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mozga-intel
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Enable pool2d operator for a ngraph engine (#15395)
* Enable pool2d operator for a ngraph engine test=develop * Update test=develop
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‎paddle/fluid/operators/ngraph/ngraph_bridge.cc

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@@ -38,6 +38,8 @@ std::map<std::string,
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{"mean_grad", NG_OPS::BuildMeanGradNode},
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{"mul", NG_OPS::BuildMulNode},
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{"mul_grad", NG_OPS::BuildMulGradNode},
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{"pool2d", NG_OPS::BuildPool2dNode},
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{"pool2d_grad", NG_OPS::BuildPool2dGradNode},
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{"softmax", NG_OPS::BuildSoftmaxNode},
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{"softmax_grad", NG_OPS::BuildSoftmaxGradNode},
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{"scale", NG_OPS::BuildScaleNode},

‎paddle/fluid/operators/ngraph/ngraph_ops.h

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@@ -26,6 +26,7 @@ limitations under the License. */
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#include "ops/fill_constant_op.h"
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#include "ops/mean_op.h"
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#include "ops/mul_op.h"
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#include "ops/pool2d_op.h"
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#include "ops/scale_op.h"
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#include "ops/softmax_op.h"
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#include "ops/top_k_op.h"

‎paddle/fluid/operators/ngraph/ops/pool2d_op.h

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/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <string>
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#include <vector>
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#include "ngraph/ngraph.hpp"
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#include "paddle/fluid/platform/ngraph_helper.h"
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namespace paddle {
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namespace operators {
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namespace ngraphs {
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void BuildPool2dNode(
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const std::shared_ptr<paddle::framework::OperatorBase>& op,
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std::shared_ptr<
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std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
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ngb_node_map) {
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auto op_attrs = paddle::framework::AttrReader(op->Attrs());
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auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
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auto x_shape = x->get_shape();
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std::string pooling_type = op_attrs.Get<std::string>("pooling_type");
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std::vector<int> ksize = op_attrs.Get<std::vector<int>>("ksize");
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std::vector<int> strides = op_attrs.Get<std::vector<int>>("strides");
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std::vector<int> paddings = op_attrs.Get<std::vector<int>>("paddings");
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PADDLE_ENFORCE_EQ(x_shape.size() - 2, ksize.size(),
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"Handling 2d pooling only");
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if (op_attrs.Get<bool>("global_pooling")) {
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for (size_t i = 0; i < ksize.size(); ++i) {
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paddings[i] = 0;
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ksize[i] = static_cast<int>(x_shape.at(i + 2));
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}
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}
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ngraph::Shape ng_padding_below{static_cast<size_t>(paddings.at(0)),
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static_cast<size_t>(paddings.at(1))};
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ngraph::Shape ng_padding_above{static_cast<size_t>(paddings.at(0)),
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static_cast<size_t>(paddings.at(1))};
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ngraph::Shape ng_ksize_shape{static_cast<size_t>(ksize.at(0)),
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static_cast<size_t>(ksize.at(1))};
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ngraph::Strides ng_strides{static_cast<size_t>(strides.at(0)),
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static_cast<size_t>(strides.at(1))};
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auto ComputeCeiledOutput = [](size_t in, size_t k, size_t p, size_t s) {
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return (in - k + 2 * p) / s + 1;
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};
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if (op_attrs.Get<bool>("ceil_mode")) {
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auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map);
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auto dummpy_shape = dummy_out->get_shape();
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for (size_t i = 0; i < ng_padding_above.size(); ++i) {
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auto desired_size = ComputeCeiledOutput(x_shape[i + 2], ksize[i],
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paddings[i], strides[i]);
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if (desired_size != dummpy_shape[i + 2]) {
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ng_padding_above[i] += strides[i];
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}
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}
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}
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bool padding_exclusive = op_attrs.Get<bool>("exclusive");
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if (pooling_type == "max") {
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auto pool2d = std::make_shared<ngraph::op::MaxPool>(
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x, ng_ksize_shape, ng_strides, ng_padding_below, ng_padding_above);
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paddle::platform::SetOutputNode(op, "Out", pool2d, ngb_node_map);
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} else if (pooling_type == "avg") {
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std::shared_ptr<ngraph::Node> pool2d;
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if (op_attrs.Get<bool>("adaptive")) {
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auto ComputeAdaptive = [](size_t in, size_t k) {
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return std::floor(in / k);
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};
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ng_strides[0] = x_shape.size() == 4
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? ComputeAdaptive(x_shape[3], ksize[0])
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: ng_strides[0];
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ng_strides[1] = x_shape.size() == 4
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? ComputeAdaptive(x_shape[3], ksize[0])
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: ng_strides[1];
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pool2d =
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std::make_shared<ngraph::op::AvgPool>(x, ng_ksize_shape, ng_strides);
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} else {
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pool2d = std::make_shared<ngraph::op::AvgPool>(
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x, ng_ksize_shape, ng_strides, ng_padding_below, ng_padding_above,
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!padding_exclusive);
