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mozga-inteltensor-tang
mozga-intel
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Enable conv2d operator for a ngraph engine (#15269)
test=develop
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‎paddle/fluid/operators/ngraph/ngraph_bridge.cc

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@@ -31,6 +31,8 @@ std::map<std::string,
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std::shared_ptr<std::unordered_map<
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std::string, std::shared_ptr<ngraph::Node>>>)>>
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NgraphBridge::NG_NODE_MAP = {
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{"conv2d", NG_OPS::BuildConv2dNode},
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{"conv2d_grad", NG_OPS::BuildConv2dGradNode},
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{"elementwise_add", NG_OPS::BuildElementwiseAddNode},
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{"elementwise_add_grad", NG_OPS::BuildElementwiseAddGradNode},
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{"fill_constant", NG_OPS::BuildFillConstantNode},

‎paddle/fluid/operators/ngraph/ngraph_ops.h

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@@ -22,6 +22,7 @@ limitations under the License. */
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#pragma once
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#include "ops/binary_unnary_op.h"
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#include "ops/conv2d_op.h"
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#include "ops/elementwise_add_op.h"
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#include "ops/fill_constant_op.h"
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#include "ops/mean_op.h"

‎paddle/fluid/operators/ngraph/ops/conv2d_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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std::shared_ptr<ngraph::Node> GroupedConvolution(
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const std::shared_ptr<ngraph::Node>& data_batch,
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const std::shared_ptr<ngraph::Node>& filters, const ngraph::Strides strides,
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const ngraph::Strides dilations, const ngraph::CoordinateDiff& paddings,
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size_t groups) {
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auto& data_shape = data_batch->get_shape();
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auto& filter_shape = filters->get_shape();
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ngraph::NodeVector ng_slices;
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for (size_t i = 0; i < groups; ++i) {
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size_t channel_step = filter_shape.at(1);
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const std::vector<size_t> lower_bound{0, i * channel_step, 0, 0};
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const std::vector<size_t> upper_bound{data_shape.at(0),
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(i + 1) * channel_step,
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data_shape.at(2), data_shape.at(3)};
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auto data_slice = std::make_shared<ngraph::op::Slice>(
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data_batch, lower_bound, upper_bound);
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size_t filter_step = filter_shape.at(0) / groups;
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const std::vector<size_t> filter_lower_bound{i * filter_step, 0, 0, 0};
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const std::vector<size_t> filter_upper_bound{
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(i + 1) * filter_step, filter_shape.at(1), filter_shape.at(2),
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filter_shape.at(3)};
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auto filter_slice = std::make_shared<ngraph::op::Slice>(
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filters, filter_lower_bound, filter_upper_bound);
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auto ng_conv = std::make_shared<ngraph::op::Convolution>(
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data_slice, filter_slice, strides, dilations, paddings, paddings);
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ng_slices.push_back(ng_conv);
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}
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size_t concat_axis = 1;
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return std::make_shared<ngraph::op::Concat>(ng_slices, concat_axis);
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}
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std::shared_ptr<ngraph::Node> GroupedGradConvolutionFilter(
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const std::shared_ptr<ngraph::Node>& data_batch,
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const std::shared_ptr<ngraph::Node>& filters,
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const std::shared_ptr<ngraph::Node>& doutput, const ngraph::Strides strides,
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const ngraph::Strides dilations, const ngraph::CoordinateDiff& paddings,
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size_t groups) {
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auto& data_shape = data_batch->get_shape();
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auto& filter_shape = filters->get_shape();
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auto& out_shape = doutput->get_shape();
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ngraph::NodeVector ng_slices;
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for (size_t i = 0; i < groups; ++i) {
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size_t channel_step = filter_shape.at(1);
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const std::vector<size_t> lower_bound{0, i * channel_step, 0, 0};
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const std::vector<size_t> upper_bound{data_shape.at(0),
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(i + 1) * channel_step,
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data_shape.at(2), data_shape.at(3)};
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auto data_slice = std::make_shared<ngraph::op::Slice>(
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data_batch, lower_bound, upper_bound);
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size_t filter_step = data_shape.at(0);
