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提交 d04c8538 编写于 作者: Y yangyaming
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Refine .cc and .h, more unit test more readable.

上级 0d9ba3da
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Showing with 46 addition and 32 deletion
paddle/operators/expand_op.cc
浏览文件 @ d04c8538
......@@ -25,13 +25,15 @@ class ExpandOp : public framework::OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null.");
std::vector<int> expand_times =
ctx->Attrs().Get<std::vector<int>>("expandTimes");
ctx->Attrs().Get<std::vector<int>>("expand_times");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims.size()), expand_times.size(),
"The number of Attr(expandTimes)'s value must be equal "
"The number of Attr(expand_times)'s value must be equal "
"to the rank of Input(X).");
PADDLE_ENFORCE_LE(x_dims.size(), 6,
"The rank of Input(X) must not be greater than 6.");
......@@ -39,14 +41,16 @@ class ExpandOp : public framework::OperatorWithKernel {
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 Attr(expandTimes) should not be "
"Each value of Attr(expand_times) should not be "
"less than 1.");
out_shape[i] = x_dims[i] * expand_times[i];
}
ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
if (out_shape[0] == x_dims[0]) {
ctx->ShareLoD("X", "Out");
}
}
};
class ExpandOpMaker : public framework::OpProtoAndCheckerMaker {
......@@ -61,13 +65,13 @@ class ExpandOpMaker : public framework::OpProtoAndCheckerMaker {
"The rank of Output(Out) is same as Input(X) except that each "
"dimension size of Output(Out) is equal to corresponding "
"dimension size of Input(X) multiplying corresponding value of "
"Attr(expandTimes).");
AddAttr<std::vector<int>>("expandTimes",
"Attr(expand_times).");
AddAttr<std::vector<int>>("expand_times",
"Expand times number for each dimension.");
AddComment(R"DOC(
Expand operator tiles the input by given times number. You should set times
number for each dimension by providing attribute 'expandTimes'. The rank of X
should be in [1, 6]. Please notice that size of 'expandTimes' must be same with
number for each dimension by providing attribute 'expand_times'. The rank of X
should be in [1, 6]. Please notice that size of 'expand_times' must be same with
X's rank.
)DOC");
}
......@@ -82,16 +86,17 @@ class ExpandGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
auto x_dims = ctx->GetInputDim("X");
std::vector<int> expand_times =
ctx->Attrs().Get<std::vector<int>>("expandTimes");
ctx->Attrs().Get<std::vector<int>>("expand_times");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
for (size_t i = 0; i < expand_times.size(); ++i) {
PADDLE_ENFORCE_EQ(x_dims[i] * expand_times[i], out_dims[i],
"Each dimension size of Input(Out@GRAD) should be "
"equal to multiplication of crroresponding dimension "
"size of Input(X) and Attr(expandTimes) value.");
"size of Input(X) and Attr(expand_times) value.");
}
auto x_grad_name = framework::GradVarName("X");
......
paddle/operators/expand_op.h
浏览文件 @ d04c8538
......@@ -25,14 +25,17 @@
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#define MAX_RANK_SUPPORTED 6
#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 COND(n) \
BOOST_PP_GREATER_EQUAL(BOOST_PP_DIV(n, MAX_RANK_SUPPORTED), \
BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
#define EXPAND_GRAD_CASE(n) \
case n: { \
ExpandBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
......@@ -46,7 +49,6 @@ 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>;
......@@ -60,7 +62,7 @@ class ExpandKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& context) const override {
auto rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
REP_EXPAND_TEMPLATE(6)
REP_EXPAND_TEMPLATE(MAX_RANK_SUPPORTED)
default:
PADDLE_ENFORCE(false,
"Only support tensor with rank being between 1 and 6.");
......@@ -71,7 +73,7 @@ class ExpandKernel : public framework::OpKernel<T> {
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& expand_times = context.Attr<std::vector<int>>("expand_times");
auto* out0 = context.Output<Tensor>("Out");
Eigen::DSizes<int, Rank> bcast_dims;
auto x_dims = in0->dims();
......@@ -91,8 +93,14 @@ class ExpandGradKernel : public framework::OpKernel<T> {
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& expand_times = context.Attr<std::vector<int>>("expand_times");
auto x_dims = in0->dims();
// 1. reshape_dims_vec is the broadcast parameter. For each dimension i,
// if expand_times[i] > 1 and x_dims[i] > 1, i will be splitted to two
// dimensions [expand_times[i], x_dims[i]].
