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Add split selected rows op (#7604)

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* add split selected rows op

* update comment

* add grad check

* registry cuda kernel

* fix ci failed
add_depthwiseConv_op_gpu
Yancey 7 years ago committed by GitHub
parent 161bd4a4c3
commit c79d530ad3
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4 changed files with 319 additions and 0 deletions
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114
paddle/operators/split_selected_rows_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/split_selected_rows_op.h"
namespace paddle {
namespace operators {
class SplitSelectedRowsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SplitSelectedRowsOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input SelectedRows.");
AddOutput("Out", "The outputs of input SelectedRows.").AsDuplicable();
AddAttr<std::vector<int>>("rows_sections", "Rows section for output.")
.SetDefault(std::vector<int>({}));
AddAttr<std::vector<int>>("height_sections",
"Height for each output SelectedRows.")
.SetDefault(std::vector<int>({}));
AddComment(R"DOC(
Split a SelectedRows with a specified rows section.
height_sections is only needed when need to split the dims of the original tensor.
Example:
Input:
X.rows = {0, 7, 5}
X.height = 12
Attr:
rows_sections = {1, 2}
height_sections = {}
Out:
out0.rows = {0}
out0.height = 12
out1.rows = {7, 5}
out2.height = 12
)DOC");
}
};
class SplitSelectedRowsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "SplitSelectedRowsOp must has input X.");
PADDLE_ENFORCE(ctx->HasOutputs("Out"),
"SplitSelectedRowsOp must has output Out.");
std::vector<int> height_sections =
ctx->Attrs().Get<std::vector<int>>("height_sections");
std::vector<int> rows_sections =
ctx->Attrs().Get<std::vector<int>>("rows_sections");
PADDLE_ENFORCE_EQ(
rows_sections.size(), ctx->Outputs("Out").size(),
"The size of rows section should be the same with Outputs size.");
int64_t n = ctx->Outputs("Out").size();
std::vector<framework::DDim> outs_dims;
outs_dims.reserve(n);
// make output dims
for (int64_t i = 0; i < n; ++i) {
auto dims = ctx->GetInputDim("X");
if (height_sections.size()) {
PADDLE_ENFORCE_EQ(
height_sections.size(), static_cast<size_t>(n),
"The size of height section should be the same with height"
" section size.");
dims[0] = height_sections[i];
}
outs_dims.push_back(dims);
}
ctx->SetOutputsDim("Out", outs_dims);
}
};
class SplitSelectedRowsGradMaker : 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("sum");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(split_selected_rows, ops::SplitSelectedRowsOp,
ops::SplitSelectedRowsOpMaker,
ops::SplitSelectedRowsGradMaker);
REGISTER_OP_CPU_KERNEL(
split_selected_rows,
ops::SplitSelectedRowsOpKernel<paddle::platform::CPUPlace, float>);

19
paddle/operators/split_selected_rows_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. */
#include "paddle/operators/split_selected_rows_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
split_selected_rows,
ops::SplitSelectedRowsOpKernel<paddle::platform::CUDADeviceContext, float>);

58
paddle/operators/split_selected_rows_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 <vector>
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class SplitSelectedRowsOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::SelectedRows>("X");
auto outs = ctx.MultiOutput<framework::SelectedRows>("Out");
auto rows_sections = ctx.Attr<std::vector<int>>("rows_sections");
auto height_sections = ctx.Attr<std::vector<int>>("height_sections");
int64_t n = outs.size();
int offset = 0;
for (int64_t i = 0; i < n; ++i) {
framework::Vector<int64_t> out_rows;
for (int64_t j = 0; j < rows_sections[i]; ++j) {
out_rows.push_back(x->rows()[offset + j]);
}
auto& out = outs[i];
auto x_dims = x->GetCompleteDims();
x_dims[0] = rows_sections[i];
out->mutable_value()->mutable_data<T>(x_dims, ctx.GetPlace());
framework::Copy(x->value().Slice(offset, rows_sections[i] + offset),
x->place(), ctx.device_context(), out->mutable_value());
outs[i]->set_rows(out_rows);
if (height_sections.size()) {
outs[i]->set_height(height_sections[i]);
}
offset += rows_sections[i];
}
}
};
} // namespace operators
} // namespace paddle

128
python/paddle/v2/fluid/tests/test_split_selected_rows_op.py
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# 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 paddle.v2.fluid.core as core
import numpy as np
from paddle.v2.fluid.op import Operator
class TestSpliteSelectedRows(unittest.TestCase):
def get_places(self):
places = [core.CPUPlace()]
if core.is_compile_gpu():
places.append(core.CUDAPlace(0))
return places
def test_check_output(self):
for place in self.get_places():
self.check_with_place(place)
def test_check_grad(self):
for place in self.get_places():
self.check_grad_with_place(place)
def check_with_place(self, place):
scope = core.Scope()
rows = [0, 5, 7, 4]
height = 10
row_numel = 2
# initialize input variable X
x = scope.var('X').get_selected_rows()
x.set_rows(rows)
x.set_height(height)
np_array = np.ones((len(rows), row_numel)).astype("float32")
np_array[0, 0] = 2.0
np_array[2, 1] = 4.0
x_tensor = x.get_tensor()
x_tensor.set(np_array, place)
rows_sections = [2, 2]
height_sections = []
# initialize output variables [out0, out1]
out0 = scope.var('out0').get_selected_rows()
out1 = scope.var('out1').get_selected_rows()
# expected output selected rows
expected_out0_rows = [0, 5]
expected_out1_rows = [7, 4]
expected_height = height
op = Operator(
"split_selected_rows",
X="X",
Out=["out0", "out1"],
rows_sections=rows_sections,
height_sections=height_sections)
op.run(scope, place)
self.assertEqual(out0.rows(), expected_out0_rows)
self.assertEqual(out1.rows(), expected_out1_rows)
self.assertEqual(out0.height(), expected_height)
self.assertEqual(out1.height(), expected_height)
self.assertAlmostEqual(2.0, np.array(out0.get_tensor())[0, 0])
self.assertAlmostEqual(4.0, np.array(out1.get_tensor())[0, 1])
def check_grad_with_place(self, place):
scope = core.Scope()
height = 10
row_numel = 2
# attr
rows_sections = [2, 2]
height_sections = []
# initialize input variable X
out0_grad = scope.var("out0@GRAD").get_selected_rows()
rows0 = [0, 5]
out0_grad.set_rows(rows0)
out0_grad.set_height(height)
out0_grad_tensor = out0_grad.get_tensor()
np_array = np.ones((len(rows0), row_numel)).astype("float32")
np_array[0, 0] = 2.0
out0_grad_tensor.set(np_array, place)
out1_grad = scope.var("out1@GRAD").get_selected_rows()
rows1 = [7, 5]
out1_grad.set_rows(rows1)
out1_grad.set_height(height)
out1_grad_tensor = out1_grad.get_tensor()
np_array = np.ones((len(rows1), row_numel)).astype("float32")
np_array[0, 1] = 4.0
out1_grad_tensor.set(np_array, place)
x_grad = scope.var("X@GRAD").get_selected_rows()
grad_op = Operator(
"sum",
X=["out0@GRAD", "out1@GRAD"],
Out="X@GRAD",
rows_sections=rows_sections,
height_sections=height_sections)
grad_op.run(scope, place)
self.assertEqual(x_grad.rows(), rows0 + rows1)
self.assertEqual(x_grad.height(), height)
self.assertAlmostEqual(2.0, np.array(x_grad.get_tensor())[0, 0])
self.assertAlmostEqual(4.0, np.array(x_grad.get_tensor())[2, 1])
if __name__ == "__main__":
unittest.main()
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