You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
270 lines
12 KiB
270 lines
12 KiB
/* 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/linear_chain_crf_op.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
LinearChainCRFOpMaker(OpProto* proto, OpAttrChecker* op_checker)
|
|
: OpProtoAndCheckerMaker(proto, op_checker) {
|
|
AddInput("Emission",
|
|
"(LoDTensor, default LoDTensor<float>) "
|
|
"A 2-D LoDTensor with shape [N x D], where N is the size of the "
|
|
"mini-batch and D is the total tag number. The unscaled emission "
|
|
"weight matrix for the linear chain CRF. ");
|
|
AddInput("Transition",
|
|
"(Tensor, default Tensor<float>) A 2-D Tensor with shape "
|
|
"[(D + 2) x D]. The learnable parameter for the linear_chain_crf "
|
|
"operator. See more details in the operator's comments.");
|
|
AddInput("Label",
|
|
"(LoDTensor, default LoDTensor<int64_t>) A LoDTensor with shape "
|
|
"[N x 1], where N is the total element number in a mini-batch. "
|
|
"The ground truth.");
|
|
AddOutput(
|
|
"Alpha",
|
|
"(Tensor, default Tensor<float>) A 2-D Tensor with shape [N x D]. "
|
|
"The forward vectors for the entire batch. Denote it as $\alpha$. "
|
|
"$\alpha$ is a memo table used to calculate the normalization "
|
|
"factor in CRF. $\alpha[k, v]$ stores the unnormalized "
|
|
"probabilites of all possible unfinished sequences of tags that end at "
|
|
"position $k$ with tag $v$. For each $k$, "
|
|
"$\alpha[k, v]$ is a vector of length $D$ with a component for "
|
|
"each tag value $v$. This vector is called a forward vecotr and "
|
|
"will also be used in backward computations.")
|
|
.AsIntermediate();
|
|
AddOutput(
|
|
"EmissionExps",
|
|
"(Tensor, default Tensor<float>) A 2-D Tensor with shape [N x D]. "
|
|
"The exponentials of Input(Emission). This is an intermediate "
|
|
"computational result in forward computation, and will be reused in "
|
|
"backward computation.")
|
|
.AsIntermediate();
|
|
AddOutput(
|
|
"TransitionExps",
|
|
"(Tensor, default Tensor<float>) A 2-D Tensor with shape "
|
|
"[(D + 2) x D]. The exponentials of Input(Transition). This is an "
|
|
"intermediate computational result in forward computation, and "
|
|
"will be reused in backward computation.")
|
|
.AsIntermediate();
|
|
AddOutput(
|
|
"LogLikelihood",
|
|
"(Tensor, default Tensor<float>) The logarithm of the conditional "
|
|
"likelihood of each training sample in a mini-batch. This is a 2-D "
|
|
"tensor with shape [S x 1], where S is the sequence number in a "
|
|
"mini-batch. Note: S is equal to the sequence number in a mini-batch. "
|
|
"The output is no longer a LoDTensor.");
|
|
AddComment(R"DOC(
|
|
LinearChainCRF Operator.
|
|
|
|
Conditional Random Field defines an undirected probabilistic graph with nodes
|
|
denoting random variables and edges denoting dependencies between these
|
|
variables. CRF learns the conditional probability $P(Y|X)$, where
|
|
$X = (x_1, x_2, ... , x_n)$ are structured inputs and
|
|
$Y = (y_1, y_2, ... , y_n)$ are labels for the inputs.
|
|
|
|
Linear chain CRF is a special case of CRF that is useful for sequence labeling
|
|
task. Sequence labeling tasks do not assume a lot of conditional
|
|
independences among inputs. The only constraint they impose is that the input
|
|
and output must be linear sequences. Thus, the graph of such a CRF is a simple
|
|
chain or a line, which results in the linear chain CRF.
|
|
|
|
This operator implements the Forward-Backward algorithm for the linear chain
|
|
CRF. Please refer to http://www.cs.columbia.edu/~mcollins/fb.pdf and
|
|
http://cseweb.ucsd.edu/~elkan/250Bwinter2012/loglinearCRFs.pdf for details.
|
|
|
|
Equation:
|
|
1. Denote Input(Emission) to this operator as $x$ here.
|
|
2. The first D values of Input(Transition) to this operator are for starting
|
|
weights, denoted as $a$ here.
