Merge pull request #4472 from Xreki/core_add_sequence_softmax_op
Add sequence softmax operator.tonyyang-svail-feed-op-desgin
Cao Ying
8 years ago
committed by
GitHub
commit
a2657fea11
@ -1,17 +1,15 @@
|
||||
if(WITH_GPU)
|
||||
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc
|
||||
im2col.cu DEPS cblas device_context operator)
|
||||
im2col.cu DEPS cblas device_context operator)
|
||||
nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
|
||||
nv_library(softmax_function SRCS softmax.cc softmax.cu
|
||||
DEPS operator)
|
||||
nv_library(cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu
|
||||
DEPS operator)
|
||||
nv_library(softmax SRCS softmax.cc softmax.cu DEPS operator)
|
||||
nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
|
||||
else()
|
||||
cc_library(math_function SRCS math_function.cc im2col.cc
|
||||
DEPS cblas device_context operator)
|
||||
DEPS cblas device_context operator)
|
||||
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
|
||||
cc_library(softmax_function SRCS softmax.cc DEPS operator)
|
||||
cc_library(cross_entropy_function SRCS cross_entropy.cc DEPS operator)
|
||||
cc_library(softmax SRCS softmax.cc DEPS operator)
|
||||
cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
|
||||
endif()
|
||||
|
||||
cc_test(im2col_test SRCS im2col_test.cc DEPS math_function tensor)
|
||||
|
||||
@ -0,0 +1,103 @@
|
||||
/* 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/sequence_softmax_op.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
class SequenceSoftmaxOp : public framework::OperatorWithKernel {
|
||||
public:
|
||||
using framework::OperatorWithKernel::OperatorWithKernel;
|
||||
|
||||
protected:
|
||||
void InferShape(framework::InferShapeContextBase* ctx) const override {
|
||||
PADDLE_ENFORCE(ctx->HasInput("X"),
|
||||
"Input(X) of SequenceSoftmaxOp should not be null.");
|
||||
PADDLE_ENFORCE(ctx->HasOutput("Out"),
|
||||
"Output(Out) of SequenceSoftmaxOp should not be null.");
|
||||
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
|
||||
ctx->ShareLoD("X", /*->*/ "Out");
|
||||
}
|
||||
};
|
||||
|
||||
class SequenceSoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
|
||||
public:
|
||||
SequenceSoftmaxOpMaker(framework::OpProto* proto,
|
||||
framework::OpAttrChecker* op_checker)
|
||||
: OpProtoAndCheckerMaker(proto, op_checker) {
|
||||
AddInput("X",
|
||||
"(LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension "
|
||||
"of length 1.");
|
||||
AddOutput("Out",
|
||||
"(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension "
|
||||
"of length 1.");
|
||||
AddComment(R"DOC(
|
||||
SequenceSoftmaxOp computes softmax activation among all time-steps for each
|
||||
sequence. The dimension of each time-step should be 1. Thus, the shape of
|
||||
input Tensor can be either [N, 1] or [N], where N is the sum of all sequences'
|
||||
lengths.
|
||||
|
||||
Equation:
|
||||
for i-th sequence in a mini-batch:
|
||||
Out(X[lod[i]:lod[i+1]], :) =
|
||||
exp(X[lod[i]:lod[i+1], :]) / sum(exp(X[lod[i]:lod[i+1], :]))
|
||||
|
||||
For example, for a mini-batch of 3 sequences with variable-length,
|
||||
each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
|
||||
then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :]
|
||||
and N turns out to be 7.
|
||||
)DOC");
|
||||
}
|
||||
};
|
||||
|
||||
class SequenceSoftmaxGradOp : public framework::OperatorWithKernel {
|
||||
public:
|
||||
using framework::OperatorWithKernel::OperatorWithKernel;
|
||||
|
||||
protected:
|
||||
void InferShape(framework::InferShapeContextBase* ctx) const override {
|
||||
PADDLE_ENFORCE(ctx->HasInput("Out"),
|
||||
"Input(Out) of SequenceSoftmaxGradOp should not be null.");
|
||||
PADDLE_ENFORCE(
|
||||
ctx->HasInput(framework::GradVarName("Out")),
|
||||
"Input(Out@GRAD) of SequenceSoftmaxGradOp should not be null.");
|
||||
PADDLE_ENFORCE(ctx->HasInput("X"),
|
||||
"Input(X) of SequenceSoftmaxOp should not be null.");
|
||||
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
|
||||
"Output(X@GRAD) of SequenceSoftmaxOp should not be null.");
|
||||
|
||||
PADDLE_ENFORCE_EQ(
|
||||
ctx->GetInputDim("Out"),
|
||||
ctx->GetInputDim(framework::GradVarName("Out")),
|
||||
"Input(Out) and Input(Out@GRAD) of SequenceSoftmaxGradOp should be of "
|
||||
"the same shape.");
|
||||
|
||||
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
|
||||
namespace ops = paddle::operators;
|
||||
REGISTER_OP(sequence_softmax, ops::SequenceSoftmaxOp,
|
||||
ops::SequenceSoftmaxOpMaker, sequence_softmax_grad,
|
||||
ops::SequenceSoftmaxGradOp);
|
||||
REGISTER_OP_CPU_KERNEL(
|
||||
sequence_softmax,
|
||||
ops::SequenceSoftmaxKernel<paddle::platform::CPUPlace, float>);
|
||||
REGISTER_OP_CPU_KERNEL(
|
||||
sequence_softmax_grad,
|
||||
