+
+## Overview
+When a model compiled using Mindspore run in graph mode `context.set_context(mode=context.GRAPH_MODE)` and `context.set_context(save_graphs=True)` is set in the configuration, some intermediate files will be generated during graph compliation. These intermediate files are called IR files. Currently, there are three IR files:
+
+- .ir file: An IR file that describes the model structure in text format and can be directly viewed using any text editing software. We will also introduce how to view it in the following sections.
+
+- .dat file: An IR file that describes the model structure more strictly than the .ir file. It contains more content and can be directly viewed using any text editing software.
+
+- .dot file: An IR file that describes the topology relationships between different nodes. You can use this file by [graphviz](http://graphviz.org/) as the input to generate images for users to view the model structure. For models with many operators, it is recommended using the visualization component [MindInsight](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/dashboard.html#id5) to visualize computing graphs.
+
+In this tutorial, we use LeNet in ModelZoo as a demonstration on the Ascend environment. The related scripts can be found in [ModelZoo/LeNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet).
+
+## Generate IR Files
+
+Add the following code to `train.py`, When the script run, Mindspore will automatically store the IR files generated during compliation to the specified path.
+
+```python
+if __name__ == "__main__":
+ context.set_context(save_graphs=True, save_graphs_path="path/to/ir/files")
+```
+
+In this tutorial, we run the training script on standalone computing device. When run on multiple computing devices, Mindspore will generate separate processes for each computing device. So, in multiple computing devices scenario, you are advised to read the ID of the current computing device from the training script and set an independent `save_graphs_path` for each decive to save the IR files to a different path. For example:
+
+```python
+device_id = os.getenv("DEVICE_ID")
+context.set_context(save_graphs=True, save_graphs_path="path/to/ir/files"+device_id)
+```
+After the training command is executed, the following files are generated in the specified directory: the IR files starting with digits and underscores are generated during the ME diagram compilation. The calculation diagram is saved in each phase of the `pipeline`. Let's see the important phases, For examples, the `parse` phase parses the `construct` function of the entrance. The `symbol_resolve` phase recursively parses other functions and objects directly or indirectly referenced by the entry function. The `abstract_specialize` phase, type derivation and `shape` derivation are performed. The `optimize` phase, hardware-independent optimization is performed, The automatic differential and automatic parallel functions are also performed. The `validate` phase, the compiled compute graph is verified. The `task_emit` phase, the computing graph is transferred to the backend for further processing. The calculation graph is executed in the `execute` phase.
+
+```text
+.
+├── 00_parse_[xxxx].ir
+├── 00_parse.dat
+├── 00_parse.dot
+├── 01_symbol_resolve_[xxxx].ir
+├── 01_symbol_resolve.dat
+├── 01_symbol_resolve.dot
+├── 02_combine_like_graphs_[xxxx].ir
+├── 02_combine_like_graphs.dat
+├── 02_combine_like_graphs.dot
+├── 03_inference_opt_prepare_[xxxx].ir
+├── 03_inference_opt_prepare.dat
+├── 03_inference_opt_prepare.dot
+├── 04_abstract_specialize_[xxxx].ir
+├── 04_abstract_specialize.dat
+├── 04_abstract_specialize.dot
+├── 05_inline_[xxxx].ir
+├── 05_inline.dat
+├── 05_inline.dot
+├── 06_py_pre_ad_[xxxx].ir
+├── 06_py_pre_ad.dat
+├── 06_py_pre_ad.dot
+├── 07_pipeline_split_[xxxx].ir
+├── 07_pipeline_split.dat
+├── 07_pipeline_split.dot
+├── 08_optimize_[xxxx].ir
+├── 08_optimize.dat
+├── 08_optimize.dot
+├── 09_py_opt_[xxxx].ir
+├── 09_py_opt.dat
+├── 09_py_opt.dot
+├── 10_validate_[xxxx].ir
+├── 10_validate.dat
+├── 10_validate.dot
+├── 11_task_emit_[xxxx].ir
+├── 11_task_emit.dat
+├── 11_task_emit.dot
+├── 12_execute_[xxxx].ir
+├── 12_execute.dat
+├── 12_execute.dot
+...
+```
+
+## IR File Contents Introduction
+
+The following is an example to describe the contents of the IR file.
