Implement nGraph transformation to decompose Einsum-7 operation (#5529)
* Implement nGraph transformation to decompose Einsum-7 operation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Use MatMul instead of Eltwise-multiplication and ReduceSum Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Add description for new methods Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code style Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code style #2 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Remove unused variables.py Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Apply feedback after review: fix comments, new_register_node use Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Add Reshape if needed and apply code-review feedback Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix code-style Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Remove unused variable Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
This commit is contained in:
Roman Kazantsev
GitHub
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commit
b1d1f9287f
@ -0,0 +1,28 @@
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// Copyright (C) 2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#pragma once
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#include <memory>
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#include <ngraph/pass/graph_rewrite.hpp>
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#include <transformations_visibility.hpp>
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namespace ngraph {
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namespace pass {
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class TRANSFORMATIONS_API EinsumDecomposition;
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} // namespace pass
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} // namespace ngraph
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/**
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* @ingroup ie_transformation_common_api
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* @brief EinsumDecomposition transformation decomposes Einsum-7 operation into a sub-graph with more simple operations:
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* Transpose, Reshape, MatMul, ReduceSum, Unsqueeze, ShapeOf, ReduceProd, StridedSlice, and Concat
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*/
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class ngraph::pass::EinsumDecomposition : public ngraph::pass::MatcherPass {
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public:
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NGRAPH_RTTI_DECLARATION;
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EinsumDecomposition();
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};
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@ -58,6 +58,7 @@
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#include "transformations/op_conversions/convert_gelu.hpp"
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#include "transformations/op_conversions/convert_interpolate1_to_interpolate4.hpp"
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#include "transformations/op_conversions/batch_norm_decomposition.hpp"
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#include "transformations/op_conversions/einsum_decomposition.hpp"
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#include "transformations/op_conversions/gelu7_downgrade.hpp"
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#include "transformations/op_conversions/reduce_l1_decomposition.hpp"
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#include "transformations/op_conversions/reduce_l2_decomposition.hpp"
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@ -146,6 +147,7 @@ bool ngraph::pass::CommonOptimizations::run_on_function(std::shared_ptr<ngraph::
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decomp->add_matcher<ngraph::pass::BatchNormDecomposition>();
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decomp->add_matcher<ngraph::pass::MVN6Decomposition>();
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decomp->add_matcher<ngraph::pass::SimplifyCTCGreedyDecoderSeqLen>();
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decomp->add_matcher<ngraph::pass::EinsumDecomposition>();
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decomp->set_name("ngraph::pass::CommonDecompositions");
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// CF is required after all decompositions
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@ -0,0 +1,683 @@
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// Copyright (C) 2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include "transformations/op_conversions/einsum_decomposition.hpp"
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#include <memory>
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#include <ngraph/opsets/opset7.hpp>
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#include <ngraph/pattern/op/wrap_type.hpp>
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#include <ngraph/rt_info.hpp>
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#include <ngraph/validation_util.hpp>
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#include <transformations/utils/utils.hpp>
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#include "itt.hpp"
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namespace {
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/// \brief Check if the EinsumDecomposition transformation is applicable to a given Einsum.
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/// The transformation is applicable if input subscript does not have repeated labels and ellipsis.
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///
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/// \param subscript A subscript to check its format
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///
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/// \return true - applicable, false - not applicable
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///
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bool is_subscript_applicable(const std::string& subscript) {
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auto labels = ngraph::opset7::Einsum::extract_labels(subscript);
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auto unique_labels = std::unordered_set<std::string>(labels.begin(), labels.end());
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return std::find(labels.begin(), labels.end(), "...") == labels.end() && unique_labels.size() == labels.size();
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}
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/// \brief Compute einsum_path for a given Einsum node meaning that the (pseudo-)optimal
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/// order of operands contraction in terms of performance and memory consumption
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///
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/// \param einsum_node An input Einsum node
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///
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/// \return a vector of pairs with input indices assuming that the intermediate result is
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/// appended in the tail
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///
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std::vector<std::pair<size_t, size_t>> compute_einsum_path(std::shared_ptr<const ngraph::opset7::Einsum> einsum_node) {
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// TODO: implement algorithm for finding (pseudo-)optimal einsum_path
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std::vector<std::pair<size_t, size_t>> einsum_path;
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const size_t num_inputs = einsum_node->get_input_size();
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NGRAPH_CHECK(num_inputs > 0);
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for (size_t input_ind = num_inputs - 1; input_ind > 0; --input_ind) {
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einsum_path.push_back(std::make_pair(0, input_ind));
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}
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return einsum_path;
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}
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/// \brief Check if the dimension with a given label is reduced. The dimension is reduced
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/// if the corresponding label is met in neither the output subscript nor the input subscripts
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/// excluding ones specified by a vector excluded_indices
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///
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/// \param input_subscripts The vector of the input subscripts
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/// \param output_subscript The output subscript
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/// \param label_to_check A label that corresponds to dimension to check
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/// \param excluded_indices A vector of input subscript indices to be excluded
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///
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/// \return true - a dimension to reduce, false - otherwise
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///
