Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into my_unpool_max_2d
sweetsky0901
7 years ago
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5b449b6021
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include <cuda_profiler_api.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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namespace paddle {
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namespace platform {
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void CudaProfilerInit(std::string output_file, std::string output_mode,
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std::vector<std::string> config_flags) {
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std::array<char, 128> buf;
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std::string tmpl = "/tmp/cuda_profile_config.XXXXXX";
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PADDLE_ENFORCE_LT(tmpl.size(), buf.size());
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memcpy(buf.data(), tmpl.data(), tmpl.size());
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auto result = mktemp(buf.data());
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PADDLE_ENFORCE(strlen(result) != 0);
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std::string config_file = result;
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{
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std::ofstream ofs(config_file, std::ios::out | std::ios::trunc);
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PADDLE_ENFORCE(ofs.is_open(), "ofstream: ", ofs.rdstate());
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for (const auto& line : config_flags) {
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ofs << line << std::endl;
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}
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}
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PADDLE_ENFORCE(output_mode == "kvp" || output_mode == "csv");
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cudaOutputMode_t mode = output_mode == "csv" ? cudaCSV : cudaKeyValuePair;
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PADDLE_ENFORCE(
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cudaProfilerInitialize(config_file.c_str(), output_file.c_str(), mode));
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}
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void CudaProfilerStart() { PADDLE_ENFORCE(cudaProfilerStart()); }
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void CudaProfilerStop() { PADDLE_ENFORCE(cudaProfilerStop()); }
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} // namespace platform
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} // namespace paddle
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import paddle.v2.fluid.core as core
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from contextlib import contextmanager
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__all__ = ['CudaProfiler']
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NVPROF_CONFIG = [
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"gpustarttimestamp",
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"gpuendtimestamp",
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"gridsize3d",
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"threadblocksize",
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"streamid",
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"enableonstart 0",
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"conckerneltrace",
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]
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@contextmanager
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def cuda_profiler(output_file, output_mode=None, config=None):
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"""The CUDA profiler.
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This fuctions is used to profile CUDA program by CUDA runtime application
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programming interface. The profiling result will be written into
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`output_file` with Key-Value pair format or Comma separated values format.
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The user can set the output mode by `output_mode` argument and set the
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counters/options for profiling by `config` argument. The default config
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caontains 'gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
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'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'.
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Args:
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output_file (string) : The output file name, the result will be
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written into this file.
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output_mode (string) : The output mode has Key-Value pair format and
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Comma separated values format. It should be 'kv' or 'csv'.
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config (string) : The profiler options and counters can refer to
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"Compute Command Line Profiler User Guide".
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"""
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if output_mode is None:
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output_mode = 'csv'
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if output_mode not in ['kv', 'csv']:
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raise ValueError("The output mode must be 'key-value' or 'csv'.")
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config = NVPROF_CONFIG if config is None else config
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core.nvprof_init(output_file, output_mode, config)
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# Enables profiler collection by the active CUDA profiling tool.
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core.nvprof_start()
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yield
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# Disables profiler collection.
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core.nvprof_stop()
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import unittest
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import numpy as np
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import paddle.v2.fluid as fluid
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import paddle.v2.fluid.profiler as profiler
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import paddle.v2.fluid.layers as layers
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class TestProfiler(unittest.TestCase):
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def test_nvprof(self):
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if not fluid.core.is_compile_gpu():
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return
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epoc = 8
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dshape = [4, 3, 28, 28]
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data = layers.data(name='data', shape=[3, 28, 28], dtype='float32')
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conv = layers.conv2d(data, 20, 3, stride=[1, 1], padding=[1, 1])
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place = fluid.GPUPlace(0)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
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for i in range(epoc):
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input = np.random.random(dshape).astype("float32")
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exe.run(fluid.default_main_program(), feed={'data': input})
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if __name__ == '__main__':
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unittest.main()
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