gangliao
8 years ago
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GitHub
commit
29b6a75ca1
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Performance Tuning
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==================
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.. toctree::
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:maxdepth: 3
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gpu_profiling.rst
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After Width: | Height: | Size: 484 KiB |
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/* Copyright (c) 2016 Baidu, Inc. 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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#ifndef PADDLE_ONLY_CPU
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#include "paddle/utils/Util.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/math/SparseMatrix.h"
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#include <gtest/gtest.h>
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#include "paddle/gserver/tests/TestUtil.h"
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#include "paddle/utils/Stat.h"
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using namespace paddle; // NOLINT
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using namespace std; // NOLINT
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void MatrixCheckErr(const Matrix& matrix1, const Matrix& matrix2) {
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CHECK(matrix1.getHeight() == matrix2.getHeight());
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CHECK(matrix1.getWidth() == matrix2.getWidth());
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#ifndef PADDLE_TYPE_DOUBLE
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real err = 1e-3;
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#else
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real err = 1e-10;
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#endif
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int height = matrix1.getHeight();
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int width = matrix1.getWidth();
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const real* data1 = matrix1.getData();
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const real* data2 = matrix2.getData();
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int count = 0;
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for (int i = 0; i < height; i++) {
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for (int j = 0; j < width; j++) {
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real a = data1[i * width + j];
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real b = data2[i * width + j];
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if (fabs(a - b) > err) {
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if ((fabsf(a - b) / fabsf(a)) > (err / 10.0f)) {
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count++;
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}
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}
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}
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}
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EXPECT_EQ(count, 0) << "There are " << count << " different element.";
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}
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void testBilinearFwdBwd(int numSamples, int imgSizeH, int imgSizeW,
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int channels) {
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int inWidth = imgSizeH * imgSizeW * channels;
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int outWidth = 2 * imgSizeH * 2 * imgSizeW * channels;
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real ratioH = 0.5;
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real ratioW = 0.5;
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// forward
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MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
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MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true);
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MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false);
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MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true);
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MatrixPtr targetCheck = CpuMatrix::create(numSamples, outWidth, false, false);
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input->randomizeUniform();
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inputGpu->copyFrom(*input);
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{
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// nvprof: GPU Proflier
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REGISTER_GPU_PROFILER("testBilinearFwdBwd");
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target->bilinearForward(*input, imgSizeH, imgSizeW,
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2 * imgSizeH, 2 * imgSizeW, channels, ratioH, ratioW);
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targetGpu->bilinearForward(*inputGpu, imgSizeH, imgSizeW,
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2 * imgSizeH, 2 * imgSizeW, channels, ratioH, ratioW);
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}
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// check
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targetCheck->copyFrom(*targetGpu);
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MatrixCheckErr(*target, *targetCheck);
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// backward
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MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false);
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MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true);
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MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false);
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MatrixPtr targetGpuGrad = GpuMatrix::create(numSamples, outWidth, false,
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true);
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MatrixPtr targetCheckGrad =
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CpuMatrix::create(numSamples, inWidth, false, false);
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inputGrad->randomizeUniform();
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targetGrad->randomizeUniform();
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inputGpuGrad->copyFrom(*inputGrad);
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targetGpuGrad->copyFrom(*targetGrad);
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inputGrad->bilinearBackward(*targetGrad, 2 * imgSizeH, 2 * imgSizeW,
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imgSizeH, imgSizeW, channels, ratioH, ratioW);
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inputGpuGrad->bilinearBackward(*targetGpuGrad, 2 * imgSizeH, 2 * imgSizeW,
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imgSizeH, imgSizeW, channels, ratioH, ratioW);
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// check
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targetCheckGrad->copyFrom(*inputGpuGrad);
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MatrixCheckErr(*inputGrad, *targetCheckGrad);
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}
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TEST(Profiler, testBilinearFwdBwd) {
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auto numSamples = 10;
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auto channels = 16;
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auto imgSize = 64;
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{
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// nvprof: GPU Proflier
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REGISTER_GPU_PROFILER("testBilinearFwdBwd");
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// Paddle built-in timer
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REGISTER_TIMER_INFO("testBilinearFwdBwd",
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"numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64");
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testBilinearFwdBwd(numSamples, imgSize, imgSize, channels);
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}
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globalStat.printAllStatus();
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}
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int main(int argc, char** argv) {
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testing::InitGoogleTest(&argc, argv);
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initMain(argc, argv);
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// nvprof: GPU Proflier
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REGISTER_GPU_PROFILER("RecursiveProfilingTest",
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"numSamples = 10, channels = 16, imgSizeX = 64, imgSizeY = 64");
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return RUN_ALL_TESTS();
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}
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#endif /* PADDLE_ONLY_CPU */
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