[CPU][Snippets] Dynamism via recompilation and cache (#15430)
This commit is contained in:
Chenhu Wang
GitHub
parent
8df85badf8
commit
28a5bf7b04
@ -116,6 +116,7 @@ public:
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const void* compile_params = nullptr);
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snippets::Schedule generate(const void* compile_params = nullptr);
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ov::PartialShape canonicalize(const BlockedShapeVector& output_shapes, const BlockedShapeVector& input_shapes);
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ov::PartialShape canonicalized_body_shape_infer(const BlockedShapeVector& input_shapes);
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std::vector<PartialShape> reshape_body(const std::vector<PartialShape>& input_shapes);
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std::vector<Shape> reshape_body(const std::vector<Shape>& input_shapes);
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@ -161,6 +162,8 @@ private:
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ov::PartialShape master_shape;
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size_t tileRank = 0; // set by plugin to specify the number of dimensions processed in a single kernel call
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size_t maxInputRank = 0;
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std::vector<size_t> appendOnesForCanonical;
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/**
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* @interface SubgraphConfig
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39
src/common/snippets/include/snippets/pass/hash.hpp
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39
src/common/snippets/include/snippets/pass/hash.hpp
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@ -0,0 +1,39 @@
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// Copyright (C) 2018-2023 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 <string>
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#include "openvino/core/model.hpp"
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#include <openvino/pass/pass.hpp>
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namespace ov {
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namespace snippets {
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namespace pass {
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/**
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* @brief Hash transformation calculates hash value for snippets. Don't care about names as no difference from execution perspective
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*/
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class Hash : public ov::pass::ModelPass {
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public:
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OPENVINO_RTTI("HashPass", "0");
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bool run_on_model(const std::shared_ptr<ov::Model>& f) override;
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/**
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* @brief Hash pass constructor
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*
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* @param output_hash_value Reference to output value. By applying hash pass on function, resulting hash value
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* will be set to this variable
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*/
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Hash(uint64_t& output_hash_value);
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private:
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uint64_t& m_hash;
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};
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} // namespace pass
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} // namespace snippets
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} // namespace ov
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@ -325,7 +325,8 @@ ov::PartialShape snippets::op::Subgraph::canonicalize(const BlockedShapeVector&
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PartialShape baseShape;
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AxisVector baseOrder;
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std::tie(baseShape, baseOrder, std::ignore) = getMaxRankBlockedShape(inputShapes);
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const auto baseRank = baseShape.size();
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maxInputRank = baseShape.size();
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appendOnesForCanonical.resize(inputShapes.size(), 0);
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const bool baseIsBlocked = baseOrder.size() != std::set<size_t>(baseOrder.begin(), baseOrder.end()).size();
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for (size_t i = 0; i < inputShapes.size(); i++) {
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const auto& blockedShape = inputShapes[i];
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@ -334,14 +335,16 @@ ov::PartialShape snippets::op::Subgraph::canonicalize(const BlockedShapeVector&
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element::Type inType;
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std::tie(inShape, inOrder, inType) = blockedShape;
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const auto inRank = inShape.size();
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NODE_VALIDATION_CHECK(this, inRank <= baseRank, "Input rank can't be larger than output rank in snippets.");
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if (inRank < baseRank) {
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PartialShape newShape(ov::Shape(baseRank, 1));
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NODE_VALIDATION_CHECK(this, inRank <= maxInputRank, "Input rank can't be larger than output rank in snippets.");
