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204 lines
8.3 KiB
204 lines
8.3 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import os
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import tempfile
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import numpy as np
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import paddle.utils as utils
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import paddle.fluid as fluid
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import paddle.fluid.profiler as profiler
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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from paddle.fluid import compiler, Program, program_guard
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import paddle.fluid.proto.profiler.profiler_pb2 as profiler_pb2
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class TestProfiler(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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os.environ['CPU_NUM'] = str(4)
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def build_program(self, compile_program=True):
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startup_program = fluid.Program()
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main_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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image = fluid.layers.data(name='x', shape=[784], dtype='float32')
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hidden1 = fluid.layers.fc(input=image, size=64, act='relu')
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i = layers.zeros(shape=[1], dtype='int64')
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counter = fluid.layers.zeros(
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shape=[1], dtype='int64', force_cpu=True)
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until = layers.fill_constant([1], dtype='int64', value=10)
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data_arr = layers.array_write(hidden1, i)
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cond = fluid.layers.less_than(x=counter, y=until)
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while_op = fluid.layers.While(cond=cond)
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with while_op.block():
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hidden_n = fluid.layers.fc(input=hidden1, size=64, act='relu')
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layers.array_write(hidden_n, i, data_arr)
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fluid.layers.increment(x=counter, value=1, in_place=True)
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layers.less_than(x=counter, y=until, cond=cond)
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hidden_n = layers.array_read(data_arr, i)
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hidden2 = fluid.layers.fc(input=hidden_n, size=64, act='relu')
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predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
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label = fluid.layers.data(name='y', shape=[1], dtype='int64')
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(cost)
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batch_size = fluid.layers.create_tensor(dtype='int64')
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batch_acc = fluid.layers.accuracy(
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input=predict, label=label, total=batch_size)
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optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
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opts = optimizer.minimize(avg_cost, startup_program=startup_program)
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if compile_program:
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# TODO(luotao): profiler tool may have bug with multi-thread parallel executor.
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# https://github.com/PaddlePaddle/Paddle/pull/25200#issuecomment-650483092
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exec_strategy = fluid.ExecutionStrategy()
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exec_strategy.num_threads = 1
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train_program = fluid.compiler.CompiledProgram(
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main_program).with_data_parallel(
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loss_name=avg_cost.name, exec_strategy=exec_strategy)
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else:
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train_program = main_program
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return train_program, startup_program, avg_cost, batch_size, batch_acc
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def get_profile_path(self):
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profile_path = os.path.join(tempfile.gettempdir(), "profile")
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open(profile_path, "w").write("")
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return profile_path
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def check_profile_result(self, profile_path):
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data = open(profile_path, 'rb').read()
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if (len(data) > 0):
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profile_pb = profiler_pb2.Profile()
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profile_pb.ParseFromString(data)
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self.assertGreater(len(profile_pb.events), 0)
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for event in profile_pb.events:
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if event.type == profiler_pb2.Event.GPUKernel:
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if not event.detail_info and not event.name.startswith(
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"MEM"):
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raise Exception(
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"Kernel %s missing event. Has this kernel been recorded by RecordEvent?"
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% event.name)
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elif event.type == profiler_pb2.Event.CPU and (
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event.name.startswith("Driver API") or
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event.name.startswith("Runtime API")):
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print("Warning: unregister", event.name)
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def run_iter(self, exe, main_program, fetch_list):
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x = np.random.random((32, 784)).astype("float32")
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y = np.random.randint(0, 10, (32, 1)).astype("int64")
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outs = exe.run(main_program,
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feed={'x': x,
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'y': y},
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fetch_list=fetch_list)
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def net_profiler(self,
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exe,
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state,
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tracer_option,
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batch_range=None,
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use_parallel_executor=False,
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use_new_api=False):
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main_program, startup_program, avg_cost, batch_size, batch_acc = self.build_program(
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compile_program=use_parallel_executor)
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exe.run(startup_program)
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profile_path = self.get_profile_path()
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if not use_new_api:
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with profiler.profiler(state, 'total', profile_path, tracer_option):
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for iter in range(10):
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if iter == 2:
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profiler.reset_profiler()
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self.run_iter(exe, main_program,
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[avg_cost, batch_acc, batch_size])
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else:
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options = utils.ProfilerOptions(options={
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'state': state,
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'sorted_key': 'total',
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'tracer_level': tracer_option,
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'batch_range': [0, 10] if batch_range is None else batch_range,
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'profile_path': profile_path
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})
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with utils.Profiler(enabled=True, options=options) as prof:
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for iter in range(10):
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self.run_iter(exe, main_program,
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[avg_cost, batch_acc, batch_size])
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utils.get_profiler().record_step()
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if batch_range is None and iter == 2:
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utils.get_profiler().reset()
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# TODO(luotao): check why nccl kernel in profile result.
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# https://github.com/PaddlePaddle/Paddle/pull/25200#issuecomment-650483092
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# self.check_profile_result(profile_path)
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def test_cpu_profiler(self):
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exe = fluid.Executor(fluid.CPUPlace())
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for use_new_api in [False, True]:
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self.net_profiler(
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exe,
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'CPU',
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"Default",
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batch_range=[5, 10],
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use_new_api=use_new_api)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"profiler is enabled only with GPU")
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def test_cuda_profiler(self):
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exe = fluid.Executor(fluid.CUDAPlace(0))
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for use_new_api in [False, True]:
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self.net_profiler(
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exe,
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'GPU',
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"OpDetail",
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batch_range=[0, 10],
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use_new_api=use_new_api)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"profiler is enabled only with GPU")
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def test_all_profiler(self):
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exe = fluid.Executor(fluid.CUDAPlace(0))
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for use_new_api in [False, True]:
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self.net_profiler(
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exe,
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'All',
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"AllOpDetail",
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batch_range=None,
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use_new_api=use_new_api)
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class TestProfilerAPIError(unittest.TestCase):
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def test_errors(self):
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options = utils.ProfilerOptions()
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self.assertTrue(options['profile_path'] is None)
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self.assertTrue(options['timeline_path'] is None)
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options = options.with_state('All')
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self.assertTrue(options['state'] == 'All')
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try:
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print(options['test'])
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except ValueError:
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pass
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global_profiler = utils.get_profiler()
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with utils.Profiler(enabled=True) as prof:
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self.assertTrue(utils.get_profiler() == prof)
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self.assertTrue(global_profiler != prof)
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if __name__ == '__main__':
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unittest.main()
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