raw().srcKind, raw().dstKind);
-}
-
-template<>
-inline ActivityType GpuActivity<CUpti_ActivityMemcpy2>::type() const {
-  return ActivityType::GPU_MEMCPY;
-}
-
-template<>
-inline const std::string GpuActivity<CUpti_ActivityMemcpy2>::name() const {
-  return memcpyName(raw().copyKind, raw().srcKind, raw().dstKind);
-}
-
-template<>
-inline const std::string GpuActivity<CUpti_ActivityMemset>::name() const {
-  const char* memory_kind =
-    memoryKindString((CUpti_ActivityMemoryKind)raw().memoryKind);
-  return fmt::format("Memset ({})", memory_kind);
-}
-
-template<>
-inline ActivityType GpuActivity<CUpti_ActivityMemset>::type() const {
-  return ActivityType::GPU_MEMSET;
-}
-
-inline void RuntimeActivity::log(ActivityLogger& logger) const {
-  logger.handleActivity(*this);
-}
-
-inline void OverheadActivity::log(ActivityLogger& logger) const {
-  logger.handleActivity(*this);
-}
-
-inline bool OverheadActivity::flowStart() const {
-  return false;
-}
-
-inline const std::string OverheadActivity::metadataJson() const {
-  return "";
-}
-
-template<class T>
-inline void GpuActivity<T>::log(ActivityLogger& logger) const {
-  logger.handleGpuActivity(*this);
-}
-
-inline bool RuntimeActivity::flowStart() const {
-  return activity_.cbid == CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000 ||
-      (activity_.cbid >= CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy_v3020 &&
-       activity_.cbid <= CUPTI_RUNTIME_TRACE_CBID_cudaMemset2DAsync_v3020) ||
-      activity_.cbid ==
-          CUPTI_RUNTIME_TRACE_CBID_cudaLaunchCooperativeKernel_v9000 ||
-      activity_.cbid ==
-          CUPTI_RUNTIME_TRACE_CBID_cudaLaunchCooperativeKernelMultiDevice_v9000;
-}
-
-inline const std::string RuntimeActivity::metadataJson() const {
-  return fmt::format(R"JSON(
-      "cbid": {}, "correlation": {})JSON",
-      activity_.cbid, activity_.correlationId);
-}
-
-template<class T>
-inline const std::string GpuActivity<T>::metadataJson() const {
-  return "";
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityApi.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiActivityApi.cpp
deleted file mode 100644
index 5718bed2f89..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityApi.cpp
+++ /dev/null
@@ -1,343 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "CuptiActivityApi.h"
-
-#include <assert.h>
-#include <chrono>
-
-#include "cupti_call.h"
-#include "Logger.h"
-
-using namespace std::chrono;
-
-namespace KINETO_NAMESPACE {
-
-// TODO: do we want this to be configurable?
-// Set to 2MB to avoid constantly creating buffers (espeically for networks
-// that has many small memcpy such as sparseNN)
-// Consider putting this on huge pages?
-constexpr size_t kBufSize(2 * 1024 * 1024);
-
-CuptiActivityApi& CuptiActivityApi::singleton() {
-  static CuptiActivityApi instance;
-  return instance;
-}
-
-void CuptiActivityApi::pushCorrelationID(int id, CorrelationFlowType type) {
-#ifdef HAS_CUPTI
-  if (!singleton().externalCorrelationEnabled_) {
-    return;
-  }
-  VLOG(2) << "pushCorrelationID(" << id << ")";
-  switch(type) {
-    case Default:
-      CUPTI_CALL(cuptiActivityPushExternalCorrelationId(
-        CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM0, id));
-        break;
-    case User:
-      CUPTI_CALL(cuptiActivityPushExternalCorrelationId(
-        CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM1, id));
-  }
-#endif
-}
-
-void CuptiActivityApi::popCorrelationID(CorrelationFlowType type) {
-#ifdef HAS_CUPTI
-  if (!singleton().externalCorrelationEnabled_) {
-    return;
-  }
-  switch(type) {
-    case Default:
-      CUPTI_CALL(cuptiActivityPopExternalCorrelationId(
-        CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM0, nullptr));
-        break;
-    case User:
-      CUPTI_CALL(cuptiActivityPopExternalCorrelationId(
-        CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM1, nullptr));
-  }
-#endif
-}
-
-static int getSMCount() {
-#ifdef HAS_CUPTI
-  // There may be a simpler way to get the number of SMs....
-  // Look for domain_d - this has 80 instances on Volta and
-  // 56 instances on Pascal, corresponding to the number of SMs
-  // FIXME: This does not work on Turing and later
-  uint32_t domainCount{0};
-  CUPTI_CALL(cuptiDeviceGetNumEventDomains(0, &domainCount));
-  std::vector<CUpti_EventDomainID> ids(domainCount);
-  size_t sz = sizeof(CUpti_EventDomainID) * domainCount;
-  CUPTI_CALL(cuptiDeviceEnumEventDomains(0, &sz, ids.data()));
-  for (CUpti_EventDomainID id : ids) {
-    char name[16];
-    name[0] = '\0';
-    sz = sizeof(name);
-    CUPTI_CALL(cuptiEventDomainGetAttribute(
-        id, CUPTI_EVENT_DOMAIN_ATTR_NAME, &sz, name));
-    if (strncmp(name, "domain_d", sz) == 0) {
-      uint32_t count{0};
-      sz = sizeof(count);
-      CUPTI_CALL(cuptiDeviceGetEventDomainAttribute(
-          0, id, CUPTI_EVENT_DOMAIN_ATTR_TOTAL_INSTANCE_COUNT, &sz, &count));
-      return count;
-    }
-  }
-#endif
-
-  return -1;
-}
-
-int CuptiActivityApi::smCount() {
-  static int sm_count = getSMCount();
-  return sm_count;
-}
-
-static bool nextActivityRecord(
-    uint8_t* buffer,
-    size_t valid_size,
-    CUpti_Activity*& record) {
-#ifdef HAS_CUPTI
-  CUptiResult status = CUPTI_CALL_NOWARN(
-      cuptiActivityGetNextRecord(buffer, valid_size, &record));
-  if (status != CUPTI_SUCCESS) {
-    if (status != CUPTI_ERROR_MAX_LIMIT_REACHED) {
-      CUPTI_CALL(status);
-    }
-    record = nullptr;
-  }
-#endif
-  return record != nullptr;
-}
-
-void CuptiActivityApi::setMaxBufferSize(int size) {
-  maxGpuBufferCount_ = 1 + size / kBufSize;
-}
-
-void CuptiActivityApi::forceLoadCupti() {
-#ifdef HAS_CUPTI
-  CUPTI_CALL(cuptiActivityEnable(CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL));
-#endif
-}
-
-#ifdef HAS_CUPTI
-void CUPTIAPI CuptiActivityApi::bufferRequestedTrampoline(
-    uint8_t** buffer,
-    size_t* size,
-    size_t* maxNumRecords) {
-  singleton().bufferRequested(buffer, size, maxNumRecords);
-}
-
-void CuptiActivityApi::bufferRequested(
-    uint8_t** buffer, size_t* size, size_t* maxNumRecords) {
-  std::lock_guard<std::mutex> guard(mutex_);
-  if (allocatedGpuTraceBuffers_.size() >= maxGpuBufferCount_) {
-    stopCollection = true;
-    LOG(WARNING) << "Exceeded max GPU buffer count ("
-                 << allocatedGpuTraceBuffers_.size()
-                 << " > " << maxGpuBufferCount_
-                 << ") - terminating tracing";
-  }
-
-  auto buf = std::make_unique<CuptiActivityBuffer>(kBufSize);
-  *buffer = buf->data();
-  *size = kBufSize;
-
-  allocatedGpuTraceBuffers_[*buffer] = std::move(buf);
-
-  *maxNumRecords = 0;
-}
-#endif
-
-std::unique_ptr<CuptiActivityBufferMap>
-CuptiActivityApi::activityBuffers() {
-  {
-    std::lock_guard<std::mutex> guard(mutex_);
-    if (allocatedGpuTraceBuffers_.empty()) {
-      return nullptr;
-    }
-  }
-
-#ifdef HAS_CUPTI
-  VLOG(1) << "Flushing GPU activity buffers";
-  time_point<system_clock> t1;
-  if (VLOG_IS_ON(1)) {
-    t1 = system_clock::now();
-  }
-  // Can't hold mutex_ during this call, since bufferCompleted
-  // will be called by libcupti and mutex_ is acquired there.
-  CUPTI_CALL(cuptiActivityFlushAll(CUPTI_ACTIVITY_FLAG_FLUSH_FORCED));
-  if (VLOG_IS_ON(1)) {
-    flushOverhead =
-        duration_cast<microseconds>(system_clock::now() - t1).count();
-  }
-#endif
-  std::lock_guard<std::mutex> guard(mutex_);
-  // Transfer ownership of buffers to caller. A new map is created on-demand.
-  return std::move(readyGpuTraceBuffers_);
-}
-
-#ifdef HAS_CUPTI
-int CuptiActivityApi::processActivitiesForBuffer(
-    uint8_t* buf,
-    size_t validSize,
-    std::function<void(const CUpti_Activity*)> handler) {
-  int count = 0;
-  if (buf && validSize) {
-    CUpti_Activity* record{nullptr};
-    while ((nextActivityRecord(buf, validSize, record))) {
-      handler(record);
-      ++count;
-    }
-  }
-  return count;
-}
-#endif
-
-const std::pair<int, int> CuptiActivityApi::processActivities(
-    CuptiActivityBufferMap& buffers,
-    std::function<void(const CUpti_Activity*)> handler) {
-  std::pair<int, int> res{0, 0};
-#ifdef HAS_CUPTI
-  for (auto& pair : buffers) {
-    // No lock needed - only accessed from this thread
-    auto& buf = pair.second;
-    res.first += processActivitiesForBuffer(buf->data(), buf->size(), handler);
-    res.second += buf->size();
-  }
-#endif
-  return res;
-}
-
-void CuptiActivityApi::clearActivities() {
-  {
-    std::lock_guard<std::mutex> guard(mutex_);
-    if (allocatedGpuTraceBuffers_.empty()) {
-      return;
-    }
-  }
-  // Can't hold mutex_ during this call, since bufferCompleted
-  // will be called by libcupti and mutex_ is acquired there.
-#ifdef HAS_CUPTI
-  CUPTI_CALL(cuptiActivityFlushAll(0));
-#endif
-  // FIXME: We might want to make sure we reuse
-  // the same memory during warmup and tracing.
-  // Also, try to use the amount of memory required
-  // for active tracing during warmup.
-  std::lock_guard<std::mutex> guard(mutex_);
-  // Throw away ready buffers as a result of above flush
-  readyGpuTraceBuffers_ = nullptr;
-}
-
-#ifdef HAS_CUPTI
-void CUPTIAPI CuptiActivityApi::bufferCompletedTrampoline(
-    CUcontext ctx,
-    uint32_t streamId,
-    uint8_t* buffer,
-    size_t /* unused */,
-    size_t validSize) {
-  singleton().bufferCompleted(ctx, streamId, buffer, 0, validSize);
-}
-
-void CuptiActivityApi::bufferCompleted(
-    CUcontext ctx,
-    uint32_t streamId,
-    uint8_t* buffer,
-    size_t /* unused */,
-    size_t validSize) {
-
-  std::lock_guard<std::mutex> guard(mutex_);
-  auto it = allocatedGpuTraceBuffers_.find(buffer);
-  if (it == allocatedGpuTraceBuffers_.end()) {
-    LOG(ERROR) << "bufferCompleted called with unknown buffer: "
-               << (void*) buffer;
-    return;
-  }
-
-  if (!readyGpuTraceBuffers_) {
-    readyGpuTraceBuffers_ = std::make_unique<CuptiActivityBufferMap>();
-  }
-  // Set valid size of buffer before moving to ready map
-  it->second->setSize(validSize);
-  (*readyGpuTraceBuffers_)[it->first] = std::move(it->second);
-  allocatedGpuTraceBuffers_.erase(it);
-
-  // report any records dropped from the queue; to avoid unnecessary cupti
-  // API calls, we make it report only in verbose mode (it doesn't happen
-  // often in our testing anyways)
-  if (VLOG_IS_ON(1)) {
-    size_t dropped = 0;
-    CUPTI_CALL(cuptiActivityGetNumDroppedRecords(ctx, streamId, &dropped));
-    if (dropped != 0) {
-      LOG(WARNING) << "Dropped " << dropped << " activity records";
-    }
-  }
-}
-#endif
-
-void CuptiActivityApi::enableCuptiActivities(
-    const std::set<ActivityType>& selected_activities) {
-#ifdef HAS_CUPTI
-  static bool registered = false;
-  if (!registered) {
-    CUPTI_CALL(
-        cuptiActivityRegisterCallbacks(bufferRequestedTrampoline, bufferCompletedTrampoline));
-  }
-
-  externalCorrelationEnabled_ = false;
-  for (const auto& activity : selected_activities) {
-    if (activity == ActivityType::GPU_MEMCPY) {
-      CUPTI_CALL(cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MEMCPY));
-    }
-    if (activity == ActivityType::GPU_MEMSET) {
-      CUPTI_CALL(cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MEMSET));
-    }
-    if (activity == ActivityType::CONCURRENT_KERNEL) {
-      CUPTI_CALL(cuptiActivityEnable(CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL));
-    }
-    if (activity == ActivityType::EXTERNAL_CORRELATION) {
-      CUPTI_CALL(cuptiActivityEnable(CUPTI_ACTIVITY_KIND_EXTERNAL_CORRELATION));
-      externalCorrelationEnabled_ = true;
-    }
-    if (activity == ActivityType::CUDA_RUNTIME) {
-      CUPTI_CALL(cuptiActivityEnable(CUPTI_ACTIVITY_KIND_RUNTIME));
-    }
-    if (activity == ActivityType::OVERHEAD) {
-      CUPTI_CALL(cuptiActivityEnable(CUPTI_ACTIVITY_KIND_OVERHEAD));
-    }
-  }
-#endif
-
-  // Explicitly enabled, so reset this flag if set
-  stopCollection = false;
-}
-
-void CuptiActivityApi::disableCuptiActivities(
-    const std::set<ActivityType>& selected_activities) {
-#ifdef HAS_CUPTI
-  for (const auto& activity : selected_activities) {
-    if (activity == ActivityType::GPU_MEMCPY) {
-      CUPTI_CALL(cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MEMCPY));
-    }
-    if (activity == ActivityType::GPU_MEMSET) {
-      CUPTI_CALL(cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MEMSET));
-    }
-    if (activity == ActivityType::CONCURRENT_KERNEL) {
-      CUPTI_CALL(cuptiActivityDisable(CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL));
-    }
-    if (activity == ActivityType::EXTERNAL_CORRELATION) {
-      CUPTI_CALL(cuptiActivityDisable(CUPTI_ACTIVITY_KIND_EXTERNAL_CORRELATION));
-    }
-    if (activity == ActivityType::CUDA_RUNTIME) {
-      CUPTI_CALL(cuptiActivityDisable(CUPTI_ACTIVITY_KIND_RUNTIME));
-    }
-    if (activity == ActivityType::OVERHEAD) {
-      CUPTI_CALL(cuptiActivityDisable(CUPTI_ACTIVITY_KIND_OVERHEAD));
-    }
-  }
-  externalCorrelationEnabled_ = false;
-#endif
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityApi.h b/plugins/tensorboard-plugins/libkineto/src/CuptiActivityApi.h
deleted file mode 100644
index 92af51ecac9..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityApi.h
+++ /dev/null
@@ -1,100 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <atomic>
-#include <functional>
-#include <list>
-#include <memory>
-#include <mutex>
-#include <set>
-
-#ifdef HAS_CUPTI
-#include <cupti.h>
-#endif
-
-#include "ActivityType.h"
-#include "CuptiActivityBuffer.h"
-
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-
-#ifndef HAS_CUPTI
-using CUpti_Activity = void;
-#endif
-
-class CuptiActivityApi {
- public:
-  enum CorrelationFlowType {
-    Default,
-    User
-  };
-
-  CuptiActivityApi() = default;
-  CuptiActivityApi(const CuptiActivityApi&) = delete;
-  CuptiActivityApi& operator=(const CuptiActivityApi&) = delete;
-
-  virtual ~CuptiActivityApi() {}
-
-  static CuptiActivityApi& singleton();
-
-  virtual int smCount();
-  static void pushCorrelationID(int id, CorrelationFlowType type);
-  static void popCorrelationID(CorrelationFlowType type);
-
-  void enableCuptiActivities(
-    const std::set<ActivityType>& selected_activities);
-  void disableCuptiActivities(
-    const std::set<ActivityType>& selected_activities);
-  void clearActivities();
-
-  virtual std::unique_ptr<CuptiActivityBufferMap> activityBuffers();
-
-  virtual const std::pair<int, int> processActivities(
-      CuptiActivityBufferMap&,
-      std::function<void(const CUpti_Activity*)> handler);
-
-  void setMaxBufferSize(int size);
-
-  std::atomic_bool stopCollection{false};
-  int64_t flushOverhead{0};
-
-  static void forceLoadCupti();
-
- private:
-#ifdef HAS_CUPTI
-  int processActivitiesForBuffer(
-      uint8_t* buf,
-      size_t validSize,
-      std::function<void(const CUpti_Activity*)> handler);
-  static void CUPTIAPI
-  bufferRequestedTrampoline(uint8_t** buffer, size_t* size, size_t* maxNumRecords);
-  static void CUPTIAPI bufferCompletedTrampoline(
-      CUcontext ctx,
-      uint32_t streamId,
-      uint8_t* buffer,
-      size_t /* unused */,
-      size_t validSize);
-#endif // HAS_CUPTI
-
-  int maxGpuBufferCount_{0};
-  CuptiActivityBufferMap allocatedGpuTraceBuffers_;
-  std::unique_ptr<CuptiActivityBufferMap> readyGpuTraceBuffers_;
-  std::mutex mutex_;
-  bool externalCorrelationEnabled_{false};
-
- protected:
-#ifdef HAS_CUPTI
-  void bufferRequested(uint8_t** buffer, size_t* size, size_t* maxNumRecords);
-  void bufferCompleted(
-      CUcontext ctx,
-      uint32_t streamId,
-      uint8_t* buffer,
-      size_t /* unused */,
-      size_t validSize);
-#endif
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityBuffer.h b/plugins/tensorboard-plugins/libkineto/src/CuptiActivityBuffer.h
deleted file mode 100644
index 1c3fbef62c8..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityBuffer.h
+++ /dev/null
@@ -1,51 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <stdlib.h>
-#include <assert.h>
-#include <cstdint>
-#include <map>
-#include <memory>
-#include <sys/types.h>
-#include <vector>
-
-#include "ITraceActivity.h"
-
-namespace KINETO_NAMESPACE {
-
-class CuptiActivityBuffer {
- public:
-  explicit CuptiActivityBuffer(size_t size) : size_(size) {
-    buf_.reserve(size);
-  }
-  CuptiActivityBuffer() = delete;
-  CuptiActivityBuffer& operator=(const CuptiActivityBuffer&) = delete;
-  CuptiActivityBuffer(CuptiActivityBuffer&&) = default;
-  CuptiActivityBuffer& operator=(CuptiActivityBuffer&&) = default;
-
-  size_t size() const {
-    return size_;
-  }
-
-  void setSize(size_t size) {
-    assert(size <= buf_.capacity());
-    size_ = size;
-  }
-
-  uint8_t* data() {
-    return buf_.data();
-  }
-
- private:
-
-  std::vector<uint8_t> buf_;
-  size_t size_;
-
-  std::vector<std::unique_ptr<const ITraceActivity>> wrappers_;
-};
-
-using CuptiActivityBufferMap =
-    std::map<uint8_t*, std::unique_ptr<CuptiActivityBuffer>>;
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityPlatform.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiActivityPlatform.cpp
deleted file mode 100644
index fa2ef2f3a8c..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityPlatform.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-#include <chrono>
-
-namespace chrono = std::chrono;
-
-namespace KINETO_NAMESPACE {
-
-#ifdef _WIN32
-uint64_t epochs_diff() {
-  // On Windows, steady_clock wraps the QueryPerformanceCounter function.
-  // https://docs.microsoft.com/en-us/cpp/standard-library/steady-clock-struct?view=msvc-160
-  auto steady =
-      chrono::time_point_cast<chrono::nanoseconds>(chrono::steady_clock::now());
-  auto system =
-      chrono::time_point_cast<chrono::nanoseconds>(chrono::system_clock::now());
-
-  auto time_since_unix = system.time_since_epoch().count();
-  auto time_since_boot = steady.time_since_epoch().count();
-  return time_since_unix - time_since_boot;
-}
-
-uint64_t unixEpochTimestamp(uint64_t ts) {
-  static uint64_t diff = epochs_diff();
-  return ts + diff;
-}
-#else
-uint64_t unixEpochTimestamp(uint64_t ts) {
-  return ts;
-}
-#endif // _WIN32
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityPlatform.h b/plugins/tensorboard-plugins/libkineto/src/CuptiActivityPlatform.h
deleted file mode 100644
index 78de8373d5f..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityPlatform.h
+++ /dev/null
@@ -1,12 +0,0 @@
-#pragma once
-
-#include <cstdint>
-
-namespace KINETO_NAMESPACE {
-
-// cupti's timestamps are platform specific. This function convert the raw
-// cupti timestamp to time since unix epoch. So that on different platform,
-// correction can work correctly.
-uint64_t unixEpochTimestamp(uint64_t ts);
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityProfiler.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiActivityProfiler.cpp
deleted file mode 100644
index 97c23ef047d..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityProfiler.cpp
+++ /dev/null
@@ -1,841 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "CuptiActivityProfiler.h"
-
-#include <fmt/format.h>
-#include <time.h>
-#include <atomic>
-#include <iomanip>
-#include <string>
-#include <thread>
-#include <vector>
-#include <limits>
-
-#ifdef HAS_CUPTI
-#include <cupti.h>
-#endif
-
-#include "Config.h"
-#include "time_since_epoch.h"
-#ifdef HAS_CUPTI
-#include "CuptiActivity.h"
-#include "CuptiActivity.tpp"
-#include "CuptiActivityApi.h"
-#endif // HAS_CUPTI
-#ifdef HAS_ROCTRACER
-#include "RoctracerActivityApi.h"
-#endif
-#include "output_base.h"
-
-#include "Logger.h"
-#include "ThreadUtil.h"
-
-using namespace std::chrono;
-using namespace libkineto;
-using std::string;
-
-namespace KINETO_NAMESPACE {
-
-void CuptiActivityProfiler::transferCpuTrace(
-    std::unique_ptr<libkineto::CpuTraceBuffer> cpuTrace) {
-  std::lock_guard<std::mutex> guard(mutex_);
-  const string& trace_name = cpuTrace->span.name;
-  if (currentRunloopState_ != RunloopState::CollectTrace &&
-      currentRunloopState_ != RunloopState::ProcessTrace) {
-    VLOG(0) << "Trace collection not in progress - discarding span "
-            << trace_name;
-    return;
-  }
-
-  cpuTrace->span.iteration = iterationCountMap_[trace_name]++;
-
-  VLOG(0) << "Received iteration " << cpuTrace->span.iteration << " of span "
-          << trace_name << " (" << cpuTrace->activities.size() << " activities / "
-          << cpuTrace->gpuOpCount << " gpu activities)";
-  traceBuffers_->cpu.push_back(std::move(cpuTrace));
-}
-
-#ifdef HAS_ROCTRACER
-CuptiActivityProfiler::CuptiActivityProfiler(RoctracerActivityApi& cupti, bool cpuOnly)
-#else
-CuptiActivityProfiler::CuptiActivityProfiler(CuptiActivityApi& cupti, bool cpuOnly)
-#endif
-    : cupti_(cupti),
-      flushOverhead_{0, 0},
-      setupOverhead_{0, 0},
-      cpuOnly_{cpuOnly},
-      currentRunloopState_{RunloopState::WaitForRequest},
-      stopCollection_{false} {}
-
-void CuptiActivityProfiler::processTraceInternal(ActivityLogger& logger) {
-  LOG(INFO) << "Processing " << traceBuffers_->cpu.size()
-      << " CPU buffers";
-  VLOG(0) << "Profile time range: " << captureWindowStartTime_ << " - "
-          << captureWindowEndTime_;
-  logger.handleTraceStart(metadata_);
-  for (auto& cpu_trace : traceBuffers_->cpu) {
-    string trace_name = cpu_trace->span.name;
-    VLOG(0) << "Processing CPU buffer for " << trace_name << " ("
-            << cpu_trace->span.iteration << ") - "
-            << cpu_trace->activities.size() << " records";
-    VLOG(0) << "Span time range: " << cpu_trace->span.startTime << " - "
-            << cpu_trace->span.endTime;
-    processCpuTrace(*cpu_trace, logger);
-    LOGGER_OBSERVER_ADD_EVENT_COUNT(cpu_trace->activities.size());
-  }
-
-#ifdef HAS_CUPTI
-  if (!cpuOnly_) {
-    VLOG(0) << "Retrieving GPU activity buffers";
-    traceBuffers_->gpu = cupti_.activityBuffers();
-    if (VLOG_IS_ON(1)) {
-      addOverheadSample(flushOverhead_, cupti_.flushOverhead);
-    }
-    if (traceBuffers_->gpu) {
-      const auto count_and_size = cupti_.processActivities(
-          *traceBuffers_->gpu,
-          std::bind(&CuptiActivityProfiler::handleCuptiActivity, this, std::placeholders::_1, &logger));
-      LOG(INFO) << "Processed " << count_and_size.first
-                << " GPU records (" << count_and_size.second << " bytes)";
-      LOGGER_OBSERVER_ADD_EVENT_COUNT(count_and_size.first);
-    }
-  }
-#endif // HAS_CUPTI
-#ifdef HAS_ROCTRACER
-  if (!cpuOnly_) {
-    VLOG(0) << "Retrieving GPU activity buffers";
-    const int count = cupti_.processActivities(logger);
-    LOG(INFO) << "Processed " << count
-              << " GPU records";
-    LOGGER_OBSERVER_ADD_EVENT_COUNT(count);
-  }
-#endif // HAS_ROCTRACER
-
-  for (const auto& session : sessions_){
-    LOG(INFO) << "Processing child profiler trace";
-    session->processTrace(logger);
-  }
-
-  finalizeTrace(*config_, logger);
-}
-
-CuptiActivityProfiler::CpuGpuSpanPair& CuptiActivityProfiler::recordTraceSpan(
-    TraceSpan& span, int gpuOpCount) {
-  TraceSpan gpu_span(gpuOpCount, span.iteration, span.name, "GPU: ");
-  auto& iterations = traceSpans_[span.name];
-  iterations.push_back({span, gpu_span});
-  return iterations.back();
-}
-
-void CuptiActivityProfiler::processCpuTrace(
-    libkineto::CpuTraceBuffer& cpuTrace,
-    ActivityLogger& logger) {
-  if (cpuTrace.activities.size() == 0) {
-    LOG(WARNING) << "CPU trace is empty!";
-    return;
-  }
-
-  CpuGpuSpanPair& span_pair = recordTraceSpan(cpuTrace.span, cpuTrace.gpuOpCount);
-  TraceSpan& cpu_span = span_pair.first;
-  for (auto const& act : cpuTrace.activities) {
-    VLOG(2) << act.correlationId() << ": OP " << act.activityName;
-    if (config_->selectedActivityTypes().count(act.type())) {
-      act.log(logger);
-    }
-    clientActivityTraceMap_[act.correlationId()] = &span_pair;
-    activityMap_[act.correlationId()] = &act;
-
-    recordThreadInfo(act.resourceId(), act.getThreadId(), act.deviceId());
-  }
-  logger.handleTraceSpan(cpu_span);
-}
-
-#ifdef HAS_CUPTI
-inline void CuptiActivityProfiler::handleCorrelationActivity(
-    const CUpti_ActivityExternalCorrelation* correlation) {
-  if (correlation->externalKind == CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM0) {
-    cpuCorrelationMap_[correlation->correlationId] = correlation->externalId;
-  } else if (correlation->externalKind == CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM1){
-    userCorrelationMap_[correlation->correlationId] = correlation->externalId;
-  } else {
-    LOG(ERROR) << "Invalid CUpti_ActivityExternalCorrelation sent to handleCuptiActivity";
-  }
-}
-#endif // HAS_CUPTI
-
-static GenericTraceActivity createUserGpuSpan(
-    const libkineto::ITraceActivity& cpuTraceActivity,
-    const libkineto::ITraceActivity& gpuTraceActivity) {
-  GenericTraceActivity res(
-      *cpuTraceActivity.traceSpan(),
-      ActivityType::GPU_USER_ANNOTATION,
-      cpuTraceActivity.name());
-  res.startTime = gpuTraceActivity.timestamp();
-  res.device = gpuTraceActivity.deviceId();
-  res.resource = gpuTraceActivity.resourceId();
-  res.endTime =
-      gpuTraceActivity.timestamp() + gpuTraceActivity.duration();
-  res.id = cpuTraceActivity.correlationId();
-  return res;
-}
-
-void CuptiActivityProfiler::GpuUserEventMap::insertOrExtendEvent(
-    const ITraceActivity& userActivity,
-    const ITraceActivity& gpuActivity) {
-  StreamKey key(gpuActivity.deviceId(), gpuActivity.resourceId());
-  CorrelationSpanMap& correlationSpanMap = streamSpanMap_[key];
-  auto it = correlationSpanMap.find(userActivity.correlationId());
-  if (it == correlationSpanMap.end()) {
-    auto it_success = correlationSpanMap.insert({
-        userActivity.correlationId(), createUserGpuSpan(userActivity, gpuActivity)
-    });
-    it = it_success.first;
-  }
-  GenericTraceActivity& span = it->second;
-  if (gpuActivity.timestamp() < span.startTime || span.startTime == 0) {
-    span.startTime = gpuActivity.timestamp();
-  }
-  int64_t gpu_activity_end = gpuActivity.timestamp() + gpuActivity.duration();
-  if (gpu_activity_end > span.endTime) {
-    span.endTime = gpu_activity_end;
-  }
-}
-
-const CuptiActivityProfiler::CpuGpuSpanPair& CuptiActivityProfiler::defaultTraceSpan() {
-  static TraceSpan span(0, 0, "Unknown", "");
-  static CpuGpuSpanPair span_pair(span, span);
-  return span_pair;
-}
-
-void CuptiActivityProfiler::GpuUserEventMap::logEvents(ActivityLogger *logger) {
-  for (auto const& streamMapPair : streamSpanMap_) {
-    for (auto const& correlationSpanPair : streamMapPair.second) {
-      correlationSpanPair.second.log(*logger);
-    }
-  }
-}
-
-#ifdef HAS_CUPTI
-inline bool CuptiActivityProfiler::outOfRange(const ITraceActivity& act) {
-  bool out_of_range = act.timestamp() < captureWindowStartTime_ ||
-      (act.timestamp() + act.duration()) > captureWindowEndTime_;
-  if (out_of_range) {
-    VLOG(2) << "TraceActivity outside of profiling window: " << act.name()
-        << " (" << act.timestamp() << " < " << captureWindowStartTime_ << " or "
-        << (act.timestamp() + act.duration()) << " > " << captureWindowEndTime_;
-  }
-  return out_of_range;
-}
-
-inline static bool isBlockListedRuntimeCbid(CUpti_CallbackId cbid) {
-  // Some CUDA calls that are very frequent and also not very interesting.
-  // Filter these out to reduce trace size.
-  if (cbid == CUPTI_RUNTIME_TRACE_CBID_cudaGetDevice_v3020 ||
-      cbid == CUPTI_RUNTIME_TRACE_CBID_cudaSetDevice_v3020 ||
-      cbid == CUPTI_RUNTIME_TRACE_CBID_cudaGetLastError_v3020 ||
-      // Don't care about cudaEvents
-      cbid == CUPTI_RUNTIME_TRACE_CBID_cudaEventCreate_v3020 ||
-      cbid == CUPTI_RUNTIME_TRACE_CBID_cudaEventCreateWithFlags_v3020 ||
-      cbid == CUPTI_RUNTIME_TRACE_CBID_cudaEventRecord_v3020 ||
-      cbid == CUPTI_RUNTIME_TRACE_CBID_cudaEventDestroy_v3020 ||
-      cbid == CUPTI_RUNTIME_TRACE_CBID_cudaEventSynchronize_v3020) {
-    return true;
-  }
-
-  return false;
-}
-
-void CuptiActivityProfiler::handleRuntimeActivity(
-    const CUpti_ActivityAPI* activity,
-    ActivityLogger* logger) {
-  if (isBlockListedRuntimeCbid(activity->cbid)) {
-    return;
-  }
-  VLOG(2) << activity->correlationId
-          << ": CUPTI_ACTIVITY_KIND_RUNTIME, cbid=" << activity->cbid
-          << " tid=" << activity->threadId;
-  int32_t tid = activity->threadId;
-  const auto& it = resourceInfo_.find({processId(), tid});
-  if (it != resourceInfo_.end()) {
-    tid = it->second.id;
-  }
-  const ITraceActivity* linked = linkedActivity(
-      activity->correlationId, cpuCorrelationMap_);
-  const auto& runtime_activity =
-      traceBuffers_->addActivityWrapper(RuntimeActivity(activity, linked, tid));
-  checkTimestampOrder(&runtime_activity);
-  if (outOfRange(runtime_activity)) {
-    return;
-  }
-  runtime_activity.log(*logger);
-}
-
-void CuptiActivityProfiler::handleOverheadActivity(
-    const CUpti_ActivityOverhead* activity,
-    ActivityLogger* logger) {
-  VLOG(2) << ": CUPTI_ACTIVITY_KIND_OVERHEAD" << " overheadKind=" << activity->overheadKind;
-
-  const auto& overhead_activity =
-      traceBuffers_->addActivityWrapper(OverheadActivity(activity, nullptr));
-  overhead_activity.log(*logger);
-}
-
-
-inline void CuptiActivityProfiler::updateGpuNetSpan(
-    const ITraceActivity& gpuOp) {
-  if (!gpuOp.linkedActivity()) {
-    VLOG(0) << "Missing linked activity";
-    return;
-  }
-  const auto& it = clientActivityTraceMap_.find(
-     gpuOp.linkedActivity()->correlationId());
-  if (it == clientActivityTraceMap_.end()) {
-    // No correlation id mapping?
-    return;
-  }
-  TraceSpan& gpu_span = it->second->second;
-  if (gpuOp.timestamp() < gpu_span.startTime || gpu_span.startTime == 0) {
-    gpu_span.startTime = gpuOp.timestamp();
-  }
-  if ((gpuOp.timestamp() + gpuOp.duration()) > gpu_span.endTime) {
-    gpu_span.endTime = gpuOp.timestamp() + gpuOp.duration();
-  }
-}
-
-// I've observed occasional broken timestamps attached to GPU events...
-void CuptiActivityProfiler::checkTimestampOrder(const ITraceActivity* act1) {
-  // Correlated GPU runtime activity cannot
-  // have timestamp greater than the GPU activity's
-  const auto& it = correlatedCudaActivities_.find(act1->correlationId());
-  if (it == correlatedCudaActivities_.end()) {
-    correlatedCudaActivities_.insert({act1->correlationId(), act1});
-    return;
-  }
-
-  // Activities may be appear in the buffers out of order.
-  // If we have a runtime activity in the map, it should mean that we
-  // have a GPU activity passed in, and vice versa.
-  const ITraceActivity* act2 = it->second;
-  if (act2->type() == ActivityType::CUDA_RUNTIME) {
-    // Buffer is out-of-order.
-    // Swap so that runtime activity is first for the comparison below.
-    std::swap(act1, act2);
-  }
-  if (act1->timestamp() > act2->timestamp()) {
-    LOG(WARNING) << "GPU op timestamp (" << act2->timestamp()
-                 << ") < runtime timestamp (" << act1->timestamp() << ") by "
-                 << act1->timestamp() - act2->timestamp() << "us";
-    LOG(WARNING) << "Name: " << act2->name()
-                 << " Device: " << act2->deviceId()
-                 << " Stream: " << act2->resourceId();
-  }
-}
-
-inline void CuptiActivityProfiler::handleGpuActivity(
-    const ITraceActivity& act,
-    ActivityLogger* logger) {
-  if (outOfRange(act)) {
-    return;
-  }
-  checkTimestampOrder(&act);
-  VLOG(2) << act.correlationId() << ": "
-          << act.name();
-  recordStream(act.deviceId(), act.resourceId(), "");
-  act.log(*logger);
-  updateGpuNetSpan(act);
-  if (config_->selectedActivityTypes().count(ActivityType::GPU_USER_ANNOTATION)) {
-    const auto& it = userCorrelationMap_.find(act.correlationId());
-    if (it != userCorrelationMap_.end()) {
-      const auto& it2 = activityMap_.find(it->second);
-      if (it2 != activityMap_.end()) {
-        recordStream(act.deviceId(), act.resourceId(), "context");
-        gpuUserEventMap_.insertOrExtendEvent(*it2->second, act);
-      }
-    }
-  }
-}
-
-const ITraceActivity* CuptiActivityProfiler::linkedActivity(
-    int32_t correlationId,
-    const std::unordered_map<int64_t, int64_t>& correlationMap) {
-  const auto& it = correlationMap.find(correlationId);
-  if (it != correlationMap.end()) {
-    const auto& it2 = activityMap_.find(it->second);
-    if (it2 != activityMap_.end()) {
-      return it2->second;
-    }
-  }
-  return nullptr;
-}
-
-template <class T>
-inline void CuptiActivityProfiler::handleGpuActivity(
-    const T* act, ActivityLogger* logger) {
-  const ITraceActivity* linked = linkedActivity(
-      act->correlationId, cpuCorrelationMap_);
-  const auto& gpu_activity =
-      traceBuffers_->addActivityWrapper(GpuActivity<T>(act, linked));
-  handleGpuActivity(gpu_activity, logger);
-}
-
-void CuptiActivityProfiler::handleCuptiActivity(const CUpti_Activity* record, ActivityLogger* logger) {
-  switch (record->kind) {
-    case CUPTI_ACTIVITY_KIND_EXTERNAL_CORRELATION:
-      handleCorrelationActivity(
-          reinterpret_cast<const CUpti_ActivityExternalCorrelation*>(
-              record));
-      break;
-    case CUPTI_ACTIVITY_KIND_RUNTIME:
-      handleRuntimeActivity(
-          reinterpret_cast<const CUpti_ActivityAPI*>(record), logger);
-      break;
-    case CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL:
-      handleGpuActivity(
-          reinterpret_cast<const CUpti_ActivityKernel4*>(record), logger);
-      break;
-    case CUPTI_ACTIVITY_KIND_MEMCPY:
-      handleGpuActivity(
-          reinterpret_cast<const CUpti_ActivityMemcpy*>(record), logger);
-      break;
-    case CUPTI_ACTIVITY_KIND_MEMCPY2:
-      handleGpuActivity(
-          reinterpret_cast<const CUpti_ActivityMemcpy2*>(record), logger);
-      break;
-    case CUPTI_ACTIVITY_KIND_MEMSET:
-      handleGpuActivity(
-          reinterpret_cast<const CUpti_ActivityMemset*>(record), logger);
-      break;
-    case CUPTI_ACTIVITY_KIND_OVERHEAD:
-      handleOverheadActivity (reinterpret_cast<const CUpti_ActivityOverhead*>(record), logger);
-      break;
-    default:
-      LOG(WARNING) << "Unexpected activity type: " << record->kind;
-      break;
-  }
-}
-#endif // HAS_CUPTI
-
-void CuptiActivityProfiler::configureChildProfilers() {
-  // If child profilers are enabled create profiler sessions
-  for (auto& profiler: profilers_) {
-    int64_t start_time_ms = duration_cast<milliseconds>(
-        profileStartTime_.time_since_epoch()).count();
-    LOG(INFO) << "Running child profiler " << profiler->name() << " for "
-            << config_->activitiesDuration().count() << " ms";
-    auto session = profiler->configure(
-        start_time_ms,
-        config_->activitiesDuration().count(),
-        config_->selectedActivityTypes(),
-        *config_
-    );
-    if (session) {
-      sessions_.push_back(std::move(session));
-    }
-  }
-}
-
-void CuptiActivityProfiler::configure(
-    const Config& config,
-    const time_point<system_clock>& now) {
-  std::lock_guard<std::mutex> guard(mutex_);
-  if (isActive()) {
-    LOG(ERROR) << "CuptiActivityProfiler already busy, terminating";
-    return;
-  }
-
-  config_ = config.clone();
-
-  if (config_->activitiesDuration().count() == 0) {
-    // Use default if not specified
-    config_->setActivitiesDuration(
-        config_->activitiesDurationDefault());
-  }
-
-  // Ensure we're starting in a clean state
-  resetTraceData();
-
-#if !USE_GOOGLE_LOG
-  // Add a LoggerObserverCollector to collect all logs during the trace.
-  loggerCollectorMetadata_ = std::make_unique<LoggerCollector>();
-  Logger::addLoggerObserver(loggerCollectorMetadata_.get());
-#endif // !USE_GOOGLE_LOG
-
-  profileStartTime_ = config_->requestTimestamp();
-
-  if (config_->hasProfileStartIteration()) {
-    profileStartIter_ = config_->profileStartIteration();
-    profileEndIter_ = profileStartIter_ + config_->activitiesRunIterations();
-  } else {
-
-    profileStartIter_ = -1;
-    profileEndIter_ = (std::numeric_limits<decltype(profileEndIter_)>::max)();
-
-    if (profileStartTime_ < now) {
-      LOG(ERROR) << "Not starting tracing - start timestamp is in the past. Time difference (ms): " << duration_cast<milliseconds>(now - profileStartTime_).count();
-      return;
-    } else if ((profileStartTime_ - now) < config_->activitiesWarmupDuration()) {
-      LOG(ERROR) << "Not starting tracing - insufficient time for warmup. Time to warmup (ms): " << duration_cast<milliseconds>(profileStartTime_ - now).count() ;
-      return;
-    }
-  }
-
-  if (LOG_IS_ON(INFO)) {
-    config_->printActivityProfilerConfig(LIBKINETO_DBG_STREAM);
-  }
-  if (!cpuOnly_ && !libkineto::api().client()) {
-    if (profileStartIter_ < 0) {
-      LOG(INFO) << "GPU-only tracing for "
-                << config_->activitiesDuration().count() << "ms";
-    } else {
-      LOG(INFO) << "GPU-only tracing for "
-                << config_->activitiesRunIterations() << " iterations";
-    }
-  }
-
-  // Set useful metadata into the logger.
-  LOGGER_OBSERVER_SET_TRACE_DURATION_MS(config_->activitiesDuration().count());
-  if (!config_->requestTraceID().empty()) {
-    LOGGER_OBSERVER_SET_TRACE_ID(config_->requestTraceID());
-  }
-  if (!config_->requestGroupTraceID().empty()) {
-    LOGGER_OBSERVER_SET_GROUP_TRACE_ID(config_->requestGroupTraceID());
-  }
-  LOGGER_OBSERVER_ADD_DESTINATION(config_->activitiesLogUrl());
-
-#if defined(HAS_CUPTI) || defined(HAS_ROCTRACER)
-  if (!cpuOnly_) {
-    // Enabling CUPTI activity tracing incurs a larger perf hit at first,
-    // presumably because structures are allocated and initialized, callbacks
-    // are activated etc. After a while the overhead decreases and stabilizes.
-    // It's therefore useful to perform some warmup before starting recording.
-    LOG(INFO) << "Enabling GPU tracing";
-    cupti_.setMaxBufferSize(config_->activitiesMaxGpuBufferSize());
-
-    time_point<system_clock> timestamp;
-    if (VLOG_IS_ON(1)) {
-      timestamp = system_clock::now();
-    }
-#ifdef HAS_CUPTI
-    cupti_.enableCuptiActivities(config_->selectedActivityTypes());
-#else
-    cupti_.enableActivities(config_->selectedActivityTypes());
-#endif
-    if (VLOG_IS_ON(1)) {
-      auto t2 = system_clock::now();
-      addOverheadSample(
-          setupOverhead_, duration_cast<microseconds>(t2 - timestamp).count());
-    }
-  }
-#endif // HAS_CUPTI || HAS_ROCTRACER
-
-  if (profilers_.size() > 0) {
-    configureChildProfilers();
-  }
-
-  if (libkineto::api().client()) {
-    libkineto::api().client()->warmup(config_->isOpInputsCollectionEnabled());
-  }
-  if (profileStartIter_ >= 0) {
-    LOG(INFO) << "Tracing starting on iteration = " << profileStartIter_;
-  } else {
-    LOG(INFO) << "Tracing starting in "
-              << duration_cast<seconds>(profileStartTime_ - now).count() << "s";
-  }
-
-  traceBuffers_ = std::make_unique<ActivityBuffers>();
-  captureWindowStartTime_ = captureWindowEndTime_ = 0;
-  currentRunloopState_ = RunloopState::Warmup;
-}
-
-void CuptiActivityProfiler::startTraceInternal(const time_point<system_clock>& now) {
-  captureWindowStartTime_ = libkineto::timeSinceEpoch(now);
-  VLOG(0) << "Warmup -> CollectTrace";
-  for (auto& session: sessions_){
-    LOG(INFO) << "Starting child profiler session";
-    session->start();
-  }
-  currentRunloopState_ = RunloopState::CollectTrace;
-}
-
-void CuptiActivityProfiler::stopTraceInternal(const time_point<system_clock>& now) {
-  if (captureWindowEndTime_ == 0) {
-    captureWindowEndTime_ = libkineto::timeSinceEpoch(now);
-  }
-#if defined(HAS_CUPTI) || defined(HAS_ROCTRACER)
-  if (!cpuOnly_) {
-    time_point<system_clock> timestamp;
-    if (VLOG_IS_ON(1)) {
-      timestamp = system_clock::now();
-    }
-#ifdef HAS_CUPTI
-    cupti_.disableCuptiActivities(config_->selectedActivityTypes());
-#else
-    cupti_.disableActivities(config_->selectedActivityTypes());
-#endif
-    if (VLOG_IS_ON(1)) {
-      auto t2 = system_clock::now();
-      addOverheadSample(
-          setupOverhead_, duration_cast<microseconds>(t2 - timestamp).count());
-    }
-  }
-#endif // HAS_CUPTI || HAS_ROCTRACER
-
-  if (currentRunloopState_ == RunloopState::CollectTrace) {
-    VLOG(0) << "CollectTrace -> ProcessTrace";
-  } else {
-    LOG(WARNING) << "Called stopTrace with state == " <<
-        static_cast<std::underlying_type<RunloopState>::type>(
-            currentRunloopState_.load());
-  }
-  for (auto& session: sessions_){
-    LOG(INFO) << "Stopping child profiler session";
-    session->stop();
-  }
-  currentRunloopState_ = RunloopState::ProcessTrace;
-}
-
-void CuptiActivityProfiler::resetInternal() {
-  resetTraceData();
-  currentRunloopState_ = RunloopState::WaitForRequest;
-}
-
-bool CuptiActivityProfiler::isWarmupDone(
-      const time_point<system_clock>& now,
-      int64_t currentIter) const {
-  // is it a time based config
-  if (profileStartIter_ < 0) {
-    // qualify that this check is not being called from application step() API
-    // this avoids races between the step() API and periodically invoked
-    // profiler run loop step() method
-    return (currentIter < 0) && (now >= profileStartTime_);
-  }
-  // this is an iteration based config
-  if (currentIter < 0) {
-    return false;
-  }
-  return currentIter >= profileStartIter_;
-}
-
-bool CuptiActivityProfiler::isCollectionDone(
-      const time_point<system_clock>& now,
-      int64_t currentIter) const {
-  // is it a time based config
-  if (profileStartIter_ < 0) {
-    // qualify that this check is not being called from application step() API
-    return (currentIter < 0) && (now >= profileEndTime_);
-  }
-  // this is an iteration based config
-  if (currentIter < 0) {
-    return false;
-  }
-  return currentIter >= profileEndIter_;
-}
-
-const time_point<system_clock> CuptiActivityProfiler::performRunLoopStep(
-    const time_point<system_clock>& now,
-    const time_point<system_clock>& nextWakeupTime,
-    int64_t currentIter) {
-  auto new_wakeup_time = nextWakeupTime;
-  bool warmup_done = false, collection_done = false;
-
-  VLOG_IF(1, currentIter >= 0) << "Run loop on application step(), iteration = "
-    << currentIter;
-
-  switch (currentRunloopState_) {
-    case RunloopState::WaitForRequest:
-      VLOG(1) << "State: WaitForRequest";
-      // Nothing to do
-      break;
-
-    case RunloopState::Warmup:
-      VLOG(1) << "State: Warmup";
-      warmup_done = isWarmupDone(now, currentIter);
-#if defined(HAS_CUPTI) || defined(HAS_ROCTRACER)
-      // Flushing can take a while so avoid doing it close to the start time
-      if (!cpuOnly_ && currentIter < 0 &&
-          (profileStartIter_ >= 0 || nextWakeupTime < profileStartTime_)) {
-        cupti_.clearActivities();
-      }
-
-      if (cupti_.stopCollection) {
-        // Go to process trace to clear any outstanding buffers etc
-        LOG(WARNING) << "Trace terminated during warmup";
-        std::lock_guard<std::mutex> guard(mutex_);
-        stopTraceInternal(now);
-        resetInternal();
-        VLOG(0) << "Warmup -> WaitForRequest";
-        break;
-      }
-#endif // HAS_CUPTI || HAS_ROCTRACER
-
-      if (warmup_done) {
-        UST_LOGGER_MARK_COMPLETED(kWarmUpStage);
-        if (profileStartIter_ < 0 &&
-            (now > profileStartTime_ + milliseconds(10))) {
-          LOG(WARNING)
-              << "Tracing started "
-              << duration_cast<milliseconds>(now - profileStartTime_).count()
-              << "ms late!";
-        } else {
-          LOG(INFO) << "Tracing started";
-        }
-        startTrace(now);
-        if (libkineto::api().client()) {
-          libkineto::api().client()->start();
-        }
-        if (nextWakeupTime > profileEndTime_) {
-          new_wakeup_time = profileEndTime_;
-        }
-      } else if (nextWakeupTime > profileStartTime_) {
-        new_wakeup_time = profileStartTime_;
-      }
-
-      break;
-
-    case RunloopState::CollectTrace:
-      VLOG(1) << "State: CollectTrace";
-      // captureWindowStartTime_ can be set by external threads,
-      // so recompute end time.
-      // FIXME: Is this a good idea for synced start?
-      if (profileStartIter_ < 0) {
-        std::lock_guard<std::mutex> guard(mutex_);
-        profileEndTime_ = time_point<system_clock>(
-                              microseconds(captureWindowStartTime_)) +
-            config_->activitiesDuration();
-      }
-
-      collection_done = isCollectionDone(now, currentIter);
-
-      // TODO revisit stopCollection_ is not used right now
-      if (collection_done || stopCollection_.exchange(false)
-#if defined(HAS_CUPTI) || defined(HAS_ROCTRACER)
-          || cupti_.stopCollection
-#endif // HAS_CUPTI || HAS_ROCTRACER
-      ){
-        // Update runloop state first to prevent further updates to shared state
-        LOG(INFO) << "Tracing complete.";
-        if (currentIter > 0) {
-          LOG(INFO) << "This state change was invoked by application's step() call";
-        }
-        // FIXME: Need to communicate reason for stopping on errors
-        if (libkineto::api().client()) {
-          libkineto::api().client()->stop();
-        }
-        std::lock_guard<std::mutex> guard(mutex_);
-        stopTraceInternal(now);
-        VLOG_IF(0, collection_done) << "Reached profile end time";
-
-        UST_LOGGER_MARK_COMPLETED(kCollectionStage);
-      } else if (profileStartIter_ >= 0) {
-        // nothing to do here
-      } else if (now < profileEndTime_ && profileEndTime_ < nextWakeupTime) {
-        new_wakeup_time = profileEndTime_;
-      }
-
-      break;
-
-    case RunloopState::ProcessTrace:
-      VLOG(1) << "State: ProcessTrace";
-      // skip this state transition if it called from the step() api
-      // of the profiler.
-      // else it could lead to a race between the profiler thread and an
-      // application thread calling step()
-      if (currentIter >= 0) {
-        return new_wakeup_time;
-      }
-      // FIXME: Probably want to allow interruption here
-      // for quickly handling trace request via synchronous API
-      std::lock_guard<std::mutex> guard(mutex_);
-      processTraceInternal(*logger_);
-      UST_LOGGER_MARK_COMPLETED(kPostProcessingStage);
-      resetInternal();
-      VLOG(0) << "ProcessTrace -> WaitForRequest";
-      break;
-  }
-
-  return new_wakeup_time;
-}
-
-void CuptiActivityProfiler::finalizeTrace(const Config& config, ActivityLogger& logger) {
-  LOG(INFO) << "Recorded nets:";
-  {
-    for (const auto& it : iterationCountMap_) {
-      LOG(INFO) << it.first << ": " << it.second << " iterations";
-    }
-    iterationCountMap_.clear();
-  }
-
-  // Process names
-  int32_t pid = processId();
-  string process_name = processName(pid);
-  if (!process_name.empty()) {
-    logger.handleDeviceInfo(
-        {pid, process_name, "CPU"}, captureWindowStartTime_);
-    if (!cpuOnly_) {
-      // GPU events use device id as pid (0-7).
-      constexpr int kMaxGpuCount = 8;
-      for (int gpu = 0; gpu < kMaxGpuCount; gpu++) {
-        logger.handleDeviceInfo(
-            {gpu, process_name, fmt::format("GPU {}", gpu)},
-            captureWindowStartTime_);
-      }
-    }
-  }
-
-  // Thread & stream info
-  for (auto pair : resourceInfo_) {
-    const auto& resource = pair.second;
-    logger.handleResourceInfo(resource, captureWindowStartTime_);
-  }
-
-  for (const auto& iterations : traceSpans_) {
-    for (const auto& span_pair : iterations.second) {
-      const TraceSpan& gpu_span = span_pair.second;
-      if (gpu_span.opCount > 0) {
-        logger.handleTraceSpan(gpu_span);
-      }
-    }
-  }
-
-  // Overhead info
-  overheadInfo_.push_back(ActivityLogger::OverheadInfo("CUPTI Overhead"));
-  for(const auto& info : overheadInfo_) {
-    logger.handleOverheadInfo(info, captureWindowStartTime_);
-  }
-
-  gpuUserEventMap_.logEvents(&logger);
-
-#if !USE_GOOGLE_LOG
-  // Save logs from LoggerCollector objects into Trace metadata.
-  auto LoggerMD = loggerCollectorMetadata_->extractCollectorMetadata();
-  std::unordered_map<std::string, std::vector<std::string>> LoggerMDString;
-  for (auto& md : LoggerMD) {
-    LoggerMDString[toString(md.first)] = md.second;
-  }
-#endif // !USE_GOOGLE_LOG
-
-  logger.finalizeTrace(config, std::move(traceBuffers_), captureWindowEndTime_, LoggerMDString);
-}
-
-void CuptiActivityProfiler::resetTraceData() {
-#if defined(HAS_CUPTI) || defined(HAS_ROCTRACER)
-  if (!cpuOnly_) {
-    cupti_.clearActivities();
-  }
-#endif // HAS_CUPTI || HAS_ROCTRACER
-  activityMap_.clear();
-  cpuCorrelationMap_.clear();
-  correlatedCudaActivities_.clear();
-  gpuUserEventMap_.clear();
-  traceSpans_.clear();
-  clientActivityTraceMap_.clear();
-  traceBuffers_ = nullptr;
-  metadata_.clear();
-  sessions_.clear();
-#if !USE_GOOGLE_LOG
-  Logger::removeLoggerObserver(loggerCollectorMetadata_.get());
-#endif // !USE_GOOGLE_LOG
-}
-
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityProfiler.h b/plugins/tensorboard-plugins/libkineto/src/CuptiActivityProfiler.h
deleted file mode 100644
index 208833a4db7..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiActivityProfiler.h
+++ /dev/null
@@ -1,364 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <atomic>
-#include <chrono>
-#include <condition_variable>
-#include <list>
-#include <map>
-#include <memory>
-#include <mutex>
-#include <queue>
-#include <string>
-#include <thread>
-#include <unordered_map>
-#include <unordered_set>
-#include <vector>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "ThreadUtil.h"
-#include "TraceSpan.h"
-#include "libkineto.h"
-#include "output_base.h"
-#include "GenericTraceActivity.h"
-#include "IActivityProfiler.h"
-#include "LoggerCollector.h"
-
-namespace KINETO_NAMESPACE {
-
-class Config;
-class CuptiActivityApi;
-class RoctracerActivityApi;
-
-class CuptiActivityProfiler {
- public:
-  CuptiActivityProfiler(CuptiActivityApi& cupti, bool cpuOnly);
-  CuptiActivityProfiler(RoctracerActivityApi& rai, bool cpuOnly);
-  CuptiActivityProfiler(const CuptiActivityProfiler&) = delete;
-  CuptiActivityProfiler& operator=(const CuptiActivityProfiler&) = delete;
-
-  bool isActive() const {
-    return currentRunloopState_ != RunloopState::WaitForRequest;
-  }
-
-  // Invoke at a regular interval to perform profiling activities.
-  // When not active, an interval of 1-5 seconds is probably fine,
-  // depending on required warm-up time and delayed start time.
-  // When active, it's a good idea to invoke more frequently to stay below
-  // memory usage limit (ACTIVITIES_MAX_GPU_BUFFER_SIZE_MB) during warmup.
-  const std::chrono::time_point<std::chrono::system_clock> performRunLoopStep(
-      const std::chrono::time_point<std::chrono::system_clock>& now,
-      const std::chrono::time_point<std::chrono::system_clock>& nextWakeupTime,
-      int64_t currentIter = -1);
-
-  // Used for async requests
-  void setLogger(ActivityLogger* logger) {
-    logger_ = logger;
-  }
-
-  // Synchronous control API
-  void startTrace(
-      const std::chrono::time_point<std::chrono::system_clock>& now) {
-    std::lock_guard<std::mutex> guard(mutex_);
-    startTraceInternal(now);
-  }
-
-  void stopTrace(const std::chrono::time_point<std::chrono::system_clock>& now) {
-    std::lock_guard<std::mutex> guard(mutex_);
-    stopTraceInternal(now);
-  }
-
-  // Process CPU and GPU traces
-  void processTrace(ActivityLogger& logger) {
-    std::lock_guard<std::mutex> guard(mutex_);
-    processTraceInternal(logger);
-  }
-
-  void reset() {
-    std::lock_guard<std::mutex> guard(mutex_);
-    resetInternal();
-  }
-
-  // Set up profiler as specified in config.
-  void configure(
-      const Config& config,
-      const std::chrono::time_point<std::chrono::system_clock>& now);
-
-  // Registered with client API to pass CPU trace events over
-  void transferCpuTrace(
-      std::unique_ptr<libkineto::CpuTraceBuffer> cpuTrace);
-
-  Config& config() {
-    return *config_;
-  }
-
-  inline void recordThreadInfo() {
-    int32_t sysTid = systemThreadId();
-    // Note we're using the lower 32 bits of the (opaque) pthread id
-    // as key, because that's what CUPTI records.
-    int32_t tid = threadId();
-    int32_t pid = processId();
-    std::lock_guard<std::mutex> guard(mutex_);
-    recordThreadInfo(sysTid, tid, pid);
-  }
-
-  // T107508020: We can deprecate the recordThreadInfo(void) once we optimized profiler_kineto
-  void recordThreadInfo(int32_t sysTid, int32_t tid, int32_t pid) {
-    if (resourceInfo_.find({pid, tid}) == resourceInfo_.end()) {
-      resourceInfo_.emplace(
-          std::make_pair(pid, tid),
-          ActivityLogger::ResourceInfo(
-              pid,
-              sysTid,
-              sysTid, // sortindex
-              fmt::format("thread {} ({})", sysTid, getThreadName())));
-    }
-  }
-
-  void addMetadata(const std::string& key, const std::string& value) {
-    std::lock_guard<std::mutex> guard(mutex_);
-    metadata_[key] = value;
-  }
-
-  void addChildActivityProfiler(
-      std::unique_ptr<IActivityProfiler> profiler) {
-    std::lock_guard<std::mutex> guard(mutex_);
-    profilers_.push_back(std::move(profiler));
-  }
-
- protected:
-
-  using CpuGpuSpanPair = std::pair<TraceSpan, TraceSpan>;
-  static const CpuGpuSpanPair& defaultTraceSpan();
-
- private:
-
-  // Map of gpu activities to user defined events
-  class GpuUserEventMap {
-   public:
-    // Insert a user defined event which maps to the gpu trace activity.
-    // If the user defined event mapping already exists this will update the
-    // gpu side span to include the span of gpuTraceActivity.
-    void insertOrExtendEvent(const ITraceActivity& cpuTraceActivity,
-      const ITraceActivity& gpuTraceActivity);
-    // Log out the events to the logger
-    void logEvents(ActivityLogger *logger);
-
-    void clear() {
-      streamSpanMap_.clear();
-    }
-
-   private:
-    // device id and stream name
-    using StreamKey = std::pair<int64_t, int64_t>;
-
-    // map of correlation id to TraceSpan
-    using CorrelationSpanMap =
-        std::unordered_map<int64_t, GenericTraceActivity>;
-    std::map<StreamKey, CorrelationSpanMap> streamSpanMap_;
-  };
-
-  GpuUserEventMap gpuUserEventMap_;
-  // id -> activity*
-  std::unordered_map<int64_t, const ITraceActivity*> activityMap_;
-  // cuda runtime id -> pytorch op id
-  // CUPTI provides a mechanism for correlating Cuda events to arbitrary
-  // external events, e.g.operator activities from PyTorch.
-  std::unordered_map<int64_t, int64_t> cpuCorrelationMap_;
-  // CUDA runtime <-> GPU Activity
-  std::unordered_map<int64_t, const ITraceActivity*>
-      correlatedCudaActivities_;
-  std::unordered_map<int64_t, int64_t> userCorrelationMap_;
-
-  // data structure to collect cuptiActivityFlushAll() latency overhead
-  struct profilerOverhead {
-    int64_t overhead;
-    int cntr;
-  };
-
-  bool isWarmupDone(
-      const std::chrono::time_point<std::chrono::system_clock>& now,
-      int64_t currentIter) const;
-
-  bool isCollectionDone(
-      const std::chrono::time_point<std::chrono::system_clock>& now,
-      int64_t currentIter) const;
-
-  void startTraceInternal(
-      const std::chrono::time_point<std::chrono::system_clock>& now);
-
-  void stopTraceInternal(
-      const std::chrono::time_point<std::chrono::system_clock>& now);
-
-  void processTraceInternal(ActivityLogger& logger);
-
-  void resetInternal();
-
-  void finalizeTrace(const Config& config, ActivityLogger& logger);
-
-  void configureChildProfilers();
-
-  // Process a single CPU trace
-  void processCpuTrace(
-      libkineto::CpuTraceBuffer& cpuTrace,
-      ActivityLogger& logger);
-
-  // Create resource names for streams
-  inline void recordStream(int device, int id, const char* postfix) {
-    if (resourceInfo_.find({device, id}) == resourceInfo_.end()) {
-      resourceInfo_.emplace(
-          std::make_pair(device, id),
-          ActivityLogger::ResourceInfo(
-              device, id, id, fmt::format(
-                  "stream {} {}", id, postfix)));
-    }
-  }
-
-  // Record client trace span for subsequent lookups from activities
-  // Also creates a corresponding GPU-side span.
-  CpuGpuSpanPair& recordTraceSpan(TraceSpan& span, int gpuOpCount);
-
-  // Returns true if net name is to be tracked for a specified number of
-  // iterations.
-  bool iterationTargetMatch(libkineto::CpuTraceBuffer& trace);
-
-  // net name to id
-  int netId(const std::string& netName);
-
-  const ITraceActivity* linkedActivity(
-      int32_t correlationId,
-      const std::unordered_map<int64_t, int64_t>& correlationMap);
-
-#ifdef HAS_CUPTI
-  // Process generic CUPTI activity
-  void handleCuptiActivity(const CUpti_Activity* record, ActivityLogger* logger);
-
-  // Process specific GPU activity types
-  void updateGpuNetSpan(const ITraceActivity& gpuOp);
-  bool outOfRange(const ITraceActivity& act);
-  void handleCorrelationActivity(
-      const CUpti_ActivityExternalCorrelation* correlation);
-  void handleRuntimeActivity(
-      const CUpti_ActivityAPI* activity, ActivityLogger* logger);
-  void handleOverheadActivity(
-      const CUpti_ActivityOverhead* activity, ActivityLogger* logger);
-  void handleGpuActivity(const ITraceActivity& act,
-      ActivityLogger* logger);
-  template <class T>
-  void handleGpuActivity(const T* act, ActivityLogger* logger);
-#endif // HAS_CUPTI
-
-  void resetTraceData();
-
-  void addOverheadSample(profilerOverhead& counter, int64_t overhead) {
-    counter.overhead += overhead;
-    counter.cntr++;
-  }
-  int64_t getOverhead(const profilerOverhead& counter) {
-    if (counter.cntr == 0) {
-      return 0;
-    }
-    return counter.overhead / counter.cntr;
-  }
-
-  void checkTimestampOrder(const ITraceActivity* act1);
-
-  // On-demand request configuration
-  std::unique_ptr<Config> config_;
-
-  // Logger used during trace processing
-  ActivityLogger* logger_;
-
-  // Calls to CUPTI is encapsulated behind this interface
-#ifdef HAS_ROCTRACER
-  RoctracerActivityApi& cupti_;		// Design failure here
-#else
-  CuptiActivityApi& cupti_;
-#endif
-
-  enum class RunloopState {
-    WaitForRequest,
-    Warmup,
-    CollectTrace,
-    ProcessTrace
-  };
-
-  // Start and end time used for triggering and stopping profiling
-  std::chrono::time_point<std::chrono::system_clock> profileStartTime_;
-  std::chrono::time_point<std::chrono::system_clock> profileEndTime_;
-  int64_t profileStartIter_ = -1, profileEndIter_ = -1;
-
-
-  // All recorded trace spans, both CPU and GPU
-  // Trace Id -> list of iterations.
-  // Using map of lists for the iterator semantics, since we are recording
-  // pointers to the elements in this structure.
-  std::map<std::string, std::list<CpuGpuSpanPair>> traceSpans_;
-
-  // Maintain a map of client trace activity to trace span.
-  // Maps correlation id -> TraceSpan* held by traceSpans_.
-  using ActivityTraceMap = std::unordered_map<int64_t, CpuGpuSpanPair*>;
-  ActivityTraceMap clientActivityTraceMap_;
-
-  // Cache thread names and system thread ids for pthread ids,
-  // and stream ids for GPU streams
-  std::map<
-      std::pair<int64_t, int64_t>,
-      ActivityLogger::ResourceInfo> resourceInfo_;
-
-  std::vector<ActivityLogger::OverheadInfo> overheadInfo_;
-
-  // the overhead to flush the activity buffer
-  profilerOverhead flushOverhead_;
-  // the overhead to enable/disable activity tracking
-  profilerOverhead setupOverhead_;
-
-  bool cpuOnly_{false};
-
-  // ***************************************************************************
-  // Below state is shared with external threads.
-  // These need to either be atomic, accessed under lock or only used
-  // by external threads in separate runloop phases from the profiler thread.
-  // ***************************************************************************
-
-  // Mutex to protect non-atomic access to below state
-  std::mutex mutex_;
-
-  // Runloop phase
-  std::atomic<RunloopState> currentRunloopState_{RunloopState::WaitForRequest};
-
-  // Keep track of the start time of the first net in the current trace.
-  // This is only relevant to Caffe2 as PyTorch does not have nets.
-  // All CUDA events before this time will be removed
-  // Can be written by external threads during collection.
-  int64_t captureWindowStartTime_{0};
-  // Similarly, all CUDA API events after the last net event will be removed
-  int64_t captureWindowEndTime_{0};
-
-  // span name -> iteration count
-  std::map<std::string, int> iterationCountMap_;
-  // Flag used to stop tracing from external api callback.
-  // Needs to be atomic since it's set from a different thread.
-  std::atomic_bool stopCollection_{false};
-
-  // Buffers where trace data is stored
-  std::unique_ptr<ActivityBuffers> traceBuffers_;
-
-  // Trace metadata
-  std::unordered_map<std::string, std::string> metadata_;
-
-  // child activity profilers
-  std::vector<std::unique_ptr<IActivityProfiler>> profilers_;
-
-  // a vector of active profiler plugin sessions
-  std::vector<std::unique_ptr<IActivityProfilerSession>> sessions_;
-
-  // LoggerCollector to collect all LOGs during the trace
-#if !USE_GOOGLE_LOG
-  std::unique_ptr<LoggerCollector> loggerCollectorMetadata_;
-#endif // !USE_GOOGLE_LOG
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApi.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApi.cpp
deleted file mode 100644
index 1876003998d..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApi.cpp
+++ /dev/null
@@ -1,260 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "CuptiCallbackApi.h"
-
-#include <assert.h>
-#include <chrono>
-#include <algorithm>
-#include <mutex>
-#include <shared_mutex>
-
-#ifdef HAS_CUPTI
-#include "cupti_call.h"
-#endif
-#include "Logger.h"
-
-
-namespace KINETO_NAMESPACE {
-
-// limit on number of handles per callback type
-constexpr size_t MAX_CB_FNS_PER_CB = 8;
-
-// Reader Writer lock types
-using ReaderWriterLock = std::shared_timed_mutex;
-using ReaderLockGuard = std::shared_lock<ReaderWriterLock>;
-using WriteLockGuard = std::unique_lock<ReaderWriterLock>;
-
-static ReaderWriterLock callbackLock_;
-
-/* Callback Table :
- *  Overall goal of the design is to optimize the lookup of function
- *  pointers. The table is structured at two levels and the leaf
- *  elements in the table are std::list to enable fast access/inserts/deletes
- *
- *   <callback domain0> |
- *                     -> cb id 0 -> std::list of callbacks
- *                     ...
- *                     -> cb id n -> std::list of callbacks
- *   <callback domain1> |
- *                    ...
- *  CallbackTable is the finaly table type above
- *  See type declrartions in header file.
- */
-
-
-/* callback_switchboard : is the global callback handler we register
- *  with CUPTI. The goal is to make it as efficient as possible
- *  to re-direct to the registered callback(s).
- *
- *  Few things to care about :
- *   a) use if/then switches rather than map/hash structures
- *   b) avoid dynamic memory allocations
- *   c) be aware of locking overheads
- */
-#ifdef HAS_CUPTI
-static void CUPTIAPI callback_switchboard(
-#else
-static void callback_switchboard(
-#endif
-   void* /* unused */,
-   CUpti_CallbackDomain domain,
-   CUpti_CallbackId cbid,
-   const CUpti_CallbackData* cbInfo) {
-
-  // below statement is likey going to call a mutex
-  // on the singleton access
-  CuptiCallbackApi::singleton().__callback_switchboard(
-      domain, cbid, cbInfo);
-}
-
-
-void CuptiCallbackApi::__callback_switchboard(
-   CUpti_CallbackDomain domain,
-   CUpti_CallbackId cbid,
-   const CUpti_CallbackData* cbInfo) {
-  VLOG(0) << "Callback: domain = " << domain << ", cbid = " << cbid;
-  CallbackList *cblist = nullptr;
-
-  switch (domain) {
-
-    // add the fastest path for kernel launch callbacks
-    // as these are the most frequent ones
-    case CUPTI_CB_DOMAIN_RUNTIME_API:
-      switch (cbid) {
-        case CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000:
-          cblist = &callbacks_.runtime[
-            CUDA_LAUNCH_KERNEL - __RUNTIME_CB_DOMAIN_START];
-          break;
-        default:
-          break;
-      }
-      break;
-
-    case CUPTI_CB_DOMAIN_RESOURCE:
-      switch (cbid) {
-        case CUPTI_CBID_RESOURCE_CONTEXT_CREATED:
-          cblist = &callbacks_.resource[
-            RESOURCE_CONTEXT_CREATED - __RESOURCE_CB_DOMAIN_START];
-          break;
-        case CUPTI_CBID_RESOURCE_CONTEXT_DESTROY_STARTING:
-          cblist = &callbacks_.resource[
-            RESOURCE_CONTEXT_DESTROYED - __RESOURCE_CB_DOMAIN_START];
-          break;
-        default:
-          break;
-      }
-      break;
-
-    default:
-      return;
-  }
-
-  // ignore callbacks that are not handled
-  if (cblist == nullptr) {
-    return;
-  }
-
-  // make a copy of the callback list so we avoid holding lock
-  // in common case this should be just one func pointer copy
-  std::array<CuptiCallbackFn, MAX_CB_FNS_PER_CB> callbacks;
-  int num_cbs = 0;
-  {
-    ReaderLockGuard rl(callbackLock_);
-    int i = 0;
-    for (auto it = cblist->begin();
-        it != cblist->end() && i < MAX_CB_FNS_PER_CB;
-        it++, i++) {
-      callbacks[i] = *it;
-    }
-    num_cbs = i;
-  }
-
-  for (int i = 0; i < num_cbs; i++) {
-    auto fn = callbacks[i];
-    fn(domain, cbid, cbInfo);
-  }
-}
-
-CuptiCallbackApi& CuptiCallbackApi::singleton() {
-  static CuptiCallbackApi instance;
-  return instance;
-}
-
-CuptiCallbackApi::CuptiCallbackApi() {
-#ifdef HAS_CUPTI
-  lastCuptiStatus_ = CUPTI_ERROR_UNKNOWN;
-  lastCuptiStatus_ = CUPTI_CALL_NOWARN(
-    cuptiSubscribe(&subscriber_,
-      (CUpti_CallbackFunc)callback_switchboard,
-      nullptr));
-
-  initSuccess_ = (lastCuptiStatus_ == CUPTI_SUCCESS);
-#endif
-}
-
-CuptiCallbackApi::CallbackList* CuptiCallbackApi::CallbackTable::lookup(
-    CUpti_CallbackDomain domain, CuptiCallBackID cbid) {
-  size_t idx;
-
-  switch (domain) {
-
-    case CUPTI_CB_DOMAIN_RESOURCE:
-      assert(cbid >= __RESOURCE_CB_DOMAIN_START);
-      assert(cbid < __RESOURCE_CB_DOMAIN_END);
-      idx = cbid - __RESOURCE_CB_DOMAIN_START;
-      return &resource.at(idx);
-
-    case CUPTI_CB_DOMAIN_RUNTIME_API:
-      assert(cbid >= __RUNTIME_CB_DOMAIN_START);
-      assert(cbid < __RUNTIME_CB_DOMAIN_END);
-      idx = cbid - __RUNTIME_CB_DOMAIN_START;
-      return &runtime.at(idx);
-
-    default:
-      LOG(WARNING) << " Unsupported callback domain : " << domain;
-      return nullptr;
-  }
-}
-
-bool CuptiCallbackApi::registerCallback(
-    CUpti_CallbackDomain domain,
-    CuptiCallBackID cbid,
-    CuptiCallbackFn cbfn) {
-  CallbackList* cblist = callbacks_.lookup(domain, cbid);
-
-  if (!cblist) {
-    LOG(WARNING) << "Could not register callback -- domain = " << domain
-                 << " callback id = " << cbid;
-    return false;
-  }
-
-  // avoid duplicates
-  auto it = std::find(cblist->begin(), cblist->end(), cbfn);
-  if (it != cblist->end()) {
-    LOG(WARNING) << "Adding duplicate callback -- domain = " << domain
-                 << " callback id = " << cbid;
-    return true;
-  }
-
-  if (cblist->size() == MAX_CB_FNS_PER_CB) {
-    LOG(WARNING) << "Already registered max callback -- domain = " << domain
-                 << " callback id = " << cbid;
-  }
-
-  WriteLockGuard wl(callbackLock_);
-  cblist->push_back(cbfn);
-  return true;
-}
-
-bool CuptiCallbackApi::deleteCallback(
-    CUpti_CallbackDomain domain,
-    CuptiCallBackID cbid,
-    CuptiCallbackFn cbfn) {
-  CallbackList* cblist = callbacks_.lookup(domain, cbid);
-  if (!cblist) {
-    LOG(WARNING) << "Attempting to remove unsupported callback -- domain = " << domain
-                 << " callback id = " << cbid;
-    return false;
-  }
-
-  // Locks are not required here as
-  //  https://en.cppreference.com/w/cpp/container/list/erase
-  //  "References and iterators to the erased elements are invalidated.
-  //   Other references and iterators are not affected."
-  auto it = std::find(cblist->begin(), cblist->end(), cbfn);
-  if (it == cblist->end()) {
-    LOG(WARNING) << "Could not find callback to remove -- domain = " << domain
-                 << " callback id = " << cbid;
-    return false;
-  }
-
-  WriteLockGuard wl(callbackLock_);
-  cblist->erase(it);
-  return true;
-}
-
-bool CuptiCallbackApi::enableCallback(
-    CUpti_CallbackDomain domain, CUpti_CallbackId cbid) {
-#ifdef HAS_CUPTI
-  if (initSuccess_) {
-    lastCuptiStatus_ = CUPTI_CALL_NOWARN(
-        cuptiEnableCallback(1, subscriber_, domain, cbid));
-    return (lastCuptiStatus_ == CUPTI_SUCCESS);
-  }
-#endif
-  return false;
-}
-
-bool CuptiCallbackApi::disableCallback(
-    CUpti_CallbackDomain domain, CUpti_CallbackId cbid) {
-#ifdef HAS_CUPTI
-  if (initSuccess_) {
-    lastCuptiStatus_ = CUPTI_CALL_NOWARN(
-        cuptiEnableCallback(0, subscriber_, domain, cbid));
-    return (lastCuptiStatus_ == CUPTI_SUCCESS);
-  }
-#endif
-  return false;
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApi.h b/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApi.h
deleted file mode 100644
index 4526f3750b4..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApi.h
+++ /dev/null
@@ -1,130 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <atomic>
-#ifdef HAS_CUPTI
-#include <cupti.h>
-#endif
-#include <array>
-#include <list>
-#include <memory>
-#include <mutex>
-#include <set>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "CuptiCallbackApiMock.h"
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-
-
-/* CuptiCallbackApi : Provides an abstraction over CUPTI callback
- *  interface. This enables various callback functions to be registered
- *  with this class. The class registers a global callback handler that
- *  redirects to the respective callbacks.
- *
- *  Note: one design choice we made is to only support simple function pointers
- *  in order to speed up the implementation for fast path.
- */
-
-using CuptiCallbackFn = void(*)(
-    CUpti_CallbackDomain domain,
-    CUpti_CallbackId cbid,
-    const CUpti_CallbackData* cbInfo);
-
-
-class CuptiCallbackApi {
-
- public:
-
-  /* Global list of supported callback ids
-   *  use the class namespace to avoid confusing with CUPTI enums*/
-  enum CuptiCallBackID {
-    CUDA_LAUNCH_KERNEL =  0,
-    // can possibly support more callback ids per domain
-    //
-    __RUNTIME_CB_DOMAIN_START = CUDA_LAUNCH_KERNEL,
-
-    // Callbacks under Resource CB domain
-    RESOURCE_CONTEXT_CREATED,
-    RESOURCE_CONTEXT_DESTROYED,
-
-    __RUNTIME_CB_DOMAIN_END = RESOURCE_CONTEXT_CREATED,
-    __RESOURCE_CB_DOMAIN_START = RESOURCE_CONTEXT_CREATED,
-
-    __RESOURCE_CB_DOMAIN_END = RESOURCE_CONTEXT_DESTROYED + 1,
-  };
-
-
-  CuptiCallbackApi(const CuptiCallbackApi&) = delete;
-  CuptiCallbackApi& operator=(const CuptiCallbackApi&) = delete;
-
-  static CuptiCallbackApi& singleton();
-
-  bool initSuccess() const {
-    return initSuccess_;
-  }
-
-#ifdef HAS_CUPTI
-  CUptiResult getCuptiStatus() const {
-    return lastCuptiStatus_;
-  }
-#endif
-
-  bool registerCallback(
-    CUpti_CallbackDomain domain,
-    CuptiCallBackID cbid,
-    CuptiCallbackFn cbfn);
-
-  // returns false if callback was not found
-  bool deleteCallback(
-    CUpti_CallbackDomain domain,
-    CuptiCallBackID cbid,
-    CuptiCallbackFn cbfn);
-
-  bool enableCallback(CUpti_CallbackDomain domain, CUpti_CallbackId cbid);
-  bool disableCallback(CUpti_CallbackDomain domain, CUpti_CallbackId cbid);
-
-
-  // Please do not use this method. This has to be exposed as public
-  // so it is accessible from the callback handler
-  void __callback_switchboard(
-      CUpti_CallbackDomain domain,
-      CUpti_CallbackId cbid,
-      const CUpti_CallbackData* cbInfo);
-
- private:
-
-  explicit CuptiCallbackApi();
-
-  // For callback table design overview see the .cpp file
-  using CallbackList = std::list<CuptiCallbackFn>;
-
-  // level 2 tables sizes are known at compile time
-  constexpr static size_t RUNTIME_CB_DOMAIN_SIZE
-    = (__RUNTIME_CB_DOMAIN_END - __RUNTIME_CB_DOMAIN_START);
-
-  constexpr static size_t RESOURCE_CB_DOMAIN_SIZE
-    = (__RESOURCE_CB_DOMAIN_END - __RESOURCE_CB_DOMAIN_START);
-
-  // level 1 table is a struct
-  struct CallbackTable {
-    std::array<CallbackList, RUNTIME_CB_DOMAIN_SIZE> runtime;
-    std::array<CallbackList, RESOURCE_CB_DOMAIN_SIZE> resource;
-
-    CallbackList* lookup(CUpti_CallbackDomain domain, CuptiCallBackID cbid);
-  };
-
-  CallbackTable callbacks_;
-  bool initSuccess_ = false;
-
-#ifdef HAS_CUPTI
-  CUptiResult lastCuptiStatus_;
-  CUpti_SubscriberHandle subscriber_;
-#endif
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApiMock.h b/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApiMock.h
deleted file mode 100644
index fd51267274f..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiCallbackApiMock.h
+++ /dev/null
@@ -1,32 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-// Provides data structures to mock CUPTI Callback API
-#ifndef HAS_CUPTI
-
-enum CUpti_CallbackDomain {
-  CUPTI_CB_DOMAIN_RESOURCE,
-  CUPTI_CB_DOMAIN_RUNTIME_API,
-};
-enum CUpti_CallbackId {
-  CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000,
-  CUPTI_CBID_RESOURCE_CONTEXT_CREATED,
-  CUPTI_CBID_RESOURCE_CONTEXT_DESTROY_STARTING,
-};
-
-using CUcontext = void*;
-
-struct CUpti_ResourceData {
-  CUcontext context;
-};
-
-constexpr int CUPTI_API_ENTER = 0;
-constexpr int CUPTI_API_EXIT = 0;
-
-struct CUpti_CallbackData {
-  CUcontext context;
-  const char* symbolName;
-  int callbackSite;
-};
-#endif // HAS_CUPTI
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiEventApi.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiEventApi.cpp
deleted file mode 100644
index 7f1d48c1d00..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiEventApi.cpp
+++ /dev/null
@@ -1,112 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "CuptiEventApi.h"
-
-#include <chrono>
-
-#include "Logger.h"
-#include "cupti_call.h"
-
-using namespace std::chrono;
-using std::vector;
-
-namespace KINETO_NAMESPACE {
-
-CuptiEventApi::CuptiEventApi(CUcontext context)
-    : context_(context) {
-  CUPTI_CALL(cuptiGetDeviceId(context_, (uint32_t*)&device_));
-}
-
-CUpti_EventGroupSets* CuptiEventApi::createGroupSets(
-    vector<CUpti_EventID>& ids) {
-  CUpti_EventGroupSets* group_sets = nullptr;
-  CUptiResult res = CUPTI_CALL(cuptiEventGroupSetsCreate(
-      context_, sizeof(CUpti_EventID) * ids.size(), ids.data(), &group_sets));
-
-  if (res != CUPTI_SUCCESS || group_sets == nullptr) {
-    const char* errstr = nullptr;
-    CUPTI_CALL(cuptiGetResultString(res, &errstr));
-    throw std::system_error(EINVAL, std::generic_category(), errstr);
-  }
-
-  return group_sets;
-}
-
-void CuptiEventApi::destroyGroupSets(CUpti_EventGroupSets* sets) {
-  CUPTI_CALL(cuptiEventGroupSetsDestroy(sets));
-}
-
-bool CuptiEventApi::setContinuousMode() {
-  // Avoid logging noise for CUPTI_ERROR_LEGACY_PROFILER_NOT_SUPPORTED
-  CUptiResult res = CUPTI_CALL_NOWARN(cuptiSetEventCollectionMode(
-      context_, CUPTI_EVENT_COLLECTION_MODE_CONTINUOUS));
-  if (res == CUPTI_ERROR_LEGACY_PROFILER_NOT_SUPPORTED) {
-    return false;
-  }
-  // Log warning on other errors
-  CUPTI_CALL(res);
-  return (res == CUPTI_SUCCESS);
-}
-
-void CuptiEventApi::enablePerInstance(CUpti_EventGroup eventGroup) {
-  uint32_t profile_all = 1;
-  CUPTI_CALL(cuptiEventGroupSetAttribute(
-      eventGroup,
-      CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES,
-      sizeof(profile_all),
-      &profile_all));
-}
-
-uint32_t CuptiEventApi::instanceCount(CUpti_EventGroup eventGroup) {
-  uint32_t instance_count = 0;
-  size_t s = sizeof(instance_count);
-  CUPTI_CALL(cuptiEventGroupGetAttribute(
-      eventGroup, CUPTI_EVENT_GROUP_ATTR_INSTANCE_COUNT, &s, &instance_count));
-  return instance_count;
-}
-
-void CuptiEventApi::enableGroupSet(CUpti_EventGroupSet& set) {
-  CUptiResult res = CUPTI_CALL_NOWARN(cuptiEventGroupSetEnable(&set));
-  if (res != CUPTI_SUCCESS) {
-    const char* errstr = nullptr;
-    CUPTI_CALL(cuptiGetResultString(res, &errstr));
-    throw std::system_error(EIO, std::generic_category(), errstr);
-  }
-}
-
-void CuptiEventApi::disableGroupSet(CUpti_EventGroupSet& set) {
-  CUPTI_CALL(cuptiEventGroupSetDisable(&set));
-}
-
-void CuptiEventApi::readEvent(
-    CUpti_EventGroup grp,
-    CUpti_EventID id,
-    vector<int64_t>& vals) {
-  size_t s = sizeof(int64_t) * vals.size();
-  CUPTI_CALL(cuptiEventGroupReadEvent(
-      grp,
-      CUPTI_EVENT_READ_FLAG_NONE,
-      id,
-      &s,
-      reinterpret_cast<uint64_t*>(vals.data())));
-}
-
-vector<CUpti_EventID> CuptiEventApi::eventsInGroup(CUpti_EventGroup grp) {
-  uint32_t group_size = 0;
-  size_t s = sizeof(group_size);
-  CUPTI_CALL(cuptiEventGroupGetAttribute(
-      grp, CUPTI_EVENT_GROUP_ATTR_NUM_EVENTS, &s, &group_size));
-  size_t events_size = group_size * sizeof(CUpti_EventID);
-  vector<CUpti_EventID> res(group_size);
-  CUPTI_CALL(cuptiEventGroupGetAttribute(
-      grp, CUPTI_EVENT_GROUP_ATTR_EVENTS, &events_size, res.data()));
-  return res;
-}
-
-CUpti_EventID CuptiEventApi::eventId(const std::string& name) {
-  CUpti_EventID id{0};
-  CUPTI_CALL(cuptiEventGetIdFromName(device_, name.c_str(), &id));
-  return id;
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiEventApi.h b/plugins/tensorboard-plugins/libkineto/src/CuptiEventApi.h
deleted file mode 100644
index 79610f93f0e..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiEventApi.h
+++ /dev/null
@@ -1,49 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <cupti.h>
-#include <queue>
-#include <string>
-
-namespace KINETO_NAMESPACE {
-
-// C++ interface to CUPTI Events C API.
-// Virtual methods are here mainly to allow easier testing.
-class CuptiEventApi {
- public:
-  explicit CuptiEventApi(CUcontext context_);
-  virtual ~CuptiEventApi() {}
-
-  CUdevice device() {
-    return device_;
-  }
-
-  virtual CUpti_EventGroupSets* createGroupSets(
-      std::vector<CUpti_EventID>& ids);
-  virtual void destroyGroupSets(CUpti_EventGroupSets* sets);
-
-  virtual bool setContinuousMode();
-
-  virtual void enablePerInstance(CUpti_EventGroup eventGroup);
-  virtual uint32_t instanceCount(CUpti_EventGroup eventGroup);
-
-  virtual void enableGroupSet(CUpti_EventGroupSet& set);
-  virtual void disableGroupSet(CUpti_EventGroupSet& set);
-
-  virtual void
-  readEvent(CUpti_EventGroup g, CUpti_EventID id, std::vector<int64_t>& vals);
-  virtual std::vector<CUpti_EventID> eventsInGroup(CUpti_EventGroup g);
-
-  virtual CUpti_EventID eventId(const std::string& name);
-
- protected:
-  // Unit testing
-  CuptiEventApi() : context_(nullptr), device_(0) {}
-
- private:
-  CUcontext context_;
-  CUdevice device_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiMetricApi.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiMetricApi.cpp
deleted file mode 100644
index 36401e74341..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiMetricApi.cpp
+++ /dev/null
@@ -1,107 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "CuptiMetricApi.h"
-
-#include <chrono>
-
-#include "Logger.h"
-#include "cupti_call.h"
-
-using namespace std::chrono;
-using std::vector;
-
-namespace KINETO_NAMESPACE {
-
-CUpti_MetricID CuptiMetricApi::idFromName(const std::string& name) {
-  CUpti_MetricID metric_id{~0u};
-  CUptiResult res =
-      CUPTI_CALL(cuptiMetricGetIdFromName(device_, name.c_str(), &metric_id));
-  if (res == CUPTI_ERROR_INVALID_METRIC_NAME) {
-    LOG(WARNING) << "Invalid metric name: " << name;
-  }
-  return metric_id;
-}
-
-// Return a map of event IDs and names for a given metric id.
-// Note that many events don't have a name. In that case the name will
-// be set to the empty string.
-std::map<CUpti_EventID, std::string> CuptiMetricApi::events(
-    CUpti_MetricID metric_id) {
-  uint32_t num_events = 0;
-  CUPTI_CALL(cuptiMetricGetNumEvents(metric_id, &num_events));
-  vector<CUpti_EventID> ids(num_events);
-  size_t array_size = num_events * sizeof(CUpti_EventID);
-  CUPTI_CALL(cuptiMetricEnumEvents(metric_id, &array_size, ids.data()));
-  std::map<CUpti_EventID, std::string> res;
-  for (CUpti_EventID id : ids) {
-    // Attempt to lookup name from CUPTI
-    constexpr size_t kMaxEventNameLength = 64;
-    char cupti_name[kMaxEventNameLength];
-    size_t size = kMaxEventNameLength;
-    CUPTI_CALL(
-        cuptiEventGetAttribute(id, CUPTI_EVENT_ATTR_NAME, &size, cupti_name));
-    cupti_name[kMaxEventNameLength - 1] = 0;
-
-    // CUPTI "helpfully" returns "event_name" when the event is unnamed.
-    if (size > 0 && strcmp(cupti_name, "event_name") != 0) {
-      res.emplace(id, cupti_name);
-    } else {
-      res.emplace(id, "");
-    }
-  }
-  return res;
-}
-
-CUpti_MetricValueKind CuptiMetricApi::valueKind(CUpti_MetricID metric) {
-  CUpti_MetricValueKind res{CUPTI_METRIC_VALUE_KIND_FORCE_INT};
-  size_t value_kind_size = sizeof(res);
-  CUPTI_CALL(cuptiMetricGetAttribute(
-      metric, CUPTI_METRIC_ATTR_VALUE_KIND, &value_kind_size, &res));
-  return res;
-}
-
-CUpti_MetricEvaluationMode CuptiMetricApi::evaluationMode(
-    CUpti_MetricID metric) {
-  CUpti_MetricEvaluationMode eval_mode{
-      CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE};
-  size_t eval_mode_size = sizeof(eval_mode);
-  CUPTI_CALL(cuptiMetricGetAttribute(
-      metric, CUPTI_METRIC_ATTR_EVALUATION_MODE, &eval_mode_size, &eval_mode));
-  return eval_mode;
-}
-
-// FIXME: Consider caching value kind here
-SampleValue CuptiMetricApi::calculate(
-    CUpti_MetricID metric,
-    CUpti_MetricValueKind kind,
-    vector<CUpti_EventID>& events,
-    vector<int64_t>& values,
-    int64_t duration) {
-  CUpti_MetricValue metric_value;
-  CUPTI_CALL(cuptiMetricGetValue(
-      device_,
-      metric,
-      events.size() * sizeof(CUpti_EventID),
-      events.data(),
-      values.size() * sizeof(int64_t),
-      reinterpret_cast<uint64_t*>(values.data()),
-      duration,
-      &metric_value));
-
-  switch (kind) {
-    case CUPTI_METRIC_VALUE_KIND_DOUBLE:
-    case CUPTI_METRIC_VALUE_KIND_PERCENT:
-      return SampleValue(metric_value.metricValueDouble);
-    case CUPTI_METRIC_VALUE_KIND_UINT64:
-    case CUPTI_METRIC_VALUE_KIND_INT64:
-    case CUPTI_METRIC_VALUE_KIND_THROUGHPUT:
-      return SampleValue(metric_value.metricValueUint64);
-    case CUPTI_METRIC_VALUE_KIND_UTILIZATION_LEVEL:
-      return SampleValue((int)metric_value.metricValueUtilizationLevel);
-    default:
-      assert(false);
-  }
-  return SampleValue(-1);
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiMetricApi.h b/plugins/tensorboard-plugins/libkineto/src/CuptiMetricApi.h
deleted file mode 100644
index f45d38cd616..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiMetricApi.h
+++ /dev/null
@@ -1,38 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <cupti.h>
-
-#include <map>
-#include <vector>
-
-#include "SampleListener.h"
-
-namespace KINETO_NAMESPACE {
-
-// C++ interface to CUPTI Metrics C API.
-// Virtual methods are here mainly to allow easier testing.
-class CuptiMetricApi {
- public:
-  explicit CuptiMetricApi(CUdevice device) : device_(device) {}
-  virtual ~CuptiMetricApi() {}
-
-  virtual CUpti_MetricID idFromName(const std::string& name);
-  virtual std::map<CUpti_EventID, std::string> events(CUpti_MetricID metric_id);
-
-  virtual CUpti_MetricValueKind valueKind(CUpti_MetricID metric);
-  virtual CUpti_MetricEvaluationMode evaluationMode(CUpti_MetricID metric);
-
-  virtual SampleValue calculate(
-      CUpti_MetricID metric,
-      CUpti_MetricValueKind kind,
-      std::vector<CUpti_EventID>& events,
-      std::vector<int64_t>& values,
-      int64_t duration);
-
- private:
-  CUdevice device_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiNvPerfMetric.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiNvPerfMetric.cpp
deleted file mode 100644
index d1b08ab2c13..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiNvPerfMetric.cpp
+++ /dev/null
@@ -1,504 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#ifdef HAS_CUPTI
-#include <cuda_runtime_api.h>
-#if defined(CUDART_VERSION) && CUDART_VERSION > 10000 && CUDART_VERSION < 11040
-#include <nvperf_cuda_host.h>
-#include <nvperf_host.h>
-#include <nvperf_target.h>
-#endif // cuda version > 10.00 and < 11.04
-#endif // HAS_CUPTI
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "ScopeExit.h"
-#include "CuptiNvPerfMetric.h"
-#include "Logger.h"
-
-namespace KINETO_NAMESPACE {
-
-// Add a namespace to isolate these utility functions that are only
-// going to be used by the CuptiRangeProfiler. These included calls
-// to NVIDIA PerfWorks APIs.
-namespace nvperf {
-
-
-// Largely based on NVIDIA sample code provided with CUDA release
-//  files Metric.cpp and Eval.cpp
-
-// -------------------------------------------------
-// Metric and Counter Data Configuration
-// -------------------------------------------------
-
-
-// Note: Be carful before modifying the code below. There is a specific
-// sequence one needs to follow to program the metrics else things may
-// stop working. We tried to keep the flow consistent with the example
-// code from NVIDIA. Since most of the programmability comes from
-// the CUPTI profiler metric names this should be okay.
-
-// Only supported on CUDA RT Version between 10.0 and 11.04.
-// After CUDA RT 11.04, the structure has changed.
-// TODO update the structure NVPA_RawMetricsConfig to support 11.04
-#if defined(CUDART_VERSION) && CUDART_VERSION > 10000 && CUDART_VERSION < 11040
-
-bool getRawMetricRequests(
-    NVPA_MetricsContext* metricsContext,
-    std::vector<std::string> metricNames,
-    std::vector<std::string>& rawMetricsDeps,
-    std::vector<NVPA_RawMetricRequest>& rawMetricRequests) {
-  bool isolated = true;
-  /* Bug in collection with collection of metrics without instances, keep it
-   * to true*/
-  bool keepInstances = true;
-
-  for (const auto& metricName : metricNames) {
-
-    NVPW_MetricsContext_GetMetricProperties_Begin_Params
-        getMetricPropertiesBeginParams = {
-            NVPW_MetricsContext_GetMetricProperties_Begin_Params_STRUCT_SIZE, nullptr};
-    getMetricPropertiesBeginParams.pMetricsContext = metricsContext;
-    getMetricPropertiesBeginParams.pMetricName = metricName.c_str();
-
-    if (!NVPW_CALL(
-        NVPW_MetricsContext_GetMetricProperties_Begin(
-            &getMetricPropertiesBeginParams))) {
-      return false;
-    }
-
-    for (const char** metricDepsIt =
-             getMetricPropertiesBeginParams.ppRawMetricDependencies;
-         *metricDepsIt;
-         ++metricDepsIt) {
-      rawMetricsDeps.push_back(*metricDepsIt);
-    }
-
-    NVPW_MetricsContext_GetMetricProperties_End_Params
-        getMetricPropertiesEndParams = {
-            NVPW_MetricsContext_GetMetricProperties_End_Params_STRUCT_SIZE, nullptr};
-    getMetricPropertiesEndParams.pMetricsContext = metricsContext;
-
-    if (!NVPW_CALL(NVPW_MetricsContext_GetMetricProperties_End(
-            &getMetricPropertiesEndParams))) {
-      return false;
-    }
-  }
-
-  for (const auto& rawMetricName : rawMetricsDeps) {
-    NVPA_RawMetricRequest metricRequest = {NVPA_RAW_METRIC_REQUEST_STRUCT_SIZE, nullptr};
-    metricRequest.pMetricName = rawMetricName.c_str();
-    metricRequest.isolated = isolated;
-    metricRequest.keepInstances = keepInstances;
-    rawMetricRequests.push_back(metricRequest);
-    VLOG(1) << "Adding raw metric struct  : raw metric = " << rawMetricName
-        << " isolated = " << isolated << " keepinst = " << keepInstances;
-  }
-
-  if (rawMetricRequests.size() == 0) {
-    LOG(WARNING) << "CUPTI Profiler was unable to configure any metrics";
-    return false;
-  }
-  return true;
-}
-
-// Setup CUPTI Profiler Config Image
-bool getProfilerConfigImage(
-    const std::string& chipName,
-    const std::vector<std::string>& metricNames,
-    std::vector<uint8_t>& configImage,
-    const uint8_t* counterAvailabilityImage) {
-
-  NVPW_CUDA_MetricsContext_Create_Params metricsContextCreateParams = {
-      NVPW_CUDA_MetricsContext_Create_Params_STRUCT_SIZE, nullptr};
-  metricsContextCreateParams.pChipName = chipName.c_str();
-
-  if (!NVPW_CALL(
-        NVPW_CUDA_MetricsContext_Create(&metricsContextCreateParams))) {
-    return false;
-  }
-
-  NVPW_MetricsContext_Destroy_Params metricsContextDestroyParams = {
-      NVPW_MetricsContext_Destroy_Params_STRUCT_SIZE, nullptr};
-  metricsContextDestroyParams.pMetricsContext =
-      metricsContextCreateParams.pMetricsContext;
-
-  SCOPE_EXIT([&]() {
-    NVPW_MetricsContext_Destroy(
-        (NVPW_MetricsContext_Destroy_Params*)&metricsContextDestroyParams);
-  });
-
-  // Get all raw metrics required for given metricNames list
-  std::vector<NVPA_RawMetricRequest> rawMetricRequests;
-
-  // note: we need a variable at this functions scope to hold the string
-  // pointers for underlying C char arrays.
-  std::vector<std::string> rawMetricDeps;
-
-  if (!getRawMetricRequests(
-      metricsContextCreateParams.pMetricsContext,
-      metricNames,
-      rawMetricDeps,
-      rawMetricRequests)) {
-    return false;
-  }
-
-  NVPA_RawMetricsConfigOptions metricsConfigOptions = {
-      NVPA_RAW_METRICS_CONFIG_OPTIONS_STRUCT_SIZE, nullptr};
-  metricsConfigOptions.activityKind = NVPA_ACTIVITY_KIND_PROFILER;
-  metricsConfigOptions.pChipName = chipName.c_str();
-  NVPA_RawMetricsConfig* rawMetricsConfig;
-  if (!NVPW_CALL(
-        NVPA_RawMetricsConfig_Create(
-          &metricsConfigOptions, &rawMetricsConfig))) {
-    return false;
-  }
-
-  // TODO check if this is required
-  if (counterAvailabilityImage) {
-    NVPW_RawMetricsConfig_SetCounterAvailability_Params
-        setCounterAvailabilityParams = {
-            NVPW_RawMetricsConfig_SetCounterAvailability_Params_STRUCT_SIZE, nullptr};
-    setCounterAvailabilityParams.pRawMetricsConfig = rawMetricsConfig;
-    setCounterAvailabilityParams.pCounterAvailabilityImage =
-        counterAvailabilityImage;
-    if (!NVPW_CALL(
-          NVPW_RawMetricsConfig_SetCounterAvailability(
-            &setCounterAvailabilityParams))) {
-      return false;
-    }
-  }
-
-  NVPW_RawMetricsConfig_Destroy_Params rawMetricsConfigDestroyParams = {
-      NVPW_RawMetricsConfig_Destroy_Params_STRUCT_SIZE, nullptr};
-  rawMetricsConfigDestroyParams.pRawMetricsConfig = rawMetricsConfig;
-  SCOPE_EXIT([&]() {
-    NVPW_RawMetricsConfig_Destroy(
-        (NVPW_RawMetricsConfig_Destroy_Params*)&rawMetricsConfigDestroyParams);
-  });
-
-  // Start a Raw Metric Pass group
-  NVPW_RawMetricsConfig_BeginPassGroup_Params beginPassGroupParams = {
-      NVPW_RawMetricsConfig_BeginPassGroup_Params_STRUCT_SIZE, nullptr};
-  beginPassGroupParams.pRawMetricsConfig = rawMetricsConfig;
-  if (!NVPW_CALL(
-        NVPW_RawMetricsConfig_BeginPassGroup(&beginPassGroupParams))) {
-    return false;
-  }
-
-  // Add all raw metrics
-  NVPW_RawMetricsConfig_AddMetrics_Params addMetricsParams = {
-      NVPW_RawMetricsConfig_AddMetrics_Params_STRUCT_SIZE, nullptr};
-  addMetricsParams.pRawMetricsConfig = rawMetricsConfig;
-  addMetricsParams.pRawMetricRequests = rawMetricRequests.data();
-  addMetricsParams.numMetricRequests = rawMetricRequests.size();
-  if (!NVPW_CALL(
-        NVPW_RawMetricsConfig_AddMetrics(&addMetricsParams))) {
-    return false;
-  }
-
-  // End pass group
-  NVPW_RawMetricsConfig_EndPassGroup_Params endPassGroupParams = {
-      NVPW_RawMetricsConfig_EndPassGroup_Params_STRUCT_SIZE, nullptr};
-  endPassGroupParams.pRawMetricsConfig = rawMetricsConfig;
-  if (!NVPW_CALL(
-        NVPW_RawMetricsConfig_EndPassGroup(&endPassGroupParams))) {
-    return false;
-  }
-
-  // Setup Config Image generation
-  NVPW_RawMetricsConfig_GenerateConfigImage_Params generateConfigImageParams = {
-      NVPW_RawMetricsConfig_GenerateConfigImage_Params_STRUCT_SIZE, nullptr};
-  generateConfigImageParams.pRawMetricsConfig = rawMetricsConfig;
-  if (!NVPW_CALL(
-        NVPW_RawMetricsConfig_GenerateConfigImage(&generateConfigImageParams))) {
-    return false;
-  }
-
-  // Get the Config Image size... nearly there
-  NVPW_RawMetricsConfig_GetConfigImage_Params getConfigImageParams = {
-      NVPW_RawMetricsConfig_GetConfigImage_Params_STRUCT_SIZE, nullptr};
-  getConfigImageParams.pRawMetricsConfig = rawMetricsConfig;
-  getConfigImageParams.bytesAllocated = 0;
-  getConfigImageParams.pBuffer = nullptr;
-  if (!NVPW_CALL(
-        NVPW_RawMetricsConfig_GetConfigImage(&getConfigImageParams))) {
-    return false;
-  }
-
-  configImage.resize(getConfigImageParams.bytesCopied);
-
-  // Write the Config image binary
-  getConfigImageParams.bytesAllocated = configImage.size();
-  getConfigImageParams.pBuffer = configImage.data();
-  if (!NVPW_CALL(
-        NVPW_RawMetricsConfig_GetConfigImage(&getConfigImageParams))) {
-    return false;
-  }
-
-  return true;
-}
-
-bool getCounterDataPrefixImage(
-    const std::string& chipName,
-    const std::vector<std::string>& metricNames,
-    std::vector<uint8_t>& counterDataImagePrefix) {
-
-  NVPW_CUDA_MetricsContext_Create_Params metricsContextCreateParams = {
-      NVPW_CUDA_MetricsContext_Create_Params_STRUCT_SIZE, nullptr};
-  metricsContextCreateParams.pChipName = chipName.c_str();
-
-  if (!NVPW_CALL(
-        NVPW_CUDA_MetricsContext_Create(&metricsContextCreateParams))) {
-    return false;
-  }
-
-  NVPW_MetricsContext_Destroy_Params metricsContextDestroyParams = {
-      NVPW_MetricsContext_Destroy_Params_STRUCT_SIZE, nullptr};
-  metricsContextDestroyParams.pMetricsContext =
-      metricsContextCreateParams.pMetricsContext;
-
-
-  SCOPE_EXIT([&]() {
-    NVPW_MetricsContext_Destroy(
-        (NVPW_MetricsContext_Destroy_Params*)&metricsContextDestroyParams);
-  });
-
-  // Get all raw metrics required for given metricNames list
-  std::vector<NVPA_RawMetricRequest> rawMetricRequests;
-
-  // note: we need a variable at this functions scope to hold the string
-  // pointers for underlying C char arrays.
-  std::vector<std::string> rawMetricDeps;
-
-  if (!getRawMetricRequests(
-      metricsContextCreateParams.pMetricsContext,
-      metricNames,
-      rawMetricDeps,
-      rawMetricRequests)) {
-    return false;
-  }
-
-  // Setup Counter Data builder
-  NVPW_CounterDataBuilder_Create_Params counterDataBuilderCreateParams = {
-      NVPW_CounterDataBuilder_Create_Params_STRUCT_SIZE, nullptr};
-  counterDataBuilderCreateParams.pChipName = chipName.c_str();
-  if (!NVPW_CALL(
-        NVPW_CounterDataBuilder_Create(&counterDataBuilderCreateParams))) {
-    return false;
-  }
-
-  NVPW_CounterDataBuilder_Destroy_Params counterDataBuilderDestroyParams = {
-      NVPW_CounterDataBuilder_Destroy_Params_STRUCT_SIZE, nullptr};
-  counterDataBuilderDestroyParams.pCounterDataBuilder =
-      counterDataBuilderCreateParams.pCounterDataBuilder;
-  SCOPE_EXIT([&]() {
-    NVPW_CounterDataBuilder_Destroy((
-        NVPW_CounterDataBuilder_Destroy_Params*)&counterDataBuilderDestroyParams);
-  });
-
-  // Add metrics to counter data image prefix
-  NVPW_CounterDataBuilder_AddMetrics_Params addMetricsParams = {
-      NVPW_CounterDataBuilder_AddMetrics_Params_STRUCT_SIZE, nullptr};
-  addMetricsParams.pCounterDataBuilder =
-      counterDataBuilderCreateParams.pCounterDataBuilder;
-  addMetricsParams.pRawMetricRequests = rawMetricRequests.data();
-  addMetricsParams.numMetricRequests = rawMetricRequests.size();
-  if (!NVPW_CALL(
-        NVPW_CounterDataBuilder_AddMetrics(&addMetricsParams))) {
-    return false;
-  }
-
-  // Get image prefix size
-  NVPW_CounterDataBuilder_GetCounterDataPrefix_Params
-      getCounterDataPrefixParams = {
-          NVPW_CounterDataBuilder_GetCounterDataPrefix_Params_STRUCT_SIZE, nullptr};
-  getCounterDataPrefixParams.pCounterDataBuilder =
-      counterDataBuilderCreateParams.pCounterDataBuilder;
-  getCounterDataPrefixParams.bytesAllocated = 0;
-  getCounterDataPrefixParams.pBuffer = nullptr;
-  if (!NVPW_CALL(
-        NVPW_CounterDataBuilder_GetCounterDataPrefix(
-          &getCounterDataPrefixParams))) {
-    return false;
-  }
-
-  counterDataImagePrefix.resize(getCounterDataPrefixParams.bytesCopied);
-
-  // Now write counter data image prefix
-  getCounterDataPrefixParams.bytesAllocated = counterDataImagePrefix.size();
-  getCounterDataPrefixParams.pBuffer = counterDataImagePrefix.data();
-  if (!NVPW_CALL(
-        NVPW_CounterDataBuilder_GetCounterDataPrefix(
-          &getCounterDataPrefixParams))) {
-    return false;
-  }
-
-  return true;
-}
-
-// -------------------------------------------------
-// Metric and Counter Evaluation Utilities
-// -------------------------------------------------
-
-std::string getRangeDescription(
-    const std::vector<uint8_t>& counterDataImage,
-    int rangeIndex) {
-  std::vector<const char*> descriptionPtrs;
-
-  NVPW_Profiler_CounterData_GetRangeDescriptions_Params getRangeDescParams = {
-      NVPW_Profiler_CounterData_GetRangeDescriptions_Params_STRUCT_SIZE, nullptr};
-  getRangeDescParams.pCounterDataImage = counterDataImage.data();
-  getRangeDescParams.rangeIndex = rangeIndex;
-
-  if (!NVPW_CALL(
-      NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams))) {
-    return "";
-  }
-
-  descriptionPtrs.resize(getRangeDescParams.numDescriptions);
-  getRangeDescParams.ppDescriptions = descriptionPtrs.data();
-
-  if (!NVPW_CALL(
-      NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams))) {
-    return "";
-  }