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if (inRank < maxInputRank) {
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appendOnesForCanonical[i] = maxInputRank - inRank;
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PartialShape newShape(ov::Shape(maxInputRank, 1));
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// todo: more complicated logics is needed if we want to merge smth else than blocked and planar
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if (baseIsBlocked) {
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const bool inIsNotBlocked = inOrder.size() == std::set<size_t>(inOrder.begin(), inOrder.end()).size();
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NODE_VALIDATION_CHECK(this, inIsNotBlocked, "Snippets don't support conversion between blocked layouts of different ranks");
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inShape.insert(inShape.end(), ov::Dimension(1));
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appendOnesForCanonical[i]--;
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}
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NODE_VALIDATION_CHECK(this, PartialShape::broadcast_merge_into(newShape, inShape, ov::op::AutoBroadcastType::NUMPY),
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"Failed to broadcast_merge inputs in snippets canonicalization");
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@ -364,6 +367,7 @@ ov::PartialShape snippets::op::Subgraph::canonicalize(const BlockedShapeVector&
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body_ptr()->replace_parameter(i, std::make_shared<ov::op::v0::Parameter>(paramType, inShape));
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}
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body_ptr()->validate_nodes_and_infer_types();
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auto skipStartEndOnes = [](const PartialShape& shape) {
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auto begin = shape.begin();
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auto end = shape.end();
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@ -415,6 +419,43 @@ ov::PartialShape snippets::op::Subgraph::canonicalize(const BlockedShapeVector&
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return master_shape;
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}
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ov::PartialShape snippets::op::Subgraph::canonicalized_body_shape_infer(const BlockedShapeVector& inputShapes) {
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std::vector<Shape> normInputShapes;
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for (size_t i = 0; i < inputShapes.size(); i++) {
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PartialShape inShape = std::get<0>(inputShapes[i]);
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const auto inRank = inShape.size();
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if (inRank < maxInputRank) {
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PartialShape newShape(ov::Shape(maxInputRank, 1));
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for (size_t ir = 0; ir < inRank; ir++) {
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newShape[appendOnesForCanonical[i] + ir] = inShape[ir];
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}
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normInputShapes.push_back(newShape.get_shape());
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} else {
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normInputShapes.push_back(inShape.get_shape());
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}
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}
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reshape_body(normInputShapes);
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const auto& body_results = body_ptr()->get_results();
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PartialShape outPShape = body_results[0]->get_input_partial_shape(0);
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const auto& result_parent = body_results[0]->get_input_node_shared_ptr(0);
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if (body_results.size() == 1 &&
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ov::is_type<ov::op::v1::Transpose>(result_parent) &&
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ov::is_type<ov::op::v0::MatMul>(result_parent->get_input_node_shared_ptr(0))) {
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outPShape = result_parent->get_input_partial_shape(0);
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} else {
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for (size_t i = 0; i < body_results.size(); i++) {
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auto shape_i = body_results[i]->get_input_partial_shape(0);
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bool compatibleWithOtherOutputs = PartialShape::broadcast_merge_into(outPShape, shape_i,
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::ov::op::AutoBroadcastType::NUMPY);
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NODE_VALIDATION_CHECK(this, compatibleWithOtherOutputs,
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"Snippets output shapes must be numpy broadcastable");
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}
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}
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master_shape = outPShape;
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return master_shape;
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}
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bool snippets::op::Subgraph::check_broadcast(const std::shared_ptr<const ov::Node>& node) noexcept {
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const auto elementwise = std::dynamic_pointer_cast<const ov::op::util::BinaryElementwiseArithmetic>(node);
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return
