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Merge pull request #4193 from luotao1/seq_pool

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implement framework of seq_pool_op and its unitest
update-doc-pybind
Tao Luo 7 years ago committed by GitHub
parent 5b5f4f5140 244efd6b48
commit 01bec25734
4 changed files with 140 additions and 53 deletions
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62
paddle/operators/sequence_avg_pool_op.cc → paddle/operators/sequence_pool_op.cc
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@ -12,22 +12,22 @@ 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_avg_pool_op.h"
#include "paddle/operators/sequence_pool_op.h"
namespace paddle {
namespace operators {
class SequenceAvgPoolOp : public framework::OperatorWithKernel {
class SequencePoolOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("X"), "Input(X) of SequenceAvgPoolOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of SequencePoolOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of SequenceAvgPoolOp should not be null.");
"Output(Out) of SequencePoolOp should not be null.");
auto* x = ctx.Input<framework::LoDTensor>("X");
auto dims = x->dims();
@ -42,21 +42,45 @@ class SequenceAvgPoolOp : public framework::OperatorWithKernel {
}
};
class SequenceAvgPoolOpMaker : public framework::OpProtoAndCheckerMaker {
class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceAvgPoolOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
SequencePoolOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of SequenceAvgPoolOp.");
AddOutput("Out", "The output of SequenceAvgPoolOp.");
AddInput("X",
"A float LoDTensor, the variable-length input of SequencePoolOp");
AddOutput(
"Out",
"A float LoDTensor, the variable-length output of SequencePoolOp.");
AddAttr<int>(
"strategy",
"(int, default AVERAGE) the pooling strategy of SequencePoolOp.")
.SetDefault(AVERAGE)
.InEnum({AVERAGE, SUM, SQRT, MAX, LAST, FIRST});
AddComment(R"DOC(
SequenceAvgPoolOp averages features of all time-steps of each instance.
More detailed comments will be added later.
SequencePoolOp pools features of all time-steps of each instance.
For a mini-batch of 3 variable lengths sentences, containing 2, 3, and 2 time-steps:
Assume X is a [7,M,N] float LoDTensor, and X->lod()[0] = [0, 2, 5, 7].
Besides, for the sake of simplicity, we assume M=1 and N=1,
and the value of X = [[1, 3], [2, 4, 6], [5, 1]].
Thus, Out is a [3,1,1] float LoDTensor, but Out->lod() is nullptr.
And for different strategy, the value of Out is as follows:
- AVERAGE: [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
- SUM: [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
- SQRT: [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
- MAX: [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
- LAST: [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
- FIRST: [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
)DOC");
}
};
class SequenceAvgPoolGradOp : public framework::OperatorWithKernel {
class SequencePoolGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
@ -84,12 +108,10 @@ class SequenceAvgPoolGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sequence_avg_pool, ops::SequenceAvgPoolOp,
ops::SequenceAvgPoolOpMaker, sequence_avg_pool_grad,
ops::SequenceAvgPoolGradOp);
REGISTER_OP(sequence_pool, ops::SequencePoolOp, ops::SequencePoolOpMaker,
sequence_pool_grad, ops::SequencePoolGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_avg_pool,
ops::SequenceAvgPoolKernel<paddle::platform::CPUPlace, float>);
sequence_pool, ops::SequencePoolKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sequence_avg_pool_grad,
ops::SequenceAvgPoolGradKernel<paddle::platform::CPUPlace, float>);
sequence_pool_grad,
ops::SequencePoolGradKernel<paddle::platform::CPUPlace, float>);

9
paddle/operators/sequence_avg_pool_op.cu → paddle/operators/sequence_pool_op.cu
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@ -14,12 +14,11 @@
#define EIGEN_USE_GPU
#include "paddle/operators/sequence_avg_pool_op.h"
#include "paddle/operators/sequence_pool_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
sequence_avg_pool,
ops::SequenceAvgPoolKernel<paddle::platform::GPUPlace, float>);
sequence_pool, ops::SequencePoolKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
sequence_avg_pool_grad,
ops::SequenceAvgPoolGradKernel<paddle::platform::GPUPlace, float>);
sequence_pool_grad,
ops::SequencePoolGradKernel<paddle::platform::GPUPlace, float>);