-
-  std::string rangeName;
-
-  for (size_t i = 0; i < getRangeDescParams.numDescriptions; i++) {
-    if (i > 0) {
-      rangeName.append("/");
-    }
-    rangeName.append(descriptionPtrs[i]);
-  }
-  return rangeName;
-}
-
-CuptiProfilerResult evalMetricValues(
-    const std::string& chipName,
-    const std::vector<uint8_t>& counterDataImage,
-    const std::vector<std::string>& metricNames,
-    bool verbose) {
-
-  if (!counterDataImage.size()) {
-    LOG(ERROR) << "Counter Data Image is empty!";
-    return {};
-  }
-
-  NVPW_CUDA_MetricsContext_Create_Params metricsContextCreateParams = {
-      NVPW_CUDA_MetricsContext_Create_Params_STRUCT_SIZE, nullptr};
-  metricsContextCreateParams.pChipName = chipName.c_str();
-  if (!NVPW_CALL(
-        NVPW_CUDA_MetricsContext_Create(&metricsContextCreateParams))) {
-    return {};
-  }
-
-  NVPW_MetricsContext_Destroy_Params metricsContextDestroyParams = {
-      NVPW_MetricsContext_Destroy_Params_STRUCT_SIZE, nullptr};
-  metricsContextDestroyParams.pMetricsContext =
-      metricsContextCreateParams.pMetricsContext;
-  SCOPE_EXIT([&]() {
-    NVPW_MetricsContext_Destroy(
-        (NVPW_MetricsContext_Destroy_Params*)&metricsContextDestroyParams);
-  });
-
-  NVPW_CounterData_GetNumRanges_Params getNumRangesParams = {
-      NVPW_CounterData_GetNumRanges_Params_STRUCT_SIZE, nullptr};
-  getNumRangesParams.pCounterDataImage = counterDataImage.data();
-  if (!NVPW_CALL(
-      NVPW_CounterData_GetNumRanges(&getNumRangesParams))) {
-    return {};
-  }
-
-  // TBD in the future support special chars in metric name
-  // for now these are default
-  const bool isolated = true;
-
-  // API takes a 2D array of chars
-  std::vector<const char*> metricNamePtrs;
-
-  for (const auto& metric : metricNames) {
-    metricNamePtrs.push_back(metric.c_str());
-  }
-
-  CuptiProfilerResult result{
-    .metricNames = metricNames};
-
-  for (size_t rangeIndex = 0; rangeIndex < getNumRangesParams.numRanges;
-       ++rangeIndex) {
-
-    CuptiRangeMeasurement rangeData {
-    .rangeName = getRangeDescription(counterDataImage, rangeIndex)};
-    rangeData.values.resize(metricNames.size());
-
-    // First set Counter data image with current range
-    NVPW_MetricsContext_SetCounterData_Params setCounterDataParams = {
-        NVPW_MetricsContext_SetCounterData_Params_STRUCT_SIZE, nullptr};
-
-    setCounterDataParams.pMetricsContext =
-        metricsContextCreateParams.pMetricsContext;
-    setCounterDataParams.pCounterDataImage = counterDataImage.data();
-    setCounterDataParams.isolated = isolated;
-    setCounterDataParams.rangeIndex = rangeIndex;
-
-    NVPW_CALL(NVPW_MetricsContext_SetCounterData(&setCounterDataParams));
-
-
-    // Now we can evaluate GPU metrics
-    NVPW_MetricsContext_EvaluateToGpuValues_Params evalToGpuParams = {
-        NVPW_MetricsContext_EvaluateToGpuValues_Params_STRUCT_SIZE, nullptr};
-    evalToGpuParams.pMetricsContext =
-        metricsContextCreateParams.pMetricsContext;
-    evalToGpuParams.numMetrics = metricNamePtrs.size();
-    evalToGpuParams.ppMetricNames = metricNamePtrs.data();
-    evalToGpuParams.pMetricValues = rangeData.values.data();
-
-    if (!NVPW_CALL(NVPW_MetricsContext_EvaluateToGpuValues(&evalToGpuParams))) {
-      LOG(WARNING) << "Failed to evaluate metris for range : "
-                   << rangeData.rangeName;
-      continue;
-    }
-
-    if (verbose) {
-      for (size_t i = 0; i < metricNames.size(); i++) {
-        LOG(INFO) << "rangeName: " << rangeData.rangeName
-                  << "\tmetricName: " << metricNames[i]
-                  << "\tgpuValue: " << rangeData.values[i];
-      }
-    }
-
-    result.rangeVals.emplace_back(std::move(rangeData));
-  }
-
-  return result;
-}
-
-#else
-
-bool getProfilerConfigImage(
-    const std::string& /*chipName*/,
-    const std::vector<std::string>& /*metricNames*/,
-    std::vector<uint8_t>& /*configImage*/,
-    const uint8_t* /*counterAvailabilityImage*/) {
-  return false;
-}
-
-bool getCounterDataPrefixImage(
-    const std::string& /*chipName*/,
-    const std::vector<std::string>& /*metricNames*/,
-    std::vector<uint8_t>& /*counterDataImagePrefix*/) {
-  return false;
-}
-
-CuptiProfilerResult evalMetricValues(
-    const std::string& /*chipName*/,
-    const std::vector<uint8_t>& /*counterDataImage*/,
-    const std::vector<std::string>& /*metricNames*/,
-    bool /*verbose*/) {
-  return {};
-}
-
-#endif // cuda version > 10.00 and < 11.04
-
-} // namespace nvperf
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiNvPerfMetric.h b/plugins/tensorboard-plugins/libkineto/src/CuptiNvPerfMetric.h
deleted file mode 100644
index d5dd1b1c1d2..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiNvPerfMetric.h
+++ /dev/null
@@ -1,71 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <string>
-#include <vector>
-#include <fmt/format.h>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "Logger.h"
-
-namespace KINETO_NAMESPACE {
-
-struct CuptiRangeMeasurement {
-  std::string rangeName;
-  std::vector<double> values;
-};
-
-struct CuptiProfilerResult {
-  std::vector<std::string> metricNames;
-  // rangeName, list<double> values
-  std::vector<CuptiRangeMeasurement> rangeVals;
-};
-
-/* Utilities for CUPTI and NVIDIA PerfWorks Metric API
- */
-
-#define NVPW_CALL(call)                            \
-  [&]() -> bool {                                  \
-    NVPA_Status _status_ = call;                   \
-    if (_status_ != NVPA_STATUS_SUCCESS) {         \
-      LOG(WARNING) << fmt::format(                 \
-          "function {} failed with error ({})",    \
-          #call,                                   \
-          (int)_status_);                          \
-      return false;                                \
-    }                                              \
-    return true;                                   \
-  }()
-
-// fixme - add a results string
-// nvpperfGetResultString(_status_, &_errstr_);
-
-namespace nvperf {
-
-// Setup CUPTI profiler configuration blob and counter data image prefix
-bool getProfilerConfigImage(
-    const std::string& chipName,
-    const std::vector<std::string>& metricNames,
-    std::vector<uint8_t>& configImage,
-    const uint8_t* counterAvailabilityImage = nullptr);
-
-// Setup CUPTI profiler configuration blob and counter data image prefix
-bool getCounterDataPrefixImage(
-    const std::string& chipName,
-    const std::vector<std::string>& metricNames,
-    std::vector<uint8_t>& counterDataImagePrefix);
-
-/* NV Perf Metric Evaluation helpers
- *   - utilities to read binary data and obtain metrics for ranges
- */
-CuptiProfilerResult evalMetricValues(
-    const std::string& chipName,
-    const std::vector<uint8_t>& counterDataImage,
-    const std::vector<std::string>& metricNames,
-    bool verbose = false);
-
-
-} // namespace nvperf
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerApi.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerApi.cpp
deleted file mode 100644
index e5f18ed7b0b..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerApi.cpp
+++ /dev/null
@@ -1,751 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <stdio.h>
-#include <stdlib.h>
-#ifdef HAS_CUPTI
-#include <cupti.h>
-#include <nvperf_host.h>
-#endif // HAS_CUPTI
-#include <mutex>
-#include <unordered_map>
-
-#ifdef HAS_CUPTI
-#include "cupti_call.h"
-#endif
-
-#include "time_since_epoch.h"
-#include "Logger.h"
-#include "Demangle.h"
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "CuptiCallbackApiMock.h"
-#include "CuptiRangeProfilerApi.h"
-
-#if HAS_CUPTI_RANGE_PROFILER
-#include <cupti.h>
-#include <nvperf_host.h>
-#include "cupti_call.h"
-#endif // HAS_CUPTI_RANGE_PROFILER
-
-namespace KINETO_NAMESPACE {
-
-#if HAS_CUPTI_RANGE_PROFILER
-constexpr char kRootUserRangeName[] = "__profile__";
-constexpr int kCallbacksCountToFlush = 500;
-
-// Should we set Counter availability image ourselves?
-// Disabled this right now as this call conflicts with DCGM
-// It is not clear why it should conflict except it being a profiler API call
-//  TODO Revisit
-constexpr bool kSetCounterAvail = false;
-
-// Shared state to track one Cupti Profiler API per Device
-namespace {
-// per device profiler maps
-std::unordered_map<uint32_t, CuptiRBProfilerSession*> profiler_map;
-std::unordered_map<uint32_t, bool> enable_flag;
-std::unordered_map<uint32_t, bool> disable_flag;
-
-std::mutex contextMutex_;
-std::unordered_map<CUcontext, int> ctx_to_dev;
-std::set<uint32_t> active_devices;
-}
-
-// forward declarations
-void __trackCudaCtx(CUcontext ctx, uint32_t device_id, CUpti_CallbackId cbid);
-void __trackCudaKernelLaunch(CUcontext ctx, const char* kernelName);
-
-/// Helper functions
-
-// Available raw counters
-std::vector<uint8_t> getCounterAvailiability(CUcontext cuContext) {
-  std::vector<uint8_t> counterAvailabilityImage;
-  CUpti_Profiler_GetCounterAvailability_Params getCounterAvailabilityParams = {
-      CUpti_Profiler_GetCounterAvailability_Params_STRUCT_SIZE, nullptr};
-  getCounterAvailabilityParams.ctx = cuContext;
-  CUPTI_CALL(
-      cuptiProfilerGetCounterAvailability(&getCounterAvailabilityParams));
-
-  counterAvailabilityImage.clear();
-  counterAvailabilityImage.resize(
-      getCounterAvailabilityParams.counterAvailabilityImageSize);
-
-  getCounterAvailabilityParams.pCounterAvailabilityImage =
-      counterAvailabilityImage.data();
-  CUPTI_CALL(
-      cuptiProfilerGetCounterAvailability(&getCounterAvailabilityParams));
-
-  return counterAvailabilityImage;
-}
-
-std::string getChipName(int deviceId) {
-  // Get chip name for the cuda device
-  CUpti_Device_GetChipName_Params getChipNameParams = {
-      CUpti_Device_GetChipName_Params_STRUCT_SIZE, nullptr};
-
-  getChipNameParams.deviceIndex = deviceId;
-  CUPTI_CALL(cuptiDeviceGetChipName(&getChipNameParams));
-
-  return getChipNameParams.pChipName;
-}
-
-inline uint32_t getDevID(CUcontext ctx) {
-  uint32_t device_id = UINT32_MAX;
-  CUPTI_CALL(cuptiGetDeviceId(ctx, &device_id));
-  if (device_id == UINT32_MAX) {
-    LOG(ERROR) << "Could not determine dev id for = " << ctx;
-  }
-  return device_id;
-}
-
-// We use CUPTI Callback functions in three ways :
-//   1. Track cuda contexts and maintain a list of active GPUs to profile
-//   2. Callbacks on kernel launches to track the name of automatic
-//      ranges that correspond to names of kernels
-//   3. Lastly CUPTI profiler has to be enabled on the same thread executing
-//      the CUDA kernels. We use Callbacks to enable the profiler
-//      asynchronously from another thread.
-
-void disableKernelCallbacks();
-
-void trackCudaCtx(
-    CUpti_CallbackDomain /*domain*/,
-    CUpti_CallbackId cbid,
-    const CUpti_CallbackData* cbInfo) {
-  auto *d = reinterpret_cast<const CUpti_ResourceData*>(cbInfo);
-  auto ctx = d->context;
-  uint32_t device_id = getDevID(ctx);
-
-  if (device_id == UINT32_MAX) {
-    return;
-  }
-
-  __trackCudaCtx(ctx, device_id, cbid);
-}
-
-void __trackCudaCtx(CUcontext ctx, uint32_t device_id, CUpti_CallbackId cbid) {
-  std::lock_guard<std::mutex> g(contextMutex_);
-  if (cbid == CUPTI_CBID_RESOURCE_CONTEXT_CREATED) {
-    VLOG(0) << "CUPTI Profiler observed CUDA Context created = "
-            << ctx << " device id = " << device_id;
-    active_devices.insert(device_id);
-    if constexpr (kSetCounterAvail) {
-      if (active_devices.size() == 1) {
-      CuptiRBProfilerSession::setCounterAvailabilityImage(
-          getCounterAvailiability(ctx));
-      }
-    }
-    ctx_to_dev[ctx] = device_id;
-
-  } else if (cbid == CUPTI_CBID_RESOURCE_CONTEXT_DESTROY_STARTING) {
-    VLOG(0) << "CUPTI Profiler observed CUDA Context destroyed = "
-            << ctx << " device id = " << device_id;
-    auto it = active_devices.find(device_id);
-    if (it != active_devices.end()) {
-      active_devices.erase(it);
-      ctx_to_dev.erase(ctx);
-    }
-  }
-}
-
-void trackCudaKernelLaunch(
-    CUpti_CallbackDomain /*domain*/,
-    CUpti_CallbackId /*cbid*/,
-    const CUpti_CallbackData* cbInfo) {
-  VLOG(1) << " Trace : Callback name = "
-          << (cbInfo->symbolName ?  cbInfo->symbolName: "")
-          << " context ptr = " << cbInfo->context;
-  auto ctx = cbInfo->context;
-  // should be in CUPTI_API_ENTER call site
-  if (cbInfo->callbackSite != CUPTI_API_ENTER) {
-    return;
-  }
-  __trackCudaKernelLaunch(ctx, cbInfo->symbolName);
-}
-
-void __trackCudaKernelLaunch(
-    CUcontext ctx,
-    const char* kernelName) {
-  VLOG(0) << " Tracking kernel name = " << (kernelName ? kernelName : "")
-          << " context ptr = " << ctx;
-
-  uint32_t device_id = 0;
-  auto it = ctx_to_dev.find(ctx);
-  if (it == ctx_to_dev.end()) {
-    // Warning here could be too noisy
-    VLOG(0) << " Could not find corresponding device to ctx = " << ctx;
-    return;
-  } else {
-    device_id = it->second;
-  }
-
-  auto pit = profiler_map.find(device_id);
-  if (pit == profiler_map.end() || pit->second == nullptr) {
-    return;
-  }
-  auto profiler = pit->second;
-
-  if (enable_flag[device_id]) {
-    LOG(INFO) << "Callback handler is enabling cupti profiler";
-    profiler->startAndEnable();
-    enable_flag[device_id] = false;
-
-  } else if (disable_flag[device_id]) {
-    LOG(INFO) << "Callback handler is disabling cupti profiler";
-    profiler->disableAndStop();
-    return;
-  }
-
-  if (profiler->curRange_ == CUPTI_AutoRange) {
-    profiler->logKernelName(kernelName ? kernelName : "__missing__");
-  }
-
-  /* TODO add per kernel time logging
-  if (measure_per_kernel) {
-    profiler->kernelStartTs_.push_back(
-        std::chrono::high_resolution_clock::now());
-  }
-  */
-
-  // periodically flush profiler data from GPU
-  if (profiler->numCallbacks_ % kCallbacksCountToFlush == 0) {
-    profiler->flushCounterData();
-  }
-  profiler->numCallbacks_++;
-}
-
-void enableKernelCallbacks() {
-  auto& cbapi = CuptiCallbackApi::singleton();
-  bool status = cbapi.enableCallback(
-      CUPTI_CB_DOMAIN_RUNTIME_API,
-      CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000);
-  if (!status) {
-    LOG(WARNING) << "CUPTI Range Profiler unable to "
-                 << "enable cuda kernel launch callback";
-    return;
-  }
-  LOG(INFO) << "CUPTI Profiler kernel callbacks enabled";
-}
-
-void disableKernelCallbacks() {
-  auto& cbapi = CuptiCallbackApi::singleton();
-  bool status = cbapi.disableCallback(
-      CUPTI_CB_DOMAIN_RUNTIME_API,
-      CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000);
-  if (!status) {
-    LOG(WARNING) << "CUPTI Range Profiler unable to "
-                 << "disable cuda kernel launch callback";
-    return;
-  }
-  LOG(INFO) << "CUPTI Profiler kernel callbacks disabled";
-}
-
-// static
-std::set<uint32_t> CuptiRBProfilerSession::getActiveDevices() {
-  std::lock_guard<std::mutex> g(contextMutex_);
-  return active_devices;
-}
-
-// static
-void CuptiRBProfilerSession::initCupti() {
-  CUpti_Profiler_Initialize_Params profilerInitializeParams = {
-      CUpti_Profiler_Initialize_Params_STRUCT_SIZE, nullptr};
-  CUPTI_CALL(cuptiProfilerInitialize(&profilerInitializeParams));
-}
-
-// static
-void CuptiRBProfilerSession::deInitCupti() {
-  CUpti_Profiler_DeInitialize_Params profilerDeInitializeParams = {
-      CUpti_Profiler_DeInitialize_Params_STRUCT_SIZE, nullptr};
-  CUPTI_CALL(cuptiProfilerDeInitialize(&profilerDeInitializeParams));
-}
-
-// static
-void CuptiRBProfilerSession::staticInit() {
-  CuptiRBProfilerSession::initCupti();
-
-  // Register CUPTI callbacks
-  auto& cbapi = CuptiCallbackApi::singleton();
-  CUpti_CallbackDomain domain = CUPTI_CB_DOMAIN_RESOURCE;
-  bool status = cbapi.registerCallback(
-      domain, CuptiCallbackApi::RESOURCE_CONTEXT_CREATED, trackCudaCtx);
-  status = status && cbapi.registerCallback(
-      domain, CuptiCallbackApi::RESOURCE_CONTEXT_DESTROYED, trackCudaCtx);
-  status = status && cbapi.enableCallback(
-      domain, CUPTI_CBID_RESOURCE_CONTEXT_CREATED);
-  status = status && cbapi.enableCallback(
-      domain, CUPTI_CBID_RESOURCE_CONTEXT_DESTROY_STARTING);
-
-  if (!status) {
-    LOG(WARNING) << "CUPTI Range Profiler unable to attach cuda context "
-                 << "create and destroy callbacks";
-    CUPTI_CALL(cbapi.getCuptiStatus());
-    return;
-  }
-
-  domain = CUPTI_CB_DOMAIN_RUNTIME_API;
-  status = cbapi.registerCallback(
-      domain, CuptiCallbackApi::CUDA_LAUNCH_KERNEL, trackCudaKernelLaunch);
-
-  if (!status) {
-    LOG(WARNING) << "CUPTI Range Profiler unable to attach cuda kernel "
-                 << "launch callback";
-    return;
-  }
-}
-
-// static
-std::vector<uint8_t>& CuptiRBProfilerSession::counterAvailabilityImage() {
-  static std::vector<uint8_t> counterAvailabilityImage_;
-  return counterAvailabilityImage_;
-}
-
-
-// Setup the profiler sessions
-CuptiRBProfilerSession::CuptiRBProfilerSession(
-    const std::vector<std::string>& metricNames,
-    int deviceId,
-    int maxRanges,
-    int numNestingLevels,
-    CUcontext cuContext)
-    : metricNames_(metricNames),
-      chipName_(getChipName(deviceId)),
-      deviceId_(deviceId),
-      maxRanges_(maxRanges),
-      numNestingLevels_(numNestingLevels),
-      cuContext_(cuContext) {
-  CuptiRBProfilerSession::initCupti();
-
-  LOG(INFO) << "Initializing CUPTI profiler session : device = " << deviceId
-            << " chip = " << chipName_;
-  /* Generate configuration for metrics, this can also be done offline*/
-  NVPW_InitializeHost_Params initializeHostParams = {
-      NVPW_InitializeHost_Params_STRUCT_SIZE, nullptr};
-  NVPW_CALL(NVPW_InitializeHost(&initializeHostParams));
-
-  if (metricNames.size()) {
-    if (!nvperf::getProfilerConfigImage(
-            chipName_,
-            metricNames,
-            configImage,
-            CuptiRBProfilerSession::counterAvailabilityImage().data())) {
-      LOG(ERROR) << "Failed to create configImage or counterDataImagePrefix";
-      return;
-    }
-    if (!nvperf::getCounterDataPrefixImage(
-            chipName_,
-            metricNames,
-            counterDataImagePrefix)) {
-      LOG(ERROR) << "Failed to create counterDataImagePrefix";
-      return;
-    }
-  } else {
-    LOG(ERROR) << "No metrics provided to profile";
-    return;
-  }
-
-  if (!createCounterDataImage()) {
-    LOG(ERROR) << "Failed to create counterDataImage";
-    return;
-  }
-
-  LOG(INFO) << "Size of structs\n"
-            << " config image size = " << configImage.size()  << " B"
-            << " counter data image prefix = "
-            << counterDataImagePrefix.size()  << " B"
-            << " counter data image size = " << counterDataImage.size() / 1024
-            << " KB"
-            << " counter sb image size = "
-            << counterDataScratchBuffer.size()  << " B";
-
-  beginPassParams_ = {CUpti_Profiler_BeginPass_Params_STRUCT_SIZE, nullptr};
-  endPassParams_ = {CUpti_Profiler_EndPass_Params_STRUCT_SIZE, nullptr};
-
-  initSuccess_ = true;
-  profiler_map[deviceId] = this;
-}
-
-// used in unittests only
-CuptiRBProfilerSession::CuptiRBProfilerSession(int deviceId, CUcontext ctx)
-  : deviceId_(deviceId), cuContext_(ctx) {
-  initSuccess_ = true;
-  profiler_map[deviceId] = this;
-}
-
-void CuptiRBProfilerSession::startInternal(
-    CUpti_ProfilerRange profilerRange,
-    CUpti_ProfilerReplayMode profilerReplayMode) {
-  LOG(INFO) << "Starting profiler session: profiler range = "
-            << ((profilerRange == CUPTI_AutoRange) ? "autorange" : "userrange")
-            << " replay mode = "
-            << ((profilerReplayMode == CUPTI_KernelReplay) ? "kernel" : "user");
-  if (!initSuccess_) {
-    LOG(WARNING) << __func__ << "() bailing out since initialization failed";
-    return;
-  }
-
-  if (cuContext_ == nullptr) {
-    for (const auto& it : ctx_to_dev) {
-      if (it.second == deviceId_) {
-        cuContext_ = it.first;
-        break;
-      }
-    }
-    LOG(INFO) << " Cupti Profiler using CUDA context = " << cuContext_;
-  }
-
-  profilerStartTs_ = std::chrono::high_resolution_clock::now();
-  curRange_ = profilerRange;
-  curReplay_ = profilerReplayMode;
-
-  CUpti_Profiler_BeginSession_Params beginSessionParams = {
-      CUpti_Profiler_BeginSession_Params_STRUCT_SIZE, nullptr};
-
-  beginSessionParams.ctx = cuContext_;
-  beginSessionParams.counterDataImageSize = counterDataImage.size();
-  beginSessionParams.pCounterDataImage = counterDataImage.data();
-  beginSessionParams.counterDataScratchBufferSize =
-      counterDataScratchBuffer.size();
-  beginSessionParams.pCounterDataScratchBuffer = counterDataScratchBuffer.data();
-  beginSessionParams.range = profilerRange;
-  beginSessionParams.replayMode = profilerReplayMode;
-  beginSessionParams.maxRangesPerPass = maxRanges_;
-  beginSessionParams.maxLaunchesPerPass = maxRanges_;
-
-  auto status = CUPTI_CALL(cuptiProfilerBeginSession(&beginSessionParams));
-  if (status != CUPTI_SUCCESS) {
-    LOG(WARNING) << "Failed to start CUPTI profiler";
-    initSuccess_ = false;
-    return;
-  }
-
-  // Set counter configuration
-  CUpti_Profiler_SetConfig_Params setConfigParams = {
-      CUpti_Profiler_SetConfig_Params_STRUCT_SIZE, nullptr};
-
-  setConfigParams.ctx = cuContext_;
-  setConfigParams.pConfig = configImage.data();
-  setConfigParams.configSize = configImage.size();
-  setConfigParams.passIndex = 0;
-  setConfigParams.minNestingLevel = 1;
-  setConfigParams.numNestingLevels = numNestingLevels_;
-  status = CUPTI_CALL(cuptiProfilerSetConfig(&setConfigParams));
-
-  if (status != CUPTI_SUCCESS) {
-    LOG(WARNING) << "Failed to configure CUPTI profiler";
-    initSuccess_ = false;
-    return;
-  }
-  profilerInitDoneTs_ = std::chrono::high_resolution_clock::now();
-
-  if (curRange_ == CUPTI_AutoRange) {
-    enableKernelCallbacks();
-  }
-  profilingActive_ = true;
-}
-
-void CuptiRBProfilerSession::stop() {
-  if (!initSuccess_) {
-    LOG(WARNING) << __func__ << "() bailing out since initialization failed";
-    return;
-  }
-  LOG(INFO) << "Stop profiler session on device = " << deviceId_;
-
-  CUpti_Profiler_UnsetConfig_Params unsetConfigParams = {
-      CUpti_Profiler_UnsetConfig_Params_STRUCT_SIZE, nullptr};
-  CUPTI_CALL(cuptiProfilerUnsetConfig(&unsetConfigParams));
-
-  CUpti_Profiler_EndSession_Params endSessionParams = {
-      CUpti_Profiler_EndSession_Params_STRUCT_SIZE, nullptr};
-  CUPTI_CALL(cuptiProfilerEndSession(&endSessionParams));
-
-  disableKernelCallbacks();
-
-  profilerStopTs_ = std::chrono::high_resolution_clock::now();
-  profilingActive_ = false;
-}
-
-void CuptiRBProfilerSession::beginPass() {
-  if (!initSuccess_) {
-    LOG(WARNING) << __func__ << "() bailing out since initialization failed";
-    return;
-  }
-  CUPTI_CALL(cuptiProfilerBeginPass(&beginPassParams_));
-}
-
-bool CuptiRBProfilerSession::endPass() {
-  if (!initSuccess_) {
-    LOG(WARNING) << __func__ << "() bailing out since initialization failed";
-    return true;
-  }
-  CUPTI_CALL(cuptiProfilerEndPass(&endPassParams_));
-  return endPassParams_.allPassesSubmitted;
-}
-
-void CuptiRBProfilerSession::flushCounterData() {
-  LOG(INFO) << "Flushing counter data on device = " << deviceId_;
-  CUpti_Profiler_FlushCounterData_Params flushCounterDataParams = {
-      CUpti_Profiler_FlushCounterData_Params_STRUCT_SIZE, nullptr};
-  CUPTI_CALL(cuptiProfilerFlushCounterData(&flushCounterDataParams));
-}
-
-/// Enable and disable the profiler
-void CuptiRBProfilerSession::enable() {
-  if (!initSuccess_) {
-    LOG(WARNING) << __func__ << "() bailing out since initialization failed";
-    return;
-  }
-  CUpti_Profiler_EnableProfiling_Params enableProfilingParams = {
-      CUpti_Profiler_EnableProfiling_Params_STRUCT_SIZE, nullptr};
-  CUPTI_CALL(cuptiProfilerEnableProfiling(&enableProfilingParams));
-}
-
-void CuptiRBProfilerSession::disable() {
-  if (!initSuccess_) {
-    LOG(WARNING) << __func__ << "() bailing out since initialization failed";
-    return;
-  }
-  CUpti_Profiler_DisableProfiling_Params disableProfilingParams = {
-      CUpti_Profiler_DisableProfiling_Params_STRUCT_SIZE, nullptr};
-  CUPTI_CALL(cuptiProfilerDisableProfiling(&disableProfilingParams));
-}
-
-/// User range based profiling
-void CuptiRBProfilerSession::pushRange(const std::string& rangeName) {
-  LOG(INFO) << " CUPTI pushrange ( " << rangeName << " )";
-  CUpti_Profiler_PushRange_Params pushRangeParams = {
-      CUpti_Profiler_PushRange_Params_STRUCT_SIZE, nullptr};
-  pushRangeParams.pRangeName = rangeName.c_str();
-  CUPTI_CALL(cuptiProfilerPushRange(&pushRangeParams));
-}
-
-void CuptiRBProfilerSession::popRange() {
-  LOG(INFO) << " CUPTI pop range";
-  CUpti_Profiler_PopRange_Params popRangeParams = {
-      CUpti_Profiler_PopRange_Params_STRUCT_SIZE, nullptr};
-  CUPTI_CALL(cuptiProfilerPopRange(&popRangeParams));
-}
-
-void CuptiRBProfilerSession::startAndEnable() {
-  startInternal(curRange_, curReplay_);
-  if (curReplay_ == CUPTI_UserReplay) {
-    beginPass();
-  }
-  enable();
-  if (curRange_ == CUPTI_UserRange) {
-    pushRange(kRootUserRangeName);
-  }
-  enable_flag[deviceId_] = false;
-}
-
-void CuptiRBProfilerSession::disableAndStop() {
-  if (curRange_ == CUPTI_UserRange) {
-    popRange();
-  }
-  disable();
-  if (curReplay_ == CUPTI_UserReplay) {
-    endPass();
-    flushCounterData();
-  }
-  stop();
-  disable_flag[deviceId_] = false;
-}
-
-void CuptiRBProfilerSession::asyncStartAndEnable(
-    CUpti_ProfilerRange profilerRange,
-    CUpti_ProfilerReplayMode profilerReplayMode) {
-  LOG(INFO) << "Starting CUPTI profiler asynchronously on device = "
-            << deviceId_ << " profiler range = "
-            << ((profilerRange == CUPTI_AutoRange) ? "autorange" : "userrange")
-            << " replay mode = "
-            << ((profilerReplayMode == CUPTI_KernelReplay) ? "kernel" : "user");
-  curReplay_ = profilerReplayMode;
-  curRange_ = profilerRange;
-  enable_flag[deviceId_] = true;
-  enableKernelCallbacks();
-}
-
-void CuptiRBProfilerSession::asyncDisableAndStop() {
-  LOG(INFO) << "Stopping CUPTI profiler asynchronously on device = "
-            << deviceId_ << " cu context = " << cuContext_;
-  disable_flag[deviceId_] = true;
-}
-
-
-CuptiProfilerResult CuptiRBProfilerSession::evaluateMetrics(
-    bool verbose) {
-  if (!initSuccess_) {
-    LOG(WARNING) << "Profiling failed, no results to return";
-    return {};
-  }
-  if (profilingActive_) {
-    disableAndStop();
-  }
-
-  LOG(INFO) << "Total kernels logged = " << kernelNames_.size();
-  if (verbose) {
-    for (const auto& kernel : kernelNames_) {
-      std::cout << demangle(kernel) << std::endl;
-    }
-    LOG(INFO) << "Profiler Range data : ";
-  }
-
-  auto results = nvperf::evalMetricValues(
-      chipName_, counterDataImage, metricNames_, verbose /*verbose*/);
-
-  // profiler end-end duration
-  auto duration_ms = std::chrono::duration_cast<std::chrono::milliseconds>(
-      profilerStopTs_ - profilerStartTs_);
-
-  auto init_dur_ms = std::chrono::duration_cast<std::chrono::milliseconds>(
-      profilerInitDoneTs_ - profilerStartTs_);
-  LOG(INFO) << "Total profiler time = " << duration_ms.count() << " ms";
-  LOG(INFO) << "Total profiler init time = " << init_dur_ms.count() << " ms";
-
-  return results;
-}
-
-std::unique_ptr<TraceSpan> CuptiRBProfilerSession::getProfilerTraceSpan() {
-  return std::make_unique<TraceSpan>(
-      timeSinceEpoch(profilerStartTs_),
-      timeSinceEpoch(profilerStopTs_),
-      "__cupti_profiler__"
-  );
-}
-
-void CuptiRBProfilerSession::saveCounterData(
-    const std::string& /*CounterDataFileName*/,
-    const std::string& /*CounterDataSBFileName*/) {
-  /* TBD write binary files for counter data and counter scratch buffer */
-}
-
-/// Setup counter data
-bool CuptiRBProfilerSession::createCounterDataImage() {
-  CUpti_Profiler_CounterDataImageOptions counterDataImageOptions;
-  counterDataImageOptions.pCounterDataPrefix = counterDataImagePrefix.data();
-  counterDataImageOptions.counterDataPrefixSize = counterDataImagePrefix.size();
-  counterDataImageOptions.maxNumRanges = maxRanges_;
-  counterDataImageOptions.maxNumRangeTreeNodes = maxRanges_;
-  counterDataImageOptions.maxRangeNameLength = 64;
-
-  // Calculate size of counter data image
-  CUpti_Profiler_CounterDataImage_CalculateSize_Params calculateSizeParams = {
-      CUpti_Profiler_CounterDataImage_CalculateSize_Params_STRUCT_SIZE, nullptr};
-  calculateSizeParams.pOptions = &counterDataImageOptions;
-  calculateSizeParams.sizeofCounterDataImageOptions =
-      CUpti_Profiler_CounterDataImageOptions_STRUCT_SIZE;
-
-  CUPTI_CALL(
-      cuptiProfilerCounterDataImageCalculateSize(&calculateSizeParams));
-  counterDataImage.resize(calculateSizeParams.counterDataImageSize);
-
-  // Initialize counter data image
-  CUpti_Profiler_CounterDataImage_Initialize_Params initializeParams = {
-    CUpti_Profiler_CounterDataImage_Initialize_Params_STRUCT_SIZE, nullptr};
-  initializeParams.sizeofCounterDataImageOptions =
-    CUpti_Profiler_CounterDataImageOptions_STRUCT_SIZE;
-  initializeParams.pOptions = &counterDataImageOptions;
-  initializeParams.counterDataImageSize =
-    calculateSizeParams.counterDataImageSize;
-  initializeParams.pCounterDataImage = counterDataImage.data();
-  CUPTI_CALL(cuptiProfilerCounterDataImageInitialize(&initializeParams));
-
-  // Calculate counter Scratch Buffer size
-  CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params
-    scratchBufferSizeParams = {
-          CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params_STRUCT_SIZE, nullptr};
-
-  scratchBufferSizeParams.counterDataImageSize =
-    calculateSizeParams.counterDataImageSize;
-  scratchBufferSizeParams.pCounterDataImage =
-    initializeParams.pCounterDataImage;
-  CUPTI_CALL(cuptiProfilerCounterDataImageCalculateScratchBufferSize(
-    &scratchBufferSizeParams));
-
-  counterDataScratchBuffer.resize(
-      scratchBufferSizeParams.counterDataScratchBufferSize);
-
-  // Initialize scratch buffer
-  CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params
-    initScratchBufferParams = {
-      CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params_STRUCT_SIZE, nullptr};
-
-  initScratchBufferParams.counterDataImageSize =
-    calculateSizeParams.counterDataImageSize;
-
-  initScratchBufferParams.pCounterDataImage =
-    initializeParams.pCounterDataImage;
-  initScratchBufferParams.counterDataScratchBufferSize =
-    scratchBufferSizeParams.counterDataScratchBufferSize;
-  initScratchBufferParams.pCounterDataScratchBuffer =
-    counterDataScratchBuffer.data();
-
-  CUPTI_CALL(cuptiProfilerCounterDataImageInitializeScratchBuffer(
-      &initScratchBufferParams));
-
-  return true;
-}
-
-#elif defined(HAS_CUPTI)
-
-// Create empty stubs for the API when CUPTI is not present.
-CuptiRBProfilerSession::CuptiRBProfilerSession(
-    const std::vector<std::string>& metricNames,
-    int deviceId,
-    int maxRanges,
-    int numNestingLevels,
-    CUcontext cuContext)
-    : metricNames_(metricNames),
-      deviceId_(deviceId),
-      maxRanges_(maxRanges),
-      numNestingLevels_(numNestingLevels),
-      cuContext_(cuContext) {}
-void CuptiRBProfilerSession::stop() {}
-void CuptiRBProfilerSession::enable() {}
-void CuptiRBProfilerSession::disable() {}
-void CuptiRBProfilerSession::beginPass() {}
-bool CuptiRBProfilerSession::endPass() { return true; }
-void CuptiRBProfilerSession::flushCounterData() {}
-void CuptiRBProfilerSession::pushRange(const std::string& /*rangeName*/) {}
-void CuptiRBProfilerSession::popRange() {}
-void CuptiRBProfilerSession::asyncStartAndEnable(
-    CUpti_ProfilerRange /*profilerRange*/,
-    CUpti_ProfilerReplayMode /*profilerReplayMode*/) {}
-void CuptiRBProfilerSession::asyncDisableAndStop() {}
-CuptiProfilerResult CuptiRBProfilerSession::evaluateMetrics(bool verbose) {
-  static CuptiProfilerResult res;
-  return res;
-};
-void CuptiRBProfilerSession::saveCounterData(
-    const std::string& /*CounterDataFileName*/,
-    const std::string& /*CounterDataSBFileName*/) {}
-void CuptiRBProfilerSession::initCupti() {}
-void CuptiRBProfilerSession::deInitCupti() {}
-void CuptiRBProfilerSession::staticInit() {}
-bool CuptiRBProfilerSession::createCounterDataImage() { return true; }
-void CuptiRBProfilerSession::startInternal(
-    CUpti_ProfilerRange /*profilerRange*/,
-    CUpti_ProfilerReplayMode /*profilerReplayMode*/) {}
-std::vector<uint8_t>& CuptiRBProfilerSession::counterAvailabilityImage() {
-  static std::vector<uint8_t> _vec;
-  return _vec;
-}
-#endif // HAS_CUPTI_RANGE_PROFILER
-
-namespace testing {
-
-void trackCudaCtx(CUcontext ctx, uint32_t device_id, CUpti_CallbackId cbid) {
-#if HAS_CUPTI_RANGE_PROFILER
-  __trackCudaCtx(ctx, device_id, cbid);
-#endif // HAS_CUPTI_RANGE_PROFILER
-}
-
-void trackCudaKernelLaunch(CUcontext ctx, const char* kernelName) {
-#if HAS_CUPTI_RANGE_PROFILER
-  __trackCudaKernelLaunch(ctx, kernelName);
-#endif // HAS_CUPTI_RANGE_PROFILER
-}
-
-} // namespace testing
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerApi.h b/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerApi.h
deleted file mode 100644
index 98a0b3ea5f4..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerApi.h
+++ /dev/null
@@ -1,220 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#ifdef HAS_CUPTI
-#include <cuda.h>
-#include <cuda_runtime_api.h>
-// Using CUDA 11 and above due to usage of API: cuptiProfilerGetCounterAvailability.
-#if defined(CUDART_VERSION) && CUDART_VERSION >= 10000 && CUDART_VERSION < 11040 && CUDA_VERSION >= 11000
-#define HAS_CUPTI_RANGE_PROFILER 1
-#endif // CUDART_VERSION > 10.00 and < 11.04 && CUDA_VERSION >= 11.00
-#endif // HAS_CUPTI
-
-#if HAS_CUPTI_RANGE_PROFILER
-#include <cupti.h>
-#include <cupti_profiler_target.h>
-#include <cupti_target.h>
-#else
-using CUpti_ProfilerRange = enum
-{
-  CUPTI_AutoRange,
-  CUPTI_UserRange,
-};
-
-using CUpti_ProfilerReplayMode = enum
-{
-  CUPTI_KernelReplay,
-  CUPTI_UserReplay,
-};
-#endif // HAS_CUPTI_RANGE_PROFILER
-
-#include <chrono>
-#include <mutex>
-#include <string>
-#include <vector>
-#include <set>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "TraceSpan.h"
-#include "CuptiCallbackApi.h"
-#include "CuptiNvPerfMetric.h"
-
-/* Cupti Range based profiler session
- * See : https://docs.nvidia.com/cupti/Cupti/r_main.html#r_profiler
- */
-
-namespace KINETO_NAMESPACE {
-
-class CuptiRBProfilerSession {
- public:
-  // Initialize and configure CUPTI Profiler counters.
-  // - Metric names must be provided as string vector.
-  // - Supported values by CUPTI can be found at -
-  //   https://docs.nvidia.com/cupti/Cupti/r_main.html#r_host_metrics_api
-  explicit CuptiRBProfilerSession(
-      const std::vector<std::string>& metricNames,
-      int deviceId,
-      int maxRanges,
-      int numNestingLevels = 1,
-      CUcontext cuContext = 0);
-
-  virtual ~CuptiRBProfilerSession() = default;
-
-  // Start profiling session
-  // This function has to be called from the CPU thread running
-  // the CUDA context. If this is not the case asyncStartAndEnable()
-  // can be used
-  void start(
-      CUpti_ProfilerRange profilerRange = CUPTI_AutoRange,
-      CUpti_ProfilerReplayMode profilerReplayMode = CUPTI_KernelReplay) {
-    startInternal(profilerRange, profilerReplayMode);
-  }
-
-  // Stop profiling session
-  virtual void stop();
-
-  virtual void enable();
-  virtual void disable();
-
-  // Profiler passes
-  //  GPU hardware has limited performance monitoring resources
-  //  the CUPTI profiler may need to run multiple passes to collect
-  //  data for a given range
-  //  If we use kernel replay model the kernels are automatically replayed
-  //  else, you can use the beginPass() and endPass() functions below
-  //  for user to manage the replays
-
-  // starts a profiler pass with given kernels in between
-  virtual void beginPass();
-
-  // end a profiler pass with given kernels in between
-  // returns true if no more passes are required
-  virtual bool endPass();
-
-  // flushes the counter data - required if you use user replay
-  virtual void flushCounterData();
-
-  // Each pass can contain multiple of ranges
-  //  metrics configured in a pass are collected per each range-stack.
-  virtual void pushRange(const std::string& rangeName);
-  virtual void popRange();
-
-  // utilities for common operations
-  void startAndEnable();
-  void disableAndStop();
-
-  // Async APIs : these will can be called from another thread
-  // outside the CUDA context being profiled
-  void asyncStartAndEnable(
-      CUpti_ProfilerRange profilerRange = CUPTI_AutoRange,
-      CUpti_ProfilerReplayMode profilerReplayMode = CUPTI_KernelReplay);
-  void asyncDisableAndStop();
-
-  void printMetrics() {
-    evaluateMetrics(true);
-  }
-
-  std::unique_ptr<TraceSpan> getProfilerTraceSpan();
-
-  virtual CuptiProfilerResult evaluateMetrics(bool verbose = false);
-
-  void saveCounterData(
-      const std::string& CounterDataFileName,
-      const std::string& CounterDataSBFileName);
-
-  // This is not thread safe so please only call after
-  // profiling has stopped
-  const std::vector<std::string>& getKernelNames() const {
-    return kernelNames_;
-  }
-
-  int deviceId() const {
-    return deviceId_;
-  }
-
-  bool profilingActive() const {
-    return profilingActive_;
-  }
-
-  static std::set<uint32_t> getActiveDevices();
-
-  static void initCupti();
-
-  static void deInitCupti();
-
-  static void staticInit();
-
-  static void setCounterAvailabilityImage(std::vector<uint8_t> img) {
-    counterAvailabilityImage() = img;
-  }
- protected:
-  CuptiRBProfilerSession(int deviceId, CUcontext ctx);
-
-  virtual void startInternal(
-      CUpti_ProfilerRange profilerRange,
-      CUpti_ProfilerReplayMode profilerReplayMode);
-
-  CUpti_ProfilerRange curRange_ = CUPTI_AutoRange;
-  CUpti_ProfilerReplayMode curReplay_ = CUPTI_KernelReplay;
-
- private:
-
-  bool createCounterDataImage();
-
-
-  // log kernel name that used with callbacks
-  void logKernelName(const char* kernel) {
-    std::lock_guard<std::mutex> lg(kernelNamesMutex_);
-    kernelNames_.emplace_back(kernel);
-  }
-
-  std::vector<std::string> metricNames_;
-  std::string chipName_;
-
-  uint32_t deviceId_ = 0;
-  int maxRanges_;
-  int numNestingLevels_;
-  CUcontext cuContext_;
-
-
-  // data buffers for configuration and counter data collection
-  std::vector<uint8_t> counterDataImagePrefix;
-  std::vector<uint8_t> configImage;
-  std::vector<uint8_t> counterDataImage;
-  std::vector<uint8_t> counterDataScratchBuffer;
-
-  std::chrono::time_point<std::chrono::high_resolution_clock> profilerStartTs_;
-  std::chrono::time_point<std::chrono::high_resolution_clock>
-      profilerInitDoneTs_;
-  std::chrono::time_point<std::chrono::high_resolution_clock> profilerStopTs_;
-
-  std::mutex kernelNamesMutex_;
-  // raw kernel names (not demangled)
-  std::vector<std::string> kernelNames_;
-
-  uint32_t numCallbacks_ = 0;
-
-  static std::vector<uint8_t>& counterAvailabilityImage();
-
-#if HAS_CUPTI_RANGE_PROFILER
-  CUpti_Profiler_BeginPass_Params beginPassParams_;
-  CUpti_Profiler_EndPass_Params endPassParams_;
-#endif
-
-  bool initSuccess_ = false;
-  bool profilingActive_ = false;
-
-  friend void __trackCudaKernelLaunch(CUcontext ctx, const char* kernelName);
-};
-
-// called directly only in unit tests
-namespace testing {
-
-void trackCudaCtx(CUcontext ctx, uint32_t device_id, CUpti_CallbackId cbid);
-void trackCudaKernelLaunch(CUcontext ctx, const char* kernelName);
-
-} // namespace testing
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerConfig.cpp b/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerConfig.cpp
deleted file mode 100644
index 04b1ad0cb3f..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerConfig.cpp
+++ /dev/null
@@ -1,68 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <CuptiRangeProfilerConfig.h>
-#include <Logger.h>
-
-#include <stdlib.h>
-#include <unistd.h>
-
-#include <fmt/format.h>
-#include <ostream>
-
-using namespace std::chrono;
-
-namespace KINETO_NAMESPACE {
-
-// number of ranges affect the size of counter data binary used by
-// the CUPTI Profiler. these defaults can be tuned
-constexpr int KMaxAutoRanges = 1500; // supports 1500 kernels
-constexpr int KMaxUserRanges = 10;   // enable upto 10 sub regions marked by user
-
-constexpr char kCuptiProfilerMetricsKey[] = "CUPTI_PROFILER_METRICS";
-constexpr char kCuptiProfilerPerKernelKey[] = "CUPTI_PROFILER_ENABLE_PER_KERNEL";
-constexpr char kCuptiProfilerMaxRangesKey[] = "CUPTI_PROFILER_MAX_RANGES";
-
-CuptiRangeProfilerConfig::CuptiRangeProfilerConfig(Config& cfg)
-    : parent_(&cfg),
-      cuptiProfilerPerKernel_(false),
-      cuptiProfilerMaxRanges_(0) {}
-
-bool CuptiRangeProfilerConfig::handleOption(const std::string& name, std::string& val) {
-  VLOG(0) << " handling : " << name << " = " << val;
-  // Cupti Range based Profiler configuration
-  if (!name.compare(kCuptiProfilerMetricsKey)) {
-    activitiesCuptiMetrics_ = splitAndTrim(val, ',');
-  } else if (!name.compare(kCuptiProfilerPerKernelKey)) {
-    cuptiProfilerPerKernel_ = toBool(val);
-  } else if (!name.compare(kCuptiProfilerMaxRangesKey)) {
-    cuptiProfilerMaxRanges_ = toInt64(val);
-  } else {
-    return false;
-  }
-  return true;
-}
-
-void CuptiRangeProfilerConfig::setDefaults() {
-  if (activitiesCuptiMetrics_.size() > 0 && cuptiProfilerMaxRanges_ == 0) {
-    cuptiProfilerMaxRanges_ =
-      cuptiProfilerPerKernel_ ? KMaxAutoRanges : KMaxUserRanges;
-  }
-}
-
-void CuptiRangeProfilerConfig::printActivityProfilerConfig(std::ostream& s) const {
-  if (activitiesCuptiMetrics_.size() > 0) {
-    s << "Cupti Profiler metrics : "
-      << fmt::format("{}", fmt::join(activitiesCuptiMetrics_, ", ")) << std::endl;
-    s << "Cupti Profiler measure per kernel : "
-      << cuptiProfilerPerKernel_ << std::endl;
-    s << "Cupti Profiler max ranges : " << cuptiProfilerMaxRanges_ << std::endl;
-  }
-}
-
-void CuptiRangeProfilerConfig::registerFactory() {
-  Config::addConfigFactory(
-      kCuptiProfilerConfigName,
-      [](Config& cfg) { return new CuptiRangeProfilerConfig(cfg); });
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerConfig.h b/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerConfig.h
deleted file mode 100644
index 549b8a4e8b4..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/CuptiRangeProfilerConfig.h
+++ /dev/null
@@ -1,86 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include "Config.h"
-
-#include <chrono>
-#include <set>
-#include <string>
-#include <vector>
-
-namespace KINETO_NAMESPACE {
-
-constexpr char kCuptiProfilerConfigName[] = "cupti_rb_profiler";
-
-class CuptiRangeProfilerConfig : public AbstractConfig {
- public:
-  bool handleOption(const std::string& name, std::string& val) override;
-
-  void validate(
-      const std::chrono::time_point<std::chrono::system_clock>&
-      fallbackProfileStartTime) override {}
-
-  static CuptiRangeProfilerConfig& get(const Config& cfg) {
-    return dynamic_cast<CuptiRangeProfilerConfig&>(cfg.feature(
-          kCuptiProfilerConfigName));
-  }
-
-  Config& parent() const {
-    return *parent_;
-  }
-
-  std::vector<std::string> activitiesCuptiMetrics() const {
-    return activitiesCuptiMetrics_;
-  }
-
-  bool cuptiProfilerPerKernel() const {
-    return cuptiProfilerPerKernel_;
-  }
-
-  int64_t cuptiProfilerMaxRanges() const {
-    return cuptiProfilerMaxRanges_;
-  }
-
-  void setSignalDefaults() override {
-    setDefaults();
-  }
-
-  void setClientDefaults() override {
-    setDefaults();
-  }
-
-  void printActivityProfilerConfig(std::ostream& s) const override;
-
-  static void registerFactory();
- protected:
-  AbstractConfig* cloneDerived(AbstractConfig& parent) const override {
-    CuptiRangeProfilerConfig* clone = new CuptiRangeProfilerConfig(*this);
-    clone->parent_ = dynamic_cast<Config*>(&parent);
-    return clone;
-  }
-
- private:
- CuptiRangeProfilerConfig() = delete;
-  explicit CuptiRangeProfilerConfig(Config& parent);
-  explicit CuptiRangeProfilerConfig(
-      const CuptiRangeProfilerConfig& other) = default;
-
-  // some defaults will depend on other configuration
-  void setDefaults();
-
-  // Associated Config object
-  Config* parent_;
-
-  // Counter metrics exposed via CUPTI Profiler API
-  std::vector<std::string> activitiesCuptiMetrics_;
-
-  // Collect profiler metrics per kernel - autorange made
-  bool cuptiProfilerPerKernel_{false};
-
-  // max number of ranges to configure the profiler for.
-  // this has to be set before hand to reserve space for the output
-  int64_t cuptiProfilerMaxRanges_ = 0;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/DaemonConfigLoader.h b/plugins/tensorboard-plugins/libkineto/src/DaemonConfigLoader.h
deleted file mode 100644
index 9b0ed928636..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/DaemonConfigLoader.h
+++ /dev/null
@@ -1,27 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <cstdint>
-#include <string>
-
-namespace KINETO_NAMESPACE {
-
-class DaemonConfigLoader {
- public:
-  virtual ~DaemonConfigLoader() {}
-
-  // Return the base config from the daemon
-  virtual std::string readBaseConfig() = 0;
-
-  // Return a configuration string from the daemon, if one has been posted.
-  virtual std::string readOnDemandConfig(bool events, bool activities) = 0;
-
-  // Returns the number of tracked contexts for this device. The daemon has a
-  // global view. If an unexpedted error occurs, return -1.
-  virtual int gpuContextCount(uint32_t device) = 0;
-
-  virtual void setCommunicationFabric(bool enabled) = 0;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/Demangle.cpp b/plugins/tensorboard-plugins/libkineto/src/Demangle.cpp
deleted file mode 100644
index f84f0b8ec36..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/Demangle.cpp
+++ /dev/null
@@ -1,49 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "Demangle.h"
-
-#ifndef _MSC_VER
-#include <cxxabi.h>
-#endif
-#include <string.h>
-#include <string>
-
-namespace KINETO_NAMESPACE {
-
-static constexpr int kMaxSymbolSize = 1024;
-
-std::string demangle(const char* name) {
-#ifndef _MSC_VER
-  if (!name) {
-    return "";
-  }
-
-  if (strlen(name) > kMaxSymbolSize) {
-    return name;
-  }
-
-  int status;
-  size_t len = 0;
-  char* demangled = abi::__cxa_demangle(name, nullptr, &len, &status);
-  if (status != 0) {
-    return name;
-  }
-  std::string res(demangled);
-  // The returned buffer must be freed!
-  free(demangled);
-  return res;
-#else
-  // TODO: demangling on Windows
-  if (!name) {
-    return "";
-  } else {
-    return name;
-  }
-#endif
-}
-
-std::string demangle(const std::string& name) {
-  return demangle(name.c_str());
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/Demangle.h b/plugins/tensorboard-plugins/libkineto/src/Demangle.h
deleted file mode 100644
index 6dcf0776f1a..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/Demangle.h
+++ /dev/null
@@ -1,12 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <string>
-
-namespace KINETO_NAMESPACE {
-
-std::string demangle(const char* name);
-std::string demangle(const std::string& name);
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/EventProfiler.cpp b/plugins/tensorboard-plugins/libkineto/src/EventProfiler.cpp
deleted file mode 100644
index dbf27552389..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/EventProfiler.cpp
+++ /dev/null
@@ -1,635 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "EventProfiler.h"
-
-#include <assert.h>
-#include <fmt/format.h>
-#include <time.h>
-#include <algorithm>
-#include <cmath>
-#include <iomanip>
-#include <iostream>
-#include <map>
-#include <mutex>
-#include <numeric>
-#include <thread>
-#include <vector>
-
-#include <cupti.h>
-
-#include "CuptiEventApi.h"
-#include "Logger.h"
-
-using namespace std::chrono;
-using std::accumulate;
-using std::endl;
-using std::map;
-using std::ostream;
-using std::string;
-using std::unique_ptr;
-using std::vector;
-
-namespace KINETO_NAMESPACE {
-
-static std::mutex& logMutex() {
-  static std::mutex instance;
-  return instance;
-}
-
-// ---------------------------------------------------------------------
-// class Event
-// ---------------------------------------------------------------------
-
-// Compute domain instance percentiles
-PercentileList& Event::percentiles(
-    PercentileList& pcs,
-    const SampleSlice& slice) const {
-  vector<int64_t> instance_values;
-  instance_values.reserve(instanceCount);
-  for (int i = 0; i < instanceCount; i++) {
-    instance_values.push_back(sumInstance(i, slice));
-  }
-  return KINETO_NAMESPACE::percentiles(instance_values, pcs);
-}
-
-// Add up all samples for a given domain instance
-int64_t Event::sumInstance(int i, const SampleSlice& slice) const {
-  auto r = toIdxRange(slice);
-  auto start = samples_.cbegin();
-  std::advance(start, r.first);
-  auto end = start;
-  std::advance(end, r.second);
-  return accumulate(start, end, 0ul, [i](int64_t a, const Sample& b) {
-    return a + b.second[i];
-  });
-}
-
-// Add up all samples across all domain instances
-int64_t Event::sumAll(const SampleSlice& slice) const {
-  int64_t res = 0;
-  for (int i = 0; i < instanceCount; i++) {
-    res += sumInstance(i, slice);
-  }
-  return res;
-}
-
-// Print raw sample values for all domains
-void Event::printSamples(ostream& s, CUdevice device) const {
-  // Don't mess up output with interleaved lines
-  // Probably OK to reuse logMutex() here since this is
-  // used for debugging, but need to keep an eye on it.
-  std::lock_guard<std::mutex> lock(logMutex());
-  s << "Device " << device << " " << name << ":" << endl;
-  for (const auto& sample : samples_) {
-    const auto& vals = sample.second;
-    for (int64_t val : vals) {
-      s << val << " ";
-    }
-    s << endl;
-  }
-}
-
-// ---------------------------------------------------------------------
-// class Metric
-// ---------------------------------------------------------------------
-Metric::Metric(
-    string name,
-    CUpti_MetricID id,
-    vector<CUpti_EventID> events,
-    CUpti_MetricEvaluationMode eval_mode,
-    CuptiMetricApi& cupti_metrics)
-    : name(std::move(name)),
-      id_(id),
-      events_(std::move(events)),
-      evalMode_(eval_mode),
-      cuptiMetrics_(cupti_metrics),
-      valueKind_(cuptiMetrics_.valueKind(id)) {}
-
-// Return per-SM vector as well as total
-struct Metric::CalculatedValues Metric::calculate(
-    map<CUpti_EventID, Event>& event_map,
-    nanoseconds sample_duration,
-    const SampleSlice& slice) {
-  vector<SampleValue> metric_values;
-  vector<int64_t> ev_values;
-  ev_values.reserve(events_.size());
-  if (evalMode_ & CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE) {
-    int instance_count = instanceCount(event_map);
-    metric_values.reserve(instance_count);
-    for (int i = 0; i < instance_count; i++) {
-      ev_values.clear();
-      for (CUpti_EventID event_id : events_) {
-        ev_values.push_back(event_map[event_id].sumInstance(i, slice));
-      }
-      metric_values.push_back(cuptiMetrics_.calculate(
-          id_, valueKind_, events_, ev_values, sample_duration.count()));
-    }
-  }
-
-  // FIXME: Check assumption that all instances are profiled
-  ev_values.clear();
-  for (CUpti_EventID event_id : events_) {
-    ev_values.push_back(event_map[event_id].sumAll(slice));
-  }
-  SampleValue total = cuptiMetrics_.calculate(
-      id_, valueKind_, events_, ev_values, sample_duration.count());
-  if (evalMode_ & CUPTI_METRIC_EVALUATION_MODE_AGGREGATE) {
-    metric_values.push_back(total);
-  }
-  return {metric_values, std::move(total)};
-}
-
-void Metric::printDescription(ostream& s) const {
-  s << fmt::format("{} ({})", name, fmt::join(events_, ",")) << endl;
-}
-
-// ---------------------------------------------------------------------
-// class EventGroupSet
-// ---------------------------------------------------------------------
-
-// Each domain has a set of counters.
-// Some counters in a domain can be collected simultaneously in a "group"
-// Counters from different domains can also be collected at the same time
-// Therefore we have a "set of groups", or group set, with counters that
-// can all be collected at once.
-EventGroupSet::EventGroupSet(
-    CUpti_EventGroupSet& set,
-    map<CUpti_EventID, Event>& events,
-    CuptiEventApi& cupti)
-    : set_(set), events_(events), cuptiEvents_(cupti), enabled_(false) {
-  for (int g = 0; g < set.numEventGroups; g++) {
-    CUpti_EventGroup grp = set.eventGroups[g];
-    // Profile all domain instances
-    cuptiEvents_.enablePerInstance(grp);
-    uint32_t instance_count = cuptiEvents_.instanceCount(grp);
-    for (const auto& id : cuptiEvents_.eventsInGroup(grp)) {
-      VLOG(0) << "Instance count for " << id << ":" << instance_count;
-      events_[id].instanceCount = instance_count;
-    }
-  }
-}
-
-EventGroupSet::~EventGroupSet() {
-  // Disable EventGroupSet in Cupti.
-  if (enabled_) {
-    setEnabled(false);
-  }
-}
-
-// Enable or disable this group set
-void EventGroupSet::setEnabled(bool enabled) {
-  if (enabled && !enabled_) {
-    cuptiEvents_.enableGroupSet(set_);
-  } else if (!enabled && enabled_) {
-    cuptiEvents_.disableGroupSet(set_);
-  }
-  enabled_ = enabled;
-}
-
-// Collect counter values for each counter in group set
-void EventGroupSet::collectSample() {
-  auto timestamp = system_clock::now();
-  for (int g = 0; g < set_.numEventGroups; g++) {
-    CUpti_EventGroup grp = set_.eventGroups[g];
-    for (const auto& id : cuptiEvents_.eventsInGroup(grp)) {
-      Event& ev = events_[id];
-      vector<int64_t> vals(ev.instanceCount);
-      // FIXME: Use cuptiEventGroupReadAllEvents
-      cuptiEvents_.readEvent(grp, id, vals);
-
-      if (VLOG_IS_ON(0)) {
-        for (int64_t v : vals) {
-          if (v == CUPTI_EVENT_OVERFLOW) {
-            LOG(WARNING) << "Counter overflow detected "
-                         << "- decrease sample period!" << endl;
-          }
-        }
-      }
-
-      ev.addSample(timestamp, vals);
-    }
-  }
-
-  if (VLOG_IS_ON(1)) {
-    auto t2 = system_clock::now();
-    VLOG(1) << "Device " << cuptiEvents_.device() << " Sample (us): "
-            << duration_cast<microseconds>(t2 - timestamp).count();
-  }
-}
-
-// Print names of events in this group set, ordered by group
-void EventGroupSet::printDescription(ostream& s) const {
-  for (int g = 0; g < set_.numEventGroups; g++) {
-    s << "  Events in group " << g << ": ";
-    for (const auto& id : cuptiEvents_.eventsInGroup(set_.eventGroups[g])) {
-      s << id << " (" << events_[id].name << ") ";
-    }
-    s << endl;
-  }
-}
-
-// ---------------------------------------------------------------------
-// class EventProfiler
-// ---------------------------------------------------------------------
-
-// Find nearest factor of a number by linear search,
-// starting at hi and lo - hi searches up and lo searches down
-static int nearestFactor(int hi, int lo, int number) {
-  return number % hi == 0
-      ? hi
-      : number % lo == 0 ? lo : nearestFactor(hi + 1, lo - 1, number);
-}
-
-static int nearestFactor(int count, int max) {
-  return nearestFactor(count, count, max);
-}
-
-void EventProfiler::initEvents(const std::set<std::string>& eventNames) {
-  events_.clear();
-  // Build event map
-  for (const auto& name : eventNames) {
-    events_.emplace(cuptiEvents_->eventId(name), name);
-  }
-}
-
-void EventProfiler::initMetrics(const std::set<std::string>& metricNames) {
-  metrics_.clear();
-  // Add events from metrics
-  metrics_.reserve(metricNames.size());
-  for (const auto& metric_name : metricNames) {
-    CUpti_MetricID metric_id = cuptiMetrics_->idFromName(metric_name);
-    if (metric_id == ~0) {
-      continue;
-    }
-
-    const auto& events = cuptiMetrics_->events(metric_id);
-    vector<CUpti_EventID> event_ids;
-    event_ids.reserve(events.size());
-    for (const auto& pair : events) {
-      CUpti_EventID id = pair.first;
-      const string& event_name = pair.second;
-      if (event_name.empty()) {
-        // For unnamed events, use metric name and event id
-        // FIXME: For subsequent metrics using the same event,
-        // this will be confusing
-        events_.emplace(id, metric_name + "_" + event_name);
-      } else {
-        events_.emplace(id, event_name);
-      }
-      event_ids.push_back(id);
-    }
-    metrics_.emplace_back(
-        metric_name,
-        metric_id,
-        event_ids,
-        cuptiMetrics_->evaluationMode(metric_id),
-        *cuptiMetrics_);
-  }
-}
-
-bool EventProfiler::initEventGroups() {
-  sets_.clear();
-  if (eventGroupSets_) {
-    cuptiEvents_->destroyGroupSets(eventGroupSets_);
-    eventGroupSets_ = nullptr;
-  }
-  if (events_.empty()) {
-    return true;
-  }
-
-  // Determine sets of groups to be collected
-  vector<CUpti_EventID> ids;
-  ids.reserve(events_.size());
-  for (const auto& ev : events_) {
-    ids.push_back(ev.first);
-  }
-  eventGroupSets_ = cuptiEvents_->createGroupSets(ids);
-  VLOG(0) << "Number of group sets: " << eventGroupSets_->numSets;
-  for (int i = 0; i < eventGroupSets_->numSets; i++) {
-    sets_.push_back(
-        EventGroupSet(eventGroupSets_->sets[i], events_, *cuptiEvents_));
-  }
-  return !sets_.empty();
-}
-
-static unique_ptr<Config> alignAndValidateConfigs(
-    Config& base,
-    Config* onDemand) {
-  auto now = system_clock::now();
-  if (!onDemand ||
-      now >
-          (onDemand->eventProfilerOnDemandStartTime() +
-           onDemand->eventProfilerOnDemandDuration())) {
-    base.validate(now);
-    return base.clone();
-  }
-
-  auto res = base.clone();
-  res->addEvents(onDemand->eventNames());
-  res->addMetrics(onDemand->metricNames());
-
-  int sample_period =
-      std::min(base.samplePeriod().count(), onDemand->samplePeriod().count());
-  if (sample_period < base.samplePeriod().count() &&
-      (base.samplePeriod().count() % sample_period) != 0) {
-    sample_period = nearestFactor(sample_period, base.samplePeriod().count());
-    LOG(WARNING)
-        << "On-demand sample period must be a factor of base sample period. "
-        << "Adjusting from " << onDemand->samplePeriod().count() << "ms to "
-        << sample_period << "ms.";
-  }
-  base.setSamplePeriod(milliseconds(sample_period));
-  base.validate(now);
-  res->setSamplePeriod(base.samplePeriod());
-  res->setMultiplexPeriod(base.multiplexPeriod());
-  res->validate(now);
-  onDemand->setSamplePeriod(base.samplePeriod());
-  onDemand->setMultiplexPeriod(base.multiplexPeriod());
-  onDemand->validate(now);
-
-  return res;
-}
-
-static milliseconds minReportPeriod(const Config& config, int num_sets) {
-  return config.multiplexPeriod() * num_sets;
-}
-
-static bool canSupportReportPeriod(const Config& config, int num_sets) {
-  // Can we get through the groups an even number per report period?
-  milliseconds min_report_period = minReportPeriod(config, num_sets);
-  return (config.reportPeriod().count() % min_report_period.count()) == 0;
-}
-
-static int completeSamplesPerReport(const Config& config, int num_sets) {
-  if (num_sets <= 1) {
-    return config.reportPeriod() / config.samplePeriod();
-  }
-  // Numnber of complete sample collections in the report period
-  // E.g. if report period is 10000ms, sample period 500ms,
-  // multiplex period 2000ms and num_sets is 5 then # of complete samples is
-  // (2000ms / 500ms) * (10000ms / 2000ms / 5) = 4 * 1 = 4
-  int samples_per_multiplex_period =
-      config.multiplexPeriod() / config.samplePeriod();
-  int multiplex_periods_per_report =
-      config.reportPeriod() / config.multiplexPeriod();
-  return (multiplex_periods_per_report / num_sets) *
-      samples_per_multiplex_period;
-}
-
-static bool canSupportSamplesPerReport(const Config& config, int num_sets) {
-  // Can samples per report can be honored with an exact *full* set of samples?
-  // We don't support partial samples at this point.
-  int full_samples_per_report = completeSamplesPerReport(config, num_sets);
-  return (full_samples_per_report % config.samplesPerReport()) == 0;
-}
-
-static void adjustConfig(Config& config, int num_sets) {
-  // Don't change sample period and multiplex period here, since that can
-  // cause overflows and perf degradation. Report period and samples per
-  // report is OK to change (with warning).
-  if (!canSupportReportPeriod(config, num_sets)) {
-    milliseconds min_report_period = minReportPeriod(config, num_sets);
-    LOG(WARNING) << "Report period must be a multiple of "
-                 << min_report_period.count() << "ms (" << num_sets
-                 << " event sets * " << config.multiplexPeriod().count()
-                 << "ms multiplex period), in order to get complete samples.";
-    auto new_report_period =
-        Config::alignUp(config.reportPeriod(), min_report_period);
-    double sf =
-        ((double)new_report_period.count()) / config.reportPeriod().count();
-    int new_samples_per_report = std::round(config.samplesPerReport() * sf);
-    LOG(WARNING) << "Adjusting report period from "
-                 << config.reportPeriod().count() << "ms to "
-                 << new_report_period.count() << "ms";
-    if (new_samples_per_report != config.samplesPerReport()) {
-      LOG(WARNING) << "Adjusting samples per report from "
-                   << config.samplesPerReport() << " to "
-                   << new_samples_per_report;
-    }
-    config.setReportPeriod(new_report_period);
-    config.setSamplesPerReport(new_samples_per_report);
-  }
-  // Ensure that samples per report can be honored with
-  // an exact *full* set of samples. Don't support partial
-  // samples at this point.
-  if (!canSupportSamplesPerReport(config, num_sets)) {
-    int full_samples_per_report = completeSamplesPerReport(config, num_sets);
-    int adjusted_count =
-        nearestFactor(config.samplesPerReport(), full_samples_per_report);
-    LOG(WARNING)
-        << "Samples per report must be such that an even number of "
-        << "complete samples can be aggregated in each report period. Adjusting"
-        << " from " << config.samplesPerReport() << " to " << adjusted_count
-        << " (complete sample count is " << full_samples_per_report << ")";
-    config.setSamplesPerReport(adjusted_count);
-  }
-}
-
-// Prepare profiler
-EventProfiler::EventProfiler(
-    std::unique_ptr<CuptiEventApi> cupti_events,
-    std::unique_ptr<CuptiMetricApi> cupti_metrics,
-    vector<unique_ptr<SampleListener>>& loggers,
-    vector<unique_ptr<SampleListener>>& onDemandLoggers)
-    : cuptiEvents_(std::move(cupti_events)),
-      cuptiMetrics_(std::move(cupti_metrics)),
-      loggers_(loggers),
-      onDemandLoggers_(onDemandLoggers) {}
-
-void EventProfiler::reportSamples() {
-  dispatchSamples(*config_, loggers_, baseSamples_);
-  baseSamples_ += completeSamplesPerReport(*config_, sets_.size());
-}
-
-void EventProfiler::reportOnDemandSamples() {
-  dispatchSamples(*onDemandConfig_, onDemandLoggers_, onDemandSamples_);
-  onDemandSamples_ += completeSamplesPerReport(*onDemandConfig_, sets_.size());
-}
-
-EventProfiler::~EventProfiler() {
-  if (eventGroupSets_) {
-    for (auto& set : sets_) {
-      set.setEnabled(false);
-    }
-    cuptiEvents_->destroyGroupSets(eventGroupSets_);
-  }
-  VLOG(0) << "Stopped event profiler for device " << device();
-}
-
-void EventProfiler::updateLoggers(Config& config, Config* on_demand_config) {
-  // Update loggers.
-  for (auto& logger : loggers_) {
-    std::lock_guard<std::mutex> lock(logMutex());
-    logger->update(config);
-  }
-
-  if (on_demand_config) {
-    // Update onDemand loggers.
-    for (auto& logger : onDemandLoggers_) {
-      std::lock_guard<std::mutex> lock(logMutex());
-      logger->update(*on_demand_config);
-    }
-  }
-}
-
-bool EventProfiler::applyConfig(const Config& config) {
-  // Initialize events, metrics, and event group sets.
-  // TODO: Send warnings / errors back to dyno for onDemand config
-  try {
-    if (!initEventsAndMetrics(config)) {
-      return false;
-    }
-  } catch (const std::exception& ex) {
-    LOG(WARNING) << "Failed to apply config (" << ex.what() << ")";
-    return false;
-  }
-
-  return true;
-}
-
-bool EventProfiler::initEventsAndMetrics(const Config& config) {
-  initEvents(config.eventNames());
-  initMetrics(config.metricNames());
-  // We now have the total list of events to collect
-  // They need to be organized into groups for multiplexing
-  if (!initEventGroups()) {
-    LOG(WARNING) << "No events/metrics initialized successfully";
-    return false;
-  }
-
-  if (VLOG_IS_ON(1)) {
-    printMetrics(LIBKINETO_DBG_STREAM);
-    printSets(LIBKINETO_DBG_STREAM);
-  }
-  return true;
-}
-
-void EventProfiler::printSets(ostream& s) const {
-  for (int i = 0; i < sets_.size(); i++) {
-    s << "Set " << i << endl;
-    sets_[i].printDescription(s);
-  }
-}
-
-void EventProfiler::printMetrics(ostream& s) const {
-  s << "Metrics:" << endl;
-  for (const Metric& m : metrics_) {
-    m.printDescription(s);
-  }
-}
-
-void EventProfiler::printAllSamples(ostream& s, CUdevice device) const {
-  for (const auto& pair : events_) {
-    const Event& ev = pair.second;
-    ev.printSamples(s, device);
-  }
-}
-
-void EventProfiler::enableNextCounterSet() {
-  if (sets_.size() > 1) {
-    auto t1 = system_clock::now();
-
-    VLOG(1) << "Disabling set " << curEnabledSet_;
-    sets_[curEnabledSet_].setEnabled(false);
-    curEnabledSet_ = (curEnabledSet_ + 1) % sets_.size();
-    VLOG(1) << "Enabling set " << curEnabledSet_;
-    sets_[curEnabledSet_].setEnabled(true);
-
-    if (VLOG_IS_ON(1)) {
-      auto t2 = system_clock::now();
-      VLOG(1) << "Switch (us): "
-              << duration_cast<microseconds>(t2 - t1).count();
-    }
-  }
-}
-
-// Notify listeners of collected samples
-void EventProfiler::dispatchSamples(
-    const Config& config,
-    const vector<unique_ptr<SampleListener>>& loggers,
-    int sample_offset) {
-  Sample sample(events_.size() + metrics_.size());
-  // Normalize values to per second
-  auto delta = config.reportPeriod() / config.samplesPerReport();
-  double sf = 1000.0 * sets_.size() / delta.count();
-  for (int i = 0; i < config.samplesPerReport(); i++) {
-    sample.stats.clear();
-    sample.deltaMsec = (delta * i).count();
-    SampleSlice slice = {sample_offset, i, config.samplesPerReport()};
-    VLOG(1) << "Slice: " << sample_offset << ", " << i << ", "
-            << config.samplesPerReport();
-    for (const auto& pair : events_) {
-      const Event& ev = pair.second;
-      int64_t total = std::round(sf * ev.sumAll(slice));
-      PercentileList pcs = initPercentiles(config.percentiles());
-      normalize(ev.percentiles(pcs, slice), sf);
-      sample.stats.push_back({ev.name, std::move(pcs), SampleValue(total)});
-    }
-
-    for (auto& m : metrics_) {
-      // calculate returns a pair of per-SM vector and a total
-      auto vals = m.calculate(events_, delta, slice);
-      PercentileList pcs = initPercentiles(config.percentiles());
-      sample.stats.push_back(
-          {m.name, std::move(percentiles(vals.perInstance, pcs)), vals.total});
-    }
-
-    for (auto& logger : loggers) {
-      std::lock_guard<std::mutex> lock(logMutex());
-      logger->handleSample(device(), sample, config.ipcFabricEnabled());
-    }
-  }
-
-  if (VLOG_IS_ON(2)) {
-    printAllSamples(LIBKINETO_DBG_STREAM, device());
-  }
-}
-
-void EventProfiler::configure(Config& config, Config* onDemandConfig) {
-  if (!sets_.empty()) {
-    sets_[curEnabledSet_].setEnabled(false);
-    clearSamples();
-  }
-
-  config_ = config.clone();
-  onDemandConfig_ = onDemandConfig ? onDemandConfig->clone() : nullptr;
-  mergedConfig_ = alignAndValidateConfigs(*config_, onDemandConfig_.get());
-  if (!applyConfig(*mergedConfig_)) {
-    LOG(WARNING) << "Failed to apply config!";
-    mergedConfig_ = config_->clone();
-    applyConfig(*config_);
-  }
-  if (!sets_.empty()) {
-    // Make timing adjustments based on multiplexing requirements.
-    adjustConfig(*config_, sets_.size());
-    if (onDemandConfig_) {
-      int duration = onDemandConfig_->eventProfilerOnDemandDuration().count();
-      LOG(INFO) << "On demand profiler activated for " << duration << " secs";
-      adjustConfig(*onDemandConfig_, sets_.size());
-    }
-    // If events or metrics were added or removed, need to tell loggers
-    updateLoggers(*config_, onDemandConfig_.get());
-  }
-
-  curEnabledSet_ = 0;
-  if (!sets_.empty()) {
-    sets_[0].setEnabled(true);
-  } else {
-    VLOG(0) << "No counters profiled!";
-  }
-
-  baseSamples_ = 0;
-  onDemandSamples_ = 0;
-}
-
-void EventProfiler::collectSample() {
-  if (sets_.empty()) {
-    return;
-  }
-  sets_[curEnabledSet_].collectSample();
-  if (VLOG_IS_ON(1)) {
-    printAllSamples(LIBKINETO_DBG_STREAM, device());
-  }
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/EventProfiler.h b/plugins/tensorboard-plugins/libkineto/src/EventProfiler.h
deleted file mode 100644
index fafd5b9bb83..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/EventProfiler.h
+++ /dev/null
@@ -1,341 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <algorithm>
-#include <atomic>
-#include <chrono>
-#include <list>
-#include <map>
-#include <set>
-#include <string>
-#include <vector>
-
-#include <cupti.h>
-
-#include "Config.h"
-#include "CuptiEventApi.h"
-#include "CuptiMetricApi.h"
-#include "SampleListener.h"
-
-namespace KINETO_NAMESPACE {
-
-// Helper function for computing percentiles (nearest-rank).
-// Modifies the input.
-template <typename T>
-inline PercentileList& percentiles(std::vector<T> values, PercentileList& pcs) {
-  auto size = values.size();
-  for (auto& x : pcs) {
-    int idx = std::min(size - 1, (x.first * size) / 100);
-    std::nth_element(values.begin(), values.begin() + idx, values.end());
-    x.second = SampleValue(values[idx]);
-  }
-  return pcs;
-}
-
-// Helper function for normalizing a percentile list
-// Modifies the input
-inline PercentileList& normalize(PercentileList& pcs, double sf) {
-  for (auto& pc : pcs) {
-    pc.second *= sf;
-  }
-  return pcs;
-}
-
-// A slice of the sample buffer
-struct SampleSlice {
-  // Start offset (samples)
-  int offset;
-  // Slice number
-  int index;
-  // Out of this many
-  int count;
-};
-
-// A sampled event
-class Event {
- public:
-  /* implicit */ Event(std::string name) : name(std::move(name)) {}
-  /* implicit */ Event(const char* name) : name(name) {}
-  Event() : name("INVALID") {}
-
-  Event(const Event&) = delete;
-  Event& operator=(const Event&) = delete;
-  Event(Event&&) = default;
-  Event& operator=(Event&&) = default;
-
-  void addSample(
-      std::chrono::time_point<std::chrono::system_clock> timestamp,
-      const std::vector<int64_t>& values) {
-    assert(values.size() == instanceCount);
-    samples_.emplace_back(timestamp, values);
-  }
-
-  // Sum samples for a single domain instance
-  int64_t sumInstance(int i, const SampleSlice& slice) const;
-
-  // Sum all samples across all domain instances
-  int64_t sumAll(const SampleSlice& slice) const;
-
-  // Create list of percentiles
-  PercentileList& percentiles(PercentileList& pcs, const SampleSlice& slice)
-      const;
-
-  void eraseSamples(int count) {
-    auto end = samples_.begin();
-    std::advance(end, count);
-    samples_.erase(samples_.begin(), end);
-  }
-
-  void clearSamples() {
-    samples_.clear();
-  }
-
-  int sampleCount() {
-    return samples_.size();
-  }
-
-  void printSamples(std::ostream& s, CUdevice device) const;
-
-  // Event name (see nvprof --query-events)
-  std::string name;
-
-  // Number of domain instances for this event, e.g. number of SMs
-  int instanceCount = 0;
-
- private:
-  std::pair<int, int> toIdxRange(const SampleSlice& slice) const {
-    int size = (samples_.size() - slice.offset) / slice.count;
-    return std::make_pair(slice.offset + (slice.index * size), size);
-  }
-
-  // List of collected samples, where each sample has values for
-  // one or more domain instances
-  using Sample = std::pair<
-      std::chrono::time_point<std::chrono::system_clock>,
-      std::vector<int64_t>>;
-  std::list<Sample> samples_;
-};
-
-class Metric {
- public:
-  Metric(
-      std::string name,
-      CUpti_MetricID id,
-      std::vector<CUpti_EventID> events,
-      CUpti_MetricEvaluationMode eval_mode,
-      CuptiMetricApi& cupti_metrics);
-
-  struct CalculatedValues {
-    std::vector<SampleValue> perInstance;
-    SampleValue total;
-  };
-
-  struct CalculatedValues calculate(
-      std::map<CUpti_EventID, Event>& events,
-      std::chrono::nanoseconds sample_duration,
-      const SampleSlice& slice);
-
-  int instanceCount(std::map<CUpti_EventID, Event>& events) {
-    return events[events_[0]].instanceCount;
-  }
-
-  void printDescription(std::ostream& s) const;
-
-  std::string name;
-
- private:
-  CUpti_MetricID id_;
-  std::vector<CUpti_EventID> events_;
-  CUpti_MetricEvaluationMode evalMode_;
-  // Calls to CUPTI is encapsulated behind this interface
-  CuptiMetricApi& cuptiMetrics_;
-  CUpti_MetricValueKind valueKind_;
-};
-
-/**
- * A set of event groups.
- * Holds all the events that may be collected in a single pass.
- * A group contains one or more counters for a single domain.
- * A group set contains zero or one groups per domain.
- */
-class EventGroupSet {
- public:
-  EventGroupSet(
-      CUpti_EventGroupSet& set,
-      std::map<CUpti_EventID, Event>& events,
-      CuptiEventApi& cupti);
-  ~EventGroupSet();
-
-  EventGroupSet(const EventGroupSet&) = delete;
-  EventGroupSet& operator=(const EventGroupSet&) = delete;
-  EventGroupSet(EventGroupSet&&) = default;
-  EventGroupSet& operator=(EventGroupSet&&) = delete;
-
-  // Number of groups = number of domains profiled
-  int groupCount() const {
-    return set_.numEventGroups;
-  }
-
-  void setEnabled(bool enabled);
-  // Take a sample of counters in this group set
-  void collectSample();
-  void printDescription(std::ostream& s) const;
-
- private:
-  CUpti_EventGroupSet& set_;
-  std::map<CUpti_EventID, Event>& events_;
-  // Calls to CUPTI is encapsulated behind this interface
-  CuptiEventApi& cuptiEvents_;
-  bool enabled_;
-};
-
-// The sampler
-class EventProfiler {
- public:
-  explicit EventProfiler(
-      std::unique_ptr<CuptiEventApi> cupti_events,
-      std::unique_ptr<CuptiMetricApi> cupti_metrics,
-      std::vector<std::unique_ptr<SampleListener>>& loggers,
-      std::vector<std::unique_ptr<SampleListener>>& onDemandLoggers);
-  EventProfiler(const EventProfiler&) = delete;
-  EventProfiler& operator=(const EventProfiler&) = delete;
-  ~EventProfiler();
-
-  void configure(Config& config, Config* onDemandConfig);
-
-  bool isOnDemandActive() {
-    return !!onDemandConfig_;
-  }
-
-  // Print the counter sets. Multiple sets will be multiplexed.
-  void printSets(std::ostream& s) const;
-
-  // Print metrics descriptions
-  void printMetrics(std::ostream& s) const;
-
-  bool enableForDevice(Config& cfg);
-
-  CUdevice device() {
-    return cuptiEvents_->device();
-  }
-
-  bool setContinuousMode() {
-    return cuptiEvents_->setContinuousMode();
-  }
-
-  std::chrono::milliseconds samplePeriod() {
-    return mergedConfig_->samplePeriod();
-  }
-
-  std::chrono::milliseconds multiplexPeriod() {
-    return mergedConfig_->multiplexPeriod();
-  }
-
-  std::chrono::milliseconds reportPeriod() {
-    return config_->reportPeriod();
-  }
-
-  std::chrono::milliseconds onDemandReportPeriod() {
-    return onDemandConfig_->reportPeriod();
-  }
-
-  // Read values of currently running counters.
-  void collectSample();
-
-  void reportSamples();
-  void reportOnDemandSamples();
-
-  bool enabled() {
-    return sets_.size() > 0;
-  }
-
-  bool multiplexEnabled() {
-    return sets_.size() > 1;
-  }
-
-  // Multiplex counters.
-  void enableNextCounterSet();
-
-  void eraseReportedSamples() {
-    int erase_count = baseSamples_;
-    if (onDemandConfig_ &&
-        onDemandConfig_->eventProfilerOnDemandDuration().count() > 0) {
-      erase_count = std::min(baseSamples_, onDemandSamples_);
-    }
-    eraseSamples(erase_count);
-    baseSamples_ -= erase_count;
-    onDemandSamples_ -= erase_count;
-  }
-
-  void clearSamples() {
-    for (auto& pair : events_) {
-      pair.second.clearSamples();
-    }
-    baseSamples_ = 0;
-    onDemandSamples_ = 0;
-  }
-
- private:
-  // Functions to initialize profiler based on Config settings.
-  bool applyConfig(const Config& config);
-  bool initEventsAndMetrics(const Config& config);
-  void initEvents(const std::set<std::string>& eventNames);
-  void initMetrics(const std::set<std::string>& metricNames);
-  bool initEventGroups();
-
-  PercentileList initPercentiles(const std::vector<int>& percentiles) {
-    PercentileList res;
-    res.reserve(percentiles.size());
-    for (int p : percentiles) {
-      res.emplace_back(p, SampleValue(0));
-    }
-    return res;
-  }
-
-  // Notify listeners of collected samples
-  void dispatchSamples(
-      const Config& config,
-      const std::vector<std::unique_ptr<SampleListener>>& loggers,
-      int report_nr);
-
-  void eraseSamples(int count) {
-    for (auto& pair : events_) {
-      pair.second.eraseSamples(count);
-    }
-  }
-
-  void updateLoggers(Config& config, Config* on_demand_config);
-
-  // Print all collected samples since last clear.
-  void printAllSamples(std::ostream& s, CUdevice device) const;
-
-  // Calls to CUPTI is encapsulated behind these interfaces
-  std::unique_ptr<CuptiEventApi> cuptiEvents_;
-  std::unique_ptr<CuptiMetricApi> cuptiMetrics_;
-  // The CUpti API reports event IDs, we must map them to our event objects
-  std::map<CUpti_EventID, Event> events_;
-  // List of metrics
-  std::vector<Metric> metrics_;
-  // The countert sets needed to collect all counters
-  std::vector<EventGroupSet> sets_;
-  // The event group set object returned by Cupti.
-  // Saved s.t. we can call cuptiEventGroupSetsDestroy to free memory when
-  // the object is no longer needed.
-  CUpti_EventGroupSets* eventGroupSets_ = nullptr;
-  // Current multiplexed counter set
-  int curEnabledSet_{0};
-
-  std::unique_ptr<Config> config_;
-  std::unique_ptr<Config> onDemandConfig_;
-  std::unique_ptr<Config> mergedConfig_;
-  int baseSamples_{0};
-  int onDemandSamples_{0};
-
-  // Shared between profiler threads
-  // Vectors are read-only but calling loggers require lock
-  const std::vector<std::unique_ptr<SampleListener>>& loggers_;
-  const std::vector<std::unique_ptr<SampleListener>>& onDemandLoggers_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/EventProfilerController.cpp b/plugins/tensorboard-plugins/libkineto/src/EventProfilerController.cpp
deleted file mode 100644
index 0427cc7a90c..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/EventProfilerController.cpp
+++ /dev/null
@@ -1,423 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "EventProfilerController.h"
-
-#include <chrono>
-#include <thread>
-#include <vector>
-
-#include "ConfigLoader.h"
-#include "CuptiEventApi.h"
-#include "CuptiMetricApi.h"
-#include "EventProfiler.h"
-#include "output_csv.h"
-
-#include "Logger.h"
-#include "ThreadUtil.h"
-
-using namespace std::chrono;
-using std::unique_ptr;
-using std::vector;
-
-namespace KINETO_NAMESPACE {
-
-namespace {
-
-vector<std::function<unique_ptr<SampleListener>(const Config&)>>&
-loggerFactories() {
-  static vector<std::function<unique_ptr<SampleListener>(const Config&)>>
-      factories;
-  return factories;
-}
-
-vector<std::function<unique_ptr<SampleListener>(const Config&)>>&
-onDemandLoggerFactories() {
-  static vector<std::function<unique_ptr<SampleListener>(const Config&)>>
-      factories;
-  return factories;
-}
-
-vector<unique_ptr<SampleListener>> makeLoggers(const Config& config) {
-  vector<unique_ptr<SampleListener>> loggers;
-  for (const auto& factory : loggerFactories()) {
-    loggers.push_back(factory(config));
-  }
-  loggers.push_back(std::make_unique<EventCSVDbgLogger>());
-  loggers.push_back(std::make_unique<EventCSVFileLogger>());
-  return loggers;
-}
-
-vector<unique_ptr<SampleListener>> makeOnDemandLoggers(
-    const Config& config) {
-  vector<unique_ptr<SampleListener>> loggers;
-  for (const auto& factory : onDemandLoggerFactories()) {
-    loggers.push_back(factory(config));
-  }
-  loggers.push_back(std::make_unique<EventCSVDbgLogger>());
-  return loggers;
-}
-
-vector<unique_ptr<SampleListener>>& loggers(const Config& config) {
-  static auto res = makeLoggers(config);
-  return res;
-}
-
-vector<unique_ptr<SampleListener>>& onDemandLoggers(
-    const Config& config) {
-  static auto res = makeOnDemandLoggers(config);
-  return res;
-}
-
-} // anon namespace
-
-// Keep an eye on profiling threads.
-// We've observed deadlocks in Cuda11 in libcuda / libcupti..
-namespace detail {
-
-class HeartbeatMonitor {
-
- public:
-  ~HeartbeatMonitor() {
-    stopMonitoring();
-  }
-
-  static HeartbeatMonitor& instance() {
-    static HeartbeatMonitor monitor;
-    return monitor;
-  }
-
-  void profilerHeartbeat() {
-    int32_t tid = systemThreadId();
-    std::lock_guard<std::mutex> lock(mutex_);
-    profilerAliveMap_[tid]++;
-  }
-
-  void setPeriod(seconds period) {
-    {
-      std::lock_guard<std::mutex> lock(mutex_);
-      if (period_ == period) {
-        return;
-      }
-      period_ = period;
-    }
-    if (period == seconds(0)) {
-      stopMonitoring();
-    } else {
-      startMonitoring();
-    }
-  }
-
- private:
-  HeartbeatMonitor() = default;
-
-  void monitorLoop() {
-    std::unique_lock<std::mutex> lock(mutex_);
-    while(!stopMonitor_) {
-      auto cv_status = condVar_.wait_for(lock, seconds(period_));
-      // Don't perform check on spurious wakeup or on notify
-      if (cv_status == std::cv_status::timeout) {
-        for (auto& pair : profilerAliveMap_) {
-          int32_t tid = pair.first;
-          int& i = pair.second;
-          if (i == 0) {
-            LOG(ERROR) << "Thread " << tid << " appears stuck!";
-          }
-          i = 0;
-        }
-      }
-    }
-  }
-
-  void startMonitoring() {
-    if (!monitorThread_) {
-      VLOG(0) << "Starting monitoring thread";
-      stopMonitor_ = false;
-      monitorThread_ = std::make_unique<std::thread>(
-          &HeartbeatMonitor::monitorLoop, this);
-    }
-  }
-
-  void stopMonitoring() {
-    if (monitorThread_) {
-      VLOG(0) << "Stopping monitoring thread";
-      stopMonitor_ = true;
-      condVar_.notify_one();
-      monitorThread_->join();
-      monitorThread_ = nullptr;
-      VLOG(0) << "Monitoring thread terminated";
-    }
-  }
-
-  std::map<int32_t, int> profilerAliveMap_;
-  std::unique_ptr<std::thread> monitorThread_;
-  std::mutex mutex_;
-  std::condition_variable condVar_;
-  std::atomic_bool stopMonitor_{false};
-  seconds period_{0};
-};
-
-} // namespace detail
-
-namespace {
-// Profiler map singleton
-std::map<CUcontext, unique_ptr<EventProfilerController>>& profilerMap() {
-  static std::map<CUcontext, unique_ptr<EventProfilerController>> instance;
-  return instance;
-}
-
-void reportLateSample(
-    int sleepMs,
-    int sampleMs,
-    int reportMs,
-    int reprogramMs) {
-  LOG_EVERY_N(WARNING, 10) << "Lost sample due to delays (ms): " << sleepMs
-                           << ", " << sampleMs << ", " << reportMs << ", "
-                           << reprogramMs;
-}
-
-void configureHeartbeatMonitor(
-    detail::HeartbeatMonitor& monitor, const Config& base, const Config* onDemand) {
-  seconds base_period =
-      base.eventProfilerHeartbeatMonitorPeriod();
-  seconds on_demand_period = !onDemand ? seconds(0) :
-      onDemand->eventProfilerHeartbeatMonitorPeriod();
-  monitor.setPeriod(
-      on_demand_period > seconds(0) ? on_demand_period : base_period);
-}
-
-} // anon namespace
-
-void EventProfilerController::addLoggerFactory(
-    std::function<unique_ptr<SampleListener>(const Config&)> factory) {
-  loggerFactories().push_back(factory);
-}
-
-void EventProfilerController::addOnDemandLoggerFactory(
-    std::function<unique_ptr<SampleListener>(const Config&)> factory) {
-  onDemandLoggerFactories().push_back(factory);
-}
-
-EventProfilerController::EventProfilerController(
-    CUcontext context,
-    ConfigLoader& configLoader,
-    detail::HeartbeatMonitor& heartbeatMonitor)
-    : configLoader_(configLoader), heartbeatMonitor_(heartbeatMonitor) {
-  auto cupti_events = std::make_unique<CuptiEventApi>(context);
-  auto cupti_metrics =
-      std::make_unique<CuptiMetricApi>(cupti_events->device());
-  configLoader_.addHandler(
-      ConfigLoader::ConfigKind::EventProfiler, this);
-  auto config = configLoader.getConfigCopy();
-  profiler_ = std::make_unique<EventProfiler>(
-      std::move(cupti_events),
-      std::move(cupti_metrics),
-      loggers(*config),
-      onDemandLoggers(*config));
-  profilerThread_ = std::make_unique<std::thread>(
-      &EventProfilerController::profilerLoop, this);
-}
-
-EventProfilerController::~EventProfilerController() {
-  if (profilerThread_) {
-    // signaling termination of the profiler loop
-    stopRunloop_ = true;
-    profilerThread_->join();
-  }
-  configLoader_.removeHandler(
-      ConfigLoader::ConfigKind::EventProfiler, this);
-  VLOG(0) << "Stopped event profiler";
-}
-
-// Must be called under lock
-void EventProfilerController::start(CUcontext ctx, ConfigLoader& configLoader) {
-  profilerMap()[ctx] = unique_ptr<EventProfilerController>(
-      new EventProfilerController(
-          ctx, configLoader, detail::HeartbeatMonitor::instance()));
-}
-
-// Must be called under lock
-void EventProfilerController::stop(CUcontext ctx) {
-  profilerMap()[ctx] = nullptr;
-}
-
-bool EventProfilerController::canAcceptConfig() {
-  std::lock_guard<std::mutex> guard(mutex_);
-  return !newOnDemandConfig_;
-}
-
-void EventProfilerController::acceptConfig(const Config& config) {
-  if (config.eventProfilerOnDemandDuration().count() == 0) {
-    // Ignore - not for this profiler
-    return;
-  }
-  std::lock_guard<std::mutex> guard(mutex_);
-  if (newOnDemandConfig_) {
-    LOG(ERROR) << "On demand request already queued - ignoring new request";
-    return;
-  }
-  newOnDemandConfig_ = config.clone();
-  LOG(INFO) << "Received new on-demand config";
-}
-
-bool EventProfilerController::enableForDevice(Config& cfg) {
-  // FIXME: Use device unique id!
-  if (!cfg.eventProfilerEnabledForDevice(profiler_->device())) {
-    return false;
-  }
-  // context count includes the new context
-  int instances = configLoader_.contextCountForGpu(profiler_->device());
-  VLOG(0) << "Device context count: " << instances;
-  return instances >= 0 && instances <= cfg.maxEventProfilersPerGpu();
-}
-
-void EventProfilerController::profilerLoop() {
-  // We limit the number of profilers that can exist per GPU
-  auto config = configLoader_.getConfigCopy();
-  if (!enableForDevice(*config)) {
-    VLOG(0) << "Not starting EventProfiler - profilers for GPU "
-            << profiler_->device() << " exceeds profilers per GPU limit ("
-            << config->maxEventProfilersPerGpu() << ")";
-    return;
-  }
-
-  if (!profiler_->setContinuousMode()) {
-    VLOG(0) << "Continuous mode not supported for GPU "
-            << profiler_->device() << ". Not starting Event Profiler.";
-    return;
-  }
-
-  VLOG(0) << "Starting Event Profiler for GPU " << profiler_->device();
-  setThreadName("CUPTI Event Profiler");
-
-  time_point<system_clock> next_sample_time;
-  time_point<system_clock> next_report_time;
-  time_point<system_clock> next_on_demand_report_time;
-  time_point<system_clock> next_multiplex_time;
-  std::unique_ptr<Config> on_demand_config = nullptr;
-  bool reconfigure = true;
-  bool restart = true;
-  int report_count = 0;
-  int on_demand_report_count = 0;
-  while (!stopRunloop_) {
-    heartbeatMonitor_.profilerHeartbeat();
-    if (configLoader_.hasNewConfig(*config)) {
-      config = configLoader_.getConfigCopy();
-      VLOG(0) << "Base config changed";
-      report_count = 0;
-      reconfigure = true;
-    }
-
-    auto now = system_clock::now();
-    if (on_demand_config &&
-        now > (on_demand_config->eventProfilerOnDemandStartTime() +
-               on_demand_config->eventProfilerOnDemandDuration())) {
-      on_demand_config = nullptr;
-      LOG(INFO) << "On-demand profiling complete";
-      reconfigure = true;
-    }
-
-    if (!profiler_->isOnDemandActive()) {
-      std::lock_guard<std::mutex> lock(mutex_);
-      if (newOnDemandConfig_) {
-        VLOG(0) << "Received on-demand config, reconfiguring";
-        on_demand_config = std::move(newOnDemandConfig_);
-        reconfigure = true;
-        on_demand_report_count = 0;
-      }
-    }
-
-    if (reconfigure) {
-      try {
-        profiler_->configure(*config, on_demand_config.get());
-      } catch (const std::exception& ex) {
-        LOG(ERROR) << "Encountered error while configuring event profiler: "
-            << ex.what();
-        // Exit profiling entirely when encountering an error here
-        // as it indicates a serious problem or bug.
-        break;
-      }
-      configureHeartbeatMonitor(
-          heartbeatMonitor_, *config, on_demand_config.get());
-      reconfigure = false;
-      restart = true;
-    }
-
-    if (restart) {
-      now = system_clock::now();
-      next_sample_time = now + profiler_->samplePeriod();
-      next_report_time = now + profiler_->reportPeriod();
-      if (profiler_->isOnDemandActive()) {
-        next_on_demand_report_time = now + profiler_->onDemandReportPeriod();
-      }
-      next_multiplex_time = now + profiler_->multiplexPeriod();
-      // Collect an initial sample and throw it away
-      // The next sample is the first valid one
-      profiler_->collectSample();
-      profiler_->clearSamples();
-      restart = false;
-    }
-
-    auto start_sleep = now;
-    while (now < next_sample_time) {
-      /* sleep override */
-      std::this_thread::sleep_for(next_sample_time - now);
-      now = system_clock::now();
-    }
-    int sleep_time = duration_cast<milliseconds>(now - start_sleep).count();
-
-    auto start_sample = now;
-    profiler_->collectSample();
-    now = system_clock::now();
-    int sample_time = duration_cast<milliseconds>(now - start_sample).count();
-
-    next_sample_time += profiler_->samplePeriod();
-    if (now > next_sample_time) {
-      reportLateSample(sleep_time, sample_time, 0, 0);
-      restart = true;
-      continue;
-    }
-
-    auto start_report = now;
-    if (now > next_report_time) {
-      VLOG(1) << "Report #" << report_count++;
-      profiler_->reportSamples();
-      next_report_time += profiler_->reportPeriod();
-    }
-    if (profiler_->isOnDemandActive() && now > next_on_demand_report_time) {
-      VLOG(1) << "OnDemand Report #" << on_demand_report_count++;
-      profiler_->reportOnDemandSamples();
-      next_on_demand_report_time += profiler_->onDemandReportPeriod();
-    }
-    profiler_->eraseReportedSamples();
-    now = system_clock::now();
-    int report_time = duration_cast<milliseconds>(now - start_report).count();
-
-    if (now > next_sample_time) {
-      reportLateSample(sleep_time, sample_time, report_time, 0);
-      restart = true;
-      continue;
-    }
-
-    auto start_multiplex = now;
-    if (profiler_->multiplexEnabled() && now > next_multiplex_time) {
-      profiler_->enableNextCounterSet();
-      next_multiplex_time += profiler_->multiplexPeriod();
-    }
-    now = system_clock::now();
-    int multiplex_time =
-        duration_cast<milliseconds>(now - start_multiplex).count();
-
-    if (now > next_sample_time) {
-      reportLateSample(sleep_time, sample_time, report_time, multiplex_time);
-      restart = true;
-    }
-
-    VLOG(0) << "Runloop execution time: "
-            << duration_cast<milliseconds>(now - start_sample).count() << "ms";
-  }
-
-  VLOG(0) << "Device " << profiler_->device()
-          << ": Exited event profiling loop";
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/EventProfilerController.h b/plugins/tensorboard-plugins/libkineto/src/EventProfilerController.h
deleted file mode 100644
index 007a82faa92..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/EventProfilerController.h
+++ /dev/null
@@ -1,63 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <atomic>
-#include <functional>
-#include <memory>
-#include <mutex>
-#include <thread>
-
-#include <cupti.h>
-
-#include "ConfigLoader.h"
-
-namespace KINETO_NAMESPACE {
-
-class Config;
-class ConfigLoader;
-class EventProfiler;
-class SampleListener;
-
-namespace detail {
-class HeartbeatMonitor;
-}
-
-class EventProfilerController : public ConfigLoader::ConfigHandler {
- public:
-  EventProfilerController(const EventProfilerController&) = delete;
-  EventProfilerController& operator=(const EventProfilerController&) = delete;
-
-  ~EventProfilerController();
-
-  static void start(CUcontext ctx, ConfigLoader& configLoader);
-  static void stop(CUcontext ctx);
-
-  static void addLoggerFactory(
-      std::function<std::unique_ptr<SampleListener>(const Config&)> factory);
-
-  static void addOnDemandLoggerFactory(
-      std::function<std::unique_ptr<SampleListener>(const Config&)> factory);
-
-  bool canAcceptConfig() override;
-
-  void acceptConfig(const Config& config) override;
-
- private:
-  explicit EventProfilerController(
-      CUcontext context,
-      ConfigLoader& configLoader,
-      detail::HeartbeatMonitor& heartbeatMonitor);
-  bool enableForDevice(Config& cfg);
-  void profilerLoop();
-
-  ConfigLoader& configLoader_;
-  std::unique_ptr<Config> newOnDemandConfig_;
-  detail::HeartbeatMonitor& heartbeatMonitor_;
-  std::unique_ptr<EventProfiler> profiler_;
-  std::unique_ptr<std::thread> profilerThread_;
-  std::atomic_bool stopRunloop_{false};
-  std::mutex mutex_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/GenericTraceActivity.cpp b/plugins/tensorboard-plugins/libkineto/src/GenericTraceActivity.cpp
deleted file mode 100644
index 4e00b1256c4..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/GenericTraceActivity.cpp
+++ /dev/null
@@ -1,10 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "GenericTraceActivity.h"
-#include "output_base.h"
-
-namespace libkineto {
-  void GenericTraceActivity::log(ActivityLogger& logger) const {
-    logger.handleGenericActivity(*this);
-  }
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/ILoggerObserver.cpp b/plugins/tensorboard-plugins/libkineto/src/ILoggerObserver.cpp
deleted file mode 100644
index f0106578811..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ILoggerObserver.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "ILoggerObserver.h"
-
-#if !USE_GOOGLE_LOG
-
-#include <array>
-#include <fmt/format.h>
-
-namespace libkineto {
-
-struct LoggerTypeName {
-  constexpr LoggerTypeName(const char* n, LoggerOutputType t) : name(n), type(t) {};
-  const char* name;
-  LoggerOutputType type;
-};
-
-static constexpr std::array<LoggerTypeName, LoggerTypeCount + 1> LoggerMap{{
-    {"VERBOSE", LoggerOutputType::VERBOSE},
-    {"INFO", LoggerOutputType::INFO},
-    {"WARNING", LoggerOutputType::WARNING},
-    {"ERROR", LoggerOutputType::ERROR},
-    {"STAGE", LoggerOutputType::STAGE},
-    {"???", LoggerOutputType::ENUM_COUNT}
-}};
-
-static constexpr bool matchingOrder(int idx = 0) {
-  return LoggerMap[idx].type == LoggerOutputType::ENUM_COUNT ||
-    ((idx == (int) LoggerMap[idx].type) && matchingOrder(idx + 1));
-}
-static_assert(matchingOrder(), "LoggerTypeName map is out of order");
-
-const char* toString(LoggerOutputType t) {
-  if(t < VERBOSE || t >= ENUM_COUNT) {
-    return LoggerMap[ENUM_COUNT].name;
-  }
-  return LoggerMap[(int)t].name;
-}
-
-LoggerOutputType toLoggerOutputType(const std::string& str) {
-  for (int i = 0; i < LoggerTypeCount; i++) {
-    if (str == LoggerMap[i].name) {
-      return LoggerMap[i].type;
-    }
-  }
-  throw std::invalid_argument(fmt::format("Invalid activity type: {}", str));
-}
-
-} // namespace libkineto
-
-
-#endif // !USE_GOOGLE_LOG
diff --git a/plugins/tensorboard-plugins/libkineto/src/Logger.cpp b/plugins/tensorboard-plugins/libkineto/src/Logger.cpp
deleted file mode 100644
index dbde765f51f..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/Logger.cpp
+++ /dev/null
@@ -1,136 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "Logger.h"
-#include "ILoggerObserver.h"
-
-#ifndef USE_GOOGLE_LOG
-
-#include <chrono>
-#include <cstring>
-#include <iomanip>
-#include <iostream>
-#include <time.h>
-
-#include <fmt/chrono.h>
-#include <fmt/format.h>
-
-#include "ThreadUtil.h"
-
-namespace KINETO_NAMESPACE {
-
-std::atomic_int Logger::severityLevel_{VERBOSE};
-std::atomic_int Logger::verboseLogLevel_{-1};
-std::atomic<uint64_t> Logger::verboseLogModules_{~0ull};
-
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wglobal-constructors"
-std::mutex Logger::loggerObserversMutex_;
-#pragma GCC diagnostic pop
-
-
-Logger::Logger(int severity, int line, const char* filePath, int errnum)
-    : buf_(), out_(LIBKINETO_DBG_STREAM), errnum_(errnum), messageSeverity_(severity) {
-  buf_ << toString((LoggerOutputType) severity) << ":";
-
-  const auto tt =
-      std::chrono::system_clock::to_time_t(std::chrono::system_clock::now());
