Yancey1989
8 years ago
parent
d7db15f3e5
commit
be3fa7926e
@ -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_concat_op.h"
|
||||||
|
|
||||||
|
namespace ops = paddle::operators;
|
||||||
|
REGISTER_OP_GPU_KERNEL(
|
||||||
|
sequence_concat,
|
||||||
|
ops::SequenceConcatOpKernel<paddle::platform::GPUPlace, float>);
|
||||||
|
REGISTER_OP_GPU_KERNEL(
|
||||||
|
sequence_concat_grad,
|
||||||
|
ops::SequenceConcatGradOpKernel<paddle::platform::GPUPlace, float>);
|
||||||
@ -0,0 +1,106 @@
|
|||||||
|
/* 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_concat_op.h"
|
||||||
|
|
||||||
|
namespace paddle {
|
||||||
|
namespace operators {
|
||||||
|
|
||||||
|
class SequenceConcatOp : public framework::OperatorWithKernel {
|
||||||
|
public:
|
||||||
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
void InferShape(framework::InferShapeContextBase* ctx) const override {
|
||||||
|
PADDLE_ENFORCE_GT(ctx->Inputs("X").size(), 0UL,
|
||||||
|
"Inputs(X) of SequenceConcatOp should not be empty.");
|
||||||
|
PADDLE_ENFORCE(ctx->HasOutput("Out"),
|
||||||
|
"Output(Out) of SequenceConcatOp should not be null.");
|
||||||
|
const size_t level = static_cast<size_t>(ctx->Attrs().Get<int>("level"));
|
||||||
|
const size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
|
||||||
|
PADDLE_ENFORCE(level == 0UL || level == 1UL,
|
||||||
|
"Sequence Concat Op only support one or two sequence now.");
|
||||||
|
auto ins_dims = ctx->GetInputsDim("X");
|
||||||
|
framework::DDim out_dims = ins_dims[0];
|
||||||
|
const size_t n = ins_dims.size();
|
||||||
|
for (size_t i = 1; i < n; i++) {
|
||||||
|
out_dims[axis] += ins_dims[i][axis];
|
||||||
|
}
|
||||||
|
ctx->SetOutputDim("Out", out_dims);
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
|
||||||
|
public:
|
||||||
|
SequenceConcatOpMaker(framework::OpProto* proto,
|
||||||
|
framework::OpAttrChecker* op_checker)
|
||||||
|
: OpProtoAndCheckerMaker(proto, op_checker) {
|
||||||
|
AddInput("X",
|
||||||
|
"Multip LodTensors, the variable-length inputs of "
|
||||||
|
"SequenceConcatOp")
|
||||||
|
.AsDuplicable();
|
||||||
|
AddOutput("Out",
|
||||||
|
"A float LodTensor, the variable-length output of "
|
||||||
|
"SequenceConcatOp.");
|
||||||
|
AddAttr<int>("axis",
|
||||||
|
"The axis which the inputs will be joined with."
|
||||||
|
"If axis is 0, the inputs will be joined with Lod index.")
|
||||||
|
.SetDefault(0);
|
||||||
|
AddAttr<int>("level",
|
||||||
|
"The level which the inputs will be joined with."
|
||||||
|
"If level is 0, the inputs will be joined with word."
|
||||||
|
"If level is 1, the inputs will be joined with sentence.")
|
||||||
|
.SetDefault(0);
|
||||||
|
AddComment(R"DOC(
|
||||||
|
SequenceConcatOp concat multip LodTensors and only supports one or two levels.