59
paddle/operators/sequence_avg_pool_op.h → paddle/operators/sequence_pool_op.h
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@ -28,54 +28,85 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
enum SeqPoolType {
AVERAGE = 0,
SUM = 1,
SQRT = 2, // square_root_n
MAX = 3,
LAST = 4,
FIRST = 5
};
template <typename Place, typename T>
class SequenceAvgPoolKernel : public framework::OpKernel {
class SequencePoolKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int strategy = context.Attr<int>("strategy");
auto dims = in->dims();
auto lod = in->lod();
auto lod = in->lod()[0];
int64_t w = in->numel() / dims[0];
out->mutable_data<T>(context.GetPlace());
auto place = context.GetEigenDevice<Place>();
for (int i = 0; i < static_cast<int>(lod[0].size()) - 1; ++i) {
Tensor in_t = in->Slice<T>(static_cast<int>(lod[0][i]),
static_cast<int>(lod[0][i + 1]));
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
Tensor in_t =
in->Slice<T>(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
Tensor out_t = out->Slice<T>(i, i + 1);
int64_t h = static_cast<int64_t>(lod[0][i + 1] - lod[0][i]);
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w}));
auto out_e = EigenVector<T>::Flatten(out_t);
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
switch (strategy) {
case AVERAGE:
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
break;
case SUM:
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}}));
break;
default:
PADDLE_THROW("unsupported pooling strategy");
}
}
}
};
template <typename Place, typename T>
class SequenceAvgPoolGradKernel : public framework::OpKernel {
class SequencePoolGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
int strategy = context.Attr<int>("strategy");
auto dims = in->dims();
auto lod = in->lod();
auto lod = in->lod()[0];
int64_t w = in->numel() / dims[0];
in_g->mutable_data<T>(context.GetPlace());
auto place = context.GetEigenDevice<Place>();
for (int i = 0; i < static_cast<int>(lod[0].size()) - 1; ++i) {
auto in_g_t = in_g->Slice<T>(static_cast<int>(lod[0][i]),
static_cast<int>(lod[0][i + 1]));
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
auto in_g_t = in_g->Slice<T>(static_cast<int>(lod[i]),
static_cast<int>(lod[i + 1]));
auto out_g_t = out_g->Slice<T>(i, i + 1);
int64_t h = static_cast<int64_t>(lod[0][i + 1] - lod[0][i]);
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
auto in_g_e = EigenMatrix<T>::From(in_g_t, {h, w});
auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
Eigen::DSizes<int, 2> bcast(h, 1);
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
switch (strategy) {
case AVERAGE:
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
break;
case SUM:
in_g_e.device(place) = (out_g_e).broadcast(bcast);
break;
default:
PADDLE_THROW("unsupported pooling strategy");
}
}
}
};

63
python/paddle/v2/framework/tests/test_seq_pool.py
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@ -3,20 +3,37 @@ import numpy as np
from op_test import OpTest
class TestSeqAvgPool1D(OpTest):
def setUp(self):
self.op_type = 'sequence_avg_pool'
class SeqPoolType(OpTest):
AVERAGE = 0
SUM = 1
SQRT = 2
MAX = 3
LAST = 4
FIRST = 5
class TestSeqAvgPool(OpTest):
def set_data(self):
self.op_type = 'sequence_pool'
# one level, batch size is 4
x = np.random.uniform(0.1, 1, [11, 23]).astype('float32')
lod = [[0, 4, 5, 8, 11]]
self.inputs = {'X': (x, lod)}
out = np.zeros((4, 23)).astype('float32')
self.outputs = {'Out': out}
def compute(self):
self.attrs = {'strategy': SeqPoolType.AVERAGE}
x, lod = self.inputs['X']
out = self.outputs['Out']
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
out[i] = sub_x.mean(axis=0)
self.inputs = {'X': (x, lod)}
self.outputs = {'Out': out}
def setUp(self):
self.set_data()
self.compute()
def test_check_output(self):
self.check_output()
@ -25,26 +42,44 @@ class TestSeqAvgPool1D(OpTest):
self.check_grad(["X"], "Out")
class TestSeqAvgPool2D(OpTest):
def setUp(self):
self.op_type = 'sequence_avg_pool'
class TestSeqAvgPool2D(TestSeqAvgPool):
def set_data(self):
self.op_type = 'sequence_pool'
# one level, batch size is 4
x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32')
lod = [[0, 4, 5, 8, 13]]
self.inputs = {'X': (x, lod)}
out = np.zeros((4, 3, 17)).astype('float32')
self.outputs = {'Out': out}
def compute(self):
self.attrs = {'strategy': SeqPoolType.AVERAGE}
x, lod = self.inputs['X']
out = self.outputs['Out']
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
out[i] = np.reshape(sub_x.mean(axis=0), (3, 17))
self.inputs = {'X': (x, lod)}
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
class TestSeqSumPool(TestSeqAvgPool):
def compute(self):
self.attrs = {'strategy': SeqPoolType.SUM}
x, lod = self.inputs['X']
out = self.outputs['Out']
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
out[i] = sub_x.sum(axis=0)
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestSeqSumPool2D(TestSeqAvgPool2D):
def compute(self):
self.attrs = {'strategy': SeqPoolType.SUM}
x, lod = self.inputs['X']
out = self.outputs['Out']
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
out[i] = np.reshape(sub_x.sum(axis=0), (3, 17))
if __name__ == '__main__':

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