wehub-resource-sync
243 lines
8.9 KiB
C++
243 lines
8.9 KiB
C++
// Copyright (c) 2021 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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// Eager Dygraph
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#include <paddle/fluid/framework/op_registry.h>
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#include <chrono>
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#include "gtest/gtest.h"
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#include "paddle/common/flags.h"
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#include "paddle/fluid/eager/api/all.h"
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#include "paddle/fluid/eager/autograd_meta.h"
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#include "paddle/fluid/eager/backward.h"
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#include "paddle/fluid/imperative/tracer.h"
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#include "test/cpp/eager/performance_tests/benchmark_utils.h"
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#include "test/cpp/eager/test_utils.h"
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#ifdef WITH_GPERFTOOLS
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#include "gperftools/profiler.h"
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#endif
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#include "paddle/phi/core/kernel_registry.h"
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using namespace egr; // NOLINT
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using namespace egr_utils_api; // NOLINT
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TEST(Benchmark, EagerScaleCPU) {
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// Prepare Device Contexts
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eager_test::InitEnv(phi::CPUPlace());
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for (const std::string mode : {"Accuracy", "Performance"}) {
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phi::DDim ddim = common::make_ddim({2, 4, 4, 4});
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paddle::Tensor tensor =
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eager_test::CreateTensorWithValue(ddim,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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5.0,
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true);
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RetainGradForTensor(tensor);
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if (mode == "Accuracy") {
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benchmark_eager_scale(tensor, true /* accuracy_check*/);
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} else if (mode == "Performance") {
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auto t_start = std::chrono::high_resolution_clock::now();
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#ifdef WITH_GPERFTOOLS
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ProfilerStart("eager_scale_cpu.out");
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#endif
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benchmark_eager_scale(tensor);
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#ifdef WITH_GPERFTOOLS
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ProfilerStop();
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#endif
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auto t_end = std::chrono::high_resolution_clock::now();
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double elapsed_time_ms =
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std::chrono::duration<double, std::milli>(t_end - t_start).count();
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std::cout << "Duration: " << elapsed_time_ms << " ms" << std::endl;
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} else {
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PADDLE_THROW(common::errors::Fatal("Unknown benchmark mode"));
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}
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}
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}
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TEST(Benchmark, EagerMatmulCPU) {
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// Prepare Device Contexts
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eager_test::InitEnv(phi::CPUPlace());
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for (const std::string mode : {"Accuracy", "Performance"}) {
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phi::DDim ddimX = common::make_ddim({2, 2});
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paddle::Tensor X = eager_test::CreateTensorWithValue(ddimX,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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1.0,
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true);
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RetainGradForTensor(X);
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phi::DDim ddimY = common::make_ddim({2, 2});
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paddle::Tensor Y = eager_test::CreateTensorWithValue(ddimY,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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2.0,
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true);
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RetainGradForTensor(Y);
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if (mode == "Accuracy") {
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benchmark_eager_matmul(X, Y, true /* accuracy_check */);
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} else if (mode == "Performance") {
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auto t_start = std::chrono::high_resolution_clock::now();
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#ifdef WITH_GPERFTOOLS
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ProfilerStart("eager_matmul_cpu.out");
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#endif
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benchmark_eager_matmul(X, Y);
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#ifdef WITH_GPERFTOOLS
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ProfilerStop();
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#endif
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auto t_end = std::chrono::high_resolution_clock::now();
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double elapsed_time_ms =
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std::chrono::duration<double, std::milli>(t_end - t_start).count();
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std::cout << "Duration: " << elapsed_time_ms << " ms" << std::endl;
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} else {
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PADDLE_THROW(common::errors::Fatal("Unknown benchmark mode"));
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}
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}
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}
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TEST(Benchmark, EagerIntermediateMatmulCPU) {
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// Prepare Device Contexts
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eager_test::InitEnv(phi::CPUPlace());
