lstm_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/operators/lstm_op.h"

namespace paddle {
namespace operators {

class LSTMOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  void InferShape(framework::InferShapeContextBase* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Input"),
                   "Input(Input) of LSTM should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
                   "Output(Hidden) of LSTM should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("H"),
                   "Output(Cell) of LSTM should not be null.");

    auto x_dims = ctx->GetInputDim("Input");
    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");

    if (ctx->HasInput("H0")) {
      PADDLE_ENFORCE(ctx->HasInput("C0"),
                     "Input(Cell) and Input(Hidden) of LSTM should not "
                     "be null at the same time.");
      auto h_dims = ctx->GetInputDim("H0");
      auto c_dims = ctx->GetInputDim("C0");
      PADDLE_ENFORCE(h_dims == c_dims,
                     "The dimension of Input(H0) and Input(C0) "
                     "should be the same.");
    }

    int frame_size = x_dims[1];
    auto w_dims = ctx->GetInputDim("Weight");
    PADDLE_ENFORCE_EQ(w_dims.size(), 2,
                      "The rank of Input(Weight) should be 2.");
    PADDLE_ENFORCE_EQ(w_dims[0], frame_size,
                      "The first dimension of Input(Weight) "
                      "should be %d.",
                      frame_size);
    PADDLE_ENFORCE_EQ(w_dims[1], 4 * frame_size,
                      "The second dimension of Input(Weight) "
                      "should be 4 * %d.",
                      frame_size);
    auto b_dims = ctx->GetInputDim("Bias");
    PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
    PADDLE_ENFORCE_EQ(b_dims[0], 1,
                      "The first dimension of Input(Bias) should be 1.");
    if (ctx->Attrs().Get<bool>("use_peepholes")) {
      PADDLE_ENFORCE_EQ(b_dims[1], 7 * frame_size,
                        "The second dimension of Input(Bias) should be "
                        "7 * %d if enable peepholes connection",
                        frame_size);
    } else {
      PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
                        "The second dimension of Input(Bias) should be "
                        "4 * %d if diable peepholes connection",
                        frame_size);
    }
    ctx->SetOutputDim("Hidden", x_dims);
    ctx->SetOutputDim("Cell", x_dims);
    ctx->SetOutputDim("Hidden", x_dims);
    ctx->ShareLoD("Input", "Hidden");
    ctx->ShareLoD("Input", "Cell");
  }
};

class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  LSTMOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("Input",
             "(LoDTensor) the first input is a LodTensor, which support "
             "variable-time length input sequence. The underlying tensor in "
             "this LoDTenosr is a matrix with shape (T X D), where, T is the "
             "total time steps in this mini-batch, D is the hidden size.");
    AddInput("H0",
             "(Tensor, optional) the initial hidden state is an optional "
             "input. This is a tensor with shape (N x D), where N is the "
             "batch size, D is the hidden size.");
    AddInput("C0",
             "(Tensor, optional) the initial cell state is an optional "
             "input. This is a tensor with shape (N x D), where N is the "
             "batch size. `H0` and `C0` can be NULL but only at the same time");
    AddInput("Weight",
             "(Tensor) the learnable hidden-hidden weights."
             " - The shape is (D x 4*D), where D is the hidden size. "
             " - Weight = {W_ih, W_fh, W_ch, W_oh}");
    AddInput("Bias",
             "(Tensor) the learnable weights, which contains two parts: "
             "input-hidden bias weight and peephole connections weight if "
             "seting `use_peepholes` True. "
             "1. `use_peepholes = False` "
             " - The shape is (1 x 4*D). "
             " - Bias = {b_i, b_f, b_c, b_o}."
             "2. `use_peepholes = True` "
             " - The shape is (1 x 7*D). "
             " - Bias = {b_i, b_f, b_c, b_o, W_ic, W_fc, W_oc}.");
    AddOutput("Batch", "(LoDTensor) save the reorganized input as batch info. ")
        .AsIntermediate();
    AddOutput("Hidden",
              "(LoDTensor) the hidden state lod tensor of LSTM operator. "
              "The shape and lod is the same with the `Input`.");
    AddOutput("Cell",
              "(LoDTensor) the cell state lod tensor of LSTM operator. "
              "The shape and lod is the same with the `Input`.");
    AddAttr<bool>("use_peepholes",
                  "(bool, defalut: True) "
                  "whether to enable diagonal/peephole connections.")
        .SetDefault(true);
    AddAttr<bool>("is_reverse",
                  "(bool, defalut: False) "
                  "whether to compute reversed LSTM.")
        .SetDefault(true);
    AddAttr<std::string>(
        "gate_activation",
        "(string, defalut: sigmoid)"
        "The activation for input gate, forget gate and output "
        "gate, `sigmoid` by defalut.")
        .SetDefault("sigmoid");
    AddAttr<std::string>("cell_activation",
                         "(string, defalut: tanh)"
                         "The activation for cell output, `tanh` by defalut.")
        .SetDefault("tanh");
    AddAttr<std::string>("candidate_activation",
                         "(string, defalut: tanh)"
                         "The activation for candidate hidden state, "
                         "`tanh` by defalut.")
        .SetDefault("tanh");
    AddComment(R"DOC(Long-Short Term Memory (LSTM) Operator