|
||||||
|
- Case1:
|
||||||
|
axis is 1, level is 1, the Lod of Inputs are the same,
|
||||||
|
LoD(x0) = {{0,2,4},{0,1,2,3,4}}; Dims(x0) = (2,3,4)
|
||||||
|
LoD(x1) = {{0,2,4},{0,1,2,3,4}}; Dims(x1) = (2,4,4)
|
||||||
|
LoD(Out) = {{0,2,4},{01,2,3,4}}; Dims(Out) = (2,7,4)
|
||||||
|
- Case2:
|
||||||
|
If axis is 0, level is 1, the Lod of inputs are different,
|
||||||
|
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (2,3,4)
|
||||||
|
LoD(x1) = {{0,3,5}, {0,1,3,4,5}}; Dims(x1) = (3,3,4)
|
||||||
|
LoD(Out) = {{0,5,9}, {0,1,2,4,5,6,7,8,9}}; Dims(Out) = (5,3,4)
|
||||||
|
)DOC");
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
class SequenceConcatGradOp : public framework::OperatorWithKernel {
|
||||||
|
public:
|
||||||
|
using framework::OperatorWithKernel::OperatorWithKernel;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
void InferShape(framework::InferShapeContextBase* ctx) const override {
|
||||||
|
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
|
||||||
|
"Gradient of Out should not be null.");
|
||||||
|
PADDLE_ENFORCE_GT(ctx->Outputs(framework::GradVarName("X")).size(), 0UL,
|
||||||
|
"Gradient of X should not be empty.")
|
||||||
|
ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
|
|
||||||
|
namespace ops = paddle::operators;
|
||||||
|
REGISTER_OP(sequence_concat, ops::SequenceConcatOp, ops::SequenceConcatOpMaker,
|
||||||
|
sequence_concat_grad, ops::SequenceConcatGradOp);
|
||||||
|
REGISTER_OP_CPU_KERNEL(
|
||||||
|
sequence_concat,
|
||||||
|
ops::SequenceConcatOpKernel<paddle::platform::CPUPlace, float>);
|
||||||
|
REGISTER_OP_CPU_KERNEL(
|
||||||
|
sequence_concat_grad,
|
||||||
|
ops::SequenceConcatGradOpKernel<paddle::platform::CPUPlace, float>);
|
||||||
@ -0,0 +1,148 @@
|
|||||||
|
/* 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/op_registry.h"
|
||||||
|
#include "paddle/operators/strided_memcpy.h"
|
||||||
|
|
||||||
|
namespace paddle {
|
||||||
|
namespace operators {
|
||||||
|
|
||||||
|
using Tensor = framework::Tensor;
|
||||||
|
using LoDTensor = framework::LoDTensor;
|
||||||
|
using LoD = framework::LoD;
|
||||||
|
|
||||||
|
// Concat Lod, the initialized Lod of Output is lod(x0),
|
||||||
|
// if axis is not 0, the LoD(Out) will be the same as Inputs, if axis is 0:
|
||||||
|
// Case1:
|
||||||
|
// There is one level, the Output LoD will be modified:
|
||||||
|
// LoD(x0) = {{0,2,4}}
|
||||||
|
// LoD(x1) = {{0,1,5}}
|
||||||
|
// LoD(Out) = {{0,3,9}}
|
||||||
|
// Case2:
|
||||||
|
// There is two level, and concat level is 1,
|
||||||
|
// the Output LoD will be modified as followed:
|
||||||
|
// LoD(x0) = {{0,2,4}, {0,1,2,3,4}}
|
||||||
|
// LoD(x1) = {{0,3,5}, {0,1,3,4,5}}
|
||||||
|
// LoD(Out) = {{0,5,9}, {0,1,2,4,5,6,7,8,9}}
|
||||||
|
template <typename T>
|
||||||
|
LoD concatLod(const std::vector<const T*> ins, const size_t axis,
|
||||||
|
const size_t level) {
|
||||||
|
auto out_lod = ins[0]->lod();
|
||||||
|
const size_t n = ins.size();
|
||||||
|
if (axis == 0UL) {
|
||||||
|
if (level == 0) {
|
||||||
|
for (size_t i = 1; i < n; i++) {
|
||||||
|
for (size_t j = 0; j < ins[i]->lod()[0].size(); j++) {
|
||||||
|