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@ -188,11 +188,10 @@ auto has_supported_in_out(const std::shared_ptr<const Node> &n) -> bool {
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auto supported = [&n](descriptor::Tensor& t) -> bool {
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// Todo: int32 isn't supported in general because i32 emitters are required for bit-exact i32 calculations in some cases
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// So i32 is supported exclusively for transposes and broadcast
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return t.get_partial_shape().is_static() &&
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(TokenizeSnippets::supported_element_types.count(t.get_element_type()) != 0 ||
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return TokenizeSnippets::supported_element_types.count(t.get_element_type()) != 0 ||
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(t.get_element_type() == ov::element::i32 &&
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(ov::is_type<const opset1::Transpose>(n) ||
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ov::is_type<const opset1::Broadcast>(n))));
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ov::is_type<const opset1::Broadcast>(n)));
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};
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const auto& inputs = n->inputs();
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const auto& outputs = n->outputs();
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411
src/common/snippets/src/pass/hash.cpp
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411
src/common/snippets/src/pass/hash.cpp
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@ -0,0 +1,411 @@
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// Copyright (C) 2018-2023 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include "snippets/pass/hash.hpp"
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#include <array>
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#include <cassert>
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#include <cstdint>
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#include <unordered_map>
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#include <unordered_set>
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#include "ngraph/ops.hpp"
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#include "ngraph/opsets/opset.hpp"
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#include "openvino/core/except.hpp"
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#include "openvino/core/meta_data.hpp"
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#include "openvino/core/model.hpp"
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#include "openvino/op/util/framework_node.hpp"
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#include "openvino/opsets/opset1.hpp"
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#include "transformations/rt_info/primitives_priority_attribute.hpp"
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namespace ov {
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namespace snippets {
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namespace pass {
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OPENVINO_SUPPRESS_DEPRECATED_START
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// helper
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namespace {
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template <typename Container>
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std::string join(const Container& c, const char* glue = ", ") {
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std::stringstream oss;
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const char* s = "";
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for (const auto& v : c) {
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oss << s << v;
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s = glue;
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}
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return oss.str();
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}
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struct Edge {
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int from_layer = 0;
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int from_port = 0;
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int to_layer = 0;
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int to_port = 0;
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};
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enum class AttrType {layers, layer, id, type, data, rt_info, attribute, name, version, input, port, precision, dimension, output, value,
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edges, edge, from_layer, from_port, to_layer, to_port, constant, size};
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template <typename T, typename std::enable_if<!std::is_enum<T>::value , int>::type = 0>
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static uint64_t hash_combine(uint64_t seed, const T &v) {
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// Hash combine formula from boost
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return seed ^= std::hash<T> {}(v) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
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}
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template <typename T, typename std::enable_if<std::is_enum<T>::value , int>::type = 0>
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static uint64_t hash_combine(uint64_t seed, const T &v) {
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using underlying_t = typename std::underlying_type<T>::type;
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return hash_combine(seed, static_cast<underlying_t>(v));
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}