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bool is_dimension_reduced(const std::vector<std::string>& input_subscripts, const std::string& output_subscript,
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const std::string label_to_check, const std::vector<size_t>& excluded_indices) {
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for (size_t input_ind = 0; input_ind < input_subscripts.size(); ++input_ind) {
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const auto& input_subscript = input_subscripts[input_ind];
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// the subscript is checked only if its index is not in excluded indices list
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bool check_subscript = (std::find(excluded_indices.begin(), excluded_indices.end(), input_ind) == excluded_indices.end());
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if (check_subscript && input_subscript.find(label_to_check) != std::string::npos) {
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return false;
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}
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}
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return output_subscript.find(label_to_check) == std::string::npos;
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}
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/// \brief Checks if input vector represents a range [0; n]
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///
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/// \param labels_inds Input vector to check
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///
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/// \return true - the input vector is a range [0; n]; false - otherwise
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///
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bool is_range_0_to_n(const std::vector<int64_t> &labels_inds) {
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int64_t check_index = 0;
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for (auto index : labels_inds) {
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if (check_index != index) {
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return false;
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}
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++check_index;
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}
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return true;
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}
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/// \brief Generate an input subscript that provides to group dimensions into the common,
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/// separate and reduced dimensions after transpose
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///
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/// \param input_subscripts A vector of the input subscripts
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/// \param common_labels_inds A vector of indices of the common dimensions
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/// \param separate_labels_inds A vector of indices of the separate dimensions
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/// \param reduced_labels_inds A vector of indices of the reduced dimensions
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/// \param is_separate_first A boolean flag. It is true if the separate dimensions
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/// goes before the reduced dimensions
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///
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/// \return An input subscript for grouping dimensions
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///
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std::string generate_grouping_subscript(const std::string& input_subscript, const std::vector<int64_t>& common_labels_inds,
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const std::vector<int64_t>& separate_labels_inds, const std::vector<int64_t>& reduced_labels_inds,
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bool& is_separate_first) {
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// transpose is not needed if common labels, reduced labels
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// and separate labels indices go concurrently
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std::vector<int64_t> labels_inds = common_labels_inds;
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labels_inds.insert(labels_inds.end(), reduced_labels_inds.begin(), reduced_labels_inds.end());
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labels_inds.insert(labels_inds.end(), separate_labels_inds.begin(), separate_labels_inds.end());
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if (is_range_0_to_n(labels_inds)) {
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is_separate_first = false;
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return input_subscript;
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}
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// transpose is not needed if common labels, separate labels
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// and reduced labels indices go concurrently
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labels_inds = common_labels_inds;
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labels_inds.insert(labels_inds.end(), separate_labels_inds.begin(), separate_labels_inds.end());
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labels_inds.insert(labels_inds.end(), reduced_labels_inds.begin(), reduced_labels_inds.end());
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if (is_range_0_to_n(labels_inds)) {
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is_separate_first = true;
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return input_subscript;
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}
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auto labels = ngraph::opset7::Einsum::extract_labels(input_subscript);
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std::string required_subscript = "";
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for (auto index : labels_inds) {
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required_subscript += labels[index];
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}
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is_separate_first = true;
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return required_subscript;
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}
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/// \brief Update a vector of input nodes and subscripts by removing items for operands
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/// with indices input_ind1 and input_ind2 and inserted new input node and the corresponsing
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/// subscript in the tail
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///
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/// \param input_nodes A vector of the input nodes to update
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/// \param input_subscripts A vector of the input subscripts to update
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/// \param input_ind1 An index of item to be removed
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/// \param input_ind2 An index of item to be removed
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/// \param new_node New input node to be inserted in the tail
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/// \param new_subscript New input subscript to be inserted in the tail
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///
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void update_operands(ngraph::OutputVector& input_nodes, std::vector<std::string>& input_subscripts, size_t input_ind1, size_t input_ind2,
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const ngraph::Output<ngraph::Node>& new_node, const std::string& new_subscript) {
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NGRAPH_CHECK(input_ind1 < input_ind2);
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NGRAPH_CHECK(input_ind2 < input_nodes.size());
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NGRAPH_CHECK(input_ind2 < input_subscripts.size());
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input_nodes.erase(input_nodes.begin() + input_ind2);
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input_nodes.erase(input_nodes.begin() + input_ind1);
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input_nodes.push_back(new_node);
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input_subscripts.erase(input_subscripts.begin() + input_ind2);
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input_subscripts.erase(input_subscripts.begin() + input_ind1);
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input_subscripts.push_back(new_subscript);
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}
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/// \brief Return input node with computed sub-shape defined by a range [s_begin;s_end)
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///
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/// \param data_shape Input node that contains some tensor shape
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/// \param s_begin Start index of dimension
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/// \param s_end End index of dimension
