cpu gpu transform function (#7191)
* add rename guard * add device_data_transform * add device_data_transform_test * modify GetExpectedKernelType * update operator.run * support test test_label_semantic_roles * optimize code * optimize code * rename GetActualKernelType to GetExpectedKernelType * fix chunk_eval_op and device_data_transform_test * add is_same_place to place * optimize code, refine rename_guard * refine rename guard, add GetKernelTypeForVar * optimize code * add some log * rename guard * use sub scope to create var * fix compile * add IsInitialized for Tensor * add VarIsTensor * fix op_registry_test * test * tmp disable priority * restore switch_kernel.md * code cleandetection_output_fixbug
Qiao Longfei
8 years ago
committed by
GitHub
parent
8814bec0c5
commit
0f353ab46e
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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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#include "paddle/framework/device_data_transform.h"
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namespace paddle {
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namespace framework {
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static const platform::DeviceContext* GetDeviceContext(
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const platform::Place& src_place, const platform::Place& dst_place) {
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platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
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if (platform::is_gpu_place(src_place) && platform::is_cpu_place(dst_place)) {
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return pool.Get(src_place);
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} else if (platform::is_cpu_place(src_place) &&
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platform::is_gpu_place(dst_place)) {
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return pool.Get(dst_place);
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} else {
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PADDLE_THROW(
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"Currently, model parallelism is only supported between CPU and CUDA");
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}
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}
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Tensor* DeviceTransform(const Tensor& in, const platform::Place& dst_place) {
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VLOG(3) << "DeviceTransform in, src_place " << in.place()
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<< " dst_place: " << dst_place;
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Tensor* out = new Tensor();
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auto* dev_ctx = GetDeviceContext(in.place(), dst_place);
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dev_ctx->Wait();
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CopyFrom(in, dst_place, *dev_ctx, out);
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dev_ctx->Wait();
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return out;
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}
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,27 @@
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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 "paddle/framework/lod_tensor.h"
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#include "paddle/framework/tensor.h"
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#include "paddle/framework/tensor_util.h"
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#include "paddle/platform/device_context.h"
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namespace paddle {
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namespace framework {
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Tensor* DeviceTransform(const Tensor& in, const platform::Place& dst_place);
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} // namespace framework
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} // namespace paddle
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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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#include "gtest/gtest.h"
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#include "paddle/framework/init.h"
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#include "paddle/framework/lod_tensor.h"
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#include "paddle/framework/op_info.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/elementwise_op_function.h"
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#include "paddle/operators/math/math_function.h"
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#include "paddle/platform/device_context.h"
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namespace paddle {
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namespace framework {
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template <typename T>
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struct AddFunctor {
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inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
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};
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class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
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public:
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OpKernelTestProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("input", "input1 of test op");
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AddOutput("output", "output of test op");
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AddAttr<bool>("use_gpu", "force to use gpu kernel").SetDefault(false);
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AddComment("This is test op");
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}
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};
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class TestOpWithKernel : public OperatorWithKernel {
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public:
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using OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {}
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OpKernelType GetExpectedKernelType(
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const ExecutionContext& ctx) const override {
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if (Attr<bool>("use_gpu")) {
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VLOG(3) << "force use gpu kernel";
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return OpKernelType(proto::DataType::FP32, platform::CUDAPlace(0));
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} else {
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VLOG(3) << "use default kernel";
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return OpKernelType(proto::DataType::FP32,
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ctx.Input<Tensor>("input")->place());
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}
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}
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};
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template <typename DeviceContext, typename T>
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class TestKernel : public OpKernel<float> {
