sequence_mask-inl.h

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 * to you under the Apache License, Version 2.0 (the
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/*!
 * \file wl_sequence_mask-inl.h
 * \brief
 * \author Sebastian Bodenstien
 */

#ifndef MXNET_OPERATOR_SEQUENCE_MASK_INL_H_
#define MXNET_OPERATOR_SEQUENCE_MASK_INL_H_

#include <dmlc/logging.h>
#include <dmlc/parameter.h>
#include <mxnet/operator.h>
#include <algorithm>
#include <map>
#include <string>
#include <utility>
#include <vector>
#include "./mshadow_op.h"
#include "./operator_common.h"

namespace mxnet {
namespace op {

namespace seq_mask {
enum SequenceMaskOpInputs { kData, kSequenceLength };
enum SequenceMaskOpOutputs { kOut };
enum SequenceMaskOpBackResource { kTempSpace };
}  // namespace seq_mask

struct SequenceMaskParam : public dmlc::Parameter<SequenceMaskParam> {
  bool use_sequence_length;
  float value;
  int axis;
  DMLC_DECLARE_PARAMETER(SequenceMaskParam) {
    DMLC_DECLARE_FIELD(use_sequence_length)
        .set_default(false)
        .describe(
            "If set to true, this layer takes in an extra input parameter "
            "`sequence_length` "
            "to specify variable length sequence");
    DMLC_DECLARE_FIELD(value).set_default(0.).describe("The value to be used as a mask.");
    DMLC_DECLARE_FIELD(axis).set_default(0).describe(
        "The sequence axis. Only values of 0 and 1 are currently supported.");
  }
};

template <typename DType, typename IType>
void SequenceMaskExec(const mshadow::Tensor<cpu, 3, DType>& data,
                      const mshadow::Tensor<cpu, 1, IType>& indices,
                      const OpReqType req,
                      mshadow::Stream<cpu>* const s,
                      int axis,
                      DType val);
#ifdef __CUDACC__
template <typename DType, typename IType>
void SequenceMaskExec(const mshadow::Tensor<gpu, 3, DType>& data,
                      const mshadow::Tensor<gpu, 1, IType>& indices,
                      const OpReqType req,
                      mshadow::Stream<gpu>* const s,
                      int axis,
                      DType val);
#endif

template <typename xpu, typename DType, typename IType>
class SequenceMaskOp : public Operator {
 public:
  explicit SequenceMaskOp(SequenceMaskParam p) {
    this->param_ = p;
  }

  virtual void Forward(const OpContext& ctx,
                       const std::vector<TBlob>& in_data,
                       const std::vector<OpReqType>& req,
                       const std::vector<TBlob>& out_data,
                       const std::vector<TBlob>& aux_args) {
    using namespace mshadow;
    using namespace mshadow::expr;
    CHECK_EQ(in_data.size(), param_.use_sequence_length ? 2U : 1U);
    CHECK_EQ(out_data.size(), 1U);
    Stream<xpu>* s = ctx.get_stream<xpu>();

    // Get any size input + output into required form
    auto d0    = in_data[seq_mask::kData].size(0);
    auto d1    = in_data[seq_mask::kData].size(1);
    auto dsize = in_data[seq_mask::kData].Size();

    if (dsize == 0) {
      return;  // noop if any input dimension is zero-sized, out_data is of a right shape
    }

    auto rest_size = dsize / (d0 * d1);

    Shape<3> s3                = Shape3(d0, d1, rest_size);
    Tensor<xpu, 3, DType> data = in_data[seq_mask::kData].get_with_shape<xpu, 3, DType>(s3, s);
    Tensor<xpu, 3, DType> out  = out_data[seq_mask::kOut].get_with_shape<xpu, 3, DType>(s3, s);
    // Actual implementation of masking
    Assign(out, req[seq_mask::kOut], F<mshadow_op::identity>(data));
    if (param_.use_sequence_length) {
      Tensor<xpu, 1, IType> indices = in_data[seq_mask::kSequenceLength].get<xpu, 1, IType>(s);
      SequenceMaskExec<DType, IType>(
          out, indices, req[seq_mask::kOut], s, param_.axis, static_cast<DType>(param_.value));
    }
  }

  virtual void Backward(const OpContext& ctx,
                        const std::vector<TBlob>& out_grad,
                        const std::vector<TBlob>& in_data,
                        const std::vector<TBlob>& out_data,
                        const std::vector<OpReqType>& req,
                        const std::vector<TBlob>& in_grad,
                        const std::vector<TBlob>& aux_args) {
    using namespace mshadow;
    using namespace mshadow::expr;
    CHECK_EQ(out_grad.size(), 1U);
    CHECK_EQ(in_data.size(), param_.use_sequence_length ? 2U : 1U);
    Stream<xpu>* s = ctx.get_stream<xpu>();

    // Get any size input + output into required form
    auto d0        = in_grad[seq_mask::kData].size(0);
    auto d1        = in_grad[seq_mask::kData].size(1);
    auto dsize     = in_grad[seq_mask::kData].Size();
    auto rest_size = dsize / (d0 * d1);

    Shape<3> s3                  = Shape3(d0, d1, rest_size);
    Tensor<xpu, 3, DType> data_g = in_grad[seq_mask::kData].get_with_shape<xpu, 3, DType>(s3, s);
    Tensor<xpu, 3, DType> out_g  = out_grad[seq_mask::kOut].get_with_shape<xpu, 3, DType>(s3, s);

