Merge pull request #4443 from guoshengCS/add-GRUStepOp

Add gru_unit_op

paddle/operators/gru_unit_op.cc

@@ -0,0 +1,210 @@
+/* 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/gru_unit_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class GRUUnitOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(%s) of GRUUnitOp should not be null.", "Input");
+    PADDLE_ENFORCE(ctx->HasInput("HiddenPrev"),
+                   "Input(%s) of GRUUnitOp should not be null.", "HiddenPrev");
+    PADDLE_ENFORCE(ctx->HasInput("Weight"),
+                   "Input(%s) of GRUUnitOp should not be null.", "Weight");
+    PADDLE_ENFORCE(ctx->HasOutput("Gate"),
+                   "Output(%s) of GRUUnitOp should not be null.", "Gate");
+    PADDLE_ENFORCE(ctx->HasOutput("ResetHiddenPrev"),
+                   "Output(%s) of GRUUnitOp should not be null.",
+                   "ResetHiddenPrev");
+    PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
+                   "Output(%s) of GRUUnitOp should not be null.", "Hidden");
+    auto input_dims = ctx->GetInputDim("Input");
+    auto hidden_prev_dims = ctx->GetInputDim("HiddenPrev");
+    auto weight_dims = ctx->GetInputDim("Weight");
+    int batch_size = input_dims[0];
+    int input_size = input_dims[1];
+    int frame_size = hidden_prev_dims[1];
+    int weight_height = weight_dims[0];
+    int weight_width = weight_dims[1];
+    PADDLE_ENFORCE_EQ(
+        input_size, frame_size * 3,
+        "The input_size must be 3 times of frame_size in GRUUnitOp.");
+    PADDLE_ENFORCE_EQ(
+        weight_height, frame_size,
+        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+    PADDLE_ENFORCE_EQ(
+        weight_width, frame_size * 3,
+        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+    auto bias = Input("Bias");
+    if (bias != framework::kEmptyVarName) {
+      auto bias_dims = ctx->GetInputDim("Bias");
+      int bias_height = bias_dims[0];
+      int bias_width = bias_dims[1];
+      PADDLE_ENFORCE_EQ(bias_height, 1,
+                        "The shape of Bias must be [1, frame_size * 3].");
+      PADDLE_ENFORCE_EQ(bias_width, frame_size * 3,
+                        "The shape of Bias must be [1, frame_size * 3].");
+    }
+    ctx->SetOutputDim("Gate", {batch_size, frame_size * 3});
+    ctx->SetOutputDim("ResetHiddenPrev", {batch_size, frame_size});
+    ctx->SetOutputDim("Hidden", {batch_size, frame_size});
+  }
+};
+
+class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  GRUUnitOpMaker(framework::OpProto* proto,
+                 framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Input",
+             "(Tensor) Matrix with shape [batch_size, frame_size * 3] for the "
+             "input.");
+    AddInput("HiddenPrev",
+             "(Tensor) Matrix with shape [batch_size, frame_size] for the "
+             "states of previous time step.");
+    AddInput("Weight",
+             "(Tensor) Weight matrix with shape [frame_size, frame_size * 3]. "
+             "The elements continuous in memory can be divided into two parts. "
+             "The first part are weights of the update gate and reset gate "
+             "with shape [frame_size, frame_size * 2], and the second part are "
+             "weights of output candidate with shape [frame_size, frame_size]");
+    AddInput("Bias",
+             "(Tensor) Bias vector with shape [1, frame_size * 3] concating "
+             "bias of the update gate, reset gate and output candidate.");
+    AddOutput("Gate",
+              "(Tensor) Matrix with shape [batch_size, frame_size * 3] for the "
+              "output of update gate, reset gate and output candidate")
+        .AsIntermediate();
+    AddOutput("ResetHiddenPrev",
+              "(Tensor) Matrix with shape [batch_size, frame_size] for the "
+              "reseted hidden state of previous time step.")
+        .AsIntermediate();
+    AddOutput("Hidden",
+              "(Tensor) The GRU hidden state of the current time step "
+              "with shape [batch_size, frame_size].");
+    AddAttr<int>("activation",
+                 "(enum int, default tanh) "
+                 "The activation type used for output candidate {h}_t.")
+        .SetDefault(tanh)
+        .InEnum({identity, sigmoid, tanh, relu});
+    AddAttr<int>("gate_activation",
+                 "(enum int, default sigmoid) "
+                 "The activation type used in update gate and reset gate.")
