tracer.h

// 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 <map>
#include <string>
#include <vector>

#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<std::string>& no_grad_set,
                  const std::vector<framework::BlockDesc*>& grad_sub_block,
                  framework::OpDesc** grad_op_desc,
                  std::unordered_map<std::string, std::string>* grad_to_var) {
  std::vector<std::unique_ptr<framework::OpDesc>> 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<framework::LoDTensor>();
  float* data =
      grad_var->GetMutable<framework::LoDTensor>()->mutable_data<float>(
          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<std::string, std::vector<VarBase*>>& inputs,
             const std::map<std::string, std::vector<VarBase*>>& outputs,
             framework::BlockDesc* block, const bool stop_gradient = false) {
    std::map<std::string, VarBase*> 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<framework::OperatorBase> 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<VarBase*>& 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<framework::LoDTensor>();
        } 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<framework::OperatorWithKernel*>(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;
      // TODO(panyx): Is this leaked?
      std::unique_ptr<std::unordered_map<std::string, std::string>> grad_to_var(
          new std::unordered_map<std::string, std::string>());
      CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var.get());
      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());
            // Forward inputs or outputs.
            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_);
            }
            // Douts.
            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;
  }

  std::vector<VarBase*> PyTrace(OpBase* op, const std::vector<VarBase*>& inputs,
                                bool stop_gradient = false) {
    VLOG(3) << "py_trace";
    op->input_vars_["X"] = inputs;
    op->output_vars_["Out"] = PyLayer::Apply(op->forward_id_, inputs);
    for (VarBase* inp : inputs) {
      if (inp->pre_op_) {
        op->pre_ops_["X"].push_back(inp->pre_op_);
        op->pre_ops_out_idx_["X"].push_back(inp->pre_op_out_idx_);
      } else {
        op->pre_ops_["X"].push_back(nullptr);
      }
    }

    auto& outputs = op->output_vars_["Out"];
    for (size_t i = 0; i < outputs.size(); ++i) {
      VarBase* out = outputs[i];
      out->stop_gradient_ = stop_gradient;
      out->pre_op_ = op;
      out->pre_op_out_name_ = "Out";
      out->pre_op_out_idx_ = i;
    }
    if (!stop_gradient) {
      auto& grad_input_vars = op->grad_input_vars_["X@GRAD"];
      auto& grad_output_vars = op->grad_output_vars_["Out@GRAD"];

      for (const VarBase* inp : inputs) {
        grad_input_vars.push_back(inp->var_);
      }
      for (VarBase* out : outputs) {
        grad_input_vars.push_back(out->var_);
      }
      for (VarBase* out : outputs) {
        grad_input_vars.push_back(out->grads_);
        if (!grad_input_vars.back()->IsInitialized()) {
          InitVar(out->var_, grad_input_vars.back());
        }
      }
      for (const VarBase* inp : inputs) {
        grad_output_vars.push_back(inp->grads_);
        if (!grad_output_vars.back()->IsInitialized()) {
          InitVar(inp->var_, grad_output_vars.back());
        }
      }
    }
    return outputs;
  }

 private:
  framework::BlockDesc* root_block_;
};

}  // namespace imperative
}  // namespace paddle