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[Snippets] Added generic check for broadcasting (openvinotoolkit#25772)
### Details: - *Created the analyzer-pass `AnalyzeBroadcastableInputs` to fill map with broadcastable inputs* - *Added tests with scalar dimension processing with domain sensitive ops* - *Fixed not-first code generation for the same Subgraph node - kernel executor table should be reset* ### Tickets: - *136158* ### Prerequsuites: - [x] openvinotoolkit#25623 - [x] openvinotoolkit#25638
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src/common/snippets/include/snippets/pass/analyze_broadcastable_inputs.hpp
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| // Copyright (C) 2024 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
| // | ||
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| #pragma once | ||
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| #include "openvino/pass/pass.hpp" | ||
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| namespace ov { | ||
| namespace snippets { | ||
| namespace pass { | ||
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| /** | ||
| * @interface AnalyzeBroadcastableInputs | ||
| * @brief Analyzes body parameters which affects inputs of broadcastable operations (If needed, `Broadcast` op should be inserted there).s | ||
| * Also the pass initializes special map `BroadcastableInputsMap` | ||
| * Notes: | ||
| * - Must be called after Canonicalization pass | ||
| * - Doesn't support `layouts` in PortDescriptors | ||
| * @ingroup snippets | ||
| */ | ||
| class AnalyzeBroadcastableInputs : public ov::pass::ModelPass { | ||
| public: | ||
| OPENVINO_RTTI("AnalyzeBroadcastableInputs"); | ||
| // [Index of Parameter -> Index of broadcastable dimension from end] | ||
| using BroadcastableInputsMap = std::map<size_t, size_t>; | ||
| AnalyzeBroadcastableInputs(BroadcastableInputsMap& map); | ||
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| bool run_on_model(const std::shared_ptr<ov::Model>& m) override; | ||
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| private: | ||
| BroadcastableInputsMap& m_broadcastable_inputs; | ||
| }; | ||
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| } // namespace pass | ||
| } // namespace snippets | ||
| } // namespace ov |
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106 changes: 106 additions & 0 deletions
106
src/common/snippets/src/pass/analyze_broadcastable_inputs.cpp
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| // Copyright (C) 2018-2024 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
| // | ||
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| #include "snippets/pass/analyze_broadcastable_inputs.hpp" | ||
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| #include "snippets/lowered/pass/insert_broadcastmove.hpp" | ||
| #include "snippets/utils/utils.hpp" | ||
| #include "snippets/itt.hpp" | ||
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| namespace ov { | ||
| namespace snippets { | ||
| namespace pass { | ||
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| AnalyzeBroadcastableInputs::AnalyzeBroadcastableInputs(BroadcastableInputsMap& map) : m_broadcastable_inputs(map) {} | ||
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| bool pass::AnalyzeBroadcastableInputs::run_on_model(const std::shared_ptr<ov::Model>& body) { | ||
| RUN_ON_MODEL_SCOPE(AnalyzeBroadcastableInputs); | ||
| OV_ITT_SCOPED_TASK(ov::pass::itt::domains::SnippetsTransform, "Snippets::AnalyzeBroadcastableInputs") | ||
| // Snippets supports tokenization of the following operations: | ||
| // - Unary, Binary and Ternary (Select) Elementwise ops | ||
| // - Softmax, MatMul, Transpose, GroupNorm | ||
| // Binary Elementwise ops (+ Select) requires explicit Broadcast op | ||
| // on inputs if broadcasting of latest dimensions is needed. | ||
| // These ops will be start points of DFS - need to go to Parameters and update `broadcastable_inputs_map`. | ||
| // We iterates through all ops by execution order. So if we already analyzied some op in the input branch - skip this branch. | ||