+        .SetDefault(sigmoid)
+        .InEnum({identity, sigmoid, tanh, relu});
+    AddComment(R"DOC(
+GRUUnitOp implements part calculations of the GRU unit as following:
+
+\f[
+update \ gate: u_t = actGate(xu_t + W_u * hidden_prev + bias_u) \\
+reset \ gate: r_t = actGate(xr_t + W_r * hidden_prev + bias_r)  \\
+output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, hidden_prev) + bias_c) \\
+output: h_t = dot((1-u_t), {h}_t) + dot(u_t, hidden_prev)
+\f]
+
+The rest of GRU unit can be completed by using FCOp's output as the input of GRUUnitOp.
+)DOC");
+  }
+};
+
+class GRUUnitGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(%s) of GRUUnitGradOp should not be null.", "Input");
+    PADDLE_ENFORCE(ctx->HasInput("HiddenPrev"),
+                   "Input(%s) of GRUUnitGradOp should not be null.",
+                   "HiddenPrev");
+    PADDLE_ENFORCE(ctx->HasInput("Weight"),
+                   "Input(%s) of GRUUnitGradOp should not be null.", "Weight");
+    PADDLE_ENFORCE(ctx->HasInput("Gate"),
+                   "Input(%s) of GRUUnitGradOp should not be null.", "Gate");
+    PADDLE_ENFORCE(ctx->HasInput("ResetHiddenPrev"),
+                   "Input(%s) of GRUUnitGradOp should not be null.",
+                   "ResetHiddenPrev");
+    PADDLE_ENFORCE(ctx->HasInput("Hidden"),
+                   "Input(%s) of GRUUnitGradOp should not be null.", "Hidden");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Gate")),
+                   "Input(%s@GRAD) of GRUUnitGradOp should not be null.",
+                   "Gate");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("ResetHiddenPrev")),
+                   "Input(%s@GRAD) of GRUUnitGradOp should not be null.",
+                   "ResetHiddenPrev");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Hidden")),
+                   "Input(%s@GRAD) of GRUUnitGradOp should not be null.",
+                   "Hidden");
+    auto input_dims = ctx->GetInputDim("Input");
+    auto hidden_prev_dims = ctx->GetInputDim("HiddenPrev");
+    auto weight_dims = ctx->GetInputDim("Weight");
+    // int batch_size = input_dims[0];
+    int input_size = input_dims[1];
+    int frame_size = hidden_prev_dims[1];
+    int weight_height = weight_dims[0];
+    int weight_width = weight_dims[1];
+    PADDLE_ENFORCE_EQ(
+        input_size, frame_size * 3,
+        "The input_size must be 3 times of frame_size in GRUUnitOp.");
+    PADDLE_ENFORCE_EQ(
+        weight_height, frame_size,
+        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+    PADDLE_ENFORCE_EQ(
+        weight_width, frame_size * 3,
+        "The shape of Weight matrix must be [frame_size, frame_size * 3].");
+    auto bias = Input("Bias");
+    if (bias != framework::kEmptyVarName) {
+      auto bias_dims = ctx->GetInputDim("Bias");
+      int bias_height = bias_dims[0];
+      int bias_width = bias_dims[1];
+      PADDLE_ENFORCE_EQ(bias_height, 1,
+                        "The shape of Bias must be [1, frame_size * 3].");
+      PADDLE_ENFORCE_EQ(bias_width, frame_size * 3,
+                        "The shape of Bias must be [1, frame_size * 3].");
+      auto bias_grad_name = framework::GradVarName("Bias");
+      if (ctx->HasOutput(bias_grad_name))
+        ctx->SetOutputDim(bias_grad_name, bias_dims);
+    }
+    auto input_grad_name = framework::GradVarName("Input");
+    if (ctx->HasOutput(input_grad_name))
+      ctx->SetOutputDim(input_grad_name, input_dims);
+    auto hidden_prev_grad_name = framework::GradVarName("HiddenPrev");
+    if (ctx->HasOutput(hidden_prev_grad_name))
+      ctx->SetOutputDim(hidden_prev_grad_name, hidden_prev_dims);
+    auto weight_grad_name = framework::GradVarName("Weight");
+    if (ctx->HasOutput(weight_grad_name))
+      ctx->SetOutputDim(weight_grad_name, weight_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(gru_unit, ops::GRUUnitOp, ops::GRUUnitOpMaker, gru_unit_grad,
+            ops::GRUUnitGradOp);
+REGISTER_OP_CPU_KERNEL(gru_unit,
+                       ops::GRUUnitKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/gru_unit_op.cu

@@ -0,0 +1,22 @@
+/* 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/gru_unit_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(gru_unit,
+                       ops::GRUUnitKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    gru_unit_grad, ops::GRUUnitGradKernel<paddle::platform::GPUPlace, float>);