ops::SequenceSoftmaxGradKernel<paddle::platform::CPUPlace, float>);
|
||||
@ -0,0 +1,25 @@
|
||||
/* 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. */
|
||||
|
||||
#define EIGEN_USE_GPU
|
||||
|
||||
#include "paddle/operators/sequence_softmax_op.h"
|
||||
|
||||
namespace ops = paddle::operators;
|
||||
REGISTER_OP_GPU_KERNEL(
|
||||
sequence_softmax,
|
||||
ops::SequenceSoftmaxKernel<paddle::platform::GPUPlace, float>)
|
||||
REGISTER_OP_GPU_KERNEL(
|
||||
sequence_softmax_grad,
|
||||
ops::SequenceSoftmaxGradKernel<paddle::platform::GPUPlace, float>);
|
||||
@ -0,0 +1,94 @@
|
||||
/* 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. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "paddle/framework/eigen.h"
|
||||
#include "paddle/framework/op_registry.h"
|
||||
#include "paddle/operators/math/softmax.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
using Tensor = framework::Tensor;
|
||||
using LoDTensor = framework::LoDTensor;
|
||||
|
||||
template <typename Place, typename T>
|
||||
class SequenceSoftmaxKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||
auto* x = ctx.Input<LoDTensor>("X");
|
||||
auto* out = ctx.Output<LoDTensor>("Out");
|
||||
|
||||
auto lod = x->lod();
|
||||
auto dims = x->dims();
|
||||
|
||||
const size_t level = lod.size() - 1;
|
||||
PADDLE_ENFORCE_EQ(dims[0], static_cast<int64_t>(lod[level].back()),
|
||||
"The first dimension of Input(X) should be equal to the "
|
||||
"sum of all sequences' lengths.");
|
||||
PADDLE_ENFORCE_EQ(dims[0], x->numel(),
|
||||
"The width of each timestep in Input(X) of "
|
||||
"SequenceSoftmaxOp should be 1.");
|
||||
|
||||
out->mutable_data<T>(ctx.GetPlace());
|
||||
for (int i = 0; i < static_cast<int>(lod[level].size()) - 1; ++i) {
|
||||
int start_pos = static_cast<int>(lod[level][i]);
|
||||
int end_pos = static_cast<int>(lod[level][i + 1]);
|
||||
Tensor x_i = x->Slice<T>(start_pos, end_pos);
|
||||
Tensor out_i = out->Slice<T>(start_pos, end_pos);
|
||||
|
||||
// Reshape from (end_pos - start_pos) x 1UL to 1UL x (end_pos - start_pos)
|
||||
framework::DDim dims_i = framework::make_ddim({1UL, end_pos - start_pos});
|
||||
x_i.Resize(dims_i);
|
||||
out_i.Resize(dims_i);
|
||||
math::SoftmaxFunctor<Place, T>()(ctx.device_context(), &x_i, &out_i);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <typename Place, typename T>
|
||||
class SequenceSoftmaxGradKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||
auto* out = ctx.Input<LoDTensor>("Out");
|
||||
auto* out_grad = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
|
||||
auto* x = ctx.Input<LoDTensor>("X");
|
||||
auto* x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
|
||||
|
||||
auto lod = x->lod();
|
||||
const size_t level = lod.size() - 1;
|
||||
|
||||
x_grad->mutable_data<T>(ctx.GetPlace());
|
||||
for (int i = 0; i < static_cast<int>(lod[level].size()) - 1; ++i) {
|
||||
int start_pos = static_cast<int>(lod[level][i]);
|
||||
int end_pos = static_cast<int>(lod[level][i + 1]);
|
||||
|
||||
Tensor out_i = out->Slice<T>(start_pos, end_pos);
|
||||
Tensor out_grad_i = out_grad->Slice<T>(start_pos, end_pos);
|
||||
Tensor x_grad_i = x_grad->Slice<T>(start_pos, end_pos);
|
||||
|
||||
// Reshape from (end_pos - start_pos) x 1UL to 1UL x (end_pos - start_pos)
|
||||
framework::DDim dims_i = framework::make_ddim({1UL, end_pos - start_pos});
|
||||
out_i.Resize(dims_i);
|
||||
out_grad_i.Resize(dims_i);
|
||||
x_grad_i.Resize(dims_i);
|
||||
math::SoftmaxGradFunctor<Place, T>()(ctx.device_context(), &out_i,
|
||||
&out_grad_i, &x_grad_i);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,38 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
from op_test import OpTest
|
||||
|
||||
|
||||
def stable_softmax(x):
|
||||
"""Compute the softmax of vector x in a numerically stable way."""
|
||||
shiftx = x - np.max(x).clip(-64.)
|
||||
exps = np.exp(shiftx)
|
||||
return exps / np.sum(exps)
|
||||
|
||||
|
||||
class TestSequenceSoftmaxOp(OpTest):
|
||||
def setUp(self):
|
||||
self.op_type = "sequence_softmax"
|
||||
x = np.random.uniform(0.1, 1, (11, 1)).astype("float32")
|
||||
lod = [[0, 4, 5, 8, 11]]
|
||||
|
||||
out = np.zeros((11, 1)).astype("float32")
|
||||
for i in range(4):
|
||||
sub_x = x[lod[0][i]:lod[0][i + 1], :]
|
||||
sub_x = sub_x.reshape(1, lod[0][i + 1] - lod[0][i])
|
||||
sub_out = stable_softmax(sub_x)
|
||||
out[lod[0][i]:lod[0][i + 1], :] = sub_out.reshape(
|
||||
lod[0][i + 1] - lod[0][i], 1)
|
||||
|
||||
self.inputs = {"X": (x, lod)}
|
||||
self.outputs = {"Out": out}
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output()
|
||||
|
||||
def test_check_grad(self):
|
||||
self.check_grad(["X"], "Out", max_relative_error=0.01)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue