未验证 提交 dc0c2214 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #14803 from mozga-intel/mozga-intel/mean_operator_ngraph

Enable mean operator for a ngraph engine
......@@ -32,6 +32,8 @@ std::map<std::string,
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {
{"fill_constant", paddle::operators::ngraphs::BuildFillConstantNode},
{"mean", paddle::operators::ngraphs::BuildMeanNode},
{"mean_grad", paddle::operators::ngraphs::BuildMeanGradNode},
{"mul", paddle::operators::ngraphs::BuildMulNode},
{"mul_grad", paddle::operators::ngraphs::BuildMulGradNode},
{"relu", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Relu>},
......
......@@ -23,5 +23,6 @@ limitations under the License. */
#include "ops/binary_unnary_op.h"
#include "ops/fill_constant_op.h"
#include "ops/mean_op.h"
#include "ops/mul_op.h"
#include "ops/top_k_op.h"
/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
template <typename T>
std::shared_ptr<ngraph::Node> ElementwiseScalar(
float scale, std::shared_ptr<ngraph::Node> node) {
auto node_shape = node->get_shape();
auto scale_const = ngraph::op::Constant::create(node->get_element_type(),
node_shape, {scale});
return std::make_shared<T>(scale_const, node);
}
template <typename T>
std::shared_ptr<ngraph::Node> ElementwiseScalar(
std::shared_ptr<ngraph::Node> scale_1d,
std::shared_ptr<ngraph::Node> node) {
auto scale_shape = scale_1d->get_shape();
PADDLE_ENFORCE_EQ(scale_shape.size(), 1, "Supporting 1d scale node");
PADDLE_ENFORCE_EQ(scale_shape.at(0), 1, "scale 1d in in shape {1}");
auto node_shape = node->get_shape();
ngraph::AxisSet axis_set;
for (size_t i = 0; i < node_shape.size(); ++i) {
axis_set.insert(i);
}
node_shape.push_back(1);
auto scale_bcast =
std::make_shared<ngraph::op::Broadcast>(scale_1d, node_shape, axis_set);
auto scale_reshape =
paddle::platform::NgReshaper(scale_bcast, node->get_shape());
return std::make_shared<T>(scale_reshape, node);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <functional>
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildMeanNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto input = paddle::platform::GetInputNode(op, "X", ngb_node_map);
ngraph::AxisSet axes;
for (size_t i = 0; i < input->get_shape().size(); ++i) {
axes.insert(i);
}
auto mean = ngraph::builder::mean(input, axes);
auto mean_1d = std::make_shared<ngraph::op::Reshape>(
mean, ngraph::AxisVector{}, ngraph::Shape{1});
paddle::platform::SetOutputNode(op, "Out", mean_1d, ngb_node_map);
}
void BuildMeanGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto og = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
auto x_shape = x->get_shape();
float x_size = std::accumulate(std::begin(x_shape), std::end(x_shape), 1,
std::multiplies<float>());
auto node_const = ngraph::op::Constant::create(og->get_element_type(),
ngraph::Shape{1}, {x_size});
auto node_div = std::make_shared<ngraph::op::Divide>(og, node_const);
auto result = ElementwiseScalar<ngraph::op::Add>(
og / node_const,
ngraph::op::Constant::create(og->get_element_type(), x_shape, {0}));
paddle::platform::SetOutputNode(op, "X@GRAD", result, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import unittest
from paddle.fluid.tests.unittests.test_mean_op import TestMeanOp, TestFP16MeanOp
class TestNGRAPHMeanOp(TestMeanOp):
def setUp(self):
super(TestNGRAPHMeanOp, self).setUp()
class TestNGRAPHFP16MeanOp(TestFP16MeanOp):
def setUp(self):
super(TestNGRAPHFP16MeanOp, self).setUp()
if __name__ == "__main__":
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册