lstmp_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/lstmp_op.h"

namespace paddle {
namespace operators {

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Input"),
                   "Input(Input) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Weight"),
                   "Input(Weight) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("ProjWeight"),
                   "Input(ProjWeight) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Bias"),
                   "Input(Bias) of LSTMP should not be null.");

    PADDLE_ENFORCE(ctx->HasOutput("Projection"),
                   "Output(Projection) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Cell"),
                   "Output(Cell) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("BatchGate"),
                   "Output(BatchGate) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
                   "Output(BatchGate) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("BatchHidden"),
                   "Output(BatchHidden) of LSTMP should not be null.");

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

    int frame_size = in_dims[1] / 4;
    auto w_dims = ctx->GetInputDim("Weight");
    auto proj_dims = ctx->GetInputDim("ProjWeight");
    PADDLE_ENFORCE_EQ(w_dims.size(), 2,
                      "The rank of Input(Weight) should be 2.");
    PADDLE_ENFORCE_EQ(w_dims[0], proj_dims[1],
                      "The first dimension of Input(Weight) "
                      "should be %d.",
                      proj_dims[1]);
    PADDLE_ENFORCE_EQ(w_dims[1], 4 * frame_size,
                      "The second dimension of Input(Weight) "
                      "should be 4 * %d.",
                      frame_size);

    PADDLE_ENFORCE_EQ(proj_dims.size(), 2,
                      "The rank of Input(ProjWeight) should be 2.");
    PADDLE_ENFORCE_EQ(proj_dims[0], frame_size,
                      "The first dimension of Input(ProjWeight) "
                      "should be %d.",
                      frame_size);

    if (ctx->HasInput("H0")) {
      PADDLE_ENFORCE(ctx->HasInput("C0"),
                     "Input(C0) and Input(H0) of LSTMP 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.");
      ctx->SetOutputDim("OrderedP0", {h_dims[0], proj_dims[1]});
    }

    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 disable peepholes connection",
                        frame_size);
    }

    framework::DDim out_dims({in_dims[0], frame_size});
    framework::DDim proj_out_dims({in_dims[0], proj_dims[1]});
    ctx->SetOutputDim("Projection", proj_out_dims);
    ctx->SetOutputDim("Cell", out_dims);
    ctx->SetOutputDim("BatchGate", in_dims);
    ctx->SetOutputDim("BatchCellPreAct", out_dims);
    ctx->SetOutputDim("BatchHidden", out_dims);
    ctx->ShareLoD("Input", "Projection");
    ctx->ShareLoD("Input", "Cell");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(
        framework::ToDataType(ctx.Input<framework::LoDTensor>("Input")->type()),
        ctx.device_context());
  }
};