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/// \param subgraph_nodes A vector of operation nodes where to add new ones
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/// \param is_product A boolean flag that indicates if to compute a product of
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/// dimension sizes in the computed sub-shape
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///
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/// \return A vector of input nodes that can be empty (if s_end <= s_begin)
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/// or contains just one input node with sub-shape or its product
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///
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ngraph::OutputVector compute_sub_shape(const ngraph::Output<ngraph::Node>& data_shape, size_t s_begin, size_t s_end, ngraph::NodeVector& subgraph_nodes,
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bool is_product = false) {
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int64_t begin = static_cast<int64_t>(s_begin);
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int64_t end = static_cast<int64_t>(s_end);
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ngraph::OutputVector sub_shape_vector;
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if (end <= begin) {
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return sub_shape_vector;
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}
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std::vector<int64_t> begin_mask(1, 0);
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std::vector<int64_t> end_mask(1, 0);
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auto begin_const = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {1}, {begin});
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auto end_const = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {1}, {end});
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auto stride_const = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {1}, {1});
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auto sub_shape = std::make_shared<ngraph::opset7::StridedSlice>(data_shape, begin_const, end_const, begin_mask, end_mask);
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if (is_product) {
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auto reduce_axis_const = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {1}, {0});
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auto separate_shape_prod = std::make_shared<ngraph::opset7::ReduceProd>(sub_shape->output(0), reduce_axis_const, true);
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sub_shape_vector.push_back(separate_shape_prod->output(0));
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subgraph_nodes.insert(subgraph_nodes.end(), {reduce_axis_const, separate_shape_prod});
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} else {
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sub_shape_vector.push_back(sub_shape->output(0));
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}
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subgraph_nodes.insert(subgraph_nodes.end(), {begin_const, end_const, stride_const, sub_shape});
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return sub_shape_vector;
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}
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/// \brief Unsqueeze input node by given dimensions if a vector of unsqueezing dimensions
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/// is not empty
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///
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/// \param input_node Input node to unsqueeze
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/// \param unsqueeze_axes A vector of dimensions to be unsqueezed
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/// \param subgraph_nodes A vector of operation nodes that is included into a
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/// sub-graph decomposing Einsum that is needed for copy_runtime_info
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///
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/// \return Unsqueezed input node if a vector of unsqueezing dimensions is not empty,
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/// otherwise, the original input node
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///
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ngraph::Output<ngraph::Node> unsqueeze_input(const ngraph::Output<ngraph::Node>& input_node, const std::vector<int64_t>& unsqueeze_axes,
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ngraph::NodeVector& subgraph_nodes) {
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if (unsqueeze_axes.empty()) {
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return input_node;
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}
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auto unsqueeze_axes_const = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {unsqueeze_axes.size()}, unsqueeze_axes);
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auto unsqueeze = std::make_shared<ngraph::opset7::Unsqueeze>(input_node, unsqueeze_axes_const);
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subgraph_nodes.insert(subgraph_nodes.end(), {unsqueeze_axes_const, unsqueeze});
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return unsqueeze->output(0);
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}
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/// \brief Reshape input node to the new shape specified by sub-shapes of the common,
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/// separate and reduced dimensions so that the reshaped input has a format acceptable by MatMul
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///
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/// \param input_node Input node to reshape
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/// \param common_sub_shape A sub-shape corresponding to the common dimensions
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/// \param separate_sub_shape A sub-shape corresponding to the separate dimensions
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/// \param reduced_sub_shape_prod A product of the separate dimensions sizes
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/// \param is_separate_first true - the separate dimensions placed before reduced
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/// dimensions, otherwise, it is after them
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/// \param subgraph_nodes A vector of operation nodes that is included into
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/// a sub-graph decomposing Einsum that is needed for copy_runtime_info
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///
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/// \return Reshaped input node
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///
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ngraph::Output<ngraph::Node> reshape_input_for_matmul(const ngraph::Output<ngraph::Node>& input_node, const ngraph::OutputVector& common_sub_shape,
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const ngraph::OutputVector& separate_sub_shape, const ngraph::OutputVector& reduced_sub_shape_prod,
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bool is_separate_first, ngraph::NodeVector& subgraph_nodes) {
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ngraph::OutputVector new_shape_parts;
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new_shape_parts.insert(new_shape_parts.end(), common_sub_shape.begin(), common_sub_shape.end());
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// compute a product of a sub-shape for separate labels
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ngraph::OutputVector separate_parts;
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if (common_sub_shape.size() > 0 && separate_sub_shape.size() == 0) {
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// in this case new dimension corresponding to separate labels must be added
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// since MatMul operation is not possible to do without separate dimensions if the
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// common dimension presents
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auto separate_new_dim = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {1}, {1});
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separate_parts.push_back(separate_new_dim);
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subgraph_nodes.insert(subgraph_nodes.end(), {separate_new_dim});
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} else if (separate_sub_shape.size() > 0) {
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// in this case compute a product of separate dimension sizes since they must be
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// presented with just one dimension for MatMul
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auto reduce_axis_const = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {1}, {0});
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auto separate_shape_prod = std::make_shared<ngraph::opset7::ReduceProd>(separate_sub_shape[0], reduce_axis_const, true);
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separate_parts.push_back(separate_shape_prod->output(0));
