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@ -24,6 +24,11 @@ class LSTMOp : public framework::OperatorWithKernel {
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Input"),
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"Input(Input) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Weight"),
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"Input(Weight) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Bias"),
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"Input(Bias) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
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"Output(Hidden) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Cell"),
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@ -59,11 +64,13 @@ class LSTMOp : public framework::OperatorWithKernel {
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"The second dimension of Input(Weight) "
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"should be 4 * %d.",
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frame_size);
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auto b_dims = ctx->GetInputDim("Bias");
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PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
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PADDLE_ENFORCE_EQ(b_dims[0], 1,
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"The first dimension of Input(Bias) should be 1.");
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if (ctx->Attrs().Get<bool>("usePeepholes")) {
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if (ctx->Attrs().Get<bool>("use_peepholes")) {
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PADDLE_ENFORCE_EQ(b_dims[1], 7 * frame_size,
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"The second dimension of Input(Bias) should be "
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"7 * %d if enable peepholes connection",
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@ -74,6 +81,7 @@ class LSTMOp : public framework::OperatorWithKernel {
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"4 * %d if disable peepholes connection",
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frame_size);
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}
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framework::DDim out_dims({in_dims[0], frame_size});
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ctx->SetOutputDim("Hidden", out_dims);
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ctx->SetOutputDim("Cell", out_dims);
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@ -118,14 +126,13 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
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AddInput("Bias",
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"(Tensor) the learnable weights, which contains two parts: "
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"input-hidden bias weight and peephole connections weight if "
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"setting `usePeepholes` True. "
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"1. `usePeepholes = False` "
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"setting `use_peepholes` True. "
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"1. `use_peepholes = False` "
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" - The shape is (1 x 4D). "
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" - Bias = {b_c, b_i, b_f, b_o}."
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"2. `usePeepholes = True` "
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"2. `use_peepholes = True` "
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" - The shape is (1 x 7D). "
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" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.")
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.AsDispensable();
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" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
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AddOutput("Hidden",
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"(LoDTensor) the hidden state of LSTM operator. "
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"The shape is (T x D), and lod is the same with the `Input`.");
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@ -145,29 +152,32 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
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"(LoDTensor) This LoDTensor is obtained in the forward and used "
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"in the backward.")
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.AsIntermediate();
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AddAttr<bool>("usePeepholes",
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"(bool, default True) "
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AddAttr<bool>("use_peepholes",
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"(bool, defalut: True) "
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"whether to enable diagonal/peephole connections.")
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.SetDefault(true);
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AddAttr<bool>("isReverse",
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"(bool, default False) "
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AddAttr<bool>("is_reverse",
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"(bool, defalut: False) "
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"whether to compute reversed LSTM.")
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.SetDefault(false);
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AddAttr<std::string>(
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"gateActivation",
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"(string, default sigmoid)"
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"gate_activation",
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"(string, default: sigmoid)"
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"The activation for input gate, forget gate and output "
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"gate, `sigmoid` by default.")
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.SetDefault("sigmoid");
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AddAttr<std::string>("cellActivation",
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"(string, default tanh)"
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.SetDefault("sigmoid")
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.InEnum({"sigmoid", "tanh", "relu", "identity"});
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AddAttr<std::string>("cell_activation",
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"(string, default: tanh)"
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"The activation for cell output, `tanh` by defalut.")
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.SetDefault("tanh");
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AddAttr<std::string>("candidateActivation",
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"(string, default tanh)"
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.SetDefault("tanh")
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.InEnum({"sigmoid", "tanh", "relu", "identity"});
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AddAttr<std::string>("candidate_activation",
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"(string, default: tanh)"
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"The activation for candidate hidden state, "
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"`tanh` by default.")
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.SetDefault("tanh");
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.SetDefault("tanh")
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.InEnum({"sigmoid", "tanh", "relu", "identity"});
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AddComment(R"DOC(
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Long-Short Term Memory (LSTM) Operator.
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@ -203,7 +213,7 @@ are the cell input and cell output activation functions and `tanh` is usually
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used for them. \f$\tilde{c_t}\f$ is also called candidate hidden state,
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which is computed based on the current input and the previous hidden state.
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Set usePeepholes False to disable peephole connection
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Set `use_peepholes` False to disable peephole connection
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(http://www.bioinf.jku.at/publications/older/2604.pdf). The formula
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is omitted here.
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@ -226,23 +236,27 @@ class LSTMGradOp : public framework::OperatorWithKernel {
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"Input(Hidden) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Cell"),
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"Input(Cell) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Weight"),
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"Input(Weight) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Bias"),
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"Input(Bias) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
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"Input(BatchGate) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("BatchCellPreAct"),
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"Input(BatchGate) of LSTM should not be null.");
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auto in_g_name = framework::GradVarName("Input");
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if (ctx->HasOutput(in_g_name))
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ctx->SetOutputDim(in_g_name, ctx->GetInputDim("Input"));
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auto w_g_name = framework::GradVarName("Weight");
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if (ctx->HasOutput(w_g_name))
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ctx->SetOutputDim(w_g_name, ctx->GetInputDim("Weight"));
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auto b_g_name = framework::GradVarName("Bias");
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if (ctx->HasOutput(b_g_name))
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ctx->SetOutputDim(b_g_name, ctx->GetInputDim("Bias"));
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auto SetOutGradDim = [&ctx](const std::string& name) {
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auto g_name = framework::GradVarName(name);
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if (ctx->HasOutput(g_name))
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ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
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};
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SetOutGradDim("Input");
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SetOutGradDim("Weight");
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SetOutGradDim("Bias");
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SetOutGradDim("H0");
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SetOutGradDim("C0");
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}
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protected:
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qingqing01
7 years ago
committed by
GitHub