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namespace rt_info {
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// some node attr is not type of ov::RuntimeAttribute, need dedicate visitor.
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const std::vector<std::string> list_of_names{
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"PrimitivesPriority",
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"alt_width",
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};
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class NodeAuxRTInfoHasher {
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public:
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explicit NodeAuxRTInfoHasher(uint64_t& hash) : m_hash(hash) {}
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void serialize(const ov::Node::RTMap& rt_info) {
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for (const auto& rt_info_name : list_of_names) {
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const auto& found_rt_info = rt_info.find(rt_info_name);
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if (found_rt_info != rt_info.end()) {
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std::stringstream strm;
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found_rt_info->second.print(strm);
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m_hash = hash_combine(m_hash, rt_info_name);
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m_hash = hash_combine(m_hash, strm.str());
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}
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}
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}
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private:
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uint64_t& m_hash;
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};
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class RTInfoHasher : public ov::AttributeVisitor {
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uint64_t& m_rt_hash;
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public:
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RTInfoHasher(uint64_t& rt_hash) : m_rt_hash(rt_hash) {}
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void on_adapter(const std::string& name, ov::ValueAccessor<void>& adapter) override {
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if (auto a = ov::as_type<ov::AttributeAdapter<std::set<std::string>>>(&adapter)) {
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const auto& value = join(a->get());
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), value);
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} else {
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OPENVINO_THROW("Unsupported attribute type for snippets hash generation: ", name);
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}
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<bool>& adapter) override {
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), adapter.get());
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::string>& adapter) override {
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), adapter.get());
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<int64_t>& adapter) override {
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), adapter.get());
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<double>& adapter) override {
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), adapter.get());
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int>>& adapter) override {
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const auto& value = join(adapter.get());
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), value);
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int64_t>>& adapter) override {
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const auto& value = join(adapter.get());
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), value);
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<uint64_t>>& adapter) override {
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const auto& value = join(adapter.get());
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), value);
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<float>>& adapter) override {
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const auto& value = join(adapter.get());
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), value);
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<std::string>>& adapter) override {
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const auto& value = join(adapter.get());
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m_rt_hash = hash_combine(hash_combine(m_rt_hash, name), value);
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::shared_ptr<ov::Model>>& adapter) override {
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OPENVINO_THROW("Model type is unsupported for snippets rt info hash generation");
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}
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};
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} // namespace rt_info
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void ovfunction_2_hash(uint64_t& hash, const ov::Model& model);
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OPENVINO_SUPPRESS_DEPRECATED_START
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class SnippetsHasher : public ov::AttributeVisitor {
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uint64_t& m_hash;
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const std::string& m_node_type_name;
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template <typename T>
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std::string create_attribute_list(ov::ValueAccessor<std::vector<T>>& adapter) {
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return join(adapter.get());
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}