-  const char* file = strrchr(filePath, '/');
-  buf_ << fmt::format("{:%Y-%m-%d %H:%M:%S}", fmt::localtime(tt)) << " "
-       << processId() << ":" << systemThreadId() << " "
-       << (file ? file + 1 : filePath) << ":" << line << "] ";
-}
-
-Logger::~Logger() {
-#ifdef __linux__
-  if (errnum_ != 0) {
-    thread_local char buf[1024];
-    buf_ << " : " << strerror_r(errnum_, buf, sizeof(buf));
-  }
-#endif
-
-  {
-    std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-    for (auto* observer : loggerObservers()) {
-      // Output to observers. Current Severity helps keep track of which bucket the output goes.
-      if (observer) {
-        observer->write(buf_.str(), (LoggerOutputType) messageSeverity_);
-      }
-    }
-  }
-
-  // Finally, print to terminal or console.
-  out_ << buf_.str() << std::endl;
-}
-
-void Logger::setVerboseLogModules(const std::vector<std::string>& modules) {
-  uint64_t mask = 0;
-  if (modules.empty()) {
-    mask = ~0ull;
-  } else {
-    for (const std::string& name : modules) {
-      mask |= hash(name.c_str());
-    }
-  }
-  verboseLogModules_ = mask;
-}
-
-void Logger::addLoggerObserver(ILoggerObserver* observer) {
-  if (observer == nullptr) {
-    return;
-  }
-  std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-  loggerObservers().insert(observer);
-}
-
-void Logger::removeLoggerObserver(ILoggerObserver* observer) {
-  std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-  loggerObservers().erase(observer);
-}
-
-void Logger::addLoggerObserverDevice(int64_t device) {
-  std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-  for (auto observer : loggerObservers()) {
-    observer->addDevice(device);
-  }
-}
-
-void Logger::addLoggerObserverEventCount(int64_t count) {
-  std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-  for (auto observer : loggerObservers()) {
-    observer->addEventCount(count);
-  }
-}
-
-void Logger::setLoggerObserverTraceDurationMS(int64_t duration) {
-  std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-  for (auto observer : loggerObservers()) {
-    observer->setTraceDurationMS(duration);
-  }
-}
-
-void Logger::setLoggerObserverTraceID(const std::string& tid) {
-  std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-  for (auto observer : loggerObservers()) {
-    observer->setTraceID(tid);
-  }
-}
-
-void Logger::setLoggerObserverGroupTraceID(const std::string& gtid) {
-  std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-  for (auto observer : loggerObservers()) {
-    observer->setGroupTraceID(gtid);
-  }
-}
-
-void Logger::addLoggerObserverDestination(const std::string& dest) {
-  std::lock_guard<std::mutex> guard(loggerObserversMutex_);
-  for (auto observer : loggerObservers()) {
-    observer->addDestination(dest);
-  }
-}
-
-} // namespace KINETO_NAMESPACE
-
-#endif // USE_GOOGLE_LOG
diff --git a/plugins/tensorboard-plugins/libkineto/src/Logger.h b/plugins/tensorboard-plugins/libkineto/src/Logger.h
deleted file mode 100644
index 868fc84b9f4..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/Logger.h
+++ /dev/null
@@ -1,244 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <iostream>
-
-#define LIBKINETO_DBG_STREAM std::cerr
-
-#if USE_GOOGLE_LOG
-
-#include <glog/logging.h>
-
-#define SET_LOG_SEVERITY_LEVEL(level)
-#define SET_LOG_VERBOSITY_LEVEL(level, modules)
-#define LOGGER_OBSERVER_ADD_DEVICE(device)
-#define LOGGER_OBSERVER_ADD_EVENT_COUNT(count)
-#define LOGGER_OBSERVER_SET_TRACE_DURATION_MS(duration)
-#define LOGGER_OBSERVER_SET_TRACE_ID(tid)
-#define LOGGER_OBSERVER_SET_GROUP_TRACE_ID(gtid)
-#define LOGGER_OBSERVER_ADD_DESTINATION(dest)
-#define UST_LOGGER_MARK_COMPLETED(stage)
-
-#else // !USE_GOOGLE_LOG
-#include <stdio.h>
-#include <atomic>
-#include <map>
-#include <mutex>
-#include <ostream>
-#include <set>
-#include <string>
-#include <sstream>
-#include <vector>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "ILoggerObserver.h"
-
-#ifdef _MSC_VER
-// unset a predefined ERROR (windows)
-#undef ERROR
-#endif // _MSC_VER
-
-namespace KINETO_NAMESPACE {
-
-class Logger {
- public:
-  Logger(int severity, int line, const char* filePath, int errnum = 0);
-  ~Logger();
-
-  inline std::ostream& stream() {
-    return buf_;
-  }
-
-  static inline void setSeverityLevel(int level) {
-    severityLevel_ = level;
-  }
-
-  static inline int severityLevel() {
-    return severityLevel_;
-  }
-
-  static inline void setVerboseLogLevel(int level) {
-    verboseLogLevel_ = level;
-  }
-
-  static inline int verboseLogLevel() {
-    return verboseLogLevel_;
-  }
-
-  // This is constexpr so that the hash for a file name is computed at compile
-  // time when used in the VLOG macros.
-  // This way, there is no string comparison for matching VLOG modules,
-  // only a comparison of pre-computed hashes.
-  // No fancy hashing needed here. It's pretty inefficient (one character
-  // at a time) but the strings are not large and it's not in the critical path.
-  static constexpr uint64_t rol(uint64_t val, int amount) {
-    return val << amount | val >> (63 - amount);
-  }
-  static constexpr uint64_t hash(const char* s) {
-    uint64_t hash = hash_rec(s, 0);
-    return hash & rol(0x41a0240682483014ull, hash & 63);
-  }
-  static constexpr uint64_t hash_rec(const char* s, int off) {
-    // Random constants!
-    return (!s[off] ? 57ull : (hash_rec(s, off + 1) * 293) ^ s[off]);
-  }
-  static constexpr const char* basename(const char* s, int off = 0) {
-    return !s[off]
-        ? s
-        : s[off] == '/' ? basename(&s[off + 1]) : basename(s, off + 1);
-  }
-
-  static void setVerboseLogModules(const std::vector<std::string>& modules);
-
-  static inline uint64_t verboseLogModules() {
-    return verboseLogModules_;
-  }
-
-  static void clearLoggerObservers() {
-    std::lock_guard<std::mutex> g(loggerObserversMutex_);
-    loggerObservers().clear();
-  }
-
-  static void addLoggerObserver(ILoggerObserver* observer);
-
-  static void removeLoggerObserver(ILoggerObserver* observer);
-
-  static void addLoggerObserverDevice(int64_t device);
-
-  static void addLoggerObserverEventCount(int64_t count);
-
-  static void setLoggerObserverTraceDurationMS(int64_t duration);
-
-  static void setLoggerObserverTraceID(const std::string& tid);
-
-  static void setLoggerObserverGroupTraceID(const std::string& gtid);
-
-  static void addLoggerObserverDestination(const std::string& dest);
-
- private:
-  std::stringstream buf_;
-  std::ostream& out_;
-  int errnum_;
-  int messageSeverity_;
-  static std::atomic_int severityLevel_;
-  static std::atomic_int verboseLogLevel_;
-  static std::atomic<uint64_t> verboseLogModules_;
-  static std::set<ILoggerObserver*>& loggerObservers() {
-    static auto* inst = new std::set<ILoggerObserver*>();
-    return *inst;
-  }
-  static std::mutex loggerObserversMutex_;
-};
-
-class VoidLogger {
- public:
-  VoidLogger() {}
-  void operator&(std::ostream&) {}
-};
-
-} // namespace KINETO_NAMESPACE
-
-#ifdef LOG // Undefine in case these are already defined (quite likely)
-#undef LOG
-#undef LOG_IS_ON
-#undef LOG_IF
-#undef LOG_EVERY_N
-#undef LOG_IF_EVERY_N
-#undef DLOG
-#undef DLOG_IF
-#undef VLOG
-#undef VLOG_IF
-#undef VLOG_EVERY_N
-#undef VLOG_IS_ON
-#undef DVLOG
-#undef LOG_FIRST_N
-#undef CHECK
-#undef DCHECK
-#undef DCHECK_EQ
-#undef PLOG
-#undef PCHECK
-#undef LOG_OCCURRENCES
-#endif
-
-#define LOG_IS_ON(severity) \
-  (severity >= libkineto::Logger::severityLevel())
-
-#define LOG_IF(severity, condition) \
-  !(LOG_IS_ON(severity) && (condition)) ? (void)0 : libkineto::VoidLogger() & \
-    libkineto::Logger(severity, __LINE__, __FILE__).stream()
-
-#define LOG(severity) LOG_IF(severity, true)
-
-#define LOCAL_VARNAME_CONCAT(name, suffix) _##name##suffix##_
-
-#define LOCAL_VARNAME(name) LOCAL_VARNAME_CONCAT(name, __LINE__)
-
-#define LOG_OCCURRENCES LOCAL_VARNAME(log_count)
-
-#define LOG_EVERY_N(severity, rate)               \
-  static int LOG_OCCURRENCES = 0;                 \
-  LOG_IF(severity, LOG_OCCURRENCES++ % rate == 0) \
-      << "(x" << LOG_OCCURRENCES << ") "
-
-template <uint64_t n>
-struct __to_constant__ {
-  static const uint64_t val = n;
-};
-#define FILENAME_HASH                             \
-  __to_constant__<libkineto::Logger::hash( \
-      libkineto::Logger::basename(__FILE__))>::val
-#define VLOG_IS_ON(verbosity)                           \
-  (libkineto::Logger::verboseLogLevel() >= verbosity && \
-   (libkineto::Logger::verboseLogModules() & FILENAME_HASH) == FILENAME_HASH)
-
-#define VLOG_IF(verbosity, condition) \
-  LOG_IF(VERBOSE, VLOG_IS_ON(verbosity) && (condition))
-
-#define VLOG(verbosity) VLOG_IF(verbosity, true)
-
-#define VLOG_EVERY_N(verbosity, rate)               \
-  static int LOG_OCCURRENCES = 0;                   \
-  VLOG_IF(verbosity, LOG_OCCURRENCES++ % rate == 0) \
-      << "(x" << LOG_OCCURRENCES << ") "
-
-#define PLOG(severity) \
-  libkineto::Logger(severity, __LINE__, __FILE__, errno).stream()
-
-#define SET_LOG_SEVERITY_LEVEL(level) \
-  libkineto::Logger::setSeverityLevel(level)
-
-#define SET_LOG_VERBOSITY_LEVEL(level, modules)   \
-  libkineto::Logger::setVerboseLogLevel(level); \
-  libkineto::Logger::setVerboseLogModules(modules)
-
-// Logging the set of devices the trace is collect on.
-#define LOGGER_OBSERVER_ADD_DEVICE(device_count) \
-  libkineto::Logger::addLoggerObserverDevice(device_count)
-
-// Incrementing the number of events collected by this trace.
-#define LOGGER_OBSERVER_ADD_EVENT_COUNT(count) \
-  libkineto::Logger::addLoggerObserverEventCount(count)
-
-// Record duration of trace in milliseconds.
-#define LOGGER_OBSERVER_SET_TRACE_DURATION_MS(duration) \
-  libkineto::Logger::setLoggerObserverTraceDurationMS(duration)
-
-// Record the trace id when given.
-#define LOGGER_OBSERVER_SET_TRACE_ID(tid) \
-  libkineto::Logger::setLoggerObserverTraceID(tid)
-
-// Record the group trace id when given.
-#define LOGGER_OBSERVER_SET_GROUP_TRACE_ID(gtid) \
-  libkineto::Logger::setLoggerObserverGroupTraceID(gtid)
-
-// Log the set of destinations the trace is sent to.
-#define LOGGER_OBSERVER_ADD_DESTINATION(dest) \
-  libkineto::Logger::addLoggerObserverDestination(dest)
-
-// UST Logger Semantics to describe when a stage is complete.
-#define UST_LOGGER_MARK_COMPLETED(stage) \
-  LOG(libkineto::LoggerOutputType::STAGE) << "Completed Stage: " << stage
-
-#endif // USE_GOOGLE_LOG
diff --git a/plugins/tensorboard-plugins/libkineto/src/LoggerCollector.h b/plugins/tensorboard-plugins/libkineto/src/LoggerCollector.h
deleted file mode 100644
index bb05aab218d..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/LoggerCollector.h
+++ /dev/null
@@ -1,70 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#if !USE_GOOGLE_LOG
-
-#include <atomic>
-#include <map>
-#include <set>
-#include <vector>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "ILoggerObserver.h"
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-
-class LoggerCollector : public ILoggerObserver {
- public:
-  LoggerCollector() : buckets_() {}
-
-  void write(const std::string& message, LoggerOutputType ot = ERROR) override {
-    // Skip STAGE output type which is only used by USTLoggerCollector.
-    if (ot != STAGE) {
-      buckets_[ot].push_back(message);
-    }
-  }
-
-  const std::map<LoggerOutputType, std::vector<std::string>> extractCollectorMetadata() override {
-    return buckets_;
-  }
-
-  void reset() override {
-    trace_duration_ms = 0;
-    event_count = 0;
-    destinations.clear();
-  }
-
-  void addDevice(const int64_t device) override {
-    devices.insert(device);
-  }
-
-  void setTraceDurationMS(const int64_t duration) override {
-    trace_duration_ms = duration;
-  }
-
-  void addEventCount(const int64_t count) override {
-    event_count += count;
-  }
-
-  void addDestination(const std::string& dest) override {
-    destinations.insert(dest);
-  }
-
- protected:
-  std::map<LoggerOutputType, std::vector<std::string>> buckets_;
-
-  // These are useful metadata to collect from CUPTIActivityProfiler for internal tracking.
-  std::set<int64_t> devices;
-  int64_t trace_duration_ms{0};
-  std::atomic<uint64_t> event_count{0};
-  std::set<std::string> destinations;
-
-};
-
-} // namespace KINETO_NAMESPACE
-
-#endif // !USE_GOOGLE_LOG
diff --git a/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityApi.cpp b/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityApi.cpp
deleted file mode 100644
index 73eff13e2a0..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityApi.cpp
+++ /dev/null
@@ -1,569 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "RoctracerActivityApi.h"
-
-#include <cstring>
-#include <chrono>
-#include <time.h>
-
-#include "Demangle.h"
-#include "output_base.h"
-#include "ThreadUtil.h"
-
-typedef uint64_t timestamp_t;
-
-static timestamp_t timespec_to_ns(const timespec& time) {
-    return ((timestamp_t)time.tv_sec * 1000000000) + time.tv_nsec;
-  }
-
-using namespace std::chrono;
-
-namespace KINETO_NAMESPACE {
-
-constexpr size_t kBufSize(2 * 1024 * 1024);
-
-RoctracerActivityApi& RoctracerActivityApi::singleton() {
-  static RoctracerActivityApi instance;
-  return instance;
-}
-
-RoctracerActivityApi::RoctracerActivityApi() {
-  gpuTraceBuffers_ = std::make_unique<std::list<RoctracerActivityBuffer>>();
-}
-
-RoctracerActivityApi::~RoctracerActivityApi() {
-  disableActivities(std::set<ActivityType>());
-  endTracing();
-}
-
-void RoctracerActivityApi::pushCorrelationID(int id, CorrelationFlowType type) {
-#ifdef HAS_ROCTRACER
-  if (!singleton().externalCorrelationEnabled_) {
-    return;
-  }
-  // placeholder
-#endif
-}
-
-void RoctracerActivityApi::popCorrelationID(CorrelationFlowType type) {
-#ifdef HAS_ROCTRACER
-  if (!singleton().externalCorrelationEnabled_) {
-    return;
-  }
-  // placeholder
-#endif
-}
-
-void RoctracerActivityApi::setMaxBufferSize(int size) {
-  maxGpuBufferCount_ = 1 + size / kBufSize;
-}
-
-int RoctracerActivityApi::processActivities(
-    ActivityLogger& logger) {
-  // Find offset to map from monotonic clock to system clock.
-  // This will break time-ordering of events but is status quo.
-
-  timespec t0, t1, t00;
-  clock_gettime(CLOCK_REALTIME, &t0);
-  clock_gettime(CLOCK_MONOTONIC, &t1);
-  clock_gettime(CLOCK_REALTIME, &t00);
-
-  const timestamp_t toffset = (timespec_to_ns(t0) >> 1) + (timespec_to_ns(t00) >> 1) - timespec_to_ns(t1);
-
-  int count = 0;
-
-  // Basic Api calls
-
-  for (auto &item : rows_) {
-    GenericTraceActivity a;
-    a.startTime = (item.begin + toffset) / 1000;
-    a.endTime = (item.end + toffset) / 1000;
-    a.id = item.id;
-    a.device = item.pid;
-    a.resource = item.tid;
-    a.activityType = ActivityType::CUDA_RUNTIME;
-    a.activityName = std::string(roctracer_op_string(ACTIVITY_DOMAIN_HIP_API, item.cid, 0));
-    a.flow.id = item.id;
-    a.flow.type = kLinkAsyncCpuGpu;
-    a.flow.start = true;
-
-    logger.handleGenericActivity(a);
-    ++count;
-  }
-
-  // Malloc/Free calls
-  for (auto &item : mallocRows_) {
-    GenericTraceActivity a;
-    a.startTime = (item.begin + toffset) / 1000;
-    a.endTime = (item.end + toffset) / 1000;
-    a.id = item.id;
-    a.device = item.pid;
-    a.resource = item.tid;
-    a.activityType = ActivityType::CUDA_RUNTIME;
-    a.activityName = std::string(roctracer_op_string(ACTIVITY_DOMAIN_HIP_API, item.cid, 0));
-    a.flow.id = item.id;
-    a.flow.type = kLinkAsyncCpuGpu;
-    a.flow.start = true;
-
-    a.addMetadata("ptr", item.ptr);
-    if (item.cid == HIP_API_ID_hipMalloc) {
-      a.addMetadata("size", item.size);
-    }
-
-    logger.handleGenericActivity(a);
-    ++count;
-  }
-
-  // HipMemcpy calls
-  for (auto &item : copyRows_) {
-    GenericTraceActivity a;
-    a.startTime = (item.begin + toffset) / 1000;
-    a.endTime = (item.end + toffset) / 1000;
-    a.id = item.id;
-    a.device = item.pid;
-    a.resource = item.tid;
-    a.activityType = ActivityType::CUDA_RUNTIME;
-    a.activityName = std::string(roctracer_op_string(ACTIVITY_DOMAIN_HIP_API, item.cid, 0));
-    a.flow.id = item.id;
-    a.flow.type = kLinkAsyncCpuGpu;
-    a.flow.start = true;
-
-    a.addMetadata("src", item.src);
-    a.addMetadata("dst", item.dst);
-    a.addMetadata("size", item.size);
-    a.addMetadata("kind", item.kind);
-    if ((item.cid == HIP_API_ID_hipMemcpyAsync) || (item.cid == HIP_API_ID_hipMemcpyWithStream)) {
-      a.addMetadata("stream", fmt::format("{}", reinterpret_cast<void*>(item.stream)));
-    }
-
-    logger.handleGenericActivity(a);
-    ++count;
-  }
-
-  // Kernel Launch Api calls
-
-  for (auto &item : kernelRows_) {
-    GenericTraceActivity a;
-    a.startTime = (item.begin + toffset) / 1000;
-    a.endTime = (item.end + toffset) / 1000;
-    a.id = item.id;
-    a.device = item.pid;
-    a.resource = item.tid;
-    a.activityType = ActivityType::CUDA_RUNTIME;
-    a.activityName = std::string(roctracer_op_string(ACTIVITY_DOMAIN_HIP_API, item.cid, 0));
-    a.flow.id = item.id;
-    a.flow.type = kLinkAsyncCpuGpu;
-    a.flow.start = true;
-
-    if (item.functionAddr != nullptr) {
-      a.addMetadataQuoted(
-          "kernel", demangle(hipKernelNameRefByPtr(item.functionAddr, item.stream)));
-    }
-    else if (item.function != nullptr) {
-      a.addMetadataQuoted(
-          "kernel", demangle(hipKernelNameRef(item.function)));
-    }
-    a.addMetadata("grid dim", fmt::format("[{}, {}, {}]", item.gridX, item.gridY, item.gridZ));
-    a.addMetadata("block dim", fmt::format("[{}, {}, {}]", item.workgroupX, item.workgroupY, item.workgroupZ));
-    a.addMetadata("shared size", item.groupSegmentSize);
-    a.addMetadata("stream", fmt::format("{}", reinterpret_cast<void*>(item.stream)));
-
-    // Stash launches to tie to the async ops
-    kernelLaunches_[a.id] = a;
-
-    // Stash kernel names to tie to the async ops
-    std::string name;
-    if (item.functionAddr != nullptr) {
-      name = demangle(hipKernelNameRefByPtr(item.functionAddr, item.stream));
-    }
-    else if (item.function != nullptr) {
-      name = demangle(hipKernelNameRef(item.function));
-    }
-    if (!name.empty()) {
-      uint32_t string_id = reverseStrings_[name];
-      if (string_id == 0) {
-        string_id = nextStringId_++;
-        reverseStrings_[name] = string_id;
-        strings_[string_id] = name;
-      }
-      kernelNames_[item.id] = string_id;
-    }
-
-    logger.handleGenericActivity(a);
-    ++count;
-  }
-
-  // Async Ops
-
-  for (auto& buffer : *gpuTraceBuffers_) {
-    const roctracer_record_t* record = (const roctracer_record_t*)(buffer.data);
-    const roctracer_record_t* end_record = (const roctracer_record_t*)(buffer.data + buffer.validSize);
-    GenericTraceActivity a;
-
-    while (record < end_record) {
-      if ((record->domain == ACTIVITY_DOMAIN_HIP_API) && (loggedIds_.contains(record->op))) {
-        const char *name = roctracer_op_string(record->domain, record->op, record->kind);
-        a.device = record->process_id;
-        a.resource = record->thread_id;
-
-        a.startTime = (record->begin_ns + toffset) / 1000;
-        a.endTime = (record->end_ns + toffset) / 1000;
-        a.id = record->correlation_id;
-
-        a.activityType = ActivityType::CUDA_RUNTIME;
-        a.activityName = std::string(name);
-        a.flow.id = record->correlation_id;
-        a.flow.type = kLinkAsyncCpuGpu;
-        a.flow.start = true;
-
-        logger.handleGenericActivity(a);
-        ++count;
-      }
-      else if (record->domain == ACTIVITY_DOMAIN_HCC_OPS) {
-        // Overlay launch metadata for kernels
-        auto kit = kernelLaunches_.find(record->correlation_id);
-        if (kit != kernelLaunches_.end()) {
-          a = (*kit).second;
-        }
-
-        const char *name = roctracer_op_string(record->domain, record->op, record->kind);
-        a.device = record->device_id;
-        a.resource = record->queue_id;
-
-        a.startTime = (record->begin_ns + toffset) / 1000;
-        a.endTime = (record->end_ns + toffset) / 1000;
-        a.id = record->correlation_id;
-
-        a.activityType = ActivityType::CONCURRENT_KERNEL;
-        a.activityName = std::string(name);
-        a.flow.id = record->correlation_id;
-        a.flow.type = kLinkAsyncCpuGpu;
-
-        auto it = kernelNames_.find(record->correlation_id);
-        if (it != kernelNames_.end()) {
-          a.activityName = strings_[it->second];
-        }
-
-        logger.handleGenericActivity(a);
-        ++count;
-      }
-
-      roctracer_next_record(record, &record);
-    }
-  }
-  return count;
-}
-
-void RoctracerActivityApi::clearActivities() {
-  gpuTraceBuffers_->clear();
-  rows_.clear();
-  kernelRows_.clear();
-  copyRows_.clear();
-  mallocRows_.clear();
-  kernelLaunches_.clear();
-}
-
-void RoctracerActivityApi::api_callback(uint32_t domain, uint32_t cid, const void* callback_data, void* arg)
-{
-  RoctracerActivityApi *dis = &singleton();
-
-  if (domain == ACTIVITY_DOMAIN_HIP_API && dis->loggedIds_.contains(cid)) {
-    const hip_api_data_t* data = (const hip_api_data_t*)(callback_data);
-
-    // Pack callbacks into row structures
-
-    static timespec timestamp;	// FIXME verify thread safety
-
-    if (data->phase == ACTIVITY_API_PHASE_ENTER) {
-      clock_gettime(CLOCK_MONOTONIC, &timestamp);  // record proper clock
-    }
-    else { // (data->phase == ACTIVITY_API_PHASE_EXIT)
-      timespec endTime;
-      timespec startTime { timestamp };
-      clock_gettime(CLOCK_MONOTONIC, &endTime);  // record proper clock
-
-      switch (cid) {
-        case HIP_API_ID_hipLaunchKernel:
-        case HIP_API_ID_hipExtLaunchKernel:
-        case HIP_API_ID_hipLaunchCooperativeKernel:     // Should work here
-          {
-          auto &args = data->args.hipLaunchKernel;
-          dis->kernelRows_.emplace_back(data->correlation_id,
-                              domain,
-                              cid,
-                              processId(),
-                              systemThreadId(),
-                              timespec_to_ns(startTime),
-                              timespec_to_ns(endTime),
-                              args.function_address,
-                              nullptr,
-                              args.numBlocks.x,
-                              args.numBlocks.y,
-                              args.numBlocks.z,
-                              args.dimBlocks.x,
-                              args.dimBlocks.y,
-                              args.dimBlocks.z,
-                              args.sharedMemBytes,
-                              args.stream
-                            );
-          }
-          break;
-        case HIP_API_ID_hipHccModuleLaunchKernel:
-        case HIP_API_ID_hipModuleLaunchKernel:
-        case HIP_API_ID_hipExtModuleLaunchKernel:
-          {
-          auto &args = data->args.hipModuleLaunchKernel;
-          dis->kernelRows_.emplace_back(data->correlation_id,
-                              domain,
-                              cid,
-                              processId(),
-                              systemThreadId(),
-                              timespec_to_ns(startTime),
-                              timespec_to_ns(endTime),
-                              nullptr,
-                              args.f,
-                              args.gridDimX,
-                              args.gridDimY,
-                              args.gridDimZ,
-                              args.blockDimX,
-                              args.blockDimY,
-                              args.blockDimZ,
-                              args.sharedMemBytes,
-                              args.stream
-                            );
-          }
-          break;
-        case HIP_API_ID_hipLaunchCooperativeKernelMultiDevice:
-        case HIP_API_ID_hipExtLaunchMultiKernelMultiDevice:
-#if 0
-          {
-            auto &args = data->args.hipLaunchCooperativeKernelMultiDevice.launchParamsList__val;
-            dis->kernelRows_.emplace_back(data->correlation_id,
-                              domain,
-                              cid,
-                              processId(),
-                              systemThreadId(),
-                              timespec_to_ns(startTime),
-                              timespec_to_ns(endTime),
-                              args.function_address,
-                              nullptr,
-                              args.numBlocks.x,
-                              args.numBlocks.y,
-                              args.numBlocks.z,
-                              args.dimBlocks.x,
-                              args.dimBlocks.y,
-                              args.dimBlocks.z,
-                              args.sharedMemBytes,
-                              args.stream
-                            );
-          }
-#endif
-          break;
-        case HIP_API_ID_hipMalloc:
-            dis->mallocRows_.emplace_back(data->correlation_id,
-                              domain,
-                              cid,
-                              processId(),
-                              systemThreadId(),
-                              timespec_to_ns(startTime),
-                              timespec_to_ns(endTime),
-                              data->args.hipMalloc.ptr__val,
-                              data->args.hipMalloc.size
-                              );
-          break;
-        case HIP_API_ID_hipFree:
-            dis->mallocRows_.emplace_back(data->correlation_id,
-                              domain,
-                              cid,
-                              processId(),
-                              systemThreadId(),
-                              timespec_to_ns(startTime),
-                              timespec_to_ns(endTime),
-                              data->args.hipFree.ptr,
-                              0
-                              );
-          break;
-        case HIP_API_ID_hipMemcpy:
-          {
-            auto &args = data->args.hipMemcpy;
-            dis->copyRows_.emplace_back(data->correlation_id,
-                              domain,
-                              cid,
-                              processId(),
-                              systemThreadId(),
-                              timespec_to_ns(startTime),
-                              timespec_to_ns(endTime),
-                              args.src,
-                              args.dst,
-                              args.sizeBytes,
-                              args.kind,
-                              static_cast<hipStream_t>(0)  // use placeholder?
-                              );
-          }
-          break;
-        case HIP_API_ID_hipMemcpyAsync:
-        case HIP_API_ID_hipMemcpyWithStream:
-          {
-            auto &args = data->args.hipMemcpyAsync;
-            dis->copyRows_.emplace_back(data->correlation_id,
-                              domain,
-                              cid,
-                              processId(),
-                              systemThreadId(),
-                              timespec_to_ns(startTime),
-                              timespec_to_ns(endTime),
-                              args.src,
-                              args.dst,
-                              args.sizeBytes,
-                              args.kind,
-                              args.stream
-                              );
-          }
-          break;
-        default:
-          dis->rows_.emplace_back(data->correlation_id,
-                              domain,
-                              cid,
-                              processId(),
-                              systemThreadId(),
-                              timespec_to_ns(startTime),
-                              timespec_to_ns(endTime)
-                              );
-          break;
-      }
-    }
-  }
-}
-
-void RoctracerActivityApi::activity_callback(const char* begin, const char* end, void* arg)
-{
-  size_t size = end - begin;
-  uint8_t *buffer = (uint8_t*) malloc(size);
-  auto &gpuTraceBuffers = singleton().gpuTraceBuffers_;
-  memcpy(buffer, begin, size);
-  gpuTraceBuffers->emplace_back(buffer, size);
-}
-
-void RoctracerActivityApi::enableActivities(
-    const std::set<ActivityType>& selected_activities) {
-#ifdef HAS_ROCTRACER
-  if (!registered_) {
-    roctracer_set_properties(ACTIVITY_DOMAIN_HIP_API, nullptr);  // Magic encantation
-
-    // Set some api calls to ignore
-    loggedIds_.setInvertMode(true);  // Omit the specified api
-    loggedIds_.add("hipGetDevice");
-    loggedIds_.add("hipSetDevice");
-    loggedIds_.add("hipGetLastError");
-    loggedIds_.add("__hipPushCallConfiguration");
-    loggedIds_.add("__hipPopCallConfiguration");
-    loggedIds_.add("hipCtxSetCurrent");
-    loggedIds_.add("hipEventRecord");
-    loggedIds_.add("hipEventQuery");
-    loggedIds_.add("hipGetDeviceProperties");
-    loggedIds_.add("hipPeekAtLastError");
-    loggedIds_.add("hipModuleGetFunction");
-    loggedIds_.add("hipEventCreateWithFlags");
-
-    // Enable API callbacks
-    if (loggedIds_.invertMode() == true) {
-        // exclusion list - enable entire domain and turn off things in list
-        roctracer_enable_domain_callback(ACTIVITY_DOMAIN_HIP_API, api_callback, nullptr);
-        const std::unordered_map<uint32_t, uint32_t> &filter = loggedIds_.filterList();
-        for (auto it = filter.begin(); it != filter.end(); ++it) {
-            roctracer_disable_op_callback(ACTIVITY_DOMAIN_HIP_API, it->first);
-        }
-    }
-    else {
-        // inclusion list - only enable things in the list
-        const std::unordered_map<uint32_t, uint32_t> &filter = loggedIds_.filterList();
-        roctracer_disable_domain_callback(ACTIVITY_DOMAIN_HIP_API);
-        for (auto it = filter.begin(); it != filter.end(); ++it) {
-            roctracer_enable_op_callback(ACTIVITY_DOMAIN_HIP_API, it->first, api_callback, nullptr);
-        }
-    }
-    //roctracer_enable_domain_callback(ACTIVITY_DOMAIN_ROCTX, api_callback, nullptr);
-
-    // Allocate default tracing pool
-    roctracer_properties_t properties;
-    memset(&properties, 0, sizeof(roctracer_properties_t));
-    properties.buffer_size = 0x1000;
-    roctracer_open_pool(&properties);
-
-    // Enable async op collection
-    roctracer_properties_t hcc_cb_properties;
-    memset(&hcc_cb_properties, 0, sizeof(roctracer_properties_t));
-    hcc_cb_properties.buffer_size = 0x4000;
-    hcc_cb_properties.buffer_callback_fun = activity_callback;
-    roctracer_open_pool_expl(&hcc_cb_properties, &hccPool_);
-    roctracer_enable_domain_activity_expl(ACTIVITY_DOMAIN_HCC_OPS, hccPool_);
-
-    registered_ = true;
-  }
-
-  for (const auto& activity : selected_activities) {
-    if (activity == ActivityType::EXTERNAL_CORRELATION) {
-        externalCorrelationEnabled_ = true;
-    }
-  }
-
-  roctracer_start();
-#endif
-}
-
-void RoctracerActivityApi::disableActivities(
-    const std::set<ActivityType>& selected_activities) {
-#ifdef HAS_ROCTRACER
-  roctracer_stop();
-  roctracer_flush_activity_expl(hccPool_);
-
-  for (const auto& activity : selected_activities) {
-    if (activity == ActivityType::EXTERNAL_CORRELATION) {
-        externalCorrelationEnabled_ = false;
-    }
-  }
-#endif
-}
-
-void RoctracerActivityApi::endTracing() {
-  if (registered_ == true) {
-    roctracer_disable_domain_callback(ACTIVITY_DOMAIN_HIP_API);
-    //roctracer_disable_domain_callback(ACTIVITY_DOMAIN_ROCTX);
-
-    roctracer_disable_domain_activity(ACTIVITY_DOMAIN_HCC_OPS);
-    roctracer_close_pool_expl(hccPool_);
-  }
-}
-
-
-ApiIdList::ApiIdList()
-: invert_(true)
-{
-}
-
-void ApiIdList::add(std::string apiName)
-{
-  uint32_t cid = 0;
-  if (roctracer_op_code(ACTIVITY_DOMAIN_HIP_API, apiName.c_str(), &cid, nullptr) == ROCTRACER_STATUS_SUCCESS) {
-    filter_[cid] = 1;
-  }
-}
-void ApiIdList::remove(std::string apiName)
-{
-  uint32_t cid = 0;
-  if (roctracer_op_code(ACTIVITY_DOMAIN_HIP_API, apiName.c_str(), &cid, nullptr) == ROCTRACER_STATUS_SUCCESS) {
-    filter_.erase(cid);
-  }
-}
-
-bool ApiIdList::loadUserPrefs()
-{
-  // placeholder
-  return false;
-}
-bool ApiIdList::contains(uint32_t apiId)
-{
-  return (filter_.find(apiId) != filter_.end()) ? !invert_ : invert_;  // XOR
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityApi.h b/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityApi.h
deleted file mode 100644
index 28280253e7c..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityApi.h
+++ /dev/null
@@ -1,171 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <functional>
-#include <list>
-#include <memory>
-#include <set>
-#include <vector>
-#include <map>
-#include <unordered_map>
-#include <deque>
-#include <atomic>
-
-#ifdef HAS_ROCTRACER
-#include <roctracer.h>
-#include <roctracer_hcc.h>
-#include <roctracer_hip.h>
-#include <roctracer_ext.h>
-#include <roctracer_roctx.h>
-#endif
-
-#include "ActivityType.h"
-#include "GenericTraceActivity.h"
-#include "RoctracerActivityBuffer.h"
-
-
-namespace KINETO_NAMESPACE {
-
-using namespace libkineto;
-
-class ApiIdList
-{
-public:
-  ApiIdList();
-  bool invertMode() { return invert_; }
-  void setInvertMode(bool invert) { invert_ = invert; }
-  void add(std::string apiName);
-  void remove(std::string apiName);
-  bool loadUserPrefs();
-  bool contains(uint32_t apiId);
-  const std::unordered_map<uint32_t, uint32_t> &filterList() { return filter_; }
-
-private:
-  std::unordered_map<uint32_t, uint32_t> filter_;
-  bool invert_;
-};
-
-struct roctracerRow {
-  roctracerRow(uint64_t id, uint32_t domain, uint32_t cid, uint32_t pid
-             , uint32_t tid, uint64_t begin, uint64_t end)
-    : id(id), domain(domain), cid(cid), pid(pid), tid(tid), begin(begin), end(end) {}
-  uint64_t id;  // correlation_id
-  uint32_t domain;
-  uint32_t cid;
-  uint32_t pid;
-  uint32_t tid;
-  uint64_t begin;
-  uint64_t end;
-};
-
-struct kernelRow : public roctracerRow {
-  kernelRow(uint64_t id, uint32_t domain, uint32_t cid, uint32_t pid
-          , uint32_t tid, uint64_t begin, uint64_t end
-          , const void *faddr, hipFunction_t function
-          , unsigned int gx, unsigned int gy, unsigned int gz
-          , unsigned int wx, unsigned int wy, unsigned int wz
-          , size_t gss, hipStream_t stream)
-    : roctracerRow(id, domain, cid, pid, tid, begin, end), functionAddr(faddr)
-    , function(function), gridX(gx), gridY(gy), gridZ(gz)
-    , workgroupX(wx), workgroupY(wy), workgroupZ(wz), groupSegmentSize(gss)
-    , stream(stream) {}
-  const void* functionAddr;
-  hipFunction_t function;
-  unsigned int gridX;
-  unsigned int gridY;
-  unsigned int gridZ;
-  unsigned int workgroupX;
-  unsigned int workgroupY;
-  unsigned int workgroupZ;
-  size_t groupSegmentSize;
-  hipStream_t stream;
-};
-
-struct copyRow : public roctracerRow {
-  copyRow(uint64_t id, uint32_t domain, uint32_t cid, uint32_t pid
-             , uint32_t tid, uint64_t begin, uint64_t end
-             , const void* src, const void *dst, size_t size, hipMemcpyKind kind
-             , hipStream_t stream)
-    : roctracerRow(id, domain, cid, pid, tid, begin, end)
-    , src(src), dst(dst), size(size), kind(kind), stream(stream) {}
-  const void *src;
-  const void *dst;
-  size_t size;
-  hipMemcpyKind kind;
-  hipStream_t stream;
-};
-
-struct mallocRow : public roctracerRow {
-  mallocRow(uint64_t id, uint32_t domain, uint32_t cid, uint32_t pid
-             , uint32_t tid, uint64_t begin, uint64_t end
-             , const void* ptr, size_t size)
-    : roctracerRow(id, domain, cid, pid, tid, begin, end)
-    , ptr(ptr), size(size) {}
-  const void *ptr;
-  size_t size;
-};
-
-
-class RoctracerActivityApi {
- public:
-  enum CorrelationFlowType {
-    Default,
-    User
-  };
-
-  RoctracerActivityApi();
-  RoctracerActivityApi(const RoctracerActivityApi&) = delete;
-  RoctracerActivityApi& operator=(const RoctracerActivityApi&) = delete;
-
-  virtual ~RoctracerActivityApi();
-
-  static RoctracerActivityApi& singleton();
-
-  static void pushCorrelationID(int id, CorrelationFlowType type);
-  static void popCorrelationID(CorrelationFlowType type);
-
-  void enableActivities(
-    const std::set<ActivityType>& selected_activities);
-  void disableActivities(
-    const std::set<ActivityType>& selected_activities);
-  void clearActivities();
-
-  int processActivities(ActivityLogger& logger);
-
-  void setMaxBufferSize(int size);
-
-  std::atomic_bool stopCollection{false};
-
- private:
-  bool registered_{false};
-  void endTracing();
-
-#ifdef HAS_ROCTRACER
-  roctracer_pool_t *hccPool_{NULL};
-  static void api_callback(uint32_t domain, uint32_t cid, const void* callback_data, void* arg);
-  static void activity_callback(const char* begin, const char* end, void* arg);
-
-  //Name cache
-  uint32_t nextStringId_{2};
-  std::map<uint32_t, std::string> strings_;
-  std::map<std::string, uint32_t> reverseStrings_;
-  std::map<activity_correlation_id_t, uint32_t> kernelNames_;
-
-  ApiIdList loggedIds_;
-
-  // Api callback data
-  std::deque<roctracerRow> rows_;
-  std::deque<kernelRow> kernelRows_;
-  std::deque<copyRow> copyRows_;
-  std::deque<mallocRow> mallocRows_;
-  std::map<activity_correlation_id_t, GenericTraceActivity> kernelLaunches_;
-#endif
-
-  int maxGpuBufferCount_{0};
-  std::unique_ptr<std::list<RoctracerActivityBuffer>> gpuTraceBuffers_;
-  bool externalCorrelationEnabled_{true};
-};
-
-} // namespace KINETO_NAMESPACE
-
diff --git a/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityBuffer.h b/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityBuffer.h
deleted file mode 100644
index cd8a5709a84..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/RoctracerActivityBuffer.h
+++ /dev/null
@@ -1,30 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <stdlib.h>
-#include <sys/types.h>
-#include <unistd.h>
-#include <cstdint>
-
-namespace KINETO_NAMESPACE {
-
-class RoctracerActivityBuffer {
- public:
-  // data must be allocated using malloc.
-  // Ownership is transferred to this object.
-  RoctracerActivityBuffer(uint8_t* data, size_t validSize)
-      : data(data), validSize(validSize) {}
-
-  ~RoctracerActivityBuffer() {
-    free(data);
-  }
-
-  // Allocated by malloc
-  uint8_t* data{nullptr};
-
-  // Number of bytes used
-  size_t validSize;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/SampleListener.h b/plugins/tensorboard-plugins/libkineto/src/SampleListener.h
deleted file mode 100644
index bff86ad122a..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/SampleListener.h
+++ /dev/null
@@ -1,146 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <assert.h>
-#include <cmath>
-#include <iostream>
-#include <string>
-#include <vector>
-
-namespace KINETO_NAMESPACE {
-
-class Config;
-
-class SampleValue {
- public:
-  template <class T>
-  explicit SampleValue(T v) {
-    init<T>(v);
-  }
-
-  SampleValue(const SampleValue&) = default;
-  SampleValue& operator=(const SampleValue&) = delete;
-  SampleValue(SampleValue&&) = default;
-  SampleValue& operator=(SampleValue&&) = default;
-
-  bool isInt() const {
-    return type_ == INT64;
-  }
-
-  int64_t getInt() const {
-    assert(isInt());
-    return int_;
-  }
-
-  bool isDouble() const {
-    return type_ == DOUBLE;
-  }
-
-  double getDouble() const {
-    assert(isDouble());
-    return dbl_;
-  }
-
-  inline void operator*=(double x) {
-    assert(isDouble() || isInt());
-    if (isDouble()) {
-      dbl_ *= x;
-    } else {
-      int_ = std::round(int_ * x);
-    }
-  }
-
-  inline bool operator<(const SampleValue& o) const {
-    if (type_ != o.type_) {
-      return type_ < o.type_;
-    } else if (type_ == INT64) {
-      return int_ < o.int_;
-    } else if (type_ == DOUBLE) {
-      return dbl_ < o.dbl_;
-    }
-    assert(false);
-    return true;
-  }
-
-  void print(std::ostream& s) const {
-    if (type_ == INT64) {
-      s << int_;
-    } else if (type_ == DOUBLE) {
-      s << dbl_;
-    } else {
-      assert(false);
-    }
-  }
-
- private:
-  enum Type { INT64, DOUBLE };
-
-  template <typename T>
-  void init(T v);
-
-  Type type_{INT64};
-  union {
-    int64_t int_{0};
-    double dbl_;
-  };
-};
-
-template <>
-inline void SampleValue::init(uint64_t v) {
-  int_ = v, type_ = INT64;
-}
-template <>
-inline void SampleValue::init(int64_t v) {
-  int_ = v, type_ = INT64;
-}
-template <>
-inline void SampleValue::init(int v) {
-  int_ = v, type_ = INT64;
-}
-template <>
-inline void SampleValue::init(double v) {
-  dbl_ = v, type_ = DOUBLE;
-}
-
-inline std::ostream& operator<<(std::ostream& out, const SampleValue& s) {
-  s.print(out);
-  return out;
-}
-
-using PercentileList = std::vector<std::pair<int, SampleValue>>;
-
-struct Stat {
-  const std::string& name;
-  const PercentileList percentileValues;
-  SampleValue total;
-};
-
-struct Sample {
-  Sample(int stats_count) {
-    stats.reserve(stats_count);
-  }
-
-  // Offset in milliseconds from first sample in report
-  int deltaMsec;
-  std::vector<Stat> stats;
-};
-
-// Inherit from this to be notified of samples
-class SampleListener {
- public:
-  SampleListener(const SampleListener&) = delete;
-  SampleListener& operator=(const SampleListener&) = delete;
-
-  virtual ~SampleListener(){};
-
-  // Report bucketed & aggregated values for event
-  virtual void handleSample(int device, const Sample& sample, bool from_new_version) = 0;
-
-  virtual void update(const Config& config) = 0;
-
- protected:
-  SampleListener() = default;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/ScopeExit.h b/plugins/tensorboard-plugins/libkineto/src/ScopeExit.h
deleted file mode 100644
index b9a6bc83ef9..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ScopeExit.h
+++ /dev/null
@@ -1,29 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-// Implement a simple scope handler allowing a function to release
-// resources when an error or exception occurs
-
-template <typename T>
-class ScopeExit {
- public:
-  explicit ScopeExit(T t) : t(t) {}
-  ~ScopeExit() {
-    t();
-  }
-  T t;
-};
-
-template <typename T>
-ScopeExit<T> makeScopeExit(T t) {
-  return ScopeExit<T>(t);
-};
-
-// Add a level of indirection so __LINE__ is expanded
-#define __kINETO_CONCAT(name, line) name##line
-#define ANON_VAR(name, line) __kINETO_CONCAT(name, line)
-
-#define SCOPE_EXIT(func)                                      \
-  const auto ANON_VAR(SCOPE_BLOCK, __LINE__) =                \
-      makeScopeExit([=]() { func; })
diff --git a/plugins/tensorboard-plugins/libkineto/src/ThreadUtil.cpp b/plugins/tensorboard-plugins/libkineto/src/ThreadUtil.cpp
deleted file mode 100644
index 0f67d54d585..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/ThreadUtil.cpp
+++ /dev/null
@@ -1,203 +0,0 @@
-#include "ThreadUtil.h"
-
-#ifndef _MSC_VER
-#include <pthread.h>
-#include <unistd.h>
-#include <sys/syscall.h>
-#include <sys/types.h>
-#else // _MSC_VER
-#include <locale>
-#include <codecvt>
-#define WIN32_LEAN_AND_MEAN
-#define NOGDI
-#include <windows.h>
-#include <processthreadsapi.h>
-#undef ERROR
-#endif // _MSC_VER
-
-#ifdef __ANDROID__
-#include <sys/prctl.h>
-#endif
-
-#include <fmt/format.h>
-#include <iostream>
-#include <string>
-
-namespace libkineto {
-
-namespace {
-thread_local int32_t _pid = 0;
-thread_local int32_t _tid = 0;
-thread_local int32_t _sysTid = 0;
-}
-
-int32_t processId() {
-  if (!_pid) {
-#ifndef _MSC_VER
-    _pid = (int32_t)getpid();
-#else
-    _pid = (int32_t)GetCurrentProcessId();
-#endif
-  }
-  return _pid;
-}
-
-int32_t systemThreadId() {
-  if (!_sysTid) {
-#ifdef __APPLE__
-    _sysTid = (int32_t)syscall(SYS_thread_selfid);
-#elif defined _MSC_VER
-    _sysTid = (int32_t)GetCurrentThreadId();
-#else
-    _sysTid = (int32_t)syscall(SYS_gettid);
-#endif
-  }
-  return _sysTid;
-}
-
-int32_t threadId() {
-  if (!_tid) {
-#ifdef __APPLE__
-    uint64_t tid;
-    pthread_threadid_np(nullptr, &tid);
-    _tid = tid;
-#elif defined _MSC_VER
-  _tid = (int32_t)GetCurrentThreadId();
-#else
-  pthread_t pth = pthread_self();
-  int32_t* ptr = reinterpret_cast<int32_t*>(&pth);
-  _tid = *ptr;
-#endif
-  }
-  return _tid;
-}
-
-namespace {
-static constexpr size_t kMaxThreadNameLength = 16;
-
-static constexpr const char* basename(const char* s, int off = 0) {
-  return !s[off]
-      ? s
-      : s[off] == '/' ? basename(&s[off + 1]) : basename(s, off + 1);
-}
-#if defined(_MSC_VER)
-void *getKernel32Func(const char* procName) {
-  return GetProcAddress(GetModuleHandleA("KERNEL32.DLL"), procName);
-}
-#endif
-}
-
-bool setThreadName(const std::string& name) {
-#ifdef __APPLE__
-  return 0 == pthread_setname_np(name.c_str());
-#elif defined _MSC_VER
-  // Per https://docs.microsoft.com/en-us/windows/win32/api/processthreadsapi/nf-processthreadsapi-setthreaddescription
-  // Use runtime linking to set thread description
-  static auto _SetThreadDescription = reinterpret_cast<decltype(&SetThreadDescription)>(getKernel32Func("SetThreadDescription"));
-  if (!_SetThreadDescription) {
-    return false;
-  }
-  std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> conv;
-  std::wstring wname = conv.from_bytes(name);
-  HRESULT hr = _SetThreadDescription(GetCurrentThread(), wname.c_str());
-  return SUCCEEDED(hr);
-#else
-  return 0 == pthread_setname_np(pthread_self(), name.c_str());
-#endif
-}
-
-std::string getThreadName() {
-#ifndef _MSC_VER
-  char buf[kMaxThreadNameLength] = "";
-  if (
-#ifndef __ANDROID__
-    pthread_getname_np(pthread_self(), buf, kMaxThreadNameLength) != 0
-#else
-    prctl(PR_GET_NAME, buf, kMaxThreadNameLength) != 0
-#endif
-  ) {
-    return "Unknown";
-  }
-  return buf;
-#else // _MSC_VER
-  static auto _GetThreadDescription = reinterpret_cast<decltype(&GetThreadDescription)>(getKernel32Func("GetThreadDescription"));
-  if (!_GetThreadDescription) {
-    return "Unknown";
-  }
-  PWSTR data;
-  HRESULT hr = _GetThreadDescription(GetCurrentThread(), &data);
-  if (!SUCCEEDED(hr)) {
-    return "";
-  }
-  std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> conv;
-  std::string name = conv.to_bytes(data);
-  LocalFree(data);
-  return name;
-#endif
-}
-
-// Linux:
-// Extract process name from /proc/pid/cmdline. This does not have
-// the 16 character limit that /proc/pid/status and /prod/pid/comm has.
-std::string processName(int32_t pid) {
-#ifdef __linux__
-  FILE* cmdfile = fopen(fmt::format("/proc/{}/cmdline", pid).c_str(), "r");
-  if (cmdfile != nullptr) {
-    char* command = nullptr;
-    int scanned = fscanf(cmdfile, "%ms", &command);
-    fclose(cmdfile);
-    if (scanned > 0 && command) {
-      std::string ret(basename(command));
-      free(command);
-      return ret;
-    }
-  }
-  std::cerr << "Failed to read process name for pid " << pid << std::endl;
-#endif
-  return "";
-}
-
-// Max number of parent pids to collect, just for extra safeguarding.
-constexpr int kMaxParentPids = 10;
-
-// Return a pair of <parent_pid, command_of_current_pid>
-static std::pair<int32_t, std::string> parentPidAndCommand(int32_t pid) {
-#ifdef __linux__
-  FILE* statfile = fopen(fmt::format("/proc/{}/stat", pid).c_str(), "r");
-  if (statfile == nullptr) {
-    return std::make_pair(0, "");
-  }
-  int32_t parent_pid;
-  char* command = nullptr;
-  int scanned = fscanf(statfile, "%*d (%m[^)]) %*c %d", &command, &parent_pid);
-  fclose(statfile);
-  std::pair<int32_t, std::string> ret;
-  if (scanned == 2) {
-    ret = std::make_pair(parent_pid, std::string(command));
-  } else {
-    std::cerr << "Failed to parse /proc/" << pid << "/stat" << std::endl;
-    ret = std::make_pair(0, "");
-  }
-
-  // The 'm' character in the format tells fscanf to allocate memory
-  // for the parsed string, which we need to free here.
-  free(command);
-  return ret;
-#else
-  return std::make_pair(0, "");
-#endif
-}
-
-std::vector<std::pair<int32_t, std::string>> pidCommandPairsOfAncestors() {
-  std::vector<std::pair<int32_t, std::string>> pairs;
-  pairs.reserve(kMaxParentPids + 1);
-  int32_t curr_pid = processId();
-  for (int i = 0; i <= kMaxParentPids && curr_pid > 1; i++) {
-    std::pair<int32_t, std::string> ppid_and_comm = parentPidAndCommand(curr_pid);
-    pairs.push_back(std::make_pair(curr_pid, ppid_and_comm.second));
-    curr_pid = ppid_and_comm.first;
-  }
-  return pairs;
-}
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/WeakSymbols.cpp b/plugins/tensorboard-plugins/libkineto/src/WeakSymbols.cpp
deleted file mode 100644
index 540a5ac8f97..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/WeakSymbols.cpp
+++ /dev/null
@@ -1,12 +0,0 @@
-#include <stdexcept>
-
-#ifndef _MSC_VER
-extern "C" {
-// This function is needed to avoid superfluous dependency on GNU OpenMP library when cuPTI is linked statically
-// For more details see https://github.com/pytorch/pytorch/issues/51026
-__attribute__((weak)) int acc_get_device_type() {
-  throw std::runtime_error("Dummy implementation of acc_get_device_type is not supposed to be called!");
-}
-
-} // extern "C"
-#endif
diff --git a/plugins/tensorboard-plugins/libkineto/src/cupti_call.h b/plugins/tensorboard-plugins/libkineto/src/cupti_call.h
deleted file mode 100644
index fd6ebae7691..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/cupti_call.h
+++ /dev/null
@@ -1,33 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <fmt/format.h>
-
-#ifdef HAS_CUPTI
-
-#include <cupti.h>
-
-#define CUPTI_CALL(call)                           \
-  [&]() -> CUptiResult {                           \
-    CUptiResult _status_ = call;                   \
-    if (_status_ != CUPTI_SUCCESS) {               \
-      const char* _errstr_ = nullptr;              \
-      cuptiGetResultString(_status_, &_errstr_);   \
-      LOG(WARNING) << fmt::format(                 \
-          "function {} failed with error {} ({})", \
-          #call,                                   \
-          _errstr_,                                \
-          (int)_status_);                          \
-    }                                              \
-    return _status_;                               \
-  }()
-
-#define CUPTI_CALL_NOWARN(call) call
-
-#else
-
-#define CUPTI_CALL(call) call
-#define CUPTI_CALL_NOWARN(call) call
-
-#endif // HAS_CUPTI
diff --git a/plugins/tensorboard-plugins/libkineto/src/cupti_strings.cpp b/plugins/tensorboard-plugins/libkineto/src/cupti_strings.cpp
deleted file mode 100644
index 4535273a277..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/cupti_strings.cpp
+++ /dev/null
@@ -1,502 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "cupti_strings.h"
-
-namespace libkineto {
-
-const char* memcpyKindString(
-    CUpti_ActivityMemcpyKind kind) {
-  switch (kind) {
-    case CUPTI_ACTIVITY_MEMCPY_KIND_HTOD:
-      return "HtoD";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_DTOH:
-      return "DtoH";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_HTOA:
-      return "HtoA";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_ATOH:
-      return "AtoH";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_ATOA:
-      return "AtoA";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_ATOD:
-      return "AtoD";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_DTOA:
-      return "DtoA";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_DTOD:
-      return "DtoD";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_HTOH:
-      return "HtoH";
-    case CUPTI_ACTIVITY_MEMCPY_KIND_PTOP:
-      return "PtoP";
-    default:
-      break;
-  }
-  return "<unknown>";
-}
-
-const char* memoryKindString(
-    CUpti_ActivityMemoryKind kind) {
-  switch (kind) {
-    case CUPTI_ACTIVITY_MEMORY_KIND_UNKNOWN:
-      return "Unknown";
-    case CUPTI_ACTIVITY_MEMORY_KIND_PAGEABLE:
-      return "Pageable";
-    case CUPTI_ACTIVITY_MEMORY_KIND_PINNED:
-      return "Pinned";
-    case CUPTI_ACTIVITY_MEMORY_KIND_DEVICE:
-      return "Device";
-    case CUPTI_ACTIVITY_MEMORY_KIND_ARRAY:
-      return "Array";
-    case CUPTI_ACTIVITY_MEMORY_KIND_MANAGED:
-      return "Managed";
-    case CUPTI_ACTIVITY_MEMORY_KIND_DEVICE_STATIC:
-      return "Device Static";
-    case CUPTI_ACTIVITY_MEMORY_KIND_MANAGED_STATIC:
-      return "Managed Static";
-    case CUPTI_ACTIVITY_MEMORY_KIND_FORCE_INT:
-      return "Force Int";
-    default:
-      return "Unrecognized";
-  }
-}
-
-const char* overheadKindString(
-    CUpti_ActivityOverheadKind kind) {
-  switch (kind) {
-    case CUPTI_ACTIVITY_OVERHEAD_UNKNOWN:
-      return "Unknown";
-    case CUPTI_ACTIVITY_OVERHEAD_DRIVER_COMPILER:
-      return "Driver Compiler";
-    case CUPTI_ACTIVITY_OVERHEAD_CUPTI_BUFFER_FLUSH:
-      return "Buffer Flush";
-    case CUPTI_ACTIVITY_OVERHEAD_CUPTI_INSTRUMENTATION:
-      return "Instrumentation";
-    case CUPTI_ACTIVITY_OVERHEAD_CUPTI_RESOURCE:
-      return "Resource";
-    case CUPTI_ACTIVITY_OVERHEAD_FORCE_INT:
-      return "Force Int";
-    default:
-      return "Unrecognized";
-  }
-}
-
-
-
-static const char* runtimeCbidNames[] = {
-    "INVALID",
-    "cudaDriverGetVersion",
-    "cudaRuntimeGetVersion",
-    "cudaGetDeviceCount",
-    "cudaGetDeviceProperties",
-    "cudaChooseDevice",
-    "cudaGetChannelDesc",
-    "cudaCreateChannelDesc",
-    "cudaConfigureCall",
-    "cudaSetupArgument",
-    "cudaGetLastError",
-    "cudaPeekAtLastError",
-    "cudaGetErrorString",
-    "cudaLaunch",
-    "cudaFuncSetCacheConfig",
-    "cudaFuncGetAttributes",
-    "cudaSetDevice",
-    "cudaGetDevice",
-    "cudaSetValidDevices",
-    "cudaSetDeviceFlags",
-    "cudaMalloc",
-    "cudaMallocPitch",
-    "cudaFree",
-    "cudaMallocArray",
-    "cudaFreeArray",
-    "cudaMallocHost",
-    "cudaFreeHost",
-    "cudaHostAlloc",
-    "cudaHostGetDevicePointer",
-    "cudaHostGetFlags",
-    "cudaMemGetInfo",
-    "cudaMemcpy",
-    "cudaMemcpy2D",
-    "cudaMemcpyToArray",
-    "cudaMemcpy2DToArray",
-    "cudaMemcpyFromArray",
-    "cudaMemcpy2DFromArray",
-    "cudaMemcpyArrayToArray",
-    "cudaMemcpy2DArrayToArray",
-    "cudaMemcpyToSymbol",
-    "cudaMemcpyFromSymbol",
-    "cudaMemcpyAsync",
-    "cudaMemcpyToArrayAsync",
-    "cudaMemcpyFromArrayAsync",
-    "cudaMemcpy2DAsync",
-    "cudaMemcpy2DToArrayAsync",
-    "cudaMemcpy2DFromArrayAsync",
-    "cudaMemcpyToSymbolAsync",
-    "cudaMemcpyFromSymbolAsync",
-    "cudaMemset",
-    "cudaMemset2D",
-    "cudaMemsetAsync",
-    "cudaMemset2DAsync",
-    "cudaGetSymbolAddress",
-    "cudaGetSymbolSize",
-    "cudaBindTexture",
-    "cudaBindTexture2D",
-    "cudaBindTextureToArray",
-    "cudaUnbindTexture",
-    "cudaGetTextureAlignmentOffset",
-    "cudaGetTextureReference",
-    "cudaBindSurfaceToArray",
-    "cudaGetSurfaceReference",
-    "cudaGLSetGLDevice",
-    "cudaGLRegisterBufferObject",
-    "cudaGLMapBufferObject",
-    "cudaGLUnmapBufferObject",
-    "cudaGLUnregisterBufferObject",
-    "cudaGLSetBufferObjectMapFlags",
-    "cudaGLMapBufferObjectAsync",
-    "cudaGLUnmapBufferObjectAsync",
-    "cudaWGLGetDevice",
-    "cudaGraphicsGLRegisterImage",
-    "cudaGraphicsGLRegisterBuffer",
-    "cudaGraphicsUnregisterResource",
-    "cudaGraphicsResourceSetMapFlags",
-    "cudaGraphicsMapResources",
-    "cudaGraphicsUnmapResources",
-    "cudaGraphicsResourceGetMappedPointer",
-    "cudaGraphicsSubResourceGetMappedArray",
-    "cudaVDPAUGetDevice",
-    "cudaVDPAUSetVDPAUDevice",
-    "cudaGraphicsVDPAURegisterVideoSurface",
-    "cudaGraphicsVDPAURegisterOutputSurface",
-    "cudaD3D11GetDevice",
-    "cudaD3D11GetDevices",
-    "cudaD3D11SetDirect3DDevice",
-    "cudaGraphicsD3D11RegisterResource",
-    "cudaD3D10GetDevice",
-    "cudaD3D10GetDevices",
-    "cudaD3D10SetDirect3DDevice",
-    "cudaGraphicsD3D10RegisterResource",
-    "cudaD3D10RegisterResource",
-    "cudaD3D10UnregisterResource",
-    "cudaD3D10MapResources",
-    "cudaD3D10UnmapResources",
-    "cudaD3D10ResourceSetMapFlags",
-    "cudaD3D10ResourceGetSurfaceDimensions",
-    "cudaD3D10ResourceGetMappedArray",
-    "cudaD3D10ResourceGetMappedPointer",
-    "cudaD3D10ResourceGetMappedSize",
-    "cudaD3D10ResourceGetMappedPitch",
-    "cudaD3D9GetDevice",
-    "cudaD3D9GetDevices",
-    "cudaD3D9SetDirect3DDevice",
-    "cudaD3D9GetDirect3DDevice",
-    "cudaGraphicsD3D9RegisterResource",
-    "cudaD3D9RegisterResource",
-    "cudaD3D9UnregisterResource",
-    "cudaD3D9MapResources",
-    "cudaD3D9UnmapResources",
-    "cudaD3D9ResourceSetMapFlags",
-    "cudaD3D9ResourceGetSurfaceDimensions",
-    "cudaD3D9ResourceGetMappedArray",
-    "cudaD3D9ResourceGetMappedPointer",
-    "cudaD3D9ResourceGetMappedSize",
-    "cudaD3D9ResourceGetMappedPitch",
-    "cudaD3D9Begin",
-    "cudaD3D9End",
-    "cudaD3D9RegisterVertexBuffer",
-    "cudaD3D9UnregisterVertexBuffer",
-    "cudaD3D9MapVertexBuffer",
-    "cudaD3D9UnmapVertexBuffer",
-    "cudaThreadExit",
-    "cudaSetDoubleForDevice",
-    "cudaSetDoubleForHost",
-    "cudaThreadSynchronize",
-    "cudaThreadGetLimit",
-    "cudaThreadSetLimit",
-    "cudaStreamCreate",
-    "cudaStreamDestroy",
-    "cudaStreamSynchronize",
-    "cudaStreamQuery",
-    "cudaEventCreate",
-    "cudaEventCreateWithFlags",
-    "cudaEventRecord",
-    "cudaEventDestroy",
-    "cudaEventSynchronize",
-    "cudaEventQuery",
-    "cudaEventElapsedTime",
-    "cudaMalloc3D",
-    "cudaMalloc3DArray",
-    "cudaMemset3D",
-    "cudaMemset3DAsync",
-    "cudaMemcpy3D",
-    "cudaMemcpy3DAsync",
-    "cudaThreadSetCacheConfig",
-    "cudaStreamWaitEvent",
-    "cudaD3D11GetDirect3DDevice",
-    "cudaD3D10GetDirect3DDevice",
-    "cudaThreadGetCacheConfig",
-    "cudaPointerGetAttributes",
-    "cudaHostRegister",
-    "cudaHostUnregister",
-    "cudaDeviceCanAccessPeer",
-    "cudaDeviceEnablePeerAccess",
-    "cudaDeviceDisablePeerAccess",
-    "cudaPeerRegister",
-    "cudaPeerUnregister",
-    "cudaPeerGetDevicePointer",
-    "cudaMemcpyPeer",
-    "cudaMemcpyPeerAsync",
-    "cudaMemcpy3DPeer",
-    "cudaMemcpy3DPeerAsync",
-    "cudaDeviceReset",
-    "cudaDeviceSynchronize",
-    "cudaDeviceGetLimit",
-    "cudaDeviceSetLimit",
-    "cudaDeviceGetCacheConfig",
-    "cudaDeviceSetCacheConfig",
-    "cudaProfilerInitialize",
-    "cudaProfilerStart",
-    "cudaProfilerStop",
-    "cudaDeviceGetByPCIBusId",
-    "cudaDeviceGetPCIBusId",
-    "cudaGLGetDevices",
-    "cudaIpcGetEventHandle",
-    "cudaIpcOpenEventHandle",
-    "cudaIpcGetMemHandle",
-    "cudaIpcOpenMemHandle",
-    "cudaIpcCloseMemHandle",
-    "cudaArrayGetInfo",
-    "cudaFuncSetSharedMemConfig",
-    "cudaDeviceGetSharedMemConfig",
-    "cudaDeviceSetSharedMemConfig",
-    "cudaCreateTextureObject",
-    "cudaDestroyTextureObject",
-    "cudaGetTextureObjectResourceDesc",
-    "cudaGetTextureObjectTextureDesc",
-    "cudaCreateSurfaceObject",
-    "cudaDestroySurfaceObject",
-    "cudaGetSurfaceObjectResourceDesc",
-    "cudaMallocMipmappedArray",
-    "cudaGetMipmappedArrayLevel",
-    "cudaFreeMipmappedArray",
-    "cudaBindTextureToMipmappedArray",
-    "cudaGraphicsResourceGetMappedMipmappedArray",
-    "cudaStreamAddCallback",
-    "cudaStreamCreateWithFlags",
-    "cudaGetTextureObjectResourceViewDesc",
-    "cudaDeviceGetAttribute",
-    "cudaStreamDestroy",
-    "cudaStreamCreateWithPriority",
-    "cudaStreamGetPriority",
-    "cudaStreamGetFlags",
-    "cudaDeviceGetStreamPriorityRange",
-    "cudaMallocManaged",
-    "cudaOccupancyMaxActiveBlocksPerMultiprocessor",
-    "cudaStreamAttachMemAsync",
-    "cudaGetErrorName",
-    "cudaOccupancyMaxActiveBlocksPerMultiprocessor",
-    "cudaLaunchKernel",
-    "cudaGetDeviceFlags",
-    "cudaLaunch_ptsz",
-    "cudaLaunchKernel_ptsz",
-    "cudaMemcpy_ptds",
-    "cudaMemcpy2D_ptds",
-    "cudaMemcpyToArray_ptds",
-    "cudaMemcpy2DToArray_ptds",
-    "cudaMemcpyFromArray_ptds",
-    "cudaMemcpy2DFromArray_ptds",
-    "cudaMemcpyArrayToArray_ptds",
-    "cudaMemcpy2DArrayToArray_ptds",
-    "cudaMemcpyToSymbol_ptds",
-    "cudaMemcpyFromSymbol_ptds",
-    "cudaMemcpyAsync_ptsz",
-    "cudaMemcpyToArrayAsync_ptsz",
-    "cudaMemcpyFromArrayAsync_ptsz",
-    "cudaMemcpy2DAsync_ptsz",
-    "cudaMemcpy2DToArrayAsync_ptsz",
-    "cudaMemcpy2DFromArrayAsync_ptsz",
-    "cudaMemcpyToSymbolAsync_ptsz",
-    "cudaMemcpyFromSymbolAsync_ptsz",
-    "cudaMemset_ptds",
-    "cudaMemset2D_ptds",
-    "cudaMemsetAsync_ptsz",
-    "cudaMemset2DAsync_ptsz",
-    "cudaStreamGetPriority_ptsz",
-    "cudaStreamGetFlags_ptsz",
-    "cudaStreamSynchronize_ptsz",
-    "cudaStreamQuery_ptsz",
-    "cudaStreamAttachMemAsync_ptsz",
-    "cudaEventRecord_ptsz",
-    "cudaMemset3D_ptds",
-    "cudaMemset3DAsync_ptsz",
-    "cudaMemcpy3D_ptds",
-    "cudaMemcpy3DAsync_ptsz",
-    "cudaStreamWaitEvent_ptsz",
-    "cudaStreamAddCallback_ptsz",
-    "cudaMemcpy3DPeer_ptds",
-    "cudaMemcpy3DPeerAsync_ptsz",
-    "cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags",
-    "cudaMemPrefetchAsync",
-    "cudaMemPrefetchAsync_ptsz",
-    "cudaMemAdvise",
-    "cudaDeviceGetP2PAttribute",
-    "cudaGraphicsEGLRegisterImage",
-    "cudaEGLStreamConsumerConnect",
-    "cudaEGLStreamConsumerDisconnect",
-    "cudaEGLStreamConsumerAcquireFrame",
-    "cudaEGLStreamConsumerReleaseFrame",
-    "cudaEGLStreamProducerConnect",
-    "cudaEGLStreamProducerDisconnect",
-    "cudaEGLStreamProducerPresentFrame",
-    "cudaEGLStreamProducerReturnFrame",
-    "cudaGraphicsResourceGetMappedEglFrame",
-    "cudaMemRangeGetAttribute",
-    "cudaMemRangeGetAttributes",
-    "cudaEGLStreamConsumerConnectWithFlags",
-    "cudaLaunchCooperativeKernel",
-    "cudaLaunchCooperativeKernel_ptsz",
-    "cudaEventCreateFromEGLSync",
-    "cudaLaunchCooperativeKernelMultiDevice",
-    "cudaFuncSetAttribute",
-    "cudaImportExternalMemory",
-    "cudaExternalMemoryGetMappedBuffer",
-    "cudaExternalMemoryGetMappedMipmappedArray",
-    "cudaDestroyExternalMemory",
-    "cudaImportExternalSemaphore",
-    "cudaSignalExternalSemaphoresAsync",
-    "cudaSignalExternalSemaphoresAsync_ptsz",
-    "cudaWaitExternalSemaphoresAsync",
-    "cudaWaitExternalSemaphoresAsync_ptsz",
-    "cudaDestroyExternalSemaphore",
-    "cudaLaunchHostFunc",
-    "cudaLaunchHostFunc_ptsz",
-    "cudaGraphCreate",
-    "cudaGraphKernelNodeGetParams",
-    "cudaGraphKernelNodeSetParams",
-    "cudaGraphAddKernelNode",
-    "cudaGraphAddMemcpyNode",
-    "cudaGraphMemcpyNodeGetParams",
-    "cudaGraphMemcpyNodeSetParams",
-    "cudaGraphAddMemsetNode",
-    "cudaGraphMemsetNodeGetParams",
-    "cudaGraphMemsetNodeSetParams",
-    "cudaGraphAddHostNode",
-    "cudaGraphHostNodeGetParams",
-    "cudaGraphAddChildGraphNode",
-    "cudaGraphChildGraphNodeGetGraph",
-    "cudaGraphAddEmptyNode",
-    "cudaGraphClone",
-    "cudaGraphNodeFindInClone",
-    "cudaGraphNodeGetType",
-    "cudaGraphGetRootNodes",
-    "cudaGraphNodeGetDependencies",
-    "cudaGraphNodeGetDependentNodes",
-    "cudaGraphAddDependencies",
-    "cudaGraphRemoveDependencies",
-    "cudaGraphDestroyNode",
-    "cudaGraphInstantiate",
-    "cudaGraphLaunch",
-    "cudaGraphLaunch_ptsz",
-    "cudaGraphExecDestroy",
-    "cudaGraphDestroy",
-    "cudaStreamBeginCapture",
-    "cudaStreamBeginCapture_ptsz",
-    "cudaStreamIsCapturing",
-    "cudaStreamIsCapturing_ptsz",
-    "cudaStreamEndCapture",
-    "cudaStreamEndCapture_ptsz",
-    "cudaGraphHostNodeSetParams",
-    "cudaGraphGetNodes",
-    "cudaGraphGetEdges",
-    "cudaStreamGetCaptureInfo",
-    "cudaStreamGetCaptureInfo_ptsz",
-    "cudaGraphExecKernelNodeSetParams",
-    "cudaThreadExchangeStreamCaptureMode",
-    "cudaDeviceGetNvSciSyncAttributes",
-    "cudaOccupancyAvailableDynamicSMemPerBlock",
-    "cudaStreamSetFlags",
-    "cudaStreamSetFlags_ptsz",
-    "cudaGraphExecMemcpyNodeSetParams",
-    "cudaGraphExecMemsetNodeSetParams",
-    "cudaGraphExecHostNodeSetParams",
-    "cudaGraphExecUpdate",
-    "cudaGetFuncBySymbol",
-    "cudaCtxResetPersistingL2Cache",
-    "cudaGraphKernelNodeCopyAttributes",
-    "cudaGraphKernelNodeGetAttribute",
-    "cudaGraphKernelNodeSetAttribute",
-    "cudaStreamCopyAttributes",
-    "cudaStreamCopyAttributes_ptsz",
-    "cudaStreamGetAttribute",
-    "cudaStreamGetAttribute_ptsz",
-    "cudaStreamSetAttribute",
-    "cudaStreamSetAttribute_ptsz",
-    "cudaDeviceGetTexture1DLinearMaxWidth",
-    "cudaGraphUpload",
-    "cudaGraphUpload_ptsz",
-    "cudaGraphAddMemcpyNodeToSymbol",
-    "cudaGraphAddMemcpyNodeFromSymbol",
-    "cudaGraphAddMemcpyNode1D",
-    "cudaGraphMemcpyNodeSetParamsToSymbol",
-    "cudaGraphMemcpyNodeSetParamsFromSymbol",
-    "cudaGraphMemcpyNodeSetParams1D",
-    "cudaGraphExecMemcpyNodeSetParamsToSymbol",
-    "cudaGraphExecMemcpyNodeSetParamsFromSymbol",
-    "cudaGraphExecMemcpyNodeSetParams1D",
-    "cudaArrayGetSparseProperties",
-    "cudaMipmappedArrayGetSparseProperties",
-    "cudaGraphExecChildGraphNodeSetParams",
-    "cudaGraphAddEventRecordNode",
-    "cudaGraphEventRecordNodeGetEvent",
-    "cudaGraphEventRecordNodeSetEvent",
-    "cudaGraphAddEventWaitNode",
-    "cudaGraphEventWaitNodeGetEvent",
-    "cudaGraphEventWaitNodeSetEvent",
-    "cudaGraphExecEventRecordNodeSetEvent",
-    "cudaGraphExecEventWaitNodeSetEvent",
-    "cudaEventRecordWithFlags",
-    "cudaEventRecordWithFlags_ptsz",
-    "cudaDeviceGetDefaultMemPool",
-    "cudaMallocAsync",
-    "cudaMallocAsync_ptsz",
-    "cudaFreeAsync",
-    "cudaFreeAsync_ptsz",
-    "cudaMemPoolTrimTo",
-    "cudaMemPoolSetAttribute",
-    "cudaMemPoolGetAttribute",
-    "cudaMemPoolSetAccess",
-    "cudaArrayGetPlane",
-    "cudaMemPoolGetAccess",
-    "cudaMemPoolCreate",
-    "cudaMemPoolDestroy",
-    "cudaDeviceSetMemPool",
-    "cudaDeviceGetMemPool",
-    "cudaMemPoolExportToShareableHandle",
-    "cudaMemPoolImportFromShareableHandle",
-    "cudaMemPoolExportPointer",
-    "cudaMemPoolImportPointer",
-    "cudaMallocFromPoolAsync",
-    "cudaMallocFromPoolAsync_ptsz",
-    "cudaSignalExternalSemaphoresAsync",
-    "cudaSignalExternalSemaphoresAsync",
-    "cudaWaitExternalSemaphoresAsync",
-    "cudaWaitExternalSemaphoresAsync",
-    "cudaGraphAddExternalSemaphoresSignalNode",
-    "cudaGraphExternalSemaphoresSignalNodeGetParams",
-    "cudaGraphExternalSemaphoresSignalNodeSetParams",
-    "cudaGraphAddExternalSemaphoresWaitNode",
-    "cudaGraphExternalSemaphoresWaitNodeGetParams",
-    "cudaGraphExternalSemaphoresWaitNodeSetParams",
-    "cudaGraphExecExternalSemaphoresSignalNodeSetParams",
-    "cudaGraphExecExternalSemaphoresWaitNodeSetParams",
-    "SIZE"
-};
-
-const char* runtimeCbidName(CUpti_CallbackId cbid) {
-  constexpr int names_size =
-      sizeof(runtimeCbidNames) / sizeof(runtimeCbidNames[0]);
-  if (cbid < 0 || cbid >= names_size) {
-    return runtimeCbidNames[CUPTI_RUNTIME_TRACE_CBID_INVALID];
-  }
-  return runtimeCbidNames[cbid];
-}
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/cupti_strings.h b/plugins/tensorboard-plugins/libkineto/src/cupti_strings.h
deleted file mode 100644
index bbfebb98364..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/cupti_strings.h
+++ /dev/null
@@ -1,14 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <cupti.h>
-
-namespace libkineto {
-
-const char* memoryKindString(CUpti_ActivityMemoryKind kind);
-const char* memcpyKindString(CUpti_ActivityMemcpyKind kind);
-const char* runtimeCbidName(CUpti_CallbackId cbid);
-const char* overheadKindString(CUpti_ActivityOverheadKind kind);
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/init.cpp b/plugins/tensorboard-plugins/libkineto/src/init.cpp
deleted file mode 100644
index 4e1022485ac..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/init.cpp
+++ /dev/null
@@ -1,139 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <memory>
-#include <mutex>
-
-#include "ActivityProfilerProxy.h"
-#include "Config.h"
-#ifdef HAS_CUPTI
-#include "CuptiCallbackApi.h"
-#include "CuptiActivityApi.h"
-#include "EventProfilerController.h"
-#endif
-#include "cupti_call.h"
-#include "libkineto.h"
-
-#include "Logger.h"
-
-namespace KINETO_NAMESPACE {
-
-#ifdef HAS_CUPTI
-static bool initialized = false;
-static std::mutex initMutex;
-
-static void initProfilers(
-    CUpti_CallbackDomain /*domain*/,
-    CUpti_CallbackId /*cbid*/,
-    const CUpti_CallbackData* cbInfo) {
-  CUpti_ResourceData* d = (CUpti_ResourceData*)cbInfo;
-  CUcontext ctx = d->context;
-
-  VLOG(0) << "CUDA Context created";
-  std::lock_guard<std::mutex> lock(initMutex);
-
-  if (!initialized) {
-    libkineto::api().initProfilerIfRegistered();
-    initialized = true;
-    VLOG(0) << "libkineto profilers activated";
-  }
-  if (getenv("KINETO_DISABLE_EVENT_PROFILER") != nullptr) {
-    VLOG(0) << "Event profiler disabled via env var";
-  } else {
-    ConfigLoader& config_loader = libkineto::api().configLoader();
-    config_loader.initBaseConfig();
-    EventProfilerController::start(ctx, config_loader);
-  }
-}
-
-// Some models suffer from excessive instrumentation code gen
-// on dynamic attach which can hang for more than 5+ seconds.
-// If the workload was meant to be traced, preload the CUPTI
-// to take the performance hit early on.
-// https://docs.nvidia.com/cupti/r_main.html#r_overhead
-static bool shouldPreloadCuptiInstrumentation() {
-  return getenv("PRELOAD_CUPTI_INSTRUMENTATION");
-}
-
-static void stopProfiler(
-    CUpti_CallbackDomain /*domain*/,
-    CUpti_CallbackId /*cbid*/,
-    const CUpti_CallbackData* cbInfo) {
-  CUpti_ResourceData* d = (CUpti_ResourceData*)cbInfo;
-  CUcontext ctx = d->context;
-
-  LOG(INFO) << "CUDA Context destroyed";
-  std::lock_guard<std::mutex> lock(initMutex);
-  EventProfilerController::stop(ctx);
-}
-#endif // HAS_CUPTI
-
-} // namespace KINETO_NAMESPACE
-
-// Callback interface with CUPTI and library constructors
-using namespace KINETO_NAMESPACE;
-extern "C" {
-
-// Return true if no CUPTI errors occurred during init
-bool libkineto_init(bool cpuOnly, bool logOnError) {
-  bool success = true;
-#ifdef HAS_CUPTI
-  if (!cpuOnly) {
-    // libcupti will be lazily loaded on this call.
-    // If it is not available (e.g. CUDA is not installed),
-    // then this call will return an error and we just abort init.
-    auto& cbapi = CuptiCallbackApi::singleton();
-    bool status = false;
-
-    if (cbapi.initSuccess()){
-      const CUpti_CallbackDomain domain = CUPTI_CB_DOMAIN_RESOURCE;
-      status = cbapi.registerCallback(
-          domain, CuptiCallbackApi::RESOURCE_CONTEXT_CREATED, initProfilers);
-      status = status && cbapi.registerCallback(
-          domain, CuptiCallbackApi::RESOURCE_CONTEXT_DESTROYED, stopProfiler);
-
-      if (status) {
-        status = cbapi.enableCallback(
-            domain, CuptiCallbackApi::RESOURCE_CONTEXT_CREATED);
-        status = status && cbapi.enableCallback(
-            domain, CuptiCallbackApi::RESOURCE_CONTEXT_DESTROYED);
-        }
-    }
-
-    if (!cbapi.initSuccess() || !status) {
-      success = false;
-      cpuOnly = true;
-      if (logOnError) {
-        CUPTI_CALL(cbapi.getCuptiStatus());
-        LOG(WARNING) << "CUPTI initialization failed - "
-                     << "CUDA profiler activities will be missing";
-        LOG(INFO) << "If you see CUPTI_ERROR_INSUFFICIENT_PRIVILEGES, refer to "
-                  << "https://developer.nvidia.com/nvidia-development-tools-solutions-err-nvgpuctrperm-cupti";
-      }
-    }
-  }
-
-  if (shouldPreloadCuptiInstrumentation()) {
-    CuptiActivityApi::forceLoadCupti();
-  }
-#endif // HAS_CUPTI
-
-  ConfigLoader& config_loader = libkineto::api().configLoader();
-  libkineto::api().registerProfiler(
-      std::make_unique<ActivityProfilerProxy>(cpuOnly, config_loader));
-
-  return success;
-}
-
-// The cuda driver calls this function if the CUDA_INJECTION64_PATH environment
-// variable is set
-int InitializeInjection(void) {
-  LOG(INFO) << "Injection mode: Initializing libkineto";
-  libkineto_init(false /*cpuOnly*/, true /*logOnError*/);
-  return 1;
-}
-
-void suppressLibkinetoLogMessages() {
-  SET_LOG_SEVERITY_LEVEL(ERROR);
-}
-
-} // extern C
diff --git a/plugins/tensorboard-plugins/libkineto/src/libkineto_api.cpp b/plugins/tensorboard-plugins/libkineto/src/libkineto_api.cpp
deleted file mode 100644
index 9a622e4f5e5..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/libkineto_api.cpp
+++ /dev/null
@@ -1,41 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "libkineto.h"
-
-#include "ConfigLoader.h"
-#include "ThreadUtil.h"
-
-namespace libkineto {
-
-LibkinetoApi& api() {
-  static LibkinetoApi instance(ConfigLoader::instance());
-  return instance;
-}
-
-void LibkinetoApi::initClientIfRegistered() {
-  if (client_) {
-    if (clientRegisterThread_ != threadId()) {
-      fprintf(
-          stderr,
-          "ERROR: External init callback must run in same thread as registerClient "
-          "(%d != %d)\n",
-          threadId(),
-          (int)clientRegisterThread_);
-    } else {
-      client_->init();
-    }
-  }
-}
-
-void LibkinetoApi::registerClient(ClientInterface* client) {
-  client_ = client;
-  if (client && activityProfiler_) {
-    // Can initialize straight away
-    client->init();
-  }
-  // Assume here that the external init callback is *not* threadsafe
-  // and only call it if it's the same thread that called registerClient
-  clientRegisterThread_ = threadId();
-}
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/src/output_base.h b/plugins/tensorboard-plugins/libkineto/src/output_base.h
deleted file mode 100644
index 29d0d57768c..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/output_base.h
+++ /dev/null
@@ -1,104 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <fstream>
-#include <map>
-#include <ostream>
-#include <thread>
-#include <unordered_map>
-
-#ifdef HAS_CUPTI
-#include <cupti.h>
-#include "CuptiActivity.h"
-#endif // HAS_CUPTI
-#include "ActivityBuffers.h"
-#include "GenericTraceActivity.h"
-#include "ThreadUtil.h"
-#include "TraceSpan.h"
-
-namespace KINETO_NAMESPACE {
-  class Config;
-  class GpuKernelActivity;
-  struct RuntimeActivity;
-}
-
-namespace libkineto {
-
-using namespace KINETO_NAMESPACE;
-
-class ActivityLogger {
- public:
-
-  virtual ~ActivityLogger() = default;
-
-  struct DeviceInfo {
-    DeviceInfo(int64_t id, const std::string& name, const std::string& label) :
-      id(id), name(name), label(label) {}
-    int64_t id;
-    const std::string name;
-    const std::string label;
-  };
-
-  struct ResourceInfo {
-    ResourceInfo(
-        int64_t deviceId,
-        int64_t id,
-        int64_t sortIndex,
-        const std::string& name) :
-        id(id), sortIndex(sortIndex), deviceId(deviceId), name(name) {}
-    int64_t id;
-    int64_t sortIndex;
-    int64_t deviceId;
-    const std::string name;
-  };
-
-  struct OverheadInfo {
-    explicit OverheadInfo(const std::string& name) : name(name) {}
-    const std::string name;
-  };
-
-  virtual void handleDeviceInfo(
-      const DeviceInfo& info,
-      uint64_t time) = 0;
-
-  virtual void handleResourceInfo(const ResourceInfo& info, int64_t time) = 0;
-
-  virtual void handleOverheadInfo(const OverheadInfo& info, int64_t time) = 0;
-
-  virtual void handleTraceSpan(const TraceSpan& span) = 0;
-
-  virtual void handleActivity(
-      const libkineto::ITraceActivity& activity) = 0;
-  virtual void handleGenericActivity(
-      const libkineto::GenericTraceActivity& activity) = 0;
-
-#ifdef HAS_CUPTI
-  virtual void handleGpuActivity(
-      const GpuActivity<CUpti_ActivityKernel4>& activity) = 0;
-  virtual void handleGpuActivity(
-      const GpuActivity<CUpti_ActivityMemcpy>& activity) = 0;
-  virtual void handleGpuActivity(
-      const GpuActivity<CUpti_ActivityMemcpy2>& activity) = 0;
-  virtual void handleGpuActivity(
-      const GpuActivity<CUpti_ActivityMemset>& activity) = 0;
-#endif // HAS_CUPTI
-
-  virtual void handleTraceStart(
-      const std::unordered_map<std::string, std::string>& metadata) = 0;
-
-  void handleTraceStart() {
-    handleTraceStart(std::unordered_map<std::string, std::string>());
-  }
-
-  virtual void finalizeTrace(
-      const KINETO_NAMESPACE::Config& config,
-      std::unique_ptr<ActivityBuffers> buffers,
-      int64_t endTime,
-      std::unordered_map<std::string, std::vector<std::string>>& metadata) = 0;
-
- protected:
-  ActivityLogger() = default;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/output_csv.cpp b/plugins/tensorboard-plugins/libkineto/src/output_csv.cpp
deleted file mode 100644
index e56c0229398..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/output_csv.cpp
+++ /dev/null
@@ -1,88 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "output_csv.h"
-
-#include <chrono>
-#include <fstream>
-#include <iomanip>
-
-#include <fmt/chrono.h>
-#include <fmt/format.h>
-
-#include "Config.h"
-#include "Logger.h"
-
-namespace KINETO_NAMESPACE {
-
-static void write_header(
-    std::ostream& out,
-    const std::vector<int>& percentiles) {
-  out << "timestamp,delta_ms,device,event_name";
-  for (int p : percentiles) {
-    out << ",p" << p;
-  }
-  out << ",total" << std::endl;
-}
-
-void EventCSVLogger::update(const Config& config) {
-  eventNames_.clear();
-  eventNames_.insert(config.eventNames().begin(), config.eventNames().end());
-  eventNames_.insert(config.metricNames().begin(), config.metricNames().end());
-  if (config.percentiles() != percentiles_) {
-    percentiles_ = config.percentiles();
-    if (out_) {
-      write_header(*out_, percentiles_);
-    }
-  }
-}
-
-void EventCSVLogger::handleSample(int device, const Sample& sample, bool from_new_version) {
-  using namespace std::chrono;
-  if (out_) {
-    auto now = system_clock::now();
-    auto time = system_clock::to_time_t(now);
-    for (const Stat& s : sample.stats) {
-      if (eventNames_.find(s.name) == eventNames_.end()) {
-        continue;
-      }
-      *out_ << fmt::format("{:%Y-%m-%d %H:%M:%S}", fmt::localtime(time)) << ",";
-      *out_ << sample.deltaMsec << ",";
-      *out_ << device << ",";
-      *out_ << s.name;
-      for (const auto& p : s.percentileValues) {
-        *out_ << "," << p.second;
-      }
-      *out_ << "," << s.total << std::endl;
-    }
-  }
-}
-
-void EventCSVFileLogger::update(const Config& config) {
-  if (config.eventLogFile() != filename_) {
-    if (of_.is_open()) {
-      of_.close();
-      out_ = nullptr;
-      percentiles_.clear();
-    }
-    filename_ = config.eventLogFile();
-    if (!filename_.empty()) {
-      of_.open(filename_, std::ios::out | std::ios::trunc);
-      out_ = &of_;
-    }
-  }
-  EventCSVLogger::update(config);
-}
-
-void EventCSVDbgLogger::update(const Config& config) {
-  if (out_ && config.verboseLogLevel() < 0) {
-    out_ = nullptr;
-  } else if (!out_ && config.verboseLogLevel() >= 0) {
-    out_ = &LIBKINETO_DBG_STREAM;
-  }
-  if (config.verboseLogLevel() >= 0) {
-    percentiles_.clear();
-    EventCSVLogger::update(config);
-  }
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/output_csv.h b/plugins/tensorboard-plugins/libkineto/src/output_csv.h
deleted file mode 100644
index bca29f4db99..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/output_csv.h
+++ /dev/null
@@ -1,39 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-#include "SampleListener.h"
-
-#include <fstream>
-#include <ostream>
-#include <set>
-
-namespace KINETO_NAMESPACE {
-
-class EventCSVLogger : public SampleListener {
- public:
-  void update(const Config& config) override;
-  void handleSample(int device, const Sample& sample, bool from_new_version) override;
-
- protected:
-  EventCSVLogger() : out_(nullptr) {}
-
-  std::ostream* out_;
-  std::set<std::string> eventNames_;
-  std::vector<int> percentiles_;
-};
-
-class EventCSVFileLogger : public EventCSVLogger {
- public:
-  void update(const Config& config) override;
-
- private:
-  std::ofstream of_;
-  std::string filename_;
-};
-
-class EventCSVDbgLogger : public EventCSVLogger {
- public:
-  void update(const Config& config) override;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/output_json.cpp b/plugins/tensorboard-plugins/libkineto/src/output_json.cpp
deleted file mode 100644
index 0ef22339fad..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/output_json.cpp
+++ /dev/null
@@ -1,583 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "output_json.h"
-
-#include <fmt/format.h>
-#include <fstream>
-#include <time.h>
-#include <map>
-
-#include "Config.h"
-#ifdef HAS_CUPTI
-#include "CuptiActivity.h"
-#include "CuptiActivity.tpp"
-#include "CuptiActivityApi.h"
-#include "CudaDeviceProperties.h"
-#endif // HAS_CUPTI
-#include "Demangle.h"
-#include "TraceSpan.h"
-
-#include "Logger.h"
-
-using std::endl;
-using namespace libkineto;
-
-namespace KINETO_NAMESPACE {
-
-static constexpr int kSchemaVersion = 1;
-static constexpr char kFlowStart = 's';
-static constexpr char kFlowEnd = 'f';
-
-#ifdef __linux__
-static constexpr char kDefaultLogFileFmt[] =
-    "/tmp/libkineto_activities_{}.json";
-#else
-static constexpr char kDefaultLogFileFmt[] = "libkineto_activities_{}.json";
-#endif
-
-std::string& ChromeTraceLogger::sanitizeStrForJSON(std::string& value) {
-// Replace all backslashes with forward slash because Windows paths causing JSONDecodeError.
-#ifdef _WIN32
-  std::replace(value.begin(), value.end(), '\\', '/');
-#endif
-  return value;
-}
-
-void ChromeTraceLogger::metadataToJSON(
-    const std::unordered_map<std::string, std::string>& metadata) {
-  for (const auto& kv : metadata) {
-    traceOf_ << fmt::format(R"JSON(
-  "{}": {},)JSON", kv.first, kv.second);
-  }
-}
-
-void ChromeTraceLogger::handleTraceStart(
-    const std::unordered_map<std::string, std::string>& metadata) {
-  traceOf_ << fmt::format(R"JSON(
-{{
-  "schemaVersion": {},)JSON", kSchemaVersion);
-
-#ifdef HAS_CUPTI
-  traceOf_ << fmt::format(R"JSON(
-  "deviceProperties": [{}
-  ],)JSON", devicePropertiesJson());
-#endif
-
-  metadataToJSON(metadata);
-  traceOf_ << R"JSON(
-  "traceEvents": [)JSON";
-}
-
-static std::string defaultFileName() {
-  return fmt::format(kDefaultLogFileFmt, processId());
-}
-
-void ChromeTraceLogger::openTraceFile() {
-  traceOf_.open(fileName_, std::ofstream::out | std::ofstream::trunc);
-  if (!traceOf_) {
-    PLOG(ERROR) << "Failed to open '" << fileName_ << "'";
-  } else {
-    LOG(INFO) << "Tracing to " << fileName_;
-  }
-}
-
-ChromeTraceLogger::ChromeTraceLogger(const std::string& traceFileName) {
-  fileName_ = traceFileName.empty() ? defaultFileName() : traceFileName;
-  traceOf_.clear(std::ios_base::badbit);
-  openTraceFile();
-}
-
-static int64_t us(int64_t timestamp) {
-  // It's important that this conversion is the same here and in the CPU trace.
-  // No rounding!
-  return timestamp / 1000;
-}
-
-void ChromeTraceLogger::handleDeviceInfo(
-    const DeviceInfo& info,
-    uint64_t time) {
-  if (!traceOf_) {
-    return;
-  }
-
-  // M is for metadata
-  // process_name needs a pid and a name arg
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "name": "process_name", "ph": "M", "ts": {}, "pid": {}, "tid": 0,
-    "args": {{
-      "name": "{}"
-    }}
-  }},
-  {{
-    "name": "process_labels", "ph": "M", "ts": {}, "pid": {}, "tid": 0,
-    "args": {{
-      "labels": "{}"
-    }}
-  }},
-  {{
-    "name": "process_sort_index", "ph": "M", "ts": {}, "pid": {}, "tid": 0,
-    "args": {{
-      "sort_index": {}
-    }}
-  }},)JSON",
-      time, info.id,
-      info.name,
-      time, info.id,
-      info.label,
-      time, info.id,
-      info.id < 8 ? info.id + 0x1000000ll : info.id);
-  // clang-format on
-}
-
-void ChromeTraceLogger::handleResourceInfo(
-    const ResourceInfo& info,
-    int64_t time) {
-  if (!traceOf_) {
-    return;
-  }
-
-  // M is for metadata
-  // thread_name needs a pid and a name arg
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "name": "thread_name", "ph": "M", "ts": {}, "pid": {}, "tid": {},
-    "args": {{
-      "name": "{}"
-    }}
-  }},
-  {{
-    "name": "thread_sort_index", "ph": "M", "ts": {}, "pid": {}, "tid": {},
-    "args": {{
-      "sort_index": {}
-    }}
-  }},)JSON",
-      time, info.deviceId, info.id,
-      info.name,
-      time, info.deviceId, info.id,
-      info.sortIndex);
-  // clang-format on
-}
-
-void ChromeTraceLogger::handleOverheadInfo(
-    const OverheadInfo& info,
-    int64_t time) {
-  if (!traceOf_) {
-    return;
-  }
-
-  // TOOD: reserve pid = -1 for overhead but we need to rethink how to scale this for
-  // other metadata
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "name": "process_name", "ph": "M", "ts": {}, "pid": -1, "tid": 0,
-    "args": {{
-      "name": "{}"
-    }}
-  }},
-  {{
-    "name": "process_sort_index", "ph": "M", "ts": {}, "pid": -1, "tid": 0,
-    "args": {{
-      "sort_index": {}
-    }}
-  }},)JSON",
-      time,
-      info.name,
-      time,
-      0x100000All);
-  // clang-format on
-}
-
-void ChromeTraceLogger::handleTraceSpan(const TraceSpan& span) {
-  if (!traceOf_) {
-    return;
-  }
-
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "ph": "X", "cat": "Trace", "ts": {}, "dur": {},
-    "pid": "Spans", "tid": "{}",
-    "name": "{}{} ({})",
-    "args": {{
-      "Op count": {}
-    }}
-  }},
-  {{
-    "name": "process_sort_index", "ph": "M", "ts": {},
-    "pid": "Spans", "tid": 0,
-    "args": {{
-      "sort_index": {}
-    }}
-  }},)JSON",
-      span.startTime, span.endTime - span.startTime,
-      span.name,
-      span.prefix, span.name, span.iteration,
-      span.opCount,
-      span.startTime,
-      // Large sort index to appear at the bottom
-      0x20000000ll);
-  // clang-format on
-
-  addIterationMarker(span);
-}
-
-void ChromeTraceLogger::addIterationMarker(const TraceSpan& span) {
-  if (!traceOf_) {
-    return;
-  }
-
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "name": "Iteration Start: {}", "ph": "i", "s": "g",
-    "pid": "Traces", "tid": "Trace {}", "ts": {}
-  }},)JSON",
-      span.name,
-      span.name, span.startTime);
-  // clang-format on
-}
-
-static std::string traceActivityJson(const ITraceActivity& activity) {
-  // clang-format off
-  int64_t ts = activity.timestamp();
-  int64_t duration = activity.duration();
-  if (activity.type() ==  ActivityType::GPU_USER_ANNOTATION) {
-    // The GPU user annotations start at the same time as the
-    // first associated GPU activity. Since they appear later
-    // in the trace file, this causes a visualization issue in Chrome.
-    // Make it start one us earlier.
-    ts--;
-    duration++; // Still need it to end at the orginal point
-  }
-  return fmt::format(R"JSON(
-    "name": "{}", "pid": {}, "tid": {},
-    "ts": {}, "dur": {})JSON",
-      activity.name(), activity.deviceId(), activity.resourceId(),
-      ts, duration);
-  // clang-format on
-}
-
-void ChromeTraceLogger::handleGenericInstantEvent(
-    const libkineto::ITraceActivity& op) {
-  if (!traceOf_) {
-    return;
-  }
-
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "ph": "i", "s": "t", "name": "{}",
-    "pid": {}, "tid": {},
-    "ts": {},
-    "args": {{
-      {}
-    }}
-  }},)JSON",
-      op.name(), op.deviceId(), op.resourceId(),
-      op.timestamp(), op.metadataJson());
-}
-
-void ChromeTraceLogger::handleActivity(
-    const libkineto::ITraceActivity& op) {
-  if (!traceOf_) {
-    return;
-  }
-
-  if (op.type() == ActivityType::CPU_INSTANT_EVENT) {
-    handleGenericInstantEvent(op);
-    return;
-  }
-
-  const std::string op_metadata = op.metadataJson();
-  std::string separator = "";
-  if (op_metadata.find_first_not_of(" \t\n") != std::string::npos) {
-    separator = ",\n      ";
-  }
-  std::string span = "";
-  if (op.traceSpan()) {
-    span = fmt::format(R"JSON(
-      "Trace name": "{}", "Trace iteration": {},)JSON",
-        op.traceSpan()->name,
-        op.traceSpan()->iteration);
-  }
-
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "ph": "X", "cat": "{}", {},
-    "args": {{{}
-      "External id": {}{}{}
-    }}
-  }},)JSON",
-          toString(op.type()), traceActivityJson(op),
-          // args
-          span,
-          op.correlationId(), separator, op_metadata);
-  // clang-format on
-  if (op.flowId() > 0) {
-    handleGenericLink(op);
-  }
-}
-
-void ChromeTraceLogger::handleGenericActivity(
-    const libkineto::GenericTraceActivity& op) {
-        handleActivity(op);
-}
-
-void ChromeTraceLogger::handleGenericLink(const ITraceActivity& act) {
-  static struct {
-    int type;
-    char longName[24];
-    char shortName[16];
-  } flow_names[] = {
-    {kLinkFwdBwd, "forward_backward", "fwd_bwd"},
-    {kLinkAsyncCpuGpu, "async_cpu_to_gpu", "async_gpu"}
-  };
-  for (auto& flow : flow_names) {
-    if (act.flowType() == flow.type) {
-      // Link the activities via flow ID in source and destination.
-      // The source node must return true from flowStart()
-      // and the destination node false.
-      if (act.flowStart()) {
-        handleLink(kFlowStart, act, act.flowId(), flow.longName, flow.shortName);
-      } else {
-        handleLink(kFlowEnd, act, act.flowId(), flow.longName, flow.shortName);
-      }
-      return;
-    }
-  }
-  LOG(ERROR) << "Unknown flow type: " << act.flowType();
-}
-
-void ChromeTraceLogger::handleLink(
-    char type,
-    const ITraceActivity& e,
-    int64_t id,
-    const std::string& cat,
-    const std::string& name) {
-  if (!traceOf_) {
-    return;
-  }
-
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "ph": "{}", "id": {}, "pid": {}, "tid": {}, "ts": {},
-    "cat": "{}", "name": "{}", "bp": "e"
-  }},)JSON",
-      type, id, e.deviceId(), e.resourceId(), e.timestamp(), cat, name);
-  // clang-format on
-}
-
-#ifdef HAS_CUPTI
-// GPU side kernel activity
-void ChromeTraceLogger::handleGpuActivity(
-    const GpuActivity<CUpti_ActivityKernel4>& activity) {
-  if (!traceOf_) {
-    return;
-  }
-  const CUpti_ActivityKernel4* kernel = &activity.raw();
-  constexpr int threads_per_warp = 32;
-  float blocks_per_sm = -1.0;
-  float warps_per_sm = -1.0;
-  int sm_count = smCount(kernel->deviceId);
-  if (sm_count) {
-    blocks_per_sm =
-        (kernel->gridX * kernel->gridY * kernel->gridZ) / (float) sm_count;
-    warps_per_sm =
-        blocks_per_sm * (kernel->blockX * kernel->blockY * kernel->blockZ)
-        / threads_per_warp;
-  }
-
-  // Calculate occupancy
-  float occupancy = KINETO_NAMESPACE::kernelOccupancy(
-      kernel->deviceId,
-      kernel->registersPerThread,
-      kernel->staticSharedMemory,
-      kernel->dynamicSharedMemory,
-      kernel->blockX,
-      kernel->blockY,
-      kernel->blockZ,
-      blocks_per_sm);
-
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "ph": "X", "cat": "Kernel", {},
-    "args": {{
-      "queued": {}, "device": {}, "context": {},
-      "stream": {}, "correlation": {},
-      "registers per thread": {},
-      "shared memory": {},
-      "blocks per SM": {},
-      "warps per SM": {},
-      "grid": [{}, {}, {}],
-      "block": [{}, {}, {}],
-      "est. achieved occupancy %": {}
-    }}
-  }},)JSON",
-      traceActivityJson(activity),
-      // args
-      us(kernel->queued), kernel->deviceId, kernel->contextId,
-      kernel->streamId, kernel->correlationId,
-      kernel->registersPerThread,
-      kernel->staticSharedMemory + kernel->dynamicSharedMemory,
-      blocks_per_sm,
-      warps_per_sm,
-      kernel->gridX, kernel->gridY, kernel->gridZ,
-      kernel->blockX, kernel->blockY, kernel->blockZ,
-      (int) (0.5 + occupancy * 100.0));
-  // clang-format on
-
-  auto to_id = activity.correlationId();
-  handleLink(kFlowEnd, activity, to_id, "async_cpu_to_gpu", "async_gpu");
-}
-
-static std::string bandwidth(uint64_t bytes, uint64_t duration) {
-  return duration == 0 ? "\"N/A\"" : fmt::format("{}", bytes * 1.0 / duration);
-}
-
-// GPU side memcpy activity
-void ChromeTraceLogger::handleGpuActivity(
-    const GpuActivity<CUpti_ActivityMemcpy>& activity) {
-  if (!traceOf_) {
-    return;
-  }
-  const CUpti_ActivityMemcpy& memcpy = activity.raw();
-  VLOG(2) << memcpy.correlationId << ": MEMCPY";
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "ph": "X", "cat": "Memcpy", {},
-    "args": {{
-      "device": {}, "context": {},
-      "stream": {}, "correlation": {},
-      "bytes": {}, "memory bandwidth (GB/s)": {}
-    }}
-  }},)JSON",
-      traceActivityJson(activity),
-      // args
-      memcpy.deviceId, memcpy.contextId,
-      memcpy.streamId, memcpy.correlationId,
-      memcpy.bytes, bandwidth(memcpy.bytes, memcpy.end - memcpy.start));
-  // clang-format on
-
-  int64_t to_id = activity.correlationId();
-  handleLink(kFlowEnd, activity, to_id, "async_cpu_to_gpu", "async_gpu");
-}
-
-// GPU side memcpy activity
-void ChromeTraceLogger::handleGpuActivity(
-    const GpuActivity<CUpti_ActivityMemcpy2>& activity) {
-  if (!traceOf_) {
-    return;
-  }
-  const CUpti_ActivityMemcpy2& memcpy = activity.raw();
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "ph": "X", "cat": "Memcpy", {},
-    "args": {{
-      "fromDevice": {}, "inDevice": {}, "toDevice": {},
-      "fromContext": {}, "inContext": {}, "toContext": {},
-      "stream": {}, "correlation": {},
-      "bytes": {}, "memory bandwidth (GB/s)": {}
-    }}
-  }},)JSON",
-      traceActivityJson(activity),
-      // args
-      memcpy.srcDeviceId, memcpy.deviceId, memcpy.dstDeviceId,
-      memcpy.srcContextId, memcpy.contextId, memcpy.dstContextId,
-      memcpy.streamId, memcpy.correlationId,
-      memcpy.bytes, bandwidth(memcpy.bytes, memcpy.end - memcpy.start));
-  // clang-format on
-
-  int64_t to_id = activity.correlationId();
-  handleLink(kFlowEnd, activity, to_id, "async_cpu_to_gpu", "async_gpu");
-}
-
-void ChromeTraceLogger::handleGpuActivity(
-    const GpuActivity<CUpti_ActivityMemset>& activity) {
-  if (!traceOf_) {
-    return;
-  }
-  const CUpti_ActivityMemset& memset = activity.raw();
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "ph": "X", "cat": "Memset", {},
-    "args": {{
-      "device": {}, "context": {},
-      "stream": {}, "correlation": {},
-      "bytes": {}, "memory bandwidth (GB/s)": {}
-    }}
-  }},)JSON",
-      traceActivityJson(activity),
-      // args
-      memset.deviceId, memset.contextId,
-      memset.streamId, memset.correlationId,
-      memset.bytes, bandwidth(memset.bytes, memset.end - memset.start));
-  // clang-format on
-
-  int64_t to_id = activity.correlationId();
-  handleLink(kFlowEnd, activity, to_id, "async_cpu_to_gpu", "async_gpu");
-}
-#endif // HAS_CUPTI
-
-void ChromeTraceLogger::finalizeTrace(
-    const Config& /*unused*/,
-    std::unique_ptr<ActivityBuffers> /*unused*/,
-    int64_t endTime,
-    std::unordered_map<std::string, std::vector<std::string>>& metadata) {
-  if (!traceOf_) {
-    LOG(ERROR) << "Failed to write to log file!";
-    return;
-  }
-  LOG(INFO) << "Chrome Trace written to " << fileName_;
-  // clang-format off
-  traceOf_ << fmt::format(R"JSON(
-  {{
-    "name": "Record Window End", "ph": "i", "s": "g",
-    "pid": "", "tid": "", "ts": {}
-  }}
-  ],)JSON",
-      endTime);
-
-#if !USE_GOOGLE_LOG
-  std::unordered_map<std::string, std::string> PreparedMetadata;
-  for (const auto& kv : metadata) {
-    // Skip empty log buckets, ex. skip ERROR if its empty.
-    if (!kv.second.empty()) {
-      std::string value = "[";
-      // Ex. Each metadata from logger is a list of strings, expressed in JSON as
-      //   "ERROR": ["Error 1", "Error 2"],
-      //   "WARNING": ["Warning 1", "Warning 2", "Warning 3"],
-      //   ...
-      int mdv_count = kv.second.size();
-      for (const auto& v : kv.second) {
-        value.append("\"" + v + "\"");
-        if(mdv_count > 1) {
-          value.append(",");
-          mdv_count--;
-        }
-      }
-      value.append("]");
-      PreparedMetadata[kv.first] = sanitizeStrForJSON(value);
-    }
-  }
-  metadataToJSON(PreparedMetadata);
-#endif // !USE_GOOGLE_LOG
-
-  // Putting this here because the last entry MUST not end with a comma.
-  traceOf_ << fmt::format(R"JSON(
-  "traceName": "{}"
-}})JSON", sanitizeStrForJSON(fileName_));
-  // clang-format on
-
-  traceOf_.close();
-}
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/output_json.h b/plugins/tensorboard-plugins/libkineto/src/output_json.h
deleted file mode 100644
index 5a8a81e4a9f..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/output_json.h
+++ /dev/null
@@ -1,91 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <fstream>
-#include <map>
-#include <ostream>
-#include <thread>
-#include <unordered_map>
-
-#ifdef HAS_CUPTI
-#include <cupti.h>
-#endif
-#include "GenericTraceActivity.h"
-#include "output_base.h"
-
-namespace KINETO_NAMESPACE {
-  // Previous declaration of TraceSpan is struct. Must match the same here.
-  struct TraceSpan;
-}
-
-namespace KINETO_NAMESPACE {
-
-class Config;
-
-class ChromeTraceLogger : public libkineto::ActivityLogger {
- public:
-  explicit ChromeTraceLogger(const std::string& traceFileName);
-
-  // Note: the caller of these functions should handle concurrency
-  // i.e., we these functions are not thread-safe
-  void handleDeviceInfo(
-      const DeviceInfo& info,
-      uint64_t time) override;
-
-  void handleOverheadInfo(const OverheadInfo& info, int64_t time) override;
-
-  void handleResourceInfo(const ResourceInfo& info, int64_t time) override;
-
-  void handleTraceSpan(const TraceSpan& span) override;
-
-  void handleActivity(const ITraceActivity& activity) override;
