// Copyright (c) 2018 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. #pragma once #include #include #include #include "paddle/fluid/framework/op_desc.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/imperative/engine.h" #include "paddle/fluid/imperative/layer.h" namespace paddle { namespace imperative { void CreateGradOp(const framework::OpDesc& op_desc, const std::unordered_set& no_grad_set, const std::vector& grad_sub_block, framework::OpDesc** grad_op_desc, std::unordered_map* grad_to_var) { std::vector> grad_op_descs = framework::OpInfoMap::Instance() .Get(op_desc.Type()) .GradOpMaker()(op_desc, no_grad_set, grad_to_var, grad_sub_block); PADDLE_ENFORCE(grad_op_descs.size() == 1, "Only support 1 grad op now."); // TODO(panyx0718): Leak? *grad_op_desc = grad_op_descs[0].release(); } void InitVar(framework::Variable* var, framework::Variable* grad_var) { auto& var_t = var->Get(); float* data = grad_var->GetMutable()->mutable_data( var_t.dims(), platform::CPUPlace()); std::fill(data, data + var_t.numel(), 0.0); } class Tracer { public: explicit Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {} virtual ~Tracer() {} void Trace(OpBase* op, const std::map>& inputs, const std::map>& outputs, framework::BlockDesc* block, const bool stop_gradient = false) { std::map vars; framework::OpDesc* op_desc = op->op_desc_; VLOG(3) << "tracer tracing " << op_desc->Type(); op_desc->InferShape(*block); op_desc->InferVarType(block); std::unique_ptr op_base = framework::OpRegistry::CreateOp(*op_desc); framework::VariableValueMap invars_map; framework::VariableValueMap outvars_map; op->input_vars_ = inputs; for (auto it : op->input_vars_) { auto& invars = invars_map[it.first]; for (VarBase* inp : it.second) { PADDLE_ENFORCE_NOT_NULL(inp->var_, "op %s input %s nullptr", op->op_desc_->Type(), inp->var_desc_->Name()); invars.push_back(inp->var_); vars[inp->var_desc_->Name()] = inp; if (inp->pre_op_) { op->pre_ops_[it.first].push_back(inp->pre_op_); op->pre_ops_out_idx_[it.first].push_back(inp->pre_op_out_idx_); } else { op->pre_ops_[it.first].push_back(nullptr); } VLOG(3) << "input vname " << inp->var_desc_->Name() << " " << inp->var_->IsInitialized(); } } op->output_vars_ = outputs; for (auto it : op->output_vars_) { auto& outvars = outvars_map[it.first]; const std::vector& outputs = it.second; for (size_t i = 0; i < outputs.size(); ++i) { VarBase* out = outputs[i]; outvars.push_back(out->var_); vars[out->var_desc_->Name()] = out; framework::VarDesc* var_desc = block->FindVar(out->var_desc_->Name()); if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) { out->var_->GetMutable(); } else { LOG(ERROR) << "tracer doesn't support yet"; } out->stop_gradient_ = stop_gradient; out->pre_op_ = op; out->pre_op_out_name_ = it.first; out->pre_op_out_idx_ = i; VLOG(3) << "output vname " << out->var_desc_->Name() << " " << out->var_->IsInitialized(); } } VLOG(3) << "tracer running " << op_desc->Type(); framework::RuntimeContext ctx(invars_map, outvars_map); // TODO(panyx0718): Cache p. framework::OperatorWithKernel* op_kernel = dynamic_cast(op_base.get()); PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel"); framework::Scope scope; platform::CPUPlace place; PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place); p.op.RuntimeInferShape(scope, place, ctx); p.func(framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx)); if (!stop_gradient) { framework::OpDesc* grad_op_desc; auto grad_to_var = new std::unordered_map(); CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var); op->grad_op_desc_ = grad_op_desc; for (auto it : grad_op_desc->Inputs()) { auto& grad_in_vars = op->grad_input_vars_[it.first]; for (const std::string& grad_invar : it.second) { block->FindRecursiveOrCreateVar(grad_invar); auto var_it = grad_to_var->find(grad_invar); if (var_it == grad_to_var->end()) { auto fwd_var_it = vars.find(grad_invar); PADDLE_ENFORCE(fwd_var_it != vars.end()); grad_in_vars.push_back(fwd_var_it->second->var_); } else { VarBase* var = vars[var_it->second]; if (!var->grads_->IsInitialized()) { InitVar(var->var_, var->grads_); } grad_in_vars.push_back(var->grads_); } } } for (auto it : grad_op_desc->Outputs()) { auto& grad_out_vars = op->grad_output_vars_[it.first]; for (const std::string& grad_outvar : it.second) { block->FindRecursiveOrCreateVar(grad_outvar); auto var_it = grad_to_var->find(grad_outvar); PADDLE_ENFORCE(var_it != grad_to_var->end()); VarBase* var = vars[var_it->second]; if (!var->grads_->IsInitialized()) { InitVar(var->var_, var->grads_); } grad_out_vars.push_back(var->grads_); } } } op->block_ = block; } private: framework::BlockDesc* root_block_; }; } // namespace imperative } // namespace paddle