The defalut implementation is diagonal/peephole connection [1], the formula is
as follows

    i_t = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)

    f_t = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)

    \tilde{c_t} = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)

    o_t = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)

    c_t = f_t ⊙ c_{t-1} + i_t ⊙ \tilde{c_t}

    h_t = o_t ⊙ act_h(c_t)

where the W terms denote weight matrices (e.g. \f$W_{xi}\f$ is the matrix
of weights from the input gate to the input), \f$W_{ic}, W_{fc}, W_{oc}\f$
are diagonal weight matrices for peephole connections. In our implenmention,
We use vectors to reprenset these diagonal weight matrices. The b terms
denote bias vectors (\f$b_i\f$ is the input gate bias vector), \f$\sigma\f$
is the non-line actications, such as logistic sigmoid function, and
\f$i, f, o\f$ and \f$c\f$ are respectively the input gate, forget gate,
output gate and cell activation vectors, all of which are the same size as
the cell output activation vector \f$h\f$.

The ⊙ is the element-wise product of the vectors, \f$act_g\f$ and \f$act_h\f$
are the cell input and cell output activation functions, `tanh` is usually
used for them. \f$\tilde{c_t}\f$ is also called candidate hidden state,
which is computed based on the current input and the previous hidden state.

Set `use_peepholes` False to disable peephole connection [2]. The formula
is omitted here.

@note These \f$W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\f$
operations on the input x_{t} were NOT included in this operator. The
users can choose to use fully-connect operator before LSTM operator.

[1] Hasim Sak, Andrew Senior, and Francoise Beaufays. Long short-term memory
recurrent neural network architectures for large scale acoustic modeling.
INTERSPEECH, 2014.

[2] S. Hochreiter and J. Schmidhuber. Long Short-Term Memory.
Neural Computation, 9(8):1735-1780, 1997.

)DOC");
  }
};

class LSTMGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  void InferShape(framework::InferShapeContextBase* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Hidden")),
                   "Input(Hidden@GRAD) should not be null");
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Cell")),
                   "Input(Cell@GRAD) should not be null");
    ctx->SetOutputDim(framework::GradVarName("Weight"),
                      ctx->GetInputDim("Weight"));
    ctx->SetOutputDim(framework::GradVarName("Bias"), ctx->GetInputDim("Bias"));
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(lstm, ops::LSTMOp, ops::LSTMOpMaker, lstm_grad, ops::LSTMGradOp);
REGISTER_OP_CPU_KERNEL(lstm, ops::LSTMKernel<paddle::platform::CPUPlace, float>,
                       ops::LSTMKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(lstm_grad,
                       ops::LSTMGradKernel<paddle::platform::CPUPlace, float>,
                       ops::LSTMGradKernel<paddle::platform::CPUPlace, double>);