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Implemented by boost preprocessor.

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mobile_baidu
yangyaming 8 years ago
parent 8be9930fdb
commit ad5e7cc031
6 changed files with 347 additions and 0 deletions
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103
paddle/operators/expand_op.cc
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/* 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/expand_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class ExpandOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
std::vector<int> expand_times = Attr<std::vector<int>>("expandTimes");
auto* x = ctx.Input<Tensor>("X");
auto x_dims = x->dims();
PADDLE_ENFORCE_EQ(static_cast<size_t>(framework::arity(x_dims)),
expand_times.size(),
"Number of attribute (expandTimes) value must be equal "
"to rank of X.");
PADDLE_ENFORCE_LE(framework::arity(x_dims), 6,
"Rank of X must not be greater than 6.");
std::vector<int64_t> out_shape(x_dims.size());
for (size_t i = 0; i < expand_times.size(); ++i) {
PADDLE_ENFORCE_GE(expand_times[i], 1,
"Each value of expand times should not be "
"less than 1.");
out_shape[i] = x_dims[i] * expand_times[i];
}
auto* out = ctx.Output<Tensor>("Out");
out->Resize(framework::make_ddim(out_shape));
}
};
class ExpandOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ExpandOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input tensor.");
AddOutput("Out", "Expanded result by tiling input X.");
AddAttr<std::vector<int>>("expandTimes",
"Expand times for each dimension.");
AddComment(R"DOC(
Expand operator tiles the input by given times. You should set times for each
dimension by providing attribute 'expandTimes'. Rank of input tensor should be
in [1, 6]. Please draw an inttention that size of 'expandTimes' must be same
with rank of input tensor.
)DOC");
}
};
class ExpandGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
std::vector<int> expand_times = Attr<std::vector<int>>("expandTimes");
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto* x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
for (size_t i = 0; i < expand_times.size(); ++i) {
PADDLE_ENFORCE_EQ(x_dims[i] * expand_times[i], out_dims[i],
"Size of each dimension of Input(Out@GRAD) should be "
"equal to multiplication of crroresponding sizes of "
"Input(X) and expandTimes.");
}
if (x_grad) x_grad->Resize(x_dims);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(expand, ops::ExpandOp, ops::ExpandOpMaker, expand_grad,
ops::ExpandGradOp);
REGISTER_OP_CPU_KERNEL(expand,
ops::ExpandKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
expand_grad, ops::ExpandGradKernel<paddle::platform::CPUPlace, float>);

23
paddle/operators/expand_op.cu
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/* 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/expand_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(expand,
ops::ExpandKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
expand_grad, ops::ExpandGradKernel<paddle::platform::GPUPlace, float>);