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subgraph_nodes.insert(subgraph_nodes.end(), {reduce_axis_const, separate_shape_prod});
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}
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// form a new shape for input so that collapsed dimensions corresponding
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// to the common, separate and reduced dimensions are placed in the correct order
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if (is_separate_first) {
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new_shape_parts.insert(new_shape_parts.end(), separate_parts.begin(), separate_parts.end());
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new_shape_parts.insert(new_shape_parts.end(), reduced_sub_shape_prod.begin(), reduced_sub_shape_prod.end());
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} else {
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new_shape_parts.insert(new_shape_parts.end(), reduced_sub_shape_prod.begin(), reduced_sub_shape_prod.end());
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new_shape_parts.insert(new_shape_parts.end(), separate_parts.begin(), separate_parts.end());
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}
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// in case of scalar reshape is not needed
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if (new_shape_parts.size() == 0) {
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return input_node;
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}
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auto new_shape_op = std::make_shared<ngraph::opset7::Concat>(new_shape_parts, 0);
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// if new shape is possible to compute on the shape infer stage, insert Constant node immediatelly
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// in order to prevent repeated computing during constant-folding pass
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std::shared_ptr<ngraph::opset7::Reshape> reshaped_input_op;
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if (auto new_shape_const = ngraph::get_constant_from_source(new_shape_op)) {
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reshaped_input_op = std::make_shared<ngraph::opset7::Reshape>(input_node, new_shape_const, false);
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subgraph_nodes.insert(subgraph_nodes.end(), {new_shape_const});
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} else {
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reshaped_input_op = std::make_shared<ngraph::opset7::Reshape>(input_node, new_shape_op->output(0), false);
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subgraph_nodes.insert(subgraph_nodes.end(), {new_shape_op});
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}
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subgraph_nodes.insert(subgraph_nodes.end(), {reshaped_input_op});
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return reshaped_input_op->output(0);
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}
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/// \brief Transpose one of the Einsum inputs to layout specified through the required
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/// subscript
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///
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/// \param input_nodes A vector of input nodes to Einsum
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/// \param input_subscripts A vector of corresponding subscripts for input nodes
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/// \param required_subscript The required subscript that defines layout to which the
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/// input is to transpose
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/// \param input_ind An index of the input node to be transposed
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/// \param subgraph_nodes A vector of operation nodes that is included into
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/// a sub-graph decomposing Einsum that is needed for copy_runtime_info
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///
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void transpose_input(ngraph::OutputVector& input_nodes, std::vector<std::string>& input_subscripts, const std::string& required_subscript, size_t input_ind,
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ngraph::NodeVector& subgraph_nodes) {
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// perform sanity check for arguments
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auto num_inputs = input_nodes.size();
|
||||
NGRAPH_CHECK(num_inputs == input_subscripts.size(), "Each input must have own subscript.");
|
||||
NGRAPH_CHECK(input_ind < num_inputs, "Input index is out of range.");
|
||||
|
||||
// generate permutation vector by searching for bijection between input_subscripts
|
||||
// and required_subscript
|
||||
std::vector<int64_t> permutation;
|
||||
const auto& input_subscript = input_subscripts[input_ind];
|
||||
|
||||
// transpose is not needed since the input subscript is not going to be changed
|
||||
if (required_subscript == input_subscript) {
|
||||
return;
|
||||
}
|
||||
|
||||
// find permutation that establishes bijection between the input subscript
|
||||
// and the required one
|
||||
auto labels = ngraph::opset7::Einsum::extract_labels(input_subscript);
|
||||
auto required_labels = ngraph::opset7::Einsum::extract_labels(required_subscript);
|
||||
NGRAPH_CHECK(labels.size() == required_labels.size());
|
||||
for (const auto& required_label : required_labels) {
|
||||
auto it = std::find(labels.begin(), labels.end(), required_label);
|
||||
NGRAPH_CHECK(it != labels.end());
|
||||
int64_t found_index = static_cast<int64_t>(it - labels.begin());
|
||||
permutation.push_back(found_index);
|
||||
}
|
||||
|
||||
// create a sub-graph for transposing into the required layout
|
||||
const auto& input_node = input_nodes[input_ind];
|
||||
auto permutation_const = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {permutation.size()}, permutation);
|
||||
auto transpose = std::make_shared<ngraph::opset7::Transpose>(input_node, permutation_const);
|
||||
|
||||
// update a vector of inputs and input subscripts
|
||||
input_nodes[input_ind] = transpose->output(0);
|
||||
input_subscripts[input_ind] = required_subscript;
|
||||
|
||||
// update a vector of nodes for copy_runtime_info
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {permutation_const, transpose});
|
||||
}
|
||||
|
||||
/// \brief Find labels (in a given input subscript) that are met once in the equation
|
||||
/// and reduce dimensions corresponding to such labels
|
||||
///
|
||||
/// \param einsum_decompose_ptr A pointer to Einsum decomposing pass
|
||||
/// \param input_nodes A vector of input nodes to Einsum operation
|
||||
/// \param input_subscripts A vector of corresponding subscripts for the input nodes
|
||||
/// \param output_subscript The output subscript
|
||||
/// \param input_ind An index of the input node for which it will check
|
||||
/// dimensions to be reduced
|
||||
/// \param subgraph_nodes A vector of operation nodes that is included into
|
||||
/// a sub-graph decomposing Einsum that is needed for copy_runtime_info
|
||||
///
|
||||
void reduce_input(ngraph::pass::EinsumDecomposition *einsum_decompose_ptr,
|
||||
ngraph::OutputVector& input_nodes, std::vector<std::string>& input_subscripts,
|
||||
const std::string& output_subscript, size_t input_ind, ngraph::NodeVector& subgraph_nodes) {
|
||||
// perform sanity check for arguments
|
||||
auto num_inputs = input_nodes.size();
|
||||
NGRAPH_CHECK(num_inputs == input_subscripts.size(), "Each input must have own subscript.");
|
||||
NGRAPH_CHECK(input_ind < num_inputs, "Input index is out of range.");
|
||||
|
||||
std::vector<int64_t> reduced_axes;
|
||||
auto labels = ngraph::opset7::Einsum::extract_labels(input_subscripts[input_ind]);
|
||||
std::string new_input_subscript = "";
|
||||
for (size_t dim_ind = 0; dim_ind < labels.size(); ++dim_ind) {
|
||||
const auto& label = labels[dim_ind];
|
||||
|
||||
// check if the current label is met in the other input subscripts
|
||||
// or the output subscript
|
||||
bool is_dim_reduced = is_dimension_reduced(input_subscripts, output_subscript, label, {input_ind});
|
||||
|
||||
// if label is not met, dimension corresponding to the label is to reduce
|
||||
if (is_dim_reduced) {
|
||||
reduced_axes.push_back(dim_ind);
|
||||
} else {
|
||||
new_input_subscript += label;
|
||||
}
|
||||
}
|
||||
|
||||
if (reduced_axes.size() == 0) {
|
||||
// there is no axis to reduce
|
||||
return;
|
||||
}
|
||||
|
||||
// reduce by summed up elements along dimension for which label is met just once
|
||||
const auto& input_node = input_nodes[input_ind];
|
||||
auto axes_const = ngraph::opset7::Constant::create(ngraph::element::Type_t::i64, ngraph::Shape {reduced_axes.size()}, reduced_axes);
|
||||
auto reduce_sum = einsum_decompose_ptr->register_new_node<ngraph::opset7::ReduceSum>(input_node, axes_const, false);
|
||||
|
||||
// update a vector of inputs and input subscripts
|
||||
input_nodes[input_ind] = reduce_sum->output(0);
|
||||
input_subscripts[input_ind] = new_input_subscript;
|
||||
|
||||
// update a vector of nodes for copy_runtime_info
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {axes_const, reduce_sum});
|
||||
}
|
||||
|
||||
/// \brief Contract two inputs of Einsum operation according to equation.