out_lod[0][j] += ins[i]->lod()[0][j];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
} else if (level == 1) {
|
||||||
|
for (size_t i = 1; i < n; i++) {
|
||||||
|
PADDLE_ENFORCE_EQ(ins[i]->NumLevels(), 2UL,
|
||||||
|
"All the LoDTensors of Inputs(X) should "
|
||||||
|
"have two level.");
|
||||||
|
for (size_t j = 0; j < ins[i]->lod()[0].size(); j++) {
|
||||||
|
out_lod[0].push_back(ins[i]->lod()[0][j]);
|
||||||
|
}
|
||||||
|
for (size_t j = 0; j < ins[i]->lod()[1].size(); j++) {
|
||||||
|
out_lod[1][j] += ins[i]->lod()[1][j];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return out_lod;
|
||||||
|
}
|
||||||
|
|
||||||
|
template <typename Place, typename T>
|
||||||
|
class SequenceConcatOpKernel : public framework::OpKernel {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||||
|
auto ins = ctx.MultiInput<LoDTensor>("X");
|
||||||
|
auto* out = ctx.Output<LoDTensor>("Out");
|
||||||
|
const size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
|
||||||
|
const size_t level = static_cast<size_t>(ctx.Attr<int>("level"));
|
||||||
|
const size_t n = ins.size();
|
||||||
|
out->mutable_data<T>(ctx.GetPlace());
|
||||||
|
auto out_lod = concatLod<LoDTensor>(ins, axis, level);
|
||||||
|
out->set_lod(out_lod);
|
||||||
|
|
||||||
|
auto out_lod_level = out_lod[level];
|
||||||
|
for (size_t i = 0; i < out_lod_level.size() - 1; i++) {
|
||||||
|
Tensor out_t = out->Slice<T>(static_cast<int>(out_lod_level[i]),
|
||||||
|
static_cast<int>(out_lod_level[i + 1]));
|
||||||
|
auto out_stride = framework::stride(out_t.dims());
|
||||||
|
size_t offset = 0;
|
||||||
|
|
||||||
|
for (size_t j = 0; j < n; j++) {
|
||||||
|
auto in_lod_level = ins[j]->lod()[level];
|
||||||
|
auto in_stride = framework::stride(ins[j]->dims());
|
||||||
|
Tensor in_t = ins[j]->Slice<T>(static_cast<int>(in_lod_level[i]),
|
||||||
|
static_cast<int>(in_lod_level[i + 1]));
|
||||||
|
size_t axis_dim = in_t.dims()[axis];
|
||||||
|
StridedMemcpy<T>(ctx.device_context(), in_t.data<T>(), in_stride,
|
||||||
|
in_t.dims(), out_stride, out_t.data<T>() + offset);
|
||||||
|
offset += axis_dim * in_stride[axis];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <typename Place, typename T>
|
||||||
|
class SequenceConcatGradOpKernel : public framework::OpKernel {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||||
|
auto ins = ctx.MultiInput<framework::LoDTensor>("X");
|
||||||
|
auto* out_grad =
|
||||||
|
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
|
||||||
|
auto x_grads =
|
||||||
|
ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
|
||||||
|
size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
|
||||||
|
size_t level = static_cast<size_t>(ctx.Attr<int>("level"));
|
||||||
|
const size_t n = x_grads.size();
|
||||||
|
|
||||||
|
// Set Grad(X) LoD as X
|
||||||
|
for (size_t i = 0; i < n; i++) {
|
||||||
|
x_grads[i]->set_lod(ins[i]->lod());
|
||||||
|
x_grads[i]->mutable_data<T>(ctx.GetPlace());
|
||||||
|
}
|
||||||
|
|
||||||
|
auto out_lod = concatLod<LoDTensor>(ins, axis, level);
|
||||||
|
auto out_lod_level = out_lod[level];
|
||||||
|
|
||||||
|
for (size_t i = 0; i < out_lod_level.size() - 1; i++) {