| // However, there some ops which can change `processing_dim_idx`: | ||
| // - Transpose has order which changes `processing_dim_idx`. But Transpose can be only after Parameters and before Results. | ||
| // - MatMul's first input doesn't affect output latest dimension - skip this branch. | ||
| // Also MatMul has `transposed_b` which changes `processing_dim_idx` | ||
| m_broadcastable_inputs.clear(); | ||
| // Currently Broadcasting can be changed only if there are several Parameters in body | ||
| if (body->get_parameters().size() < 2) | ||
| return false; | ||
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| const auto& ops = body->get_ordered_ops(); | ||
| std::set<std::shared_ptr<ov::Node>> visited_ops = {}; | ||
| for (const auto& op : ops) { | ||
| if (!ov::snippets::lowered::pass::InsertBroadcastMove::is_broadcasting_supported(op)) | ||
| continue; | ||
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| size_t processing_dim_idx = 0; | ||
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| // We need to propagate `processing_dim_idx` from input of the current node to the parameter. | ||
| // To do it we use DFS | ||
| std::stack<std::shared_ptr<ov::Node>> nodes_to_calculate; | ||
| nodes_to_calculate.push(op); | ||
| while (!nodes_to_calculate.empty()) { | ||
| auto current_node = nodes_to_calculate.top(); | ||
| nodes_to_calculate.pop(); | ||
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| if (const auto& param = ov::as_type_ptr<ov::op::v0::Parameter>(current_node)) { | ||
| const auto consumers = param->get_output_target_inputs(0); | ||
| if (std::any_of(consumers.cbegin(), consumers.cend(), | ||
| [](const ov::Input<ov::Node>& in) { return ov::is_type<ov::op::v1::Transpose>(in.get_node()); })) { | ||
| OPENVINO_ASSERT(consumers.size() == 1, "Incorrect count of outputs of Parameter!"); | ||
| const auto transpose = consumers.begin()->get_node(); | ||
| std::vector<size_t> order; | ||
| const auto& constant = ov::as_type_ptr<const opset1::Constant>(transpose->get_input_node_shared_ptr(1)); | ||
| OPENVINO_ASSERT(constant, "Unsupported order node of Transpose"); | ||
| order = constant->cast_vector<size_t>(); | ||
| if (order.empty()) { | ||
| order.resize(transpose->get_output_partial_shape(0).size()); | ||
| std::iota(order.rbegin(), order.rend(), 0); | ||
| } | ||
| // `processing_dim_idx` starts from the end | ||
| processing_dim_idx = order.size() - 1 - ov::snippets::utils::get_input_dim_idx(order, processing_dim_idx); | ||
| } | ||
| const auto param_idx = body->get_parameter_index(param); | ||
| if (m_broadcastable_inputs.count(param_idx) == 0) { | ||
| m_broadcastable_inputs[param_idx] = processing_dim_idx; | ||
| } else { | ||
| OPENVINO_ASSERT(m_broadcastable_inputs.at(param_idx) == processing_dim_idx, | ||
| "Parameter has been already analyzed and has another processing dim index!"); | ||
| } | ||
| processing_dim_idx = 0; | ||
| continue; | ||
| } else if (ov::is_type<ov::op::v0::Constant>(current_node)) { | ||
| visited_ops.insert(op); | ||
| continue; | ||
| } | ||
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| ov::OutputVector inputs = current_node->input_values(); | ||
| if (const auto mm = ov::as_type_ptr<ov::op::v0::MatMul>(current_node)) { | ||
| inputs = { current_node->input_value(1) }; | ||
| processing_dim_idx = static_cast<size_t>(mm->get_transpose_b()); | ||
| } | ||
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| // not a leaf - continue to search | ||
| for (const auto& input_value : inputs) { | ||
| const auto& input_node = input_value.get_node()->shared_from_this(); | ||
| if (visited_ops.count(input_node) == 0) { | ||
| nodes_to_calculate.push(input_node); | ||
| } | ||
| } | ||
| } | ||
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| visited_ops.insert(op); | ||
| } | ||
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| return true; | ||
| } | ||
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| } // namespace pass | ||
| } // namespace snippets | ||
| } // namespace ov |
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