提交 91019652 编写于 作者: B baojun 提交者: tensor-tang

NGraph Added dropout and dropout_grad to ngraph test=develop (#17320)

上级 b1894807
......@@ -61,6 +61,7 @@ static std::map<framework::proto::VarType::Type, ngraph::element::Type>
{framework::proto::VarType::FP64, ngraph::element::f64},
{framework::proto::VarType::INT32, ngraph::element::i32},
{framework::proto::VarType::INT64, ngraph::element::i64},
{framework::proto::VarType::UINT8, ngraph::element::u8},
{framework::proto::VarType::BOOL, ngraph::element::boolean}};
static std::map<ngraph::element::Type, framework::proto::VarType::Type>
......@@ -69,6 +70,7 @@ static std::map<ngraph::element::Type, framework::proto::VarType::Type>
{ngraph::element::f64, framework::proto::VarType::FP64},
{ngraph::element::i32, framework::proto::VarType::INT32},
{ngraph::element::i64, framework::proto::VarType::INT64},
{ngraph::element::u8, framework::proto::VarType::UINT8},
{ngraph::element::boolean, framework::proto::VarType::BOOL}};
std::vector<std::string> NgraphEngine::feed_vars = {};
......
/*Copyright (c) 2019 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. */
#pragma once
#include <functional>
#include <memory>
#include <string>
#include <unordered_map>
#include "ngraph/ngraph.hpp"
#include "ngraph/op/experimental/generate_mask.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
static void BuildDropoutNode(
const std::shared_ptr<framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto input = platform::GetInputNode(op, "X", ngb_node_map);
auto op_attrs = framework::AttrReader(op->Attrs());
auto dropout_prob = op_attrs.Get<float>("dropout_prob");
auto dropout_implementation =
op_attrs.Get<std::string>("dropout_implementation");
auto is_test = op_attrs.Get<bool>("is_test");
auto seed = op_attrs.Get<int>("seed");
float value = 1.0f - dropout_prob;
bool upscale_in_train = (dropout_implementation == "upscale_in_train");
if (is_test) {
if (upscale_in_train) {
platform::SetOutputNode(op, "Out", input, ngb_node_map);
} else {
auto mask_val = paddle::platform::CreateConstant(
input->get_element_type(), input->get_shape(), {value});
auto out = input * mask_val;
platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
} else {
auto one = paddle::platform::CreateConstant(input->get_element_type(),
ngraph::Shape{}, {1});
auto gen_mask = std::make_shared<ngraph::op::GenerateMask>(
one, input->get_shape(), input->get_element_type(), seed, value);
if (upscale_in_train) {
auto mask_val = paddle::platform::CreateConstant(
input->get_element_type(), input->get_shape(), {value});
auto out = value ? input * gen_mask / mask_val : input * gen_mask;
platform::SetOutputNode(op, "Mask", gen_mask, ngb_node_map);
platform::SetOutputNode(op, "Out", out, ngb_node_map);
} else {
auto out = input * gen_mask;
platform::SetOutputNode(op, "Mask", gen_mask, ngb_node_map);
platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
}
}
static void BuildDropoutGradNode(
const std::shared_ptr<framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto dy = platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
auto mask = platform::GetInputNode(op, "Mask", ngb_node_map);
if (dy->get_element_type() != mask->get_element_type()) {
mask = std::make_shared<ngraph::op::Convert>(mask, dy->get_element_type());
}
auto op_attrs = framework::AttrReader(op->Attrs());
auto dropout_prob = op_attrs.Get<float>("dropout_prob");
auto dropout_implementation =
op_attrs.Get<std::string>("dropout_implementation");
auto dx = dy * mask;
if (dropout_implementation == "upscale_in_train") {
if (dropout_prob == 1.0f) {
dx = ElementwiseScalar<ngraph::op::Multiply>(0., dy);
} else {
dx =
ElementwiseScalar<ngraph::op::Multiply>(1. / (1. - dropout_prob), dx);
}
}
platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(dropout, BuildDropoutNode);
REGISTER_NG_OP(dropout_grad, BuildDropoutGradNode);
# Copyright (c) 2019 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_dropout_op import TestDropoutOp, TestDropoutOp2, TestDropoutOp3, TestDropoutOp4, TestDropoutOp5, TestDropoutOp6, TestDropoutOp7, TestDropoutOp8, TestDropoutOp9
if __name__ == '__main__':
unittest.main()
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