From 475dcd4a6ae2323a11e94076782c4a5ecc218a3c Mon Sep 17 00:00:00 2001 From: fanxiaotong Date: Thu, 3 Aug 2023 14:41:24 +0800 Subject: [PATCH] cluster op summary analysis --- OWNERS | 12 +- .../cluster_op_summary_analysis.py | 273 ++++++++++++++++++ 2 files changed, 284 insertions(+), 1 deletion(-) create mode 100644 profiler/cluster_analyse/cluster_kernels_analysis/cluster_op_summary_analysis.py diff --git a/OWNERS b/OWNERS index 276171cc2..a2670df50 100644 --- a/OWNERS +++ b/OWNERS @@ -6,6 +6,8 @@ approvers: - aerfaliang - wangchao285 - brightlyking +- chenhao_1209 +- feng123www reviewers: - leo920320 - wo-wenjie @@ -13,4 +15,12 @@ reviewers: - wuyulong11 - alysongirl - wangchao285 -- brightlyking \ No newline at end of file +- brightlyking +- chenhao_1209 +- feng123www +- zhang-mingyu-0813 +- snowflakephoenix +- Seanesmhxocism +- augboost +- fanxiaotong1995 +- sunboquan \ No newline at end of file diff --git a/profiler/cluster_analyse/cluster_kernels_analysis/cluster_op_summary_analysis.py b/profiler/cluster_analyse/cluster_kernels_analysis/cluster_op_summary_analysis.py new file mode 100644 index 000000000..cdd4b9e0e --- /dev/null +++ b/profiler/cluster_analyse/cluster_kernels_analysis/cluster_op_summary_analysis.py @@ -0,0 +1,273 @@ +#!/bin/bash +# 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. + + +from pathlib import Path + +import pandas as pd +import argparse +import re +import plotly.graph_objects as go +from plotly.subplots import make_subplots +from plotly.offline import plot +from threading import Thread +import os +import json +import warnings + + +class FormDataProcessor: + def __init__(self, path, form_name): + self.form_name = form_name + self.files = self.get_files_with_prefix_recursive(path, form_name) + + def get_files_with_prefix_recursive(self, csv_path, match_str): + matched_ir_files = list(Path(csv_path).rglob(match_str)) + assert len(matched_ir_files) > 0, f"Didn't find any file in folder {csv_path} that matches {match_str}" + return [str(item) for item in matched_ir_files] + + def readSummaryData(self, columns_to_keep): + # 存储所有合并后的数据 + all_data = pd.DataFrame() + for f in self.files: + # 读取CSV文件 + df = pd.read_csv(f) + # 保留需要的列 + df = df[columns_to_keep] + + # 从文件名提取设备ID + # 添加新列 "device_id" + df['device_id'] = self.getDeviceId(f) + df['node_id'] = self.getNodeId(f) + + # 将数据添加到最终的数据框中 + all_data = all_data.append(df, ignore_index=True) + return all_data + + def getChipType(self): + file = self.files[0] + df = pd.read_csv(file) + if 'aiv_time(us)' in df.columns: + return "ASCEND_910B" + return "ASCEND_OTHER" + + def getDeviceId(self, dir_path): + device_id = re.search(r'device_(\d+)', dir_path).group(1) + return device_id + + def getNodeId(self, dir_path): + node_id = re.search(r'node(\d+)', dir_path).group(1) + return int(node_id) + + def getRankNum(self): + return len(self.files) + + +# 表驱动，获取不同芯片类型不同交付件的所需的列 +class ViewInfoManager: + def __init__(self, chip_type): + self.chip_type = chip_type + self.op_summary_columns_dict = [] + self.setOpSummaryColumnsParams() + + def setOpSummaryColumnsParams(self): + # 有些数据除了用表格的列进行分组之外，还添加了其他属性对数据进行分类，这部分数据放在extend_attr_to_group里面 + self.op_summary_columns_dict = { + 'ASCEND_910B': { + 'TimeToCsvAnalyzer': + {'columns_to_group': ["Op Name", "Input Shapes", "Input Data Types", "Output Shapes"], + 'extend_attr_to_group': ["device_id", "node_id"], + 'columns_to_view': ["Task Duration(us)"], + 'calculate_fun': ['mean', 'var', 'max', 'min'] + }, + 