// 2. reduce_dims_vec is the dimension parameter to compute gradients. For
// each dimension expanded, the gradients should be summed to original
// size.
std::vector<int> reshape_dims_vec;
std::vector<int> reduce_dims_vec;
for (size_t i = 0; i < expand_times.size(); ++i) {
......@@ -110,7 +118,8 @@ class ExpandGradKernel : public framework::OpKernel<T> {
}
}
int dims = reshape_dims_vec.size() * 6 + reduce_dims_vec.size() - 7;
int dims = reshape_dims_vec.size() * MAX_RANK_SUPPORTED +
reduce_dims_vec.size() - MAX_RANK_SUPPORTED - 1;
// no need reduce, just copy
if (reduce_dims_vec.size() == 0) {
auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
......@@ -132,8 +141,8 @@ class ExpandGradKernel : public framework::OpKernel<T> {
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;
size_t reshape_size = Dims / MAX_RANK_SUPPORTED + 1;
size_t reduce_size = Dims % MAX_RANK_SUPPORTED + 1;
PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
"Inconsistent size between template Dims and "
"reshape dimensions.");
......@@ -145,11 +154,11 @@ class ExpandGradKernel : public framework::OpKernel<T> {
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;
Eigen::DSizes<int, Dims / MAX_RANK_SUPPORTED + 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;
Eigen::DSizes<int, Dims % MAX_RANK_SUPPORTED + 1> reduce_dims;
for (size_t i = 0; i < reduce_size; ++i) {
reduce_dims[i] = reduce_dims_vec[i];
}
......
python/paddle/v2/framework/tests/test_expand_op.py
浏览文件 @ d04c8538
......@@ -7,7 +7,7 @@ class TestExpandOpRank1(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random(12).astype("float32")}
self.attrs = {'expandTimes': [2]}
self.attrs = {'expand_times': [2]}
output = np.tile(self.inputs['X'], 2)
self.outputs = {'Out': output}
......@@ -18,11 +18,11 @@ class TestExpandOpRank1(OpTest):
self.check_grad(['X'], 'Out')
class TestExpandOpRank2_1(OpTest):
class TestExpandOpRank2_Corner(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((12, 14)).astype("float32")}
self.attrs = {'expandTimes': [1, 1]}
self.attrs = {'expand_times': [1, 1]}
output = np.tile(self.inputs['X'], (1, 1))
self.outputs = {'Out': output}
......@@ -33,11 +33,11 @@ class TestExpandOpRank2_1(OpTest):
self.check_grad(['X'], 'Out')
class TestExpandOpRank2_2(OpTest):
class TestExpandOpRank2(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((12, 14)).astype("float32")}
self.attrs = {'expandTimes': [2, 3]}
self.attrs = {'expand_times': [2, 3]}
output = np.tile(self.inputs['X'], (2, 3))
self.outputs = {'Out': output}
......@@ -48,11 +48,11 @@ class TestExpandOpRank2_2(OpTest):
self.check_grad(['X'], 'Out')
class TestExpandOpRank3_1(OpTest):
class TestExpandOpRank3_Corner(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((2, 4, 5)).astype("float32")}
self.attrs = {'expandTimes': [1, 1, 1]}
self.attrs = {'expand_times': [1, 1, 1]}
output = np.tile(self.inputs['X'], (1, 1, 1))
self.outputs = {'Out': output}
......@@ -63,11 +63,11 @@ class TestExpandOpRank3_1(OpTest):
self.check_grad(['X'], 'Out')
class TestExpandOpRank3_2(OpTest):
class TestExpandOpRank3(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((2, 4, 5)).astype("float32")}
self.attrs = {'expandTimes': [2, 1, 4]}
self.attrs = {'expand_times': [2, 1, 4]}
output = np.tile(self.inputs['X'], (2, 1, 4))
self.outputs = {'Out': output}
......@@ -82,7 +82,7 @@ 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]}
self.attrs = {'expand_times': [3, 2, 1, 2]}
output = np.tile(self.inputs['X'], (3, 2, 1, 2))
self.outputs = {'Out': output}
......
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