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public:
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SnippetsHasher(uint64_t& hash,
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const std::string& node_type_name)
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: m_hash(hash),
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m_node_type_name(node_type_name) {}
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void on_adapter(const std::string& name, ov::ValueAccessor<void>& adapter) override {
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if (const auto& a = ov::as_type<ov::AttributeAdapter<std::shared_ptr<ngraph::Variable>>>(&adapter)) {
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m_hash = hash_combine(hash_combine(m_hash, name), a->get()->get_info().variable_id);
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} else if (const auto& a =
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ov::as_type<ov::AttributeAdapter<std::shared_ptr<ngraph::runtime::AlignedBuffer>>>(&adapter)) {
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if (name == "value" && m_node_type_name == "Constant") {
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m_hash = hash_combine(m_hash, AttrType::constant);
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const int64_t size = a->get()->size();
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m_hash = hash_combine(hash_combine(m_hash, AttrType::size), size);
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auto data = static_cast<const char*>(a->get()->get_ptr());
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for (int64_t i = 0; i < size; i++) {
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m_hash = hash_combine(m_hash, data[i]);
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}
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}
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} else if (const auto& a = ov::as_type<ov::AttributeAdapter<ov::op::util::FrameworkNodeAttrs>>(&adapter)) {
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const auto& attrs = a->get();
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// Update node attributes in data field
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for (const auto& attr : attrs) {
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m_hash = hash_combine(hash_combine(m_hash, attr.first), attr.second);
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}
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} else if (const auto& a = ov::as_type<ov::AttributeAdapter<ov::element::TypeVector>>(&adapter)) {
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const auto& attrs = a->get();
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m_hash = hash_combine(hash_combine(m_hash, name), join(attrs));
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} else if (const auto& a = ov::as_type<ov::AttributeAdapter<ov::PartialShape>>(&adapter)) {
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const auto& attrs = a->get();
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auto shape_str = attrs.to_string();
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m_hash = hash_combine(hash_combine(m_hash, name), shape_str);
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} else if (const auto& a = ov::as_type<ov::AttributeAdapter<ov::Dimension>>(&adapter)) {
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const auto& attrs = a->get();
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std::stringstream dim_str_stream;
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dim_str_stream << attrs;
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auto dim_str = dim_str_stream.str();
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m_hash = hash_combine(hash_combine(m_hash, name), dim_str);
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} else {
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OPENVINO_THROW("Unsupported attribute type for snippets hash generation: ", name);
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}
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<bool>& adapter) override {
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m_hash = hash_combine(hash_combine(m_hash, name), adapter.get());
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::string>& adapter) override {
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m_hash = hash_combine(hash_combine(m_hash, name), adapter.get());
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<int64_t>& adapter) override {
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m_hash = hash_combine(hash_combine(m_hash, name), static_cast<long long>(adapter.get()));
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<double>& adapter) override {
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m_hash = hash_combine(hash_combine(m_hash, name), adapter.get());
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int>>& adapter) override {
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m_hash = hash_combine(hash_combine(m_hash, name), create_attribute_list(adapter));
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int64_t>>& adapter) override {
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m_hash = hash_combine(hash_combine(m_hash, name), create_attribute_list(adapter));
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<uint64_t>>& adapter) override {
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m_hash = hash_combine(hash_combine(m_hash, name), create_attribute_list(adapter));
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}
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void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<float>>& adapter) override {
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||||
m_hash = hash_combine(hash_combine(m_hash, name), create_attribute_list(adapter));
|
||||
}
|
||||
void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<std::string>>& adapter) override {
|
||||