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auto tracer = std::make_shared<paddle::imperative::Tracer>();
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paddle::imperative::SetCurrentTracer(tracer);
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for (const std::string mode : {"Accuracy", "Performance"}) {
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phi::DDim ddimX = common::make_ddim({2, 2});
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paddle::Tensor X = eager_test::CreateTensorWithValue(ddimX,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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1.0,
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true);
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RetainGradForTensor(X);
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phi::DDim ddimY = common::make_ddim({2, 2});
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paddle::Tensor Y = eager_test::CreateTensorWithValue(ddimY,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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2.0,
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true);
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RetainGradForTensor(Y);
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if (mode == "Accuracy") {
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benchmark_eager_intermediate_matmul(X, Y, true /* accuracy_check */);
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} else if (mode == "Performance") {
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auto t_start = std::chrono::high_resolution_clock::now();
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#ifdef WITH_GPERFTOOLS
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ProfilerStart("eager_intermediate_matmul_cpu.out");
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#endif
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benchmark_eager_intermediate_matmul(X, Y);
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#ifdef WITH_GPERFTOOLS
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ProfilerStop();
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#endif
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auto t_end = std::chrono::high_resolution_clock::now();
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double elapsed_time_ms =
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std::chrono::duration<double, std::milli>(t_end - t_start).count();
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std::cout << "Duration: " << elapsed_time_ms << " ms" << std::endl;
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} else {
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PADDLE_THROW(common::errors::Fatal("Unknown benchmark mode"));
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}
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}
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}
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TEST(Benchmark, EagerIntermediateMLPCPU) {
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// Prepare Device Contexts
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eager_test::InitEnv(phi::CPUPlace());
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auto tracer = std::make_shared<paddle::imperative::Tracer>();
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paddle::imperative::SetCurrentTracer(tracer);
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for (const std::string mode : {"Accuracy", "Performance"}) {
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phi::DDim ddimX = common::make_ddim({MLP_M, MLP_N});
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paddle::Tensor X = eager_test::CreateTensorWithValue(ddimX,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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MLP_X_VAL,
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true);
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RetainGradForTensor(X);
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std::vector<paddle::Tensor> Ws;
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std::vector<paddle::Tensor> Bs;
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for (size_t i = 0; i < MLP_NUM_LINEAR; i++) {
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phi::DDim ddimW = common::make_ddim({MLP_N, MLP_K});
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paddle::Tensor W =
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eager_test::CreateTensorWithValue(ddimW,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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MLP_W_VAL,
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true);
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RetainGradForTensor(W);
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phi::DDim ddimB = common::make_ddim({MLP_K});
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paddle::Tensor B =
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eager_test::CreateTensorWithValue(ddimB,
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phi::CPUPlace(),
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phi::DataType::FLOAT32,
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phi::DataLayout::NCHW,
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MLP_B_VAL,
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true);
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RetainGradForTensor(B);
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Ws.emplace_back(std::move(W));
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Bs.emplace_back(std::move(B));
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}
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if (mode == "Accuracy") {
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benchmark_eager_intermediate_mlp(X, Ws, Bs, true /* accuracy_check */);
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} else if (mode == "Performance") {
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auto t_start = std::chrono::high_resolution_clock::now();
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#ifdef WITH_GPERFTOOLS
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ProfilerStart("eager_intermediate_mlp_cpu.out");
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#endif
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benchmark_eager_intermediate_mlp(X, Ws, Bs);
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#ifdef WITH_GPERFTOOLS
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ProfilerStop();
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#endif
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auto t_end = std::chrono::high_resolution_clock::now();
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double elapsed_time_ms =
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std::chrono::duration<double, std::milli>(t_end - t_start).count();
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std::cout << "Duration: " << elapsed_time_ms << " ms" << std::endl;
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} else {
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PADDLE_THROW(common::errors::Fatal("Unknown benchmark mode"));
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}
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}
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}
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