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[GNA] Add support for DWSC, other fixes and code refactoring.
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inference-engine/src/gna_plugin/transformations/convert_dwsc_to_scaleshifts.cpp
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| // Copyright (C) 2021 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
| // | ||
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| #include <openvino/cc/ngraph/itt.hpp> | ||
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| #include "transformations/convert_dwsc_to_scaleshifts.hpp" | ||
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| #include <ngraph/opsets/opset7.hpp> | ||
| #include <ngraph/pattern/op/wrap_type.hpp> | ||
| #include <transformations/utils/utils.hpp> | ||
| #include <ngraph/rt_info.hpp> | ||
| #include <ie_common.h> | ||
| #include "utils/transformation_helper.hpp" | ||
| //#include "backend/gna_limitations.hpp" | ||
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| using namespace GNAPluginNS; | ||
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| NGRAPH_RTTI_DEFINITION(ConvertDWSCToScaleShifts, "ConvertDWSCToScaleShifts", 0); | ||
| NGRAPH_RTTI_DEFINITION(ConvertDWSCBiasToScaleShifts, "ConvertDWSCBiasToScaleShifts", 0); | ||
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| static bool VerifyDWSC(std::shared_ptr<ngraph::opset7::GroupConvolution> dwsc) { | ||
| // Verify it's a 1D convolution | ||
| // Verify that filter group count == input channel count | ||
| // Verify that per group filter output channel count == 1 | ||
| if (dwsc->get_input_shape(1)[3] != 1 || dwsc->get_input_shape(0)[2] != 1 || dwsc->get_output_shape(0)[2] != 1 || | ||
| dwsc->get_input_shape(1)[0] != dwsc->get_input_shape(0)[1] || | ||
| dwsc->get_input_shape(1)[1] != 1) | ||
| return false; | ||
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| return true; | ||
| } | ||
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| static std::shared_ptr<ngraph::Node> DecomposeDWSC(std::shared_ptr<ngraph::opset7::GroupConvolution> dwsc, std::shared_ptr<ngraph::opset7::Constant> bias_const, | ||
| std::shared_ptr<ngraph::opset7::Reshape> flat_input_plane, std::shared_ptr<ngraph::Node> flat_filters_plane) { | ||
| std::shared_ptr<ngraph::opset7::Constant> const_zero_padding; | ||
| std::shared_ptr<ngraph::Node> reshaped_bias; | ||
| ngraph::OutputVector output_chunks; | ||
| auto input_channel_count = dwsc->get_input_shape(0)[1]; | ||
| auto input_width = dwsc->get_input_shape(0)[3]; | ||
| auto output_width = dwsc->get_output_shape(0)[3]; | ||
| auto filter_width = dwsc->get_input_shape(1)[4]; | ||
| auto pads_begin = dwsc->get_pads_begin()[1]; | ||
| auto stride_width = dwsc->get_strides()[1]; | ||
| auto dilation_width = dwsc->get_dilations()[1]; | ||
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| // Constant with zero padding | ||
| if (pads_begin) { | ||
| const_zero_padding = std::make_shared<ngraph::opset7::Constant>(dwsc->get_element_type(), ngraph::Shape{1, input_channel_count}, 0); | ||
| copy_runtime_info(dwsc, const_zero_padding); | ||
| } | ||
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| // Reshape bias const | ||
| if (bias_const) { | ||
| auto bias_size = shape_size(bias_const->get_shape()); | ||
| reshaped_bias = ngraph::op::util::make_try_fold<ngraph::opset7::Reshape>(bias_const, | ||
| ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, ngraph::Shape{1, bias_size}), false); | ||
| } | ||
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| // Move filter over input performing multiplication and addition (scaleshift), take padding, stride, dilation and bias into account | ||
| for (int32_t input_position = -pads_begin, o = 0; o < output_width; input_position += stride_width, o++) { | ||
| std::shared_ptr<ngraph::Node> previous_layer_output, last_layer_output; | ||
| int32_t filter_end = input_position + filter_width * dilation_width; | ||
| bool first = true; | ||
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| filter_end = filter_end < input_width ? filter_end : input_width; | ||
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| for (int32_t filter_pos = input_position, filter_idx = 0; filter_pos < filter_end; filter_pos += dilation_width, filter_idx++) { | ||
| if (filter_pos >= 0) { | ||
| auto conv_input_slice = FlatCrop(flat_input_plane, filter_pos * input_channel_count, input_channel_count); | ||
| auto conv_filter_slice = FlatCrop(flat_filters_plane, filter_idx * input_channel_count, input_channel_count); | ||
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| if (first) { | ||
| first = false; | ||
| previous_layer_output = std::make_shared<ngraph::opset7::Multiply>(conv_input_slice, conv_filter_slice); | ||