152
paddle/operators/expand_op.h
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/* 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 <boost/preprocessor/arithmetic/div.hpp>
#include <boost/preprocessor/arithmetic/mod.hpp>
#include <boost/preprocessor/comparison/greater.hpp>
#include <boost/preprocessor/comparison/greater_equal.hpp>
#include <boost/preprocessor/control/if.hpp>
#include <boost/preprocessor/repetition/repeat.hpp>
#include <iostream>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#define EXPAND_TEMPLATE(z, n, data) \
case n + 1: { \
Expand<n + 1>(context); \
break; \
}
#define REP_EXPAND_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_TEMPLATE, ~)
#define COND(n) BOOST_PP_GREATER_EQUAL(BOOST_PP_DIV(n, 6), BOOST_PP_MOD(n, 6))
#define EXPAND_GRAD_CASE(n) \
case n: { \
ExpandBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
break; \
}
#define EXPAND_TEMPLATE_GRAD(z, n, data) \
BOOST_PP_IF(COND(n), EXPAND_GRAD_CASE(n), )
#define REP_EXPAND_GRAD_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_TEMPLATE_GRAD, ~)
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename Place, typename T>
class ExpandKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto rank = framework::arity(context.Input<Tensor>("X")->dims());
switch (rank) {
REP_EXPAND_TEMPLATE(6)
default:
PADDLE_ENFORCE(false, "Only support tensor whose rank in [1, 6].");
};
}
protected:
template <int Rank>
void Expand(const framework::ExecutionContext& context) const {
auto* in0 = context.Input<Tensor>("X");
auto expand_times = context.Attr<std::vector<int>>("expandTimes");
auto* out0 = context.Output<Tensor>("Out");
Eigen::DSizes<int, Rank> bcast_dims;
auto x_dims = in0->dims();
for (size_t i = 0; i < expand_times.size(); ++i) {
bcast_dims[i] = expand_times[i];
}
auto x = EigenTensor<T, Rank>::From(*in0);
out0->mutable_data<T>(context.GetPlace());
auto y = EigenTensor<T, Rank>::From(*out0);
auto place = context.GetEigenDevice<Place>();
y.device(place) = x.broadcast(bcast_dims);
}
};
template <typename Place, typename T>
class ExpandGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in0 = context.Input<Tensor>("X");
auto expand_times = context.Attr<std::vector<int>>("expandTimes");
auto x_dims = in0->dims();
std::vector<int> reshape_dims_vec;
std::vector<int> reduce_dims_vec;
for (size_t i = 0; i < expand_times.size(); ++i) {
if (expand_times[i] == 1) {
reshape_dims_vec.push_back(x_dims[i]);
} else {
if (x_dims[i] == 1) {
reduce_dims_vec.push_back(reshape_dims_vec.size());
reshape_dims_vec.push_back(expand_times[i]);
} else {
reduce_dims_vec.push_back(reshape_dims_vec.size());
reshape_dims_vec.push_back(expand_times[i]);
reshape_dims_vec.push_back(x_dims[i]);
}
}
}
int dims = reshape_dims_vec.size() * 6 + reduce_dims_vec.size() - 7;
switch (dims) {
REP_EXPAND_GRAD_TEMPLATE(72)
default:
PADDLE_ENFORCE(false, "Only support tensor whose rank in [1, 6].");
};
}
protected:
template <int Dims>
void ExpandBackward(const framework::ExecutionContext& context,
const std::vector<int>& reshape_dims_vec,
const std::vector<int>& reduce_dims_vec) const {
size_t reshape_size = Dims / 6 + 1;
size_t reduce_size = Dims % 6 + 1;
PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
"Inconsistent size between Dims and "
"reshape dimensions.");
PADDLE_ENFORCE_EQ(reduce_size, reduce_dims_vec.size(),
"Inconsistent size between Dims and "
"reduce dimensions.");
auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
auto x = EigenVector<T>::Flatten(*(context.Input<Tensor>("X")));
out0->mutable_data<T>(context.GetPlace());
auto x_grad = EigenVector<T>::Flatten(*out0);
Eigen::DSizes<int, Dims / 6 + 1> reshape_dims;
for (size_t i = 0; i < reshape_size; ++i) {
reshape_dims[i] = reshape_dims_vec[i];
}
Eigen::DSizes<int, Dims % 6 + 1> reduce_dims;
for (size_t i = 0; i < reduce_size; ++i) {
reduce_dims[i] = reduce_dims_vec[i];
}
auto out_grad = EigenVector<T>::Flatten(*in0);
x_grad.device(context.GetEigenDevice<Place>()) =
out_grad.reshape(reshape_dims).sum(reduce_dims).reshape(x.dimensions());
}
};
} // operators
} // paddle

1
paddle/pybind/pybind.cc
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@ -54,6 +54,7 @@ USE_CPU_ONLY_OP(concat);
USE_OP(top_k);
USE_OP(squared_l2_distance);
USE_OP(sum);
USE_OP(expand);
namespace paddle {
namespace framework {

1
python/paddle/v2/framework/tests/CMakeLists.txt
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@ -35,3 +35,4 @@ py_test(test_sum_op SRCS test_sum_op.py)
py_test(mnist SRCS mnist.py)
py_test(test_concat_op SRCS test_concat_op.py)
py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py)
py_test(test_expand_op SRCS test_expand_op.py)

67
python/paddle/v2/framework/tests/test_expand_op.py
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import unittest
import numpy as np
from op_test import OpTest
class TestExpandOpRank1(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random(12).astype("float32")}
self.attrs = {'expandTimes': [2]}
output = np.tile(self.inputs['X'], 2)
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandOpRank2(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((12, 14)).astype("float32")}
self.attrs = {'expandTimes': [3, 4]}
output = np.tile(self.inputs['X'], (3, 4))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandOpRank3(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((2, 4, 5)).astype("float32")}
self.attrs = {'expandTimes': [3, 2, 1]}
output = np.tile(self.inputs['X'], (3, 2, 1))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandOpRank4(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((2, 4, 5, 7)).astype("float32")}
self.attrs = {'expandTimes': [3, 2, 1, 2]}
output = np.tile(self.inputs['X'], (3, 2, 1, 2))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == "__main__":
unittest.main()
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