+
+```python
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+context.set_context(save_graphs=True, save_graphs_path="./ir_files")
+
+class Net(nn.Cell):
+ def __init__(self):
+ super().__init__()
+
+ def construct(self, x, y):
+ x = x + y
+ x = x * y
+ return x
+
+x = Tensor(3, mstype.float32)
+y = Tensor(2, mstype.float32)
+net = Net()
+out = net(x, y)
+print(out)
+```
+Use a text editing software (for example, vi) to open the `12_execute_[xxxx].ir` file. The file contents are as follows:
+```text
+1 #IR entry : @6_5_1_construct_wrapper.15
+ 2 #attrs :
+ 3 check_set_strategy_valid_once_only : 1
+ 4 #Total params : 2
+ 5
+ 6 %para1_x : Notice:
+>After several optimizations by the complier, the node may undergo several changes (such as operator splitting and operator merging). The source code parsing call stack information of the node may not be in a one-to-one correspondence with the script. This is only an auxiliary method.
+ +## Dump Required Data from the IR File +The following code comes from `lenet.py` in ModelZoo LeNet sample, Assume that you want to dump the first convolutional layer, that is, the `x = self.conv1(x)` data in the following code. +```python +class LeNet5(nn.Cell): + def __init__(self, num_class=10, num_channel=1, include_top=True): + super(LeNet5, self).__init__() + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.include_top = include_top + if self.include_top: + self.flatten = nn.Flatten() + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) + + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + if not self.include_top: + return x + x = self.flatten(x) + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.fc3(x) + return x +``` +Generally, graph 0, `hwopt_d_end_graph_0_[xxxx].ir`, indicates the data subgraph (if the dataset_sink_mode is enabled), and graph 1 indicates the backbone network. So, search for `x = self.conv1(x)` in the dumped `hwopt_d_end_graph_1_[xxxx].ir`file, 4 results are obtained, 3 of them are `Cast` and `TransData`, skipping these operators generated by the precision conversion and format conversion optimization, we finally locate in lines 213 to 221, that is, `%24(equivoutput) = Conv2D(%23, %19)...`, corresponding to `conv1` in the network. In this way, you can obtain the op name (in the brackets of line 216, `Default/network-TrainOneStepWithLossScaleCell/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Conv2D-op89`) in the compiled diagram from the following information. + +```text +... + 213 %24(equivoutput) = Conv2D(%23, %19) {instance name: conv2d} primitive_attrs: {pri_format: NC1HWC0, stride: (1, 1, 1, 1), pad: (0, 0, 0, 0), pad_mode: valid, out_channel: 6, mode: 1 , dilation: (1, 1, 1, 1), output_names: [output], group: 1, format: NCHW, visited: true, offset_a: 0, kernel_size: (5, 5), groups: 1, input_names: [x, w], pad_list: (0, 0, 0, 0), IsF eatureMapOutput: true, IsFeatureMapInputList: (0)} + 214 : (Notice:
->After several optimizations by the complier, the node may undergo several changes (such as operator splitting and operator merging). The source code parsing call stack information of the node may not be in a one-to-one correspondence with the script. This is only an auxiliary method.
+>After several optimizations by the compiler, the node may undergo several changes (such as operator splitting and operator merging). The source code parsing call stack information of the node may not be in a one-to-one correspondence with the script. This is only an auxiliary method.
## Dump Required Data from the IR File The following code comes from `lenet.py` in ModelZoo LeNet sample, Assume that you want to dump the first convolutional layer, that is, the `x = self.conv1(x)` data in the following code. -- Gitee From fe16dd9f1f2a0e7197d8e003ddabfd046ce66ad6 Mon Sep 17 00:00:00 2001 From: xiaoxiaozhang <345368251@qq.com> Date: Wed, 17 Feb 2021 15:25:47 +0800 Subject: [PATCH 3/5] fix blank lines --- .../advanced_use/dump_data_from_ir_files.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md b/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md index 9598206ca7..c300ca3b52 100644 --- a/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md +++ b/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md @@ -15,6 +15,7 @@
## Overview
+
When a model compiled using Mindspore run in graph mode `context.set_context(mode=context.GRAPH_MODE)` and `context.set_context(save_graphs=True)` is set in the configuration, some intermediate files will be generated during graph compliation. These intermediate files are called IR files. Currently, there are three IR files:
- .ir file: An IR file that describes the model structure in text format and can be directly viewed using any text editing software. We will also introduce how to view it in the following sections.