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const std::vector<size_t> filter_lower_bound{i * filter_step, 0, 0, 0};
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const std::vector<size_t> filter_upper_bound{
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(i + 1) * filter_step, filter_shape.at(1), filter_shape.at(2),
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filter_shape.at(3)};
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auto filter_slice = std::make_shared<ngraph::op::Slice>(
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filters, filter_lower_bound, filter_upper_bound);
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const std::vector<size_t> olower_bound{0, i * filter_step, 0, 0};
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const std::vector<size_t> oupper_bound{out_shape.at(0),
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(i + 1) * filter_step,
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out_shape.at(2), out_shape.at(3)};
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auto out_slice = std::make_shared<ngraph::op::Slice>(doutput, olower_bound,
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oupper_bound);
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auto ng_conv = std::make_shared<ngraph::op::ConvolutionBackpropFilters>(
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data_slice, filter_slice->get_shape(), out_slice, strides, dilations,
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paddings, paddings, ngraph::Strides{1, 1});
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ng_slices.push_back(ng_conv);
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}
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size_t concat_axis = 0;
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return std::make_shared<ngraph::op::Concat>(ng_slices, concat_axis);
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}
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std::shared_ptr<ngraph::Node> GroupedGradConvolutionData(
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const std::shared_ptr<ngraph::Node>& data_batch,
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const std::shared_ptr<ngraph::Node>& filters,
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const std::shared_ptr<ngraph::Node>& doutput, const ngraph::Strides strides,
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const ngraph::Strides dilations, const ngraph::CoordinateDiff& paddings,
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size_t groups) {
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auto& data_shape = data_batch->get_shape();
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auto& filter_shape = filters->get_shape();
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auto& out_shape = doutput->get_shape();
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ngraph::NodeVector ng_slices;
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for (size_t i = 0; i < groups; ++i) {
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size_t channel_step = filter_shape.at(1);
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const std::vector<size_t> lower_bound{0, i * channel_step, 0, 0};
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const std::vector<size_t> upper_bound{data_shape.at(0),
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(i + 1) * channel_step,
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data_shape.at(2), data_shape.at(3)};
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auto data_slice = std::make_shared<ngraph::op::Slice>(
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data_batch, lower_bound, upper_bound);
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size_t filter_step = data_shape.at(0);
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const std::vector<size_t> filter_lower_bound{i * filter_step, 0, 0, 0};
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const std::vector<size_t> filter_upper_bound{
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(i + 1) * filter_step, filter_shape.at(1), filter_shape.at(2),
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filter_shape.at(3)};
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auto filter_slice = std::make_shared<ngraph::op::Slice>(
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filters, filter_lower_bound, filter_upper_bound);
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const std::vector<size_t> olower_bound{0, i * filter_step, 0, 0};
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const std::vector<size_t> oupper_bound{out_shape.at(0),
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(i + 1) * filter_step,
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out_shape.at(2), out_shape.at(3)};
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auto out_slice = std::make_shared<ngraph::op::Slice>(doutput, olower_bound,
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oupper_bound);
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auto ng_conv = std::make_shared<ngraph::op::ConvolutionBackpropData>(
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data_slice->get_shape(), filter_slice, out_slice, strides, dilations,
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paddings, paddings, ngraph::Strides{1, 1});
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ng_slices.push_back(ng_conv);
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}
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size_t concat_axis = 1;
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return std::make_shared<ngraph::op::Concat>(ng_slices, concat_axis);
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}
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void BuildConv2dNode(
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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 filters = paddle::platform::GetInputNode(op, "Filter", ngb_node_map);
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auto input = paddle::platform::GetInputNode(op, "Input", ngb_node_map);
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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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std::vector<int> dilations = op_attrs.Get<std::vector<int>>("dilations");
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const 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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const ngraph::Strides ng_dilations{static_cast<size_t>(dilations.at(0)),
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static_cast<size_t>(dilations.at(1))};
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const ngraph::CoordinateDiff ng_paddings{