'StatisticalInfoToHtmlAnalyzer': + {'columns_to_group': ["Op Name", "Input Shapes", "Input Data Types", "Output Shapes"], + "columns_to_view": ["Task Duration(us)", "aiv_time(us)", "aiv_vec_ratio", + "aiv_scalar_ratio", "aiv_mte2_ratio", "aiv_mte3_ratio", + "aicore_time(us)", "aic_mac_ratio", "aic_scalar_ratio", + "aic_mte1_ratio", "aic_mte2_ratio", "aic_fixpipe_ratio" + ], + 'calculate_fun': ['mean', 'var', 'max', 'min'] + } + }, + 'ASCEND_OTHER': { + 'TimeToCsvAnalyzer': + {'columns_to_group': ["Op Name", "Input Shapes", "Input Data Types", "Output Shapes"], + 'extend_attr_to_group': ["device_id", "node_id"], + "columns_to_view": ["aicore_time(us)"], + 'calculate_fun': ['mean', 'var', 'max', 'min'] + }, + 'StatisticalInfoToHtmlAnalyzer': + {'columns_to_group': ["Op Name", "Input Shapes", "Input Data Types", "Output Shapes"], + "columns_to_view": ["aicore_time(us)", "Task Duration(us)", "mac_ratio", "vec_ratio", + "scalar_ratio", "mte1_ratio", "mte2_ratio", "mte3_ratio"], + 'calculate_fun': ['mean', 'var', 'max', 'min'] + } + } + } + + def getColumnsInfo(self, analyzer_type): + return self.op_summary_columns_dict[self.chip_type][analyzer_type] + + +class OpSummaryAnalyzerBase: + def __init__(self, chip_type, analyzer_type, dir_path): + self.chip_type = chip_type + self.result_dir = f"{dir_path}/result" + os.makedirs(self.result_dir, exist_ok=True) # 文件路径不存在则创建 + view_info = ViewInfoManager(chip_type).getColumnsInfo(analyzer_type) + self.columns_to_view = view_info['columns_to_view'] + self.calculate_fun = view_info['calculate_fun'] + self.columns_to_group = view_info['columns_to_group'] + self.attrs_to_group = self.columns_to_group.copy() + if 'extend_attr_to_group' in view_info: + extend_attr_to_group = view_info['extend_attr_to_group'] + self.attrs_to_group.extend(extend_attr_to_group) + + def getColumnsToGroup(self): + return self.columns_to_group + + def getColumnsToView(self): + return self.columns_to_view + + def calculateViewData(self, summary_data): + # 存储所有合并后的数据 + calculate_dict = {self.columns_to_view[i]: self.calculate_fun for i in range(len(self.columns_to_view))} + view_data = summary_data.groupby(self.attrs_to_group).agg(calculate_dict).reset_index() + return view_data + +class TimeToCsvAnalyzer(OpSummaryAnalyzerBase): + def __init__(self, chip_type, dir_path): + super().__init__(chip_type, "TimeToCsvAnalyzer", dir_path) + + def GenerateDeliverable(self, summary_data, rank_num): + view_data = self.calculateViewData(summary_data) + # 规范化列名 + view_data.columns = [''.join(col) if col[1] == "" else '_'.join(col) for col in view_data.columns] + for column in self.columns_to_view: + view_data[column + '_range'] = view_data[column + '_max'] - view_data[column + '_min'] + view_data.to_csv(self.result_dir + "/cluster_duration_time_analysis.csv", index=False) + return view_data + + +class StatisticalInfoToHtmlAnalyzer(OpSummaryAnalyzerBase): + def __init__(self, chip_type, top_n, dir_path): + super().