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cumsum operator (#8288)

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emailweixu-patch-1
emailweixu 7 years ago committed by qingqing01
parent 69712ef276
commit 725e64486a
8 changed files with 352 additions and 2 deletions
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5
paddle/framework/grad_op_desc_maker.h
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@ -122,6 +122,11 @@ class GradOpDescMakerBase {
return it->second;
}
template <typename T>
inline const T& Attr(const std::string& name) const {
return boost::get<T>(GetAttr(name));
}
std::string ForwardOpType() const { return this->fwd_op_.Type(); }
private:

2
paddle/framework/op_registry.h
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@ -143,7 +143,7 @@ class OpKernelRegistrar : public Registrar {
/**
* Macro to register Operator. When the input is duplicable, you should
* use REGISTER_OP_EX with deop_empty_grad=false instead.
* use REGISTER_OP_EX with drop_empty_grad=false instead.
*/
#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \
grad_op_class) \

111
paddle/operators/cum_op.h
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@ -0,0 +1,111 @@
/* Copyright (c) 2018 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/framework/operator.h"
#include "paddle/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename Functor>
class CumKernel : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
public:
using T = typename Functor::ELEMENT_TYPE;
void Compute(const framework::ExecutionContext& context) const override {
auto& X = detail::Ref(context.Input<framework::Tensor>("X"),
"Cannot get input tensor X, variable name = %s",
context.op().Input("X"));
auto& Out = detail::Ref(context.Output<framework::Tensor>("Out"),
"Cannot get output tensor Out, variable name = %s",
context.op().Output("Out"));
int axis = context.Attr<int>("axis");
bool exclusive = context.Attr<bool>("exclusive");
bool reverse = context.Attr<bool>("reverse");
auto x_dims = X.dims();
if (axis == -1) {
axis = x_dims.size() - 1;
}
PADDLE_ENFORCE_LT(
axis, x_dims.size(),
"axis should be less than the dimensiotn of the input tensor");
Out.mutable_data<T>(context.GetPlace());
int pre = 1;
int post = 1;
int mid = x_dims[axis];
for (int i = 0; i < axis; ++i) {
pre *= x_dims[i];
}
for (int i = axis + 1; i < x_dims.size(); ++i) {
post *= x_dims[i];
}
auto x = framework::EigenVector<T>::Flatten(X);
auto out = framework::EigenVector<T>::Flatten(Out);
auto* place =
context.template device_context<DeviceContext>().eigen_device();
using IndexT = Eigen::DenseIndex;
if (pre == 1) {
if (post == 1) {
ComputeImp(*place, Eigen::DSizes<IndexT, 1>(mid), x, out,
/* axis= */ 0, reverse, exclusive);
} else {
ComputeImp(*place, Eigen::DSizes<IndexT, 2>(mid, post), x, out,
/* axis= */ 0, reverse, exclusive);
}
} else {
if (post == 1) {
ComputeImp(*place, Eigen::DSizes<IndexT, 2>(pre, mid), x, out,
/* axis= */ 1, reverse, exclusive);
} else {
ComputeImp(*place, Eigen::DSizes<IndexT, 3>(pre, mid, post), x, out,
/* axis= */ 1, reverse, exclusive);
}
}
}
private:
template <typename Device, typename Dim, typename X, typename Out>
void ComputeImp(Device d, const Dim& dims, X x, Out out, int axis,
bool reverse, bool exclusive) const {
if (!reverse) {
out.reshape(dims).device(d) = Functor()(x.reshape(dims), axis, exclusive);
} else {
std::array<bool, Dim::count> rev;
rev.fill(false);
rev[axis] = reverse;
out.reshape(dims).device(d) =
Functor()(x.reshape(dims).reverse(rev), axis, exclusive).reverse(rev);
}
}
};
template <typename T>
struct CumsumFunctor {
using ELEMENT_TYPE = T;
template <typename X>
const typename X::TensorScanSumOp operator()(X x, int axis,
bool exclusive) const {
return x.cumsum(axis, exclusive);
}
};
} // namespace operators
} // namespace paddle

