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Add LSTM backward implenmentation.

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fix-typo
dangqingqing 7 years ago
parent 3f1062d711
commit 3d8b6ebcf8
3 changed files with 237 additions and 45 deletions
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56
paddle/operators/lstm_op.cc
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@ -21,7 +21,6 @@ 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.");
@ -30,8 +29,8 @@ class LSTMOp : public framework::OperatorWithKernel {
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.");
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 +43,7 @@ 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.");
@ -71,9 +70,11 @@ 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");
}
@ -86,7 +87,7 @@ 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 "
@ -110,21 +111,25 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
"2. `usePeepholes = True` "
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
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("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 get in the forward and used "
"in the backward.")
.AsIntermediate();
AddAttr<bool>("usePeepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
@ -202,15 +207,28 @@ 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"));
ctx->SetOutputDim(framework::GradVarName("Input"),
ctx->GetInputDim("Input"));
if (ctx->HasInput("Weight")) {
ctx->SetOutputDim(framework::GradVarName("Weight"),
ctx->GetInputDim("Weight"));
}
if (ctx->HasInput("Bias")) {
ctx->SetOutputDim(framework::GradVarName("Bias"),
ctx->GetInputDim("Bias"));
}
if (ctx->HasInput("H0")) {
ctx->SetOutputDim(framework::GradVarName("H0"), ctx->GetInputDim("H0"));
}
if (ctx->HasInput("C0")) {
ctx->SetOutputDim(framework::GradVarName("C0"), ctx->GetInputDim("C0"));
}
}
};

214
paddle/operators/lstm_op.h
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12
paddle/operators/math/sequence2batch.h
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@ -53,7 +53,17 @@ class LoDTensor2BatchFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::LoDTensor& lod_tensor,
framework::LoDTensor& batch, bool is_reverse) const {
framework::LoDTensor& batch, bool is_cal_batch_lod,
bool is_reverse = false) const {
if (!is_cal_batch_lod) {
auto lods = batch.lod();
PADDLE_ENFORCE_EQ(lods.size(), 2UL);
PADDLE_ENFORCE_EQ(lods[1].size(), lod_tensor.dims()[1]);
CopyMatrixRowsFunctor<Place, T> to_batch;
to_batch(context, lod_tensor, lods[1].data(), batch, true);
return;
}
auto lods = lod_tensor.lod();
PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");
auto lod = lods[0];

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