wehub-resource-sync
266 lines
10 KiB
C++
266 lines
10 KiB
C++
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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 <string>
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle::operators {
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using BlockDesc = framework::BlockDesc;
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class RunProgramOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(ctx->HasInputs("X"),
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true,
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common::errors::NotFound(
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"Input(X) of RunProgramOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasOutputs("Out"),
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true,
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common::errors::NotFound(
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"Output(Out) of RunProgramOp should not be null."));
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}
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protected:
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/* [Why use single type kernel]:
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*
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* This op is similar to a control flow op, it doses not need
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* a op kernel, but in order to make it execute under dynamic
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* graph mode, implement it with op kernel.
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*
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* So whether the kernel data type is int, float or other type,
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* which has no effect on its execution logic, so directly
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* specified a data type here.
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*
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* Of course, the data type here is also not important.
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*/
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phi::KernelKey GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return phi::KernelKey(framework::proto::VarType::FP32, ctx.GetPlace());
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}
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phi::KernelKey GetKernelTypeForVar(
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const std::string& var_name,
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const phi::DenseTensor& tensor,
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const phi::KernelKey& expected_kernel_type) const override {
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return phi::KernelKey(phi::Backend::ALL_BACKEND,
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expected_kernel_type.layout(),
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expected_kernel_type.dtype());
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}
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};
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class RunProgramOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(vector<DenseTensor>)"
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"The input tensors of RunProgram operator, also the feed targets "
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"of loaded program.")
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.AsDuplicable();
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AddInput("Params",
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"(vector<DenseTensor or SelectedRows>)"
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"The input parameter of RunProgram operator, also the parameters "
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"of the loaded program.")
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.AsDuplicable()
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.AsDispensable();
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AddOutput("Out",
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"(vector<DenseTensor>)"
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"The output tensors of RunProgram operator, also the fetch "
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"targets of the loaded program.")
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.AsDuplicable();
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AddOutput("OutScope",
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"(StepScopeVar)"
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"A vector of execution scope in RunProgram operator, which "
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"contains at most one scope."
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"NOTE: Do not use Scope directly because Scope output is not "
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"currently supported.");
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AddOutput("DOut",
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"(vector<DenseTensor>)"
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"The output tensors for GRAD Tensors in RunProgram forward "
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"operator, the forward operator contains GRAD Tensors when it "
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"computes double grad.")
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.AsDuplicable()
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.AsDispensable();
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AddOutput("CUDAGraph", "The output CUDA Graph when use_cuda_graph=True.")
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.AsDispensable();
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AddAttr<BlockDesc*>("global_block",
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"(BlockDesc *)"
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"The global block of executed program desc.");
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AddAttr<int64_t>("start_op_index",
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"(int64_t)"
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"The index of the op to start execution");
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AddAttr<int64_t>("end_op_index",
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"(int64_t)"
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"The index of the op to stop execution");
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AddAttr<bool>("is_test",
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"(bool, default false) Set to true for inference only, false "
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"for training.")
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.SetDefault(false);
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AddAttr<bool>(
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"in_pir_pt_mode",
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"(bool, default false) Set to true when need to run in pir mode")
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.SetDefault(false);
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AddAttr<int64_t>(
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"program_id",
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"(int64_t)"
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"The unique hash id used as cache key for ExecutorInfoCache.");
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AddAttr<std::string>("cuda_graph_capture_mode",
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"(str, default '') The CUDA Graph capture mode. "
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"Default '' means no CUDA Graph capturing.")
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.SetDefault("");
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AddAttr<int64_t>("cuda_graph_pool_id",
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"(int64_t, default 0) The CUDA Graph memory pool ID.")
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.SetDefault(0);
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AddAttr<bool>("use_interpretorcore",
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"(bool, default false) Set to true for use interpretercore.")
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.SetDefault(false);
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AddAttr<BlockDesc*>("forward_global_block",
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"(BlockDesc *)"
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"The global block of executed forward program desc.")
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.SetDefault(nullptr);
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AddAttr<BlockDesc*>("backward_global_block",
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"(BlockDesc *)"
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"The global block of executed backward program desc.")