82
paddle/operators/cumsum_op.cc
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@ -0,0 +1,82 @@
/* Copyright (c) 2018 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/cum_op.h"
namespace paddle {
namespace operators {
class CumOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class CumsumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CumsumOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Cumsum operator");
AddOutput("Out", "Output of Cumsum operator");
AddAttr<int>("axis",
"(int, default -1). The dimenstion to accumulate along. "
"-1 means the last dimenstion")
.SetDefault(-1)
.EqualGreaterThan(-1);
AddAttr<bool>("exclusive",
"bool, default false). Whether to perform exclusive cumsum")
.SetDefault(false);
AddAttr<bool>("reverse",
"bool, default false). If true, the cumsum is performed in "
"the reversed direction")
.SetDefault(false);
AddComment(R"DOC(
The cumulative sum of the elements along a given axis.
By default, the first element of the result is the same of the first element of
the input. If exlusive is true, the first element of the result is 0.
)DOC");
}
};
class CumsumGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("cumsum");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttr("axis", Attr<int>("axis"));
grad_op->SetAttr("reverse", !Attr<bool>("reverse"));
grad_op->SetAttr("exclusive", Attr<bool>("exclusive"));
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
using CPU = paddle::platform::CPUDeviceContext;
REGISTER_OPERATOR(cumsum, ops::CumOp, ops::CumsumOpMaker, ops::CumsumGradMaker);
REGISTER_OP_CPU_KERNEL(cumsum, ops::CumKernel<CPU, ops::CumsumFunctor<float>>,
ops::CumKernel<CPU, ops::CumsumFunctor<double>>,
ops::CumKernel<CPU, ops::CumsumFunctor<int>>)

22
paddle/operators/cumsum_op.cu
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@ -0,0 +1,22 @@
/* Copyright (c) 2018 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/cum_op.h"
namespace ops = paddle::operators;
using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(cumsum, ops::CumKernel<CUDA, ops::CumsumFunctor<float>>,
ops::CumKernel<CUDA, ops::CumsumFunctor<double>>,
ops::CumKernel<CUDA, ops::CumsumFunctor<int>>)

2
python/paddle/v2/fluid/layers/ops.py
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@ -65,6 +65,8 @@ __all__ = [
'logical_or',
'logical_xor',
'logical_not',
'uniform_random',
'cumsum',
] + __activations__
for _OP in set(__all__):

3
python/paddle/v2/fluid/tests/op_test.py
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@ -326,7 +326,8 @@ class OpTest(unittest.TestCase):
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol),
"Output (" + out_name + ") has diff at " + str(place))
"Output (" + out_name + ") has diff at " + str(place) +
str(actual_t) + str(expect_t))
if isinstance(expect, tuple):
self.assertListEqual(actual.lod(), expect[1],
"Output (" + out_name +

127
python/paddle/v2/fluid/tests/test_cumsum_op.py
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@ -0,0 +1,127 @@
# Copyright (c) 2018 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.
import unittest
import numpy as np
from op_test import OpTest
class TestSumOp1(OpTest):
def setUp(self):
self.op_type = "cumsum"
self.attrs = {'axis': 2}
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].cumsum(axis=2)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSumOp2(OpTest):
def setUp(self):
self.op_type = "cumsum"
self.attrs = {'axis': -1, 'reverse': True}
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
self.outputs = {
'Out': np.flip(
np.flip(
self.inputs['X'], axis=2).cumsum(axis=2), axis=2)
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSumOp3(OpTest):
def setUp(self):
self.op_type = "cumsum"
self.attrs = {'axis': 1}
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSumOp4(OpTest):
def setUp(self):
self.op_type = "cumsum"
self.attrs = {'axis': 0}
self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSumOp5(OpTest):
def setUp(self):
self.op_type = "cumsum"
self.inputs = {'X': np.random.random((5, 6)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSumOp7(OpTest):
def setUp(self):
self.op_type = "cumsum"
self.inputs = {'X': np.random.random((6)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSumOp8(OpTest):
def setUp(self):
self.op_type = "cumsum"
self.attrs = {'axis': 2, "exclusive": True}
a = np.random.random((5, 6, 3)).astype("float64")
self.inputs = {'X': a}
self.outputs = {
'Out': np.concatenate(
(np.zeros(
(5, 6, 1), dtype=np.float64), a[:, :, :-1].cumsum(axis=2)),
axis=2)
}
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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