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static_cast<std::ptrdiff_t>(paddings.at(0)),
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static_cast<std::ptrdiff_t>(paddings.at(1))};
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int groups = static_cast<size_t>(op_attrs.Get<int>("groups"));
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PADDLE_ENFORCE_GE(groups, 1, "conv groups needs be no less than 1");
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std::shared_ptr<ngraph::Node> result;
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if (groups == 1) {
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result = std::make_shared<ngraph::op::Convolution>(
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input, filters, ng_strides, ng_dilations, ng_paddings, ng_paddings);
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} else {
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result = GroupedConvolution(input, filters, ng_strides, ng_dilations,
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ng_paddings, groups);
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}
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paddle::platform::SetOutputNode(op, "Output", result, ngb_node_map);
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}
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void BuildConv2dGradNode(
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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 filter = paddle::platform::GetInputNode(op, "Filter", ngb_node_map);
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auto input = paddle::platform::GetInputNode(op, "Input", ngb_node_map);
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auto doutput =
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paddle::platform::GetInputNode(op, "Output@GRAD", ngb_node_map);
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int groups = op_attrs.Get<int>("groups");
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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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std::vector<int> dilations = op_attrs.Get<std::vector<int>>("dilations");
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const 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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const ngraph::Strides ng_dilations{static_cast<size_t>(dilations.at(0)),
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static_cast<size_t>(dilations.at(1))};
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const ngraph::CoordinateDiff ng_paddings{
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static_cast<std::ptrdiff_t>(paddings.at(0)),
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static_cast<std::ptrdiff_t>(paddings.at(1))};
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std::shared_ptr<ngraph::Node> dfilter;
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std::shared_ptr<ngraph::Node> dinput;
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if (groups == 1) {
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dfilter = std::make_shared<ngraph::op::ConvolutionBackpropFilters>(
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input, filter->get_shape(), doutput, ng_strides, ng_dilations,
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ng_paddings, ng_paddings, ngraph::Strides{1, 1});
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dinput = std::make_shared<ngraph::op::ConvolutionBackpropData>(
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input->get_shape(), filter, doutput, ng_strides, ng_dilations,
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ng_paddings, ng_paddings, ngraph::Strides{1, 1});
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} else {
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dfilter = GroupedGradConvolutionFilter(input, filter, doutput, ng_strides,
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ng_dilations, ng_paddings, groups);
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dinput = GroupedGradConvolutionData(input, filter, doutput, ng_strides,
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ng_dilations, ng_paddings, groups);
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}
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paddle::platform::SetOutputNode(op, "Filter@GRAD", dfilter, ngb_node_map);
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paddle::platform::SetOutputNode(op, "Input@GRAD", dinput, ngb_node_map);
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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_conv2d_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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import unittest
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from paddle.fluid.tests.unittests.test_conv2d_op import *
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class TestNGRAPH(TestConv2dOp):
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def init_kernel_type(self):
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super(TestNGRAPH, self).init_kernel_type()
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class TestNGRAPHWithPad(TestWithPad):
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def init_kernel_type(self):
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super(TestNGRAPHWithPad, self).init_kernel_type()
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class TestNGRAPHWithStride(TestWithStride):
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def init_kernel_type(self):
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super(TestNGRAPHWithStride, self).init_kernel_type()
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class TestNGRAPHWithGroup(TestWithGroup):
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def init_kernel_type(self):
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super(TestNGRAPHWithGroup, self).init_kernel_type()
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class TestNGRAPHWith1x1(TestWith1x1):
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def init_kernel_type(self):
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super(TestNGRAPHWith1x1, self).init_kernel_type()
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class TestNGRAPHWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
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def init_kernel_type(self):
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super(TestNGRAPHWithInput1x1Filter1x1, self).init_kernel_type()
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if __name__ == '__main__':
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unittest.main()

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