|
||||
/// The result of the contraction is appended into input_nodes along with its subscript.
|
||||
/// The input nodes for these two operands are removed from input_nodes along with their input
|
||||
/// subscripts
|
||||
///
|
||||
/// \param einsum_decompose_ptr A pointer to Einsum decomposing pass
|
||||
/// \param input_nodes A vector of input nodes to Einsum operation
|
||||
/// \param input_subscripts A vector of corresponding subscripts for the input nodes
|
||||
/// \param output_subscript The output subscript
|
||||
/// \param input_ind1 An index of the first operand
|
||||
/// \param input_ind2 An index of the second operand
|
||||
/// \param subgraph_nodes A vector of operation nodes that is included into a
|
||||
/// sub-graph decomposing Einsum that is needed for copy_runtime_info
|
||||
///
|
||||
void contract_two_inputs(ngraph::pass::EinsumDecomposition* einsum_decompose_ptr,
|
||||
ngraph::OutputVector& input_nodes, std::vector<std::string>& input_subscripts,
|
||||
const std::string& output_subscript, size_t input_ind1,
|
||||
size_t input_ind2, ngraph::NodeVector& subgraph_nodes) {
|
||||
// assume that input_ind1 < input_ind2 without loss of generality, otherwise, just swap them
|
||||
if (input_ind2 < input_ind1) {
|
||||
std::swap(input_ind1, input_ind2);
|
||||
}
|
||||
|
||||
// perform sanity check for arguments
|
||||
auto num_inputs = input_nodes.size();
|
||||
NGRAPH_CHECK(num_inputs == input_subscripts.size(), "Each input must have own subscript.");
|
||||
NGRAPH_CHECK(input_ind2 < num_inputs && input_ind1 != input_ind2, "Incorrect input index is specified.");
|
||||
|
||||
const auto& input_node1 = input_nodes[input_ind1];
|
||||
const auto& input_node2 = input_nodes[input_ind2];
|
||||
|
||||
// reduce dimensions for input operands if possible
|
||||
reduce_input(einsum_decompose_ptr, input_nodes, input_subscripts, output_subscript, input_ind1, subgraph_nodes);
|
||||
reduce_input(einsum_decompose_ptr, input_nodes, input_subscripts, output_subscript, input_ind2, subgraph_nodes);
|
||||
|
||||
// step 0. split dimensions of both operands into three groups:
|
||||
// 1. dimension indices with the same labels (in both subscripts) that are NOT reduced -
|
||||
// common labels (dimensions)
|
||||
// 2. dimension indices with labels that are met only in one of two subscripts - separate
|
||||
// labels (dimensions)
|
||||
// 3. dimension indices with the same labels (in both subscripts) that are reduced - reduced
|
||||
// labels (dimensions) NOTE: dimension is reduced iff. the corresponding label are met in
|
||||
// neither the output subscript nor the input subscripts for other Einsum inputs excluding
|
||||
// two given inputs
|
||||
auto& input_subscript1 = input_subscripts[input_ind1];
|
||||
auto labels1 = ngraph::opset7::Einsum::extract_labels(input_subscript1);
|
||||
auto& input_subscript2 = input_subscripts[input_ind2];
|
||||
auto labels2 = ngraph::opset7::Einsum::extract_labels(input_subscript2);
|
||||
std::string common_part = "";
|
||||
std::string separate_part1 = "";
|
||||
std::string separate_part2 = "";
|
||||
std::vector<int64_t> common_labels_inds1, common_labels_inds2;
|
||||
std::vector<int64_t> separate_labels_inds1, separate_labels_inds2;
|
||||
std::vector<int64_t> reduced_labels_inds1, reduced_labels_inds2;
|
||||
for (size_t label_ind = 0; label_ind < labels1.size(); ++label_ind) {
|
||||
const auto& label = labels1[label_ind];
|
||||
auto iter = std::find(labels2.begin(), labels2.end(), label);
|
||||
if (iter != labels2.end()) {
|
||||
bool is_dim_reduced = is_dimension_reduced(input_subscripts, output_subscript, label, {input_ind1, input_ind2});
|
||||
common_part += label;
|
||||
if (is_dim_reduced) {
|
||||
reduced_labels_inds1.push_back(static_cast<int64_t>(label_ind));
|
||||
reduced_labels_inds2.push_back(static_cast<int64_t>(iter - labels2.begin()));
|
||||
} else {
|
||||
common_labels_inds1.push_back(static_cast<int64_t>(label_ind));
|
||||
common_labels_inds2.push_back(static_cast<int64_t>(iter - labels2.begin()));
|
||||
}
|
||||
} else {
|
||||
separate_part1 += label;
|
||||
separate_labels_inds1.push_back(static_cast<int64_t>(label_ind));
|
||||
}
|
||||
}
|
||||
for (size_t label_ind = 0; label_ind < labels2.size(); ++label_ind) {
|
||||
const auto& label = labels2[label_ind];
