diff --git a/python/paddle/fluid/tests/unittests/test_profiler.py b/python/paddle/fluid/tests/unittests/test_profiler.py index 17ba2c605e8dbf161fa12c1fb51e8a1271e7ca42..97ced3d99eac2b8f616caa74e0029c5a25c9a74b 100644 --- a/python/paddle/fluid/tests/unittests/test_profiler.py +++ b/python/paddle/fluid/tests/unittests/test_profiler.py @@ -18,6 +18,7 @@ import unittest import os import tempfile import numpy as np +import paddle.utils as utils import paddle.fluid as fluid import paddle.fluid.profiler as profiler import paddle.fluid.layers as layers @@ -31,16 +32,9 @@ class TestProfiler(unittest.TestCase): def setUpClass(cls): os.environ['CPU_NUM'] = str(4) - def net_profiler(self, - state, - option, - iter_range=None, - use_parallel_executor=False): - profile_path = os.path.join(tempfile.gettempdir(), "profile") - open(profile_path, "w").write("") + def build_program(self, compile_program=True): startup_program = fluid.Program() main_program = fluid.Program() - with fluid.program_guard(main_program, startup_program): image = fluid.layers.data(name='x', shape=[784], dtype='float32') hidden1 = fluid.layers.fc(input=image, size=64, act='relu') @@ -70,34 +64,19 @@ class TestProfiler(unittest.TestCase): optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9) opts = optimizer.minimize(avg_cost, startup_program=startup_program) - place = fluid.CPUPlace() if state == 'CPU' else fluid.CUDAPlace(0) - exe = fluid.Executor(place) - exe.run(startup_program) - if use_parallel_executor: - pe = fluid.ParallelExecutor( - state != 'CPU', - loss_name=avg_cost.name, - main_program=main_program) - - pass_acc_calculator = fluid.average.WeightedAverage() - with profiler.profiler(state, 'total', profile_path, option) as prof: - for iter in range(10): - if iter == 2: - profiler.reset_profiler() - x = np.random.random((32, 784)).astype("float32") - y = np.random.randint(0, 10, (32, 1)).astype("int64") - - if use_parallel_executor: - pe.run(feed={'x': x, 'y': y}, fetch_list=[avg_cost.name]) - continue - outs = exe.run(main_program, - feed={'x': x, - 'y': y}, - fetch_list=[avg_cost, batch_acc, batch_size]) - acc = np.array(outs[1]) - b_size = np.array(outs[2]) - pass_acc_calculator.add(value=acc, weight=b_size) - pass_acc = pass_acc_calculator.eval() + if compile_program: + train_program = fluid.compiler.CompiledProgram( + main_program).with_data_parallel(loss_name=avg_cost.name) + else: + train_program = main_program + return train_program, startup_program, avg_cost, batch_size, batch_acc + + def get_profile_path(self): + profile_path = os.path.join(tempfile.gettempdir(), "profile") + open(profile_path, "w").write("") + return profile_path + + def check_profile_result(self, profile_path): data = open(profile_path, 'rb').read() if (len(data) > 0): profile_pb = profiler_pb2.Profile() @@ -115,21 +94,114 @@ class TestProfiler(unittest.TestCase): event.name.startswith("Runtime API")): print("Warning: unregister", event.name) + def run_iter(self, exe, main_program, fetch_list, pass_acc_calculator): + x = np.random.random((32, 784)).astype("float32") + y = np.random.randint(0, 10, (32, 1)).astype("int64") + outs = exe.run(main_program, + feed={'x': x, + 'y': y}, + fetch_list=fetch_list) + acc = np.array(outs[1]) + b_size = np.array(outs[2]) + pass_acc_calculator.add(value=acc, weight=b_size) + pass_acc = pass_acc_calculator.eval() + + def net_profiler(self, + exe, + state, + tracer_option, + batch_range=None, + use_parallel_executor=False, + use_new_api=False): + main_program, startup_program, avg_cost, batch_size, batch_acc = self.build_program( + compile_program=use_parallel_executor) + exe.run(startup_program) + + profile_path = self.get_profile_path() + if not use_new_api: + with profiler.profiler(state, 'total', profile_path, tracer_option): + pass_acc_calculator = fluid.average.WeightedAverage() + for iter in range(10): + if iter == 2: + profiler.reset_profiler() + self.run_iter(exe, main_program, + [avg_cost, batch_acc, batch_size], + pass_acc_calculator) + else: + options = utils.ProfilerOptions(options={ + 'state': state, + 'sorted_key': 'total', + 'tracer_level': tracer_option, + 'batch_range': [0, 10] if batch_range is None else batch_range, + 'profile_path': profile_path + }) + with utils.Profiler(enabled=True, options=options) as prof: + pass_acc_calculator = fluid.average.WeightedAverage() + for iter in range(10): + self.run_iter(exe, main_program, + [avg_cost, batch_acc, batch_size], + pass_acc_calculator) + utils.get_profiler().record_step() + if batch_range is None and iter == 2: + utils.get_profiler().reset() + + self.check_profile_result(profile_path) + def test_cpu_profiler(self): - self.net_profiler('CPU', "Default") - #self.net_profiler('CPU', "Default", use_parallel_executor=True) + exe = fluid.Executor(fluid.CPUPlace()) + for use_new_api in [False, True]: + self.net_profiler( + exe, + 'CPU', + "Default", + batch_range=[5, 10], + use_new_api=use_new_api) + #self.net_profiler('CPU', "Default", use_parallel_executor=True) @unittest.skipIf(not core.is_compiled_with_cuda(), "profiler is enabled only with GPU") def test_cuda_profiler(self): - self.net_profiler('GPU', "OpDetail") - #self.net_profiler('GPU', "OpDetail", use_parallel_executor=True) + exe = fluid.Executor(fluid.CUDAPlace(0)) + for use_new_api in [False, True]: + self.net_profiler( + exe, + 'GPU', + "OpDetail", + batch_range=[0, 100], + use_new_api=use_new_api) + #self.net_profiler('GPU', "OpDetail", use_parallel_executor=True) @unittest.skipIf(not core.is_compiled_with_cuda(), "profiler is enabled only with GPU") def test_all_profiler(self): - self.net_profiler('All', "AllOpDetail") - #self.net_profiler('All', "AllOpDetail", use_parallel_executor=True) + exe = fluid.Executor(fluid.CUDAPlace(0)) + for use_new_api in [False, True]: + self.net_profiler( + exe, + 'All', + "AllOpDetail", + batch_range=None, + use_new_api=use_new_api) + #self.net_profiler('All', "AllOpDetail", use_parallel_executor=True) + + +class TestProfilerAPIError(unittest.TestCase): + def test_errors(self): + options = utils.ProfilerOptions() + self.assertTrue(options['profile_path'] is None) + self.assertTrue(options['timeline_path'] is None) + + options = options.with_state('All') + self.assertTrue(options['state'] == 'All') + try: + print(options['test']) + except ValueError: + pass + + global_profiler = utils.get_profiler() + with utils.Profiler(enabled=True) as prof: + self.assertTrue(utils.get_profiler() == prof) + self.assertTrue(global_profiler != prof) if __name__ == '__main__': diff --git a/python/paddle/utils/__init__.py b/python/paddle/utils/__init__.py index d8f629224d6738db000c6f7194c2516c630ef670..33537929044dbaa6c86ddce3dd972c02603eb0aa 100644 --- a/python/paddle/utils/__init__.py +++ b/python/paddle/utils/__init__.py @@ -13,15 +13,13 @@ # limitations under the License. from .plot import Ploter +from .profiler import ProfilerOptions +from .profiler import Profiler +from .profiler import get_profiler + __all__ = ['dump_config', 'Ploter'] #TODO: define new api under this directory -# __all__ = ['profiler', -# 'profiler.cuda_profiler', -# 'profiler.profiler', -# 'profiler.reset_profiler', -# 'profiler.start_profiler', -# 'profiler.stop_profiler', -# 'unique_name', +# __all__ = ['unique_name', # 'load_op_library', # 'require_version'] diff --git a/python/paddle/utils/profiler.py b/python/paddle/utils/profiler.py index 4b0b2c96bbb891217f7b78b512ed4d61c908a5b2..89c0d2cac68a9779bfde85bc87790b64fc38fbd6 100644 --- a/python/paddle/utils/profiler.py +++ b/python/paddle/utils/profiler.py @@ -1,4 +1,4 @@ -# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved +# Copyright (c) 2020 PaddlePaddle Authors. 