| copy_runtime_info(dwsc, previous_layer_output); | ||
| if (bias_const) { | ||
| previous_layer_output = std::make_shared<ngraph::opset7::Add>(previous_layer_output, reshaped_bias); | ||
| copy_runtime_info(dwsc, previous_layer_output); | ||
| } | ||
| last_layer_output = previous_layer_output; | ||
| } else { | ||
| last_layer_output = std::make_shared<ngraph::opset7::Multiply>(conv_input_slice, conv_filter_slice); | ||
| copy_runtime_info(dwsc, last_layer_output); | ||
| last_layer_output = std::make_shared<ngraph::opset7::Add>(last_layer_output, previous_layer_output); | ||
| copy_runtime_info(dwsc, last_layer_output); | ||
| previous_layer_output = last_layer_output; | ||
| } | ||
| } | ||
| } | ||
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| if (!last_layer_output) { | ||
| IE_ASSERT(const_zero_padding); | ||
| last_layer_output = const_zero_padding; | ||
| } | ||
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| output_chunks.push_back(last_layer_output); | ||
| } | ||
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| // Concat and transpose is only needed when output width > 1 | ||
| if (output_chunks.size() > 1) { | ||
| auto concat_output_plane = std::make_shared<ngraph::opset7::Concat>(output_chunks, 0); | ||
| auto transposed_concat_output_plane = std::make_shared<ngraph::opset7::Transpose>(concat_output_plane, | ||
| ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, ngraph::Shape{1, 0})); | ||
| copy_runtime_info(dwsc, {concat_output_plane, transposed_concat_output_plane}); | ||
| return transposed_concat_output_plane; | ||
| } | ||
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| return output_chunks[0].get_node_shared_ptr(); | ||
| } | ||
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| static bool Convert(std::shared_ptr<ngraph::Node> dwsc_node, | ||
| std::shared_ptr<ngraph::Node> bias_node, | ||
| std::shared_ptr<ngraph::Node> bias_const_node) { | ||
| auto dwsc = std::dynamic_pointer_cast<ngraph::opset7::GroupConvolution>(dwsc_node); | ||
| auto bias = std::dynamic_pointer_cast<ngraph::opset7::Add>(bias_node); | ||
| auto bias_const = std::dynamic_pointer_cast<ngraph::opset7::Constant>(bias_const_node); | ||
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| if (!VerifyDWSC(dwsc)) | ||
| return false; | ||
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| auto input_channel_count = dwsc->get_input_shape(0)[1]; | ||
| auto input_width = dwsc->get_input_shape(0)[3]; | ||
| auto output_channel_count = dwsc->get_output_shape(0)[1]; | ||
| auto output_width = dwsc->get_output_shape(0)[3]; | ||
| auto original_last_node = (bias_const ? bias_node : dwsc_node); | ||
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| // Prepare flat input data | ||
| auto reshaped_input_plane = std::make_shared<ngraph::opset7::Reshape>(dwsc->input_value(0), | ||
| ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, | ||
| ngraph::Shape{input_channel_count, input_width}), false); | ||
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| auto transposed_input_plane = std::make_shared<ngraph::opset7::Transpose>(reshaped_input_plane, | ||
| ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, ngraph::Shape{1, 0})); | ||
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| auto flat_input_plane = std::make_shared<ngraph::opset7::Reshape>(transposed_input_plane, | ||
| ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, | ||
| ngraph::Shape{1, shape_size(dwsc->input_value(0).get_shape())}), false); | ||
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| // Prepare flat filter data | ||
| auto filters_const = std::dynamic_pointer_cast<ngraph::opset7::Constant>(dwsc->input_value(1).get_node_shared_ptr()); | ||
| auto filters_size = shape_size(filters_const->get_shape()); | ||
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| auto transposed_filters_const = ngraph::op::util::make_try_fold<ngraph::opset7::Transpose>(filters_const, | ||
| ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{5}, ngraph::Shape{4, 1, 2, 3, 0})); | ||
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| auto flat_filters_plane = ngraph::op::util::make_try_fold<ngraph::opset7::Reshape>(transposed_filters_const, | ||
| ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, ngraph::Shape{1, filters_size}), false); | ||
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| copy_runtime_info(dwsc, {reshaped_input_plane, transposed_input_plane, flat_input_plane, transposed_filters_const, flat_filters_plane}); | ||
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| // Convert DWSC to a set of diagonal layers | ||
| auto output_plane = DecomposeDWSC(dwsc, bias_const, flat_input_plane, flat_filters_plane); | ||