-  void handleGenericActivity(const GenericTraceActivity& activity) override;
-
-#ifdef HAS_CUPTI
-  void handleGpuActivity(const GpuActivity<CUpti_ActivityKernel4>& activity) override;
-  void handleGpuActivity(const GpuActivity<CUpti_ActivityMemcpy>& activity) override;
-  void handleGpuActivity(const GpuActivity<CUpti_ActivityMemcpy2>& activity) override;
-  void handleGpuActivity(const GpuActivity<CUpti_ActivityMemset>& activity) override;
-#endif // HAS_CUPTI
-
-  void handleTraceStart(
-      const std::unordered_map<std::string, std::string>& metadata) override;
-
-  void finalizeTrace(
-      const Config& config,
-      std::unique_ptr<ActivityBuffers> buffers,
-      int64_t endTime,
-      std::unordered_map<std::string, std::vector<std::string>>& metadata) override;
-
-  std::string traceFileName() const {
-    return fileName_;
-  }
-
- private:
-
-  // Create a flow event (arrow)
-  void handleLink(
-      char type,
-      const ITraceActivity& e,
-      int64_t id,
-      const std::string& cat,
-      const std::string& name);
-
-  void addIterationMarker(const TraceSpan& span);
-
-  void openTraceFile();
-
-  void handleGenericInstantEvent(const ITraceActivity& op);
-
-  void handleGenericLink(const ITraceActivity& activity);
-
-  void metadataToJSON(const std::unordered_map<std::string, std::string>& metadata);
-
-  std::string& sanitizeStrForJSON(std::string& value);
-
-  std::string fileName_;
-  std::ofstream traceOf_;
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/src/output_membuf.h b/plugins/tensorboard-plugins/libkineto/src/output_membuf.h
deleted file mode 100644
index ef6aadeb657..00000000000
--- a/plugins/tensorboard-plugins/libkineto/src/output_membuf.h
+++ /dev/null
@@ -1,130 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <map>
-#include <memory>
-#include <unordered_map>
-#include <vector>
-
-#ifdef HAS_CUPTI
-#include <cupti.h>
-#endif
-
-#include "Config.h"
-#include "GenericTraceActivity.h"
-#ifdef HAS_CUPTI
-#include "CuptiActivity.h"
-#include "CuptiActivity.tpp"
-#endif // HAS_CUPTI
-#include "output_base.h"
-
-namespace KINETO_NAMESPACE {
-
-class Config;
-
-class MemoryTraceLogger : public ActivityLogger {
- public:
-  MemoryTraceLogger(const Config& config) : config_(config.clone()) {
-    activities_.reserve(100000);
-  }
-
-  // Note: the caller of these functions should handle concurrency
-  // i.e., these functions are not thread-safe
-  void handleDeviceInfo(
-      const DeviceInfo& info,
-      uint64_t time) override {
-    deviceInfoList_.emplace_back(info, time);
-  }
-
-  void handleResourceInfo(const ResourceInfo& info, int64_t time) override {
-    resourceInfoList_.emplace_back(info, time);
-  }
-
-  void handleOverheadInfo(const OverheadInfo& info, int64_t time) override {}
-
-  void handleTraceSpan(const TraceSpan& span) override {
-    // Handled separately
-  }
-
-  template<class T>
-  void addActivityWrapper(const T& act) {
-    wrappers_.push_back(std::make_unique<T>(act));
-    activities_.push_back(wrappers_.back().get());
-  }
-
-  // Just add the pointer to the list - ownership of the underlying
-  // objects must be transferred in ActivityBuffers via finalizeTrace
-  void handleActivity(const ITraceActivity& activity) override {
-    activities_.push_back(&activity);
-  }
-  void handleGenericActivity(const GenericTraceActivity& activity) override {
-    addActivityWrapper(activity);
-  }
-
-#ifdef HAS_CUPTI
-  void handleGpuActivity(const GpuActivity<CUpti_ActivityKernel4>& activity) override {
-    addActivityWrapper(activity);
-  }
-  void handleGpuActivity(const GpuActivity<CUpti_ActivityMemcpy>& activity) override {
-    addActivityWrapper(activity);
-  }
-  void handleGpuActivity(const GpuActivity<CUpti_ActivityMemcpy2>& activity) override {
-    addActivityWrapper(activity);
-  }
-  void handleGpuActivity(const GpuActivity<CUpti_ActivityMemset>& activity) override {
-    addActivityWrapper(activity);
-  }
-#endif // HAS_CUPTI
-
-  void handleTraceStart(
-      const std::unordered_map<std::string, std::string>& metadata) override {
-    metadata_ = metadata;
-  }
-
-  void finalizeTrace(
-      const Config& config,
-      std::unique_ptr<ActivityBuffers> buffers,
-      int64_t endTime,
-      std::unordered_map<std::string, std::vector<std::string>>& metadata) override {
-    buffers_ = std::move(buffers);
-    endTime_ = endTime;
-  }
-
-  const std::vector<const ITraceActivity*>* traceActivities() {
-    return &activities_;
-  }
-
-  void log(ActivityLogger& logger) {
-    logger.handleTraceStart(metadata_);
-    for (auto& activity : activities_) {
-      activity->log(logger);
-    }
-    for (auto& p : deviceInfoList_) {
-      logger.handleDeviceInfo(p.first, p.second);
-    }
-    for (auto& p : resourceInfoList_) {
-      logger.handleResourceInfo(p.first, p.second);
-    }
-    for (auto& cpu_trace_buffer : buffers_->cpu) {
-      logger.handleTraceSpan(cpu_trace_buffer->span);
-    }
-    // Hold on to the buffers
-    logger.finalizeTrace(*config_, nullptr, endTime_, loggerMetadata_);
-  }
-
- private:
-
-  std::unique_ptr<Config> config_;
-  // Optimization: Remove unique_ptr by keeping separate vector per type
-  std::vector<const ITraceActivity*> activities_;
-  std::vector<std::unique_ptr<const ITraceActivity>> wrappers_;
-  std::vector<std::pair<DeviceInfo, int64_t>> deviceInfoList_;
-  std::vector<std::pair<ResourceInfo, int64_t>> resourceInfoList_;
-  std::unique_ptr<ActivityBuffers> buffers_;
-  std::unordered_map<std::string, std::string> metadata_;
-  std::unordered_map<std::string, std::vector<std::string>> loggerMetadata_;
-  int64_t endTime_{0};
-};
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/test/CMakeLists.txt b/plugins/tensorboard-plugins/libkineto/test/CMakeLists.txt
deleted file mode 100644
index ca54460b36c..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/CMakeLists.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-cmake_minimum_required(VERSION 3.5 FATAL_ERROR)
-
-# TODO
diff --git a/plugins/tensorboard-plugins/libkineto/test/ConfigTest.cpp b/plugins/tensorboard-plugins/libkineto/test/ConfigTest.cpp
deleted file mode 100644
index 16bc86e751c..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/ConfigTest.cpp
+++ /dev/null
@@ -1,315 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "include/Config.h"
-
-#include <fmt/format.h>
-#include <gtest/gtest.h>
-#include <time.h>
-#include <chrono>
-
-using namespace std::chrono;
-using namespace KINETO_NAMESPACE;
-
-TEST(ParseTest, Whitespace) {
-  Config cfg;
-  // Check that various types of whitespace is ignored
-  EXPECT_TRUE(cfg.parse(""));
-  EXPECT_TRUE(cfg.parse(" "));
-  EXPECT_TRUE(cfg.parse("\t"));
-  EXPECT_TRUE(cfg.parse("\n"));
-  EXPECT_TRUE(cfg.parse("    "));
-  EXPECT_TRUE(cfg.parse("\t \n  \t\t\n\n"));
-  // Only the above characters are supported
-  EXPECT_FALSE(cfg.parse("\r\n"));
-}
-
-TEST(ParseTest, Comment) {
-  Config cfg;
-  // Anything following a '#' should be ignored, up to a newline
-  EXPECT_TRUE(cfg.parse("# comment"));
-  EXPECT_TRUE(cfg.parse("   # ~!@#$"));
-  EXPECT_TRUE(cfg.parse("\t#abc"));
-  EXPECT_TRUE(cfg.parse("###\n##"));
-  EXPECT_TRUE(cfg.parse("EVENTS=util ##ok"));
-  EXPECT_TRUE(cfg.parse("EVENTS=util ## EVENTS=instruction"));
-  // Whatever appears before the comment must be valid format
-  EXPECT_FALSE(cfg.parse("util ## not ok"));
-  EXPECT_FALSE(cfg.parse("## ok \n blah # not OK"));
-  // Check that a comment does not affect config parsing
-  EXPECT_TRUE(cfg.parse("SAMPLE_PERIOD_MSECS = 1 # Sample every millisecond"));
-  EXPECT_EQ(cfg.samplePeriod(), milliseconds(1));
-}
-
-TEST(ParseTest, Format) {
-  Config cfg;
-  // The basic format is just "name = value".
-  // Where both value and name can be almost anything.
-  // Leading and trailing whitespace should be removed
-  // for both 'name' and 'value', but internal whitespace is not.
-  EXPECT_FALSE(cfg.parse("events"));
-  EXPECT_TRUE(cfg.parse("events="));
-  EXPECT_FALSE(cfg.parse("=events="));
-  EXPECT_TRUE(cfg.parse("events=1,2,3"));
-  // Only one setting per line
-  EXPECT_FALSE(cfg.parse("events = 1,2,3 ; metrics = 4,5,6"));
-  // Names are case sensitive
-  EXPECT_TRUE(cfg.parse("EVENTS = 1,2,3 \n metrics = 4,5,6"));
-  EXPECT_EQ(cfg.eventNames(), std::set<std::string>({"1", "2", "3"}));
-  EXPECT_EQ(cfg.metricNames().size(), 0);
-  // Leading and trailing whitespace removed for event and metric names,
-  // but not internal.
-  EXPECT_TRUE(
-      cfg.parse("EVENTS = 1, 2, 3 \n \tMETRICS\t = \t4,\t5\t,\ts i x  "));
-  EXPECT_EQ(cfg.eventNames(), std::set<std::string>({"1", "2", "3"}));
-  EXPECT_EQ(cfg.metricNames(), std::set<std::string>({"4", "5", "s i x"}));
-}
-
-TEST(ParseTest, DefaultActivityTypes) {
-  Config cfg;
-  cfg.validate(std::chrono::system_clock::now());
-  auto all_activities = activityTypes();
-  // TODO: introduce optional activities
-  EXPECT_EQ(cfg.selectedActivityTypes(),
-    std::set<ActivityType>(all_activities.begin(), all_activities.end() - 1));
-}
-
-TEST(ParseTest, ActivityTypes) {
-  Config cfg;
-  EXPECT_FALSE(cfg.parse("ACTIVITY_TYPES"));
-  EXPECT_TRUE(cfg.parse("ACTIVITY_TYPES="));
-  EXPECT_FALSE(cfg.parse("=ACTIVITY_TYPES="));
-
-  EXPECT_EQ(cfg.selectedActivityTypes(),
-    std::set<ActivityType>({ActivityType::CPU_OP,
-                            ActivityType::CPU_INSTANT_EVENT,
-                            ActivityType::PYTHON_FUNCTION,
-                            ActivityType::USER_ANNOTATION,
-                            ActivityType::GPU_USER_ANNOTATION,
-                            ActivityType::GPU_MEMCPY,
-                            ActivityType::GPU_MEMSET,
-                            ActivityType::CONCURRENT_KERNEL,
-                            ActivityType::EXTERNAL_CORRELATION,
-                            ActivityType::GLOW_RUNTIME,
-                            ActivityType::CUDA_RUNTIME,
-                            ActivityType::CUDA_PROFILER_RANGE}));
-
-  Config cfg2;
-  EXPECT_TRUE(cfg2.parse("ACTIVITY_TYPES=gpu_memcpy,gpu_MeMsEt,kernel"));
-  EXPECT_EQ(cfg2.selectedActivityTypes(),
-    std::set<ActivityType>({ActivityType::GPU_MEMCPY,
-                            ActivityType::GPU_MEMSET,
-                            ActivityType::CONCURRENT_KERNEL}));
-
-  EXPECT_TRUE(cfg2.parse("ACTIVITY_TYPES = cuda_Runtime,"));
-  EXPECT_EQ(cfg2.selectedActivityTypes(),
-    std::set<ActivityType>({ActivityType::CUDA_RUNTIME}));
-
-  // Should throw an exception because incorrect activity name
-  EXPECT_FALSE(cfg2.parse("ACTIVITY_TYPES = memcopy,cuda_runtime"));
-
-  EXPECT_TRUE(cfg2.parse("ACTIVITY_TYPES = cpu_op"));
-  EXPECT_EQ(cfg2.selectedActivityTypes(),
-    std::set<ActivityType>({ActivityType::CPU_OP}));
-}
-
-TEST(ParseTest, SamplePeriod) {
-  Config cfg;
-  EXPECT_TRUE(cfg.parse("SAMPLE_PERIOD_MSECS=10"));
-  EXPECT_EQ(cfg.samplePeriod(), milliseconds(10));
-  EXPECT_TRUE(cfg.parse("SAMPLE_PERIOD_MSECS=0"));
-  cfg.validate(std::chrono::system_clock::now());
-  // 0 should be adjustd up to 1
-  EXPECT_EQ(cfg.samplePeriod(), milliseconds(1));
-  // Negative and non-int values should fail
-  EXPECT_FALSE(cfg.parse("SAMPLE_PERIOD_MSECS=-10"));
-  EXPECT_FALSE(cfg.parse("SAMPLE_PERIOD_MSECS=1.5"));
-  EXPECT_FALSE(cfg.parse("SAMPLE_PERIOD_MSECS="));
-  EXPECT_FALSE(cfg.parse("SAMPLE_PERIOD_MSECS=string"));
-  EXPECT_EQ(cfg.samplePeriod(), milliseconds(1));
-}
-
-TEST(ParseTest, MultiplexPeriod) {
-  Config cfg;
-  auto now = std::chrono::system_clock::now();
-
-  EXPECT_TRUE(cfg.parse("SAMPLE_PERIOD_MSECS=100\nMULTIPLEX_PERIOD_MSECS=100"));
-  EXPECT_EQ(cfg.multiplexPeriod(), milliseconds(100));
-  EXPECT_TRUE(cfg.parse("MULTIPLEX_PERIOD_MSECS = 0"));
-  cfg.validate(now);
-  // Adjusted to match sample period
-  EXPECT_EQ(cfg.multiplexPeriod(), milliseconds(100));
-  EXPECT_TRUE(cfg.parse("MULTIPLEX_PERIOD_MSECS \t= \t 750 \n"));
-  cfg.validate(now);
-  // Adjusted to match multiple of sample period
-  EXPECT_EQ(cfg.multiplexPeriod(), milliseconds(800));
-  EXPECT_FALSE(cfg.parse("MULTIPLEX_PERIOD_MSECS=-10"));
-  EXPECT_FALSE(cfg.parse("MULTIPLEX_PERIOD_MSECS=1.5"));
-  EXPECT_FALSE(cfg.parse("MULTIPLEX_PERIOD_MSECS="));
-  EXPECT_FALSE(cfg.parse("MULTIPLEX_PERIOD_MSECS=string"));
-  // Previous value not affected
-  EXPECT_EQ(cfg.multiplexPeriod(), milliseconds(800));
-}
-
-TEST(ParseTest, ReportPeriod) {
-  Config cfg;
-  EXPECT_TRUE(cfg.parse("REPORT_PERIOD_SECS=1"));
-  EXPECT_EQ(cfg.reportPeriod(), seconds(1));
-  // Whitespace
-  EXPECT_TRUE(cfg.parse("REPORT_PERIOD_SECS  =  \t100"));
-  EXPECT_EQ(cfg.reportPeriod(), seconds(100));
-  // Invalid types
-  EXPECT_FALSE(cfg.parse("REPORT_PERIOD_SECS=-1"));
-  EXPECT_EQ(cfg.reportPeriod(), seconds(100));
-}
-
-TEST(ParseTest, SamplesPerReport) {
-  Config cfg;
-  auto now = std::chrono::system_clock::now();
-
-  EXPECT_TRUE(cfg.parse(R"(
-    SAMPLE_PERIOD_MSECS = 1000
-    REPORT_PERIOD_SECS  =    1
-    SAMPLES_PER_REPORT  =   10)"));
-  cfg.validate(now);
-  // Adjusted down to one sample per report
-  EXPECT_EQ(cfg.samplesPerReport(), 1);
-  EXPECT_TRUE(cfg.parse(R"(
-    SAMPLE_PERIOD_MSECS = 1000
-    REPORT_PERIOD_SECS  =   10
-    SAMPLES_PER_REPORT  =   10)"));
-  cfg.validate(now);
-  // No adjustment needed
-  EXPECT_EQ(cfg.samplesPerReport(), 10);
-  EXPECT_TRUE(cfg.parse(R"(
-    SAMPLE_PERIOD_MSECS = 1000
-    REPORT_PERIOD_SECS  =    2
-    SAMPLES_PER_REPORT  =   10)"));
-  cfg.validate(now);
-  // Adjusted to 2 samples per report
-  EXPECT_EQ(cfg.samplesPerReport(), 2);
-  EXPECT_TRUE(cfg.parse(R"(
-    SAMPLE_PERIOD_MSECS = 200
-    REPORT_PERIOD_SECS  =   2
-    SAMPLES_PER_REPORT  =  10)"));
-  cfg.validate(now);
-  // No adjustment needed
-  EXPECT_EQ(cfg.samplesPerReport(), 10);
-  EXPECT_TRUE(cfg.parse("SAMPLES_PER_REPORT=0"));
-  cfg.validate(now);
-  // Adjusted up to 1
-  EXPECT_EQ(cfg.samplesPerReport(), 1);
-  // Invalid value types
-  EXPECT_FALSE(cfg.parse("SAMPLES_PER_REPORT=-10"));
-  EXPECT_FALSE(cfg.parse("SAMPLES_PER_REPORT=1.5"));
-  EXPECT_EQ(cfg.samplesPerReport(), 1);
-
-  EXPECT_TRUE(cfg.parse(R"(
-    SAMPLE_PERIOD_MSECS=1000
-    MULTIPLEX_PERIOD_MSECS=500 # Must be a multiple of sample period
-    REPORT_PERIOD_SECS=0       # Must be non-zero multiple of multiplex period
-    SAMPLES_PER_REPORT=5       # Max report period / multiplex period)"));
-  cfg.validate(now);
-  // Multiple adjustments
-  EXPECT_EQ(cfg.samplePeriod(), milliseconds(1000));
-  EXPECT_EQ(cfg.multiplexPeriod(), milliseconds(1000));
-  EXPECT_EQ(cfg.reportPeriod(), seconds(1));
-  EXPECT_EQ(cfg.samplesPerReport(), 1);
-}
-
-TEST(ParseTest, EnableSigUsr2) {
-  Config cfg;
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=yes"));
-  EXPECT_TRUE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=no"));
-  EXPECT_FALSE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=YES"));
-  EXPECT_TRUE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=NO"));
-  EXPECT_FALSE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=Y"));
-  EXPECT_TRUE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=N"));
-  EXPECT_FALSE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=T"));
-  EXPECT_TRUE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=F"));
-  EXPECT_FALSE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=true"));
-  EXPECT_TRUE(cfg.sigUsr2Enabled());
-  EXPECT_TRUE(cfg.parse("ENABLE_SIGUSR2=false"));
-  EXPECT_FALSE(cfg.sigUsr2Enabled());
-  EXPECT_FALSE(cfg.parse("ENABLE_SIGUSR2=  "));
-  EXPECT_FALSE(cfg.parse("ENABLE_SIGUSR2=2"));
-  EXPECT_FALSE(cfg.parse("ENABLE_SIGUSR2=-1"));
-  EXPECT_FALSE(cfg.parse("ENABLE_SIGUSR2=yep"));
-}
-
-TEST(ParseTest, DeviceMask) {
-  Config cfg;
-  // Single device
-  EXPECT_TRUE(cfg.parse("EVENTS_ENABLED_DEVICES = 0"));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(0));
-  EXPECT_FALSE(cfg.eventProfilerEnabledForDevice(1));
-
-  // Two devices, internal whitespace
-  EXPECT_TRUE(cfg.parse("EVENTS_ENABLED_DEVICES = 1, 2"));
-  EXPECT_FALSE(cfg.eventProfilerEnabledForDevice(0));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(1));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(2));
-  EXPECT_FALSE(cfg.eventProfilerEnabledForDevice(3));
-
-  // Three devices, check that previous devices are ignored
-  EXPECT_TRUE(cfg.parse("EVENTS_ENABLED_DEVICES = 0, 2,4"));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(0));
-  EXPECT_FALSE(cfg.eventProfilerEnabledForDevice(1));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(2));
-  EXPECT_FALSE(cfg.eventProfilerEnabledForDevice(3));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(4));
-  EXPECT_FALSE(cfg.eventProfilerEnabledForDevice(5));
-
-  // Repeated numbers have no effect
-  EXPECT_TRUE(cfg.parse("EVENTS_ENABLED_DEVICES = 0,1,1,1,2,3,2,1,3,7,7,3"));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(0));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(1));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(2));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(3));
-  EXPECT_FALSE(cfg.eventProfilerEnabledForDevice(4));
-  EXPECT_FALSE(cfg.eventProfilerEnabledForDevice(6));
-  EXPECT_TRUE(cfg.eventProfilerEnabledForDevice(7));
-
-  // 8 is larger than the max allowed
-  EXPECT_FALSE(cfg.parse("EVENTS_ENABLED_DEVICES = 3,8"));
-
-  // 300 cannot be held in an uint8_t
-  EXPECT_FALSE(cfg.parse("EVENTS_ENABLED_DEVICES = 300"));
-
-  // Various illegal cases
-  EXPECT_FALSE(cfg.parse("EVENTS_ENABLED_DEVICES = 0,1,two,three"));
-  EXPECT_FALSE(cfg.parse("EVENTS_ENABLED_DEVICES = 0,1,,2"));
-  EXPECT_FALSE(cfg.parse("EVENTS_ENABLED_DEVICES = -1"));
-  EXPECT_FALSE(cfg.parse("EVENTS_ENABLED_DEVICES = 1.0"));
-}
-
-TEST(ParseTest, RequestTime) {
-  Config cfg;
-  system_clock::time_point now = system_clock::now();
-  int64_t tgood_ms =
-      duration_cast<milliseconds>(now.time_since_epoch()).count();
-  EXPECT_TRUE(cfg.parse(fmt::format("REQUEST_TIMESTAMP = {}", tgood_ms)));
-
-  tgood_ms = duration_cast<milliseconds>((now - seconds(5)).time_since_epoch())
-                 .count();
-  EXPECT_TRUE(cfg.parse(fmt::format("REQUEST_TIMESTAMP = {}", tgood_ms)));
-
-  int64_t tbad_ms =
-      duration_cast<milliseconds>((now - seconds(20)).time_since_epoch())
-          .count();
-  EXPECT_FALSE(cfg.parse(fmt::format("REQUEST_TIMESTAMP = {}", tbad_ms)));
-
-  EXPECT_FALSE(cfg.parse("REQUEST_TIMESTAMP = 0"));
-  EXPECT_FALSE(cfg.parse("REQUEST_TIMESTAMP = -1"));
-
-  tbad_ms = duration_cast<milliseconds>((now + seconds(10)).time_since_epoch())
-                .count();
-  EXPECT_FALSE(cfg.parse(fmt::format("REQUEST_TIMESTAMP = {}", tbad_ms)));
-}
diff --git a/plugins/tensorboard-plugins/libkineto/test/CuptiActivityProfilerTest.cpp b/plugins/tensorboard-plugins/libkineto/test/CuptiActivityProfilerTest.cpp
deleted file mode 100644
index 6e67980ee31..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/CuptiActivityProfilerTest.cpp
+++ /dev/null
@@ -1,629 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <fmt/format.h>
-#include <gmock/gmock.h>
-#include <gtest/gtest.h>
-#include <strings.h>
-#include <time.h>
-#include <chrono>
-
-#ifdef __linux__
-#include <sys/stat.h>
-#include <sys/types.h>
-#include <fcntl.h>
-#endif
-
-#include "include/libkineto.h"
-#include "include/Config.h"
-#include "src/CuptiActivityProfiler.h"
-#include "src/ActivityTrace.h"
-#include "src/CuptiActivityApi.h"
-#include "src/output_base.h"
-#include "src/output_json.h"
-#include "src/output_membuf.h"
-
-#include "src/Logger.h"
-#include "test/MockActivitySubProfiler.h"
-
-using namespace std::chrono;
-using namespace KINETO_NAMESPACE;
-
-#define CUDA_LAUNCH_KERNEL CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000
-#define CUDA_MEMCPY CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy_v3020
-
-namespace {
-const TraceSpan& defaultTraceSpan() {
-  static TraceSpan span(0, 0, "Unknown", "");
-  return span;
-}
-}
-
-// Provides ability to easily create a few test CPU-side ops
-struct MockCpuActivityBuffer : public CpuTraceBuffer {
-  MockCpuActivityBuffer(int64_t startTime, int64_t endTime) {
-    span = TraceSpan(startTime, endTime,"Test trace");
-    gpuOpCount = 0;
-  }
-
-  void addOp(std::string name, int64_t startTime, int64_t endTime, int64_t correlation) {
-    GenericTraceActivity op(span, ActivityType::CPU_OP, name);
-    op.startTime = startTime;
-    op.endTime = endTime;
-    op.resource = systemThreadId();
-    op.id = correlation;
-    activities.push_back(std::move(op));
-    span.opCount++;
-  }
-};
-
-// Provides ability to easily create a few test CUPTI ops
-struct MockCuptiActivityBuffer {
-  void addCorrelationActivity(int64_t correlation, CUpti_ExternalCorrelationKind externalKind, int64_t externalId) {
-    auto& act = *(CUpti_ActivityExternalCorrelation*) malloc(sizeof(CUpti_ActivityExternalCorrelation));
-    act.kind = CUPTI_ACTIVITY_KIND_EXTERNAL_CORRELATION;
-    act.externalId = externalId;
-    act.externalKind = externalKind;
-    act.correlationId = correlation;
-    activities.push_back(reinterpret_cast<CUpti_Activity*>(&act));
-  }
-
-  void addRuntimeActivity(
-      CUpti_runtime_api_trace_cbid_enum cbid,
-      int64_t start_us, int64_t end_us, int64_t correlation) {
-    auto& act = createActivity<CUpti_ActivityAPI>(
-        start_us, end_us, correlation);
-    act.kind = CUPTI_ACTIVITY_KIND_RUNTIME;
-    act.cbid = cbid;
-    act.threadId = threadId();
-    activities.push_back(reinterpret_cast<CUpti_Activity*>(&act));
-  }
-
-  void addKernelActivity(
-      int64_t start_us, int64_t end_us, int64_t correlation) {
-    auto& act = createActivity<CUpti_ActivityKernel4>(
-        start_us, end_us, correlation);
-    act.kind = CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL;
-    act.deviceId = 0;
-    act.streamId = 1;
-    act.name = "kernel";
-    act.gridX = act.gridY = act.gridZ = 1;
-    act.blockX = act.blockY = act.blockZ = 1;
-    activities.push_back(reinterpret_cast<CUpti_Activity*>(&act));
-  }
-
-  void addMemcpyActivity(
-      int64_t start_us, int64_t end_us, int64_t correlation) {
-    auto& act = createActivity<CUpti_ActivityMemcpy>(
-        start_us, end_us, correlation);
-    act.kind = CUPTI_ACTIVITY_KIND_MEMCPY;
-    act.deviceId = 0;
-    act.streamId = 2;
-    act.copyKind = CUPTI_ACTIVITY_MEMCPY_KIND_HTOD;
-    act.srcKind = CUPTI_ACTIVITY_MEMORY_KIND_PINNED;
-    act.dstKind = CUPTI_ACTIVITY_MEMORY_KIND_DEVICE;
-    activities.push_back(reinterpret_cast<CUpti_Activity*>(&act));
-  }
-
-  template<class T>
-  T& createActivity(
-      int64_t start_us, int64_t end_us, int64_t correlation) {
-    T& act = *static_cast<T*>(malloc(sizeof(T)));
-    bzero(&act, sizeof(act));
-    act.start = start_us * 1000;
-    act.end = end_us * 1000;
-    act.correlationId = correlation;
-    return act;
-  }
-
-  ~MockCuptiActivityBuffer() {
-    for (CUpti_Activity* act : activities) {
-      free(act);
-    }
-  }
-
-  std::vector<CUpti_Activity*> activities;
-};
-
-// Mock parts of the CuptiActivityApi
-class MockCuptiActivities : public CuptiActivityApi {
- public:
-  virtual int smCount() override {
-    return 10;
-  }
-
-  virtual const std::pair<int, int> processActivities(
-      CuptiActivityBufferMap&, /*unused*/
-      std::function<void(const CUpti_Activity*)> handler) override {
-    for (CUpti_Activity* act : activityBuffer->activities) {
-      handler(act);
-    }
-    return {activityBuffer->activities.size(), 100};
-  }
-
-  virtual std::unique_ptr<CuptiActivityBufferMap>
-  activityBuffers() override {
-    auto map = std::make_unique<CuptiActivityBufferMap>();
-    auto buf = std::make_unique<CuptiActivityBuffer>(100);
-    uint8_t* addr = buf->data();
-    (*map)[addr] = std::move(buf);
-    return map;
-  }
-
-  void bufferRequestedOverride(uint8_t** buffer, size_t* size, size_t* maxNumRecords) {
-    this->bufferRequested(buffer, size, maxNumRecords);
-  }
-
-  std::unique_ptr<MockCuptiActivityBuffer> activityBuffer;
-};
-
-
-// Common setup / teardown and helper functions
-class CuptiActivityProfilerTest : public ::testing::Test {
- protected:
-  void SetUp() override {
-    profiler_ = std::make_unique<CuptiActivityProfiler>(
-        cuptiActivities_, /*cpu only*/ false);
-    cfg_ = std::make_unique<Config>();
-    cfg_->validate(std::chrono::system_clock::now());
-    loggerFactory.addProtocol("file", [](const std::string& url) {
-        return std::unique_ptr<ActivityLogger>(new ChromeTraceLogger(url));
-    });
-  }
-
-  std::unique_ptr<Config> cfg_;
-  MockCuptiActivities cuptiActivities_;
-  std::unique_ptr<CuptiActivityProfiler> profiler_;
-  ActivityLoggerFactory loggerFactory;
-};
-
-void checkTracefile(const char* filename) {
-#ifdef __linux__
-  // Check that the expected file was written and that it has some content
-  int fd = open(filename, O_RDONLY);
-  if (!fd) {
-    perror(filename);
-  }
-  EXPECT_TRUE(fd);
-  // Should expect at least 100 bytes
-  struct stat buf{};
-  fstat(fd, &buf);
-  EXPECT_GT(buf.st_size, 100);
-  close(fd);
-#endif
-}
-
-TEST(CuptiActivityProfiler, AsyncTrace) {
-  std::vector<std::string> log_modules(
-      {"CuptiActivityProfiler.cpp", "output_json.cpp"});
-  SET_LOG_VERBOSITY_LEVEL(1, log_modules);
-
-  MockCuptiActivities activities;
-  CuptiActivityProfiler profiler(activities, /*cpu only*/ true);
-
-  char filename[] = "/tmp/libkineto_testXXXXXX.json";
-  mkstemps(filename, 5);
-
-  Config cfg;
-
-  int iter = 0;
-  int warmup = 5;
-  auto now = system_clock::now();
-  auto startTime = now + seconds(10);
-
-  bool success = cfg.parse(fmt::format(R"CFG(
-    ACTIVITIES_WARMUP_PERIOD_SECS = {}
-    ACTIVITIES_DURATION_SECS = 1
-    ACTIVITIES_LOG_FILE = {}
-    PROFILE_START_TIME = {}
-  )CFG", warmup, filename, duration_cast<milliseconds>(startTime.time_since_epoch()).count()));
-
-  EXPECT_TRUE(success);
-  EXPECT_FALSE(profiler.isActive());
-
-  auto logger = std::make_unique<ChromeTraceLogger>(cfg.activitiesLogFile());
-
-  // Usually configuration is done when now is startTime - warmup to kick off warmup
-  // but start right away in the test
-  profiler.configure(cfg, now);
-  profiler.setLogger(logger.get());
-
-  EXPECT_TRUE(profiler.isActive());
-
-  // fast forward in time and we have reached the startTime
-  now = startTime;
-
-  // Run the profiler
-  // Warmup
-  // performRunLoopStep is usually called by the controller loop and takes
-  // the current time and the controller's next wakeup time.
-  profiler.performRunLoopStep(
-      /* Current time */ now, /* Next wakeup time */ now);
-
-  auto next = now + milliseconds(1000);
-
-  // performRunLoopStep can also be called by an application thread to update iteration count
-  // since this config does not use iteration this should have no effect on the state
-  while (++iter < 20) {
-    profiler.performRunLoopStep(now, now, iter);
-  }
-
-  // Runloop should now be in collect state, so start workload
-  // Perform another runloop step, passing in the end profile time as current.
-  // This should terminate collection
-  profiler.performRunLoopStep(
-      /* Current time */ next, /* Next wakeup time */ next);
-  // One step needed for each of the Process and Finalize phases
-  // Doesn't really matter what times we pass in here.
-
-  EXPECT_TRUE(profiler.isActive());
-
-  auto nextnext = next + milliseconds(1000);
-
-  while (++iter < 40) {
-    profiler.performRunLoopStep(next, next, iter);
-  }
-
-  EXPECT_TRUE(profiler.isActive());
-
-  profiler.performRunLoopStep(nextnext,nextnext);
-  profiler.performRunLoopStep(nextnext,nextnext);
-
-  // Assert that tracing has completed
-  EXPECT_FALSE(profiler.isActive());
-
-  checkTracefile(filename);
-}
-
-TEST(CuptiActivityProfiler, AsyncTraceUsingIter) {
-  std::vector<std::string> log_modules(
-      {"CuptiActivityProfiler.cpp", "output_json.cpp"});
-  SET_LOG_VERBOSITY_LEVEL(1, log_modules);
-
-  auto runIterTest = [&](
-    int start_iter, int warmup_iters, int trace_iters) {
-
-    LOG(INFO ) << "Async Trace Test: start_iteration = " << start_iter
-               << " warmup iterations = " << warmup_iters
-               << " trace iterations = " << trace_iters;
-
-    MockCuptiActivities activities;
-    CuptiActivityProfiler profiler(activities, /*cpu only*/ true);
-
-    char filename[] = "/tmp/libkineto_testXXXXXX.json";
-    mkstemps(filename, 5);
-
-    Config cfg;
-
-    int iter = 0;
-    auto now = system_clock::now();
-
-    bool success = cfg.parse(fmt::format(R"CFG(
-      PROFILE_START_ITERATION = {}
-      ACTIVITIES_WARMUP_ITERATIONS={}
-      ACTIVITIES_ITERATIONS={}
-      ACTIVITIES_DURATION_SECS = 1
-      ACTIVITIES_LOG_FILE = {}
-    )CFG", start_iter, warmup_iters, trace_iters, filename));
-
-    EXPECT_TRUE(success);
-    EXPECT_FALSE(profiler.isActive());
-
-    auto logger = std::make_unique<ChromeTraceLogger>(cfg.activitiesLogFile());
-
-    // Usually configuration is done when now is startIter - warmup iter to kick off warmup
-    // but start right away in the test
-    while (iter < (start_iter - warmup_iters)) {
-      profiler.performRunLoopStep(now, now, iter++);
-    }
-
-    profiler.configure(cfg, now);
-    profiler.setLogger(logger.get());
-
-    EXPECT_TRUE(profiler.isActive());
-
-    // fast forward in time, mimicking what will happen in reality
-    now += seconds(10);
-    auto next = now + milliseconds(1000);
-
-    // this call to runloop step should not be effecting the state
-    profiler.performRunLoopStep(now, next);
-    EXPECT_TRUE(profiler.isActive());
-
-    // start trace collection
-    while (iter < start_iter) {
-      profiler.performRunLoopStep(now, next, iter++);
-    }
-
-    // Runloop should now be in collect state, so start workload
-
-    while (iter < (start_iter + trace_iters)) {
-      profiler.performRunLoopStep(now, next, iter++);
-    }
-
-    // One step is required for each of the Process and Finalize phases
-    // Doesn't really matter what times we pass in here.
-    if (iter >= (start_iter + trace_iters)) {
-      profiler.performRunLoopStep(now, next, iter++);
-    }
-    EXPECT_TRUE(profiler.isActive());
-
-    auto nextnext = next + milliseconds(1000);
-
-    profiler.performRunLoopStep(nextnext, nextnext);
-    profiler.performRunLoopStep(nextnext, nextnext);
-
-    // Assert that tracing has completed
-    EXPECT_FALSE(profiler.isActive());
-
-    checkTracefile(filename);
-  };
-
-  // start iter = 50, warmup iters = 5, trace iters = 10
-  runIterTest(50, 5, 10);
-  // should be able to start at 0 iteration
-  runIterTest(0, 0, 2);
-  runIterTest(0, 5, 5);
-}
-
-TEST_F(CuptiActivityProfilerTest, SyncTrace) {
-  using ::testing::Return;
-  using ::testing::ByMove;
-
-  // Verbose logging is useful for debugging
-  std::vector<std::string> log_modules(
-      {"CuptiActivityProfiler.cpp"});
-  SET_LOG_VERBOSITY_LEVEL(2, log_modules);
-
-  // Start and stop profiling
-  CuptiActivityProfiler profiler(cuptiActivities_, /*cpu only*/ false);
-  int64_t start_time_us = 100;
-  int64_t duration_us = 300;
-  auto start_time = time_point<system_clock>(microseconds(start_time_us));
-  profiler.configure(*cfg_, start_time);
-  profiler.startTrace(start_time);
-  profiler.stopTrace(start_time + microseconds(duration_us));
-
-  profiler.recordThreadInfo();
-
-  // Log some cpu ops
-  auto cpuOps = std::make_unique<MockCpuActivityBuffer>(
-      start_time_us, start_time_us + duration_us);
-  cpuOps->addOp("op1", 120, 150, 1);
-  cpuOps->addOp("op2", 130, 140, 2);
-  cpuOps->addOp("op3", 200, 250, 3);
-  profiler.transferCpuTrace(std::move(cpuOps));
-
-  // And some GPU ops
-  auto gpuOps = std::make_unique<MockCuptiActivityBuffer>();
-  gpuOps->addRuntimeActivity(CUDA_LAUNCH_KERNEL, 133, 138, 1);
-  gpuOps->addRuntimeActivity(CUDA_MEMCPY, 210, 220, 2);
-  gpuOps->addRuntimeActivity(CUDA_LAUNCH_KERNEL, 230, 245, 3);
-  gpuOps->addKernelActivity(150, 170, 1);
-  gpuOps->addMemcpyActivity(240, 250, 2);
-  gpuOps->addKernelActivity(260, 320, 3);
-  cuptiActivities_.activityBuffer = std::move(gpuOps);
-
-  // Have the profiler process them
-  auto logger = std::make_unique<MemoryTraceLogger>(*cfg_);
-  profiler.processTrace(*logger);
-
-  // Profiler can be reset at this point - logger owns the activities
-  profiler_->reset();
-
-  // Wrapper that allows iterating over the activities
-  ActivityTrace trace(std::move(logger), loggerFactory);
-  EXPECT_EQ(trace.activities()->size(), 9);
-  std::map<std::string, int> activityCounts;
-  std::map<int64_t, int> resourceIds;
-  for (auto& activity : *trace.activities()) {
-    activityCounts[activity->name()]++;
-    resourceIds[activity->resourceId()]++;
-  }
-  for (const auto& p : activityCounts) {
-    LOG(INFO) << p.first << ": " << p.second;
-  }
-  EXPECT_EQ(activityCounts["op1"], 1);
-  EXPECT_EQ(activityCounts["op2"], 1);
-  EXPECT_EQ(activityCounts["op3"], 1);
-  EXPECT_EQ(activityCounts["cudaLaunchKernel"], 2);
-  EXPECT_EQ(activityCounts["cudaMemcpy"], 1);
-  EXPECT_EQ(activityCounts["kernel"], 2);
-  EXPECT_EQ(activityCounts["Memcpy HtoD (Pinned -> Device)"], 1);
-
-  auto sysTid = systemThreadId();
-  // Ops and runtime events are on thread sysTid
-  EXPECT_EQ(resourceIds[sysTid], 6);
-  // Kernels are on stream 1, memcpy on stream 2
-  EXPECT_EQ(resourceIds[1], 2);
-  EXPECT_EQ(resourceIds[2], 1);
-
-#ifdef __linux__
-  char filename[] = "/tmp/libkineto_testXXXXXX.json";
-  mkstemps(filename, 5);
-  trace.save(filename);
-  // Check that the expected file was written and that it has some content
-  int fd = open(filename, O_RDONLY);
-  if (!fd) {
-    perror(filename);
-  }
-  EXPECT_TRUE(fd);
-  // Should expect at least 100 bytes
-  struct stat buf{};
-  fstat(fd, &buf);
-  EXPECT_GT(buf.st_size, 100);
-#endif
-}
-
-TEST_F(CuptiActivityProfilerTest, GpuUserAnnotationTest) {
-  // Verbose logging is useful for debugging
-  std::vector<std::string> log_modules(
-      {"CuptiActivityProfiler.cpp"});
-  SET_LOG_VERBOSITY_LEVEL(2, log_modules);
-
-  // Start and stop profiling
-  CuptiActivityProfiler profiler(cuptiActivities_, /*cpu only*/ false);
-  int64_t start_time_us = 100;
-  int64_t duration_us = 300;
-  auto start_time = time_point<system_clock>(microseconds(start_time_us));
-  profiler.configure(*cfg_, start_time);
-  profiler.startTrace(start_time);
-  profiler.stopTrace(start_time + microseconds(duration_us));
-
-  int64_t kernelLaunchTime = 120;
-  profiler.recordThreadInfo();
-
-  // set up CPU event
-  auto cpuOps = std::make_unique<MockCpuActivityBuffer>(
-      start_time_us, start_time_us + duration_us);
-  cpuOps->addOp("annotation", kernelLaunchTime, kernelLaunchTime + 10, 1);
-  profiler.transferCpuTrace(std::move(cpuOps));
-
-  // set up a couple of GPU events and correlate with above CPU event.
-  // CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM1 is used for user annotations.
-  auto gpuOps = std::make_unique<MockCuptiActivityBuffer>();
-  gpuOps->addCorrelationActivity(1, CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM1, 1);
-  gpuOps->addKernelActivity(kernelLaunchTime + 5, kernelLaunchTime + 10, 1);
-  gpuOps->addCorrelationActivity(1, CUPTI_EXTERNAL_CORRELATION_KIND_CUSTOM1, 1);
-  gpuOps->addKernelActivity(kernelLaunchTime + 15, kernelLaunchTime + 25, 1);
-  cuptiActivities_.activityBuffer = std::move(gpuOps);
-
-  // process trace
-  auto logger = std::make_unique<MemoryTraceLogger>(*cfg_);
-  profiler.processTrace(*logger);
-
-  ActivityTrace trace(std::move(logger), loggerFactory);
-  std::map<std::string, int> counts;
-  for (auto& activity : *trace.activities()) {
-    counts[activity->name()]++;
-  }
-
-  // We should now have an additional annotation activity created
-  // on the GPU timeline.
-  EXPECT_EQ(counts["annotation"], 2);
-  EXPECT_EQ(counts["kernel"], 2);
-
-  auto& annotation = trace.activities()->at(0);
-  auto& kernel1 = trace.activities()->at(1);
-  auto& kernel2 = trace.activities()->at(2);
-  auto& gpu_annotation = trace.activities()->at(3);
-  EXPECT_EQ(gpu_annotation->type(), ActivityType::GPU_USER_ANNOTATION);
-  EXPECT_EQ(gpu_annotation->timestamp(), kernel1->timestamp());
-  EXPECT_EQ(
-      gpu_annotation->duration(),
-      kernel2->timestamp() + kernel2->duration() - kernel1->timestamp());
-  EXPECT_EQ(gpu_annotation->deviceId(), kernel1->deviceId());
-  EXPECT_EQ(gpu_annotation->resourceId(), kernel1->resourceId());
-  EXPECT_EQ(gpu_annotation->correlationId(), annotation->correlationId());
-  EXPECT_EQ(gpu_annotation->name(),  annotation->name());
-}
-
-TEST_F(CuptiActivityProfilerTest, SubActivityProfilers) {
-  using ::testing::Return;
-  using ::testing::ByMove;
-
-  // Verbose logging is useful for debugging
-  std::vector<std::string> log_modules(
-      {"CuptiActivityProfiler.cpp"});
-  SET_LOG_VERBOSITY_LEVEL(2, log_modules);
-
-  // Setup example events to test
-  GenericTraceActivity ev{defaultTraceSpan(), ActivityType::GLOW_RUNTIME, ""};
-  ev.device = 1;
-  ev.resource = 0;
-
-  int64_t start_time_us = 100;
-  int64_t duration_us = 1000;
-  auto start_time = time_point<system_clock>(microseconds(start_time_us));
-
-  std::vector<GenericTraceActivity> test_activities{3, ev};
-  test_activities[0].startTime = start_time_us;
-  test_activities[0].endTime = start_time_us + 5000;
-  test_activities[0].activityName = "SubGraph A execution";
-  test_activities[1].startTime = start_time_us;
-  test_activities[1].endTime = start_time_us + 2000;
-  test_activities[1].activityName = "Operator foo";
-  test_activities[2].startTime = start_time_us + 2500;
-  test_activities[2].endTime = start_time_us + 2900;
-  test_activities[2].activityName = "Operator bar";
-
-  auto mock_activity_profiler =
-    std::make_unique<MockActivityProfiler>(test_activities);
-
-  MockCuptiActivities activities;
-  CuptiActivityProfiler profiler(activities, /*cpu only*/ true);
-  profiler.addChildActivityProfiler(
-      std::move(mock_activity_profiler));
-
-  profiler.configure(*cfg_, start_time);
-  profiler.startTrace(start_time);
-  EXPECT_TRUE(profiler.isActive());
-
-  profiler.stopTrace(start_time + microseconds(duration_us));
-  EXPECT_TRUE(profiler.isActive());
-
-  char filename[] = "/tmp/libkineto_testXXXXXX.json";
-  mkstemps(filename, 5);
-  LOG(INFO) << "Logging to tmp file " << filename;
-
-  // process trace
-  auto logger = std::make_unique<MemoryTraceLogger>(*cfg_);
-  profiler.processTrace(*logger);
-  profiler.setLogger(logger.get());
-
-  ActivityTrace trace(std::move(logger), loggerFactory);
-  trace.save(filename);
-  const auto& traced_activites = trace.activities();
-
-  // Test we have all the events
-  EXPECT_EQ(traced_activites->size(), test_activities.size());
-
-  // Check that the expected file was written and that it has some content
-  int fd = open(filename, O_RDONLY);
-  if (!fd) {
-    perror(filename);
-  }
-  EXPECT_TRUE(fd);
-
-  // Should expect at least 100 bytes
-  struct stat buf{};
-  fstat(fd, &buf);
-  EXPECT_GT(buf.st_size, 100);
-}
-
-TEST_F(CuptiActivityProfilerTest, BufferSizeLimitTestWarmup) {
-  CuptiActivityProfiler profiler(cuptiActivities_, /*cpu only*/ false);
-
-  auto now = system_clock::now();
-  auto startTime = now + seconds(10);
-
-  int maxBufferSizeMB = 3;
-
-  auto startTimeEpoch = std::to_string(duration_cast<milliseconds>(startTime.time_since_epoch()).count());
-  std::string maxBufferSizeMBStr = std::to_string(maxBufferSizeMB);
-  cfg_->handleOption("ACTIVITIES_MAX_GPU_BUFFER_SIZE_MB", maxBufferSizeMBStr);
-  cfg_->handleOption("PROFILE_START_TIME", startTimeEpoch);
-
-
-  EXPECT_FALSE(profiler.isActive());
-  profiler.configure(*cfg_, now);
-  EXPECT_TRUE(profiler.isActive());
-
-  for (size_t i = 0; i < maxBufferSizeMB; i++) {
-    uint8_t* buf;
-    size_t gpuBufferSize;
-    size_t maxNumRecords;
-    cuptiActivities_.bufferRequestedOverride(&buf, &gpuBufferSize, &maxNumRecords);
-  }
-
-  // fast forward to startTime and profiler is now running
-  now = startTime;
-
-  profiler.performRunLoopStep(now, now);
-
-  auto next = now + milliseconds(1000);
-  profiler.performRunLoopStep(next, next);
-  profiler.performRunLoopStep(next, next);
-  profiler.performRunLoopStep(next, next);
-
-  EXPECT_FALSE(profiler.isActive());
-}
diff --git a/plugins/tensorboard-plugins/libkineto/test/CuptiCallbackApiTest.cpp b/plugins/tensorboard-plugins/libkineto/test/CuptiCallbackApiTest.cpp
deleted file mode 100644
index 253b696da54..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/CuptiCallbackApiTest.cpp
+++ /dev/null
@@ -1,239 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "src/Logger.h"
-#include "src/CuptiCallbackApi.h"
-
-#include <gtest/gtest.h>
-#include <atomic>
-#include <chrono>
-#include <thread>
-
-using namespace std::chrono;
-using namespace KINETO_NAMESPACE;
-using namespace libkineto;
-
-const size_t some_data = 42;
-
-std::atomic<int> simple_cb_calls = 0;
-
-void simple_cb(
-    CUpti_CallbackDomain domain,
-    CUpti_CallbackId cbid,
-    const CUpti_CallbackData* cbInfo) {
-
-  // simple arg check
-  EXPECT_EQ(domain, CUPTI_CB_DOMAIN_RUNTIME_API);
-  EXPECT_EQ(cbid, CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000);
-  EXPECT_EQ(*reinterpret_cast<const size_t*>(cbInfo), some_data);
-
-  simple_cb_calls++;
-}
-
-void atomic_cb(
-    CUpti_CallbackDomain /*domain*/,
-    CUpti_CallbackId /*cbid*/,
-    const CUpti_CallbackData* /*cbInfo)*/) {
-  // do some atomics in a loop
-  for (int i = 0; i < 1000; i++) {
-    // would have used release consistency but this is fine
-    simple_cb_calls++;
-  }
-}
-
-void empty_cb(
-    CUpti_CallbackDomain /*domain*/,
-    CUpti_CallbackId /*cbid*/,
-    const CUpti_CallbackData* /*cbInfo*/) {
-}
-
-TEST(CuptiCallbackApiTest, SimpleTest) {
-  auto& api = CuptiCallbackApi::singleton();
-
-  auto addSimpleCallback = [&]() -> bool {
-    bool ret = api.registerCallback(
-        CUPTI_CB_DOMAIN_RUNTIME_API,
-        CuptiCallbackApi::CUDA_LAUNCH_KERNEL,
-        &simple_cb
-    );
-    return ret;
-  };
-  EXPECT_TRUE(addSimpleCallback()) << "Failed to add callback";
-
-  // duplicate add should be okay
-  EXPECT_TRUE(addSimpleCallback()) << "Failed to re-add callback";
-
-  simple_cb_calls = 0;
-
-  // simulate callback
-  api.__callback_switchboard(
-      CUPTI_CB_DOMAIN_RUNTIME_API,
-      CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000,
-      reinterpret_cast<const CUpti_CallbackData*>(&some_data));
-
-  EXPECT_EQ(simple_cb_calls, 1);
-
-  bool ret = api.deleteCallback(
-      CUPTI_CB_DOMAIN_RUNTIME_API,
-      CuptiCallbackApi::CUDA_LAUNCH_KERNEL,
-      &simple_cb
-  );
-
-  EXPECT_TRUE(ret) << "Failed to remove callback";
-
-  ret = api.deleteCallback(
-      CUPTI_CB_DOMAIN_RUNTIME_API,
-      CuptiCallbackApi::CUDA_LAUNCH_KERNEL,
-      &atomic_cb
-  );
-
-  EXPECT_FALSE(ret) << "oops! deleted a callback that was never added";
-}
-
-TEST(CuptiCallbackApiTest, AllCallbacks) {
-  auto& api = CuptiCallbackApi::singleton();
-
-  auto testCallback = [&](
-      CUpti_CallbackDomain domain,
-      CUpti_CallbackId cbid,
-      CuptiCallbackApi::CuptiCallBackID kineto_cbid) -> bool {
-
-    bool ret = api.registerCallback(domain, kineto_cbid, atomic_cb);
-    EXPECT_TRUE(ret) << "Failed to add callback";
-
-    if (!ret) {
-      return false;
-    }
-
-    simple_cb_calls = 0;
-    api.__callback_switchboard(domain, cbid, nullptr);
-    EXPECT_EQ(simple_cb_calls, 1000);
-    ret = simple_cb_calls == 1000;
-
-    EXPECT_TRUE(api.deleteCallback(domain, kineto_cbid, atomic_cb));
-
-    return ret;
-  };
-
-  EXPECT_TRUE(
-      testCallback(
-        CUPTI_CB_DOMAIN_RESOURCE,
-        CUPTI_CBID_RESOURCE_CONTEXT_CREATED,
-        CuptiCallbackApi::RESOURCE_CONTEXT_CREATED))
-    << "Failed to run callback for RESOURCE_CONTEXT_CREATED";
-
-  EXPECT_TRUE(
-      testCallback(
-        CUPTI_CB_DOMAIN_RESOURCE,
-        CUPTI_CBID_RESOURCE_CONTEXT_DESTROY_STARTING,
-        CuptiCallbackApi::RESOURCE_CONTEXT_DESTROYED))
-    << "Failed to run callback for RESOURCE_CONTEXT_DESTROYED";
-
-  EXPECT_TRUE(
-      testCallback(
-        CUPTI_CB_DOMAIN_RUNTIME_API,
-        CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000,
-        CuptiCallbackApi::CUDA_LAUNCH_KERNEL))
-    << "Failed to run callback for CUDA_LAUNCH_KERNEL";
-
-}
-
-TEST(CuptiCallbackApiTest, ContentionTest) {
-  auto& api = CuptiCallbackApi::singleton();
-  const CUpti_CallbackDomain domain = CUPTI_CB_DOMAIN_RUNTIME_API;
-  const CUpti_CallbackId cbid = CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000;
-  const CuptiCallbackApi::CuptiCallBackID kineto_cbid =
-    CuptiCallbackApi::CUDA_LAUNCH_KERNEL;
-
-  bool ret = api.registerCallback(domain, kineto_cbid, empty_cb);
-  EXPECT_TRUE(ret) << "Failed to add callback";
-
-  const int iters = 10000;
-  const int num_readers = 8;
-
-  simple_cb_calls = 0;
-
-  // simulate callbacks being executed on multiple threads in parallel
-  //  during this interval add a new atomic_callback.
-  //  this test ensured mutual exclusion is working fine
-  auto read_fn = [&](int tid){
-    auto start_ts = high_resolution_clock::now();
-    for (int i = 0; i < iters; i++) {
-      api.__callback_switchboard(domain, cbid, nullptr);
-    }
-    auto runtime_ms = duration_cast<milliseconds>(
-      high_resolution_clock::now() - start_ts);
-    LOG(INFO) << "th " << tid << " done in " << runtime_ms.count() << " ms";
-  };
-
-
-  std::vector<std::thread> read_ths;
-  for (int i = 0; i< num_readers; i++) {
-    read_ths.emplace_back(read_fn, i);
-  }
-
-  ret = api.registerCallback(domain, kineto_cbid, atomic_cb);
-  EXPECT_TRUE(ret) << "Failed to add callback";
-
-  for (auto& t : read_ths) {
-    t.join();
-  }
-
-  //EXPECT_GT(simple_cb_calls, 0)
-  //  << "Atomic callback should have been called at least once.";
-
-  api.deleteCallback(domain, kineto_cbid, empty_cb);
-  api.deleteCallback(domain, kineto_cbid, atomic_cb);
-}
-
-TEST(CuptiCallbackApiTest, Bechmark) {
-
-  constexpr int iters = 1000;
-  // atomic bench a number of times to get a baseline
-
-  const CUpti_CallbackDomain domain = CUPTI_CB_DOMAIN_RUNTIME_API;
-  const CUpti_CallbackId cbid = CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000;
-  const CuptiCallbackApi::CuptiCallBackID kineto_cbid =
-    CuptiCallbackApi::CUDA_LAUNCH_KERNEL;
-
-  LOG(INFO) << "Iteration count = " << iters;
-
-  const bool use_empty = true;
-  auto cbfn = use_empty ? &empty_cb : &atomic_cb;
-
-  // warmup
-  for (int i = 0; i < 50; i++) {
-    (*cbfn)(domain, cbid, nullptr);
-  }
-
-  auto start_ts = high_resolution_clock::now();
-  for (int i = 0; i < iters; i++) {
-    (*cbfn)(domain, cbid, nullptr);
-  }
-  auto delta_baseline_ns = duration_cast<nanoseconds>(
-      high_resolution_clock::now() - start_ts);
-  LOG(INFO) << "Baseline runtime  = " << delta_baseline_ns.count() << " ns";
-
-
-  auto& api = CuptiCallbackApi::singleton();
-  bool ret = api.registerCallback(domain, kineto_cbid, cbfn);
-  EXPECT_TRUE(ret) << "Failed to add callback";
-
-  // warmup
-  for (int i = 0; i < 50; i++) {
-    api.__callback_switchboard(domain, cbid, nullptr);
-  }
-
-  start_ts = high_resolution_clock::now();
-  for (int i = 0; i < iters; i++) {
-    api.__callback_switchboard(domain, cbid, nullptr);
-  }
-
-  auto delta_callback_ns = duration_cast<nanoseconds>(
-      high_resolution_clock::now() - start_ts);
-  LOG(INFO) << "Callback runtime  = " << delta_callback_ns.count() << " ns";
-
-  LOG(INFO) << "Callback runtime per iteration = " <<
-    (delta_callback_ns.count() - delta_baseline_ns.count()) / (double) iters
-    << " ns";
-
-}
diff --git a/plugins/tensorboard-plugins/libkineto/test/CuptiProfilerApiTest.cu b/plugins/tensorboard-plugins/libkineto/test/CuptiProfilerApiTest.cu
deleted file mode 100644
index 54ad51b0a1f..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/CuptiProfilerApiTest.cu
+++ /dev/null
@@ -1,353 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <string>
-#include <fmt/format.h>
-#include <gtest/gtest.h>
-
-#include <cuda.h>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "src/Logger.h"
-#include "src/CuptiRangeProfilerApi.h"
-
-#define DRIVER_API_CALL(apiFuncCall)                           \
-  do {                                                         \
-    CUresult _status = apiFuncCall;                            \
-    if (_status != CUDA_SUCCESS) {                             \
-      LOG(ERROR) << "Failed invoking CUDA driver function "    \
-                 << #apiFuncCall << " status = "               \
-                 << _status;                                   \
-      exit(-1);                                                \
-    }                                                          \
-  } while (0)
-
-#define EXPECT(expr)\
-  if (!(expr)) {\
-  };
-
-using namespace KINETO_NAMESPACE;
-
-static int numRanges = 1;
-
-using Type = double;
-
-// Device code
-__global__ void VecAdd(const Type* A, const Type* B, Type* C, int N) {
-  int i = blockDim.x * blockIdx.x + threadIdx.x;
-  if (i < N) {
-    C[i] = A[i] + B[i];
-  }
-}
-
-// Device code
-__global__ void VecSub(const Type* A, const Type* B, Type* C, int N) {
-  int i = blockDim.x * blockIdx.x + threadIdx.x;
-  if (i < N) {
-    C[i] = A[i] - B[i];
-  }
-}
-
-static void initVec(Type* vec, int n) {
-  for (int i = 0; i < n; i++) {
-    vec[i] = i;
-  }
-}
-
-static void cleanUp(
-    Type* h_A,
-    Type* h_B,
-    Type* h_C,
-    Type* h_D,
-    Type* d_A,
-    Type* d_B,
-    Type* d_C,
-    Type* d_D) {
-  if (d_A)
-    cudaFree(d_A);
-  if (d_B)
-    cudaFree(d_B);
-  if (d_C)
-    cudaFree(d_C);
-  if (d_D)
-    cudaFree(d_D);
-
-  // Free host memory
-  if (h_A)
-    free(h_A);
-  if (h_B)
-    free(h_B);
-  if (h_C)
-    free(h_C);
-  if (h_D)
-    free(h_D);
-}
-
-/* Benchmark application used to test profiler measurements
- * This simply runs two kernels vector Add and Vector Subtract
- */
-
-void VectorAddSubtract() {
-  int N = 50000;
-  size_t size = N * sizeof(Type);
-  int threadsPerBlock = 0;
-  int blocksPerGrid = 0;
-  Type *h_A, *h_B, *h_C, *h_D;
-  Type *d_A, *d_B, *d_C, *d_D;
-  int i;
-  Type sum, diff;
-
-  // Allocate input vectors h_A and h_B in host memory
-  h_A = (Type*)malloc(size);
-  h_B = (Type*)malloc(size);
-  h_C = (Type*)malloc(size);
-  h_D = (Type*)malloc(size);
-
-  // Initialize input vectors
-  initVec(h_A, N);
-  initVec(h_B, N);
-  memset(h_C, 0, size);
-  memset(h_D, 0, size);
-
-  // Allocate vectors in device memory
-  cudaMalloc((void**)&d_A, size);
-  cudaMalloc((void**)&d_B, size);
-  cudaMalloc((void**)&d_C, size);
-  cudaMalloc((void**)&d_D, size);
-
-  // Copy vectors from host memory to device memory
-  cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
-  cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
-
-  // Invoke kernel
-  threadsPerBlock = 256;
-  blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
-  LOG(INFO) << fmt::format(
-      "Launching kernel: blocks {}, thread/block {}",
-      blocksPerGrid,
-      threadsPerBlock);
-
-  VecAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, N);
-
-  VecSub<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_D, N);
-
-  // Copy result from device memory to host memory
-  // h_C contains the result in host memory
-  cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
-  cudaMemcpy(h_D, d_D, size, cudaMemcpyDeviceToHost);
-
-  // Verify result
-  for (i = 0; i < N; ++i) {
-    sum = h_A[i] + h_B[i];
-    diff = h_A[i] - h_B[i];
-    if (h_C[i] != sum || h_D[i] != diff) {
-      LOG(ERROR) << "Result verification failed";
-      break;
-    }
-  }
-
-  cleanUp(h_A, h_B, h_C, h_D, d_A, d_B, d_C, d_D);
-}
-
-#if HAS_CUPTI_RANGE_PROFILER
-bool runTestWithAutoRange(
-    int deviceNum,
-    const std::vector<std::string>& metricNames,
-    CUcontext cuContext,
-    bool async) {
-
-  // create a CUPTI range based profiling profiler
-  //  this configures the counter data as well
-  CuptiRBProfilerSession profiler(
-      metricNames, deviceNum, 2, 1, async ? nullptr : cuContext);
-
-  CUpti_ProfilerRange profilerRange = CUPTI_AutoRange;
-  CUpti_ProfilerReplayMode profilerReplayMode = CUPTI_KernelReplay;
-
-  if (async) {
-    profiler.asyncStartAndEnable(profilerRange, profilerReplayMode);
-  } else {
-    profiler.start(profilerRange, profilerReplayMode);
-    profiler.enable();
-  }
-
-  VectorAddSubtract();
-
-  if (!async) {
-    profiler.disable();
-    // stop profiler
-    profiler.stop();
-  } else {
-    profiler.asyncDisableAndStop();
-  }
-
-  auto result = profiler.evaluateMetrics(true);
-
-  // check results
-  EXPECT_EQ(result.metricNames.size(), 3);
-  EXPECT_EQ(result.rangeVals.size(), 2);
-
-  for (const auto& measurement : result.rangeVals) {
-    EXPECT_EQ(measurement.values.size(), 3);
-
-    if (measurement.values.size() == 3) {
-      // smsp__warps_launched.avg
-      EXPECT_NE(measurement.values[0], 0);
-      // smsp__sass_thread_inst_executed_op_dadd_pred_on.sum
-      // each kernel has 50000 dadd ops
-      EXPECT_EQ(measurement.values[1], 50000);
-      // sm__inst_executed_pipe_tensor.sum
-      //EXPECT_EQ(measurement.values[2], 0);
-    }
-  }
-  return true;
-}
-
-bool runTestWithUserRange(
-    int deviceNum,
-    const std::vector<std::string>& metricNames,
-    CUcontext cuContext,
-    bool async = false) {
-
-  // create a CUPTI range based profiling profiler
-  //  this configures the counter data as well
-  CuptiRBProfilerSession profiler(
-      metricNames, deviceNum, numRanges, 1, async ? nullptr : cuContext);
-
-  CUpti_ProfilerRange profilerRange = CUPTI_UserRange;
-  CUpti_ProfilerReplayMode profilerReplayMode = CUPTI_UserReplay;
-
-  if (async) {
-    profiler.asyncStartAndEnable(profilerRange, profilerReplayMode);
-    { VectorAddSubtract(); }
-    profiler.disableAndStop();
-  } else {
-    profiler.start(profilerRange, profilerReplayMode);
-
-    /* User takes the resposiblity of replaying the kernel launches */
-    bool replay = true;
-    do {
-      profiler.beginPass();
-      {
-        profiler.enable();
-
-        std::string rangeName = "vecAddSub";
-        profiler.pushRange(rangeName);
-
-        { VectorAddSubtract(); }
-
-        profiler.popRange();
-        profiler.disable();
-      }
-      LOG(INFO) << "Replay starting.";
-      replay = profiler.endPass();
-
-    } while (!replay);
-
-    // stop profiler
-    profiler.stop();
-  }
-  VectorAddSubtract();
-  auto result = profiler.evaluateMetrics(true);
-
-  // check results
-  EXPECT_EQ(result.metricNames.size(), 3);
-  EXPECT_EQ(result.rangeVals.size(), 1);
-
-  if (result.rangeVals.size() > 0) {
-    const auto& measurement = result.rangeVals[0];
-    EXPECT_EQ(measurement.values.size(), 3);
-
-    if (measurement.values.size() == 3) {
-      // smsp__warps_launched.avg
-      EXPECT_NE(measurement.values[0], 0);
-      // smsp__sass_thread_inst_executed_op_dadd_pred_on.sum
-      // in async mode multiple passes are not supported yet
-      if (!async) {
-        EXPECT_EQ(measurement.values[1], 100000);
-      }
-      // sm__inst_executed_pipe_tensor.sum
-      //EXPECT_EQ(measurement.values[2], 0);
-    }
-  }
-  return true;
-}
-#endif // HAS_CUPTI_RANGE_PROFILER
-
-int main(int argc, char* argv[]) {
-
-  CUdevice cuDevice;
-
-  int deviceCount, deviceNum;
-  int computeCapabilityMajor = 0, computeCapabilityMinor = 0;
-
-  printf("Usage: %s [device_num]\n", argv[0]);
-
-  DRIVER_API_CALL(cuInit(0));
-  DRIVER_API_CALL(cuDeviceGetCount(&deviceCount));
-
-  if (deviceCount == 0) {
-    LOG(ERROR) << "There is no device supporting CUDA.";
-    return -2;
-  }
-
-  if (argc > 1)
-    deviceNum = atoi(argv[1]);
-  else
-    deviceNum = 0;
-  LOG(INFO) << "CUDA Device Number: " << deviceNum;
-
-  DRIVER_API_CALL(cuDeviceGet(&cuDevice, deviceNum));
-  DRIVER_API_CALL(cuDeviceGetAttribute(
-      &computeCapabilityMajor,
-      CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR,
-      cuDevice));
-  DRIVER_API_CALL(cuDeviceGetAttribute(
-      &computeCapabilityMinor,
-      CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR,
-      cuDevice));
-
-  LOG(INFO) << "Compute Cabapbility = "
-            << fmt::format("{},{}",computeCapabilityMajor, computeCapabilityMinor);
-
-  if (computeCapabilityMajor < 7) {
-    LOG(ERROR) << "CUPTI Profiler is not supported  with compute capability < 7.0";
-    return -2;
-  }
-
-  CuptiRBProfilerSession::staticInit();
-
-  // metrics to profile
-  std::vector<std::string> metricNames = {
-    "smsp__warps_launched.avg",
-    "smsp__sass_thread_inst_executed_op_dadd_pred_on.sum",
-    "sm__inst_executed_pipe_tensor.sum",
-  };
-
-  CUcontext cuContext;
-  DRIVER_API_CALL(cuCtxCreate(&cuContext, 0, cuDevice));
-
-  VectorAddSubtract();
-
-#if HAS_CUPTI_RANGE_PROFILER
-  CuptiRBProfilerSession::staticInit();
-
-  if (!runTestWithUserRange(deviceNum, metricNames, cuContext, false)) {
-    LOG(ERROR) << "Failed to profiler test benchmark in user range";
-  } else if (!runTestWithAutoRange(deviceNum, metricNames, cuContext, false)) {
-    LOG(ERROR) << "Failed to profiler test benchmark in auto range";
-  } else if (!runTestWithUserRange(deviceNum, metricNames, cuContext, true)) {
-    LOG(ERROR) << "Failed to profiler test benchmark in user range async";
-  } else if (!runTestWithAutoRange(deviceNum, metricNames, cuContext, true)) {
-    LOG(ERROR) << "Failed to profiler test benchmark in auto range async";
-  }
-
-  CuptiRBProfilerSession::deInitCupti();
-#else
-  LOG(WARNING) << "CuptiRBProfilerSession is not supported.";
-#endif // HAS_CUPTI_RANGE_PROFILER
-  DRIVER_API_CALL(cuCtxDestroy(cuContext));
-
-
-  return 0;
-}
diff --git a/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerApiTest.cpp b/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerApiTest.cpp
deleted file mode 100644
index 28cad722c53..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerApiTest.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <gtest/gtest.h>
-#include <array>
-#include <set>
-
-#include "include/libkineto.h"
-#include "include/Config.h"
-#include "src/CuptiRangeProfilerApi.h"
-
-#include "src/Logger.h"
-#include "test/CuptiRangeProfilerTestUtil.h"
-
-using namespace KINETO_NAMESPACE;
-
-#if HAS_CUPTI_PROFILER
-
-TEST(CuptiRangeProfilerApiTest, contextTracking) {
-  std::vector<std::string> log_modules(
-      {"CuptiRangeProfilerApi.cpp"});
-  SET_LOG_VERBOSITY_LEVEL(1, log_modules);
-
-  std::array<int64_t, 3> data;
-  std::array<CUcontext, 3> contexts;
-  for (int i = 0; i < data.size(); i++) {
-    contexts[i] = reinterpret_cast<CUcontext>(&data[i]);
-  }
-
-  // simulate creating contexts, this calls the trackCudaContexts
-  // function that would otherwise be called via a callback
-  uint32_t dev = 0;
-  for (auto ctx : contexts) {
-    simulateCudaContextCreate(ctx, dev++);
-  }
-
-  EXPECT_EQ(
-      CuptiRBProfilerSession::getActiveDevices(),
-      std::set<uint32_t>({0, 1, 2}));
-
-  simulateCudaContextDestroy(contexts[1], 1);
-
-  EXPECT_EQ(
-      CuptiRBProfilerSession::getActiveDevices(),
-      std::set<uint32_t>({0, 2}));
-
-  simulateCudaContextDestroy(contexts[0], 0);
-  simulateCudaContextDestroy(contexts[2], 2);
-
-  EXPECT_TRUE(
-      CuptiRBProfilerSession::getActiveDevices().empty());
-}
-
-TEST(CuptiRangeProfilerApiTest, asyncLaunchUserRange) {
-  std::vector<std::string> log_modules(
-      {"CuptiRangeProfilerApi.cpp"});
-  SET_LOG_VERBOSITY_LEVEL(1, log_modules);
-
-  // this is bad but the pointer is never accessed
-  CUcontext ctx0 = reinterpret_cast<CUcontext>(10);
-  simulateCudaContextCreate(ctx0, 0 /*device_id*/);
-
-  auto session = std::make_unique<MockCuptiRBProfilerSession>(0, ctx0);
-  session->asyncStartAndEnable(CUPTI_UserRange, CUPTI_UserReplay);
-
-  simulateKernelLaunch(ctx0, "hello");
-  simulateKernelLaunch(ctx0, "foo");
-  simulateKernelLaunch(ctx0, "bar");
-
-  session->asyncDisableAndStop();
-  // stop happens after next kernel is run
-  simulateKernelLaunch(ctx0, "bar");
-  simulateCudaContextDestroy(ctx0, 0 /*device_id*/);
-
-  EXPECT_EQ(session->passes_ended, 1);
-  EXPECT_EQ(session->ranges_ended, 1);
-  EXPECT_TRUE(session->enabled);
-}
-
-TEST(CuptiRangeProfilerApiTest, asyncLaunchAutoRange) {
-  std::vector<std::string> log_modules(
-      {"CuptiRangeProfilerApi.cpp"});
-  SET_LOG_VERBOSITY_LEVEL(1, log_modules);
-
-  // this is bad but the pointer is never accessed
-  CUcontext ctx0 = reinterpret_cast<CUcontext>(10);
-  CUcontext ctx1 = reinterpret_cast<CUcontext>(11);
-
-  simulateCudaContextCreate(ctx0, 0 /*device_id*/);
-
-  auto session = std::make_unique<MockCuptiRBProfilerSession>(0, ctx0);
-  session->asyncStartAndEnable(CUPTI_AutoRange, CUPTI_KernelReplay);
-
-  simulateKernelLaunch(ctx0, "hello");
-  simulateKernelLaunch(ctx0, "foo");
-  simulateKernelLaunch(ctx1, "kernel_on_different_device");
-  simulateKernelLaunch(ctx0, "bar");
-
-  session->asyncDisableAndStop();
-  // stop happens after next kernel is run
-  simulateKernelLaunch(ctx0, "bar");
-  simulateCudaContextDestroy(ctx0, 0 /*device_id*/);
-
-  EXPECT_EQ(session->passes_ended, 0);
-  EXPECT_EQ(session->ranges_ended, 0);
-  EXPECT_TRUE(session->enabled);
-
-  EXPECT_EQ(
-      session->getKernelNames(),
-      std::vector<std::string>({"hello", "foo", "bar"}))
-    << "Kernel names were not tracked";
-}
-
-#endif // HAS_CUPTI_PROFILER
diff --git a/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerConfigTest.cpp b/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerConfigTest.cpp
deleted file mode 100644
index 3f568968238..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerConfigTest.cpp
+++ /dev/null
@@ -1,67 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "include/Config.h"
-#include "src/CuptiRangeProfilerConfig.h"
-
-#include <fmt/format.h>
-#include <gtest/gtest.h>
-#include <time.h>
-#include <chrono>
-
-using namespace std::chrono;
-using namespace KINETO_NAMESPACE;
-
-class CuptiRangeProfilerConfigTest : public ::testing::Test {
- protected:
-  void SetUp() override {
-    CuptiRangeProfilerConfig::registerFactory();
-  }
-};
-
-TEST_F(CuptiRangeProfilerConfigTest, ConfigureProfiler) {
-  Config cfg;
-  std::vector<std::string> metrics = {
-    "kineto__cuda_core_flops",
-    "sm__inst_executed.sum",
-    "l1tex__data_bank_conflicts_pipe_lsu.sum",
-  };
-  auto metricsConfigStr =
-        fmt::format("CUPTI_PROFILER_METRICS = {}", fmt::join(metrics, ","));
-
-  EXPECT_TRUE(cfg.parse(metricsConfigStr));
-  EXPECT_TRUE(cfg.parse("CUPTI_PROFILER_ENABLE_PER_KERNEL = true"));
-  EXPECT_TRUE(cfg.parse("CUPTI_PROFILER_MAX_RANGES = 42"));
-
-  const CuptiRangeProfilerConfig& cupti_cfg =
-    CuptiRangeProfilerConfig::get(cfg);
-
-  EXPECT_EQ(cupti_cfg.activitiesCuptiMetrics(), metrics);
-  EXPECT_EQ(cupti_cfg.cuptiProfilerPerKernel(), true);
-  EXPECT_EQ(cupti_cfg.cuptiProfilerMaxRanges(), 42);
-
-}
-
-TEST_F(CuptiRangeProfilerConfigTest, RangesDefaults) {
-  Config cfg, cfg_auto;
-
-  // do not set max ranges in config, check defaults are sane
-  EXPECT_TRUE(cfg.parse("CUPTI_PROFILER_METRICS = kineto__cuda_core_flops"));
-  EXPECT_TRUE(cfg.parse("CUPTI_PROFILER_ENABLE_PER_KERNEL = false"));
-
-  cfg.setSignalDefaults();
-
-  EXPECT_TRUE(cfg_auto.parse("CUPTI_PROFILER_METRICS = kineto__cuda_core_flops"));
-  EXPECT_TRUE(cfg_auto.parse("CUPTI_PROFILER_ENABLE_PER_KERNEL = true"));
-
-  cfg_auto.setClientDefaults();
-
-  int user_ranges, auto_ranges;
-
-  user_ranges = CuptiRangeProfilerConfig::get(cfg).cuptiProfilerMaxRanges();
-  auto_ranges = CuptiRangeProfilerConfig::get(cfg_auto).cuptiProfilerMaxRanges();
-
-  EXPECT_GE(user_ranges, 1) << " in user range mode default to at least 1 ranges";
-  EXPECT_GE(auto_ranges, 1000) << " in auto range mode default to at least 1000 ranges";
-
-  EXPECT_GT(auto_ranges, user_ranges);
-}
diff --git a/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerTestUtil.h b/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerTestUtil.h
deleted file mode 100644
index 861b65fd701..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/CuptiRangeProfilerTestUtil.h
+++ /dev/null
@@ -1,96 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <stdlib.h>
-#include <gtest/gtest.h>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "CuptiRangeProfilerApi.h"
-
-namespace KINETO_NAMESPACE {
-
-#if HAS_CUPTI_PROFILER
-
-class MockCuptiRBProfilerSession : public CuptiRBProfilerSession {
- public:
-  MockCuptiRBProfilerSession(int deviceId, CUcontext ctx)
-    : CuptiRBProfilerSession(deviceId, ctx) {}
-
-  void beginPass() override {
-    LOG(INFO) << " Mock CUPTI begin pass";
-    passes_started++;
-  }
-
-  bool endPass() override {
-    passes_ended++;
-    return true;
-  }
-
-  void flushCounterData() override {}
-
-  void pushRange(const std::string& rangeName) override {
-    LOG(INFO) << " Mock CUPTI pushrange ( " << rangeName << " )";
-    ranges_started++;
-  }
-
-  void popRange() override {
-    LOG(INFO) << " Mock CUPTI poprange";
-    ranges_ended++;
-  }
-
-  void stop() override {
-    runChecks();
-  }
-
-  void enable() override {
-    enabled = true;
-  }
-  void disable() override {}
-
-  CuptiProfilerResult evaluateMetrics(bool /*verbose*/) override {
-    return result;
-  }
-
-protected:
-  void startInternal(
-      CUpti_ProfilerRange profilerRange,
-      CUpti_ProfilerReplayMode profilerReplayMode) override {
-    curRange_ = profilerRange;
-    curReplay_ = profilerReplayMode;
-  }
-
-private:
-  void runChecks() {
-    EXPECT_EQ(passes_started, passes_ended);
-    EXPECT_EQ(ranges_started, ranges_ended);
-  }
-
- public:
-  int passes_started = 0;
-  int passes_ended = 0;
-  int ranges_started = 0;
-  int ranges_ended = 0;
-  bool enabled = false;
-
-  CuptiProfilerResult result;
-
-};
-
-inline void simulateCudaContextCreate(CUcontext context, uint32_t dev) {
-  testing::trackCudaCtx(
-      context, dev, CUPTI_CBID_RESOURCE_CONTEXT_CREATED);
-}
-
-inline void simulateCudaContextDestroy(CUcontext context, uint32_t dev) {
-  testing::trackCudaCtx(
-      context, dev, CUPTI_CBID_RESOURCE_CONTEXT_DESTROY_STARTING);
-}
-
-inline void simulateKernelLaunch(
-    CUcontext context, const std::string& kernelName) {
-  testing::trackCudaKernelLaunch(context, kernelName.c_str());
-}
-
-#endif // HAS_CUPTI_PROFILER
-
-} // namespace KINETO_NAMESPACE
diff --git a/plugins/tensorboard-plugins/libkineto/test/CuptiStringsTest.cpp b/plugins/tensorboard-plugins/libkineto/test/CuptiStringsTest.cpp
deleted file mode 100644
index 405f9404a49..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/CuptiStringsTest.cpp
+++ /dev/null
@@ -1,29 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <gtest/gtest.h>
-
-#include "src/cupti_strings.h"
-
-using namespace KINETO_NAMESPACE;
-
-TEST(CuptiStringsTest, Valid) {
-  ASSERT_STREQ(
-      runtimeCbidName(CUPTI_RUNTIME_TRACE_CBID_INVALID), "INVALID");
-  ASSERT_STREQ(
-      runtimeCbidName(CUPTI_RUNTIME_TRACE_CBID_cudaDriverGetVersion_v3020),
-      "cudaDriverGetVersion");
-  ASSERT_STREQ(runtimeCbidName
-      (CUPTI_RUNTIME_TRACE_CBID_cudaDeviceSynchronize_v3020),
-      "cudaDeviceSynchronize");
-  ASSERT_STREQ(
-      runtimeCbidName(CUPTI_RUNTIME_TRACE_CBID_cudaStreamSetAttribute_ptsz_v11000),
-      "cudaStreamSetAttribute_ptsz");
-}
-
-TEST(CuptiStringsTest, Invalid) {
-  ASSERT_STREQ(runtimeCbidName(-1), "INVALID");
-  // We can't actually use CUPTI_RUNTIME_TRACE_CBID_SIZE here until we
-  // auto-generate the string table, since it may have more entries than
-  // the enum in the version used to compile.
-  ASSERT_STREQ(runtimeCbidName(1000), "INVALID");
-}
diff --git a/plugins/tensorboard-plugins/libkineto/test/EventProfilerTest.cpp b/plugins/tensorboard-plugins/libkineto/test/EventProfilerTest.cpp
deleted file mode 100644
index cb36c826a7f..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/EventProfilerTest.cpp
+++ /dev/null
@@ -1,578 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "src/EventProfiler.h"
-
-#include <gmock/gmock.h>
-#include <gtest/gtest.h>
-#include <time.h>
-
-using namespace std::chrono;
-using namespace KINETO_NAMESPACE;
-
-TEST(PercentileTest, Create) {
-  PercentileList pct = {{10, SampleValue(0)},
-                        {49, SampleValue(0)},
-                        {50, SampleValue(0)},
-                        {90, SampleValue(0)}};
-
-  percentiles<int>({0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100}, pct);
-  EXPECT_EQ(pct[0].second.getInt(), 10);
-  EXPECT_EQ(pct[1].second.getInt(), 50);
-  EXPECT_EQ(pct[2].second.getInt(), 50);
-  EXPECT_EQ(pct[3].second.getInt(), 90);
-
-  percentiles<int>({80, 10, 20, 70, 60, 40, 90, 30, 50, 0, 100}, pct);
-  EXPECT_EQ(pct[0].second.getInt(), 10);
-  EXPECT_EQ(pct[1].second.getInt(), 50);
-  EXPECT_EQ(pct[2].second.getInt(), 50);
-  EXPECT_EQ(pct[3].second.getInt(), 90);
-
-  percentiles<int>({80}, pct);
-  EXPECT_EQ(pct[0].second.getInt(), 80);
-  EXPECT_EQ(pct[1].second.getInt(), 80);
-  EXPECT_EQ(pct[2].second.getInt(), 80);
-  EXPECT_EQ(pct[3].second.getInt(), 80);
-
-  percentiles<int>({80, 50}, pct);
-  EXPECT_EQ(pct[0].second.getInt(), 50);
-  EXPECT_EQ(pct[1].second.getInt(), 50);
-  EXPECT_EQ(pct[2].second.getInt(), 80);
-  EXPECT_EQ(pct[3].second.getInt(), 80);
-}
-
-TEST(PercentileTest, Normalize) {
-  PercentileList pct = {
-      {10, SampleValue(10)}, {50, SampleValue(100.0)}, {90, SampleValue(2000)}};
-
-  normalize(pct, 2.5);
-
-  EXPECT_EQ(pct[0].second.getInt(), 25);
-  EXPECT_EQ((int)pct[1].second.getDouble(), 250);
-  EXPECT_EQ(pct[2].second.getInt(), 5000);
-}
-
-TEST(EventTest, SumSamples) {
-  Event ev;
-  ev.instanceCount = 4;
-  auto t = system_clock::now();
-  ev.addSample(t, {1, 2, 3, 4});
-  ev.addSample(t, {10, 20, 30, 40});
-  ev.addSample(t, {100, 200, 300, 400});
-
-  EXPECT_EQ(ev.sumInstance(0, {0, 0, 3}), 1);
-  EXPECT_EQ(ev.sumInstance(0, {0, 1, 3}), 10);
-  EXPECT_EQ(ev.sumInstance(0, {0, 2, 3}), 100);
-
-  EXPECT_EQ(ev.sumInstance(0, {0, 0, 1}), 111);
-
-  EXPECT_EQ(ev.sumInstance(3, {0, 0, 1}), 444);
-
-  // Non-zero offset
-  EXPECT_EQ(ev.sumInstance(0, {1, 0, 2}), 10);
-  EXPECT_EQ(ev.sumInstance(0, {1, 1, 2}), 100);
-  EXPECT_EQ(ev.sumInstance(0, {1, 0, 1}), 110);
-
-  ev.addSample(t, {1000, 2000, 3000, 4000});
-
-  EXPECT_EQ(ev.sumInstance(0, {1, 0, 3}), 10);
-  EXPECT_EQ(ev.sumInstance(0, {1, 1, 3}), 100);
-  EXPECT_EQ(ev.sumInstance(0, {2, 1, 2}), 1000);
-  EXPECT_EQ(ev.sumInstance(0, {2, 0, 1}), 1100);
-
-  EXPECT_EQ(ev.sumAll({0, 0, 4}), 10);
-  EXPECT_EQ(ev.sumAll({1, 0, 3}), 100);
-  EXPECT_EQ(ev.sumAll({2, 1, 2}), 10000);
-  EXPECT_EQ(ev.sumAll({0, 1, 2}), 11000);
-  EXPECT_EQ(ev.sumAll({0, 0, 1}), 11110);
-}
-
-TEST(EventTest, Percentiles) {
-  Event ev;
-  ev.instanceCount = 4;
-  auto t = system_clock::now();
-  ev.addSample(t, {3, 2, 1, 4});
-  ev.addSample(t, {30, 20, 10, 40});
-  ev.addSample(t, {300, 200, 100, 400});
-
-  PercentileList pct = {
-      {10, SampleValue(0)}, {50, SampleValue(0)}, {90, SampleValue(0)}};
-
-  ev.percentiles(pct, {0, 0, 3});
-  EXPECT_EQ(pct[0].second.getInt(), 1);
-  EXPECT_EQ(pct[1].second.getInt(), 3);
-  EXPECT_EQ(pct[2].second.getInt(), 4);
-
-  ev.percentiles(pct, {0, 0, 1});
-  EXPECT_EQ(pct[0].second.getInt(), 111);
-  EXPECT_EQ(pct[1].second.getInt(), 333);
-  EXPECT_EQ(pct[2].second.getInt(), 444);
-}
-
-class MockCuptiMetrics : public CuptiMetricApi {
- public:
-  MockCuptiMetrics() : CuptiMetricApi(0) {}
-  MOCK_METHOD1(idFromName, CUpti_MetricID(const std::string& name));
-  MOCK_METHOD1(
-      events,
-      std::map<CUpti_EventID, std::string>(CUpti_MetricID metric_id));
-  MOCK_METHOD1(valueKind, CUpti_MetricValueKind(CUpti_MetricID metric));
-  MOCK_METHOD1(
-      evaluationMode,
-      CUpti_MetricEvaluationMode(CUpti_MetricID metric));
-  MOCK_METHOD5(
-      calculate,
-      SampleValue(
-          CUpti_MetricID metric,
-          CUpti_MetricValueKind kind,
-          std::vector<CUpti_EventID>& events,
-          std::vector<int64_t>& values,
-          int64_t duration));
-};
-
-TEST(MetricTest, Calculate) {
-  using ::testing::Return;
-  MockCuptiMetrics metrics;
-
-  // The events used for the ipc metrics: instructions and cycles
-  // Pretend we have 2 SMs and 2 samples of each event
-  Event instr("instructions");
-  instr.instanceCount = 2;
-  auto t = system_clock::now();
-  instr.addSample(t, {100, 200});
-  instr.addSample(t, {300, 400});
-
-  Event cycles("cycles");
-  cycles.instanceCount = 2;
-  cycles.addSample(t, {1000, 1200});
-  cycles.addSample(t, {1300, 1300});
-
-  // 2 & 3 are the event ids we specified in the metric
-  std::map<CUpti_EventID, Event> events;
-  events[2] = std::move(instr);
-  events[3] = std::move(cycles);
-
-  // Define an ipc metric
-  EXPECT_CALL(metrics, valueKind(1))
-      .Times(1)
-      .WillOnce(Return(CUPTI_METRIC_VALUE_KIND_DOUBLE));
-  Metric m(
-      "ipc", 1, {2, 3}, CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE, metrics);
-
-  // Calculate metric for first sample
-  // Since evaluation mode is CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE,
-  // Cupti API will be called three times: once for each SM (2) and once
-  // to get the total across SMs.
-  std::vector<CUpti_EventID> ids = {2, 3};
-  std::vector<int64_t> vals = {100, 1000};
-  EXPECT_CALL(
-      metrics, calculate(1, CUPTI_METRIC_VALUE_KIND_DOUBLE, ids, vals, 1000))
-      .Times(1)
-      .WillOnce(Return(SampleValue(0.1)));
-  vals = {200, 1200};
-  EXPECT_CALL(
-      metrics, calculate(1, CUPTI_METRIC_VALUE_KIND_DOUBLE, ids, vals, 1000))
-      .Times(1)
-      .WillOnce(Return(SampleValue(0.17)));
-  vals = {300, 2200};
-  EXPECT_CALL(
-      metrics, calculate(1, CUPTI_METRIC_VALUE_KIND_DOUBLE, ids, vals, 1000))
-      .Times(1)
-      .WillOnce(Return(SampleValue(0.14)));
-  auto v = m.calculate(events, nanoseconds(1000), {0, 0, 2});
-
-  EXPECT_EQ(v.perInstance.size(), 2);
-  EXPECT_EQ(v.perInstance[0].getDouble(), 0.1);
-  EXPECT_EQ(v.perInstance[1].getDouble(), 0.17);
-  EXPECT_EQ(v.total.getDouble(), 0.14);
-
-  // Calculate second sample.
-  // Change evaluation mode to CUPTI_METRIC_EVALUATION_MODE_AGGREGATE.
-  // Now we should get only one call to the Cupti API for the total.
-  EXPECT_CALL(metrics, valueKind(1))
-      .Times(1)
-      .WillOnce(Return(CUPTI_METRIC_VALUE_KIND_DOUBLE));
-  Metric m2("ipc", 1, {2, 3}, CUPTI_METRIC_EVALUATION_MODE_AGGREGATE, metrics);
-  vals = {700, 2600};
-  EXPECT_CALL(
-      metrics, calculate(1, CUPTI_METRIC_VALUE_KIND_DOUBLE, ids, vals, 1000))
-      .Times(1)
-      .WillOnce(Return(SampleValue(0.27)));
-  v = m2.calculate(events, nanoseconds(1000), {0, 1, 2});
-
-  EXPECT_EQ(v.perInstance.size(), 1);
-  EXPECT_EQ(v.perInstance[0].getDouble(), 0.27);
-  EXPECT_EQ(v.total.getDouble(), 0.27);
-}
-
-class MockCuptiEvents : public CuptiEventApi {
- public:
-  MOCK_METHOD1(
-      createGroupSets,
-      CUpti_EventGroupSets*(std::vector<CUpti_EventID>& ids));
-  MOCK_METHOD1(destroyGroupSets, void(CUpti_EventGroupSets* sets));
-  MOCK_METHOD0(setContinuousMode, bool());
-  MOCK_METHOD1(enablePerInstance, void(CUpti_EventGroup eventGroup));
-  MOCK_METHOD1(instanceCount, uint32_t(CUpti_EventGroup eventGroup));
-  MOCK_METHOD1(enableGroupSet, void(CUpti_EventGroupSet& set));
-  MOCK_METHOD1(disableGroupSet, void(CUpti_EventGroupSet& set));
-  MOCK_METHOD3(
-      readEvent,
-      void(CUpti_EventGroup g, CUpti_EventID id, std::vector<int64_t>& vals));
-  MOCK_METHOD1(eventsInGroup, std::vector<CUpti_EventID>(CUpti_EventGroup g));
-  MOCK_METHOD1(eventId, CUpti_EventID(const std::string& name));
-};
-
-TEST(EventGroupSetTest, CollectSample) {
-  using ::testing::_;
-  using ::testing::Return;
-  using ::testing::SetArgPointee;
-  const CUpti_EventGroup g1{nullptr};
-  const CUpti_EventGroup g2{reinterpret_cast<void*>(0x1000)};
-  CUpti_EventGroup groups[] = {g1, g2};
-  CUpti_EventGroupSet set;
-  set.eventGroups = groups;
-  set.numEventGroups = 2;
-
-  std::map<CUpti_EventID, Event> events;
-  Event instr("instructions");
-  events[4] = std::move(instr);
-  Event cycles("cycles");
-  events[5] = std::move(cycles);
-  Event branches("branches");
-  events[10] = std::move(branches);
-
-  MockCuptiEvents cupti_events;
-  EXPECT_CALL(cupti_events, enablePerInstance(g1)).Times(1);
-  EXPECT_CALL(cupti_events, enablePerInstance(g2)).Times(1);
-  EXPECT_CALL(cupti_events, instanceCount(g1)).Times(1).WillOnce(Return(80));
-  EXPECT_CALL(cupti_events, instanceCount(g2)).Times(1).WillOnce(Return(40));
-  std::vector<CUpti_EventID> events_in_group1 = {4, 5};
-  EXPECT_CALL(cupti_events, eventsInGroup(g1))
-      .Times(1)
-      .WillOnce(Return(events_in_group1));
-  std::vector<CUpti_EventID> events_in_group2 = {10};
-  EXPECT_CALL(cupti_events, eventsInGroup(g2))
-      .Times(1)
-      .WillOnce(Return(events_in_group2));
-  EventGroupSet group_set(set, events, cupti_events);
-
-  EXPECT_EQ(group_set.groupCount(), 2);
-  EXPECT_EQ(events[4].instanceCount, 80);
-  EXPECT_EQ(events[5].instanceCount, 80);
-  EXPECT_EQ(events[10].instanceCount, 40);
-
-  // This should not cause any Cupti API action as the group
-  // set is already disabled
-  group_set.setEnabled(false);
-
-  // Activate group set - if activated twice, only the first
-  // should cause cupti API to be called
-  EXPECT_CALL(cupti_events, enableGroupSet(_)).Times(1);
-  group_set.setEnabled(false);
-  group_set.setEnabled(true);
-
-  EXPECT_CALL(cupti_events, eventsInGroup(g1))
-      .Times(1)
-      .WillOnce(Return(events_in_group1));
-  EXPECT_CALL(cupti_events, eventsInGroup(g2))
-      .Times(1)
-      .WillOnce(Return(events_in_group2));
-  EXPECT_CALL(cupti_events, readEvent(g1, 4, _)).Times(1);
-  EXPECT_CALL(cupti_events, readEvent(g1, 5, _)).Times(1);
-  EXPECT_CALL(cupti_events, readEvent(g2, 10, _)).Times(1);
-  group_set.collectSample();
-
-  EXPECT_EQ(events[4].sampleCount(), 1);
-  EXPECT_EQ(events[5].sampleCount(), 1);
-  EXPECT_EQ(events[10].sampleCount(), 1);
-}
-
-class MockLogger : public SampleListener {
- public:
-  MOCK_METHOD3(handleSample, void(int device, const Sample& sample, bool from_new_version));
-  MOCK_METHOD1(update, void(const Config& config));
-};
-
-class EventProfilerTest : public ::testing::Test {
- protected:
-  void SetUp() override {
-    auto cupti_events_ptr = std::make_unique<MockCuptiEvents>();
-    auto cupti_metrics_ptr = std::make_unique<MockCuptiMetrics>();
-    cuptiEvents_ = cupti_events_ptr.get();
-    cuptiMetrics_ = cupti_metrics_ptr.get();
-    loggers_.push_back(std::make_unique<MockLogger>());
-    onDemandLoggers_.push_back(std::make_unique<MockLogger>());
-    profiler_ = std::make_unique<EventProfiler>(
-        std::move(cupti_events_ptr),
-        std::move(cupti_metrics_ptr),
-        loggers_,
-        onDemandLoggers_);
-
-    for (int i = 0; i < kEventGroupCount; i++) {
-      eventGroups_[i] = &eventGroups_[i];
-    }
-    for (int i = 0; i < kGroupSetCount; i++) {
-      // Default size to 1 but can be changed by test
-      groupSet_[i].numEventGroups = 1;
-      // Two groups per set
-      groupSet_[i].eventGroups = &eventGroups_[i * 2];
-    }
-    groupSets_.numSets = 1;
-    groupSets_.sets = groupSet_;
-  }
-
-  MockCuptiEvents* cuptiEvents_;
-  MockCuptiMetrics* cuptiMetrics_;
-  std::vector<std::unique_ptr<SampleListener>> loggers_;
-  std::vector<std::unique_ptr<SampleListener>> onDemandLoggers_;
-  constexpr static int kEventGroupCount = 4;
-  constexpr static int kGroupSetCount = 2;
-  CUpti_EventGroup eventGroups_[kEventGroupCount];
-  CUpti_EventGroupSet groupSet_[kGroupSetCount];
-  CUpti_EventGroupSets groupSets_;
-  std::unique_ptr<EventProfiler> profiler_;
-};
-
-TEST_F(EventProfilerTest, ConfigureFailure) {
-  using namespace testing;
-
-  // Default config has no counters enabled.
-  // Check that profiler remains disabled.
-  Config cfg;
-  profiler_->configure(cfg, nullptr);
-
-  EXPECT_FALSE(profiler_->enabled());
-
-  // There is no event named "cycles"
-  // In this case the profiler should print a warning and remain disabled
-  bool parsed = cfg.parse("EVENTS = cycles");
-  EXPECT_TRUE(parsed);
-
-  // EventProfiler should handle exception thrown from createGroupSets
-  // Configuration will be applied twice - once for combined base + on-demand
-  // and then again falling back to base
-  EXPECT_CALL(*cuptiEvents_, eventId("cycles"))
-      .Times(2)
-      .WillRepeatedly(Return(0));
-  std::vector<CUpti_EventID> ids = {0};
-  EXPECT_CALL(*cuptiEvents_, createGroupSets(ids))
-      .Times(2)
-      .WillRepeatedly(Throw(
-          std::system_error(EINVAL, std::generic_category(), "Event ID")));
-  profiler_->configure(cfg, nullptr);
-
-  EXPECT_FALSE(profiler_->enabled());
-}
-
-TEST_F(EventProfilerTest, ConfigureBase) {
-  using namespace testing;
-
-  // Test normal path, simple base config
-  Config cfg;
-  bool parsed = cfg.parse("EVENTS = elapsed_cycles_sm");
-  EXPECT_TRUE(parsed);
-
-  // One valid event - expect one call to eventId and createGroupSets
-  EXPECT_CALL(*cuptiEvents_, eventId("elapsed_cycles_sm"))
-      .Times(1)
-      .WillOnce(Return(5));
-  std::vector<CUpti_EventID> ids = {5};
-  EXPECT_CALL(*cuptiEvents_, createGroupSets(ids))
-      .Times(1)
-      .WillOnce(Return(&groupSets_));
-  EXPECT_CALL(*cuptiEvents_, enablePerInstance(eventGroups_[0])).Times(1);
-  EXPECT_CALL(*cuptiEvents_, instanceCount(eventGroups_[0]))
-      .Times(1)
-      .WillOnce(Return(80));
-  EXPECT_CALL(*cuptiEvents_, eventsInGroup(eventGroups_[0]))
-      .Times(1)
-      .WillOnce(Return(ids));
-  EXPECT_CALL(*cuptiEvents_, enableGroupSet(_)).Times(1);
-
-  profiler_->configure(cfg, nullptr);
-
-  EXPECT_TRUE(profiler_->enabled());
-}
-
-TEST_F(EventProfilerTest, ConfigureOnDemand) {
-  using namespace testing;
-
-  // Test base + on-demand config, one event and one metric
-  Config cfg, on_demand_cfg;
-  bool parsed = cfg.parse(R"(
-    EVENTS = active_cycles
-    SAMPLE_PERIOD_MSECS=500
-    REPORT_PERIOD_SECS=10
-    SAMPLES_PER_REPORT=5
-  )");
-  EXPECT_TRUE(parsed);
-
-  parsed = on_demand_cfg.parse(R"(
-    METRICS = ipc
-    EVENTS_DURATION_SECS=60
-    SAMPLE_PERIOD_MSECS=200
-    MULTIPLEX_PERIOD_MSECS=2000
-    REPORT_PERIOD_SECS=3
-    SAMPLES_PER_REPORT=10
-  )");
-  EXPECT_TRUE(parsed);
-
-  // One event
-  EXPECT_CALL(*cuptiEvents_, eventId("active_cycles"))
-      .Times(1)
-      .WillOnce(Return(3));
-  // One metric
-  EXPECT_CALL(*cuptiMetrics_, idFromName("ipc")).Times(1).WillOnce(Return(10));
-  std::map<CUpti_EventID, std::string> ipc_events;
-  ipc_events[4] = "instructions";
-  ipc_events[5] = "elapsed_cycles_sm";
-  EXPECT_CALL(*cuptiMetrics_, events(10)).Times(1).WillOnce(Return(ipc_events));
-  EXPECT_CALL(*cuptiMetrics_, evaluationMode(10))
-      .Times(1)
-      .WillOnce(Return(CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE));
-  EXPECT_CALL(*cuptiMetrics_, valueKind(10))
-      .Times(1)
-      .WillOnce(Return(CUPTI_METRIC_VALUE_KIND_DOUBLE));
-  std::vector<CUpti_EventID> ids = {3, 4, 5};
-  groupSet_[0].numEventGroups = 2;
-  groupSets_.numSets = 2;
-  EXPECT_CALL(*cuptiEvents_, createGroupSets(ids))
-      .Times(1)
-      .WillOnce(Return(&groupSets_));
-  // Specified CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE per instance above
-  // So check that it's enabled
-  EXPECT_CALL(*cuptiEvents_, enablePerInstance(eventGroups_[0])).Times(1);
-  EXPECT_CALL(*cuptiEvents_, enablePerInstance(eventGroups_[1])).Times(1);
-  EXPECT_CALL(*cuptiEvents_, enablePerInstance(eventGroups_[2])).Times(1);
-  std::vector<CUpti_EventID> ids_g1{3}, ids_g2{4}, ids_g3{5};
-  EXPECT_CALL(*cuptiEvents_, eventsInGroup(eventGroups_[0]))
-      .Times(1)
-      .WillOnce(Return(ids_g1));
-  EXPECT_CALL(*cuptiEvents_, eventsInGroup(eventGroups_[1]))
-      .Times(1)
-      .WillOnce(Return(ids_g2));
-  EXPECT_CALL(*cuptiEvents_, eventsInGroup(eventGroups_[2]))
-      .Times(1)
-      .WillOnce(Return(ids_g3));
-  EXPECT_CALL(*cuptiEvents_, enableGroupSet(_)).Times(1);
-
-  profiler_->configure(cfg, &on_demand_cfg);
-
-  EXPECT_TRUE(profiler_->enabled());
-  EXPECT_EQ(profiler_->samplePeriod().count(), 250);
-  EXPECT_EQ(profiler_->multiplexPeriod().count(), 1000);
-  EXPECT_EQ(profiler_->reportPeriod().count(), 10000);
-  EXPECT_EQ(profiler_->onDemandReportPeriod().count(), 4000);
-}
-
-TEST_F(EventProfilerTest, ReportSample) {
-  using namespace testing;
-
-  // Test base + on-demand config, one event and one metric
-  Config cfg, on_demand_cfg;
-  bool parsed = cfg.parse("EVENTS = active_cycles");
-  EXPECT_TRUE(parsed);
-
-  parsed = on_demand_cfg.parse(R"(
-    METRICS = ipc
-    EVENTS_DURATION_SECS=60
-  )");
-  EXPECT_TRUE(parsed);
-
-  // One event
-  EXPECT_CALL(*cuptiEvents_, eventId("active_cycles"))
-      .Times(1)
-      .WillOnce(Return(3));
-  // One metric
-  EXPECT_CALL(*cuptiMetrics_, idFromName("ipc")).Times(1).WillOnce(Return(10));
-  std::map<CUpti_EventID, std::string> ipc_events;
-  ipc_events[4] = "instructions";
-  ipc_events[5] = "elapsed_cycles_sm";
-  EXPECT_CALL(*cuptiMetrics_, events(10)).Times(1).WillOnce(Return(ipc_events));
-  EXPECT_CALL(*cuptiMetrics_, evaluationMode(10))
-      .Times(1)
-      .WillOnce(Return(CUPTI_METRIC_EVALUATION_MODE_PER_INSTANCE));
-  EXPECT_CALL(*cuptiMetrics_, valueKind(10))
-      .Times(1)
-      .WillOnce(Return(CUPTI_METRIC_VALUE_KIND_DOUBLE));
-  std::vector<CUpti_EventID> ids = {3, 4, 5};
-  groupSet_[0].numEventGroups = 2;
-  groupSets_.numSets = 2;
-  EXPECT_CALL(*cuptiEvents_, createGroupSets(ids))
-      .Times(1)
-      .WillOnce(Return(&groupSets_));
-  EXPECT_CALL(*cuptiEvents_, instanceCount(_))
-      .Times(3)
-      .WillRepeatedly(Return(4));
-  std::vector<CUpti_EventID> ids_g1{3}, ids_g2{4}, ids_g3{5};
-  // These will be called by collectSample() as well, which is called twice
-  // per group set
-  EXPECT_CALL(*cuptiEvents_, eventsInGroup(eventGroups_[0]))
-      .Times(3)
-      .WillRepeatedly(Return(ids_g1));
-  EXPECT_CALL(*cuptiEvents_, eventsInGroup(eventGroups_[1]))
-      .Times(3)
-      .WillRepeatedly(Return(ids_g2));
-  EXPECT_CALL(*cuptiEvents_, eventsInGroup(eventGroups_[2]))
-      .Times(3)
-      .WillRepeatedly(Return(ids_g3));
-  EXPECT_CALL(*cuptiEvents_, enableGroupSet(_)).Times(1);
-
-  profiler_->configure(cfg, &on_demand_cfg);
-
-  EXPECT_TRUE(profiler_->enabled());
-
-  EXPECT_CALL(*cuptiEvents_, readEvent(_, _, _))
-      .Times(6)
-      .WillRepeatedly(Invoke(
-          [](CUpti_EventGroup g, CUpti_EventID id, std::vector<int64_t>& vals) {
-            vals = {1, 2, 3, 4};
-          }));
-
-  // Need to collect four times - twice for each group set
-  profiler_->collectSample();
-  profiler_->collectSample();
-  EXPECT_CALL(*cuptiEvents_, disableGroupSet(_)).Times(1);
-  EXPECT_CALL(*cuptiEvents_, enableGroupSet(_)).Times(1);
-  profiler_->enableNextCounterSet();
-  profiler_->collectSample();
-  profiler_->collectSample();
-
-  std::vector<CUpti_EventID> ipc_ids = {4, 5};
-  // Called once for each instance (4) and once for the total.
-  // x2 since we recompute per logger.
-  EXPECT_CALL(
-      *cuptiMetrics_,
-      calculate(10, CUPTI_METRIC_VALUE_KIND_DOUBLE, ipc_ids, _, 2000000000))
-      .Times(10)
-      .WillRepeatedly(Return(SampleValue(0.3)));
-  auto& logger = dynamic_cast<MockLogger&>(*loggers_[0]);
-  EXPECT_CALL(logger, handleSample(0, _, _))
-      .Times(1)
-      .WillOnce(Invoke([](int device, const Sample& sample, bool from_new_version) {
-        // Sample will include all stats - logger must pick the
-        // ones it wants.
-        EXPECT_EQ(sample.stats.size(), 4);
-        EXPECT_EQ(sample.stats[0].name, "active_cycles");
-        EXPECT_EQ(sample.stats[1].name, "instructions");
-        EXPECT_EQ(sample.stats[2].name, "elapsed_cycles_sm");
-        EXPECT_EQ(sample.stats[3].name, "ipc");
-        // 2 samples, each with values {1, 2, 3, 4}
-        // i.e. {2, 4, 6, 8} total
-        EXPECT_EQ(sample.stats[0].total.getInt(), 20);
-        EXPECT_EQ(sample.stats[0].percentileValues[0].second.getInt(), 2);
-        EXPECT_EQ(sample.stats[0].percentileValues.back().second.getInt(), 8);
-        // ipc is always 0.3 from mocked calculate function above
-        EXPECT_EQ(sample.stats[3].total.getDouble(), 0.3);
-        EXPECT_EQ(sample.stats[3].percentileValues[0].second.getDouble(), 0.3);
-        EXPECT_EQ(
-            sample.stats[3].percentileValues.back().second.getDouble(), 0.3);
-      }));
-  profiler_->reportSamples();
-
-  auto& on_demand_logger = dynamic_cast<MockLogger&>(*onDemandLoggers_[0]);
-  EXPECT_CALL(on_demand_logger, handleSample(0, _, _)).Times(1);
-  profiler_->reportOnDemandSamples();
-
-  EXPECT_CALL(*cuptiEvents_, disableGroupSet(_)).Times(1);
-}
diff --git a/plugins/tensorboard-plugins/libkineto/test/LoggerObserverTest.cpp b/plugins/tensorboard-plugins/libkineto/test/LoggerObserverTest.cpp
deleted file mode 100644
index 30ba4a824af..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/LoggerObserverTest.cpp
+++ /dev/null
@@ -1,96 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <gtest/gtest.h>
-#include <memory>
-
-// TODO(T90238193)
-// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
-#include "include/libkineto.h"
-#include "src/Logger.h"
-#include "LoggerCollector.h"
-
-using namespace KINETO_NAMESPACE;
-
-#if !USE_GOOGLE_LOG
-
-constexpr char InfoTestStr[] = "Checking LOG(INFO)";
-constexpr char WarningTestStr[] = "Checking LOG(WARNING)";
-constexpr char ErrorTestStr[] = "Checking LOG(ERROR)";
-
-TEST(LoggerObserverTest, SingleCollectorObserver) {
-  // Add a LoggerObserverCollector to collect all logs during the trace.
-  std::unique_ptr<LoggerCollector> lCollector = std::make_unique<LoggerCollector>();
-  Logger::addLoggerObserver(lCollector.get());
-
-  LOG(INFO) << InfoTestStr;
-  LOG(WARNING) << WarningTestStr;
-  LOG(ERROR) << ErrorTestStr;
-
-  auto LoggerMD = lCollector->extractCollectorMetadata();
-  EXPECT_TRUE(LoggerMD[LoggerOutputType::INFO][0].find(InfoTestStr) != std::string::npos);
-  EXPECT_TRUE(LoggerMD[LoggerOutputType::WARNING][0].find(WarningTestStr) != std::string::npos);
-  EXPECT_TRUE(LoggerMD[LoggerOutputType::ERROR][0].find(ErrorTestStr) != std::string::npos);
-
-  Logger::removeLoggerObserver(lCollector.get());
-}
-
-#define NUM_OF_MESSAGES_FOR_EACH_TYPE 10
-#define NUM_OF_WRITE_THREADS 200
-
-// Writes NUM_OF_MESSAGES_FOR_EACH_TYPE messages for each INFO, WARNING, and ERROR.
-// NOLINTNEXTLINE(clang-diagnostic-unused-parameter)
-void* writeSeveralMessages(void* ptr) {
-  for(int i=0; i<NUM_OF_MESSAGES_FOR_EACH_TYPE; i++) {
-    LOG(INFO) << InfoTestStr;
-    LOG(WARNING) << WarningTestStr;
-    LOG(ERROR) << ErrorTestStr;
-  }
-  return nullptr;
-}
-
-TEST(LoggerObserverTest, FourCollectorObserver) {
-  // There shouldn't be too many CUPTIActivityProfilers active at the same time.
-  std::unique_ptr<LoggerCollector> lc1 = std::make_unique<LoggerCollector>();
-  std::unique_ptr<LoggerCollector> lc2 = std::make_unique<LoggerCollector>();
-  std::unique_ptr<LoggerCollector> lc3 = std::make_unique<LoggerCollector>();
-  std::unique_ptr<LoggerCollector> lc4 = std::make_unique<LoggerCollector>();
-  Logger::addLoggerObserver(lc1.get());
-  Logger::addLoggerObserver(lc2.get());
-  Logger::addLoggerObserver(lc3.get());
-  Logger::addLoggerObserver(lc4.get());
-
-  // Launch NUM_OF_WRITE_THREADS threads writing several messages.
-  pthread_t ListOfThreads[NUM_OF_WRITE_THREADS];
-  for (int i=0; i<NUM_OF_WRITE_THREADS; i++) {
-    ::pthread_create(&ListOfThreads[i], nullptr, writeSeveralMessages, nullptr);
-  }
-
-  // Wait for all threads to finish.
-  for (int i=0; i<NUM_OF_WRITE_THREADS; i++) {
-    ::pthread_join(ListOfThreads[i], nullptr);
-  }
-
-  auto lc1MD = lc1->extractCollectorMetadata();
-  int InfoCount = 0, WarnCount = 0, ErrorCount = 0;
-  for (auto& md : lc1MD) {
-    InfoCount += md.first == LoggerOutputType::INFO ? md.second.size() : 0;
-    WarnCount += md.first == LoggerOutputType::WARNING ? md.second.size() : 0;
-    ErrorCount += md.first == LoggerOutputType::ERROR ? md.second.size() : 0;
-  }
-
-  EXPECT_EQ(InfoCount, NUM_OF_WRITE_THREADS * NUM_OF_MESSAGES_FOR_EACH_TYPE);
-  EXPECT_EQ(WarnCount, NUM_OF_WRITE_THREADS * NUM_OF_MESSAGES_FOR_EACH_TYPE);
-  EXPECT_EQ(ErrorCount, NUM_OF_WRITE_THREADS * NUM_OF_MESSAGES_FOR_EACH_TYPE);
-
-  Logger::removeLoggerObserver(lc1.get());
-  Logger::removeLoggerObserver(lc2.get());
-  Logger::removeLoggerObserver(lc3.get());
-  Logger::removeLoggerObserver(lc4.get());
-}
-
-#endif // !USE_GOOGLE_LOG
-
-int main(int argc, char **argv) {
-  ::testing::InitGoogleTest(&argc, argv);
-  return RUN_ALL_TESTS();
-}
diff --git a/plugins/tensorboard-plugins/libkineto/test/MockActivitySubProfiler.cpp b/plugins/tensorboard-plugins/libkineto/test/MockActivitySubProfiler.cpp
deleted file mode 100644
index 89f1d536ca8..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/MockActivitySubProfiler.cpp
+++ /dev/null
@@ -1,49 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include <memory>
-#include <set>
-#include <vector>
-
-#include "test/MockActivitySubProfiler.h"
-
-namespace libkineto {
-
-const std::set<ActivityType> supported_activities {ActivityType::CPU_OP};
-const std::string profile_name{"MockProfiler"};
-
-void MockProfilerSession::processTrace(ActivityLogger& logger) {
-  for (const auto& activity: activities()) {
-    activity.log(logger);
-  }
-}
-
-const std::string& MockActivityProfiler::name() const {
-  return profile_name;
-}
-
-const std::set<ActivityType>& MockActivityProfiler::availableActivities() const {
-  return supported_activities;
-}
-
-MockActivityProfiler::MockActivityProfiler(
-    std::vector<GenericTraceActivity>& activities) :
-  test_activities_(activities) {};
-
-std::unique_ptr<IActivityProfilerSession> MockActivityProfiler::configure(
-      const std::set<ActivityType>& /*activity_types*/,
-      const Config& /*config*/) {
-  auto session = std::make_unique<MockProfilerSession>();
-	session->set_test_activities(std::move(test_activities_));
-  return session;
-};
-
-std::unique_ptr<IActivityProfilerSession> MockActivityProfiler::configure(
-      int64_t /*ts_ms*/,
-      int64_t /*duration_ms*/,
-      const std::set<ActivityType>& activity_types,
-      const Config& config) {
-  return configure(activity_types, config);
-};
-
-} // namespace libkineto
-
diff --git a/plugins/tensorboard-plugins/libkineto/test/MockActivitySubProfiler.h b/plugins/tensorboard-plugins/libkineto/test/MockActivitySubProfiler.h
deleted file mode 100644
index 36eaa13d1a5..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/MockActivitySubProfiler.h
+++ /dev/null
@@ -1,72 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#pragma once
-
-#include <memory>
-#include <set>
-#include <vector>
-
-#include "include/IActivityProfiler.h"
-
-namespace libkineto {
-
-class MockProfilerSession: public IActivityProfilerSession {
-
-  public:
-    explicit MockProfilerSession() {}
-
-    void start() override {
-      start_count++;
-      status_ = TraceStatus::RECORDING;
-    }
-
-    void stop() override {
-      stop_count++;
-      status_ = TraceStatus::PROCESSING;
-    }
-
-    std::vector<GenericTraceActivity>& activities() override {
-      return test_activities_;
-    }
-
-    std::vector<std::string> errors() override {
-      return {};
-    }
-
-    void processTrace(ActivityLogger& logger) override;
-
-    void set_test_activities(std::vector<GenericTraceActivity>&& acs) {
-      test_activities_ = std::move(acs);
-    }
-
-    int start_count = 0;
-    int stop_count = 0;
-  private:
-    std::vector<GenericTraceActivity> test_activities_;
-};
-
-
-class MockActivityProfiler: public IActivityProfiler {
-
- public:
-  explicit MockActivityProfiler(std::vector<GenericTraceActivity>& activities);
-
-  const std::string& name() const override;
-
-  const std::set<ActivityType>& availableActivities() const override;
-
-  std::unique_ptr<IActivityProfilerSession> configure(
-      const std::set<ActivityType>& activity_types,
-      const Config& config) override;
-
-  std::unique_ptr<IActivityProfilerSession> configure(
-      int64_t ts_ms,
-      int64_t duration_ms,
-      const std::set<ActivityType>& activity_types,
-      const Config& config) override;
-
- private:
-  std::vector<GenericTraceActivity> test_activities_;
-};
-
-} // namespace libkineto
diff --git a/plugins/tensorboard-plugins/libkineto/test/PidInfoTest.cpp b/plugins/tensorboard-plugins/libkineto/test/PidInfoTest.cpp
deleted file mode 100644
index b86cfb36d05..00000000000
--- a/plugins/tensorboard-plugins/libkineto/test/PidInfoTest.cpp
+++ /dev/null
@@ -1,27 +0,0 @@
-// (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
-
-#include "include/ThreadUtil.h"
-
-#include <atomic>
-#include <thread>
-
-#include <fmt/format.h>
-#include <gtest/gtest.h>
-
-using namespace KINETO_NAMESPACE;
-
-TEST(ThreadNameTest, setAndGet) {
-  setThreadName("ThreadNameTest");
-  EXPECT_EQ(getThreadName(), "ThreadNameTest");
-
-  setThreadName("");
-  EXPECT_EQ(getThreadName(), "");
-
-  // Spaces etc are ok
-  setThreadName("Name w/ spaces");
-  EXPECT_EQ(getThreadName(), "Name w/ spaces");
-
-  // More than 16 chars is not OK
-  setThreadName("More than 16 characters");
-  EXPECT_EQ(getThreadName(), "Name w/ spaces");
-}
diff --git a/profiler/README.md b/profiler/README.md
index 1669e3524e5..549ffefc14c 100644
--- a/profiler/README.md
+++ b/profiler/README.md
@@ -91,6 +91,7 @@ ascend pytorch profiler数据目录结构如下：
 
