Forward implementation for LSTM operator.

paddle/operators/CMakeLists.txt

@@ -115,7 +115,8 @@ set(DEPS_OPS
     softmax_with_cross_entropy_op
     sum_op
     pool_op
-    pool_with_index_op)
+    pool_with_index_op
+    lstm_op)
 
 
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
@@ -126,6 +127,7 @@ op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
 op_library(sum_op DEPS net_op)
 op_library(pool_op DEPS pooling)
 op_library(pool_with_index_op DEPS pooling)
+op_library(lstm_op DEPS sequence2batch lstm_compute math_function)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})

paddle/operators/lstm_op.cc

@@ -0,0 +1,226 @@
+/* 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::InferShapeContext* 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("Cell"),
+                   "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] / 4;
+    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>("usePeepholes")) {
+      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[0], frame_size});
+    ctx->SetOutputDim("Cell", {x_dims[0], frame_size});
+    ctx->SetOutputDim("BatchGate", 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 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, 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 `usePeepholes` True. "
+             "1. `usePeepholes = False` "
+             " - The shape is (1 x 4*D). "
+             " - Bias = {b_i, b_f, b_c, b_o}."
+             "2. `usePeepholes = True` "
+             " - The shape is (1 x 7*D). "
+             " - Bias = {b_i, b_f, b_c, b_o, W_ic, W_fc, W_oc}.");
+    AddOutput("BatchGate",
+              "(LoDTensor) This LoDTensor contains input gate, forget gate "
+              "and output gate aftern the nonlinear computation. This "
+              "LoDTensor has the same shape with the reorganized input, which "
+              "was 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("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>("usePeepholes",
+                  "(bool, defalut: True) "
+                  "whether to enable diagonal/peephole connections.")
+        .SetDefault(true);
+    AddAttr<bool>("isReverse",
+                  "(bool, defalut: False) "
+                  "whether to compute reversed LSTM.")
+        .SetDefault(false);
+    AddAttr<std::string>(
+        "gateActivation",
+        "(string, defalut: sigmoid)"
+        "The activation for input gate, forget gate and output "
+        "gate, `sigmoid` by defalut.")
+        .SetDefault("sigmoid");
+    AddAttr<std::string>("cellActivation",
+                         "(string, defalut: tanh)"
+                         "The activation for cell output, `tanh` by defalut.")
+        .SetDefault("tanh");
+    AddAttr<std::string>("candidateActivation",
+                         "(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 `usePeepholes` 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::InferShapeContext* 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>);

paddle/operators/lstm_op.cu

@@ -0,0 +1,23 @@
+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/lstm_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(lstm, ops::LSTMKernel<paddle::platform::GPUPlace, float>,
+                       ops::LSTMKernel<paddle::platform::GPUPlace, double>);
+REGISTER_OP_GPU_KERNEL(lstm_grad,
+                       ops::LSTMGradKernel<paddle::platform::GPUPlace, float>,
+                       ops::LSTMGradKernel<paddle::platform::GPUPlace, double>);