|
||||
auto iter = std::find(labels1.begin(), labels1.end(), label);
|
||||
if (iter == labels1.end()) {
|
||||
separate_part2 += label;
|
||||
separate_labels_inds2.push_back(static_cast<int64_t>(label_ind));
|
||||
}
|
||||
}
|
||||
|
||||
// if there is no common dimension to reduce, apply eltwise multiplication
|
||||
if (reduced_labels_inds1.empty()) {
|
||||
std::string convenient_subscript = common_part + separate_part2;
|
||||
std::string resultant_subscript = input_subscript1 + separate_part2;
|
||||
|
||||
// transpose the second operand in order to get the convenient layout
|
||||
// for further unsqueezing
|
||||
transpose_input(input_nodes, input_subscripts, convenient_subscript, input_ind2, subgraph_nodes);
|
||||
|
||||
// unsqueeze the first operand with new dimensions in the tail
|
||||
// and the number of them is equal to the number of separate labels in the second
|
||||
// subscript
|
||||
int64_t unsqueeze_dim = labels1.size();
|
||||
std::vector<int64_t> unsqueeze_axis1;
|
||||
for (size_t label_ind = 0; label_ind < separate_labels_inds2.size(); ++label_ind) {
|
||||
unsqueeze_axis1.push_back(unsqueeze_dim++);
|
||||
}
|
||||
const auto& unsqueeze_axis2 = separate_labels_inds1;
|
||||
|
||||
// unsqueeze input operands for elementwise-multiplication with broadcasting
|
||||
auto unsqueeze_output1 = unsqueeze_input(input_node1, unsqueeze_axis1, subgraph_nodes);
|
||||
auto unsqueeze_output2 = unsqueeze_input(input_node2, unsqueeze_axis2, subgraph_nodes);
|
||||
|
||||
// multiply both operands with broadcasting
|
||||
auto mul = std::make_shared<ngraph::opset7::Multiply>(unsqueeze_output1, unsqueeze_output2, ngraph::op::AutoBroadcastSpec::NUMPY);
|
||||
|
||||
// update input operand and input subscript for Einsum operation
|
||||
update_operands(input_nodes, input_subscripts, input_ind1, input_ind2, mul->output(0), resultant_subscript);
|
||||
|
||||
// update a vector of nodes for copy_runtime_info
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {mul});
|
||||
return;
|
||||
}
|
||||
|
||||
// in this case a set of reduced labels is not empty and it can apply MatMul operation
|
||||
// step 1. transpose both operands so that common labels, separated and reduced labels
|
||||
// are grouped for both operands
|
||||
bool is_separate_first1 = false;
|
||||
auto int_subscript1 = generate_grouping_subscript(input_subscript1, common_labels_inds1, separate_labels_inds1,
|
||||
reduced_labels_inds1, is_separate_first1);
|
||||
transpose_input(input_nodes, input_subscripts, int_subscript1, input_ind1, subgraph_nodes);
|
||||
bool is_separate_first2 = false;
|
||||
auto int_subscript2 = generate_grouping_subscript(input_subscript2, common_labels_inds2, separate_labels_inds2,
|
||||
reduced_labels_inds2, is_separate_first2);
|
||||
transpose_input(input_nodes, input_subscripts, int_subscript2, input_ind2, subgraph_nodes);
|
||||
|
||||
// step 2. reshape both operands so that separate labels and reduced labels are represented
|
||||
// with just one dimension this is needed by MatMul operation requirement to operands
|
||||
// format. For example, the shape must be in a format [B1, ..., Bm, X1, Y] or [B1, ..., Bm,
|
||||
// Y, X2], where B1, ..., Bm are common dimensions, X1 and X2 are collapsed dimensions
|
||||
// for separate labels and Y is collapsed dimension for reduced labels
|
||||
// this step is not needed for the operand if it satisfies to one of the requirements:
|
||||
// 1. there is just one separate dimension and just one reduced dimension
|
||||
// 2. there is no separate dimension, no common dimensions, and just one reduced dimension
|
||||
bool no_reshape_for_matmul1 = (reduced_labels_inds1.size() == 1 && separate_labels_inds1.size() == 1) ||
|
||||
(reduced_labels_inds1.size() == 1 && common_labels_inds1.size() == 0
|
||||
&& separate_labels_inds1.size() == 0);
|
||||
bool no_reshape_for_matmul2 = (reduced_labels_inds2.size() == 1 && separate_labels_inds2.size() == 1) ||
|
||||
(reduced_labels_inds2.size() == 1 && common_labels_inds2.size() == 0
|
||||
&& separate_labels_inds2.size() == 0);
|
||||
// reshape back after MatMul is not needed if one of two requrements satisfies for both operands:
|
||||
// 1. there is just one separate dimension
|
||||
// 2. there is no separate dimension and no common dimensions present.