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}
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paddle::platform::SetOutputNode(op, "Out", pool2d, ngb_node_map);
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} else {
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PADDLE_THROW("Support max and avg pooling only");
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}
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}
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void BuildPool2dGradNode(
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const std::shared_ptr<paddle::framework::OperatorBase>& op,
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std::shared_ptr<
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std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
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ngb_node_map) {
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auto op_attrs = paddle::framework::AttrReader(op->Attrs());
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auto out = paddle::platform::GetInputNode(op, "Out", ngb_node_map);
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auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
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auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
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auto x_shape = x->get_shape();
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std::string pooling_type = op_attrs.Get<std::string>("pooling_type");
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std::vector<int> ksize = op_attrs.Get<std::vector<int>>("ksize");
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std::vector<int> strides = op_attrs.Get<std::vector<int>>("strides");
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std::vector<int> paddings = op_attrs.Get<std::vector<int>>("paddings");
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PADDLE_ENFORCE_EQ(x_shape.size() - 2, ksize.size(),
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"Handling 2d pooling only");
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if (op_attrs.Get<bool>("global_pooling")) {
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for (size_t i = 0; i < ksize.size(); ++i) {
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paddings[i] = 0;
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ksize[i] = static_cast<int>(x_shape.at(i + 2));
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}
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}
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ngraph::Shape ng_padding_below{static_cast<size_t>(paddings.at(0)),
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static_cast<size_t>(paddings.at(1))};
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ngraph::Shape ng_padding_above{static_cast<size_t>(paddings.at(0)),
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static_cast<size_t>(paddings.at(1))};
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ngraph::Shape ng_ksize_shape{static_cast<size_t>(ksize.at(0)),
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static_cast<size_t>(ksize.at(1))};
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ngraph::Strides ng_strides{static_cast<size_t>(strides.at(0)),
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static_cast<size_t>(strides.at(1))};
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bool padding_exclusive = op_attrs.Get<bool>("exclusive");
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if (pooling_type == "max") {
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auto pool2d_grad = std::make_shared<ngraph::op::MaxPoolBackprop>(
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x, dout, out, ng_ksize_shape, ng_strides, ng_padding_below,
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ng_padding_above);
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paddle::platform::SetOutputNode(op, "X@GRAD", pool2d_grad, ngb_node_map);
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} else if (pooling_type == "avg") {
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std::shared_ptr<ngraph::Node> pool2d_grad;
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if (op_attrs.Get<bool>("adaptive")) {
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auto ComputeAdaptive = [](size_t in, size_t k) {
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return std::floor(in / k);
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};
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ng_strides[0] = x_shape.size() == 4
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? ComputeAdaptive(x_shape[3], ksize[0])
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: ng_strides[0];
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ng_strides[1] = x_shape.size() == 4
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? ComputeAdaptive(x_shape[3], ksize[0])
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: ng_strides[1];
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pool2d_grad = std::make_shared<ngraph::op::AvgPoolBackprop>(
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x->get_shape(), dout, ng_ksize_shape, ng_strides, ng_padding_below,
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ng_padding_above, !padding_exclusive);
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} else {
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pool2d_grad = std::make_shared<ngraph::op::AvgPoolBackprop>(
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x->get_shape(), dout, ng_ksize_shape, ng_strides, ng_padding_below,
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ng_padding_above, !padding_exclusive);
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}
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paddle::platform::SetOutputNode(op, "X@GRAD", pool2d_grad, ngb_node_map);
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} else {
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PADDLE_THROW("Support max and avg pooling only");
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}
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}
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} // namespace ngraphs
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} // namespace operators
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} // namespace paddle

‎python/paddle/fluid/tests/unittests/ngraph/test_pool2d_ngraph_op.py

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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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from paddle.fluid.tests.unittests.test_pool2d_op import *
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class TestNGRAPHPool2D_Op(TestPool2D_Op):
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def init_test_case(self):
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super(TestNGRAPHPool2D_Op, self).init_test_case()
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class TestNGRAPHCase1(TestCase1):
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def init_test_case(self):
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super(TestNGRAPHCase1, self).init_test_case()
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class TestNGRAPHCase2(TestCase2):
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def init_test_case(self):
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super(TestNGRAPHCase2, self).init_test_case()
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class TestNGRAPHCase3(TestCase3):
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def init_pool_type(self):
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super(TestNGRAPHCase3, self).init_pool_type()
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class TestNGRAPHCase4(TestCase4):
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def init_pool_type(self):
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super(TestNGRAPHCase4, self).init_pool_type()
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class TestNGRAPHCase5(TestCase5):
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def init_pool_type(self):
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super(TestNGRAPHCase5, self).init_pool_type()
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if __name__ == '__main__':
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unittest.main()

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