|
||||||
|
Tensor out_grad_t =
|
||||||
|
out_grad->Slice<T>(static_cast<int>(out_lod_level[i]),
|
||||||
|
static_cast<int>(out_lod_level[i + 1]));
|
||||||
|
auto out_grad_stride = framework::stride(out_grad_t.dims());
|
||||||
|
size_t offset = 0;
|
||||||
|
|
||||||
|
for (size_t j = 0; j < n; j++) {
|
||||||
|
auto x_grad_lod_level = x_grads[j]->lod()[level];
|
||||||
|
auto x_grad_stride = framework::stride(x_grads[j]->dims());
|
||||||
|
Tensor x_grad_t =
|
||||||
|
x_grads[j]->Slice<T>(static_cast<int>(x_grad_lod_level[i]),
|
||||||
|
static_cast<int>(x_grad_lod_level[i + 1]));
|
||||||
|
size_t axis_dim = x_grad_t.dims()[axis];
|
||||||
|
StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>() + offset,
|
||||||
|
out_grad_stride, out_grad_t.dims(), x_grad_stride,
|
||||||
|
x_grad_t.data<T>());
|
||||||
|
offset += axis_dim * out_grad_stride[axis];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
@ -0,0 +1,57 @@
|
|||||||
|
import unittest
|
||||||
|
import numpy as np
|
||||||
|
from op_test import OpTest
|
||||||
|
|
||||||
|
|
||||||
|
class TestConcatOp(OpTest):
|
||||||
|
def set_data(self):
|
||||||
|
# two level, batch size is 3
|
||||||
|
x0 = np.random.random((11, 6, 3)).astype('float32')
|
||||||
|
lod0 = [[0, 2, 5, 11], [0, 1, 2, 5, 7, 11]]
|
||||||
|
x1 = np.random.random((11, 8, 3)).astype('float32')
|
||||||
|
lod1 = [[0, 2, 5, 11], [0, 1, 2, 5, 7, 11]]
|
||||||
|
axis = 1
|
||||||
|
level = 1
|
||||||
|
self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
|
||||||
|
self.attrs = {'axis': axis, 'level': level}
|
||||||
|
outs = []
|
||||||
|
for i in range(5):
|
||||||
|
sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
|
||||||
|
sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
|
||||||
|
outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
|
||||||
|
|
||||||
|
self.outputs = {'Out': np.concatenate(outs, axis=0)}
|
||||||
|
|
||||||
|
def setUp(self):
|
||||||
|
self.op_type = "sequence_concat"
|
||||||
|
self.set_data()
|
||||||
|
|
||||||
|
def test_check_output(self):
|
||||||
|
self.check_output()
|
||||||
|
|
||||||
|
def test_check_grad(self):
|
||||||
|
self.check_grad(['x0'], 'Out')
|
||||||
|
|
||||||
|
|
||||||
|
class TestConcatOpDiffLod(TestConcatOp):
|
||||||
|
def set_data(self):
|
||||||
|
# two level, batch size is 3
|
||||||
|
x0 = np.random.random((12, 6, 3)).astype('float32')
|
||||||
|
lod0 = [[0, 3, 9, 12], [0, 2, 3, 5, 9, 12]]
|
||||||
|
x1 = np.random.random((11, 6, 3)).astype('float32')
|
||||||
|
lod1 = [[0, 2, 5, 11], [0, 1, 2, 5, 7, 11]]
|
||||||
|
axis = 0
|
||||||
|
level = 1
|
||||||
|
self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
|
||||||
|
self.attrs = {'axis': axis, 'level': level}
|
||||||
|
outs = []
|
||||||
|
for i in range(5):
|
||||||
|
sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
|
||||||
|
sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
|
||||||
|
outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
|
||||||
|
|
||||||
|
self.outputs = {'Out': np.concatenate(outs, axis=0)}
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
unittest.main()
|
||||||
Loading…
Reference in new issue