|
||||
// If there is no separate dimension and common dimensions present, reshape is needed
|
||||
// because auxiliary separate dimension has been added by Unsqueeze operation
|
||||
// in the purpose for MatMul
|
||||
bool no_reshape_back1 = (separate_labels_inds1.size() == 1) ||
|
||||
(common_labels_inds1.size() == 0 && separate_labels_inds1.size() == 0);
|
||||
bool no_reshape_back2 = (separate_labels_inds2.size() == 1) ||
|
||||
(common_labels_inds2.size() == 0 && separate_labels_inds2.size() == 0);
|
||||
bool no_reshape_after_matmul = no_reshape_back1 && no_reshape_back2;
|
||||
|
||||
auto matmul_operand1 = input_node1;
|
||||
auto matmul_operand2 = input_node2;
|
||||
int64_t common_dims_begin = 0;
|
||||
int64_t common_dims_end = common_labels_inds1.size();
|
||||
ngraph::OutputVector common_sub_shape, separate1_sub_shape, separate2_sub_shape;
|
||||
if (no_reshape_for_matmul1 == false || no_reshape_for_matmul2 == false) {
|
||||
auto data_shape1 = std::make_shared<ngraph::opset7::ShapeOf>(input_node1);
|
||||
common_sub_shape = compute_sub_shape(data_shape1, common_dims_begin, common_dims_end, subgraph_nodes);
|
||||
int64_t reduced_dims_begin = (is_separate_first1 ? common_labels_inds1.size() + separate_labels_inds1.size() : common_labels_inds1.size());
|
||||
int64_t reduced_dims_end = reduced_dims_begin + reduced_labels_inds1.size();
|
||||
auto reduced_sub_shape_prod = compute_sub_shape(data_shape1, reduced_dims_begin, reduced_dims_end, subgraph_nodes, true);
|
||||
|
||||
if (no_reshape_for_matmul1 == false || no_reshape_after_matmul == false) {
|
||||
int64_t separate1_dims_begin = (is_separate_first1 ? common_labels_inds1.size() : common_labels_inds1.size() + reduced_labels_inds1.size());
|
||||
int64_t separate1_dims_end = separate1_dims_begin + separate_labels_inds1.size();
|
||||
separate1_sub_shape = compute_sub_shape(data_shape1, separate1_dims_begin, separate1_dims_end, subgraph_nodes);
|
||||
matmul_operand1 = reshape_input_for_matmul(input_node1, common_sub_shape, separate1_sub_shape,
|
||||
reduced_sub_shape_prod, is_separate_first1, subgraph_nodes);
|
||||
}
|
||||
|
||||
if (no_reshape_for_matmul2 == false || no_reshape_after_matmul == false) {
|
||||
auto data_shape2 = std::make_shared<ngraph::opset7::ShapeOf>(input_node2);
|
||||
int64_t separate2_dims_begin = (is_separate_first2 ? common_labels_inds2.size() : common_labels_inds2.size() + reduced_labels_inds2.size());
|
||||
int64_t separate2_dims_end = separate2_dims_begin + separate_labels_inds2.size();
|
||||
separate2_sub_shape = compute_sub_shape(data_shape2, separate2_dims_begin, separate2_dims_end, subgraph_nodes);
|
||||
matmul_operand2 = reshape_input_for_matmul(input_node2, common_sub_shape, separate2_sub_shape,
|
||||
reduced_sub_shape_prod, is_separate_first2, subgraph_nodes);
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {data_shape2});
|
||||
}
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {data_shape1});
|
||||
}
|
||||
|
||||
// step 3. apply MatMul operation for formatted inputs
|
||||
bool transpose_a = (is_separate_first1 ? false : true);
|
||||
bool transpose_b = (is_separate_first2 ? true : false);
|
||||
auto matmul = std::make_shared<ngraph::opset7::MatMul>(matmul_operand1, matmul_operand2, transpose_a, transpose_b);
|
||||
|
||||
// step 4. reshape back by unrolling dimensions corresponding to separate labels if needed
|
||||
// now dimensions corresponding to reduced labels are reduced by the MatMul operation
|
||||
std::string resultant_subscript = input_subscript1.substr(common_dims_begin, common_dims_end) + separate_part1 + separate_part2;
|
||||
if (no_reshape_after_matmul) {
|
||||
// this is a case when Reshape is not needed after MatMul operation
|
||||
// since there are no collapsed (or auxiliary added) separated dimensions
|
||||
update_operands(input_nodes, input_subscripts, input_ind1, input_ind2, matmul->output(0), resultant_subscript);
|
||||
} else {
|
||||
ngraph::OutputVector new_shape;
|
||||
new_shape.insert(new_shape.end(), common_sub_shape.begin(), common_sub_shape.end());
|
||||
new_shape.insert(new_shape.end(), separate1_sub_shape.begin(), separate1_sub_shape.end());
|
||||
new_shape.insert(new_shape.end(), separate2_sub_shape.begin(), separate2_sub_shape.end());
|
||||
auto result_shape_op = std::make_shared<ngraph::opset7::Concat>(new_shape, 0);
|
||||
|
||||
// if new shape is possible to compute on the shape infer stage, insert Constant node immediatelly
|
||||
// in order to prevent repeated computing during constant-folding pass
|
||||
std::shared_ptr<ngraph::opset7::Reshape> result_op;
|
||||
if (auto new_shape_const = ngraph::get_constant_from_source(result_shape_op)) {
|
||||
result_op = std::make_shared<ngraph::opset7::Reshape>(matmul->output(0), new_shape_const, false);
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {new_shape_const});
|
||||
} else {
|
||||
result_op = std::make_shared<ngraph::opset7::Reshape>(matmul->output(0), result_shape_op->output(0), false);
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {result_shape_op});
|
||||
}
|
||||
|
||||
// update input operand and input subscript for Einsum operation
|
||||
update_operands(input_nodes, input_subscripts, input_ind1, input_ind2, result_op->output(0), resultant_subscript);
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {result_op});
|
||||
}
|
||||
|
||||
// update a vector of nodes for copy_runtime_info
|
||||
subgraph_nodes.insert(subgraph_nodes.end(), {matmul});
|
||||
}
|
||||
} // namespace
|
||||
|
||||
NGRAPH_RTTI_DEFINITION(ngraph::pass::EinsumDecomposition, "EinsumDecomposition", 0);
|
||||
|
||||
ngraph::pass::EinsumDecomposition::EinsumDecomposition() {
|
||||
// NOTE: The transformation is applicable if Einsum equation does not contain ellipsis label
|
||||
// and does not contain subscripts with repeated labels.
|
||||
// For example, the transformation is applicable to Einsum with equation="abc,bd->ad"
|
||||
// but not applicable to a case with equation="aabc,bd->ad" due to repeated labels
|
||||
// in the first input subscript.
