// 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. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include #include #include #include #include "gtest/gtest.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/platform/device_context.h" #include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/place.h" USE_OP_ITSELF(elementwise_add); USE_OP_DEVICE_KERNEL(elementwise_add, MKLDNN); USE_OP(elementwise_mul); USE_OP_DEVICE_KERNEL(elementwise_mul, MKLDNN); USE_OP(relu); USE_OP_DEVICE_KERNEL(relu, MKLDNN); USE_OP(softmax); USE_OP_DEVICE_KERNEL(softmax, MKLDNN); USE_OP(conv2d); USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, FP32); namespace paddle { namespace operators { struct InputVars { std::string name; framework::LoDTensor *tensor; }; class CacheTester { public: CacheTester() { // Clear oneDNN cache auto &pool = platform::DeviceContextPool::Instance(); platform::CPUPlace place; onednn_dev_ctx_ = dynamic_cast(pool.Get(place)); onednn_dev_ctx_->ResetBlobMap(nullptr); } bool Analyze(unsigned short int num_entries) { // Number of created objects in cache should be as expected (num_entries) return onednn_dev_ctx_->GetCachedObjectsNumber() == num_entries; } private: platform::MKLDNNDeviceContext *onednn_dev_ctx_; }; template void RunOperator(const platform::Place &place, const std::string &op_type, const framework::DDim &dims, const std::string &first_input) { framework::Scope scope; std::map num_inputs = {{"softmax", 1}, {"relu", 1}, {"conv2d", 2}, {"elementwise_add", 2}, {"elementwise_mul", 2}}; std::string first_input_var_name = (op_type == "conv2d") ? "Input" : "X"; std::string second_input_var_name = (op_type == "conv2d") ? "Filter" : "Y"; std::string output_var_name = (op_type == "conv2d") ? "Output" : "Out"; std::string output_name = "output"; std::vector input_names = { {first_input, scope.Var(first_input)->GetMutable()}, {"x1", num_inputs[op_type] > 1 ? scope.Var("x1")->GetMutable() : nullptr}, {"x2", num_inputs[op_type] > 2 ? scope.Var("x2")->GetMutable() : nullptr}, {"x3", num_inputs[op_type] > 3 ? scope.Var("x3")->GetMutable() : nullptr}, {"x4", num_inputs[op_type] > 4 ? scope.Var("x4")->GetMutable() : nullptr}}; auto *y = scope.Var(output_name)->GetMutable(); // Initialize input data std::uniform_real_distribution dist(static_cast(10.0), static_cast(20.0)); std::mt19937 engine; size_t numel = static_cast(pten::product(dims)); for (int i = 0; i < num_inputs[op_type]; ++i) { input_names[i].tensor->Resize(dims); auto data_ptr = input_names[i].tensor->mutable_data(place); for (size_t i = 0; i < numel; ++i) { data_ptr[i] = dist(engine); } } // Initialize output y->Resize(dims); auto y_ptr = y->mutable_data(place); for (size_t i = 0; i < numel; ++i) { y_ptr[i] = static_cast(0); } auto &pool = platform::DeviceContextPool::Instance(); auto op = num_inputs[op_type] > 1 ? framework::OpRegistry::CreateOp( op_type, {{first_input_var_name, {first_input}}, {second_input_var_name, {"x1"}}}, {{output_var_name, {output_name}}}, {{"use_mkldnn", {true}}}) : framework::OpRegistry::CreateOp( op_type, {{first_input_var_name, {first_input}}}, {{output_var_name, {output_name}}}, {{"use_mkldnn", {true}}}); op->Run(scope, place); pool.Get(place)->Wait(); } TEST(test_conv2d_reuse_cache, cpu_place) { framework::DDim dims({1, 16, 32, 64}); platform::CPUPlace p; CacheTester ct; RunOperator(p, "conv2d", dims, "input_signal"); RunOperator(p, "conv2d", dims, "input_signal"); PADDLE_ENFORCE_EQ(ct.Analyze(9), true, platform::errors::InvalidArgument( "Invalid number of cached oneDNN objects")); } TEST(test_conv2d_noreuse_cache, cpu_place) { framework::DDim dims({1, 16, 32, 64}); platform::CPUPlace p; CacheTester ct; RunOperator(p, "conv2d", dims, "input_signal"); RunOperator(p, "conv2d", dims, "input_signal2"); PADDLE_ENFORCE_EQ(ct.Analyze(18), true, platform::errors::InvalidArgument( "Invalid number of cached oneDNN objects")); } } // namespace operators } // namespace paddle