    | profiler版本 | 发布日期   | 下载链接                                                     | 校验码                                                       |
    | ------------ | ---------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+   | 1.2.0        | 2024-07-25 | [msprof_analyze-1.2.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.2.0/msprof_analyze-1.2.0-py3-none-any.whl) | 6a4366e3beca40b4a8305080e6e441d6ecafb5c05489e5905ac0265787555f37 |
    | 1.1.2        | 2024-07-12 | [msprof_analyze-1.1.2-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.2/msprof_analyze-1.1.2-py3-none-any.whl) | af62125b1f9348bf491364e03af712fc6d0282ccee3fb07458bc9bbef82dacc6 |
    | 1.1.1        | 2024-06-20 | [msprof_analyze-1.1.1-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.1/msprof_analyze-1.1.1-py3-none-any.whl) | 76aad967a3823151421153d368d4d2f8e5cfbcb356033575e0b8ec5acea8e5e4 |
    | 1.1.0        | 2024-05-28 | [msprof_analyze-1.1.0-py3-none-any.whl](https://ptdbg.obs.myhuaweicloud.com/profiler/package/1.1.0/msprof_analyze-1.1.0-py3-none-any.whl) | b339f70e7d1e45e81f289332ca64990a744d0e7ce6fdd84a8d82e814fa400698 |
diff --git a/profiler/advisor/README.md b/profiler/advisor/README.md
index 47e64a90ba4..77027110559 100644
--- a/profiler/advisor/README.md
+++ b/profiler/advisor/README.md
@@ -72,6 +72,7 @@ msprof-analyze的advisor功能是将Ascend PyTorch Profiler或者msprof采集的
 |            | block_dim_analysis         | block dim算子调优                        |
 |            | operator_no_bound_analysis | operator no bound                        |
 |            | graph                      | 融合算子图调优                           |
+|            | freq_analysis              | AI Core算子降频分析                      |
 | scheduling | timeline_fusion_ops        | 亲和API替换调优                          |
 |            | timeline_op_dispatch       | 识别算子下发问题(路径3/路径5)            |
 