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. @@ -12,9 +12,124 @@ # See the License for the specific language governing permissions and # limitations under the License. -#TODO: define new api of profiler -# __all__ = ['cuda_profiler', -# 'profiler', -# 'reset_profiler', -# 'start_profiler', -# 'stop_profiler'] +from __future__ import print_function + +import sys +import warnings + +from ..fluid import core +from ..fluid.profiler import * + +__all__ = ['ProfilerOptions', 'Profiler', 'get_profiler'] + + +class ProfilerOptions(object): + def __init__(self, options=None): + self.options = { + 'state': 'All', + 'sorted_key': 'default', + 'tracer_level': 'Default', + 'batch_range': [0, sys.maxsize], + 'output_thread_detail': False, + 'profile_path': 'none', + 'timeline_path': 'none', + 'op_summary_path': 'none' + } + if options is not None: + for key in self.options.keys(): + if options.get(key, None) is not None: + self.options[key] = options[key] + + # function to set one specified option + def with_state(self, state): + self.options['state'] = state + return self + + def __getitem__(self, name): + if self.options.get(name, None) is None: + raise ValueError( + "ProfilerOptions does not have an option named %s." % name) + else: + if isinstance(self.options[name], + str) and self.options[name] == 'none': + return None + else: + return self.options[name] + + +_current_profiler = None + + +class Profiler(object): + def __init__(self, enabled=True, options=None): + if options is not None: + self.profiler_options = options + else: + self.profiler_options = ProfilerOptions() + self.batch_id = 0 + self.enabled = enabled + + def __enter__(self): + # record current profiler + global _current_profiler + self.previous_profiler = _current_profiler + _current_profiler = self + + if self.enabled: + if self.profiler_options['batch_range'][0] == 0: + self.start() + return self + + def __exit__(self, exception_type, exception_value, traceback): + global _current_profiler + _current_profiler = self.previous_profiler + + if self.enabled: + self.stop() + + def start(self): + if self.enabled: + try: + start_profiler( + state=self.profiler_options['state'], + tracer_option=self.profiler_options['tracer_level']) + except Exception as e: + warnings.warn( + "Profiler is not enabled becuase following exception:\n{}". + format(e)) + + def stop(self): + if self.enabled: + try: + stop_profiler( + sorted_key=self.profiler_options['sorted_key'], + profile_path=self.profiler_options['profile_path']) + except Exception as e: + warnings.warn( + "Profiler is not disabled becuase following exception:\n{}". + format(e)) + + def reset(self): + if self.enabled and core.is_profiler_enabled(): + reset_profiler() + + def record_step(self, change_profiler_status=True): + if not self.enabled: + return + self.batch_id = self.batch_id + 1 + if change_profiler_status: + if self.batch_id == self.profiler_options['batch_range'][0]: + if core.is_profiler_enabled(): + self.reset() + else: + self.start() + + if self.batch_id == self.profiler_options['batch_range'][1]: + self.stop() + + +def get_profiler(): + global _current_profiler + if _current_profiler is None: + _current_profiler = Profiler() + return _current_profiler