class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  LSTMPOpMaker(OpProto* proto, 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 LoDTensor is a matrix with shape (T X 4D), 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 and D is the hidden size.")
        .AsDispensable();
    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")
        .AsDispensable();
    AddInput("Weight",
             "(Tensor) the learnable hidden-hidden weights."
             " - The shape is (P x 4D), where P is the recurrent projection "
             "layer size and  D is the hidden size. "
             " - Weight = {W_cr, W_ir, W_fr, W_or}");
    AddInput("ProjWeight",
             "(Tensor) the learnable weight `W_rh` of the projection layer."
             " - The shape is (D x P), where P is the recurrent projection "
             "layer size and  D is the hidden size.");
    AddInput("Bias",
             "(Tensor) the learnable weights, which contains two parts: "
             "input-hidden bias weight and peephole connections weight if "
             "setting `use_peepholes` True. "
             "1. `use_peepholes = False` "
             " - The shape is (1 x 4D). "
             " - Bias = {b_c, b_i, b_f, b_o}."
             "2. `use_peepholes = True` "
             " - The shape is (1 x 7D). "
             " - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
    AddOutput("Projection",
              "(LoDTensor) the projection of the hidden state of LSTMP "
              "operator. The shape is (T x P), and lod is the same with the "
              "`Input`.");
    AddOutput("Cell",
              "(LoDTensor) the cell state of LSTMP operator. "
              "The shape is (T x D), and lod is the same with the `Input`.");
    AddOutput("BatchGate",
              "(LoDTensor) This LoDTensor contains input gate, forget gate "
              "and output gate after the nonlinear computation. This "
              "LoDTensor has the same shape as the reorganized input, which "
              "is also be called batch input. The LoD size is 2. The first "
              "LoD is the batch offsets and the second LoD contains the "
              "indexes, which denote the position of reorganized sequence "
              "in the raw input.")
        .AsIntermediate();
    AddOutput("BatchCellPreAct",
              "(LoDTensor) This LoDTensor is obtained in the forward and used "
              "in the backward.")
        .AsIntermediate();
    AddOutput("BatchHidden",
              "(LoDTensor) This LoDTensor is obtained in the forward and used "
              "in the backward.")
        .AsIntermediate();
    AddOutput("OrderedP0",
              "(Tensor) the projection of the initial hidden state "
              "H0. This is a tensor with shape (N x P), where N is the "
              "batch size and P is the hidden size.")
        .AsIntermediate();
    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 LSTMP.")
        .SetDefault(false);
    AddAttr<bool>("share_cell_act",
                  "(bool, defalut: True) "
                  "whether to share activation with cell output. "
                  "If false, the projection would be linear, else "
                  "through an activation same with the cell output.")
        .SetDefault(true);
    AddAttr<std::string>(
        "gate_activation",
        "(string, default: sigmoid)"
        "The activation for input gate, forget gate and output "
        "gate, `sigmoid` by default.")
        .SetDefault("sigmoid")
        .InEnum({"sigmoid", "tanh", "relu", "identity"});
    AddAttr<std::string>("cell_activation",
                         "(string, default: tanh)"
                         "The activation for cell output, `tanh` by defalut.")
        .SetDefault("tanh")
        .InEnum({"sigmoid", "tanh", "relu", "identity"});
    AddAttr<std::string>("candidate_activation",
                         "(string, default: tanh)"
                         "The activation for candidate hidden state, "
                         "`tanh` by default.")
        .SetDefault("tanh")
        .InEnum({"sigmoid", "tanh", "relu", "identity"});
    AddComment(R"DOC(
Long-Short Term Memory with Recurrent Projection (LSTMP) Operator.

LSTMP is stand LSTM appended by a recurrent projection layer to reduce the
number of parameters, espeacially when the output size is relative large. 
The formula is as follows:

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

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

c_t = f_t \odot c_{t-1} + i_t \odot act_g(W_{cx}x_t + W_{ch}r_{t-1} + b_c) \\

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

h_t = o_t \odot act_h(c_t)

r_t = act_{h'}(W_{rh}h_t)
$$

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

The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
are the cell input and cell output activation functions and `tanh` is usually
used for them. If `share_cell_act` setted to `False`, $act_h'$ will be linear
else will be same with $act_h$.

Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
operations on the input $x_{t}$ are NOT included in this operator.
Users can choose to use fully-connect operator before LSTMP operator.

)DOC");
  }
};

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

  void InferShape(framework::InferShapeContext* ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Input"),
                   "Input(Input) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Projection"),
                   "Input(Projection) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Cell"),
                   "Input(Cell) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Weight"),
                   "Input(Weight) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("ProjWeight"),
                   "Input(ProjWeight) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Bias"),
                   "Input(Bias) of LSTMP should not be null.");

    PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
                   "Input(BatchGate) of LSTMP should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("BatchCellPreAct"),
                   "Input(BatchGate) of LSTMP should not be null.");

    auto SetOutGradDim = [&ctx](const std::string& name) {
      auto g_name = framework::GradVarName(name);
      if (ctx->HasOutput(g_name))
        ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
    };

    SetOutGradDim("Input");
    SetOutGradDim("Weight");
    SetOutGradDim("ProjWeight");
    SetOutGradDim("Bias");
    SetOutGradDim("H0");
    SetOutGradDim("C0");
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return framework::OpKernelType(
        framework::ToDataType(ctx.Input<framework::LoDTensor>("Input")->type()),
        ctx.device_context());
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker, lstmp_grad,
            ops::LSTMPGradOp);
REGISTER_OP_CPU_KERNEL(
    lstmp, ops::LSTMPKernel<paddle::platform::CPUDeviceContext, float>,
    ops::LSTMPKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
    lstmp_grad, ops::LSTMPGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::LSTMPGradKernel<paddle::platform::CPUDeviceContext, double>);