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Make GRU Operator adapt to the latest code

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mobile_baidu
guosheng 8 years ago
parent 9162629b04
commit a4d54b83d4
2 changed files with 39 additions and 33 deletions
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66
paddle/operators/gru_op.cc
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@ -43,14 +43,12 @@ class GRUOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
weight_dims[1], frame_size * 3,
"The shape of Weight matrix must be [frame_size, frame_size * 3].");
auto h0 = Input("H0");
if (h0 != framework::kEmptyVarName) {
if (ctx->HasInput("H0")) {
auto h0_dims = ctx->GetInputDim("H0");
PADDLE_ENFORCE_EQ(h0_dims[1], frame_size,
"The width of H0 must be equal to frame_size.");
}
auto bias = Input("Bias");
if (bias != framework::kEmptyVarName) {
if (ctx->HasInput("Bias")) {
auto bias_dims = ctx->GetInputDim("Bias");
int bias_height = bias_dims[0];
int bias_width = bias_dims[1];
@ -74,42 +72,52 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
GRUOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(LoDTensor) the first input is a LodTensor, which support "
"(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 3D), 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 "
"(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(
"Weight",
"(Tensor) Weight matrix with shape [hidden_size, hidden_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 [hidden_size, hidden_size * 2], and the second part are "
"weights of output candidate with shape [hidden_size, hidden_size]");
"(Tensor) The learnable hidden-hidden weight matrix with shape "
"(D x 3D), where D is the hidden size. 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 (D x 2D), and the second "
"part are weights of output candidate with shape (D x D).");
AddInput("Bias",
"(Tensor) Bias vector with shape [1, hidden_size * 3] concating "
"bias of the update gate, reset gate and output candidate.");
"(Tensor, optional) Bias vector with shape (1 x 3D) concating "
"bias of the update gate, reset gate and output candidate.")
.AsDispensable();
AddOutput("BatchGate",
"(LoDTensor) the update gata, reset gate and output candidate "
"lod tensor of GRU operator. "
"The shape and lod is the same with the `Input`.")
"(LoDTensor) To compute with batches, sequence data will be "
"reorganized into several successive batches each containing "
"data from the same time step. The LoDTensor BatchGate contains "
"the update gate, reset gate and output candidate values "
"organized in batches. The LoD size is 2. The first LoD contains "
"the batch offsets and the second LoD contains the indexes in "
"the raw sequence data.")
.AsIntermediate();
AddOutput(
"BatchResetHiddenPrev",
"(LoDTensor) the reseted hidden state lod tensor of GRU operator. "
"The shape and lod is the same with the `Input`.")
"(LoDTensor) The reseted hidden state LoDTensor organized in batches. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
"with `BatchGate`.")
.AsIntermediate();
AddOutput(
"BatchHidden",
"(LoDTensor) the reseted hidden state lod tensor of GRU operator. "
"The shape and lod is the same with the `Input`.")
"(LoDTensor) The hidden state LoDTensor organized in batches. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
"with `BatchGate`.")
.AsIntermediate();
AddOutput("Hidden",
"(LoDTensor) the hidden state lod tensor of GRU operator. "
"The shape and lod is the same with the `Input`.");
AddOutput(
"Hidden",
"(LoDTensor) the hidden state LoDTensor organized in sequences. "
"This LoDTensor is a matrix with shape (T X D) and has the same LoD "
"with `BatchGate`.");
AddAttr<std::string>("activation",
"(string, default tanh) "
"The activation type used for output candidate {h}_t.")
@ -124,14 +132,14 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
"whether to compute reversed GRU.")
.SetDefault(false);
AddComment(R"DOC(
GRUOp implements part calculations of the GRU unit as following:
GRUOp implements part calculations of the GRU 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), hidden_prev) + dot(u_t, {h}_t)
\f]
The rest of GRU unit can be completed by using FCOp's output as the input of GRUOp.
The rest of GRU can be completed by using FCOp's output as the input of GRUOp.
)DOC");
}
};
@ -170,8 +178,7 @@ class GRUGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
weight_width, frame_size * 3,
"The shape of Weight matrix must be [frame_size, frame_size * 3].");
auto h0 = Input("H0");
if (h0 != framework::kEmptyVarName) {
if (ctx->HasInput("H0")) {
auto h0_dims = ctx->GetInputDim("H0");
PADDLE_ENFORCE_EQ(h0_dims[1], frame_size,
"The width of H0 must be equal to frame_size.");
@ -179,8 +186,7 @@ class GRUGradOp : public framework::OperatorWithKernel {
if (ctx->HasOutput(h0_grad_name))
ctx->SetOutputDim(h0_grad_name, h0_dims);
}
auto bias = Input("Bias");
if (bias != framework::kEmptyVarName) {
if (ctx->HasInput("Bias")) {
auto bias_dims = ctx->GetInputDim("Bias");
int bias_height = bias_dims[0];
int bias_width = bias_dims[1];

6
python/paddle/v2/framework/tests/test_gru_op.py
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@ -62,7 +62,7 @@ class TestGRUOp(OpTest):
return idx_in_seq_list
def gru_step(self, x, h_p, w, b):
print x.shape, h_p.shape, w.shape, b.shape
# print x.shape, h_p.shape, w.shape, b.shape
batch_size = x.shape[0]
frame_size = w.shape[0]
g = x + np.tile(b, (batch_size, 1))
@ -96,7 +96,7 @@ class TestGRUOp(OpTest):
num_batch = len(idx_in_seq_list)
end_idx = 0
for batch_idx in range(num_batch):
print idx_in_seq_list[batch_idx]
# print idx_in_seq_list[batch_idx]
x = input[idx_in_seq_list[batch_idx]]
g, r_h_p, h = self.gru_step(x, h_p, w, b)
if batch_idx < (num_batch - 1):
@ -112,7 +112,7 @@ class TestGRUOp(OpTest):
def set_data(self):
lod = [[0, 2, 6, 9]] #[[0, 1, 2, 3]]
self.idx_in_seq_list = self.seq_to_batch(lod, self.is_reverse)
print self.idx_in_seq_list
# print self.idx_in_seq_list
batch_size = self.batch_size
frame_size = self.frame_size
input = np.random.rand(batch_size, frame_size * 3).astype('float64')

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