@@ -40,6 +41,7 @@ In this tutorial, we run the training script on standalone computing device. Whe
device_id = os.getenv("DEVICE_ID")
context.set_context(save_graphs=True, save_graphs_path="path/to/ir/files"+device_id)
```
+
After the training command is executed, the following files are generated in the specified directory: the IR files starting with digits and underscores are generated during the ME diagram compilation. The calculation diagram is saved in each phase of the `pipeline`. Let's see the important phases, For examples, the `parse` phase parses the `construct` function of the entrance. The `symbol_resolve` phase recursively parses other functions and objects directly or indirectly referenced by the entry function. The `abstract_specialize` phase, type derivation and `shape` derivation are performed. The `optimize` phase, hardware-independent optimization is performed, The automatic differential and automatic parallel functions are also performed. The `validate` phase, the compiled compute graph is verified. The `task_emit` phase, the computing graph is transferred to the backend for further processing. The calculation graph is executed in the `execute` phase.
```text
@@ -114,7 +116,9 @@ net = Net()
out = net(x, y)
print(out)
```
+
Use a text editing software (for example, vi) to open the `12_execute_[xxxx].ir` file. The file contents are as follows:
+
```text
1 #IR entry : @6_5_1_construct_wrapper.15
2 #attrs :
@@ -141,6 +145,7 @@ Use a text editing software (for example, vi) to open the `12_execute_[xxxx].ir`
23 : (Notice:
>After several optimizations by the compiler, the node may undergo several changes (such as operator splitting and operator merging). The source code parsing call stack information of the node may not be in a one-to-one correspondence with the script. This is only an auxiliary method.
## Dump Required Data from the IR File + The following code comes from `lenet.py` in ModelZoo LeNet sample, Assume that you want to dump the first convolutional layer, that is, the `x = self.conv1(x)` data in the following code. + ```python class LeNet5(nn.Cell): def __init__(self, num_class=10, num_channel=1, include_top=True): @@ -186,6 +194,7 @@ class LeNet5(nn.Cell): x = self.fc3(x) return x ``` + Generally, graph 0, `hwopt_d_end_graph_0_[xxxx].ir`, indicates the data subgraph (if the dataset_sink_mode is enabled), and graph 1 indicates the backbone network. So, search for `x = self.conv1(x)` in the dumped `hwopt_d_end_graph_1_[xxxx].ir`file, 4 results are obtained, 3 of them are `Cast` and `TransData`, skipping these operators generated by the precision conversion and format conversion optimization, we finally locate in lines 213 to 221, that is, `%24(equivoutput) = Conv2D(%23, %19)...`, corresponding to `conv1` in the network. In this way, you can obtain the op name (in the brackets of line 216, `Default/network-TrainOneStepWithLossScaleCell/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Conv2D-op89`) in the compiled diagram from the following information. ```text @@ -206,6 +215,7 @@ After obtaining the op name, we can execute the dump process to save the input a 1.Create the configuration file `data_dump.json`, this file stores the operators information to be dumped, copy the op name obtained from previous step to the `kernels` key. For details about this file, see the [custom debugging info](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/custom_debugging_info.html#id5). + ```text { "common_dump_settings": { @@ -223,10 +233,13 @@ After obtaining the op name, we can execute the dump process to save the input a } } ``` + 2.Configure environment variables and specify the path of the configuration file. + ```bash export MINDSPORE_DUMP_CONFIG={Absolute path of data_dump.json} ``` + 3.Execute the case to dump data. During the execution, Mindspore dumps the input and output data of a specified operator to the specified path. In this example, the following files are obtained, which correspond to the input and output of the operator. @@ -240,12 +253,15 @@ In this example, the following files are obtained, which correspond to the input 4.Parse the dump data. You can use numpy.fromfile to read the file generated in previous step. The ndarray obtained after reading is the input/output of the corresponding operator. + ```python import numpy output = numpy.fromfile("Default--network-TrainOneStepWithLossScaleCell--network-WithLossCell--_backbone-LeNet5--conv1-Conv2d--Conv2D-op89_input_0_shape_32_1_32_32_16_Float16_NC1HWC0.bin") print(output) ``` + The output is: + ```text [1.17707155e-17 4.07526143e-17 5.84038559e-18 ... 