|
||||
MATCHER_SCOPE(EinsumDecomposition);
|
||||
auto einsum = ngraph::pattern::wrap_type<opset7::Einsum>();
|
||||
ngraph::matcher_pass_callback callback = [this](ngraph::pattern::Matcher& m) {
|
||||
auto einsum_node = std::dynamic_pointer_cast<ngraph::opset7::Einsum>(m.get_match_root());
|
||||
if (!einsum_node) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto equation = einsum_node->get_equation();
|
||||
std::vector<std::string> input_subscripts;
|
||||
std::string output_subscript;
|
||||
ngraph::opset7::Einsum::parse_equation(equation, input_subscripts, output_subscript);
|
||||
|
||||
// check that the transformation is applicable
|
||||
if (std::any_of(input_subscripts.cbegin(), input_subscripts.cend(), [](const std::string& subscript) {
|
||||
return is_subscript_applicable(subscript) == false;
|
||||
})) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// create a list of input nodes with preserving their order
|
||||
// and a vector of sub-graph nodes for copy_runtime_info
|
||||
ngraph::OutputVector input_nodes = einsum_node->input_values();
|
||||
ngraph::NodeVector subgraph_nodes;
|
||||
|
||||
// compute einsum path that is used to contract a pair of operands
|
||||
// in more optimal order
|
||||
auto einsum_path = compute_einsum_path(einsum_node);
|
||||
|
||||
// contract inputs by Einsum until just one is remained
|
||||
for (auto const& inds_pair : einsum_path) {
|
||||
contract_two_inputs(this, input_nodes, input_subscripts, output_subscript, inds_pair.first, inds_pair.second, subgraph_nodes);
|
||||
}
|
||||
|
||||
// reduce dimensions for the remained input node
|
||||
NGRAPH_CHECK(input_nodes.size() == 1);
|
||||
reduce_input(this, input_nodes, input_subscripts, output_subscript, 0, subgraph_nodes);
|
||||
|
||||
// transpose dimensions to layout required by the output subscript
|
||||
transpose_input(input_nodes, input_subscripts, output_subscript, 0, subgraph_nodes);
|
||||
|
||||
// replace the original Einsum node with the last node from decomposing sub-graph
|
||||
// preserve the original node name
|
||||
auto last_node = input_nodes[0].get_node_shared_ptr();
|
||||
last_node->set_friendly_name(einsum_node->get_friendly_name());
|
||||
ngraph::copy_runtime_info(einsum_node, subgraph_nodes);
|
||||
ngraph::replace_node(einsum_node, last_node);
|
||||
return true;
|
||||
};
|
||||
|
||||
auto m = std::make_shared<ngraph::pattern::Matcher>(einsum, matcher_name);
|
||||
register_matcher(m, callback);
|
||||
}
|
||||
@ -3,10 +3,14 @@
|
||||
//
|
||||
|
||||
#include <string>
|
||||
|
||||
#include "common_test_utils/xml_net_builder/ir_net.hpp"
|
||||
#include "ngraph_reader_tests.hpp"
|
||||
|
||||
TEST_F(NGraphReaderTests, ReadEinsumNetwork) {
|
||||
std::string model = R"V0G0N(
|
||||
// since EinsumDecomposition is applied, disable these two tests
|
||||
// until ngraph_reader_test checks only correctness of IR reading
|
||||
TEST_F(NGraphReaderTests, DISABLED_ReadEinsumNetwork) {
|
||||
std::string model = R"V0G0N(
|
||||
<net name="saved_model" version="10">
|
||||
<layers>
|
||||
<layer id="0" name="input_a" type="Parameter" version="opset1">
|
||||
@ -66,7 +70,7 @@ TEST_F(NGraphReaderTests, ReadEinsumNetwork) {
|
||||
</edges>
|
||||
</net>
|
||||
)V0G0N";
|
||||
std::string modelV7 = R"V0G0N(
|
||||
std::string modelV7 = R"V0G0N(
|
||||
<net name="saved_model" version="7">
|
||||
<layers>
|
||||
<layer id="0" name="input_a" type="Input" version="opset1">
|
||||
@ -115,11 +119,11 @@ TEST_F(NGraphReaderTests, ReadEinsumNetwork) {
|
||||
</edges>
|
||||
</net>
|
||||
)V0G0N";
|
||||
compareIRs(model, modelV7);
|
||||
compareIRs(model, modelV7);
|
||||
}
|
||||
|
||||
TEST_F(NGraphReaderTests, ReadEinsumNetwork2) {
|
||||
std::string model = R"V0G0N(
|
||||
TEST_F(NGraphReaderTests, DISABLED_ReadEinsumNetwork2) {
|
||||
std::string model = R"V0G0N(
|
||||
<net name="saved_model" version="10">
|
||||
<layers>
|
||||
<layer id="0" name="input_a" type="Parameter" version="opset1">
|
||||
@ -199,7 +203,7 @@ TEST_F(NGraphReaderTests, ReadEinsumNetwork2) {
|
||||
</edges>
|
||||
</net>
|
||||
)V0G0N";
|
||||
std::string modelV7 = R"V0G0N(
|
||||
std::string modelV7 = R"V0G0N(
|
||||
<net name="saved_model" version="7">
|
||||
<layers>
|
||||
<layer id="0" name="input_a" type="Input" version="opset1">
|
||||
@ -266,6 +270,5 @@ TEST_F(NGraphReaderTests, ReadEinsumNetwork2) {
|
||||
</edges>
|
||||
</net>
|
||||
)V0G0N";
|
||||
compareIRs(model, modelV7);
|
||||
compareIRs(model, modelV7);
|
||||
}
|
||||
|
||||
|
||||
@ -38,6 +38,12 @@ namespace ngraph
|
||||
std::shared_ptr<Node>
|
||||
clone_with_new_inputs(const OutputVector& new_args) const override;
|
||||
|
||||
/// \brief Get an equation of Einsum operation
|
||||
///
|
||||
/// \return Einsum equation
|
||||
///
|
||||
std::string get_equation() const { return m_equation; }
|
||||
|
||||
/// \brief Check correctness of equation format and extract input subscripts
|
||||
/// and output subscript
|
||||
///
|
||||
|
||||
Loading…
Reference in New Issue
Block a user