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| // Restore the original output shape | ||
| auto result = std::make_shared<ngraph::opset7::Reshape>(output_plane, | ||
| ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{4}, | ||
| ngraph::Shape{1, output_channel_count, 1, output_width}), false); | ||
| copy_runtime_info(dwsc, result); | ||
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| // We need to put here the original Group Convolution layer name, so the new layer output can be used as a network result | ||
| std::string result_name = original_last_node->get_friendly_name(); | ||
| replace_node(original_last_node, result); | ||
| result->set_friendly_name(result_name); | ||
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| return true; | ||
| } | ||
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| ConvertDWSCToScaleShifts::ConvertDWSCToScaleShifts() { | ||
| MATCHER_SCOPE(ConvertDWSCToScaleShifts); | ||
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| auto dwsc = ngraph::pattern::wrap_type<ngraph::opset7::GroupConvolution>( | ||
| {ngraph::pattern::any_input(), ngraph::pattern::wrap_type<ngraph::opset7::Constant>(ngraph::pattern::rank_equals(5))}, | ||
| ngraph::pattern::rank_equals(4)); | ||
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| ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) { | ||
| const auto& pattern_map = m.get_pattern_value_map(); | ||
| return Convert(pattern_map.at(dwsc).get_node_shared_ptr(), nullptr, nullptr); | ||
| }; | ||
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| auto m = std::make_shared<ngraph::pattern::Matcher>(dwsc, matcher_name); | ||
| this->register_matcher(m, callback); | ||
| } | ||
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| ConvertDWSCBiasToScaleShifts::ConvertDWSCBiasToScaleShifts() { | ||
| MATCHER_SCOPE(ConvertDWSCBiasToScaleShifts); | ||
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| auto dwsc = ngraph::pattern::wrap_type<ngraph::opset7::GroupConvolution>( | ||
| {ngraph::pattern::any_input(), ngraph::pattern::wrap_type<ngraph::opset7::Constant>(ngraph::pattern::rank_equals(5))}, | ||
| consumers_and_rank(1, 4)); | ||
| auto const_input = ngraph::pattern::wrap_type<ngraph::opset7::Constant>(); | ||
| auto bias = ngraph::pattern::wrap_type<ngraph::opset7::Add>({dwsc, const_input}); | ||
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| ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) { | ||
| const auto& pattern_map = m.get_pattern_value_map(); | ||
| auto bias_it = pattern_map.find(bias); | ||
| auto bias_node = (bias_it == std::end(pattern_map) ? nullptr : bias_it->second.get_node_shared_ptr()); | ||
| std::shared_ptr<ngraph::Node> bias_const = nullptr; | ||
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| if (bias_node && (bias_const = VerifyBiasGetConst(pattern_map.at(dwsc).get_node_shared_ptr(), bias_node)) == nullptr) | ||
| return false; | ||
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| return Convert(pattern_map.at(dwsc).get_node_shared_ptr(), bias_node, bias_const); | ||
| }; | ||
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| auto m = std::make_shared<ngraph::pattern::Matcher>(bias, matcher_name); | ||
| this->register_matcher(m, callback); | ||
| } |
29 changes: 29 additions & 0 deletions
29
inference-engine/src/gna_plugin/transformations/convert_dwsc_to_scaleshifts.hpp
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,29 @@ | ||
| // Copyright (C) 2021 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
| // | ||
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| #pragma once | ||
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| #include <ngraph/pass/graph_rewrite.hpp> | ||
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| namespace GNAPluginNS { | ||
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| /** | ||
| * @brief Convert a depthwise separable convolution (represented by a GroupConvolution) to a set of ScaleShift layers (MatMul + Add) | ||
| */ | ||
| class ConvertDWSCToScaleShifts : public ngraph::pass::MatcherPass { | ||
| public: | ||
| NGRAPH_RTTI_DECLARATION; | ||
| ConvertDWSCToScaleShifts(); | ||
| }; | ||
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| /** | ||
| * @brief Convert a depthwise separable convolution with bias (represented by a GroupConvolution + Add) to a set of ScaleShift layers (MatMul + Add) | ||
| */ | ||
| class ConvertDWSCBiasToScaleShifts : public ngraph::pass::MatcherPass { | ||
| public: | ||
| NGRAPH_RTTI_DECLARATION; | ||
| ConvertDWSCBiasToScaleShifts(); | ||
| }; | ||
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| } // namespace GNAPluginNS |
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