0.00000000e+00 0.00000000e+00 0.00000000e+00] ``` -- Gitee From 3d3ef1ee88ddf744f3f6abc61c48e3d6cc2b6137 Mon Sep 17 00:00:00 2001 From: xiaoxiaozhang <345368251@qq.com> Date: Wed, 17 Feb 2021 15:28:49 +0800 Subject: [PATCH 4/5] fix MD012 Multiple consecutive blank lines --- .../training/source_en/advanced_use/dump_data_from_ir_files.md | 1 - 1 file changed, 1 deletion(-) diff --git a/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md b/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md index c300ca3b52..3b1347f237 100644 --- a/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md +++ b/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md @@ -213,7 +213,6 @@ Generally, graph 0, `hwopt_d_end_graph_0_[xxxx].ir`, indicates the data subgraph After obtaining the op name, we can execute the dump process to save the input and output of the operator for debugging. Here, we will introduce a method called synchronous dump. - 1.Create the configuration file `data_dump.json`, this file stores the operators information to be dumped, copy the op name obtained from previous step to the `kernels` key. For details about this file, see the [custom debugging info](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/custom_debugging_info.html#id5). ```text -- Gitee From 159c836bd6e16da40b813e0020649ba32f42d13f Mon Sep 17 00:00:00 2001 From: xiaoxiaozhang <345368251@qq.com> Date: Wed, 17 Feb 2021 15:07:55 +0800 Subject: [PATCH 5/5] add tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md. dump_data_from_ir_files.md --- .../advanced_use/dump_data_from_ir_files.md | 267 ++++++++++++++++++ 1 file changed, 267 insertions(+) create mode 100644 tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md diff --git a/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md b/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md new file mode 100644 index 0000000000..3b1347f237 --- /dev/null +++ b/tutorials/training/source_en/advanced_use/dump_data_from_ir_files.md @@ -0,0 +1,267 @@ +# Debugging with IR Diagrams + +`Linux` `Ascend` `GPU` `Model Development` `Junior` `Intermediate` `Expert` + + + +- [debugging with IR Diagrams](#debugging with IR Diagrams) + - [Overview](#Overview) + - [Generate IR Files](#Generate IR Files) + - [IR File Contents Introduction](#IR File Contents Introduction) + - [Dump Required Data from the IR File](#Dump Required Data from the IR File) + + + +
+
+## Overview
+
+When a model compiled using Mindspore run in graph mode `context.set_context(mode=context.GRAPH_MODE)` and `context.set_context(save_graphs=True)` is set in the configuration, some intermediate files will be generated during graph compliation. These intermediate files are called IR files. Currently, there are three IR files:
+
+- .ir file: An IR file that describes the model structure in text format and can be directly viewed using any text editing software. We will also introduce how to view it in the following sections.
+
+- .dat file: An IR file that describes the model structure more strictly than the .ir file. It contains more content and can be directly viewed using any text editing software.
+
+- .dot file: An IR file that describes the topology relationships between different nodes. You can use this file by [graphviz](http://graphviz.org/) as the input to generate images for users to view the model structure. For models with many operators, it is recommended using the visualization component [MindInsight](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/dashboard.html#id5) to visualize computing graphs.
+
+In this tutorial, we use LeNet in ModelZoo as a demonstration on the Ascend environment. The related scripts can be found in [ModelZoo/LeNet](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet).
+
+## Generate IR Files
+
+Add the following code to `train.py`, When the script run, Mindspore will automatically store the IR files generated during compliation to the specified path.
+
+```python
+if __name__ == "__main__":
+ context.set_context(save_graphs=True, save_graphs_path="path/to/ir/files")
+```
+
+In this tutorial, we run the training script on standalone computing device. When run on multiple computing devices, Mindspore will generate separate processes for each computing device. So, in multiple computing devices scenario, you are advised to read the ID of the current computing device from the training script and set an independent `save_graphs_path` for each decive to save the IR files to a different path. For example:
+
+```python
+device_id = os.getenv("DEVICE_ID")
+context.set_context(save_graphs=True, save_graphs_path="path/to/ir/files"+device_id)
+```
+
+After the training command is executed, the following files are generated in the specified directory: the IR files starting with digits and underscores are generated during the ME diagram compilation. The calculation diagram is saved in each phase of the `pipeline`. Let's see the important phases, For examples, the `parse` phase parses the `construct` function of the entrance. The `symbol_resolve` phase recursively parses other functions and objects directly or indirectly referenced by the entry function. The `abstract_specialize` phase, type derivation and `shape` derivation are performed. The `optimize` phase, hardware-independent optimization is performed, The automatic differential and automatic parallel functions are also performed. The `validate` phase, the compiled compute graph is verified. The `task_emit` phase, the computing graph is transferred to the backend for further processing. The calculation graph is executed in the `execute` phase.
+
+```text
+.
