Add backward implementation for LSTM operator.

paddle/framework/lod_tensor_test.cu

@@ -36,15 +36,15 @@ TEST(LoDTensor, LoDInGPU) {
   lod_tensor.mutable_data<float>(place);
 
   lod_tensor.set_lod(src_lod);
-  CHECK_EQ(lod_tensor.lod_element(0, 2).first, 4UL);
-  CHECK_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
+  EXPECT_EQ(lod_tensor.lod_element(0, 2).first, 4UL);
+  EXPECT_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
 
   auto lod = lod_tensor.lod();
 
   test<<<1, 8>>>(lod[0].data(), lod[0].size());
   cudaDeviceSynchronize();
 
   for (size_t i = 0; i < src_lod[0].size(); ++i) {
-    CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
+    EXPECT_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
   }
-}
+}

paddle/operators/lstm_op.cc

@@ -21,17 +21,20 @@ 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.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchGate"),
+                   "Output(BatchGate) of LSTM should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
+                   "Output(BatchGate) 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.");
+    auto in_dims = ctx->GetInputDim("Input");
+    PADDLE_ENFORCE_EQ(in_dims.size(), 2, "Input(X)'s rank must be 2.");
 
     if (ctx->HasInput("H0")) {
       PADDLE_ENFORCE(ctx->HasInput("C0"),
@@ -44,7 +47,7 @@ class LSTMOp : public framework::OperatorWithKernel {
                      "should be the same.");
     }
 
-    int frame_size = x_dims[1] / 4;
+    int frame_size = in_dims[1] / 4;
     auto w_dims = ctx->GetInputDim("Weight");
     PADDLE_ENFORCE_EQ(w_dims.size(), 2,
                       "The rank of Input(Weight) should be 2.");
@@ -71,12 +74,21 @@ class LSTMOp : public framework::OperatorWithKernel {
                         "4 * %d if disable 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);
+    framework::DDim out_dims({in_dims[0], frame_size});
+    ctx->SetOutputDim("Hidden", out_dims);
+    ctx->SetOutputDim("Cell", out_dims);
+    ctx->SetOutputDim("BatchGate", in_dims);
+    ctx->SetOutputDim("BatchCellPreAct", out_dims);
     ctx->ShareLoD("Input", "Hidden");
     ctx->ShareLoD("Input", "Cell");
   }
+
+ protected:
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(
+        ctx.Input<framework::LoDTensor>("Input")->type());
+  }
 };
 
 class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -86,16 +98,18 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
     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 "
+             "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, D is the hidden size.");
+             "batch size, 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");
+             "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 (D x 4D), where D is the hidden size. "
@@ -109,22 +123,27 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
              " - Bias = {b_c, b_i, b_f, b_o}."
              "2. `usePeepholes = True` "
              " - The shape is (1 x 7D). "
-             " - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
+             " - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.")
+        .AsDispensable();
+    AddOutput("Hidden",
+              "(LoDTensor) the hidden state of LSTM operator. "
+              "The shape is (T x D), and lod is the same with the `Input`.");
+    AddOutput("Cell",
+              "(LoDTensor) the cell state of LSTM 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 with the reorganized input, which "
-              "was also be called batch input. The LoD size is 2. The first "
+              "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("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`.");
+    AddOutput("BatchCellPreAct",
+              "(LoDTensor) This LoDTensor is got in the forward and used "
+              "in the backward.")
+        .AsIntermediate();
     AddAttr<bool>("usePeepholes",
                   "(bool, defalut: True) "
                   "whether to enable diagonal/peephole connections.")
@@ -202,15 +221,37 @@ 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"));
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(Input) of LSTM should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Hidden"),
+                   "Input(Hidden) of LSTM should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Cell"),
+                   "Input(Cell) of LSTM should not be null.");
+
+    PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
+                   "Input(BatchGate) of LSTM should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("BatchCellPreAct"),
+                   "Input(BatchGate) of LSTM should not be null.");
+
+    auto in_g_name = framework::GradVarName("Input");
+    if (ctx->HasOutput(in_g_name))
+      ctx->SetOutputDim(in_g_name, ctx->GetInputDim("Input"));
+
+    auto w_g_name = framework::GradVarName("Weight");
+    if (ctx->HasOutput(w_g_name))
+      ctx->SetOutputDim(w_g_name, ctx->GetInputDim("Weight"));
+
+    auto b_g_name = framework::GradVarName("Bias");
+    if (ctx->HasOutput(b_g_name))
+      ctx->SetOutputDim(b_g_name, ctx->GetInputDim("Bias"));
+  }
+
+ protected:
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(
+        ctx.Input<framework::LoDTensor>("Input")->type());
   }
 };