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public:
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void Compute(const ExecutionContext& ctx) const {
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std::cout << ctx.op().DebugString() << std::endl;
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const Tensor* input = ctx.Input<Tensor>("input");
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std::cout << "input place:" << input->place() << std::endl;
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auto* output = ctx.Output<framework::LoDTensor>("output");
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output->Resize(input->dims());
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output->mutable_data<T>(ctx.GetPlace());
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operators::TransformFunctor<AddFunctor<T>, T, DeviceContext> functor(
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input, input, output, ctx.template device_context<DeviceContext>(),
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AddFunctor<T>());
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functor.Run();
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}
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};
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} // namespace framework
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} // namespace paddle
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REGISTER_OP_WITHOUT_GRADIENT(
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test_op, paddle::framework::TestOpWithKernel,
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paddle::framework::OpKernelTestProtoAndCheckerMaker);
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REGISTER_OP_CPU_KERNEL(
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test_op,
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paddle::framework::TestKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CUDA_KERNEL(
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test_op,
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paddle::framework::TestKernel<paddle::platform::CUDADeviceContext, float>);
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static void BuildVar(const std::string& param_name,
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std::initializer_list<const char*> arguments,
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paddle::framework::proto::OpDesc::Var* var) {
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var->set_parameter(param_name);
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for (auto& arg_name : arguments) {
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*var->mutable_arguments()->Add() = arg_name;
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}
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}
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TEST(Operator, CPUtoGPU) {
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using namespace paddle::framework;
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using namespace paddle::platform;
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ASSERT_EQ(InitDevices({"CPU", "GPU:0"}), true);
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paddle::framework::Scope scope;
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paddle::platform::CPUPlace cpu_place;
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// create an op to run on CPU
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paddle::framework::proto::OpDesc cpu_op_desc;
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cpu_op_desc.set_type("test_op");
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BuildVar("input", {"IN1"}, cpu_op_desc.add_inputs());
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BuildVar("output", {"OUT1"}, cpu_op_desc.add_outputs());
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auto cpu_op = paddle::framework::OpRegistry::CreateOp(cpu_op_desc);
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// prepare input
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auto* in_t = scope.Var("IN1")->GetMutable<LoDTensor>();
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auto* src_ptr = in_t->mutable_data<float>({2, 3}, CPUPlace());
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for (int i = 0; i < 2 * 3; ++i) {
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src_ptr[i] = static_cast<float>(i);
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}
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// get output
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auto* output = scope.Var("OUT1");
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cpu_op->Run(scope, cpu_place);
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auto* output_ptr = output->Get<LoDTensor>().data<float>();
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for (int i = 0; i < 2 * 3; ++i) {
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ASSERT_EQ(output_ptr[i], static_cast<float>(i) * 2);
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}
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// create an op to run on GPU
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paddle::framework::proto::OpDesc gpu_op_desc;
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gpu_op_desc.set_type("test_op");
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BuildVar("input", {"OUT1"}, gpu_op_desc.add_inputs());
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BuildVar("output", {"OUT2"}, gpu_op_desc.add_outputs());
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auto attr = gpu_op_desc.mutable_attrs()->Add();
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attr->set_name("use_gpu");
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attr->set_type(paddle::framework::proto::AttrType::BOOLEAN);
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attr->set_b(true);
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auto gpu_op = paddle::framework::OpRegistry::CreateOp(gpu_op_desc);
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paddle::platform::CUDAPlace cuda_place(0);
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// get output
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auto* output2 = scope.Var("OUT2");
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gpu_op->Run(scope, cuda_place);
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// auto* output2_ptr = output2->Get<LoDTensor>().data<float>();
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DeviceContextPool& pool = DeviceContextPool::Instance();
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auto dev_ctx = pool.Get(cuda_place);
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paddle::framework::Tensor output_tensor;
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CopyFrom(output2->Get<LoDTensor>(), paddle::platform::CPUPlace(), *dev_ctx,
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&output_tensor);
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dev_ctx->Wait();
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float* output2_ptr = output_tensor.data<float>();
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for (int i = 0; i < 2 * 3; ++i) {
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ASSERT_EQ(output2_ptr[i], static_cast<float>(i) * 4);
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}
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}
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