|
|
3. The next D values of Input(Transition) of this operator are for ending
|
|
weights, denoted as $b$ here.
|
|
4. The remaning values of Input(Transition) are for transition weights,
|
|
denoted as $w$ here.
|
|
5. Denote Input(Label) as $s$ here.
|
|
|
|
The probability of a sequence $s$ of length $L$ is defined as:
|
|
$$P(s) = (1/Z) \exp(a_{s_1} + b_{s_L}
|
|
+ \sum_{l=1}^L x_{s_l}
|
|
+ \sum_{l=2}^L w_{s_{l-1},s_l})$$
|
|
|
|
where $Z$ is a normalization value so that the sum of $P(s)$ over
|
|
all possible sequences is 1, and $x$ is the emission feature weight
|
|
to the linear chain CRF.
|
|
|
|
Finally, the linear chain CRF operator outputs the logarithm of the conditional
|
|
likelihood of each training sample in a mini-batch.
|
|
|
|
NOTE:
|
|
1. The feature function for a CRF is made up of the emission features and the
|
|
transition features. The emission feature weights are NOT computed in
|
|
this operator. They MUST be computed first before this operator is called.
|
|
|
|
2. Because this operator performs global normalization over all possible
|
|
sequences internally, it expects UNSCALED emission feature weights.
|
|
Please do not call this op with the emission feature being output of any
|
|
nonlinear activation.
|
|
|
|
3. The 2nd dimension of Input(Emission) MUST be equal to the tag number.
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class LinearChainCRFOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("Emission"),
|
|
"Input(Emission) should be not null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Transition"),
|
|
"Input(Transition) should be not null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
|
|
|
|
PADDLE_ENFORCE(ctx->HasOutput("Alpha"),
|
|
"Output(Alpha) should be not null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("EmissionExps"),
|
|
"Output(EmissionExps) should be not null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("TransitionExps"),
|
|
"Output(TransitionExps) should be not null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("LogLikelihood"),
|
|
"Output(LogLikelihood) should be not null.");
|
|
|
|
auto emission_dims = ctx->GetInputDim("Emission");
|
|
PADDLE_ENFORCE_EQ(emission_dims.size(), 2UL,
|
|
"The Input(Emission) should be a 2-D tensor.");
|
|
PADDLE_ENFORCE(emission_dims[0], "An empty mini-batch is not allowed.");
|
|
|
|
auto transition_dims = ctx->GetInputDim("Transition");
|
|
PADDLE_ENFORCE_EQ(transition_dims.size(), 2UL,
|
|
"The Input(Transition) should be a 2-D tensor.");
|
|
PADDLE_ENFORCE_EQ(
|
|
transition_dims[0] - 2, transition_dims[1],
|
|
"An invalid dimension for the Input(Transition), which should "
|
|
"be a 2-D tensor with shape [(D + 2) x D].");
|
|
PADDLE_ENFORCE_EQ(
|
|
emission_dims[1], transition_dims[1],
|
|
"The 2nd dimension of the Input(Emission) and the Input(Transition) "
|
|
"should be equal to the tag number.");
|
|
|
|
auto label_dims = ctx->GetInputDim("Label");
|
|
PADDLE_ENFORCE(label_dims.size() == 2UL && label_dims[1] == 1UL,
|
|
"The Input(Label) should be a 2-D tensor with the 2nd "
|
|
"dimensions fixed to 1.");
|
|
PADDLE_ENFORCE_EQ(
|
|
emission_dims[0], label_dims[0],
|
|
"The height of Input(Emission) and the height of Input(Label) "
|
|
"should be the same.");
|
|
|
|
ctx->SetOutputDim("Alpha", emission_dims);
|
|
ctx->SetOutputDim("EmissionExps", emission_dims);
|
|
ctx->SetOutputDim("TransitionExps", transition_dims);
|
|
// TODO(caoying) This is tricky. The 1st dimension of Output(LogLikelihood)
|
|
// is the sequence number in a mini-batch. The dimension set here should be
|
|
// resized to its correct size in the function Compute. Fix this once we can
|
|
// get LoD information in the InferShape interface.