+├── 00_parse_[xxxx].ir
+├── 00_parse.dat
+├── 00_parse.dot
+├── 01_symbol_resolve_[xxxx].ir
+├── 01_symbol_resolve.dat
+├── 01_symbol_resolve.dot
+├── 02_combine_like_graphs_[xxxx].ir
+├── 02_combine_like_graphs.dat
+├── 02_combine_like_graphs.dot
+├── 03_inference_opt_prepare_[xxxx].ir
+├── 03_inference_opt_prepare.dat
+├── 03_inference_opt_prepare.dot
+├── 04_abstract_specialize_[xxxx].ir
+├── 04_abstract_specialize.dat
+├── 04_abstract_specialize.dot
+├── 05_inline_[xxxx].ir
+├── 05_inline.dat
+├── 05_inline.dot
+├── 06_py_pre_ad_[xxxx].ir
+├── 06_py_pre_ad.dat
+├── 06_py_pre_ad.dot
+├── 07_pipeline_split_[xxxx].ir
+├── 07_pipeline_split.dat
+├── 07_pipeline_split.dot
+├── 08_optimize_[xxxx].ir
+├── 08_optimize.dat
+├── 08_optimize.dot
+├── 09_py_opt_[xxxx].ir
+├── 09_py_opt.dat
+├── 09_py_opt.dot
+├── 10_validate_[xxxx].ir
+├── 10_validate.dat
+├── 10_validate.dot
+├── 11_task_emit_[xxxx].ir
+├── 11_task_emit.dat
+├── 11_task_emit.dot
+├── 12_execute_[xxxx].ir
+├── 12_execute.dat
+├── 12_execute.dot
+...
+```
+
+## IR File Contents Introduction
+
+The following is an example to describe the contents of the IR file.
+
+```python
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+context.set_context(save_graphs=True, save_graphs_path="./ir_files")
+
+class Net(nn.Cell):
+ def __init__(self):
+ super().__init__()
+
+ def construct(self, x, y):
+ x = x + y
+ x = x * y
+ return x
+
+x = Tensor(3, mstype.float32)
+y = Tensor(2, mstype.float32)
+net = Net()
+out = net(x, y)
+print(out)
+```
+
+Use a text editing software (for example, vi) to open the `12_execute_[xxxx].ir` file. The file contents are as follows:
+
+```text
+1 #IR entry : @6_5_1_construct_wrapper.15
+ 2 #attrs :
+ 3 check_set_strategy_valid_once_only : 1
+ 4 #Total params : 2
+ 5
+ 6 %para1_x : Notice:
+>After several optimizations by the compiler, the node may undergo several changes (such as operator splitting and operator merging). The source code parsing call stack information of the node may not be in a one-to-one correspondence with the script. This is only an auxiliary method.
+ +## Dump Required Data from the IR File + +The following code comes from `lenet.py` in ModelZoo LeNet sample, Assume that you want to dump the first convolutional layer, that is, the `x = self.conv1(x)` data in the following code. + +```python +class LeNet5(nn.Cell): + def __init__(self, num_class=10, num_channel=1, include_top=True): + super(LeNet5, self).__init__() + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.include_top = include_top + if self.include_top: + self.flatten = nn.Flatten() + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) + + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + if not self.include_top: + return x + x = self.flatten(x) + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.fc3(x) + return x +``` + +Generally, graph 0, `hwopt_d_end_graph_0_[xxxx].ir`, indicates the data subgraph (if the dataset_sink_mode is enabled), and graph 1 indicates the backbone network. So, search for `x = self.conv1(x)` in the dumped `hwopt_d_end_graph_1_[xxxx].ir`file, 4 results are obtained, 3 of them are `Cast` and `TransData`, skipping these operators generated by the precision conversion and format conversion optimization, we finally locate in lines 213 to 221, that is, `%24(equivoutput) = Conv2D(%23, %19)...`, corresponding to `conv1` in the network. In this way, you can obtain the op name (in the brackets of line 216, `Default/network-TrainOneStepWithLossScaleCell/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/Conv2D-op89`) in the compiled diagram from the following information. + +```text +... + 213 %24(equivoutput) = Conv2D(%23, %19) {instance name: conv2d} primitive_attrs: {pri_format: NC1HWC0, stride: (1, 1, 1, 1), pad: (0, 0, 0, 0), pad_mode: valid, out_channel: 6, mode: 1 , dilation: (1, 1, 1, 1), output_names: [output], group: 1, format: NCHW, visited: true, offset_a: 0, kernel_size: (5, 5), groups: 1, input_names: [x, w], pad_list: (0, 0, 0, 0), IsF eatureMapOutput: true, IsFeatureMapInputList: (0)} + 214 : (