@@ -132,6 +133,8 @@ cluster模块的分析包含快慢卡和快慢链路分析，仅识别问题，
 
 overall模块的分析包含当前训练任务慢卡的性能拆解，按照计算、通信和下发三个维度进行耗时的统计，可以基于该分析识别到训练性能瓶颈是计算、通信还是下发问题，同样不提供调优建议。
 
+![输入图片说明](./img/overall_0.png)
+
 ![输入图片说明](./img/overall.png)
 
 schedule模块包含亲和API、aclOpCompile、syncBatchNorm、SynchronizeStream等多项检测。
@@ -152,7 +155,7 @@ torch_npu.npu.config.allow_internal_format = False
 
 ![schedule_3](./img/schedule_3.png)
 
-computation模块从device计算性能维度进行分析，能够识别AI CPU、计算bound、动态Shape等问题并给出相应建议。此处不再详细展开，按照报告进行调优即可。
+computation模块从device计算性能维度进行分析，能够识别AI CPU、计算bound、动态Shape、AI Core算子降频分析等问题并给出相应建议。此处不再详细展开，按照报告进行调优即可。
 
 ![computation_1](./img/computation_1.png)
 
diff --git a/profiler/advisor/common/profiling/ge_info.py b/profiler/advisor/common/profiling/ge_info.py
index 9996ec611a2..4fd5846d88d 100644
--- a/profiler/advisor/common/profiling/ge_info.py
+++ b/profiler/advisor/common/profiling/ge_info.py
@@ -17,12 +17,13 @@ class GeInfo(ProfilingParser):
     """
     ge info file
     """
-    FILE_PATTERN = r"ge_info.db"
     FILE_PATTERN_MSG = "ge_info.db"
     FILE_INFO = "ge info"
     STATIC_OP_STATE = "0"
     DYNAMIC_OP_STATE = "1"
 
+    file_pattern_list = [r"ge_info.db"]
+
     def __init__(self, path: str) -> None:
         super().__init__(path)
         self.op_state_info_list = None
diff --git a/profiler/advisor/common/profiling/msprof.py b/profiler/advisor/common/profiling/msprof.py
index 9453986b822..750c5481e67 100644
--- a/profiler/advisor/common/profiling/msprof.py
+++ b/profiler/advisor/common/profiling/msprof.py
@@ -33,10 +33,11 @@ class Msprof(ProfilingParser):
     msprof
 
     """
-    FILE_PATTERN = r"^msprof[_\d]+.json$"
     FILE_PATTERN_MSG = "msprof_*.json"
     FILE_INFO = "msprof"
 
+    file_pattern_list = [r"^msprof[_\d]+.json$"]
+
     def __init__(self, path: str) -> None:
         super().__init__(path)
         self._tasks: List[TaskInfo] = []
diff --git a/profiler/advisor/common/profiling/op_summary.py b/profiler/advisor/common/profiling/op_summary.py
index d79439dbad8..4744b5029ad 100644
--- a/profiler/advisor/common/profiling/op_summary.py
+++ b/profiler/advisor/common/profiling/op_summary.py
@@ -16,13 +16,13 @@ class OpSummary(ProfilingParser):
     """
     op summary
     """
-
-    FILE_PATTERN = r"^op_summary_[_\d]+\.csv$"
     FILE_PATTERN_MSG = "op_summary_*.csv"
     FILE_INFO = "op summary"
     STATIC_OP_STATE = "static"
     DYNAMIC_OP_STATE = "dynamic"
 
+    file_pattern_list = [r"^op_summary_[_\d]+\.csv$"]
+
     def __init__(self, path: str) -> None:
         super().__init__(path)
         self.op_list: List[OpInfo] = []
diff --git a/profiler/advisor/common/profiling/tasktime.py b/profiler/advisor/common/profiling/tasktime.py
index 3ce09a78385..732ff0f3679 100644
--- a/profiler/advisor/common/profiling/tasktime.py
+++ b/profiler/advisor/common/profiling/tasktime.py
@@ -17,11 +17,11 @@ class TaskTime(ProfilingParser):
     """
     task time info
     """
-
-    FILE_PATTERN = r"^task_time_[_\d]+\.json$"
     FILE_PATTERN_MSG = "task_time*.json"
     FILE_INFO = "task time"
 
+    file_pattern_list = [r"^task_time_[_\d]+\.json$"]
+
     def __init__(self, path: str) -> None:
         super().__init__(path)
         self._tasks: List[TaskInfo] = []
diff --git a/profiler/advisor/config/profiling_data_version_config.yaml b/profiler/advisor/config/profiling_data_version_config.yaml
index 4ef76105a07..b8c92fe074d 100644
--- a/profiler/advisor/config/profiling_data_version_config.yaml
+++ b/profiler/advisor/config/profiling_data_version_config.yaml
@@ -1,18 +1,19 @@
 versions:
   - version: 8.0.RC1
     dirs_pattern:
+      ASCEND_PROFILER_OUTPUT: [ op_summary ]
       ^PROF_\d{6}_\d{17}_\w+$:
-        mindstudio_profiler_output:
-          [ op_summary, msprof ]
+        mindstudio_profiler_output: [ op_summary, msprof ]
     class_attr:
       op_summary: OpSummary
       msprof: Msprof
     file_attr:
-      op_summary: ^op_summary_\d{14}\.csv$
       msprof: ^msprof_\d{14}\.json$
+      op_summary: [ kernel_details.csv, '^op_summary_\d{14}\.csv$' ]
 
   - version: 7.0.0
     dirs_pattern:
+      ASCEND_PROFILER_OUTPUT: [ op_summary ]
       ^PROF_\d{6}_\d{17}_\w+$:
         ^device_\d+$:
           summary:
@@ -28,13 +29,14 @@ versions:
       msprof: Msprof
       ge_info: GeInfo
     file_attr:
-      op_summary: ^op_summary_\d+_\d+_\d{14}\.csv$
+      op_summary: [ kernel_details.csv, '^op_summary_\d+_\d+_\d{14}\.csv$']
       task_time: ^task_time_\d+_\d+_\d{14}\.json$
       msprof: ^msprof_\d+_\d+_\d{14}\.json$
       ge_info: ge_info.db
 
   - version: 7.0.RC1
     dirs_pattern:
+      ASCEND_PROFILER_OUTPUT: [ op_summary ]
       ^PROF_\d{6}_\d{17}_\w+$:
         ^device_\d+$:
           summary:
@@ -50,13 +52,14 @@ versions:
       msprof: Msprof
       ge_info: GeInfo
     file_attr:
-      op_summary: ^op_summary_\d+_\d+_\d+_\d{14}\.csv$
+      op_summary: [ kernel_details.csv, '^op_summary_\d+_\d+_\d+_\d{14}\.csv$']
       task_time: ^task_time_\d+_\d+_\d+_\d{14}\.json$
       msprof: ^msprof_\d+_\d+_\d+_\d{14}\.json$
       ge_info: ge_info.db
 
   - version: 6.3.RC2
     dirs_pattern:
+      ASCEND_PROFILER_OUTPUT: [ op_summary ]
       ^PROF_\d{6}_\d{17}_\w+$:
         ^device_\d+$:
           summary:
@@ -72,9 +75,7 @@ versions:
       msprof: Msprof
       ge_info: GeInfo
     file_attr:
-      op_summary: ^op_summary_\d+_\d+\.csv$
+      op_summary: [ kernel_details.csv, '^op_summary_\d+_\d+\.csv$']
       task_time: ^task_time_\d+_\d+\.json$
       msprof: ^msprof_\d+_\d+\.json$
       ge_info: ge_info.db
-
-
diff --git a/profiler/advisor/dataset/profiling/profiling_dataset.py b/profiler/advisor/dataset/profiling/profiling_dataset.py
index 99a19d3b60e..ebd90951abf 100644
--- a/profiler/advisor/dataset/profiling/profiling_dataset.py
+++ b/profiler/advisor/dataset/profiling/profiling_dataset.py
@@ -43,14 +43,21 @@ class ProfilingDataset(Dataset):
                 self.build_from_pattern(value, join_prof_path(current_path, key))
         elif isinstance(dirs_pattern, list):
             for item in dirs_pattern:
+                if hasattr(self, item) and getattr(self, item):
+                    # 避免重复构建kernel_details.csv, op_summary.csv的数据对象
+                    continue
+                file_pattern_list = self.current_version_pattern.get('file_attr').get(item)
                 data_class = globals()[self.current_version_pattern.get('class_attr').get(item)]
-                data_class.FILE_PATTERN = self.current_version_pattern.get('file_attr').get(item)
+                if not hasattr(data_class, "file_pattern_list"):
+                    continue
+                setattr(data_class, "file_pattern_list", self.current_version_pattern.get('file_attr').get(item))
                 data_object = data_class(current_path)
                 is_success = data_object.parse_data()
                 if is_success:
                     setattr(self, item, data_object)
                 else:
-                    logger.warning("Skip parse %s from local path %s", self.current_version_pattern.get('class_attr').get(item), current_path)
+                    logger.info("Skip parse %s with file pattern %s from local path %s", 
+                                   self.current_version_pattern.get('class_attr').get(item), file_pattern_list, current_path)
         else:
             logger.warning(f"Unsupported arguments : %s to build %s", dirs_pattern, self.__class__.__name__)
 
diff --git a/profiler/advisor/dataset/profiling/profiling_parser.py b/profiler/advisor/dataset/profiling/profiling_parser.py
index bb4caeb29e5..51996617c2b 100644
--- a/profiler/advisor/dataset/profiling/profiling_parser.py
+++ b/profiler/advisor/dataset/profiling/profiling_parser.py
@@ -12,10 +12,10 @@ class ProfilingParser:
     """
     profiling
     """
-    FILE_PATTERN = ""
     FILE_PATTERN_MSG = ""
     FILE_INFO = ""
-    FILE_PATH = ""
+
+    file_pattern_list = []
 
     def __init__(self, path: str) -> None:
         self._path = path
@@ -37,15 +37,20 @@ class ProfilingParser:
         return False
 
     def _parse_from_file(self):
-        file_list = get_file_path_from_directory(self._path, self.file_match_func(self.FILE_PATTERN))
-        if not file_list:
-            return False
-        ## get last file
-        file = file_list[-1]
-        self.FILE_PATH = file
-        if len(file_list) > 1:
-            logger.warning("Multiple copies of %s were found, use %s", self.FILE_INFO, file)
-        return self.parse_from_file(file)
+
+        if not isinstance(self.file_pattern_list, list):
+            self.file_pattern_list = [self.file_pattern_list]
+
+        for file_pattern in self.file_pattern_list:
+            file_list = get_file_path_from_directory(self._path, self.file_match_func(file_pattern))
+            if not file_list:
+                continue
+            ## get last file
+            target_file = file_list[-1]
+            if len(file_list) > 1:
+                logger.warning("Multiple copies of %s were found, use %s", self.FILE_INFO, target_file)
+            return self.parse_from_file(target_file)
+        return False
 
     @staticmethod
     def get_float(data) -> float:
diff --git a/profiler/advisor/img/overall.png b/profiler/advisor/img/overall.png
index 6d5da107a3f7f6c8c655922bd80d193708fe71aa..1883d4c97388b1cfb774d05fc9e0d368d0c66901 100644
GIT binary patch
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HcmV?d00001

diff --git a/profiler/advisor/utils/utils.py b/profiler/advisor/utils/utils.py
index b373d7badaa..83f304c2d3c 100644
--- a/profiler/advisor/utils/utils.py
+++ b/profiler/advisor/utils/utils.py
@@ -414,7 +414,17 @@ def format_excel_title(title: str) -> str:
     title = title.replace("(ns)", '')
     title = title.replace("(%)", '')
     title = title.replace(" ", "_")
-    return title
+
+    # 将kernel_details中的列名转为与op_summary_x.csv中一致
+    kernel_details_col_name_map = {
+        "name": "op_name",
+        "type": "op_type",
+        "accelerator_core": "task_type",
+        "start_time": "task_start_time",
+        "duration": "task_duration",
+        "wait_time": "wait_time"
+    }
+    return kernel_details_col_name_map.get(title, title)
 
 
 def format_float(num: float) -> float:
diff --git a/profiler/affinity_cpu_bind/README.md b/profiler/affinity_cpu_bind/README.md
deleted file mode 100644
index 8c3b47ed518..00000000000
--- a/profiler/affinity_cpu_bind/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-# 昇腾亲和性CPU绑核工具
-
-昇腾亲和性CPU绑核工具支持用户无需修改代码，直接运行工具即可实现按CPU亲和性策略绑核，提升推理或训练性能。
-
-绑核工具用户arm服务器环境，对于训练或推理任务因为CPU资源调度等出现host_bound问题时使用，可改善该问题；对于非host_bound的场景无明显改善效果。
-
-## 使用须知
-
-使用绑核工具前手动执行npu-smi info -t topo，出现以下类似信息，说明环境支持绑核，否则请将环境HDK包升级到Ascend HDK 23.0.RC2及以上版本。
-
-            NPU0   NPU1    NPU2    NPU3    NPU4    NPU5    NPU6    NPU7    NPUx   CPU Affinity 
-    NPU0    X      HCCS    HCCS    HCCS    HCCS    HCCS    HCCS    HCCS    ...    xx-xx
-    NPU1    HCCS   X       HCCS    HCCS    HCCS    HCCS    HCCS    HCCS    ...    xx-xx
-    NPU2    HCCS   HCCS    X       HCCS    HCCS    HCCS    HCCS    HCCS    ...    xx-xx
-    NPU3    HCCS   HCCS    HCCS    X       HCCS    HCCS    HCCS    HCCS    ...    xx-xx
-    NPU4    HCCS   HCCS    HCCS    HCCS    X       HCCS    HCCS    HCCS    ...    xx-xx
-    NPU5    HCCS   HCCS    HCCS    HCCS    HCCS    X       HCCS    HCCS    ...    xx-xx
-    NPU6    HCCS   HCCS    HCCS    HCCS    HCCS    HCCS    X       HCCS    ...    xx-xx
-    NPU7    HCCS   HCCS    HCCS    HCCS    HCCS    HCCS    HCCS    X       ...    xx-xx
-    NPUx    ...    ...     ...     ...     ...     ...     ...     ...     ...    ...
-
-##  使用方式
-
-1.执行以下命令实施绑核：
-
- - 直接执行绑核命令
-```bash
-python3 bind_core.py -app/--application="inferenec/train cmd"
-```
-该方式会自动拉起训练或推理任务，检测任务进程，并实施绑核。
-
- - 手动拉起训练或推理任务后再执行绑核
-```bash
-python3 bind_core.py
-```
-该方式会循环查找(循环5次，每次10s，若找不到进程，则直接退出)使用到NPU的任务进程，并实施绑核。
-
-2.绑核运行过程的日志会保存到当前路径的bind_core_时间戳.log。
-
-3.如果推理或训练进程拉起后需要一定时间预处理，才会真正执行任务，可在执行绑核命令时设置-t/--time参数(单位秒)，绑核工具会在延迟配置的时间后，再实施绑核动作。例如：python3 bind_core.py -app="cmd" -t=10，配置后工具会在10秒后执行绑核操作。
\ No newline at end of file
diff --git a/profiler/affinity_cpu_bind/bind_core.py b/profiler/affinity_cpu_bind/bind_core.py
deleted file mode 100644
index 7f27e924238..00000000000
--- a/profiler/affinity_cpu_bind/bind_core.py
+++ /dev/null
@@ -1,213 +0,0 @@
-import subprocess
-import argparse
-import os
-import time
-import logging
-from datetime import datetime
-from datetime import timezone
-
-
-class PathManager:
-    DATA_FILE_AUTHORITY = 0o640
-
-    @classmethod
-    def create_file_safety(cls, path: str):
-        base_name = os.path.basename(path)
-        msg = f"Failed to create file: {base_name}"
-        if os.path.islink(path):
-            raise RuntimeError(msg)
-        if os.path.exists(path):
-            return
-        try:
-            os.close(os.open(path, os.O_WRONLY | os.O_CREAT, cls.DATA_FILE_AUTHORITY))
-        except Exception as err:
-            raise RuntimeError(msg) from err
-
-
-class BindCoreManager():
-    DEFAULT_FIND_RUNNING_PID_TIMES = 5
-
-    def __init__(self):
-        self.npu_id_list = []
-        self.running_pid_on_npu = {}
-        self.find_running_pid_times = self.DEFAULT_FIND_RUNNING_PID_TIMES
-        self.npu_affinity_cpu_dict = {}
-        self.log_file = ''
-        self._init_log_file()
-
-
-    def _init_log_file(self):
-        now_time = datetime.now(tz=timezone.utc)
-        time_stamp = str(now_time.year) + '_' + \
-                     str(now_time.month) + '_' + \
-                     str(now_time.day) + '_' + \
-                     str(now_time.hour) + '_' + \
-                     str(now_time.minute) + '_' + \
-                     str(now_time.second)
-        log_file_name = 'bind_core_' + time_stamp + '.log'
-        msg = f"Failed to create file: {log_file_name}"
-        try:
-            PathManager.create_file_safety(os.path.join(os.getcwd(), log_file_name))
-        except RuntimeError as err:
-            raise RuntimeError(msg) from err
-        self.log_file = log_file_name
-        logging.basicConfig(filename=self.log_file,
-                            level=logging.INFO,
-                            filemode='w',
-                            format='%(asctime)s-%(name)s-%(levelname)s-%(message)s')
-
-    def _get_all_npu_id(self) -> None:
-        get_npu_info_cmd = 'npu-smi info -l'
-        get_npu_info_process = subprocess.run(get_npu_info_cmd.split(), shell=False, capture_output=True)
-        get_npu_id_cmd = 'grep ID'
-        get_npu_id_process = subprocess.run(get_npu_id_cmd.split(), shell=False, input=get_npu_info_process.stdout, capture_output=True)
-        res = get_npu_id_process.stdout.decode('utf-8').split()
-        for i in res:
-            if i.isdigit():
-                self.npu_id_list.append(int(i))
-        logging.info(f'NPU total id list: {self.npu_id_list}')
-
-    def _get_npu_affinity(self) -> bool:
-        cpu_num = os.cpu_count()
-        cpu_num_for_each_npu = cpu_num // len(self.npu_id_list)
-        get_npu_topo_cmd = 'npu-smi info -t topo'
-        p = subprocess.run(get_npu_topo_cmd.split(), shell=False, capture_output=True)
-        res = p.stdout.decode('utf-8').split()
-        if not res:
-            print('[ERROR] Failed to run get npu affinity info, please check if driver version support cmd npu-smi info -t topo')
-            return False
-
-        index = 0
-        for v in res:
-            if '-' in v:
-                affinity_cpus = []
-                cpu_lists = v.split(',')
-                for cpu_list in cpu_lists:
-                    cpus = cpu_list.split('-')
-                    if len(cpus) != 2:
-                        continue
-                    if int(cpus[1]) - int(cpus[0]) == cpu_num_for_each_npu - 1:
-                        cpus[1] = str(int(cpus[1]) + cpu_num_for_each_npu)
-                    affinity_cpus.append(cpus[0] + '-' + cpus[1])
-                if index < len(self.npu_id_list):
-                    self.npu_affinity_cpu_dict[self.npu_id_list[index]] = ','.join(affinity_cpu for affinity_cpu in affinity_cpus)
-                    index += 1
-                else:
-                    print('[ERROR] Get affinity_cpu_list for {} npus, more than real npu num: {}'.format(index + 1, len(self.npu_id_list)))
-                    return False
-
-        for k in self.npu_affinity_cpu_dict.keys():
-            logging.info(f'Affinity CPU list {self.npu_affinity_cpu_dict[k]} for NPU {k}')
-        return True
-
-    def get_running_pid_on_npu(self) -> bool:
-        no_running_pids_on_npu_msg = '[INFO] Now there is no running process on all NPUs, stop bind cores'
-        logging.info('Begin to find running process on all NPUs')
-        # get running process on NPUs
-        for times in range(self.find_running_pid_times):
-            running_pid_on_npu = {}
-            for npu_id in self.npu_id_list:
-                get_npu_pids_cmd = 'npu-smi info -t proc-mem -i {} -c 0'.format(npu_id)
-                get_npu_pids_process = subprocess.run(get_npu_pids_cmd.split(), shell=False, capture_output=True)
-                res = get_npu_pids_process.stdout.decode('utf-8').split()
-                pid_list = []
-                for value in res:
-                    if value.startswith('id:'):
-                        pid = value.split(':')[1]
-                        pid_list.append(pid)
-                if pid_list:
-                    running_pid_on_npu[npu_id] = list(set(pid_list))
-            
-            if len(self.running_pid_on_npu.keys()) == len(running_pid_on_npu.keys()) and running_pid_on_npu:
-                self.running_pid_on_npu = running_pid_on_npu
-                break
-
-            self.running_pid_on_npu = running_pid_on_npu
-            time.sleep(5)
-
-        # delete repeat pid
-        for npu_id in self.npu_id_list:
-            if npu_id not in self.running_pid_on_npu:
-                continue
-            pids_on_npu = self.running_pid_on_npu[npu_id]
-            for npu_id_with_pids, pids in self.running_pid_on_npu.items():
-                if npu_id == npu_id_with_pids:
-                    continue
-                pids_on_npu = list(set(pids_on_npu) - set(pids))
-            self.running_pid_on_npu[npu_id] = pids_on_npu
-
-        if_running_process = False
-        for npu_id, pids in self.running_pid_on_npu.items():
-            if not pids:
-                logging.info(f'There is no running process on NPU {npu_id}')
-            else:
-                logging.info(f'Succeed to find running process {pids} on NPU {npu_id}')
-                if_running_process = True
-        if not if_running_process:
-            print(no_running_pids_on_npu_msg)
-        return if_running_process
-
-    def get_npu_info(self) -> bool:
-        try:
-            self._get_all_npu_id()
-            if not self._get_npu_affinity():
-                return False
-        except subprocess.CalledProcessError:
-            return False
-        return True
-
-    def run_bind_core(self):
-        if not self.running_pid_on_npu:
-            return
-        for npu, pid_list in self.running_pid_on_npu.items():
-            if npu not in self.npu_affinity_cpu_dict.keys():
-                logging.warning(f'Cannot find affinity cpu for npu: {npu}')
-                continue
-            affinity_cpu = self.npu_affinity_cpu_dict.get(npu)
-            for pid in pid_list:
-                try:
-                    logging.info(f'Begin to bind cores for process {pid} on NPU {npu}')
-                    set_affinity_cpu_cmd = 'taskset -pc {} {}'.format(affinity_cpu, pid)
-                    p = subprocess.run(set_affinity_cpu_cmd.split(), shell=False, capture_output=True)
-                    logging.info(p.stdout.decode('utf-8'))
-                except subprocess.CalledProcessError:
-                    print('[ERROR] Failed to bind process {} on NPU {} with cpu cores list {}'.format(pid, npu, affinity_cpu))
-            
-                logging.info(f'Succeed to bind process {pid} on NPU {npu} with cpu cores list {affinity_cpu}')
-
-    def args_parse(self):
-        parser = argparse.ArgumentParser(description='This is a affinity cpu core bind script.')
-        parser.add_argument('-t', '--time', type=int, metavar='', help='Wait time before bind cores that you want to set. The unit is \'s\'.')
-        parser.add_argument('-app', '--application', metavar='', nargs='+', help='Training or inference command that you want to run.')
-        args = parser.parse_args()
-        if args.application:
-            application_cmd = ' '.join(args.application)
-            self.launch_process(application_cmd)
-            time.sleep(2)
-        # if time is set, wait for setting time before bind cores
-        if args.time:
-            time.sleep(args.time)
-
-    def launch_process(self, cmd: list):
-        logging.info(f'Start to execute cmd: {cmd}')
-        try:
-            subprocess.Popen(cmd.split(), shell=False)
-        except subprocess.CalledProcessError as e:
-            raise RuntimeError(f'Failed to run cmd: {cmd}') from e
-
-
-if __name__ == '__main__':
-    print('[INFO] Begin to run bind-cores script...')
-    bind_core_manager = BindCoreManager()
-    bind_core_manager.args_parse()
-
-    if not bind_core_manager.get_npu_info():
-        print('[ERROR] Failed to get current npus info')
-        exit()
-
-    if not bind_core_manager.get_running_pid_on_npu():
-        exit()
-    bind_core_manager.run_bind_core()
-    print('[INFO] End to run bind-cores script, the log is saved in {}'.format(bind_core_manager.log_file))
-
-
diff --git a/profiler/cli/cluster_cli.py b/profiler/cli/cluster_cli.py
index 93a4a638f27..c1563898d70 100644
--- a/profiler/cli/cluster_cli.py
+++ b/profiler/cli/cluster_cli.py
@@ -21,7 +21,7 @@ sys.path.append(os.path.dirname(os.path.dirname(__file__)))
 from profiler.advisor.utils.tools import CONTEXT_SETTINGS, ClickAliasedGroup
 from profiler.advisor.utils.utils import debug_option
 from profiler.prof_common.constant import Constant
-from profiler.cluster_analyse.cluster_analysis import ALL_FEATURE_LIST
+from profiler.cluster_analyse.cluster_analysis import COMM_FEATURE_LIST
 from profiler.cluster_analyse.cluster_analysis import cluster_analysis_main
 
 
@@ -33,7 +33,7 @@ context_settings['ignore_unknown_options'] = True
                short_help='Analyze cluster data to locate slow nodes and slow links.')
 @click.option('--profiling_path', '-d', type=click.Path(), required=True,
               help='path of the profiling data')
-@click.option('--mode', '-m', type=click.Choice(ALL_FEATURE_LIST), default='all')
+@click.option('--mode', '-m', type=click.Choice(COMM_FEATURE_LIST), default='all')
 @click.argument('args', nargs=-1)
 def cluster_cli(profiling_path, mode, args) -> None:
     required_args = ('-d', profiling_path, '-m', mode)
diff --git a/profiler/cli/compare_cli.py b/profiler/cli/compare_cli.py
index e794578da8c..f9add948ea9 100644
--- a/profiler/cli/compare_cli.py
+++ b/profiler/cli/compare_cli.py
@@ -32,6 +32,8 @@ from profiler.compare_tools.compare_backend.comparison_generator import Comparis
 @click.option('--enable_operator_compare', is_flag=True)
 @click.option('--enable_memory_compare', is_flag=True)
 @click.option('--enable_communication_compare', is_flag=True)
+@click.option('--enable_api_compare', is_flag=True)
+@click.option('--enable_kernel_compare', is_flag=True)
 @click.option('--disable_details', is_flag=True)
 @click.option('--output_path', '-o', 'output_path', type=click.Path())
 @click.option('--max_kernel_num', 'max_kernel_num', type=int, help="The number of kernels per torch op is limited.")
diff --git a/profiler/cluster_analyse/README.md b/profiler/cluster_analyse/README.md
index fdd43ca965f..4a394e09a48 100644
--- a/profiler/cluster_analyse/README.md
+++ b/profiler/cluster_analyse/README.md
@@ -54,10 +54,7 @@ experimental_config = torch_npu.profiler._ExperimentalConfig(
    | --------------------- | ------------------------------------------------------------ | -------- |
    | --collection_path或-d | 性能数据汇集目录，运行分析脚本之后会在该目录下自动创建cluster_analysis_output文件夹，保存分析数据。 | 是       |
    | --mode或-m            | 数据解析模式，取值详见“**--mode参数说明**”表。               | 否       |
-   | --parallel_mode       | 设置收集多卡、多节点db数据时的并发方式。取值为concurrent（使用concurrent.feature进程池实现并发）。<br/>**只有-m配置cann_api_sum、compute_op_sum、hccl_sum、mstx_sum时可配置此参数。** | 否       |
-   | --export_type         | 设置导出的数据形式。取值为db（.db格式文件）和notebook（Jupyter Notebook文件），默认值为db。<br/>**只有-m配置cann_api_sum、compute_op_sum、hccl_sum、mstx_sum时可配置此参数。** | 否       |
-   | --rank_list           | 对特定Rank上的数据进行统计，默认值为all（表示对所有Rank进行统计），须根据实际卡的Rank ID配置。应配置为大于等于0的整数，若所配置的值大于实际训练所运行的卡的Rank ID，则仅解析合法的RankID的数据，比如当前环境Rank ID为0到7，实际训练运行0到3卡，此时若配置Rank ID为0, 3, 4或不存在的10等其他值，则仅解析0和3。配置示例：--rank_list 0, 1, 2。<br/>**只有-m配置cann_api_sum、compute_op_sum、hccl_sum、mstx_sum时可配置此参数。** | 否       |
-   | --top_num             | 设置TopN耗时的通信算子的数量，默认值为15，配置示例：--top_num 20。<br/>**只有-m配置hccl_sum时可配置此参数。** | 否       |
+
    
    --mode参数说明：
    
@@ -66,22 +63,7 @@ experimental_config = torch_npu.profiler._ExperimentalConfig(
    | communication_matrix | 解析通信矩阵数据。                                           | 否       |
    | communication_time   | 解析通信耗时数据。                                           | 否       |
    | all                  | 同时解析通信矩阵communication_matrix和通信耗时数据communication_time，--mode参数默认值为all。 | 否       |
-   | cann_api_sum         | 集群API性能数据汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/CannApiSum目录下输出交付件stats.ipynb。 | 否       |
-   | compute_op_sum       | 集群场景性能数据的device运行算子信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/ComputeOpSum目录下输出交付件stats.ipynb。 | 否       |
-   | hccl_sum             | 集合通信算子耗时分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/HcclSum目录下输出交付件stats.ipynb。 | 否       |
-   | mstx_sum             | 集群场景mstx打点信息汇总分析，输入性能数据需要基于ascend_pytorch_profiler_{rank_id}.db文件。--export_type为db时，输出交付件cluster_analysis.db；--export_type为notebook时，在cluster_analysis_output/MstxSum目录下输出交付件stats.ipynb。 | 否       |
-   
-   --parallel_mode参数示例如下：
-   
-   ```bash
-   msprof-analyze cluster -d {cluster profiling data path} -m cann_api_sum --parallel_mode concurrent
-   ```
-   
-   或
-   
-   ```bash
-   python3 cluster_analysis.py -d {cluster profiling data path} -m cann_api_sum --parallel_mode concurrent
-   ```
+
    
 
 ### 交付件
@@ -158,23 +140,6 @@ L列：Preparing，指迭代开始到首个计算或通信算子运行的时间
 
 解析analysis.db或ascend_pytorch_profiler_{rank_id}.db生成的交付件，根据数据解析模式不同而解析不同的数据，可以使用MindStudio Insight工具展示。
 
-#### stats.ipynb
-
-- 数据解析模式为cann_api_sum时生成，保存在cluster_analysis_output/CannApiSum目录下。
-
-  可使用jupyter notebook工具或MindStudio Insight工具打开，主要展示集群API耗时信息。
-
-- 数据解析模式为compute_op_sum时生成，保存在cluster_analysis_output/ComputeOpSum目录下。
-
-  可使用jupyter notebook工具或MindStudio Insight工具打开，主要展示集群计算算子耗时分析（将集群所有计算算子进行汇总并以图表展示），集群Rank计算算子耗时分析（将每个Rank的计算算子进行各自汇总）。
-  
-- 数据解析模式为hccl_sum时生成，保存在cluster_analysis_output/HcclSum目录下。
-
-  可使用jupyter notebook工具或MindStudio Insight工具打开，主要展示集群通信算子耗时分析（将集群所有通信算子进行汇总并以图表展示），集群Rank通信算子耗时分析（将每个Rank的通信算子进行各自汇总）、Top通信算子信息展示。
-  
-- 数据解析模式为mstx_sum时生成，保存在cluster_analysis_output/MstxSum目录下。
-
-  可使用jupyter notebook工具或MindStudio Insight工具打开，主要展示集群场景mstx打点信息，分为框架侧、CANN侧和Device侧三部分的打点信息。
 
 
 
diff --git a/profiler/cluster_analyse/analysis/analysis_facade.py b/profiler/cluster_analyse/analysis/analysis_facade.py
index 435d77b21bf..c438fbcf322 100644
--- a/profiler/cluster_analyse/analysis/analysis_facade.py
+++ b/profiler/cluster_analyse/analysis/analysis_facade.py
@@ -19,8 +19,6 @@ from analysis.communication_analysis import CommunicationAnalysis
 from analysis.comm_matrix_analysis import CommMatrixAnalysis
 from analysis.step_trace_time_analysis import StepTraceTimeAnalysis
 from analysis.host_info_analysis import HostInfoAnalysis
-from common_func.context import Context
-from common_func.constant import Constant
 
 class AnalysisFacade:
     default_module = {CommunicationAnalysis, StepTraceTimeAnalysis, CommMatrixAnalysis, HostInfoAnalysis}
@@ -38,13 +36,3 @@ class AnalysisFacade:
 
         for process in process_list:
             process.join()
-    
-    def recipe_analyze(self):
-        HostInfoAnalysis(self.params).run()
-        print("[INFO] Recipe analysis launched.")
-        try:
-            with Context.create_context(self.params.get(Constant.PARALLEL_MODE)) as context:
-                with self.params.get(Constant.RECIPE_CLASS)(self.params) as recipe:
-                    recipe.run(context)
-        except Exception as e:
-            print("[ERROR] Recipe analysis launched failed, %s." % str(e))
diff --git a/profiler/cluster_analyse/analysis/base_analysis.py b/profiler/cluster_analyse/analysis/base_analysis.py
index 7209e9b56f0..d7be4fc9cf6 100644
--- a/profiler/cluster_analyse/analysis/base_analysis.py
+++ b/profiler/cluster_analyse/analysis/base_analysis.py
@@ -22,8 +22,6 @@ from abc import abstractmethod
 
 from common_func.constant import Constant
 from common_func.file_manager import FileManager
-from common_func.db_manager import DBManager
-from common_func.utils import convert_unit
 from cluster_utils.data_transfer_adapter import DataTransferAdapter
 