|
|
ctx->SetOutputDim("LogLikelihood", {emission_dims[0], 1});
|
|
}
|
|
|
|
protected:
|
|
// Explicitly set that the data type of computation kernel of linear_chain_crf
|
|
// is determined by its input "Emission".
|
|
framework::OpKernelType GetActualKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
return framework::OpKernelType(
|
|
framework::ToDataType(ctx.Input<LoDTensor>("Emission")->type()),
|
|
ctx.device_context());
|
|
}
|
|
};
|
|
|
|
class LinearChainCRFGradOp : public framework::OperatorWithKernel {
|
|
public:
|
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
|
|
|
void InferShape(framework::InferShapeContext* ctx) const override {
|
|
PADDLE_ENFORCE(ctx->HasInput("EmissionExps"),
|
|
"Input(EmissionExps) should be not null.");
|
|
PADDLE_ENFORCE(ctx->HasInput("TransitionExps"),
|
|
"Input(TransitionExps) should be not null.");
|
|
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("LogLikelihood")),
|
|
"Input(LogLikelihood@GRAD) shoudl be not null.");
|
|
|
|
auto emission_exps_dims = ctx->GetInputDim("EmissionExps");
|
|
PADDLE_ENFORCE_EQ(emission_exps_dims.size(), 2UL,
|
|
"The Input(EmissionExps) should be a 2-D tensor.");
|
|
PADDLE_ENFORCE(emission_exps_dims[0],
|
|
"An empty mini-batch is not allowed.");
|
|
|
|
auto transition_exps_dims = ctx->GetInputDim("TransitionExps");
|
|
PADDLE_ENFORCE_EQ(transition_exps_dims.size(), 2UL,
|
|
"The Input(TransitionExps) should be a 2-D tensor.");
|
|
PADDLE_ENFORCE_EQ(
|
|
transition_exps_dims[0] - 2, transition_exps_dims[1],
|
|
"An invalid dimension for the Input(TransitionExps), which should "
|
|
"be a 2-D tensor with shape [(D + 2) x D].");
|
|
PADDLE_ENFORCE_EQ(
|
|
emission_exps_dims[1], transition_exps_dims[1],
|
|
"The 2nd dimension of the Input(EmissionExps) and the "
|
|
"Input(TransitionExps) should be equal to the tag number.");
|
|
|
|
auto label_dims = ctx->GetInputDim("Label");
|
|
PADDLE_ENFORCE(label_dims.size() == 2UL && label_dims[1] == 1UL,
|
|
"The Input(Label) should be a 2-D tensor with the 2nd "
|
|
"dimensions fixed to 1.");
|
|
PADDLE_ENFORCE_EQ(
|
|
emission_exps_dims[0], label_dims[0],
|
|
"The height of Input(EmissionExps) and the height of Input(Label) "
|
|
"should be the same.");
|
|
|
|
if (ctx->HasOutput(framework::GradVarName("Emission"))) {
|
|
ctx->SetOutputDim(framework::GradVarName("Emission"), emission_exps_dims);
|
|
}
|
|
if (ctx->HasOutput(framework::GradVarName("Transition"))) {
|
|
ctx->SetOutputDim(framework::GradVarName("Transition"),
|
|
transition_exps_dims);
|
|
}
|
|
}
|
|
|
|
protected:
|
|
// Explicitly set that the data type of output of the linear_chain_crf_grad
|
|
// operator is determined by its input: gradients of LogLikelihood.
|
|
framework::OpKernelType GetActualKernelType(
|
|
const framework::ExecutionContext& ctx) const override {
|
|
return framework::OpKernelType(
|
|
framework::ToDataType(
|
|
ctx.Input<LoDTensor>(framework::GradVarName("LogLikelihood"))
|
|
->type()),
|
|
ctx.device_context());
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
REGISTER_OP(linear_chain_crf, ops::LinearChainCRFOp, ops::LinearChainCRFOpMaker,
|
|
linear_chain_crf_grad, ops::LinearChainCRFGradOp);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
linear_chain_crf,
|
|
ops::LinearChainCRFOpKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::LinearChainCRFOpKernel<paddle::platform::CPUDeviceContext, double>);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
linear_chain_crf_grad,
|
|
ops::LinearChainCRFGradOpKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::LinearChainCRFGradOpKernel<paddle::platform::CPUDeviceContext,
|
|
double>);
|