shrink_rnn_memory_op.cc

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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 "paddle/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/operators/array_operator.h"
#include "paddle/operators/math/math_function.h"

namespace paddle {
namespace operators {

class ShrinkRNNMemoryOp : public ArrayOp {
 public:
  ShrinkRNNMemoryOp(const std::string &type,
                    const framework::VariableNameMap &inputs,
                    const framework::VariableNameMap &outputs,
                    const framework::AttributeMap &attrs)
      : ArrayOp(type, inputs, outputs, attrs) {}

  void Run(const framework::Scope &scope,
           const platform::Place &place) const override {
    auto *x_var = scope.FindVar(Input("X"));
    PADDLE_ENFORCE(x_var != nullptr, "Input X must be set");
    auto &x_tensor = x_var->Get<framework::LoDTensor>();
    size_t offset = this->GetOffset(scope, place);
    auto *rank_table_var = scope.FindVar(Input("RankTable"));
    PADDLE_ENFORCE(rank_table_var != nullptr, "RankTable must be set");
    auto &rank_table = rank_table_var->Get<framework::LoDRankTable>();

    auto &rank_items = rank_table.items();
    int dst_num_rows =
        std::lower_bound(rank_items.begin(), rank_items.end(), offset,
                         [](const framework::LoDRankTable::TableItem &a,
                            size_t b) { return a.length > b; }) -
        rank_items.begin();

    auto *out_var = scope.FindVar(Output("Out"));
    PADDLE_ENFORCE(out_var != nullptr, "Output Out must be set");
    auto &out_tensor = *out_var->GetMutable<framework::LoDTensor>();

    // should consider multiple levels
    size_t height = dst_num_rows;
    auto lod_level = rank_table.level();

    if (x_tensor.lod().size() > lod_level &&
        x_tensor.lod()[lod_level].size() > static_cast<size_t>(dst_num_rows)) {
      auto lod_offset = framework::GetSubLoDAndAbsoluteOffset(
          x_tensor.lod(), 0, dst_num_rows, lod_level);
      height = lod_offset.second.second;
      auto out_lod = out_tensor.mutable_lod();
      auto x_lod = x_tensor.lod();
      out_lod->reserve(lod_level + lod_offset.first.size());
      for (size_t i = 0; i < lod_level; ++i) {
        out_lod->emplace_back(x_lod.at(i));
      }
      framework::LoD remain;
      framework::AppendLoD(&remain, lod_offset.first);
      for (size_t j = 0; j < remain.size(); ++j) {
        out_lod->emplace_back(remain[j]);
      }
    }

    if (dst_num_rows != 0) {
      out_tensor.ShareDataWith(x_tensor.Slice(0, height));
    }
  }
};

class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
 public:
  ShrinkRNNMemoryOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X", "(LoDTensor) The RNN step memory to be shrinked.");
    AddInput("RankTable", "(LoDRankTable) The lod_rank_table of dynamic RNN.");
    AddInput("I",
             "(LoDTensor) The step index. The RNN step memory 'X' will be "
             "shrinked to match the size of the input of the index'th step.");
    AddOutput("Out", "(LoDTensor) The shrinked RNN step memory.");
    AddComment(
        R"DOC(
        In dynamic RNN, we are able to handle sequences of different lengths.
        Because of the multiple lengths, the size of each step input can be
        different, which may lead to a mismatching between the input of
        the current step and the memory generated by the previous one. This
        operator shrinks memory according to the size of the next step input,
        to make sure that they can match each other.
        )DOC");
  }
};

class ShrinkRNNMemoryInferShape : public framework::InferShapeBase {
 public:
  void operator()(framework::InferShapeContext *context) const override {
    PADDLE_ENFORCE(context->HasInput("X"));
    PADDLE_ENFORCE(context->HasInput("I"));
    PADDLE_ENFORCE(context->HasInput("RankTable"));
    context->SetOutputDim("Out", context->GetInputDim("X"));
  }
};

class ShrinkRNNMemoryGradOp : public ArrayOp {
 public:
  ShrinkRNNMemoryGradOp(const std::string &type,
                        const framework::VariableNameMap &inputs,
                        const framework::VariableNameMap &outputs,
                        const framework::AttributeMap &attrs)
      : ArrayOp(type, inputs, outputs, attrs) {}

  void Run(const framework::Scope &scope,
           const platform::Place &place) const override {
    auto *dout_var = scope.FindVar(Input(framework::GradVarName("Out")));
    auto *dx_var = scope.FindVar(Output(framework::GradVarName("X")));
    PADDLE_ENFORCE(dx_var != nullptr, "Input Gradient should not be nullptr");
    auto *x_var = scope.FindVar(Input("X"));
    PADDLE_ENFORCE(x_var != nullptr);

    auto &x_tensor = x_var->Get<framework::LoDTensor>();
    auto &dx_tensor = *dx_var->GetMutable<framework::LoDTensor>();
    dx_tensor.Resize(x_tensor.dims());
    dx_tensor.mutable_data(x_tensor.place(), x_tensor.type());

    // get device context from pool
    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
    auto &dev_ctx = *pool.Get(place);

    if (dout_var == nullptr) {  // dx_tensor fill zero
      math::set_constant(dev_ctx, &dx_tensor, 0.0f);
    } else {
      auto &dout_tensor = dout_var->Get<framework::LoDTensor>();
      auto height = dout_tensor.dims()[0];
      auto slice = dx_tensor.Slice(0, static_cast<int>(height));
      framework::CopyFrom(dout_tensor, dout_tensor.place(), dev_ctx, &slice);
      if (dx_tensor.dims()[0] > height) {
        auto rest_tensor = dx_tensor.Slice(
            static_cast<int>(height), static_cast<int>(dx_tensor.dims()[0]));
        math::set_constant(dev_ctx, &rest_tensor, 0.0f);
      }
    }
  }
};

class ShrinkRNNMemoryGradInferShape : public framework::InferShapeBase {
 public:
  void operator()(framework::InferShapeContext *context) const override {
    PADDLE_ENFORCE(context->HasInput("X"));
    PADDLE_ENFORCE(context->HasOutput(framework::GradVarName("X")));
    context->SetOutputDim(framework::GradVarName("X"),
                          context->GetInputDim("X"));
  }
};

class ShrinkRNNGradOpMaker : public framework::SingleGradOpDescMaker {
 public:
  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;

 protected:
  std::unique_ptr<framework::OpDesc> Apply() const override {
    auto *op = new framework::OpDesc();
    op->SetType("shrink_rnn_memory_grad");
    op->SetInput("X", Input("X"));
    op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
    op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
    op->SetAttrMap(Attrs());
    return std::unique_ptr<framework::OpDesc>(op);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(shrink_rnn_memory, ops::ShrinkRNNMemoryOp,
                  ops::ShrinkRNNMemoryInferShape,
                  ops::ShrinkRNNMemoryOpProtoMaker, ops::ShrinkRNNGradOpMaker);
REGISTER_OPERATOR(shrink_rnn_memory_grad, ops::ShrinkRNNMemoryGradOp,
                  ops::ShrinkRNNMemoryGradInferShape);