    // Actual implementation of masking
    if (req[seq_mask::kData] == kNullOp)
      return;
    if (!param_.use_sequence_length) {
      Assign(data_g, req[seq_mask::kData], F<mshadow_op::identity>(out_g));
    } else {
      Tensor<xpu, 1, IType> indices = in_data[seq_mask::kSequenceLength].get<xpu, 1, IType>(s);
      if (req[seq_mask::kData] == kAddTo) {
        Tensor<xpu, 3, DType> out_g_temp =
            ctx.requested[seq_mask::kTempSpace].get_space_typed<xpu, 3, DType>(s3, s);
        out_g_temp = F<mshadow_op::identity>(out_g);
        out_g      = out_g_temp;
        SequenceMaskExec<DType, IType>(out_g, indices, kWriteInplace, s, param_.axis, DType(0.));
        Assign(data_g, kAddTo, F<mshadow_op::identity>(out_g));
      } else {
        Assign(data_g, req[seq_mask::kData], F<mshadow_op::identity>(out_g));
        SequenceMaskExec<DType, IType>(
            data_g, indices, req[seq_mask::kData], s, param_.axis, DType(0.));
      }
    }
  }

 private:
  SequenceMaskParam param_;
};  // class SequenceMaskOp

template <typename xpu>
Operator* CreateOp(SequenceMaskParam param, int dtype, int itype);

#if DMLC_USE_CXX11
class SequenceMaskProp : public OperatorProperty {
 public:
  int NumVisibleOutputs() const override {
    return 1;
  }

  int NumOutputs() const override {
    return 1;
  }

  std::vector<std::string> ListArguments() const override {
    if (param_.use_sequence_length)
      return {"data", "sequence_length"};
    else
      return {"data"};
  }

  std::vector<std::string> ListOutputs() const override {
    return {"output"};
  }

  void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) override {
    param_.Init(kwargs);
  }

  std::map<std::string, std::string> GetParams() const override {
    return param_.__DICT__();
  }

  bool InferShape(mxnet::ShapeVector* in_shape,
                  mxnet::ShapeVector* out_shape,
                  mxnet::ShapeVector* aux_shape) const override {
    using namespace mshadow;
    CHECK_EQ(in_shape->size(), param_.use_sequence_length ? 2U : 1U)
        << "Input:[data, sequence_length]";

    const mxnet::TShape& dshape = (*in_shape)[seq_mask::kData];
    CHECK_GT(dshape.ndim(), 1U) << "The data array must be of rank 2 or greater.";
    CHECK((param_.axis == 0) || (param_.axis == 1))
        << "Current implementation expects axis to be 0 or 1.";

    // seq length vector is same as batch size
    int sbatch = param_.axis ? dshape[0] : dshape[1];
    if (param_.use_sequence_length)
      SHAPE_ASSIGN_CHECK(*in_shape, seq_mask::kSequenceLength, Shape1(sbatch));

    const mxnet::TShape& oshape = dshape;
    out_shape->clear();
    out_shape->push_back(oshape);
    return true;
  }

  bool InferType(std::vector<int>* in_type,
                 std::vector<int>* out_type,
                 std::vector<int>* aux_type) const override {
    CHECK_GE(in_type->size(), param_.use_sequence_length ? 2U : 1U);
    int dtype = (*in_type)[0];
    CHECK_NE(dtype, -1) << "First input must have specified type";
    for (size_t i = 0; i < in_type->size(); ++i) {
      if ((*in_type)[i] == -1) {
        (*in_type)[i] = dtype;
      }
    }
    out_type->clear();
    out_type->push_back(dtype);
    return true;
  }

  OperatorProperty* Copy() const override {
    auto ptr    = new SequenceMaskProp();
    ptr->param_ = param_;
    return ptr;
  }

  std::string TypeString() const override {
    return "SequenceMask";
  }

  std::vector<int> DeclareBackwardDependency(const std::vector<int>& out_grad,
                                             const std::vector<int>& in_data,
                                             const std::vector<int>& out_data) const override {
    if (param_.use_sequence_length)
      return {out_grad[seq_mask::kOut], in_data[seq_mask::kSequenceLength]};
    else
      return {out_grad[seq_mask::kOut]};
  }

  std::vector<ResourceRequest> BackwardResource(const mxnet::ShapeVector& in_shape) const override {
    return {ResourceRequest::kTempSpace};
  }

  std::vector<std::pair<int, void*> > BackwardInplaceOption(
      const std::vector<int>& out_grad,
      const std::vector<int>& in_data,
      const std::vector<int>& out_data,
      const std::vector<void*>& in_grad) const override {
    return {{out_grad[seq_mask::kOut], in_grad[seq_mask::kData]}};
  }

  std::vector<std::pair<int, void*> > ForwardInplaceOption(
      const std::vector<int>& in_data,
      const std::vector<void*>& out_data) const override {
    return {{in_data[seq_mask::kData], out_data[seq_mask::kOut]}};
  }

  Operator* CreateOperator(Context ctx) const override {
    LOG(FATAL) << "Not Implemented.";
    return nullptr;
  }

  Operator* CreateOperatorEx(Context ctx,
                             mxnet::ShapeVector* in_shape,
                             std::vector<int>* in_type) const override;

 private:
  SequenceMaskParam param_;
};      // class SequenceMaskProp
#endif  // DMLC_USE_CXX11
}  // namespace op
}  // namespace mxnet
#endif  // MXNET_OPERATOR_SEQUENCE_MASK_INL_H_