__init__(chip_type, "StatisticalInfoToHtmlAnalyzer", dir_path) + self.top_n = top_n if top_n % 2 == 0 else top_n + 1 + # top_n 如果不符合要求，报警告 + + def GenerateDeliverable(self, summary_data, rank_num): + view_data = self.calculateViewData(summary_data) + # 规范化列名 op_name/ --> op_name time/var 这种不变 + view_data.columns = [''.join(col) if col[1] == "" else col for col in view_data.columns] + + # 对使用到的变量进行初始设置 + self.top_n = min(max(self.top_n, 1), len(view_data)) + top_n_data = view_data.sort_values(("Task Duration(us)", 'var'), ascending=False).head(self.top_n) + + threads = [] + for column in self.columns_to_view: + # 分别给每一种特性画图 + draw_thread = Thread(target=self.drawPloty, args=(column, summary_data, top_n_data, rank_num)) + threads.append(draw_thread) + draw_thread.start() + + for draw_thread in threads: + draw_thread.join() + + def drawPloty(self, column, summary_data, top_n_data, rank_num): + col_num = self.getCalNum(rank_num) + row_num = self.top_n // col_num + fig = make_subplots(rows=row_num, cols=col_num, vertical_spacing=0.03) + for i, (_, operation) in enumerate(top_n_data.iterrows()): + op_data = summary_data[(summary_data["Op Name"] == operation["Op Name"]) & + (summary_data["Input Shapes"] == operation["Input Shapes"]) & + (summary_data["Input Data Types"] == operation["Input Data Types"])] + op_data = op_data.sort_values(by=["node_id", "device_id"]) + node_ids = op_data['node_id'].unique() + device_ids = op_data['device_id'].unique() + + for node_id in node_ids: + for device_id in device_ids: + draw_data = op_data[(op_data['node_id'] == node_id) & (op_data['device_id'] == device_id)] + fig.add_trace(go.Box(y=draw_data[column], + name=f'{node_id}_{device_id}', + marker_color='green', showlegend=False), (i // col_num) + 1, (i % col_num) + 1) + + fig.update_xaxes(title_text=f'{operation["Op Name"]}-{operation["Input Shapes"]}', row=(i // col_num) + 1, + col=(i % col_num) + 1) + fig.update_layout(margin=dict(l=20, r=20, t=20, b=20), + height=int(500 * row_num), + width=int(rank_num * 50 * col_num), + title_text="Op Performance Comparison") + plot(fig, filename=self.result_dir + "/" + column + "_Info.html") + + def getCalNum(self, rank_num): + # 计算每行应该画多少个子图 + if rank_num <= 8: + return 3 + elif rank_num <= 16: + return 2 + else: + return 1 + +class DeliverableGenerator: + def __init__(self, args): + self.args = args + self.formProcess = FormDataProcessor(args.dir, 'op_summary*.csv') + self.analyzers = [] + self.columns_to_keep = [] + self.setAnalyzers(args) + self.setColumnsToKeep() + + def run(self): + summary_data = self.formProcess.readSummaryData(self.columns_to_keep) + rank_num = self.formProcess.getRankNum() + for analyzer in self.analyzers: + analyzer.GenerateDeliverable(summary_data, rank_num) + + def setAnalyzers(self, args): + chip_type = self.formProcess.getChipType() + if args.type == "all": + self.analyzers = [TimeToCsvAnalyzer(chip_type, args.dir), StatisticalInfoToHtmlAnalyzer(chip_type, args.top_n, args.dir)] + elif args.type == "html": + self.analyzers = [StatisticalInfoToHtmlAnalyzer(chip_type, args.top_n, args.dir)] + elif args.type == "csv": + self.analyzers = [TimeToCsvAnalyzer(chip_type, args.dir)] + else: + warnings.warn("参数错误，请输入 all html csv 这三种类型") # 发出一个警告信息 + + + def setColumnsToKeep(self): + columns_to_keep = [] + for analyzer in self.analyzers: + columns_to_keep.extend(analyzer.getColumnsToGroup()) + columns_to_keep.extend(analyzer.getColumnsToView()) + self.columns_to_keep = list(set(columns_to_keep)) + + +def main(): + # 解析命令行参数 + parser = argparse.ArgumentParser() + parser.add_argument("--dir", "-d", default=None, help="root dir of PROF_* data") + parser.add_argument("--top_n", "-n", default=10, help="how many operators to show", type=int) + parser.add_argument("--type", "-t", default='html', help="compare ratio or aicore-time", type=str) + args = parser.parse_args() + + deviverable_gen = DeliverableGenerator(args) + deviverable_gen.run() + +if __name__ == "__main__": + main() -- Gitee