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.SetDefault(nullptr);
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AddAttr<std::vector<std::string>>("param_grad_names",
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"std::vector<std::string>"
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"The names of parameter gradients.")
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.SetDefault({});
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AddAttr<std::vector<std::string>>("out_grad_names",
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"std::vector<std::string>"
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"The names of output gradients.")
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.SetDefault({});
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AddAttr<std::vector<std::string>>("x_names",
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"std::vector<std::string>"
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"The names of input tensors.")
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.SetDefault({});
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AddAttr<std::vector<std::string>>("x_grad_names",
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"std::vector<std::string>"
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"The names of input gradients.")
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.SetDefault({});
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AddComment(R"DOC(
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RunProgram operator.
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The RunProgram operator receives a program's feed targets, fetch targets,
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and parameters, and receives the forward and backward program desc
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as attributes, and then executes the program by executor.
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NOTE: This operator is added so that the inference model stored by
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`fluid.io.save_inference_model` under the static graph mode can be loaded
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under the dynamic graph mode for fine-tuning or inferencing.
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)DOC");
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}
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};
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class RunProgramGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(ctx->HasInputs("X"),
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true,
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common::errors::NotFound(
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"Input(X) of RunProgramGradOp should not be null."));
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PADDLE_ENFORCE_EQ(
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ctx->HasInputs(framework::GradVarName("Out")),
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true,
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common::errors::NotFound(
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"Input(Out@GRAD) of RunProgramGradOp should not be null."));
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// NOTE: The X@GRAD and Params@GRAD may not exist,
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// because they can be set stop_gradient = True
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}
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protected:
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/* see [Why use single type kernel] */
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phi::KernelKey GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return phi::KernelKey(framework::proto::VarType::FP32, ctx.GetPlace());
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}
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phi::KernelKey GetKernelTypeForVar(
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const std::string& var_name,
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const phi::DenseTensor& tensor,
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const phi::KernelKey& expected_kernel_type) const override {
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return phi::KernelKey(phi::Backend::ALL_BACKEND,
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expected_kernel_type.layout(),
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expected_kernel_type.dtype());
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}
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};
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template <typename T>
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struct FilterHelper {};
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template <>
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struct FilterHelper<imperative::OpBase> {
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static void filter(const BlockDesc* desc,
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imperative::TracedVarList<imperative::VarBase,
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imperative::kBackward>* vec) {
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auto f = [desc](std::shared_ptr<imperative::VarBase> ptr) {
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return !desc->HasVar(ptr->Name());
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};
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auto new_end = std::remove_if(vec->begin(), vec->end(), f);
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vec->resize(new_end - vec->begin());
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}
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};
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template <>
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struct FilterHelper<framework::OpDesc> {
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static void filter(const BlockDesc* desc, std::vector<std::string>* vec) {
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auto f = [desc](const std::string& name) { return !desc->HasVar(name); };
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auto new_end = std::remove_if(vec->begin(), vec->end(), f);
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vec->resize(new_end - vec->begin());
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}
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};
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template <typename T>
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class RunProgramGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> grad_op) const override {
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grad_op->SetType("run_program_grad");
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grad_op->SetInput("X", this->Input("X"));
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grad_op->SetInput("Params", this->Input("Params"));
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grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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grad_op->SetInput("OutScope", this->Output("OutScope"));
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grad_op->SetInput("DOut", this->Output("DOut"));
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if (this->HasOutput("CUDAGraph")) {
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grad_op->SetInput("CUDAGraph", this->Output("CUDAGraph"));
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}
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grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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auto block_desc =
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PADDLE_GET_CONST(BlockDesc*, this->GetAttr("global_block"));
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auto params_grad = this->InputGrad("Params");
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FilterHelper<T>::filter(block_desc, ¶ms_grad); // filter the vector.
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grad_op->SetOutput(framework::GradVarName("Params"), params_grad);
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grad_op->SetAttrMap(this->Attrs());
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}
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};
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} // namespace paddle::operators
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(run_program,
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ops::RunProgramOp,
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ops::RunProgramOpMaker,
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ops::RunProgramGradOpMaker<paddle::framework::OpDesc>,
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ops::RunProgramGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(run_program_grad, ops::RunProgramGradOp);
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