 
@@ -102,154 +100,3 @@ class BaseAnalysis:
         for rank_tup, group_dict in self.comm_ops_struct.items():
             for step_id, communication_ops in group_dict.items():
                 self.compute_total_info(communication_ops)
-
-
-class BaseRecipeAnalysis:
-    
-    UNIT = "Us"
-    DB_UNIT = "Ns"
-
-    RANK_LIST = "rank_list"
-
-    def __init__(self, params):
-        self._params = params
-        self._collection_dir = params.get(Constant.COLLECTION_PATH, "")
-        self._data_map = params.get(Constant.DATA_MAP, {})
-        self._recipe_name = params.get(Constant.RECIPE_NAME, "")
-        self._mode = params.get(Constant.PARALLEL_MODE, "")
-        self._export_type = params.get(Constant.EXPORT_TYPE, "")
-        self._output_dir = None
-        self._rank_list = params.get(self.RANK_LIST, 'all')
-
-    def __enter__(self):
-        return self
-    
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if self._params is not None and exc_type is not None:
-            print(f"[ERROR] Failed to exit analysis: {exc_val}")
-            traceback.print_exc(file=sys.stdout)
-
-    def run(self, context):
-        pass
-
-    @property
-    def base_dir(self):
-        return os.path.basename(os.path.dirname(__file__))
-
-    def _get_rank_db(self):
-        invalid_rank_id = []
-        if self._rank_list == 'all':
-            rank_ids = list(self._data_map.keys())
-        else:
-            rank_ids = []
-            for rank_id in self._rank_list:
-                if rank_id in self._data_map.keys():
-                    rank_ids.append(rank_id)
-                else:
-                    invalid_rank_id.append(str(rank_id))
-        db_paths = []
-        for rank_id in rank_ids:
-            rank_path = self._data_map[rank_id]
-            db_path = os.path.join(rank_path, Constant.SINGLE_OUTPUT, f"ascend_pytorch_profiler_{rank_id}.db")
-            if os.path.exists(db_path):
-                db_paths.append((rank_id, db_path))
-            else:
-                print(f"[WARNING] DB file not found, rank id: {rank_id}, db path: {db_path}.")
-        if invalid_rank_id:
-            print(f"[WARNING] Invalid Rank id : [{','.join(invalid_rank_id)}].")
-        return db_paths
-
-    def get_mode(self):
-        return self._mode
-    
-    def get_recipe_name(self):
-        return self._recipe_name
-    
-    def dump_data(self, data, file_name, table_name=None, index=True):
-        output_path = os.path.join(self._collection_dir, Constant.CLUSTER_ANALYSIS_OUTPUT)
-        if table_name:
-            result_db = os.path.join(output_path, file_name)
-            conn, cursor = DBManager.create_connect_db(result_db)
-            if isinstance(data, pd.DataFrame):
-                data.to_sql(table_name, conn, if_exists='replace', index=True)
-            else:
-                print(f"[ERROR] Unknown dump data type: {type(data)}")
-            DBManager.destroy_db_connect(conn, cursor)
-        else:
-            result_csv = os.path.join(output_path, file_name)
-            if isinstance(data, pd.DataFrame):
-                data = convert_unit(data, self.DB_UNIT, self.UNIT)
-                data.to_csv(result_csv, index=index)
-            else:
-                print(f"[ERROR] Unknown dump data type: {type(data)}")
-
-    def _create_output_dir_name(self, name):
-        i = 1
-        while os.path.exists(f"{name}-{i}"):
-            i += 1
-        return f"{name}-{i}"
-    
-    def _create_unique_output_dir(self):
-        output_dir = os.path.join(self._collection_dir, Constant.CLUSTER_ANALYSIS_OUTPUT, self._recipe_name)
-        
-        if os.path.exists(output_dir):
-            return self._create_output_dir_name(output_dir)
-        return output_dir
-        
-    def _get_output_dir(self):
-        if self._output_dir is None:
-            self._output_dir = self._create_unique_output_dir()
-            os.makedirs(self._output_dir)
-        return self._output_dir
-    
-    def create_notebook(self, filename, notebook_template_dir=None, replace_dict=None):
-        if notebook_template_dir is None:
-            template_path = os.path.dirname(__file__)
-        else:
-            template_path = notebook_template_dir
-        output_path = os.path.join(self._get_output_dir(), filename)
-        template_file = os.path.join(template_path, self.base_dir, filename)
-        if replace_dict is None:
-            shutil.copy(template_file, output_path)
-        else:
-            with open(template_file, 'r') as f:
-                template_content = f.read()
-                for key, value in replace_dict.items():
-                    template_content = template_content.replace(str(key), str(value))
-            with open(output_path, 'w') as f:
-                f.write(template_content)
-        print(f"[INFO] Notebook export path is: {self._get_output_dir()}")
-
-    def add_helper_file(self, helper_file):
-        helper_output_path = os.path.join(self._get_output_dir(), helper_file)
-        helper_file_path = os.path.join(os.path.dirname(__file__), helper_file)
-
-        if helper_file_path is not None:
-            shutil.copy(helper_file_path, helper_output_path)
-
-    @staticmethod
-    def _filter_data(mapper_data):
-        return [(rank, data) for rank, data in mapper_data if data is not None and len(data) != 0]
-
-    @classmethod
-    def add_parser_argument(cls, parser):
-        parser.add_argument("--rank_list", type=str, help="Rank id list", default='all')
-
-    @classmethod
-    def parse_argument(cls, args_parsed) -> dict:
-        if args_parsed.rank_list == 'all':
-            return {
-                cls.RANK_LIST: 'all'
-            }
-        else:
-            rank_str_list = args_parsed.rank_list.split(",")
-            rank_list = [int(rank) for rank in rank_str_list if rank.isdigit()]
-            return {
-                cls.RANK_LIST: rank_list
-            }
-    
-    @classmethod
-    def get_extra_argument(cls, params) -> dict:
-        return {
-            cls.RANK_LIST: params.get(cls.RANK_LIST, "all")
-        }
diff --git a/profiler/cluster_analyse/analysis/cann_api_sum/__init__.py b/profiler/cluster_analyse/analysis/cann_api_sum/__init__.py
deleted file mode 100644
index 7101187a2c2..00000000000
--- a/profiler/cluster_analyse/analysis/cann_api_sum/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/profiler/cluster_analyse/analysis/cann_api_sum/cann_api_sum.py b/profiler/cluster_analyse/analysis/cann_api_sum/cann_api_sum.py
deleted file mode 100644
index db37b004b15..00000000000
--- a/profiler/cluster_analyse/analysis/cann_api_sum/cann_api_sum.py
+++ /dev/null
@@ -1,108 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import pandas as pd
-
-from analysis.base_analysis import BaseRecipeAnalysis
-from common_func.constant import Constant
-from common_func.utils import stdev
-from cluster_statistics_export.cann_api_sum_export import CannApiSumExport
-
-
-class CannApiSum(BaseRecipeAnalysis):
-
-    def __init__(self, params):
-        super().__init__(params)
-        print("[INFO] CannApiSum init.")
-
-    @property
-    def base_dir(self):
-        return os.path.basename(os.path.dirname(__file__))
-
-    @staticmethod
-    def _mapper_func(data_map, analysis_class):
-        df = CannApiSumExport(data_map[1], analysis_class).read_export_db()
-        
-        if df is None or df.empty:
-            print(f"[WARNING] There is no stats data in {data_map[1]}.")
-            return None
-        return data_map[0], df
-    
-    def mapper_func(self, context):
-        return context.wait(
-            context.map(
-            self._mapper_func,
-            self._get_rank_db(),
-            analysis_class=self._recipe_name
-            )
-        )
-    
-    def reducer_func(self, mapper_res):
-        stats_rank_data = self._filter_data(mapper_res)
-        if not stats_rank_data:
-            print("[ERROR] Mapper data is None.")
-            return
-        stats_rank_data = [df.assign(rank=rank) for rank, df in stats_rank_data]
-        stats_rank_data = pd.concat(stats_rank_data)
-        stats_data = self._aggregate_stats(stats_rank_data)
-        if self._export_type == "db":
-            self.dump_data(stats_rank_data, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, "CannApiSumRank")
-            self.dump_data(stats_data, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, "CannApiSum")
-        elif self._export_type == "notebook":
-            self.dump_data(stats_rank_data, os.path.join(self._get_output_dir(), "rank_stats.csv"), index=False)
-            self.dump_data(stats_data, os.path.join(self._get_output_dir(), "all_stats.csv"))
-            self.save_notebook()
-        else:
-            print("[ERROR] Unknown export type.")
-
-    def run(self, context):
-        mapper_res = self.mapper_func(context)
-        self.reducer_func(mapper_res)
-
-    @staticmethod
-    def _aggregate_stats(stats_res):
-        grouped = stats_res.groupby("name")
-        res = {}
-        total_time = grouped["totalTimeNs"].sum()
-        res["timeRatio"] = total_time / total_time.sum() * 100.0
-        res["totalTimeNs"] = total_time
-        res["totalCount"] = grouped["totalCount"].sum()
-        res["averageNs"] = res["totalTimeNs"] / res["totalCount"]
-        res["Q1Ns"] = grouped["Q1Ns"].min()
-        res["medNs"] = grouped["medNs"].median()
-        res["Q3Ns"] = grouped["Q3Ns"].max()
-        res["minNs"] = grouped["minNs"].min()
-        res["maxNs"] = grouped["maxNs"].max()
-        res["stdev"] = grouped.apply(lambda x: stdev(x, res))
-        min_value = grouped["minNs"].min()
-        res["minRank"] = grouped.apply(
-            lambda x: ", ".join(
-                x.loc[x["minNs"] == min_value.loc[x.name], "rank"].astype(str)
-            )
-        )
-        max_value = grouped["maxNs"].max()
-        res["maxRank"] = grouped.apply(
-            lambda x: ", ".join(
-                x.loc[x["maxNs"] == max_value.loc[x.name], "rank"].astype(str)
-            )
-        )
-        res = pd.concat(res.values(), axis=1, keys=res.keys()).round(1)
-        res.sort_values(by="totalTimeNs", ascending=False, inplace=True)
-        return res
-
-    def save_notebook(self):
-        self.create_notebook("stats.ipynb")
-        self.add_helper_file("cluster_display.py")
diff --git a/profiler/cluster_analyse/analysis/cann_api_sum/stats.ipynb b/profiler/cluster_analyse/analysis/cann_api_sum/stats.ipynb
deleted file mode 100644
index c97f039c5a0..00000000000
--- a/profiler/cluster_analyse/analysis/cann_api_sum/stats.ipynb
+++ /dev/null
@@ -1,86 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# CANN_API_SUM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import plotly.offline as pyo\n",
-    "\n",
-    "from IPython.display import display, HTML\n",
-    "\n",
-    "import cluster_display\n",
-    "\n",
-    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
-    "pd.set_option('display.max_columns', None)\n",
-    "pd.set_option('display.max_rows', None)\n",
-    "pyo.init_notebook_mode()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "## 集群场景CANN层API统计分析\n",
-    "该分析脚本展示了集群场景的统计数据分析结果。需要注意以下几点：\n",
-    "1. 所有的时间信息单位是微秒(us);\n",
-    "2. Q1表示单个API耗时的25%分位数，最终结果取自所有卡的Q1值中最小值；\n",
-    "3. Q3表示单个API耗时的75%分位数，最终结果取自所有卡的Q3值中最大值；\n",
-    "4. 'minRank'展示了API最小耗时所在卡；\n",
-    "5. 'maxRank'展示了API最大耗时所在卡。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(\"all_stats.csv\")\n",
-    "display(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cluster_display.display_box(df, xaxis_title=\"name\", yaxis_title=\"duration (ns)\")\n",
-    "cluster_display.display_stats_scatter(df, xaxis_title=\"name\", yaxis_title=\"duration (ns)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "per_rank_df = pd.read_csv(\"rank_stats.csv\")\n",
-    "cluster_display.display_stats_per_operation(per_rank_df, xaxis_title='rank', yaxis_title='duration (ns)')"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/profiler/cluster_analyse/analysis/cluster_display.py b/profiler/cluster_analyse/analysis/cluster_display.py
deleted file mode 100644
index 8fc6040ccaf..00000000000
--- a/profiler/cluster_analyse/analysis/cluster_display.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import pandas as pd
-import plotly.graph_objects as go
-from IPython.display import display, HTML
-from ipywidgets import Dropdown, fixed, interact
-
-
-def get_stats_cols(df):
-    cols = df.columns.tolist()
-    q1 = "Q1(Us)" if "Q1(Us)" in cols else "Q1~"
-    q3 = "Q3(Us)" if "Q3(Us)" in cols else "Q3~"
-    med = "med(Us)" if "med(Us)" in cols else "med~"
-    std = "stdev" if "stdev" in cols else "stdev~"
-    return q1, q3, med, std
-
-
-def display_box(df, x=None, **layout_args):
-    if x is None:
-        x = df.columns[0]
-    q1, q3, med, std = get_stats_cols(df)
-    fig = go.Figure()
-    fig.add_trace(
-        go.Box(
-            x=df[x],
-            q1=df[q1],
-            median=df[med],
-            q3=df[q3],
-            sd=df[std],
-            lowerfence=df["minRank"],
-            upperfence=df["maxRank"]
-        )
-    )
-    fig.update_layout(**layout_args)
-    fig.show()
-    
-
-def display_stats_scatter(df, x=None, **layout_args):
-    if x is None:
-        x = df.columns[0]
-    q1, q3, med, _ = get_stats_cols(df)
-    fig = go.Figure()
-    col_names = [q1, med, q3, "minRank", "maxRank"]
-    for name in col_names:
-        fig.add_trace(
-            go.Scatter(
-            x=df[x],
-            y=df[name],
-            name=name
-        )
-    )
-    fig.update_layout(**layout_args)
-    fig.show()
-    
-
-def display_table_per_rank(df):
-    if df.empty:
-        display(df)
-        return
-    
-    rank_groups = df.groupby("rank")
-    def display_table(name):
-        rank_df = rank_groups.get_group(name)
-        rank_df = rank_df.drop(columns=["rank"])
-        display(rank_df)
-    
-    dropdown = Dropdown(
-        options=rank_groups.groups.keys(),
-        description="rank:",
-        disabled=False,
-    )
-    interact(
-        display_table,
-        name=dropdown
-    )
-
-
-def display_stats_per_operation(df, x=None, box=True, scatter=True, table=True, **layout_args):
-    if df.empty:
-        display(df)
-        return
-    
-    if x is None:
-        x = df.columns[0]
-    
-    op_groups = df.groupby(x)
-    
-    def display_graphs(name):
-        op_df = op_groups.get_group(name)
-        if table:
-            display(op_df.reset_index(drop=True).set_index("rank"))
-        if box:
-            display_box(op_df, x=op_df["rank"], **layout_args)
-        if scatter:
-            display_stats_scatter(op_df, x=op_df["rank"], **layout_args)
-        
-    operations = list(op_groups.groups.keys())
-    
-    if len(operations) > 1:
-        dropdown = Dropdown(
-            options=operations,
-            description="Operation:",
-            disabled=False,
-            value=operations[1]
-        )
-        interact(
-            display_graphs,
-            name=dropdown
-        )
-        dropdown.value = operations[0]
-    else:
-        display_graphs(operations[0])
-
-
-def display_duration_boxplots(figs, stats_df: pd.DataFrame, orientation="v", title=None,
-                              x_title="Names", y_title="Time", legend_title="Legend"):
-    mean_ds = stats_df.get("Mean(Us)", None)
-    min_ds = stats_df.get("Min(Us)", None)
-    max_ds = stats_df.get("Max(Us)", None)
-    q1_ds = stats_df.get("Q1(Us)", None)
-    median_ds = stats_df.get('Median(Us)', None)
-    q3_ds = stats_df.get('Q3(Us)', None)
-    return display_boxplot(figs, stats_df.index, min_ds, q1_ds, median_ds, q3_ds, max_ds, mean_ds,
-                           orientation=orientation, title=title, x_title=x_title, y_title=y_title,
-                           legend_title=legend_title)
-
-
-def display_boxplot(figs, x_axis, min_ds, q1_ds, median_ds, q3_ds, max_ds, mean_ds, orientation="v",
-                    title=None, x_title=None, y_title="Time", legend_title="Legend"):
-    fig = go.Figure()
-    fig.add_trace(
-        go.Box(
-            x=x_axis,
-            lowerfence=min_ds,
-            q1=q1_ds,
-            median=median_ds,
-            q3=q3_ds,
-            upperfence=max_ds,
-            mean=mean_ds
-        )
-    )
-    fig.update_traces(orientation=orientation)
-    fig.update_layout(
-        xaxis_title=x_title, yaxis_title=y_title, legend_title=legend_title,
-        title=title, height=1024
-    )
-    fig.show()
-    if isinstance(figs, list):
-        figs.append(fig)
-    return fig
-
-
-def display_graph(figs, x_axis, y_axes, title=None,
-                  x_title=None, y_title=None, legend_title="Legend"):
-    data = None
-    if isinstance(y_axes, pd.DataFrame):
-        data = y_axes.set_index(x_axis)
-    elif isinstance(y_axes, dict):
-        data = pd.DataFrame(y_axes, index=x_axis)
-    elif isinstance(y_axes, pd.Series):
-        data = pd.DataFrame({"": y_axes}, index=x_axis)
-    elif isinstance(y_axes, np.ndarray):
-        data = pd.DataFrame({"": pd.Series(y_axes)}, index=x_axis)
-    else:
-        return
-
-    fig = data.plot.line()
-    fig.update_layout(
-        title=title, xaxis_title=x_title, yaxis_title=y_title, legend_title=legend_title
-    )
-    fig.show()
-    if isinstance(figs, list):
-        figs.append(fig)
-    return fig
-
-
-def display_stats_per_rank_groups_combobox(rank_stats_gdf):
-    names = list(rank_stats_gdf.groups.keys())
-    if len(names) > 1:
-        dropdown = Dropdown(
-            options=names, layout={"width": "max-content"}, value=names[1]
-        )
-        interact(
-            __display_stats_per_rank_group,
-            selected=dropdown,
-            rank_stats_gdf=fixed(rank_stats_gdf)
-        )
-        dropdown.value = names[0]
-    elif len(names) == 1:
-        __display_stats_per_rank_group(names[0], rank_stats_gdf)
-    else:
-        print("cluster_display func:input rank_stats_gdf groups is null so no need to display")
-
-
-def __display_stats_per_rank_group(selected, rank_stats_gdf):
-    df = rank_stats_gdf.get_group(selected)
-    df = df.reset_index(drop=True)
-    df = df.set_index(df["Rank"])
-    display(df)
-
-    figs = []
-    display_duration_boxplots(figs, df, x_title="Ranks")
-    display_graph(
-        figs,
-        df.index,
-        df[["Q1(Us)", "Median(Us)", "Q3(Us)"]],
-        title="50% of Distribution",
-        x_title="Ranks"
-    )
-
-
-def display_stats_optional_combobox(options, display_func, args, description="Option:"):
-    if len(options) > 1:
-        dropdown = Dropdown(
-            options=options, layout={"width": "max-content"}, value=options[1],
-            description=description
-        )
-        interact(
-            display_func,
-            selected=dropdown,
-            args=fixed(args)
-        )
-        dropdown.value = options[0]
-    elif len(options) == 1:
-        display_func(options[0], args)
diff --git a/profiler/cluster_analyse/analysis/compute_op_sum/__init__.py b/profiler/cluster_analyse/analysis/compute_op_sum/__init__.py
deleted file mode 100644
index 7101187a2c2..00000000000
--- a/profiler/cluster_analyse/analysis/compute_op_sum/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/profiler/cluster_analyse/analysis/compute_op_sum/compute_op_sum.py b/profiler/cluster_analyse/analysis/compute_op_sum/compute_op_sum.py
deleted file mode 100644
index e71cf868ac9..00000000000
--- a/profiler/cluster_analyse/analysis/compute_op_sum/compute_op_sum.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import pandas as pd
-from analysis.base_analysis import BaseRecipeAnalysis
-from common_func.constant import Constant
-from common_func.utils import describe_duration
-from cluster_statistics_export.compute_op_sum_export import ComputeOpSumExport
-
-
-class ComputeOpSum(BaseRecipeAnalysis):
-
-    TABLE_ALL_RANK_STATS = "ComputeOpAllRankStats"
-    TABLE_PER_RANK_STATS_BY_OPTYPE = "ComputeOpPerRankStatsByOpType"
-    TABLE_PER_RANK_STATS_BY_OPNAME = "ComputeOpPerRankStatsByOpName"
-
-    def __init__(self, params):
-        super().__init__(params)
-        print("[INFO] ComputeOpSum init.")
-        self.all_rank_stats = None
-        self.per_rank_stats_by_optype = None
-        self.per_rank_stats_by_opname = None
-
-    @property
-    def base_dir(self):
-        return os.path.basename(os.path.dirname(__file__))
-
-    @staticmethod
-    def _mapper_func(data_map, analysis_class):
-        df = ComputeOpSumExport(data_map[1], analysis_class).read_export_db()
-
-        if df is None or df.empty:
-            print(f"[WARNING] There is no stats data in {data_map[1]}.")
-            return None
-
-        df["Rank"] = data_map[0]
-        return df
-    
-    def mapper_func(self, context):
-        return context.wait(
-            context.map(
-            self._mapper_func,
-            self._get_rank_db(),
-            analysis_class=self._recipe_name
-            )
-        )
-    
-    def reducer_func(self, mapper_res):
-        mapper_res = list(filter(lambda df: df is not None, mapper_res))
-        if not mapper_res:
-            print("[ERROR] Mapper data is None.")
-            return
-        # get per rank stats by optype
-        self.per_rank_stats_by_optype = pd.concat(
-            describe_duration(df.groupby(["OpType", "TaskType"])["Duration"]).assign(Rank=df["Rank"][0]) for df in mapper_res)
-        self.per_rank_stats_by_optype.sort_values(by=["SumNs"], inplace=True, ascending=False)
-
-        # get all rank stats by optype
-        all_op_data = pd.concat(mapper_res)
-        self.all_rank_stats = describe_duration(all_op_data.groupby(["OpType", "TaskType"])["Duration"])
-        self.all_rank_stats.sort_values(by=["SumNs"], inplace=True, ascending=False)
-
-        # get per rank stats by opname
-        self.per_rank_stats_by_opname = pd.concat(
-            describe_duration(df.groupby(["OpName", "OpType", "TaskType", "InputShapes"])["Duration"]).assign(Rank=df["Rank"][0]) for df in mapper_res)
-        self.per_rank_stats_by_opname.sort_values(by=["SumNs"], inplace=True, ascending=False)
-
-    def run(self, context):
-        super().run(context)
-        mapper_res = self.mapper_func(context)
-        self.reducer_func(mapper_res)
-
-        if self._export_type == "db":
-            self.save_db()    
-        elif self._export_type == "notebook":
-            self.save_notebook()
-        else:
-            print("[ERROR] Unknown export type.")
-        
-    def save_notebook(self):
-        self.dump_data(self.all_rank_stats, os.path.join(self._get_output_dir(), "all_stats.csv"))
-        self.dump_data(self.per_rank_stats_by_optype, os.path.join(self._get_output_dir(), "rank_stats_by_optype.csv"))
-        self.dump_data(self.per_rank_stats_by_opname, os.path.join(self._get_output_dir(), "rank_stats_by_opname.csv"))
-        self.create_notebook("stats.ipynb")
-        self.add_helper_file("cluster_display.py")
-    
-    def save_db(self):
-        self.dump_data(self.all_rank_stats, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_ALL_RANK_STATS)
-        self.dump_data(self.per_rank_stats_by_optype, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_PER_RANK_STATS_BY_OPTYPE)
-        self.dump_data(self.per_rank_stats_by_opname, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_PER_RANK_STATS_BY_OPNAME)
diff --git a/profiler/cluster_analyse/analysis/compute_op_sum/stats.ipynb b/profiler/cluster_analyse/analysis/compute_op_sum/stats.ipynb
deleted file mode 100644
index c88d2684c1f..00000000000
--- a/profiler/cluster_analyse/analysis/compute_op_sum/stats.ipynb
+++ /dev/null
@@ -1,164 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Compute Op Summary\n",
-    "\n",
-    "集群场景计算类算子数据分析\n",
-    "\n",
-    "主要包含以下3个统计内容：\n",
-    "1. 按算子类型和任务类型分组的，整个集群通信算子耗时的统计情况\n",
-    "2. 按算子类型和任务类型分组的，每个Rank上计算类算子的耗时情况\n",
-    "3. 按算子名称、任务类型、输入shape分组的，每个Rank上的计算类算子的耗时情况"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 数据准备"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
-    "\n",
-    "import plotly.offline as pyo\n",
-    "\n",
-    "def is_lab_notebook():\n",
-    "    import re\n",
-    "    import psutil\n",
-    "    return any(re.search('jupyter--lab-script', x) for x in psutil.Process().parent().cmdline())\n",
-    "\n",
-    "if is_lab_notebook():\n",
-    "    pyo.init_notebook_mode()\n",
-    "\n",
-    "import pandas as pd\n",
-    "pd.options.plotting.backend = \"plotly\"\n",
-    "pd.set_option(\"display.max_rows\", 100)\n",
-    "pd.set_option(\"display.width\", 1000)\n",
-    "\n",
-    "import cluster_display\n",
-    "\n",
-    "all_stats_df = pd.read_csv(\"all_stats.csv\", index_col=\"OpType\")\n",
-    "rank_stats_by_optype_df = pd.read_csv(\"rank_stats_by_optype.csv\", index_col=\"OpType\")\n",
-    "rank_stats_by_opname_df = pd.read_csv(\"rank_stats_by_opname.csv\", index_col=\"OpName\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 计算类算子耗时分析\n",
-    "\n",
-    "将整个集群所有Rank的计算类算子进行汇总，按算子类型和任务类型分类，统计分析耗时情况，时间单位为微秒(us)\n",
-    "\n",
-    "包含以下统计项：\n",
-    "- Count：算子数量\n",
-    "- Mean：平均耗时\n",
-    "- Std：标准差\n",
-    "- Min：最小值\n",
-    "- Q1：四分之一分位数\n",
-    "- Median：中位数\n",
-    "- Q3：四分之三分位数\n",
-    "- Max：最大值\n",
-    "- Sum：总耗时"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display(all_stats_df)\n",
-    "fig_all_rank = cluster_display.display_duration_boxplots(None, all_stats_df, x_title=\"OpType\")\n",
-    "fig_per_rank = cluster_display.display_graph(None, all_stats_df.index, all_stats_df[[\"Q1(Us)\", \"Median(Us)\", \"Q3(Us)\"]], title=\"50% of Distribution\", x_title=\"OpType\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 单个Rank的计算类算子基于算子类型的耗时分析\n",
-    "将集群内每个Rank的计算类算子进行汇总，按算子类型和任务类型分类，统计分析耗时情况，时间单位为微秒(us)\n",
-    "\n",
-    "包含以下统计项：\n",
-    "- Count：算子数量\n",
-    "- Mean：平均耗时\n",
-    "- Std：标准差\n",
-    "- Min：最小值\n",
-    "- Q1：四分之一分位数\n",
-    "- Median：中位数\n",
-    "- Q3：四分之三分位数\n",
-    "- Max：最大值\n",
-    "- Sum：总耗时"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rank_stats_gdf = rank_stats_by_optype_df.groupby(rank_stats_by_optype_df.index)\n",
-    "cluster_display.display_stats_per_rank_groups_combobox(rank_stats_gdf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 单个Rank的计算类算子基于算子名的耗时分析\n",
-    "\n",
-    "将集群内每个Rank的计算类算子进行汇总，按算子名称、任务类型、输入shape分类，统计分析耗时情况，时间单位为微秒(us)\n",
-    "\n",
-    "包含以下统计项：\n",
-    "- Count：算子数量\n",
-    "- Mean：平均耗时\n",
-    "- Std：标准差\n",
-    "- Min：最小值\n",
-    "- Q1：四分之一分位数\n",
-    "- Median：中位数\n",
-    "- Q3：四分之三分位数\n",
-    "- Max：最大值\n",
-    "- Sum：总耗时"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rank_stats_gdf = rank_stats_by_opname_df.groupby(rank_stats_by_opname_df.index)\n",
-    "cluster_display.display_stats_per_rank_groups_combobox(rank_stats_gdf)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.12.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/profiler/cluster_analyse/analysis/hccl_sum/__init__.py b/profiler/cluster_analyse/analysis/hccl_sum/__init__.py
deleted file mode 100644
index 7101187a2c2..00000000000
--- a/profiler/cluster_analyse/analysis/hccl_sum/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/profiler/cluster_analyse/analysis/hccl_sum/hccl_sum.py b/profiler/cluster_analyse/analysis/hccl_sum/hccl_sum.py
deleted file mode 100644
index da0c575e468..00000000000
--- a/profiler/cluster_analyse/analysis/hccl_sum/hccl_sum.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import pandas as pd
-from analysis.base_analysis import BaseRecipeAnalysis
-from common_func.constant import Constant
-from common_func.utils import describe_duration
-from cluster_statistics_export.hccl_sum_export import HcclSumExport
-
-
-class HcclSum(BaseRecipeAnalysis):
-
-    TABLE_ALL_RANK_STATS = "HcclAllRankStats"
-    TABLE_PER_RANK_STATS = "HcclPerRankStats"
-    TABLE_TOP_OP_STATS = "HcclTopOpStats"
-
-    TOP_NUM = "top_num"
-    DEFAULT_TOP_NUM = 15
-
-    def __init__(self, params):
-        super().__init__(params)
-        print("[INFO] HcclSum init.")
-        self.per_rank_stats = None
-        self.all_rank_stats = None
-        self.top_op_stats = None
-        self.top_num = params.get(self.TOP_NUM, self.DEFAULT_TOP_NUM)
-
-    @property
-    def base_dir(self):
-        return os.path.basename(os.path.dirname(__file__))
-
-    @staticmethod
-    def _mapper_func(data_map, analysis_class):
-        df = HcclSumExport(data_map[1], analysis_class).read_export_db()
-
-        if df is None or df.empty:
-            print(f"[WARNING] There is no stats data in {data_map[1]}.")
-            return None
-
-        df["Rank"] = data_map[0]
-        return df
-
-    def mapper_func(self, context):
-        return context.wait(
-            context.map(
-            self._mapper_func,
-            self._get_rank_db(),
-            analysis_class=self._recipe_name
-            )
-        )
-
-    def reducer_func(self, mapper_res):
-        mapper_res = list(filter(lambda df: df is not None, mapper_res))
-        if not mapper_res:
-            print("[ERROR] Mapper data is None.")
-            return
-        self.per_rank_stats = pd.concat(
-            describe_duration(df.groupby("OpType")["Duration"]).assign(Rank=df["Rank"][0]) for df in mapper_res)
-        self.per_rank_stats.sort_values(by=["Rank"], inplace=True)
-        all_op_data = pd.concat(mapper_res)
-        self.all_rank_stats = describe_duration(all_op_data.groupby("OpType")["Duration"])
-        grouped_op_stats = all_op_data.groupby("OpName")
-        self.top_op_stats = describe_duration(grouped_op_stats["Duration"]).nlargest(self.top_num, "MeanNs")
-        min_rank = []
-        max_rank = []
-        for op_name in self.top_op_stats.index:
-            df = grouped_op_stats.get_group(op_name)
-            min_rank.append(df[df["Duration"] == df["Duration"].min()]["Rank"].values[0])
-            max_rank.append(df[df["Duration"] == df["Duration"].max()]["Rank"].values[0])
-        self.top_op_stats["MinRank"] = min_rank
-        self.top_op_stats["MaxRank"] = max_rank
-
-    def run(self, context):
-        super().run(context)
-        if self.top_num <= 0:
-            print(f"[WARNING] HcclSum: top_num is set to a invalid value, "
-                  f"it will be reset to default value({self.DEFAULT_TOP_NUM}).")
-            self.top_num = self.DEFAULT_TOP_NUM
-        mapper_res = self.mapper_func(context)
-        self.reducer_func(mapper_res)
-
-        if self._export_type == "db":
-            self.save_db()    
-        elif self._export_type == "notebook":
-            self.save_notebook()
-        else:
-            print("[ERROR] Unknown export type.")
-
-    def save_notebook(self):
-        self.dump_data(self.all_rank_stats, os.path.join(self._get_output_dir(), "all_stats.csv"))
-        self.dump_data(self.per_rank_stats, os.path.join(self._get_output_dir(), "rank_stats.csv"))
-        self.dump_data(self.top_op_stats, os.path.join(self._get_output_dir(), "top_op_stats.csv"))
-        self.create_notebook("stats.ipynb")
-        self.add_helper_file("cluster_display.py")
-
-    def save_db(self):
-        self.dump_data(self.all_rank_stats, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_ALL_RANK_STATS)
-        self.dump_data(self.per_rank_stats, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_PER_RANK_STATS)
-        self.dump_data(self.top_op_stats, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_TOP_OP_STATS)
-
-    @classmethod
-    def add_parser_argument(cls, parser):
-        BaseRecipeAnalysis.add_parser_argument(parser)
-        parser.add_argument("--top_num", type=int, help="Duration cost top count", default=cls.DEFAULT_TOP_NUM)
-
-    @classmethod
-    def parse_argument(cls, args_parsed) -> dict:
-        argument_dict = BaseRecipeAnalysis.parse_argument(args_parsed)
-        argument_dict.update({
-            cls.TOP_NUM: args_parsed.top_num
-        })
-        return argument_dict
-
-    @classmethod
-    def get_extra_argument(cls, params) -> dict:
-        argument_dict = BaseRecipeAnalysis.get_extra_argument(params)
-        argument_dict.update({
-            cls.TOP_NUM: params.get(cls.TOP_NUM, cls.DEFAULT_TOP_NUM)
-        })
-        return argument_dict
diff --git a/profiler/cluster_analyse/analysis/hccl_sum/stats.ipynb b/profiler/cluster_analyse/analysis/hccl_sum/stats.ipynb
deleted file mode 100644
index 87f8c6d7362..00000000000
--- a/profiler/cluster_analyse/analysis/hccl_sum/stats.ipynb
+++ /dev/null
@@ -1,162 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# HCCL Summary\n",
-    "\n",
-    "集群场景Hccl算子数据分析\n",
-    "\n",
-    "主要包含以下3个统计内容：\n",
-    "1. 按算子类型分组的，整个集群通信算子耗时的统计情况\n",
-    "2. 按算子类型分组的，每个Rank上通信算子的耗时情况\n",
-    "3. 整个集群平均耗时最久的TOP通信算子"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 数据准备"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
-    "\n",
-    "import plotly.offline as pyo\n",
-    "\n",
-    "def is_lab_notebook():\n",
-    "    import re\n",
-    "    import psutil\n",
-    "    return any(re.search('jupyter--lab-script', x) for x in psutil.Process().parent().cmdline())\n",
-    "\n",
-    "if is_lab_notebook():\n",
-    "    pyo.init_notebook_mode()\n",
-    "\n",
-    "import pandas as pd\n",
-    "pd.options.plotting.backend = \"plotly\"\n",
-    "pd.set_option(\"display.max_rows\", 100)\n",
-    "pd.set_option(\"display.width\", 1000)\n",
-    "\n",
-    "import cluster_display\n",
-    "\n",
-    "all_stats_df = pd.read_csv(\"all_stats.csv\", index_col=\"OpType\")\n",
-    "rank_stats_df = pd.read_csv(\"rank_stats.csv\", index_col=\"OpType\")\n",
-    "top_op_stats_df = pd.read_csv(\"top_op_stats.csv\", index_col=\"OpName\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 集群通信算子耗时分析\n",
-    "\n",
-    "将整个集群所有Rank的通信算子进行汇总，按算子类型分类，统计分析耗时情况，时间单位为微秒(us)\n",
-    "\n",
-    "包含以下统计项：\n",
-    "- Count：算子数量\n",
-    "- Mean：平均耗时\n",
-    "- Std：标准差\n",
-    "- Min：最小值\n",
-    "- Q1：四分之一分位数\n",
-    "- Median：中位数\n",
-    "- Q3：四分之三分位数\n",
-    "- Max：最大值\n",
-    "- Sum：总耗时"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display(all_stats_df)\n",
-    "fig_all_rank = cluster_display.display_duration_boxplots(None, all_stats_df, x_title=\"Hccl OpType\")\n",
-    "fig_per_rank = cluster_display.display_graph(None, all_stats_df.index, all_stats_df[[\"Q1(Us)\", \"Median(Us)\", \"Q3(Us)\"]], title=\"50% of Distribution\", x_title=\"Hccl OpType\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 集群Rank通信算子耗时分析\n",
-    "\n",
-    "将集群内每个Rank的通信算子进行汇总，按算子类型分类，统计分析耗时情况，时间单位为微秒(us)\n",
-    "\n",
-    "包含以下统计项：\n",
-    "- Count：算子数量\n",
-    "- Mean：平均耗时\n",
-    "- Std：标准差\n",
-    "- Min：最小值\n",
-    "- Q1：四分之一分位数\n",
-    "- Median：中位数\n",
-    "- Q3：四分之三分位数\n",
-    "- Max：最大值\n",
-    "- Sum：总耗时"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rank_stats_gdf = rank_stats_df.groupby(rank_stats_df.index)\n",
-    "cluster_display.display_stats_per_rank_groups_combobox(rank_stats_gdf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 集群TOP-N通信算子耗时分析\n",
-    "\n",
-    "统计集群内耗时最多的TOP-N通信算子，时间单位为微秒(us)\n",
-    "\n",
-    "包含以下统计项：\n",
-    "- Count：算子数量\n",
-    "- Mean：平均耗时\n",
-    "- Std：标准差\n",
-    "- Min：最小值\n",
-    "- Q1：四分之一分位数\n",
-    "- Median：中位数\n",
-    "- Q3：四分之三分位数\n",
-    "- Max：最大值\n",
-    "- Sum：总耗时\n",
-    "- MinRank：耗时最少算子所在的Rank\n",
-    "- MaxRank：耗时最长算子所在的Rank"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display(top_op_stats_df)\n",
-    "fig_top_op = cluster_display.display_duration_boxplots(None, top_op_stats_df, x_title=\"Hccl OpName\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.12.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/profiler/cluster_analyse/analysis/mstx_sum/__init__.py b/profiler/cluster_analyse/analysis/mstx_sum/__init__.py
deleted file mode 100644
index 7101187a2c2..00000000000
--- a/profiler/cluster_analyse/analysis/mstx_sum/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/profiler/cluster_analyse/analysis/mstx_sum/mstx_sum.py b/profiler/cluster_analyse/analysis/mstx_sum/mstx_sum.py
deleted file mode 100644
index 46a0e18abee..00000000000
--- a/profiler/cluster_analyse/analysis/mstx_sum/mstx_sum.py
+++ /dev/null
@@ -1,204 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import pandas as pd
-from collections import namedtuple
-from analysis.base_analysis import BaseRecipeAnalysis
-from common_func.constant import Constant
-from common_func.utils import describe_duration
-from cluster_statistics_export.mstx_mark_export import MstxMarkExport
-from cluster_statistics_export.mstx_step_export import MstxStepExport
-
-
-MarkInfo = namedtuple("MarkInfo", ["name", "framework_duration", "cann_duration", "device_duration",
-                                   "tid", "start_ns"])
-
-
-def format_mark_info(df: pd.DataFrame, start_idx, stop_idx, name) -> MarkInfo:
-    start_series = df.iloc[start_idx]
-    stop_series = df.iloc[stop_idx]
-    return MarkInfo(
-        name=name,
-        framework_duration=float(stop_series["framework_ts"]-start_series["framework_ts"]),
-        cann_duration=float(stop_series["cann_ts"]-start_series["cann_ts"]),
-        device_duration=float(stop_series["device_ts"]-start_series["device_ts"]),
-        tid=start_series["tid"],
-        start_ns=start_series["cann_ts"]
-    )
-
-
-def rename_mark_msg_name(mark_stats_df: pd.DataFrame):
-    msg_idx_counter = {}
-    for idx, mark_info in enumerate(mark_stats_df.itertuples(index=False)):
-        msg_idx_counter.setdefault(mark_info.step_id, {}).setdefault(mark_info.name, []).append(idx)
-    for msg_dict in msg_idx_counter.values():
-        for msg, idx_list in msg_dict.items():
-            if len(idx_list) <= 1:
-                continue
-            for i, idx in enumerate(idx_list):
-                mark_stats_df.loc[idx, 'name'] = f"{msg}_{i}"
-
-
-def compute_step_id(mark_stat, step_stats_df: pd.DataFrame):
-    for step_info in step_stats_df.itertuples(index=False):
-        if step_info.start_ns <= mark_stat.start_ns <= step_info.end_ns:
-            return step_info.step_id
-    print(f"[WARNING] {mark_stat.name} is not in any step.")
-    return 0
-
-
-def format_columns(df: pd.DataFrame):
-    formatted_df = df.rename(
-        {
-            "framework_duration": "FrameworkDurationNs",
-            "cann_duration": "CannDurationNs",
-            "device_duration": "DeviceDurationNs",
-            "duration": "DurationNs",
-            "step_id": "StepId",
-            "tid": "Tid",
-            "name": "Name"
-        },
-        axis="columns"
-    )
-    cols = [col for col in formatted_df.columns if not col.endswith("_ns") and col not in {"Tid"}]
-    return formatted_df[cols]
-
-
-class MstxSum(BaseRecipeAnalysis):
-
-    TABLE_FRAMEWORK_STATS = "MSTXAllFrameworkStats"
-    TABLE_CANN_STATS = "MSTXAllCannStats"
-    TABLE_DEVICE_STATS = "MSTXAllDeviceStats"
-    TABLE_MARK_STATS = "MSTXMarkStats"
-
-    START_SUFFIX = "_start"
-    STOP_SUFFIX = "_stop"
-
-    def __init__(self, params):
-        super().__init__(params)
-        print("[INFO] MstxSum init.")
-        self.mark_stats = None
-        self.all_fwk_stats = None
-        self.all_cann_stats = None
-        self.all_device_stats = None
-
-    @property
-    def base_dir(self):
-        return os.path.basename(os.path.dirname(__file__))
-
-    @staticmethod
-    def _mapper_func(data_map, analysis_class):
-        step_df = MstxStepExport(data_map[1], analysis_class).read_export_db()
-        if step_df is None or step_df.empty:
-            step_df = pd.DataFrame({"start_ns": [0], "end_ns": [float("inf")], "step_id": [0]})
-        mark_df = MstxMarkExport(data_map[1], analysis_class).read_export_db()
-        if mark_df is None or mark_df.empty:
-            print(f"[WARNING] There is no mark data in {data_map[1]}.")
-            return None
-        mark_df["framework_ts"] = mark_df["framework_ts"].astype("int64")
-
-        mark_info = {}
-        mark_res = []
-        mismatch_msg = []
-        for idx, row in enumerate(mark_df.itertuples(index=False)):
-            if row.msg.endswith(MstxSum.START_SUFFIX):
-                msg = row.msg[:-len(MstxSum.START_SUFFIX)]
-                mark_info.setdefault(row.tid, {}).setdefault(msg, []).append(idx)
-            elif row.msg.endswith(MstxSum.STOP_SUFFIX):
-                msg = row.msg[:-len(MstxSum.STOP_SUFFIX)]
-                idx_list = mark_info.get(row.tid, {}).get(msg, [])
-                if not idx_list:
-                    mismatch_msg.append((row.msg, idx))
-                    continue
-                start_idx = idx_list.pop()
-                mark_res.append(format_mark_info(mark_df, start_idx, idx, msg))
-
-        # 统计未匹配上的mark信息
-        for msg_info in mark_info.values():
-            for msg, idx_list in msg_info.items():
-                if not idx_list:
-                    continue
-                mismatch_msg.extend((msg + MstxSum.START_SUFFIX, idx) for idx in idx_list)
-        if mismatch_msg:
-            mismatch_msg.sort(key=lambda msg: msg[1])
-            print(f"[WARNING] The following mark messages do not match anyone in "
-                  f"rank {data_map[0]}: {','.join(msg[0] for msg in mismatch_msg)}.")
-
-        mark_stats_df = pd.DataFrame(mark_res).assign(Rank=data_map[0])
-        mark_stats_df["step_id"] = mark_stats_df.apply(compute_step_id, axis=1, step_stats_df=step_df)
-        rename_mark_msg_name(mark_stats_df)
-        mark_stats_df = format_columns(mark_stats_df).set_index("Name", drop=True)
-        return mark_stats_df
-
-    def mapper_func(self, context):
-        return context.wait(
-            context.map(
-            self._mapper_func,
-            self._get_rank_db(),
-            analysis_class=self._recipe_name
-            )
-        )
-
-    def reducer_func(self, mapper_res):
-        mapper_res = list(filter(lambda df: df is not None, mapper_res))
-        if not mapper_res:
-            print("[ERROR] Mapper data is None.")
-            return
-        self.mark_stats = pd.concat(mapper_res)
-        all_fwk_stats = []
-        all_cann_stats = []
-        all_device_stats = []
-        mark_step_df = self.mark_stats.groupby("StepId")
-        for step_id, df in mark_step_df:
-            name_gdf = df.groupby("Name")
-            fwk_stats = describe_duration(name_gdf["FrameworkDurationNs"]).assign(StepId=step_id)
-            fwk_stats.sort_values(by=["SumNs"], inplace=True, ascending=False)
-            all_fwk_stats.append(fwk_stats)
-            cann_stats = describe_duration(name_gdf["CannDurationNs"]).assign(StepId=step_id)
-            cann_stats.sort_values(by=["SumNs"], inplace=True, ascending=False)
-            all_cann_stats.append(cann_stats)
-            device_stats = describe_duration(name_gdf["DeviceDurationNs"]).assign(StepId=step_id)
-            device_stats.sort_values(by=["SumNs"], inplace=True, ascending=False)
-            all_device_stats.append(device_stats)
-        self.all_fwk_stats = pd.concat(all_fwk_stats)
-        self.all_cann_stats = pd.concat(all_cann_stats)
-        self.all_device_stats = pd.concat(all_device_stats)
-
-    def run(self, context):
-        super().run(context)
-        mapper_res = self.mapper_func(context)
-        self.reducer_func(mapper_res)
-
-        if self._export_type == "db":
-            self.save_db()    
-        elif self._export_type == "notebook":
-            self.save_notebook()
-        else:
-            print("[ERROR] Unknown export type.")
-
-    def save_notebook(self):
-        self.dump_data(self.mark_stats, os.path.join(self._get_output_dir(), "mark_stats.csv"))
-        self.dump_data(self.all_fwk_stats, os.path.join(self._get_output_dir(), "all_fwk_stats.csv"))
-        self.dump_data(self.all_cann_stats, os.path.join(self._get_output_dir(), "all_cann_stats.csv"))
-        self.dump_data(self.all_device_stats, os.path.join(self._get_output_dir(), "all_device_stats.csv"))
-        self.create_notebook("stats.ipynb")
-        self.add_helper_file("cluster_display.py")
-
-    def save_db(self):
-        self.dump_data(self.mark_stats, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_MARK_STATS)
-        self.dump_data(self.all_fwk_stats, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_FRAMEWORK_STATS)
-        self.dump_data(self.all_cann_stats, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_CANN_STATS)
-        self.dump_data(self.all_device_stats, Constant.DB_CLUSTER_COMMUNICATION_ANALYZER, self.TABLE_DEVICE_STATS)
diff --git a/profiler/cluster_analyse/analysis/mstx_sum/stats.ipynb b/profiler/cluster_analyse/analysis/mstx_sum/stats.ipynb
deleted file mode 100644
index 84672bc72b9..00000000000
--- a/profiler/cluster_analyse/analysis/mstx_sum/stats.ipynb
+++ /dev/null
@@ -1,180 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MSTX Summary\n",
-    "\n",
-    "集群场景MSTX打点数据分析\n",
-    "\n",
-    "主要包含以下2个统计内容：\n",
-    "1. 按Step分组的，整个集群MSTX打点数据的统计情况\n",
-    "2. 按Name分组的，每个Rank上MSTX打点数据的统计情况"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 数据准备"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import display, HTML\n",
-    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
-    "\n",
-    "import plotly.offline as pyo\n",
-    "\n",
-    "def is_lab_notebook():\n",
-    "    import re\n",
-    "    import psutil\n",
-    "    return any(re.search('jupyter--lab-script', x) for x in psutil.Process().parent().cmdline())\n",
-    "\n",
-    "if is_lab_notebook():\n",
-    "    pyo.init_notebook_mode()\n",
-    "\n",
-    "import pandas as pd\n",
-    "pd.options.plotting.backend = \"plotly\"\n",
-    "pd.set_option(\"display.max_rows\", 100)\n",
-    "pd.set_option(\"display.width\", 1000)\n",
-    "\n",
-    "import cluster_display\n",
-    "\n",
-    "all_fwk_stats_gdf = pd.read_csv(\"all_fwk_stats.csv\", index_col=\"Name\").groupby(\"StepId\")\n",
-    "all_cann_stats_gdf = pd.read_csv(\"all_cann_stats.csv\", index_col=\"Name\").groupby(\"StepId\")\n",
-    "all_device_stats_gdf = pd.read_csv(\"all_device_stats.csv\", index_col=\"Name\").groupby(\"StepId\")\n",
-    "mark_stats_df = pd.read_csv(\"mark_stats.csv\", index_col=\"Name\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 集群MSTX数据分析\n",
-    "\n",
-    "将整个集群所有Rank的MSTX数据进行汇总，按Step划分，统计分析耗时情况，时间单位为微秒(us)\n",
-    "打点数据分为三种：\n",
-    "1. 框架侧耗时：Framework Time\n",
-    "2. Cann侧耗时：Cann Time\n",
-    "3. Device侧耗时：Devcie Time\n",
-    "\n",
-    "3种数据都包含以下统计项：\n",
-    "- Count：数量\n",
-    "- Mean：平均耗时\n",
-    "- Std：标准差\n",
-    "- Min：最小值\n",
-    "- Q1：四分之一分位数\n",
-    "- Median：中位数\n",
-    "- Q3：四分之三分位数\n",
-    "- Max：最大值\n",
-    "- Sum：总耗时"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def display_stats_mstx_step_combobox(selected, args):\n",
-    "    step = selected\n",
-    "    fwk_stats_gdf, cann_stats_gdf, device_stats_gdf = args\n",
-    "    fwk_df = fwk_stats_gdf.get_group(step)\n",
-    "    cann_df = cann_stats_gdf.get_group(step)\n",
-    "    device_df = device_stats_gdf.get_group(step)\n",
-    "    figs = []\n",
-    "    display(HTML(\"<p><b>Framework Time Stats</b></p>\"))\n",
-    "    display(fwk_df)\n",
-    "    cluster_display.display_duration_boxplots(figs, fwk_df, title=\"Framework Time\", x_title=\"Name\", y_title=\"Time\")\n",
-    "    display(HTML(\"<p><b>Cann Time Stats</b></p>\"))\n",
-    "    display(cann_df)\n",
-    "    cluster_display.display_duration_boxplots(figs, cann_df, title=\"Cann Time\", x_title=\"Name\", y_title=\"Time\")\n",
-    "    display(HTML(\"<p><b>Device Time Stats</b></p>\"))\n",
-    "    display(device_df)\n",
-    "    cluster_display.display_duration_boxplots(figs, device_df, title=\"Device Time\", x_title=\"Name\", y_title=\"Time\")\n",
-    "\n",
-    "steps = list(all_fwk_stats_gdf.groups.keys())\n",
-    "if steps:\n",
-    "    cluster_display.display_stats_optional_combobox(steps, display_stats_mstx_step_combobox, \n",
-    "                                                    [all_fwk_stats_gdf, all_cann_stats_gdf, all_device_stats_gdf], \"Step:\")\n",
-    "else:\n",
-    "    print(\"There is no step in stats, so no need to display\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 集群Rank MSTX数据分析\n",
-    "\n",
-    "将集群内每个Rank的MSTX数据进行汇总，按打点Name分类，统计分析耗时情况，时间单位为微秒(us)\n",
-    "\n",
-    "包含以下统计项：\n",
-    "- Name：打点名称\n",
-    "- FrameworkDuration(Us)：框架侧耗时\n",
-    "- CannDuration(Us)：Cann侧耗时\n",
-    "- DeviceDuration(Us)：Device侧耗时\n",
-    "- Rank：Rank序号\n",
-    "- StepId：Step序号"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def display_mstx_duration_by_rank(selected, args):\n",
-    "    mark_stats_gdf = args\n",
-    "    df = mark_stats_gdf.get_group(selected).sort_values(\"Rank\")\n",
-    "    display(df)\n",
-    "    fwk_duration = []\n",
-    "    cann_duration = []\n",
-    "    device_duration = []\n",
-    "    step_ids = []\n",
-    "    for step_id, step_df in df.groupby(\"StepId\"):\n",
-    "        fwk_duration.append((step_id, step_df[\"FrameworkDuration(Us)\"].values))\n",
-    "        cann_duration.append((step_id, step_df[\"CannDuration(Us)\"].values))\n",
-    "        device_duration.append((step_id, step_df[\"DeviceDuration(Us)\"].values))\n",
-    "        step_ids.append(step_id)\n",
-    "    fwk_df = pd.concat([pd.Series(dur, name=step_id) for step_id, dur in fwk_duration], axis=1)\n",
-    "    cann_df = pd.concat([pd.Series(dur, name=step_id) for step_id, dur in cann_duration], axis=1)\n",
-    "    device_df = pd.concat([pd.Series(dur, name=step_id) for step_id, dur in device_duration], axis=1)\n",
-    "    figs = []\n",
-    "    ranks = df[\"Rank\"].drop_duplicates()\n",
-    "    cluster_display.display_graph(figs, ranks, fwk_df[step_ids],\n",
-    "                                  title=\"Framework Time\", x_title=\"Rank\", y_title=\"Time\", legend_title=\"Step\")\n",
-    "    cluster_display.display_graph(figs, ranks, cann_df[step_ids],\n",
-    "                                  title=\"Cann Time\", x_title=\"Rank\", y_title=\"Time\", legend_title=\"Step\")\n",
-    "    cluster_display.display_graph(figs, ranks, device_df[step_ids],\n",
-    "                                  title=\"Device Time\", x_title=\"Rank\", y_title=\"Time\", legend_title=\"Step\")\n",
-    "\n",
-    "mark_stats_gdf = mark_stats_df.groupby(mark_stats_df.index)\n",
-    "names = list(mark_stats_gdf.groups.keys())\n",
-    "if steps:\n",
-    "    cluster_display.display_stats_optional_combobox(names, display_mstx_duration_by_rank, mark_stats_gdf, \"Name:\")\n",
-    "else:\n",
-    "    print(\"There is no mark name in stats, so no need to display\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.12.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/profiler/cluster_analyse/cluster_analysis.py b/profiler/cluster_analyse/cluster_analysis.py
index a8d01dcfe34..171417c8879 100644
--- a/profiler/cluster_analyse/cluster_analysis.py
+++ b/profiler/cluster_analyse/cluster_analysis.py
@@ -22,36 +22,9 @@ from communication_group.communication_group_generator import CommunicationGroup
 from common_func.constant import Constant
 from common_func.file_manager import FileManager
 from common_func.path_manager import PathManager
-from common_func import analysis_loader
 from analysis.analysis_facade import AnalysisFacade
 
 COMM_FEATURE_LIST = ['all', 'communication_time', 'communication_matrix']
-ALL_FEATURE_LIST = ['all', 'communication_time', 'communication_matrix', 'cann_api_sum', 'hccl_sum', 'compute_op_sum',
-                    'mstx_sum']
-
-
-def get_analysis_args(analysis_class, analysis_args):
-    parser = argparse.ArgumentParser(description="custom analysis args")
-    parser.add_argument("--parallel_mode", type=str, help="context mode", default="concurrent")
-    parser.add_argument("--export_type", type=str, help="export type", default="db")
-    analysis_class[1].add_parser_argument(parser)
-    return parser.parse_args(analysis_args)
-
-def parse_specific_params(analysis_name, analysis_args):
-    analysis_class = analysis_loader.get_class_from_name(analysis_name)
-    if not analysis_class:
-        print("[ERROR] undefined analysis.")
-        return None
-    
-    args_parsed = get_analysis_args(analysis_class, analysis_args)
-    specific_params = {
-        Constant.RECIPE_NAME: analysis_class[0],
-        Constant.RECIPE_CLASS: analysis_class[1],
-        Constant.PARALLEL_MODE: args_parsed.parallel_mode,
-        Constant.EXPORT_TYPE: args_parsed.export_type
-    }
-    specific_params.update(analysis_class[1].parse_argument(args_parsed))
-    return specific_params
 
 class Interface:
     ASCEND_PT = "ascend_pt"
@@ -96,51 +69,29 @@ class Interface:
         if data_type == Constant.INVALID:
             print("[ERROR] The current folder contains both DB and other files. Please check.")
             return
-        if self.analysis_mode not in COMM_FEATURE_LIST:
-            if data_type != Constant.DB:
-                print("[ERROR] The current analysis node only supports DB as input data. Please check.")
-                return
-            FileManager.create_output_dir(self.collection_path, is_overwrite=True)
-            params = {
-                Constant.COLLECTION_PATH: self.collection_path,
-                Constant.DATA_MAP: data_map,
-                Constant.DATA_TYPE: data_type,
-                Constant.RECIPE_NAME: self.origin_params.get(Constant.RECIPE_NAME, ""),
-                Constant.RECIPE_CLASS: self.origin_params.get(Constant.RECIPE_CLASS),
-                Constant.PARALLEL_MODE: self.origin_params.get(Constant.PARALLEL_MODE, ""),
-                Constant.EXPORT_TYPE: self.origin_params.get(Constant.EXPORT_TYPE, "")
-            }
-            params.update(params[Constant.RECIPE_CLASS].get_extra_argument(self.origin_params))
-            AnalysisFacade(params).recipe_analyze()
-        else:
-            FileManager.create_output_dir(self.collection_path)
-            params = {
-                Constant.COLLECTION_PATH: self.collection_path,
-                Constant.DATA_MAP: data_map,
-                Constant.ANALYSIS_MODE: self.analysis_mode,
-                Constant.DATA_TYPE: data_type
-            }
-            comm_data_dict = CommunicationGroupGenerator(params).generate()
-            params[Constant.COMM_DATA_DICT] = comm_data_dict
-            AnalysisFacade(params).cluster_analyze()
+        FileManager.create_output_dir(self.collection_path)
+        params = {
+            Constant.COLLECTION_PATH: self.collection_path,
+            Constant.DATA_MAP: data_map,
+            Constant.ANALYSIS_MODE: self.analysis_mode,
+            Constant.DATA_TYPE: data_type
+        }
+        comm_data_dict = CommunicationGroupGenerator(params).generate()
+        params[Constant.COMM_DATA_DICT] = comm_data_dict
+        AnalysisFacade(params).cluster_analyze()
 
 
 def cluster_analysis_main(args=None):
     parser = argparse.ArgumentParser(description="cluster analysis module")
     parser.add_argument('-d', '--collection_path', type=str, required=True, help="profiling data path")
-    parser.add_argument('-m', '--mode', choices=ALL_FEATURE_LIST,
+    parser.add_argument('-m', '--mode', choices=COMM_FEATURE_LIST,
                         default='all', help="different analysis mode")
-    args_parsed, args_remained = parser.parse_known_args(args=args)
+    args_parsed, _ = parser.parse_known_args(args=args)
     parameter = {
         Constant.COLLECTION_PATH: args_parsed.collection_path,
         Constant.ANALYSIS_MODE: args_parsed.mode
     }
-    if args_parsed.mode in COMM_FEATURE_LIST:
-        if args_remained:
-            print(f"[ERROR] The specific argument {args_remained} is not supported for communication analysis.")
-            return
-    else:
-        parameter.update(parse_specific_params(args_parsed.mode, args_remained))
+
     Interface(parameter).run()
 
 
diff --git a/profiler/cluster_analyse/cluster_statistics_export/__init__.py b/profiler/cluster_analyse/cluster_statistics_export/__init__.py
deleted file mode 100644
index 7101187a2c2..00000000000
--- a/profiler/cluster_analyse/cluster_statistics_export/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/profiler/cluster_analyse/cluster_statistics_export/cann_api_sum_export.py b/profiler/cluster_analyse/cluster_statistics_export/cann_api_sum_export.py
deleted file mode 100644
index 578ee937be5..00000000000
--- a/profiler/cluster_analyse/cluster_statistics_export/cann_api_sum_export.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from cluster_statistics_export.stats_export import StatsExport
-
-QUERY = """
-WITH
-    summary as (
-        SELECT
-            name,
-            sum(endNs - startNs) AS duration,
-            count (*) AS num,
-            avg(endNs - startNs) AS avg_duration,
-            min(endNs - startNs) AS min_duration,
-            median(endNs - startNs) AS med_duration,
-            max(endNs - startNs) AS max_duration,
-            stdev(endNs - startNs) AS stdev_duration,
-            lower_quartile(endNs - startNs) AS lower_quartile_duration,
-            upper_quartile(endNs - startNs) AS upper_quartile_duration
-        FROM
-            CANN_API
-        GROUP BY name
-    ),
-    totals AS (
-        SELECT sum(duration) AS total
-        FROM summary
-    )
-SELECT
-    ids.value AS "name",
-    round(summary.duration * 100.0 / (SELECT total FROM totals), 2) AS "durationRatio",
-    summary.duration AS "totalTimeNs",
-    summary.num AS "totalCount",
-    round(summary.avg_duration, 1) AS "averageNs",
-    round(summary.min_duration, 1) AS "minNs",
-    round(summary.lower_quartile_duration, 1) AS "Q1Ns",
-    round(summary.med_duration, 1) AS "medNs",
-    round(summary.upper_quartile_duration, 1) AS "Q3Ns",
-    round(summary.max_duration, 1) AS "maxNs",
-    round(summary.stdev_duration, 1) AS "stdev"
-FROM
-    summary
-LEFT JOIN
-    STRING_IDS AS ids
-    ON ids.id == summary.name
-ORDER BY 2 DESC;
-    """
-
-
-class CannApiSumExport(StatsExport):
-    
-    def __init__(self, db_path, recipe_name):
-        super().__init__(db_path, recipe_name)
-        self._query = QUERY
diff --git a/profiler/cluster_analyse/cluster_statistics_export/compute_op_sum_export.py b/profiler/cluster_analyse/cluster_statistics_export/compute_op_sum_export.py
deleted file mode 100644
index d70c696100b..00000000000
--- a/profiler/cluster_analyse/cluster_statistics_export/compute_op_sum_export.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from cluster_statistics_export.stats_export import StatsExport
-
-
-QUERY = """
-SELECT
-    NAME_IDS.value AS "OpName",
-    OPTYPE_IDS.value AS "OpType",
-    TASKTYPE_IDS.value AS "TaskType",
-    INPUTSHAPES_IDS.value AS "InputShapes",
-    round(TASK.endNs - TASK.startNs) AS "Duration"
-FROM
-    COMPUTE_TASK_INFO
-LEFT JOIN TASK
-    ON TASK.globalTaskId == COMPUTE_TASK_INFO.globalTaskId
-LEFT JOIN
-    STRING_IDS AS NAME_IDS
-    ON NAME_IDS.id == COMPUTE_TASK_INFO.name
-LEFT JOIN
-    STRING_IDS AS OPTYPE_IDS
-    ON OPTYPE_IDS.id == COMPUTE_TASK_INFO.opType
-LEFT JOIN
-    STRING_IDS AS TASKTYPE_IDS
-    ON TASKTYPE_IDS.id == COMPUTE_TASK_INFO.taskType
-LEFT JOIN
-    STRING_IDS AS INPUTSHAPES_IDS
-    ON INPUTSHAPES_IDS.id == COMPUTE_TASK_INFO.inputShapes
-    """
-
-
-class ComputeOpSumExport(StatsExport):
-    
-    def __init__(self, db_path, recipe_name):
-        super().__init__(db_path, recipe_name)
-        self._query = QUERY
diff --git a/profiler/cluster_analyse/cluster_statistics_export/hccl_sum_export.py b/profiler/cluster_analyse/cluster_statistics_export/hccl_sum_export.py
deleted file mode 100644
index f695949de1a..00000000000
--- a/profiler/cluster_analyse/cluster_statistics_export/hccl_sum_export.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from cluster_statistics_export.stats_export import StatsExport
-
-
-QUERY = """
-SELECT
-    NAME_IDS.value AS "OpName",
-    TYPE_IDS.value AS "OpType",
-    round(endNs - startNs) AS "Duration"
-FROM
-    COMMUNICATION_OP
-LEFT JOIN
-    STRING_IDS AS TYPE_IDS
-    ON TYPE_IDS.id == COMMUNICATION_OP.opType
-LEFT JOIN
-    STRING_IDS AS NAME_IDS
-    ON NAME_IDS.id == COMMUNICATION_OP.opName
-    """
-
-
-class HcclSumExport(StatsExport):
-    
-    def __init__(self, db_path, recipe_name):
-        super().__init__(db_path, recipe_name)
-        self._query = QUERY
diff --git a/profiler/cluster_analyse/cluster_statistics_export/mstx_mark_export.py b/profiler/cluster_analyse/cluster_statistics_export/mstx_mark_export.py
deleted file mode 100644
index ac5355c0200..00000000000
--- a/profiler/cluster_analyse/cluster_statistics_export/mstx_mark_export.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from cluster_statistics_export.stats_export import StatsExport
-
-
-QUERY = """
-WITH
-    FRAMEWORK_API AS (
-        SELECT
-            PYTORCH_API.startNs,
-            CONNECTION_IDS.connectionId
-        FROM
-            PYTORCH_API
-        LEFT JOIN
-            CONNECTION_IDS
-            ON PYTORCH_API.connectionId == CONNECTION_IDS.id
-    )
-SELECT
-    MSG_IDS.value AS "msg",
-    MSTX_EVENTS.startNs AS "cann_ts",
-    TASK.startNs AS "device_ts",
-    FRAMEWORK_API.startNs AS "framework_ts",
-    MSTX_EVENTS.globalTid AS "tid"
-FROM
-    MSTX_EVENTS
-LEFT JOIN
-    TASK
-    ON MSTX_EVENTS.connectionId == TASK.connectionId
-LEFT JOIN
-    FRAMEWORK_API
-    ON MSTX_EVENTS.connectionId == FRAMEWORK_API.connectionId
-LEFT JOIN
-    STRING_IDS AS MSG_IDS
-    ON MSTX_EVENTS.message == MSG_IDS.id
-ORDER BY
-    MSTX_EVENTS.startNs
-    """
-
-
-class MstxMarkExport(StatsExport):
-
-    def __init__(self, db_path, recipe_name):
-        super().__init__(db_path, recipe_name)
-        self._query = QUERY
diff --git a/profiler/cluster_analyse/cluster_statistics_export/mstx_step_export.py b/profiler/cluster_analyse/cluster_statistics_export/mstx_step_export.py
deleted file mode 100644
index c257ce675fe..00000000000
--- a/profiler/cluster_analyse/cluster_statistics_export/mstx_step_export.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from cluster_statistics_export.stats_export import StatsExport
-
-
-QUERY = """
-SELECT
-    id AS "step_id",
-    startNs AS "start_ns",
-    endNs AS "end_ns"
-FROM
-    STEP_TIME
-ORDER BY
-    startNs
-    """
-
-
-class MstxStepExport(StatsExport):
-
-    def __init__(self, db_path, recipe_name):
-        super().__init__(db_path, recipe_name)
-        self._query = QUERY
diff --git a/profiler/cluster_analyse/cluster_statistics_export/stats_export.py b/profiler/cluster_analyse/cluster_statistics_export/stats_export.py
deleted file mode 100644
index e6d98f48ef8..00000000000
--- a/profiler/cluster_analyse/cluster_statistics_export/stats_export.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pandas as pd
-
-from common_func.db_manager import DBManager
-from common_func.constant import Constant
-
-
-class StatsExport:
-
-    def __init__(self, db_path, analysis_class):
-        self._db_path = db_path
-        self._analysis_class = analysis_class
-        self._query = None
-
-    def get_query(self):
-        return self._query
-
-    def read_export_db(self):
-        query = self.get_query()
-        if query is None:
-            print(f"[ERROR] query is None.")
-            return
-        conn, cursor = DBManager.create_connect_db(self._db_path, Constant.ANALYSIS)
-        data = pd.read_sql(query, conn)
-        DBManager.destroy_db_connect(conn, cursor)
-        return data
diff --git a/profiler/cluster_analyse/common_func/analysis_loader.py b/profiler/cluster_analyse/common_func/analysis_loader.py
deleted file mode 100644
index 55e7dbc6ea9..00000000000
--- a/profiler/cluster_analyse/common_func/analysis_loader.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import importlib
-import inspect
-import sys
-
-from common_func.constant import Constant
-from analysis.base_analysis import BaseRecipeAnalysis
-
-def is_analysis_class(obj):
-    return inspect.isclass(obj) and issubclass(obj, BaseRecipeAnalysis) and obj != BaseRecipeAnalysis
-
-def get_class_from_name(analysis_name : str):
-    sys.path.append(Constant.ANALYSIS_PATH)
-    analysis_path = f"analysis.{analysis_name}.{analysis_name}"
-    module = None
-    try:
-        module = importlib.import_module(analysis_path)
-    except Exception as e:
-        print(f"[ERROR] {analysis_path} not find:{e}")
-    
-    specific_analysis = inspect.getmembers(module, is_analysis_class)
-    if not specific_analysis:
-        print(f"[ERROR] {analysis_name} not found.")
-    return specific_analysis[0]
diff --git a/profiler/cluster_analyse/common_func/constant.py b/profiler/cluster_analyse/common_func/constant.py
index 80f0374c1d1..2922d6a900f 100644
--- a/profiler/cluster_analyse/common_func/constant.py
+++ b/profiler/cluster_analyse/common_func/constant.py
@@ -106,13 +106,3 @@ class Constant(object):
     CONFIG = "config"
     EXPER_CONFIG = "experimental_config"
     EXPORT_TYPE = "_export_type"
-
-    # recipe config
-    ANALYSIS = "analysis"
-    RECIPE_NAME = "recipe_name"
-    RECIPE_CLASS = "recipe_class"
-    PARALLEL_MODE = "parallel_mode"
-    CLUSTER_CUSTOM_ANALYSE_PATH = os.path.abspath(os.path.dirname(__file__))
-    ANALYSIS_PATH = os.path.join(CLUSTER_CUSTOM_ANALYSE_PATH, 'analysis')
-    
-    CONCURRENT_MODE = "concurrent"
\ No newline at end of file
diff --git a/profiler/cluster_analyse/common_func/context.py b/profiler/cluster_analyse/common_func/context.py
deleted file mode 100644
index 4e3d544d376..00000000000
--- a/profiler/cluster_analyse/common_func/context.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-from functools import partial
-from concurrent import futures
-from common_func.constant import Constant
-
-
-class Context(object):
-    """abstract base class"""
-
-    ctx_map = None
-
-    @classmethod
-    def create_context(cls, mode=Constant.CONCURRENT_MODE):
-        if cls.ctx_map is None:
-            keys = [Constant.CONCURRENT_MODE]
-            values = [ConcurrentContext]
-            cls.ctx_map = dict(zip(keys, values))
-
-        if mode not in cls.ctx_map:
-            raise NotImplementedError("mode must be in {}".format(keys))
-
-        return cls.ctx_map[mode]()
-
-    def __init__(self):
-        print("[INFO] context {} initialized.".format(self._mode))
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.close()
-        if exc_type is not None:
-            print(f"[ERROR] Failed to exit context: {exc_val}")
-
-    def launch(self, func, *args, **kwargs):
-        raise NotImplementedError
-
-    def map(self, func, *iterables, **kwargs):
-        raise NotImplementedError
-    
-    def wait(self, waitable):
-        raise NotImplementedError
-
-class ConcurrentContext(Context):
-
-    def __init__(self, executor=None):
-        self._mode = Constant.CONCURRENT_MODE
-        super().__init__()
-        self._custom = executor is None
-        self._executor = executor or futures.ProcessPoolExecutor(max_workers=os.cpu_count())
-
-    def __enter__(self):
-        if self._executor is None:
-            raise RuntimeError("executor is None")
-        return self
-
-    def close(self):
-        if self._custom:
-            self._executor.shutdown(wait=True)
-            self._executor = None
-
-    def launch(self, func, *args, **kwargs):
-        return self._executor.submit(func, *args, **kwargs).result()
-
-    def map(self, func, *iterables, **kwargs):
-        partial_func = partial(func, **kwargs)
-        return list(self._executor.map(partial_func, *iterables))
-    
-    def wait(self, waitable):
-        return waitable
\ No newline at end of file
diff --git a/profiler/cluster_analyse/common_func/db_manager.py b/profiler/cluster_analyse/common_func/db_manager.py
index c0d6ad89be8..1aa7ed8740e 100644
--- a/profiler/cluster_analyse/common_func/db_manager.py
+++ b/profiler/cluster_analyse/common_func/db_manager.py
@@ -20,7 +20,6 @@ from common_func.constant import Constant
 from common_func.empty_class import EmptyClass
 from common_func.file_manager import check_db_path_valid
 from common_func.tables_config import TablesConfig
-from common_func.sql_extention_func import SqlExtentionAggregateFunc
 
 class DBManager:
     """
@@ -42,12 +41,6 @@ class DBManager:
                 print(f"[ERROR] {err}")
                 return EmptyClass("empty conn"), EmptyClass("empty curs")
             try:
-                if mode == Constant.ANALYSIS:
-                    try:
-                        for func_name, params_count, class_name in SqlExtentionAggregateFunc:
-                            conn.create_aggregate(func_name, params_count, class_name)
-                    except sqlite3.Error as err:
-                        print(f"[ERROR] {err}")
                 if isinstance(conn, sqlite3.Connection):
                     curs = conn.cursor()
                     os.chmod(db_path, Constant.FILE_AUTHORITY)
diff --git a/profiler/cluster_analyse/common_func/sql_extention_func.py b/profiler/cluster_analyse/common_func/sql_extention_func.py
deleted file mode 100644
index 987a0d43653..00000000000
--- a/profiler/cluster_analyse/common_func/sql_extention_func.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-
-
-class Median:
-
-    def __init__(self) -> None:
-        self.data = []
-
-    def step(self, value) -> None:
-        self.data.append(value)
-
-    def finalize(self):
-        return np.median(self.data)
-
-
-class LowerQuartile:
-
-    def __init__(self) -> None:
-        self.data = []
-
-    def step(self, value) -> None:
-        self.data.append(value)
-
-    def finalize(self):
-        return np.quantile(self.data, 0.25)
-
-
-class UpperQuartile:
-
-    def __init__(self) -> None:
-        self.data = []
-
-    def step(self, value) -> None:
-        self.data.append(value)
-
-    def finalize(self):
-        return np.quantile(self.data, 0.75)
-    
-
-class StandardDeviation:
-
-    def __init__(self) -> None:
-        self.data = []
-
-    def step(self, value) -> None:
-        self.data.append(value)
-
-    def finalize(self):
-        return np.std(self.data)
-
-
-# func_name, params_count, class
-SqlExtentionAggregateFunc = [
-    ('median', 1, Median),
-    ('lower_quartile', 1, LowerQuartile),
-    ('upper_quartile', 1, UpperQuartile),
-    ('stdev', 1, StandardDeviation)
-]
diff --git a/profiler/cluster_analyse/common_func/utils.py b/profiler/cluster_analyse/common_func/utils.py
deleted file mode 100644
index 0a20a5c237f..00000000000
--- a/profiler/cluster_analyse/common_func/utils.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright (c) 2024, Huawei Technologies Co., Ltd.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import pandas as pd
-
-
-def format_columns(df: pd.DataFrame):
-    formatted_df = df.rename(
-        {
-            "25%": "Q1Ns",
-            "50%": "MedianNs",
-            "75%": "Q3Ns",
-            0.25: "Q1Ns",
-            0.5: "MedianNs",
-            0.75: "Q3Ns",
-            "Q1": "Q1Ns",
-            "Q3": "Q3Ns",
-            "min": "MinNs",
-            "max": "MaxNs",
-            "median": "MedianNs",
-            "sum": "SumNs",
-            "std": "StdNs",
-            "mean": "MeanNs",
-            "count": "Count"
-        },
-        axis="columns"
-    )
-
-    stats_cols = ["Count", "MeanNs", "StdNs", "MinNs", "Q1Ns", "MedianNs", "Q3Ns", "MaxNs", "SumNs"]
-    other_cols = [col for col in formatted_df.columns if col not in stats_cols]
-    return formatted_df[stats_cols + other_cols]
-
-
-def describe_duration(series_groupby):
-    agg_df = series_groupby.agg(["min", "max", "count", "std", "mean", "sum"])
-    quantile_df = series_groupby.quantile([0.25, 0.5, 0.75])
-
-    quantile_df = quantile_df.unstack()
-    quantile_df.columns = ["25%", "50%", "75%"]
-
-    stats_df = pd.merge(agg_df, quantile_df, left_index=True, right_index=True)
-    formated_df = format_columns(stats_df)
-    formated_df.index.name = stats_df.index.name
-    return formated_df
-
-
-def stdev(df, aggregated):
-    if len(df) <= 1:
-        return df["stdevNs"].iloc[0]
-    instance = aggregated["totalCount"].loc[df.name]
-    var_sum = np.dot(df["totalCount"] - 1, df["stdev"] ** 2)
-    deviation = df["averageNs"] - aggregated["averageNs"].loc[df.name]
-    dev_sum = np.dot(df["totalCount"], deviation ** 2)
-    return np.sqrt((var_sum + dev_sum) / (instance - 1))
-
-
-def convert_unit(df: pd.DataFrame, src_unit, dst_unit):
-    df.loc[:, df.columns.str.endswith(src_unit)] = df.loc[:, df.columns.str.endswith(src_unit)].apply(lambda x: x / 1000.0)
-    df = df.rename(columns=lambda x: x.replace(src_unit, "".join(["(", dst_unit, ")"])))
-    return df
diff --git a/profiler/compare_tools/README.md b/profiler/compare_tools/README.md
index 78ea5d89717..b40f19e92fa 100644
--- a/profiler/compare_tools/README.md
+++ b/profiler/compare_tools/README.md
@@ -145,6 +145,8 @@ python performance_compare.py [基准性能数据文件所在路径] [比对性
 | --enable_operator_compare      | 开启算子性能比对。MindSpore场景暂不支持。该开关较耗时，建议只采集一个step的性能数据。 | 否       |
 | --enable_communication_compare | 开启通信性能比对。                                           | 否       |
 | --enable_memory_compare        | 开启算子内存比对。MindSpore场景暂不支持。该开关较耗时，建议只采集一个step的性能数据。 | 否       |
+| --enable_kernel_compare        | 开启kernel性能比对。仅针对NPU与NPU比对的场景。需要使用性能数据中的kernel_details.csv文件。 | 否       |
+| --enable_api_compare           | 开启API性能比对。需要使用性能数据中的trace_view.csv文件。    | 否       |
 | --disable_details              | 隐藏明细比对，只进行统计级比对。                             | 否       |
 
 说明：以上开关均不设置的情况下，**工具默认开启所有的性能比对**，当用户设置了以上开关，则按照用户设置的开关进行性能比对，示例如下：
@@ -174,9 +176,13 @@ python performance_compare.py [基准性能数据文件] [比对性能数据文
 
 MindSpore场景仅支持**总体性能**和**通信性能**的对比。
 
+比对结果分为打屏和performance_comparison_result_{timestamp}.csv两种形式输出，其中打屏输出为概要信息，csv文件保存详细结果。
+
 ### 总体性能
 
-总体性能比对结果以打屏的形式呈现。
+#### 打屏结果
+
+总体性能比对结果以打屏的形式呈现时，字段如下：
 
 | 字段                                    | 说明                                                         |
 | --------------------------------------- | ------------------------------------------------------------ |
@@ -196,6 +202,54 @@ MindSpore场景仅支持**总体性能**和**通信性能**的对比。
 | E2E Time(Not minimal profiling)         | E2E总耗时，计算流端到端耗时。当存在Not minimal profiling时，表示该时间存在性能膨胀，会影响通信和调度耗时。 |
 | Other Time                              | AI CPU、DSA、TensorMove等其他算子耗时。                      |
 
+#### csv文件结果
+
+总体性能比对结果在performance_comparison_result_*.xlsx中OverallMetrics的sheet页呈现时，示例如下：
+
+![OverallMetrics](./img/OverallMetrics.png)
+
+表头字段说明：
+
+| 字段           | 说明                        |
+| -------------- | --------------------------- |
+| Index          | 指标。                      |
+| Duration(ms)   | 执行耗时，单位ms。          |
+| Duration Ratio | 执行耗时占E2E总耗时的比例。 |
+| Number         | 计算算子的数量。            |
+
+Index列字段说明：
+
+| 字段                         |                    |                                     | 说明                                                         |
+| ---------------------------- | ------------------ | ----------------------------------- | ------------------------------------------------------------ |
+| Computing Time               |                    |                                     | 计算流耗时，计算流所有event耗时总和。如果有多条并发计算，计算流耗时对重叠部分只会计算一次。 |
+|                              | Flash Attention    |                                     | Flash Attention算子。                                        |
+|                              |                    | Flash Attention (Forward) (Cube)    | Flash Attention前向算子下发的所有Cube类Kernel的总耗时，一般为执行该算子核心计算的算子。 |
+|                              |                    | Flash Attention (Forward) (Vector)  | Flash Attention前向算子下发的所有Vector类Kernel的总耗时，一般为插入的转换类算子，如TransData。 |
+|                              |                    | Flash Attention (Backward) (Cube)   | Flash Attention反向算子下发的所有Cube类Kernel的总耗时，一般为执行该算子核心计算的算子。 |
+|                              |                    | Flash Attention (Backward) (Vector) | Flash Attention反向算子下发的所有Vector类Kernel的总耗时，一般为插入的转换类算子，如TransData。 |
+|                              | Conv               |                                     | Conv算子。                                                   |
+|                              |                    | Conv (Forward) (Cube)               | Conv前向算子下发的所有Cube类Kernel的总耗时，一般为执行该算子核心计算的算子。 |
+|                              |                    | Conv (Forward)  (Vector)            | Conv前向Vector算子。Conv前向算子下发的所有Vector类Kernel的总耗时，一般为插入的转换类算子，如TransData。 |
+|                              |                    | Conv (Backward) (Cube)              | Conv反向算子下发的所有Cube类Kernel的总耗时，一般为执行该算子核心计算的算子。 |
+|                              |                    | Conv (Backward) (Vector)            | Conv反向算子下发的所有Vector类Kernel的总耗时，一般为插入的转换类算子，如TransData。 |
+|                              | Matmul             |                                     | Matmul算子。                                                 |
+|                              |                    | Matmul (Cube)                       | Matmul算子下发的所有Cube类Kernel的总耗时，一般为执行该算子核心计算的算子。 |
+|                              |                    | Matmul (Vector)                     | Matmul算子下发的所有Vector类Kernel的总耗时，一般为插入的转换类算子，如TransData。 |
+|                              | Paged Attention    |                                     | Paged Attention算子。                                        |
+|                              | Vector             |                                     | Vector算子。                                                 |
+|                              |                    | Vector (Trans)                      | 转换类Vector算子，主要包含Cast、TransPose、TransData算子。（仅针对NPU数据） |
+|                              |                    | Vector ( No Trans)                  | 非转换类Vector算子。                                         |
+|                              | Cube               |                                     | 未识别出Flash Attention、Conv和Matmul的Cube算子。            |
+|                              | SDMA (Tensor Move) |                                     | 拷贝类任务。                                                 |
+|                              | Other              |                                     | AI CPU、DSA等其他算子。                                      |
+| Uncovered Communication Time |                    |                                     | 通信未掩盖耗时，包含卡间等待时间。                           |
+|                              | Wait               |                                     | 卡间同步等待耗时。（仅针对NPU数据）                          |
+|                              | Transmit           |                                     | 通信传输耗时。                                               |
+| Free Time                    |                    |                                     | 调度耗时 = E2E耗时 - 算子耗时 - 通信不可掩盖耗时。Free的定义为Device侧既不在通信又不在计算的时间，因此包含拷贝时间（SDMA Time）。 |
+|                              | SDMA               |                                     | NPU为除Tensor Move外的拷贝类任务，GPU为所有拷贝类任务。      |
+|                              | Free               |                                     | 排除SDMA的空闲耗时。                                         |
+| E2E Time                     |                    |                                     | E2E总耗时，计算流端到端耗时。当存在Not minimal profiling时，表示该时间存在性能膨胀，会影响通信和调度耗时。 |
+
 可以采取最简性能数据采集的方式来减少E2E耗时的性能膨胀，示例代码如下：
 
 ```python
@@ -300,3 +354,29 @@ MindSpore场景暂不支持。
 
 步骤1：查看MemoryCompareStatistic页，找出内存占用差距TOP的算子。
 步骤2：查看MemoryCompare页，搜索内存占用差距TOP的算子，查看具体占用的子算子。
+
+### kernel性能
+
+仅针对NPU与NPU比对的场景。
+
+kernel比对结果在performance_comparison_result_*.xlsx中KernelCompare页呈现。
+
+按照Kernel（Kernel类型）和Input Shapes（输入Shape）分组统计，统计信息包括：
+
+- Total Duration(us)：总耗时，单位us。
+- Avg Duration(us)：平均耗时，单位us。
+- Max Duration(us)：最大耗时，单位us。
+- Min Duration(us)：最小耗时，单位us。
+- Calls：调用次数。
+
+### API性能
+
+API比对结果在performance_comparison_result_*.xlsx中ApiCompare页呈现。
+
+按照api name（API名称）组统计，统计信息包括：
+
+- Total Duration(ms)：总耗时，单位ms。
+- Self Time(ms)：Self耗时（排除掉子event），单位ms。
+- Avg Duration(ms)：平均耗时，单位ms。
+- Calls：调用次数。
+
diff --git a/profiler/compare_tools/compare_backend/comparator/api_compare_comparator.py b/profiler/compare_tools/compare_backend/comparator/api_compare_comparator.py
new file mode 100644
index 00000000000..bc5810068b0
--- /dev/null
+++ b/profiler/compare_tools/compare_backend/comparator/api_compare_comparator.py
@@ -0,0 +1,32 @@
+from compare_backend.comparator.base_comparator import BaseComparator
+from compare_backend.utils.constant import Constant
+from compare_backend.utils.common_func import update_order_id
+
+
+class ApiCompareComparator(BaseComparator):
+    def __init__(self, origin_data: list, bean: any):
+        super().__init__(origin_data, bean)
+
+    @classmethod
+    def _aggregated_api_by_name(cls, ops: list):
+        ops_dict = {}
+        for op in ops:
+            ops_dict.setdefault(op.name, []).append(op)
+        return ops_dict
+
+    def _compare(self):
+        if not self._origin_data:
+            return
+        base_ops = self._origin_data.get(Constant.BASE_DATA, {})
+        comparison_ops = self._origin_data.get(Constant.COMPARISON_DATA, {})
+        if not base_ops or not comparison_ops:
+            return
+        base_aggregated_ops = self._aggregated_api_by_name(base_ops)
+        comparison_aggregated_ops = self._aggregated_api_by_name(comparison_ops)
+        for op_name, base_data in base_aggregated_ops.items():
+            comparsion_data = comparison_aggregated_ops.pop(op_name, [])
+            self._rows.append(self._bean(op_name, base_data, comparsion_data).row)
+        if comparison_aggregated_ops:
+            for op_name, comparison_data in comparison_aggregated_ops.items():
+                self._rows.append(self._bean(op_name, [], comparison_data).row)
+        update_order_id(self._rows)
diff --git a/profiler/compare_tools/compare_backend/comparator/kernel_compare_comparator.py b/profiler/compare_tools/compare_backend/comparator/kernel_compare_comparator.py
new file mode 100644
index 00000000000..13c0f776af6
--- /dev/null
+++ b/profiler/compare_tools/compare_backend/comparator/kernel_compare_comparator.py
@@ -0,0 +1,35 @@
+from compare_backend.comparator.base_comparator import BaseComparator
+from compare_backend.utils.constant import Constant
+from compare_backend.utils.common_func import update_order_id
+
+
+class KernelCompareComparator(BaseComparator):
+    def __init__(self, origin_data: list, bean: any):
+        super().__init__(origin_data, bean)
+
+    @classmethod
+    def _aggregated_kernel_by_type_and_shape(cls, kernels: dict):
+        result_dict = {}
+        for type_shape, shape_values in kernels.items():
+            for shape, kernel_data in shape_values.items():
+                kernel = [single[1] for single in kernel_data]
+                result_list = [type_shape, shape, sum(kernel), len(kernel), max(kernel), min(kernel)]
+                result_dict.setdefault(f"{type_shape}{shape}", []).extend(result_list)
+        return result_dict
+
+    def _compare(self):
+        if not self._origin_data:
+            return
+        base_kernels = self._origin_data.get(Constant.BASE_DATA, {})
+        comparison_kernels = self._origin_data.get(Constant.COMPARISON_DATA, {})
+        if not base_kernels or not comparison_kernels:
+            return
+        base_aggregated_kernels = self._aggregated_kernel_by_type_and_shape(base_kernels)
+        comparison_aggregated_kernels = self._aggregated_kernel_by_type_and_shape(comparison_kernels)
+        for type_shape, base_data in base_aggregated_kernels.items():
+            comparsion_data = comparison_aggregated_kernels.pop(type_shape, [])
+            self._rows.append(self._bean(base_data, comparsion_data).row)
+        if comparison_aggregated_kernels:
+            for _, comparison_data in comparison_aggregated_kernels.items():
+                self._rows.append(self._bean([], comparison_data).row)
+        update_order_id(self._rows)
\ No newline at end of file
diff --git a/profiler/compare_tools/compare_backend/compare_bean/api_compare_bean.py b/profiler/compare_tools/compare_backend/compare_bean/api_compare_bean.py
new file mode 100644
index 00000000000..55e08a86be8
--- /dev/null
+++ b/profiler/compare_tools/compare_backend/compare_bean/api_compare_bean.py
@@ -0,0 +1,47 @@
+from compare_backend.utils.common_func import calculate_diff_ratio
+from compare_backend.utils.constant import Constant
+from compare_backend.utils.excel_config import ExcelConfig
+
+
+class ApiInfo:
+    def __init__(self, op_name: str, data_list: list):
+        self._data_list = data_list
+        self.name = op_name
+        self.total_dur = 0.0
+        self.self_time = 0.0
+        self.avg_dur = 0.0
+        self.number = len(data_list)
+        self._get_info()
+
+    def _get_info(self):
+        for data in self._data_list:
+            self.total_dur += data.api_dur
+            self.self_time += data.api_self_time
+        self.total_dur /= 1000.0
+        self.self_time /= 1000.0
+        self.avg_dur = self.total_dur / self.number if self.number else 0.0
+
+
+class ApiCompareBean:
+    TABLE_NAME = Constant.API_TABLE
+    HEADERS = ExcelConfig.HEADERS.get(TABLE_NAME)
+    OVERHEAD = ExcelConfig.OVERHEAD.get(TABLE_NAME)
+
+    def __init__(self, op_name: str, base_api: list, comparison_api: list):
+        self._name = op_name
+        self._base_api = ApiInfo(op_name, base_api)
+        self._comparison_api = ApiInfo(op_name, comparison_api)
+
+    @property
+    def row(self):
+        row = [None, self._name,
+               self._base_api.total_dur, self._base_api.self_time, self._base_api.avg_dur, self._base_api.number,
+               self._comparison_api.total_dur, self._comparison_api.self_time,
+               self._comparison_api.avg_dur, self._comparison_api.number]
+        diff_fields = [calculate_diff_ratio(self._base_api.total_dur, self._comparison_api.total_dur)[1],
+                       calculate_diff_ratio(self._base_api.self_time, self._comparison_api.self_time)[1],
+                       calculate_diff_ratio(self._base_api.avg_dur, self._comparison_api.avg_dur)[1],
+                       calculate_diff_ratio(self._base_api.number, self._comparison_api.number)[1]]
+        row.extend(diff_fields)
+        return row
+
diff --git a/profiler/compare_tools/compare_backend/compare_bean/kernel_compare_bean.py b/profiler/compare_tools/compare_backend/compare_bean/kernel_compare_bean.py
new file mode 100644
index 00000000000..df96addc4fe
--- /dev/null
+++ b/profiler/compare_tools/compare_backend/compare_bean/kernel_compare_bean.py
@@ -0,0 +1,75 @@
+from compare_backend.utils.common_func import calculate_diff_ratio
+from compare_backend.utils.constant import Constant
+from compare_backend.utils.excel_config import ExcelConfig
+
+
+class KernelCompareInfo:
+    def __init__(self, data_list: list):
+        self._kernel_type = None
+        self._input_shapes = None
+        self._total_dur = None
+        self._number = None
+        self._max_dur = None
+        self._min_dur = None
+        if not data_list:
+            return
+        self._kernel_type = data_list[0]
+        self._input_shapes = data_list[1]
+        self._total_dur = data_list[2]
+        self._number = data_list[3]
+        self._max_dur = data_list[4]
+        self._min_dur = data_list[5]
+
+    @property
+    def kernel_type(self):
+        return self._kernel_type
+
+    @property
+    def input_shapes(self):
+        return self._input_shapes
+    
+    @property
+    def total_dur(self):
+        return self._total_dur if self._total_dur else 0.0
+    
+    @property
+    def number(self):
+        return self._number
+    
+    @property
+    def max_dur(self):
+        return self._max_dur
+    
+    @property
+    def min_dur(self):
+        return self._min_dur
+    
+    @property
+    def avg_dur(self):
+        return self._total_dur / self._number if self._total_dur and self._number else 0.0
+
+
+class KernelCompareBean:
+    TABLE_NAME = Constant.KERNEL_TABLE
+    HEADERS = ExcelConfig.HEADERS.get(TABLE_NAME)
+    OVERHEAD = ExcelConfig.OVERHEAD.get(TABLE_NAME)
+
+    def __init__(self, base_kernel: list, comparison_kernel: list):
+        self._base_kernel = KernelCompareInfo(base_kernel)
+        self._comparison_kernel = KernelCompareInfo(comparison_kernel)
+        self._kernel_type = self._base_kernel.kernel_type \
+            if self._base_kernel.kernel_type else self._comparison_kernel.kernel_type
+        self._input_shapes = self._base_kernel.input_shapes \
+            if self._base_kernel.input_shapes else self._comparison_kernel.input_shapes
+
+    @property
+    def row(self):
+        row = [None, self._kernel_type, self._input_shapes,
+               self._base_kernel.total_dur, self._base_kernel.avg_dur,
+               self._base_kernel.max_dur, self._base_kernel.min_dur, self._base_kernel.number,
+               self._comparison_kernel.total_dur, self._comparison_kernel.avg_dur,
+               self._comparison_kernel.max_dur, self._comparison_kernel.min_dur, self._comparison_kernel.number]
+        diff_fields = [calculate_diff_ratio(self._base_kernel.total_dur, self._comparison_kernel.total_dur)[1],
+                       calculate_diff_ratio(self._base_kernel.avg_dur, self._comparison_kernel.avg_dur)[1]]
+        row.extend(diff_fields)
+        return row
\ No newline at end of file
diff --git a/profiler/compare_tools/compare_backend/compare_bean/origin_data_bean/kernel_details_bean.py b/profiler/compare_tools/compare_backend/compare_bean/origin_data_bean/kernel_details_bean.py
index 9c4825c0e8e..c15396e9c59 100644
--- a/profiler/compare_tools/compare_backend/compare_bean/origin_data_bean/kernel_details_bean.py
+++ b/profiler/compare_tools/compare_backend/compare_bean/origin_data_bean/kernel_details_bean.py
@@ -12,6 +12,7 @@ class KernelDetailsBean:
         self._data = data
         self._op_type = ""
         self._name = ""
+        self._input_shapes = ""
         self._aiv_vec_time = 0.0
         self._aicore_time = 0.0
         self._mac_time = 0.0
@@ -27,6 +28,10 @@ class KernelDetailsBean:
     def name(self) -> str:
         return self._name
 
+    @property
+    def input_shapes(self) -> str:
+        return self._input_shapes
+
     @property
     def aiv_vec_time(self) -> float:
         if self._aiv_vec_time == "" or self._aiv_vec_time == "N/A":
@@ -109,6 +114,7 @@ class KernelDetailsBean:
     def init(self):
         self._op_type = self._data.get('Type', "")
         self._name = self._data.get('Name', "")
+        self._input_shapes = self._data.get('Input Shapes', "")
         self._aiv_vec_time = self._data.get('aiv_vec_time(us)', "")
         self._aicore_time = self._data.get("aicore_time(us)", "")
         self._mac_time = self._data.get('mac_time(us)', "")
diff --git a/profiler/compare_tools/compare_backend/data_prepare/operator_data_prepare.py b/profiler/compare_tools/compare_backend/data_prepare/operator_data_prepare.py
index fdce23c6ab4..3106527c419 100644
--- a/profiler/compare_tools/compare_backend/data_prepare/operator_data_prepare.py
+++ b/profiler/compare_tools/compare_backend/data_prepare/operator_data_prepare.py
@@ -17,3 +17,20 @@ class OperatorDataPrepare:
             else:
                 result_data.append(level1_node)
         return result_data
+
+    def get_all_layer_ops(self) -> any:
+        root_node = TreeBuilder.build_tree(self.profiling_data.torch_op_data, [], [])
+        level1_child_nodes = root_node.child_nodes
+        node_queue = []
+        result_data = []
+        for level1_node in level1_child_nodes:
+            if level1_node.is_step_profiler():
+                node_queue.extend(level1_node.child_nodes)
+            else:
+                node_queue.append(level1_node)
+        while len(node_queue) > 0:
+            node = node_queue.pop(0)
+            result_data.append(node)
+            if node.child_nodes:
+                node_queue.extend(node.child_nodes)
+        return result_data
\ No newline at end of file
diff --git a/profiler/compare_tools/compare_backend/generator/detail_performance_generator.py b/profiler/compare_tools/compare_backend/generator/detail_performance_generator.py
index 292e3128154..6fe693fb067 100644
--- a/profiler/compare_tools/compare_backend/generator/detail_performance_generator.py
+++ b/profiler/compare_tools/compare_backend/generator/detail_performance_generator.py
@@ -8,6 +8,8 @@ from compare_backend.comparator.module_comparetor import ModuleComparator
 from compare_backend.comparator.module_statistic_comparator import ModuleStatisticComparator
 from compare_backend.comparator.operator_comparator import OperatorComparator
 from compare_backend.comparator.operator_statistic_comparator import OperatorStatisticComparator
+from compare_backend.comparator.api_compare_comparator import ApiCompareComparator
+from compare_backend.comparator.kernel_compare_comparator import KernelCompareComparator
 from compare_backend.comparator.overall_metrics_comparator import OverallMetricsComparator
 from compare_backend.compare_bean.communication_bean import CommunicationBean
 from compare_backend.compare_bean.memory_compare_bean import MemoryCompareBean
@@ -16,6 +18,8 @@ from compare_backend.compare_bean.module_compare_bean import ModuleCompareBean
 from compare_backend.compare_bean.module_statistic_bean import ModuleStatisticBean
 from compare_backend.compare_bean.operator_compare_bean import OperatorCompareBean
 from compare_backend.compare_bean.operator_statistic_bean import OperatorStatisticBean
+from compare_backend.compare_bean.api_compare_bean import ApiCompareBean
+from compare_backend.compare_bean.kernel_compare_bean import KernelCompareBean
 from compare_backend.compare_bean.overall_metrics_bean import OverallMetricsBean
 from compare_backend.data_prepare.module_data_prepare import ModuleDataPrepare
 from compare_backend.data_prepare.operator_data_prepare import OperatorDataPrepare
@@ -39,8 +43,10 @@ class DetailPerformanceGenerator(BaseGenerator):
         return op_compare_result
 
     def compare(self):
-        if self._args.enable_operator_compare or self._args.enable_memory_compare or \
-                self._args.enable_communication_compare:
+        enable_compare = [self._args.enable_operator_compare, self._args.enable_memory_compare,
+                          self._args.enable_communication_compare, self._args.enable_api_compare,
+                          self._args.enable_kernel_compare]
+        if any(enable_compare):
             print("[INFO] Start to compare performance detail data, please wait.")
             comparator_list = self._create_comparator()
         else:
@@ -97,6 +103,18 @@ class DetailPerformanceGenerator(BaseGenerator):
             comparator_list.append(OperatorStatisticComparator(op_compare_result, MemoryStatisticBean))
             if not self._args.disable_details:
                 comparator_list.append(OperatorComparator(op_compare_result, MemoryCompareBean))
+        if self._args.enable_api_compare:
+            api_compare_result = {
+                Constant.BASE_DATA: OperatorDataPrepare(
+                    self._profiling_data_dict.get(Constant.BASE_DATA)).get_all_layer_ops(),
+                Constant.COMPARISON_DATA: OperatorDataPrepare(
+                    self._profiling_data_dict.get(Constant.COMPARISON_DATA)).get_all_layer_ops()}
+            comparator_list.append(ApiCompareComparator(api_compare_result, ApiCompareBean))
+        if self._args.enable_kernel_compare:
+            kernel_compare_result = {
+                Constant.BASE_DATA: self._profiling_data_dict.get(Constant.BASE_DATA).kernel_details,
+                Constant.COMPARISON_DATA: self._profiling_data_dict.get(Constant.COMPARISON_DATA).kernel_details}
+            comparator_list.append(KernelCompareComparator(kernel_compare_result, KernelCompareBean))
         return comparator_list
 
     def match_torch_op(self) -> list:
diff --git a/profiler/compare_tools/compare_backend/profiling_parser/base_profiling_parser.py b/profiler/compare_tools/compare_backend/profiling_parser/base_profiling_parser.py
index 6ee07a65696..9daaa55ef16 100644
--- a/profiler/compare_tools/compare_backend/profiling_parser/base_profiling_parser.py
+++ b/profiler/compare_tools/compare_backend/profiling_parser/base_profiling_parser.py
@@ -20,6 +20,7 @@ class ProfilingResult:
         self.overall_metrics = ProfilingInfo(profiling_type)
         self.python_function_data = []
         self.fwdbwd_dict = {}
+        self.kernel_details = {}
 
     def update_torch_op_data(self, event: TraceEventBean):
         event.is_torch_op = True
@@ -43,6 +44,9 @@ class ProfilingResult:
     def update_comm_task_data(self, comm_name: str, task_event: TraceEventBean):
         self.communication_dict.setdefault(comm_name, {}).setdefault("comm_task", {}).setdefault(
             task_event.name, []).append(task_event.dur)
+    
+    def update_kernel_details(self, kernels: dict):
+        self.kernel_details = kernels
 
 
 class BaseProfilingParser(ABC):
@@ -57,6 +61,8 @@ class BaseProfilingParser(ABC):
         self._enable_operator_compare = args.enable_operator_compare
         self._enable_memory_compare = args.enable_memory_compare
         self._enable_communication_compare = args.enable_communication_compare
+        self._enable_api_compare = args.enable_api_compare
+        self._enable_kernel_compare = args.enable_kernel_compare
         self._dispatch_func = self._get_dispatch_func()
         self._result_data = ProfilingResult(self._profiling_type)
         self._memory_events = []
@@ -80,6 +86,10 @@ class BaseProfilingParser(ABC):
         self._cpu_cube_op = cpu_cube_op
         return self._cpu_cube_op
 
+    @abstractmethod
+    def _update_kernel_details(self):
+        raise NotImplementedError("Function _update_kernel_details need to be implemented.")
+
     @abstractmethod
     def _update_memory_list(self):
         raise NotImplementedError("Function _update_memory_list need to be implemented.")
@@ -112,6 +122,8 @@ class BaseProfilingParser(ABC):
             self._update_memory_list()
         if self._enable_profiling_compare:
             self._update_overall_metrics()
+        if self._enable_kernel_compare:
+            self._update_kernel_details()
         self._check_result_data()
         return self._result_data
 
@@ -291,7 +303,7 @@ class BaseProfilingParser(ABC):
                 task_index += 1
 
     def _check_result_data(self):
-        if self._enable_operator_compare or self._enable_memory_compare:
+        if self._enable_operator_compare or self._enable_memory_compare or self._enable_api_compare:
             if not self._result_data.torch_op_data:
                 print(f"[WARNING] Can't find any torch op in the file: {self._profiling_path}")
         if self._enable_operator_compare and not self._result_data.kernel_dict:
@@ -300,6 +312,11 @@ class BaseProfilingParser(ABC):
             print(f"[WARNING] Can't find any memory event in the file: {self._profiling_path}")
         if self._enable_communication_compare and not self._result_data.communication_dict:
             print(f"[WARNING] Can't find any communication op in the file: {self._profiling_path}")
+        if self._enable_kernel_compare and not self._result_data.kernel_details:
+            if self._profiling_type == Constant.GPU:
+                print(f"[WARNING] kernel compare between GPU data and NPU data is not supported.")
+            else:
+                print(f"[WARNING] Can't find any kernel details in the file: {self._profiling_path}")
 
     def _read_trace_event(self):
         try:
diff --git a/profiler/compare_tools/compare_backend/profiling_parser/gpu_profiling_parser.py b/profiler/compare_tools/compare_backend/profiling_parser/gpu_profiling_parser.py
index 7b1ae1a5a12..0aeeba83efb 100644
--- a/profiler/compare_tools/compare_backend/profiling_parser/gpu_profiling_parser.py
+++ b/profiler/compare_tools/compare_backend/profiling_parser/gpu_profiling_parser.py
@@ -33,6 +33,9 @@ class GPUProfilingParser(BaseProfilingParser):
     def __is_sdma_time(cls, name: str):
         return any(mask in name.lower() for mask in cls.SDMA_MARK_LIST)
 
+    def _update_kernel_details(self):
+        pass
+
     def _update_memory_list(self):
         if not self._enable_memory_compare:
             return
@@ -171,6 +174,8 @@ class GPUProfilingParser(BaseProfilingParser):
             func_set.add(self._picking_memory_event)
         if self._enable_profiling_compare:
             func_set.add(self._picking_flow_event)
+        if self._enable_api_compare:
+            func_set.add(self._picking_torch_op_event)
         return list(func_set)
 
     def _infer_compute_stream_id(self):
diff --git a/profiler/compare_tools/compare_backend/profiling_parser/npu_profiling_parser.py b/profiler/compare_tools/compare_backend/profiling_parser/npu_profiling_parser.py
index 457a3b6be5e..cb25c252c6c 100644
--- a/profiler/compare_tools/compare_backend/profiling_parser/npu_profiling_parser.py
+++ b/profiler/compare_tools/compare_backend/profiling_parser/npu_profiling_parser.py
@@ -53,8 +53,32 @@ class NPUProfilingParser(BaseProfilingParser):
             func_list.add(self._picking_kernel_event)
             func_list.add(self._picking_hccl_event)
             func_list.add(self._picking_flow_event)
+        if self._enable_api_compare:
+            func_list.add(self._picking_torch_op_event)
         return list(func_list)
 
+    def _update_kernel_details(self):
+        try:
+            kernel_details = FileReader.read_csv_file(self._kernel_detail_path, KernelDetailsBean)
+        except FileNotFoundError:
+            print("[WARNING] The file kernel_details.csv does not exist.")
+        except Exception:
+            print("[ERROR] Failed to read kernel_details.csv.")
+            return
+        if not kernel_details:
+            return
+        kernels_dict = {}
+        for kernel in kernel_details:
+            if kernel.is_invalid():
+                continue
+            input_shapes = kernel.input_shapes if kernel.input_shapes else 'N/A'
+            kernels_dict.setdefault(kernel.op_type, {}).setdefault(input_shapes, []).append(
+                [kernel.name, kernel.duration])
+        if len(kernels_dict) == 1:
+            print("[ERROR] Failed to enable enable_kernel_compare, type of kernel_details.csv is null.")
+            return
+        self._result_data.update_kernel_details(kernels_dict)
+
     def _update_memory_list(self):
         try:
             memory_data = FileReader.read_csv_file(self._operator_memory_path, OperatorMemoryBean)
diff --git a/profiler/compare_tools/compare_backend/utils/args_manager.py b/profiler/compare_tools/compare_backend/utils/args_manager.py
index 4b5947fa7bc..ab9fb43a968 100644
--- a/profiler/compare_tools/compare_backend/utils/args_manager.py
+++ b/profiler/compare_tools/compare_backend/utils/args_manager.py
@@ -69,6 +69,14 @@ class ArgsManager:
     def enable_communication_compare(self):
         return self._args.enable_communication_compare
 
+    @property
+    def enable_api_compare(self):
+        return self._args.enable_api_compare
+    
+    @property
+    def enable_kernel_compare(self):
+        return self._args.enable_kernel_compare
+
     @classmethod
     def check_profiling_path(cls, file_path: str):
         PathManager.input_path_common_check(file_path)
@@ -119,11 +127,14 @@ class ArgsManager:
             raise RuntimeError(msg)
 
         if not any([self._args.enable_profiling_compare, self._args.enable_operator_compare,
-                    self._args.enable_memory_compare, self._args.enable_communication_compare]):
+                    self._args.enable_memory_compare, self._args.enable_communication_compare,
+                    self._args.enable_api_compare, self._args.enable_kernel_compare]):
             self._args.enable_profiling_compare = True
             self._args.enable_operator_compare = True
             self._args.enable_memory_compare = True
             self._args.enable_communication_compare = True
+            self._args.enable_api_compare = True
+            self._args.enable_kernel_compare = True
 
         base_profiling_path = PathManager.get_realpath(self._args.base_profiling_path)
         self.check_profiling_path(base_profiling_path)
diff --git a/profiler/compare_tools/compare_backend/utils/compare_args.py b/profiler/compare_tools/compare_backend/utils/compare_args.py
index ab9bc364f44..9e6291e89e0 100644
--- a/profiler/compare_tools/compare_backend/utils/compare_args.py
+++ b/profiler/compare_tools/compare_backend/utils/compare_args.py
@@ -6,6 +6,8 @@ class Args:
                  enable_operator_compare: bool = False,
                  enable_memory_compare: bool = False,
                  enable_communication_compare: bool = False,
+                 enable_api_compare: bool = False,
+                 enable_kernel_compare: bool = False,
                  output_path: str = "",
                  max_kernel_num: int = None,
                  op_name_map: dict = {},
@@ -17,6 +19,8 @@ class Args:
         self.enable_operator_compare = enable_operator_compare
         self.enable_memory_compare = enable_memory_compare
         self.enable_communication_compare = enable_communication_compare
+        self.enable_api_compare = enable_api_compare
+        self.enable_kernel_compare = enable_kernel_compare
         self.output_path = output_path
         self.max_kernel_num = max_kernel_num
         self.op_name_map = op_name_map
diff --git a/profiler/compare_tools/compare_backend/utils/constant.py b/profiler/compare_tools/compare_backend/utils/constant.py
index e2002588024..252aa536e1c 100644
--- a/profiler/compare_tools/compare_backend/utils/constant.py
+++ b/profiler/compare_tools/compare_backend/utils/constant.py
@@ -39,13 +39,16 @@ class Constant(object):
     # compare type
     OPERATOR_COMPARE = "OperatorCompare"
     MEMORY_COMPARE = "MemoryCompare"
-
+    API_COMPARE = "ApiCompare"
+    KERNEL_COMPARE = "KernelCompare"
     # sheet name
     OPERATOR_SHEET = "OperatorCompare"
     MEMORY_SHEET = "MemoryCompare"
     OPERATOR_TOP_SHEET = "OperatorCompareStatistic"
     MEMORY_TOP_SHEET = "MemoryCompareStatistic"
     COMMUNICATION_SHEET = "CommunicationCompare"
+    API_SHEET = "ApiCompare"
+    KERNEL_SHEET = "KernelCompare"
 
     # table name
     OPERATOR_TABLE = "OperatorCompare"
@@ -57,6 +60,8 @@ class Constant(object):
     MODULE_TABLE = "ModuleCompare"
     MODULE_TOP_TABLE = "ModuleCompareStatistic"
     OVERALL_METRICS_TABLE = "OverallMetrics"
+    API_TABLE = "ApiCompare"
+    KERNEL_TABLE = "KernelCompare"    
 
     # memory
     SIZE = "Size(KB)"
diff --git a/profiler/compare_tools/compare_backend/utils/excel_config.py b/profiler/compare_tools/compare_backend/utils/excel_config.py
index ae808863e77..b6be0ae2ebc 100644
--- a/profiler/compare_tools/compare_backend/utils/excel_config.py
+++ b/profiler/compare_tools/compare_backend/utils/excel_config.py
@@ -57,7 +57,7 @@ class ExcelConfig(object):
     DEVICE_SELF_TIME = "Device Self Time(ms)"
     DEVICE_TOTAL_TIME = "Device Total Time(ms)"
     DIFF_SELF_TIME = "Device Self Time Diff(ms)"
-    DIFF_TOTAL_RATIO = "Total Diff Ratio"
+    DIFF_TOTAL_RATIO = "Diff Total Ratio"
     DIFF_TOTAL_TIME = "Device Total Time Diff(ms)"
     DEVICE_SELF_TIME_US = "Device Self Time(us)"
     DEVICE_TOTAL_TIME_US = "Device Total Time(us)"
@@ -71,6 +71,14 @@ class ExcelConfig(object):
     DURATION = "Duration(ms)"
     DURATION_RATIO = "Duration Ratio"
     DIFF_DUR_MS = "Diff Duration(ms)"
+    API_NAME = "api name"
+    TOTAL_DURATION_MS = "Total Duration(ms)"
+    AVG_DURATION_MS = "Avg Duration(ms)"
+    SELF_TIME_MS = "Self Time(ms)"
+    DIFF_SELF_RATIO = "Diff Self Ratio"
+    DIFF_AVG_RATIO = "Diff Avg Ratio"
+    DIFF_CALLS_RATIO = "Diff Calls Ratio"
+    KERNEL = "Kernel"
 
     HEADERS = {
         Constant.OPERATOR_TABLE: [
@@ -193,7 +201,39 @@ class ExcelConfig(object):
             {"name": NUMBER, "type": CellFormatType.DEFAULT, "width": 10},
             {"name": DIFF_DUR_MS, "type": CellFormatType.DEFAULT_FLOAT, "width": 20},
             {"name": DIFF_RATIO, "type": CellFormatType.DEFAULT_RATIO, "width": 10},
-
+        ],
+        Constant.API_TABLE: [
+            {"name": ORDER, "type": CellFormatType.DEFAULT, "width": 10},
+            {"name": API_NAME, "type": CellFormatType.BOLD_STR, "width": 30},
+            {"name": TOTAL_DURATION_MS, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": SELF_TIME_MS, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": AVG_DURATION_MS, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": CALLS, "type": CellFormatType.DEFAULT,"width": 20},
+            {"name": TOTAL_DURATION_MS, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": SELF_TIME_MS, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": AVG_DURATION_MS, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": CALLS, "type": CellFormatType.DEFAULT,"width": 20},
+            {"name": DIFF_TOTAL_RATIO, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": DIFF_SELF_RATIO, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": DIFF_AVG_RATIO, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": DIFF_CALLS_RATIO, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+        ],
+        Constant.KERNEL_COMPARE: [
+            {"name": ORDER, "type": CellFormatType.DEFAULT, "width": 10},
+            {"name": KERNEL, "type": CellFormatType.BOLD_STR, "width": 30},
+            {"name": INPUT_SHAPE, "type": CellFormatType.DEFAULT,"width": 20},
+            {"name": TOTAL_DURATION, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": AVG_DURATION, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": MAX_DURATION, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": MIN_DURATION, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": CALLS, "type": CellFormatType.DEFAULT,"width": 20},
+            {"name": TOTAL_DURATION, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": AVG_DURATION, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": MAX_DURATION, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": MIN_DURATION, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": CALLS, "type": CellFormatType.DEFAULT,"width": 20},
+            {"name": DIFF_TOTAL_RATIO, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
+            {"name": DIFF_AVG_RATIO, "type": CellFormatType.DEFAULT_FLOAT,"width": 20},
         ]
     }
 
@@ -201,7 +241,9 @@ class ExcelConfig(object):
                 Constant.COMMUNICATION_TABLE: ["B1:H1", "I1:O1"], Constant.OPERATOR_TOP_TABLE: ["C1:D1", "E1:F1"],
                 Constant.MEMORY_TOP_TABLE: ["C1:E1", "F1:H1"], Constant.MODULE_TOP_TABLE: ["F1:I1", "J1:M1"],
                 Constant.MODULE_TABLE: ["E1:H1", "I1:L1"],
-                Constant.OVERALL_METRICS_TABLE: ["B1:D1", "E1:G1"]}
+                Constant.OVERALL_METRICS_TABLE: ["B1:D1", "E1:G1"],
+                Constant.API_TABLE: ["C1:F1", "G1:J1"],
+                Constant.KERNEL_TABLE: ["D1:H1", "I1:M1"]}
 
     # overall metrics index
     # computing time
diff --git a/profiler/compare_tools/compare_backend/utils/torch_op_node.py b/profiler/compare_tools/compare_backend/utils/torch_op_node.py
index 690c46cd51c..69ee92d1232 100644
--- a/profiler/compare_tools/compare_backend/utils/torch_op_node.py
+++ b/profiler/compare_tools/compare_backend/utils/torch_op_node.py
@@ -64,6 +64,14 @@ class TorchOpNode:
     def device_dur(self):
         return sum([kernel.device_dur for kernel in self._kernel_list])
 
+    @property
+    def api_dur(self):
+        return self._event.dur
+    
+    @property
+    def api_self_time(self):
+        return self.api_dur - sum(child.api_dur for child in self._child_nodes)
+
     def add_child_node(self, child_node):
         self._child_nodes.append(child_node)
 
diff --git a/profiler/compare_tools/compare_backend/utils/tree_builder.py b/profiler/compare_tools/compare_backend/utils/tree_builder.py
index 34c1fe1a1f4..d5aa787ac2c 100644
--- a/profiler/compare_tools/compare_backend/utils/tree_builder.py
+++ b/profiler/compare_tools/compare_backend/utils/tree_builder.py
@@ -23,7 +23,8 @@ class TreeBuilder:
                     tree_node = TorchOpNode(event, last_node)
                     last_node.add_child_node(tree_node)
                     last_node = tree_node
-                    tree_node.set_kernel_list(kernel_dict.get(event.start_time, []))
+                    if kernel_dict:
+                        tree_node.set_kernel_list(kernel_dict.get(event.start_time, []))
                 else:
                     event.set_name(last_node.name)
                     last_node.set_memory_allocated(event)
diff --git a/profiler/compare_tools/compare_backend/view/work_sheet_creator.py b/profiler/compare_tools/compare_backend/view/work_sheet_creator.py
index dffb7549fcd..58bad621b03 100644
--- a/profiler/compare_tools/compare_backend/view/work_sheet_creator.py
+++ b/profiler/compare_tools/compare_backend/view/work_sheet_creator.py
@@ -12,7 +12,7 @@ class WorkSheetCreator:
         self._work_sheet = None
         self._row_id = 1
         self._field_format = {}
-        self._diff_ratio_index = None
+        self._diff_ratio_index = []
         self._col_ids = "ABCDEFGHIJKLMNOPQRSTUVW"
 
     def create_sheet(self):
@@ -47,8 +47,10 @@ class WorkSheetCreator:
             self._work_sheet.set_column(f"{col_id}:{col_id}", header.get("width"))
             self._work_sheet.write(f"{col_id}{self._row_id}", header.get("name"), header_format)
             self._field_format[index] = header.get("type")
-            if header.get("name") in (ExcelConfig.DIFF_RATIO, ExcelConfig.DIFF_TOTAL_RATIO):
-                self._diff_ratio_index = index
+            ratio_white_list = [ExcelConfig.DIFF_RATIO, ExcelConfig.DIFF_TOTAL_RATIO,
+                                ExcelConfig.DIFF_AVG_RATIO, ExcelConfig.DIFF_CALLS_RATIO, ExcelConfig.DIFF_SELF_RATIO]
+            if header.get("name") in ratio_white_list:
+                self._diff_ratio_index.append(index)
         self._row_id += 1
 
     def _write_data(self):
@@ -56,7 +58,7 @@ class WorkSheetCreator:
         for data in self._data.get("rows"):
             for index, cell_data in enumerate(data):
                 cell_format = self._work_book.add_format(self._field_format.get(index))
-                if index == self._diff_ratio_index and cell_data and cell_data > 1:
+                if index in self._diff_ratio_index and cell_data and cell_data > 1:
                     cell_format = red_ratio_format
                     cell_data = "INF" if cell_data == float('inf') else cell_data
                 self._work_sheet.write(f"{self._col_ids[index]}{self._row_id}", cell_data, cell_format)
@@ -76,7 +78,7 @@ class WorkSheetCreator:
                 if index == 0:  # 0 for Index field
                     cell_style["indent"] = cell_data.count("\t")
                 cell_format = self._work_book.add_format(cell_style)
-                if index == self._diff_ratio_index and cell_data and cell_data > 1:
+                if index in self._diff_ratio_index and cell_data and cell_data > 1:
                     cell_format = red_ratio_format
                     cell_data = "INF" if cell_data == float('inf') else cell_data
                 self._work_sheet.write(f"{self._col_ids[index]}{self._row_id}", cell_data, cell_format)
diff --git a/profiler/compare_tools/img/OverallMetrics.png b/profiler/compare_tools/img/OverallMetrics.png
new file mode 100644
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diff --git a/profiler/compare_tools/performance_compare.py b/profiler/compare_tools/performance_compare.py
index 8de0a72cbdc..7c9d60aac0a 100644
--- a/profiler/compare_tools/performance_compare.py
+++ b/profiler/compare_tools/performance_compare.py
@@ -18,6 +18,8 @@ def main():
     parser.add_argument("--enable_operator_compare", default=False, action='store_true', help="开启算子性能比较")
     parser.add_argument("--enable_memory_compare", default=False, action='store_true', help="开启算子内存比较")
     parser.add_argument("--enable_communication_compare", default=False, action='store_true', help="开启通信性能比较")
+    parser.add_argument("--enable_api_compare", default=False, action='store_true', help="开启host api性能比较")
+    parser.add_argument("--enable_kernel_compare", default=False, action='store_true', help="开启kernel性能比较")
     parser.add_argument("--disable_details", default=False, action='store_true', help="不展示比对明细")
     parser.add_argument("--output_path", type=str, default='', help="性能数据比对结果的存放路径")
     parser.add_argument("--max_kernel_num", type=int, help="每个torch op的kernel数量限制")
diff --git a/profiler/merge_profiling_timeline/README.md b/profiler/merge_profiling_timeline/README.md
deleted file mode 100644
index 907a39a6e79..00000000000
--- a/profiler/merge_profiling_timeline/README.md
+++ /dev/null
@@ -1,115 +0,0 @@
-# 合并大json工具
-
-merge_profiling_timeline（合并大json工具）支持合并Profiling的timeline数据，支持合并指定rank的timline、合并指定timeline中的item。
-
-
-## 多timeline融合
-
-### 性能数据采集
-
-使用Ascend PyTorch Profiler或者E2E性能采集工具采集性能数据，E2E profiling将被废弃，不建议使用。Ascend PyTorch Profiler采集方式参考：[Profiling数据采集](https://gitee.com/ascend/mstt/tree/master/profiler)。将采集到的所有节点的性能数据拷贝到当前环境同一目录下，以下假设数据在/home/test/cann_profiling下。
-
-E2E Profiling数据目录结构示例如下：
-
-```bash
-|- cann_profiling
-    |- PROF_***
-        |- timeline
-            |- msprof.json
-        |- device_*
-            |- info.json.*
-        ...
-    |- PROF_***
-    ...
-```
-
-Ascend PyTorch Profiler数据目录结构示例如下：
-
-```bash
-|- ascend_pytorch_profiling
-    |- **_ascend_pt
-        |- ASCEND_PROFILER_OUTPUT
-            |- trace_view.json
-        |- FRAMEWORK
-        |- PROF_***
-    |- **_ascend_pt
-```
-
-### 参数说明
-
-| 参数名称 | 说明                                                         | 是否必选 |
-| -------- | ------------------------------------------------------------ | -------- |
-| -i       | 指定Profiling数据目录路径。                                  | 是       |
-| --type   | 指定需要合并timeline场景，可选取值：`pytorch`（通过Ascend PyTorch Profiler方式采集profiling数据，合并所有卡的trace_view.json）、`e2e`（通过E2E Profiling方式采集Profiling数据，优先合并总timeline，没有生成则选择合并device目录下的msprof_*.json）、`custom` （自定义需要合并的timeline数据，具体参考**使用示例**）。 | 是       |
-| -o       | 指定合并后的timeline文件输出的路径（路径末尾可以设置文件名，具体用法参考**使用示例**），不设置该参数的情况下默认文件输出的路径为当前目录（默认文件名为merged.json）。 | 否       |
-| --rank   | 指定需要合并timeline的Rank ID，默认全部合并。                | 否       |
-| --items  | 指定需要合并的Profiling数据项，包括：python、Ascend Hardware、CANN、HCCL、PTA、Overlap Analysis，默认全部合并。 | 否       |
-
-### 使用示例
-
-1. 合并单机多卡timeline，默认合并所有卡、所有数据项，生成first.json在path/to/cann_profiling/output/目录下
-
-   ```bash
-   python3 main.py -i path/to/cann_profiling/ -o path/to/cann_profiling/output/first --type pytorch
-   ```
-
-2. 合并单机多卡timeline，默认合并所有卡、所有数据项，不设置-o参数时默认生成merge.json在当前目录下
-
-   ```bash
-   python3 main.py -i path/to/cann_profiling/ --type pytorch
-   ```
-
-3. 合并单机多卡timeline，只合并0卡和1卡
-
-   ```bash
-   python3 main.py -i path/to/cann_profiling/ -o path/to/cann_profiling/output/2p --type pytorch --rank 0,1
-   ```
-
-4. 合并单机多卡timeline，合并所有卡的CANN层和Ascend_Hardware层数据
-
-   ```bash
-   python3 main.py -i path/to/cann_profiling/ --type pytorch --items "CANN,Ascend Hardware"
-   ```
-
-5. 合并多timeline（自定义）
-
-   以上场景不支持的情况下，可以使用自定义的合并方式，将需要合并的timeline文件放在同一目录下（附：该场景比较特殊，与正常合并不同，无法直接读取info.json中的rank_id，因此该场景下的rank_id为默认分配的序号，用于区分不同文件的相同层，不代表实际rank_id）
-   数据目录结构示意如下：
-
-   ```bash
-   |- timeline
-       |- msprof_0.json
-       |- msprof_1.json
-       |- msprof_2.json
-       |- hccl_3.json
-       |- hccl_4.json
-       ...
-   ```
-
-   通过下面的命令合并所有timeline，同样支持-o、--rank、--items等参数。
-
-   ```bash
-   python3 main.py -i path/to/timeline/ -o path/to/timeline/xxx --type custom
-   ```
-
-   合并timeline查看：在 -o 指定的目录（不设置-o时默认在当前目录下的merged.json）的xxx.json为合并后的文件。
-
-
-## 超大timeline文件查看
-
-[下载whl](https://gitee.com/aerfaliang/trace_processor/releases/download/trace_processor_37.0/trace_processor-37.0-py3-none-any.whl)包并执行如下命令安装（windows）：
-
-```bash
-pip3 install trace_processor-37.0-py3-none-any.whl
-```
-
-安装完成后直接执行如下命令：
-
-```bash
-python -m trace_processor --httpd path/to/xxx_merged.json 
-```
-
-等待加载完毕，刷新[perfetto](https://ui.perfetto.dev/)界面，单击Use old version regardless，再单击`YES, use loaded trace`即可展示timeline（通过W放大、S缩小、A左移、D右移来查看timeline文件）。
-
-![输入图片说明](perfetto%E4%BD%BF%E7%94%A8%E6%8C%87%E5%AF%BC%E6%88%AA%E5%9B%BE1.png)
-![输入图片说明](perfetto%E4%BD%BF%E7%94%A8%E6%8C%87%E5%AF%BC%E6%88%AA%E5%9B%BE2.png)
\ No newline at end of file
diff --git a/profiler/merge_profiling_timeline/main.py b/profiler/merge_profiling_timeline/main.py
deleted file mode 100644
index 678f5d5a8f7..00000000000
--- a/profiler/merge_profiling_timeline/main.py
+++ /dev/null
@@ -1,233 +0,0 @@
-#! /usr/bin/python3
-# Copyright 2023 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-import re
-
-from functools import partial
-from argparse import ArgumentParser
-from decimal import Decimal
-
-
-FILTER_DIRS = [".profiler", "HCCL_PROF", "timeline", "query", 'sqlite', 'log']
-RANK_ID_POS = 1000
-
-def get_path_dir(path: str) -> list:
-    """
-    check result path exist JOB dir
-    path : result path
-    """
-    path_dir_filter = filter(partial(_path_dir_filter_func, root_dir=path), os.listdir(path))
-    sub_dirs = list(path_dir_filter)
-    return sub_dirs
-
-
-def _path_dir_filter_func(sub_path, root_dir):
-    return sub_path not in FILTER_DIRS and os.path.isdir(os.path.realpath(os.path.join(root_dir, sub_path)))
-
-
-def natural_sort(files):
-    convert = lambda text: int(text) if text.isdigit() else text.lower()
-    alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
-    return sorted(files, key=alphanum_key)
-
-
-def get_timeline_info(args, prof_dirs):
-    timeline_info = {}
-
-    for prof in prof_dirs:
-        pro_path = os.path.join(args.input, prof)
-
-        # 从info.json读取rank_id
-        rank_id = get_rank_id_from_info_json(pro_path)
-        if rank_id is None:
-            print(f"WARN, There is not rank id info in {pro_path}")
-            continue
-
-        timeline_path = get_timeline_path(pro_path, args.type)
-
-        if os.path.exists(timeline_path):
-            timeline_info[rank_id] = timeline_path
-        else:
-            print(f"WARN, The file \"{timeline_path}\" does not exist.")
-    return timeline_info
-
-
-def get_timeline_path(pro_path, type):
-    for root, dirs, files in os.walk(pro_path):
-        for dir_ in dirs:
-            if 'ASCEND_PROFILER_OUTPUT' == dir_ and type == 'pytorch':
-                timeline_path = os.path.realpath(os.path.join(root, dir_, 'trace_view.json'))
-                return timeline_path
-
-        for file_ in sorted(files, reverse=True):
-            if 'msprof' in file_:
-                timeline_path = os.path.join(root, file_)
-                return timeline_path
-    return
-
-def get_rank_id_from_info_json(pro_path):
-    info_json = ""
-    rank_id = None
-    for root, dirs, files in os.walk(pro_path):
-        for file in files:
-            if "info.json." in file and ".done" not in file:
-                info_json = os.path.join(root, file)
-                break
-
-    if info_json:
-        if os.path.islink(info_json):
-            print(f"The file: \"{info_json}\" is link. Please check the path.")
-            return
-        try:
-            with open(info_json, "r+") as f:
-                info = json.load(f)
-            rank_id = info.get("rank_id")
-        except Exception as err:
-            print("[ERROR] %s" % err)
-            return
-    return rank_id
-
-
-def merge_timeline_general(args):
-    """合并e2e profiling生成的msprof*.json"""
-    if not os.path.isdir(args.input):
-        print(f"No such file or directory: \"{args.input}\". Please check the path.")
-        return
-    prof_dir = get_path_dir(args.input)
-    if not prof_dir:
-        message = f"The path \"{args.input}\" does not have PROF dir. Please check the path."
-        print(message)
-        return
-    timeline_info = get_timeline_info(args, prof_dir)
-    timeline_files_dict = {}
-
-    # 合并部分profiling items
-    process_list = args.items.split(",") if args.items else None
-
-    # 合并部分rank
-    if args.rank:
-        rank_ids = [int(rank_id) for rank_id in args.rank.split(",")]
-    else:
-        rank_ids = list(timeline_info.keys())
-
-    for rank_id in rank_ids:
-        if not timeline_info.get(rank_id):
-            print(f"main.py: error rank_id '{rank_id}' ")
-            return
-        timeline_files_dict[rank_id] = timeline_info.get(rank_id)
-    merge_timeline_events(timeline_files_dict, process_list)
-
-
-def merge_timeline_custom(args):
-    """合并指定目录里所有timeline文件"""
-    timeline_files = natural_sort(os.listdir(args.input))
-    timeline_files_dict = {}
-    for idx, timeline_file in enumerate(timeline_files):
-        timeline_files_dict[idx] = os.path.join(args.input, timeline_file)
-    # 合并部分profiling items
-    process_list = args.items.split(",") if args.items else None
-    merge_timeline_events(timeline_files_dict, process_list)
-
-
-def merge_timeline_events(timeline_file_dict, process_list):
-    """
-    输入需要合并的timeline文件路径及对应的rank_id/id、需要合并的process_list
-    输出合并timeline
-    """
-    new_events = []
-    for rank_id, timeline_path in timeline_file_dict.items():
-        node = rank_id // 8
-        print("rank id: ", rank_id, "timeline file: ", timeline_path)
-        if os.path.islink(timeline_path):
-            print(f"The file: \"{timeline_path}\" is link. Please check the path.")
-            return
-        try:
-            with open(timeline_path, 'r+') as f:
-                cur_events = json.load(f)
-        except Exception as err:
-            print("[ERROR] %s" % err)
-            return
-
-        proc_pid_dict = {}
-        for event in cur_events:
-            if event.get("name") == "process_name" and event.get("ph") == "M":
-                if event.get("args"):
-                    proc_pid_dict[event["args"].get("name")] = event.get("pid")
-        process_list_tmp = process_list if process_list else list(proc_pid_dict.keys())
-        # 提取待合并的items的pid
-        merged_pids = set()
-        for pro in process_list_tmp:
-            if pro not in proc_pid_dict.keys():
-                print(f"main.py: error argument --items: invalid choice: '{pro}' (choose from {list(proc_pid_dict.keys())})")
-                return
-            merged_pids.add(proc_pid_dict.get(pro))
-
-        for event in cur_events:
-
-            # 只合并特定数据项
-            if merged_pids and event.get('pid') not in merged_pids:
-                continue
-
-            # convert tid to int
-            if not isinstance(event['tid'], int):
-                print(f"[WARNNING] {event['tid']} is not int type")
-
-            # 进程名加上rank_id区分不同rank
-            if event.get("name") == "process_name" and event.get("ph") == "M":
-                if event.get("args") is not None and event["args"].get("name") is not None:
-                    event["args"]["name"] = event["args"]["name"] + f"_{rank_id}"
-
-            #modify connect id
-            if event.get('id') and (event.get('ph') == 's' or event.get('ph') == 'f'):
-                event['id'] = float(event.get('id')) * RANK_ID_POS + rank_id
-
-            new_events.append(event)
-    out_path = f"{args.output}.json"
-    if os.path.islink(out_path):
-        print(f"The file: \"{out_path}\" is link. Please check the path.")
-        return
-    if os.path.exists(out_path):
-        print(f"File {out_path} existed before and is now overwritten.")
-        os.remove(out_path)
-    try:
-        # 设置文件权限为640，安全考虑
-        with os.fdopen(os.open(out_path, os.O_WRONLY | os.O_CREAT, 0o640), 'w') as f:
-            json.dump(new_events, f)
-    except FileNotFoundError:
-        print(f"Param -o (output path) is not exists, please check it.")
-        return
-    print(f"timeline merged output path: {out_path}")
-
-
-def parse_args():
-    parser = ArgumentParser(description="Merge timeline for multi card")
-    parser.add_argument("-i", "--input", default=None, help="root dir of PROF_* data")
-    parser.add_argument("-o", "--output", default="./merged", help="save path of merged.json ")
-    parser.add_argument("--rank", default=None, help="List of ranks to be merged. By default, all ranks are merged")
-    parser.add_argument("--items", default=None, help="Specify the data items (python，CANN，Ascend Hardware，HCCL，..)to be merged. in the timeline.")
-    parser.add_argument("--type", choices=('pytorch', 'e2e', 'custom'), help="Customize the timeline file to be merged.")
-    arg = parser.parse_args()
-    return arg
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    print("========================== start merge timeline ====================")
-    if args.type == "custom":
-        merge_timeline_custom(args)
-    else:
-        merge_timeline_general(args)
\ No newline at end of file
diff --git "a/profiler/merge_profiling_timeline/perfetto\344\275\277\347\224\250\346\214\207\345\257\274\346\210\252\345\233\2761.png" "b/profiler/merge_profiling_timeline/perfetto\344\275\277\347\224\250\346\214\207\345\257\274\346\210\252\345\233\2761.png"
deleted file mode 100644
index beef396ce2996c25ecd74298285ccab5011ddea1..0000000000000000000000000000000000000000
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literal 0
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diff --git a/profiler/module_visualization/__init__.py b/profiler/module_visualization/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/profiler/module_visualization/graph/__init__.py b/profiler/module_visualization/graph/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/profiler/module_visualization/graph/prof_node.py b/profiler/module_visualization/graph/prof_node.py
deleted file mode 100644
index cfcdabbb991..00000000000
--- a/profiler/module_visualization/graph/prof_node.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Copyright (c) 2024 Huawei Technologies Co., Ltd
-# All rights reserved.
-#
-# Licensed under the BSD 3-Clause License  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# https://opensource.org/licenses/BSD-3-Clause
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from profiler.prof_common.constant import Constant
-from profiler.prof_common.base_node import BaseNode
-from profiler.prof_common.trace_event_bean import TraceEventBean
-
-
-class ProfNode(BaseNode):
-    MODULE_TYPE = 1
-
-    def __init__(self, event: TraceEventBean, parent_node=None):
-        super().__init__(event, parent_node)
-        self._kernel_total_list = []
-
-    @property
-    def node_id(self):
-        return self._event.unique_id
-
-    @property
-    def total_kernels(self):
-        return self._kernel_total_list
-
-    @property
-    def host_total_dur(self):
-        if self.is_root_node:
-            return sum((node.host_total_dur for node in self.child_nodes))
-        return self._event.dur
-
-    @property
-    def host_self_dur(self):
-        return self.host_total_dur - sum((node.host_total_dur for node in self.child_nodes))
-
-    @property
-    def device_total_dur(self):
-        if self.is_root_node:
-            return sum((node.device_total_dur for node in self.child_nodes))
-        return sum((kernel.dur for kernel in self._kernel_total_list))
-
-    @property
-    def device_self_dur(self):
-        return self.device_total_dur - sum((node.device_total_dur for node in self.child_nodes))
-
-    @property
-    def input_data(self) -> dict:
-        data = {}
-        input_dim = self._event.args.get("Input Dims")
-        if input_dim:
-            data["Input Dims"] = input_dim
-        input_type = self._event.args.get("Input type")
-        if input_type:
-            data["Input type"] = input_type
-        return data
-
-    @property
-    def data(self):
-        return {"Input Data": self.input_data,
-                "Host Self Duration(us)": round(self.host_self_dur, 2),
-                "Host Total Duration(us)": round(self.host_total_dur, 2),
-                "Device Self Duration(us)": round(self.device_self_dur, 2),
-                "Device Total Duration(us)": round(self.device_total_dur, 2)}
-
-    @property
-    def info(self):
-        return {"id": self.node_id,
-                "node_type": self.MODULE_TYPE,
-                "data": self.data,
-                "upnode": self.parent_node.node_id if self.parent_node else "None",
-                "subnodes": [node.node_id for node in iter(self.child_nodes)]}
-
-    @property
-    def is_root_node(self):
-        return self.node_id == Constant.NPU_ROOT_ID
-
-    def update_child_nodes(self, node):
-        self._child_nodes.append(node)
-
-    def update_kernel_total_list(self, kernel_list: list):
-        self._kernel_total_list.extend(kernel_list)
diff --git a/profiler/module_visualization/graph_build/__init__.py b/profiler/module_visualization/graph_build/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/profiler/module_visualization/graph_build/fwd_module_node.py b/profiler/module_visualization/graph_build/fwd_module_node.py
deleted file mode 100644
index 34d7ab82964..00000000000
--- a/profiler/module_visualization/graph_build/fwd_module_node.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) 2024 Huawei Technologies Co., Ltd
-# All rights reserved.
-#
-# Licensed under the BSD 3-Clause License  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# https://opensource.org/licenses/BSD-3-Clause
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from profiler.prof_common.base_node import BaseNode
-from profiler.prof_common.trace_event_bean import TraceEventBean
-
-
-class FwdModuleNode(BaseNode):
-    def __init__(self, event: TraceEventBean, parent_node=None):
-        super().__init__(event, parent_node)
-        self._bwd_op_list = []
-
-    @property
-    def bwd_op_list(self):
-        return self._bwd_op_list
-
-    def update_bwd_op(self, bwd_op_list: list):
-        self._bwd_op_list.extend(bwd_op_list)
diff --git a/profiler/module_visualization/graph_build/prof_graph_builder.py b/profiler/module_visualization/graph_build/prof_graph_builder.py
deleted file mode 100644
index 83331b62502..00000000000
--- a/profiler/module_visualization/graph_build/prof_graph_builder.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) 2024 Huawei Technologies Co., Ltd
-# All rights reserved.
-#
-# Licensed under the BSD 3-Clause License  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# https://opensource.org/licenses/BSD-3-Clause
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from profiler.module_visualization.graph.prof_node import ProfNode
-from profiler.module_visualization.graph_build.fwd_module_node import FwdModuleNode
-from profiler.prof_common.tree_builder import TreeBuilder
-from profiler.prof_common.trace_event_bean import TraceEventBean
-from profiler.prof_common.constant import Constant
-from profiler.module_visualization.prof_parse.prof_data_pre_process import ProfDataPreProcess
-
-
-class ProfGraphBuilder:
-    def __init__(self, prof_data_path: str):
-        self._prof_data_path = prof_data_path
-        self._prof_data = {}
-
-    @classmethod
-    def _create_event_bean_from_ops(cls, op_list: list, name: str) -> TraceEventBean:
-        min_start = min((op.start_time for op in iter(op_list)))
-        max_end = max((op.end_time for op in iter(op_list)))
-        # 以反向算子的区间作为反向module的区间范围，为了module包含算子，做了+1 +2处理
-        return TraceEventBean({"ts": min_start - 1, "dur": float(max_end - min_start) + 2, "name": name})
-
-    @classmethod
-    def _trans_flow_to_dict(cls, flow_events: dict, end_events: list) -> dict:
-        end_event_dict = {}
-        for event in end_events:
-            end_event_dict[event.start_time] = event
-        result_data = {}
-        for flow in flow_events.values():
-            start_point = flow.get("start")
-            end_point = flow.get("end")
-            if not start_point or not end_point:
-                continue
-            end_event = end_event_dict.get(end_point.start_time)
-            if end_event:
-                result_data.setdefault(start_point.start_time, []).append(end_event)
-        return result_data
-
-    def build_graph(self):
-        self._prof_data = ProfDataPreProcess(self._prof_data_path).run()
-        all_data = [*self._prof_data.get(Constant.MODULE_EVENT, []),
-                    *self.find_bwd_module(),
-                    *self._prof_data.get(Constant.CPU_OP_EVENT, [])]
-        all_data.sort(key=lambda x: x.start_time)
-        name_dict = {}
-        for event in all_data:
-            order_id = name_dict.get(event.name, 0)
-            event.set_id(f"{event.name}_{order_id}")
-            name_dict[event.name] = order_id + 1
-        root_node = TreeBuilder.build_tree(all_data, ProfNode, TraceEventBean({}, Constant.NPU_ROOT_ID))
-        kernel_flow_dict = self._trans_flow_to_dict(self._prof_data.get(Constant.TORCH_TO_NPU_FLOW, {}),
-                                                    self._prof_data.get(Constant.KERNEL_EVENT, []))
-        for start_time, kernels in kernel_flow_dict.items():
-            matched_node = root_node.binary_search(start_time)
-            while matched_node != Constant.INVALID_RETURN:
-                matched_node.update_kernel_total_list(kernels)
-                matched_node = matched_node.binary_search(start_time)
-        all_data = root_node.find_all_child_nodes()
-        all_data.append(root_node)
-        return all_data
-
-    def find_bwd_module(self) -> list:
-        bwd_module_list = []
-        fwdbwd_flow = self._prof_data.get(Constant.FWD_BWD_FLOW, {})
-        module_list = self._prof_data.get(Constant.MODULE_EVENT, [])
-        cpu_op_list = self._prof_data.get(Constant.CPU_OP_EVENT, [])
-        if not fwdbwd_flow or not module_list or not cpu_op_list:
-            return bwd_module_list
-        fwd_tid = module_list[0].tid
-        bwd_tid = fwd_tid
-        for end_point in (flow.get("end") for flow in fwdbwd_flow.values()):
-            if end_point:
-                bwd_tid = end_point.tid
-                break
-        if fwd_tid == bwd_tid:
-            return bwd_module_list
-        # 将每一个反向包成一个module，名字叫“nn.Module: BACKWARD_0”
-        cpu_op_list.sort(key=lambda x: x.start_time)
-        pre_status = Constant.FWD_OR_OPT
-        bwd_op_list = []
-        for op in cpu_op_list:
-            if op.tid == bwd_tid:
-                bwd_op_list.append(op)
-                pre_status = Constant.BACKWARD
-            elif pre_status == Constant.BACKWARD:
-                bwd_module_list.append(self._create_event_bean_from_ops(bwd_op_list, "nn.Module: BACKWARD"))
-                bwd_op_list.clear()
-                pre_status = Constant.FWD_OR_OPT
-
-        # 通过连线匹配正向module，构建出反向的整体module关系
-        root_node = TreeBuilder.build_tree(module_list, FwdModuleNode, TraceEventBean({}))
-        fwdbwd_flow_dict = self._trans_flow_to_dict(fwdbwd_flow, cpu_op_list)
-        for start_time, end_events in fwdbwd_flow_dict.items():
-            matched_node = root_node.binary_search(start_time)
-            while matched_node != Constant.INVALID_RETURN:
-                matched_node.update_bwd_op(end_events)
-                matched_node = matched_node.binary_search(start_time)
-        all_nodes = root_node.find_all_child_nodes()
-        for module_node in all_nodes:
-            if module_node.bwd_op_list:
-                bwd_module_list.append(
-                    self._create_event_bean_from_ops(module_node.bwd_op_list, f"{module_node.name} [BACKWARD]"))
-        return bwd_module_list
diff --git a/profiler/module_visualization/prof_graph_export.py b/profiler/module_visualization/prof_graph_export.py
deleted file mode 100644
index d336e97f741..00000000000
--- a/profiler/module_visualization/prof_graph_export.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2024 Huawei Technologies Co., Ltd
-# All rights reserved.
-#
-# Licensed under the BSD 3-Clause License  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# https://opensource.org/licenses/BSD-3-Clause
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import logging
-from datetime import datetime
-
-from profiler.prof_common.constant import Constant
-from profiler.prof_common.file_reader import FileReader
-from profiler.prof_common.path_manager import PathManager
-from profiler.module_visualization.graph_build.prof_graph_builder import ProfGraphBuilder
-
-
-class ProfGraphExport:
-    @staticmethod
-    def export_to_json(prof_data_path: str, output_path: str):
-        logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
-        try:
-            PathManager.input_path_common_check(prof_data_path)
-            PathManager.check_input_directory_path(output_path)
-            PathManager.make_dir_safety(output_path)
-            all_nodes = ProfGraphBuilder(prof_data_path).build_graph()
-            result_data = {"root": Constant.NPU_ROOT_ID, "node": {}}
-            for node in all_nodes:
-                result_data["node"][node.node_id] = node.info
-            file_name = "prof_graph_json_{}.vis".format(datetime.utcnow().strftime("%Y%m%d%H%M%S%f")[:-3])
-            FileReader.write_json_file(output_path, result_data, file_name)
-        except RuntimeError as err:
-            logging.error(err)
diff --git a/profiler/module_visualization/prof_parse/__init__.py b/profiler/module_visualization/prof_parse/__init__.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/profiler/module_visualization/prof_parse/prof_data_pre_process.py b/profiler/module_visualization/prof_parse/prof_data_pre_process.py
deleted file mode 100644
index 9dc820e4ca5..00000000000
--- a/profiler/module_visualization/prof_parse/prof_data_pre_process.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# Copyright (c) 2024 Huawei Technologies Co., Ltd
-# All rights reserved.
-#
-# Licensed under the BSD 3-Clause License  (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# https://opensource.org/licenses/BSD-3-Clause
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-
-from profiler.prof_common.file_reader import FileReader
-from profiler.prof_common.constant import Constant
-from profiler.prof_common.trace_event_bean import TraceEventBean
-
-
-class ProfDataPreProcess:
-    def __init__(self, prof_data_path: str):
-        self._prof_data_path = prof_data_path
-        self._trace_path = ""
-        self._kernel_pid = None
-        self._result_data = {Constant.CPU_OP_EVENT: [], Constant.MODULE_EVENT: [], Constant.KERNEL_EVENT: [],
-                             Constant.TORCH_TO_NPU_FLOW: {}, Constant.FWD_BWD_FLOW: {}}
-
-    def run(self) -> dict:
-        self._check_trace_path()
-        self._parse_trace_events()
-        self._check_result_data()
-        return self._result_data
-
-    def _check_trace_path(self):
-        if os.path.isfile(self._prof_data_path):
-            (split_file_path, split_file_name) = os.path.split(self._prof_data_path)
-            (shot_name, extension) = os.path.splitext(split_file_name)
-            if extension != ".json":
-                msg = f"Invalid profiling path suffix: {self._prof_data_path}. " \
-                      f"You should input in a json file path, such as trace_view.json."
-                raise RuntimeError(msg)
-            self._trace_path = self._prof_data_path
-            return
-        ascend_output = os.path.join(self._prof_data_path, "ASCEND_PROFILER_OUTPUT")
-        profiler_output = ascend_output if os.path.isdir(ascend_output) else self._prof_data_path
-        json_path = os.path.join(profiler_output, "trace_view.json")
-        if not os.path.isfile(json_path):
-            msg = f"Invalid profiling path: {self._prof_data_path}. The data path should be the " \
-                  f"folder that ends with the ascend_pt collected by the Ascend PyTorch Profiler."
-            raise RuntimeError(msg)
-        self._trace_path = json_path
-
-    def _parse_trace_events(self):
-        trace_data = FileReader.read_json_file(self._trace_path)
-        self._check_trace_data(trace_data)
-        iter_trace_data = iter(trace_data)
-        for event in iter_trace_data:
-            bean = TraceEventBean(event)
-            if bean.is_optimizer():
-                self._result_data[Constant.MODULE_EVENT].append(bean)
-            elif bean.is_cpu_op():
-                if not bean.is_step():
-                    self._result_data[Constant.CPU_OP_EVENT].append(bean)
-            elif bean.is_nn_module():
-                self._result_data[Constant.MODULE_EVENT].append(bean)
-            elif bean.is_torch_to_npu():
-                if bean.is_flow_start():
-                    self._result_data[Constant.TORCH_TO_NPU_FLOW].setdefault(bean.id, {})["start"] = bean
-                else:
-                    self._result_data[Constant.TORCH_TO_NPU_FLOW].setdefault(bean.id, {})["end"] = bean
-            elif bean.is_fwd_bwd_flow():
-                if bean.is_flow_start():
-                    self._result_data[Constant.FWD_BWD_FLOW].setdefault(bean.id, {})["start"] = bean
-                else:
-                    self._result_data[Constant.FWD_BWD_FLOW].setdefault(bean.id, {})["end"] = bean
-            elif bean.is_kernel_event(self._kernel_pid):
-                self._result_data[Constant.KERNEL_EVENT].append(bean)
-
-    def _check_trace_data(self, trace_data):
-        if not isinstance(trace_data, list):
-            msg = f"Invalid profiling data path, this feature only supports performance data " \
-                  f"collected by Ascend PyTorch Profiler."
-            raise RuntimeError(msg)
-        iter_trace_data = iter(trace_data)
-        for event in iter_trace_data:
-            bean = TraceEventBean(event)
-            if bean.is_npu_process():
-                self._kernel_pid = bean.pid
-                break
-        if self._kernel_pid is None:
-            msg = f"There is no operator on the NPU side for this data, please check whether the NPU switch is enabled."
-            raise RuntimeError(msg)
-
-    def _check_result_data(self):
-        if not self._result_data.get(Constant.CPU_OP_EVENT):
-            msg = f"This data does not have any aten operator, please make sure to enable the CPU switch."
-            raise RuntimeError(msg)
-        if not self._result_data.get(Constant.MODULE_EVENT):
-            msg = f"This data does not collect any modules, please make sure to turn on the with_stack switch."
-            raise RuntimeError(msg)
diff --git a/profiler/test/ut/compare_tools/profiling_parser/test_base_profiling_parser.py b/profiler/test/ut/compare_tools/profiling_parser/test_base_profiling_parser.py
index 44d97b248e6..80734635929 100644
--- a/profiler/test/ut/compare_tools/profiling_parser/test_base_profiling_parser.py
+++ b/profiler/test/ut/compare_tools/profiling_parser/test_base_profiling_parser.py
@@ -24,6 +24,11 @@ class ProfilingParser(BaseProfilingParser):
         self._enable_operator_compare = True
         self._enable_memory_compare = True
         self._enable_communication_compare = True
+        self._enable_kernel_compare = True
+        self._enable_api_compare = True
+
+    def _update_kernel_details(self):
+        pass
 
     def _update_memory_list(self):
         pass
-- 
Gitee


From f557dfbbf0c96b57069f816ac2b70463145cc79f Mon Sep 17 00:00:00 2001
From: yangxinxian <947098055@qq.com>
Date: Tue, 6 Aug 2024 15:06:44 +0800
Subject: [PATCH 67/67] Delete api_registry copy.py

---
 .../dump/hook_cell/api_registry copy.py       | 198 ------------------
 1 file changed, 198 deletions(-)
 delete mode 100644 debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry copy.py

diff --git a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry copy.py b/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry copy.py
deleted file mode 100644
index ad73bcd9119..00000000000
--- a/debug/accuracy_tools/msprobe/mindspore/dump/hook_cell/api_registry copy.py	
+++ /dev/null
@@ -1,198 +0,0 @@
-# Copyright 2024 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-
-import os
-import functools
-import mindspore as ms
-from mindspore import ops
-from mindspore.common.tensor import Tensor
-from msprobe.core.common.utils import Const
-from msprobe.core.data_dump.data_processor.base import ModuleBackwardInputsOutputs, ModuleForwardInputsOutputs
-from msprobe.mindspore.dump.hook_cell.wrap_functional import get_functional_ops, setup_hooks, \
-    HOOKFunctionalOP, HOOKMintOP, HOOKMintNNFunctionalOP
-from msprobe.mindspore.dump.hook_cell.wrap_tensor import get_tensor_ops, wrap_tensor_ops_and_bind, HOOKTensor
-from msprobe.core.common.utils import Const
-
-PRIMITIVE_PREFIX = "Primitive"
-
-class ApiRegistry:
-    def __init__(self):
-        self.tensor_ori_attr = {}
-        self.functional_ori_attr = {}
-        self.mint_ops_ori_attr = {}
-        self.mint_func_ops_ori_attr = {}
-        self.norm_inner_ops_ori_attr = {}
-
-        self.tensor_hook_attr = {}
-        self.functional_hook_attr = {}
-        self.mint_ops_hook_attr = {}
-        self.mint_func_ops_hook_attr = {}
-        self.norm_inner_ops_hook_attr = {}
-
-        self.norm_inner_ops = ["norm", "square", "sqrt", "is_complex"]
-        self.primitive_counters = {}
-
-    @staticmethod
-    def store_ori_attr(ori_api_group, api_list, api_ori_attr):
-        for api in api_list:
-            if Const.SEP in api:
-                sub_module_name, sub_op = api.rsplit(Const.SEP, 1)
-                sub_module = getattr(ori_api_group, sub_module_name)
-                api_ori_attr[api] = getattr(sub_module, sub_op)
-            else:
-                api_ori_attr[api] = getattr(ori_api_group, api)
-
-    @staticmethod
-    def set_api_attr(api_group, attr_dict):
-        for api, api_attr in attr_dict.items():
-            if Const.SEP in api:
-                sub_module_name, sub_op = api.rsplit(Const.SEP, 1)
-                sub_module = getattr(api_group, sub_module_name, None)
-                if sub_module is not None:
-                    setattr(sub_module, sub_op, api_attr)
-            else:
-                setattr(api_group, api, api_attr)
-
-    def norm_inner_op_set_hook_func(self):
-        self.set_api_attr(ms.ops, self.norm_inner_ops_hook_attr)
-
-    def norm_inner_op_set_ori_func(self):
-        self.set_api_attr(ms.ops, self.norm_inner_ops_ori_attr)
-
-    def api_set_hook_func(self):
-        self.set_api_attr(ms.Tensor, self.tensor_hook_attr)
-        self.set_api_attr(ms.ops, self.functional_hook_attr)
-        self.set_api_attr(ms.mint, self.mint_ops_hook_attr)
-        self.set_api_attr(ms.mint.nn.functional, self.mint_func_ops_hook_attr)
-
-    def api_set_ori_func(self):
-        self.set_api_attr(ms.Tensor, self.tensor_ori_attr)
-        self.set_api_attr(ms.ops, self.functional_ori_attr)
-        self.set_api_attr(ms.mint, self.mint_ops_ori_attr)
-        self.set_api_attr(ms.mint.nn.functional, self.mint_func_ops_ori_attr)
-
-    def initialize_hook(self, hook):
-        self.store_ori_attr(ms.Tensor, get_tensor_ops(), self.tensor_ori_attr)
-        wrap_tensor_ops_and_bind(hook)
-        for attr_name in dir(HOOKTensor):
-            if attr_name.startswith(Const.ATTR_NAME_PREFIX):
-                self.tensor_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKTensor, attr_name)
-
-        functional_ops, mint_ops, mint_func_ops = get_functional_ops()
-        self.store_ori_attr(ms.ops, self.norm_inner_ops, self.norm_inner_ops_ori_attr)
-        self.store_ori_attr(ms.ops, functional_ops, self.functional_ori_attr)
-        self.store_ori_attr(ms.mint, mint_ops, self.mint_ops_ori_attr)
-        self.store_ori_attr(ms.mint.nn.functional, mint_func_ops, self.mint_func_ops_ori_attr)
-        setup_hooks(hook)
-        for attr_name in dir(HOOKFunctionalOP):
-            if attr_name.startswith(Const.ATTR_NAME_PREFIX):
-                self.functional_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKFunctionalOP, attr_name)
-                if attr_name[Const.ATTR_NAME_PREFIX_LEN:] in self.norm_inner_ops:
-                    self.norm_inner_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKFunctionalOP, attr_name)
-        for attr_name in dir(HOOKMintOP):
-            if attr_name.startswith(Const.ATTR_NAME_PREFIX):
-                self.mint_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintOP, attr_name)
-        for attr_name in dir(HOOKMintNNFunctionalOP):
-            if attr_name.startswith(Const.ATTR_NAME_PREFIX):
-                self.mint_func_ops_hook_attr[attr_name[Const.ATTR_NAME_PREFIX_LEN:]] = getattr(HOOKMintNNFunctionalOP, attr_name)    
-
-    def wrap_primitive(self, origin_func, primitive_name, service_instance):
-        primitive_instance = self
-        def func(self, *args, **kwargs):
-            if primitive_name not in primitive_instance.primitive_counters:
-                primitive_instance.primitive_counters[primitive_name] = 0
-            else:
-                primitive_instance.primitive_counters[primitive_name] += 1
-
-            current_count = primitive_instance.primitive_counters[primitive_name]
-            updated_primitive_name = f"{PRIMITIVE_PREFIX}.{primitive_name}.{current_count}"
-            captured_grads_input = []
-            captured_grads_output = []
-
-            def input_backward_hook(grad):
-                print(f"Grad input length: {len(grad)}")
-                print("Captured input grad:", grad)
-                captured_grads_input.append(grad)
-                backward_primitive_name = updated_primitive_name + Const.BACKWARD
-                new_module_input_output = ModuleBackwardInputsOutputs(
-                    grad_input=tuple(captured_grads_input),
-                    grad_output=tuple(captured_grads_output) if captured_grads_output else None
-                )
-                service_instance.data_collector.backward_data_collect(
-                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
-                )
-#1未考虑多输出场景
-# 如果时多grad呢
-# 3 输出的序号问题
-            def output_backward_hook(grad):
-                captured_grads_output.append(grad)
-                backward_primitive_name = f"{updated_primitive_name}.{Const.BACKWARD}"
-                new_module_input_output = ModuleBackwardInputsOutputs(
-                    grad_input=None,
-                    grad_output=tuple(captured_grads_output)
-                )
-                service_instance.data_collector.backward_data_collect(
-                    backward_primitive_name + Const.BACKWARD, self, os.getpid(), new_module_input_output
-                )
-
-            if not service_instance.switch:
-                return origin_func(*args, **kwargs)
-
-            print(f"Entering {updated_primitive_name} hook, number of args: {len(args)}, name: {self.name}")
-            hooked_inputs = []
-
-            # for idx, arg in enumerate(args):
-            #     if isinstance(arg, Tensor):
-            #         arg_hooked = ops.HookBackward(input_backward_hook)(arg)
-            #         hooked_inputs.append(arg_hooked)
-            #     else:
-            #         hooked_inputs.append(arg)
-
-            out = origin_func(*args, **kwargs)
-            forward_primitive_name = f"{updated_primitive_name}.{Const.FORWARD}"
-
-            if service_instance.data_collector:
-                module_input_output = ModuleForwardInputsOutputs(args=args, kwargs=kwargs, output=out)
-                service_instance.data_collector.forward_data_collect(forward_primitive_name, self, os.getpid(), module_input_output)
-                if service_instance.data_collector.if_return_forward_new_output():
-                    out = service_instance.data_collector.get_forward_new_output()
-
-            if isinstance(out, Tensor):
-                out = ops.HookBackward(output_backward_hook)(out)
-            elif isinstance(out, tuple):
-                hooked_outputs = []
-                for tensor in out:
-                    if isinstance(tensor, Tensor):
-                        hooked_outputs.append(ops.HookBackward(output_backward_hook)(tensor))
-                    else:
-                        hooked_outputs.append(tensor)
-                out = tuple(hooked_outputs)
-
-            return out
-
-        return func
-
-    def register_hooks(self, service_instance):
-        primitive_set = set()
-        for name, cell in service_instance.model.cells_and_names():
-            for pname, primitive in cell._primitives.items():
-                primitive_set.add((pname, primitive))
-
-        for pname, primitive in primitive_set:
-            print("primitive name is", pname)
-            NewPrimitive = type('NewPrimitive', (primitive.__class__,), {'__call__': self.wrap_primitive(primitive.__call__, pname, service_instance)})
-            primitive.__class__ = NewPrimitive
-
-api_register = ApiRegistry()
-- 
Gitee