m_hash = hash_combine(hash_combine(m_hash, name), create_attribute_list(adapter));
|
||||
}
|
||||
void on_adapter(const std::string& name, ov::ValueAccessor<std::shared_ptr<ov::Model>>& adapter) override {
|
||||
ovfunction_2_hash(m_hash, *adapter.get());
|
||||
}
|
||||
};
|
||||
OPENVINO_SUPPRESS_DEPRECATED_END
|
||||
|
||||
std::unordered_map<ov::Node*, int> create_layer_ids(const ov::Model& model) {
|
||||
std::unordered_map<ov::Node*, int> layer_ids;
|
||||
int id = 0;
|
||||
for (const auto& node : model.get_ordered_ops()) {
|
||||
layer_ids[node.get()] = id++;
|
||||
}
|
||||
return layer_ids;
|
||||
}
|
||||
|
||||
std::vector<Edge> create_edge_mapping(const std::unordered_map<ov::Node*, int>& layer_ids,
|
||||
const ov::Model& model) {
|
||||
std::vector<Edge> edges;
|
||||
for (const auto& node : model.get_ordered_ops()) {
|
||||
if (ov::op::util::is_parameter(node)) {
|
||||
continue;
|
||||
}
|
||||
|
||||
for (const auto& i : node->inputs()) {
|
||||
auto source_output = i.get_source_output();
|
||||
auto source_node = source_output.get_node();
|
||||
auto current_node = i.get_node();
|
||||
|
||||
if (layer_ids.find(source_node) == layer_ids.end() || layer_ids.find(current_node) == layer_ids.end()) {
|
||||
OPENVINO_THROW("Failed creat edge map in snippets hash generation");
|
||||
}
|
||||
|
||||
Edge e{};
|
||||
e.from_layer = layer_ids.find(source_node)->second;
|
||||
e.from_port = static_cast<int>(source_node->get_input_size() + source_output.get_index());
|
||||
e.to_layer = layer_ids.find(current_node)->second;
|
||||
e.to_port = static_cast<int>(i.get_index());
|
||||
edges.push_back(e);
|
||||
}
|
||||
}
|
||||
std::sort(begin(edges), end(edges), [](const Edge& a, const Edge& b) -> bool {
|
||||
return a.from_layer < b.from_layer;
|
||||
});
|
||||
return edges;
|
||||
}
|
||||
|
||||
void hash_rt_info(uint64_t& hash, const std::string& name, const ov::Any& data) {
|
||||
if (data.is<std::shared_ptr<ov::Meta>>()) {
|
||||
std::shared_ptr<ov::Meta> meta = data.as<std::shared_ptr<ov::Meta>>();
|
||||
ov::AnyMap& map = *meta;
|
||||
for (const auto& it : map) {
|
||||
hash_rt_info(hash, it.first, it.second);
|
||||
}
|
||||
} else if (data.is<ov::AnyMap>()) {
|
||||
const ov::AnyMap& any_map = data.as<ov::AnyMap>();
|
||||
for (const auto& it : any_map) {
|
||||
hash_rt_info(hash, it.first, it.second);
|
||||
}
|
||||
} else {
|
||||
std::string value = data.as<std::string>();
|
||||
hash = hash_combine(hash_combine(hash, AttrType::value), value);
|
||||
}
|
||||
}
|
||||
|
||||
void ovfunction_2_hash(uint64_t& hash,
|
||||
const ov::Model& model) {
|
||||
hash = hash_combine(hash, AttrType::layers);
|
||||
|
||||
const std::unordered_map<ov::Node*, int> layer_ids = create_layer_ids(model);
|
||||
std::unordered_set<std::string> unique_names;
|
||||
|
||||
auto sorted_ops = model.get_ordered_ops();
|
||||
|
||||
for (const auto& n : sorted_ops) {
|
||||
ov::Node* node = n.get();
|
||||
const std::string& node_type_name{node->get_type_name()};
|
||||
|
||||
if (layer_ids.find(node) == layer_ids.end())
|
||||
OPENVINO_THROW("Failed to find layer's id in snippets hash generation.");
|
||||
// <layers>
|
||||
hash = hash_combine(hash, AttrType::layer);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::id), layer_ids.find(node)->second);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::type), node_type_name);
|
||||
|
||||
// <layers/data> general attributes
|
||||
hash = hash_combine(hash, AttrType::data);
|
||||
auto append_runtime_info = [&](uint64_t& hash, ov::RTMap& attributes) {
|
||||
hash = hash_combine(hash, AttrType::rt_info);
|
||||
for (auto& item : attributes) {
|
||||
if (item.second.is<ov::RuntimeAttribute>()) {
|
||||
auto& rt_attribute = item.second.as<ov::RuntimeAttribute>();
|
||||
const auto& type_info = rt_attribute.get_type_info();
|
||||
if (!strcmp(type_info.name, "fused_names")) {
|
||||
continue;
|
||||
}
|
||||
hash = hash_combine(hash, AttrType::attribute);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::name), type_info.name);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::version), type_info.get_version());
|
||||
|
||||
rt_info::RTInfoHasher rt_info_visitor(hash);
|
||||
rt_attribute.visit_attributes(rt_info_visitor);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
append_runtime_info(hash, node->get_rt_info());
|
||||
|
||||
int port_id = 0;
|
||||
// <layers/input>
|
||||
if (node->get_input_size() > 0) {
|
||||
hash = hash_combine(hash, AttrType::input);
|
||||
for (auto& i : node->inputs()) {
|
||||
hash = hash_combine(hash, AttrType::port);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::id), port_id++);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::precision), i.get_element_type().hash());
|
||||
hash = hash_combine(hash_combine(hash, AttrType::dimension), i.get_partial_shape().to_string());
|
||||
append_runtime_info(hash, i.get_rt_info());
|
||||
}
|
||||
}
|
||||
// <layers/output>
|
||||
if ((node->get_output_size() > 0) && !ov::op::util::is_output(node)) {
|
||||
hash = hash_combine(hash, AttrType::output);
|
||||
for (auto& o : node->outputs()) {
|
||||
hash = hash_combine(hash, AttrType::port);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::id), port_id++);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::precision), o.get_element_type().hash());
|
||||
hash = hash_combine(hash_combine(hash, AttrType::dimension), o.get_partial_shape().to_string());
|
||||
append_runtime_info(hash, o.get_rt_info());
|
||||
}
|
||||
}
|
||||
|
||||
// fill <data> general attributes
|
||||
{
|
||||
SnippetsHasher visitor(hash, node_type_name);
|
||||
if (!node->visit_attributes(visitor)) {
|
||||
OPENVINO_THROW("Visitor API is not supported in " + node_type_name + " in snippets hash generation");
|
||||
}
|
||||
}
|
||||
rt_info::NodeAuxRTInfoHasher{hash}.serialize(node->get_rt_info());
|
||||
}
|
||||
// <edges>
|
||||
const std::vector<Edge> edge_mapping = create_edge_mapping(layer_ids, model);
|
||||
hash = hash_combine(hash, AttrType::edges);
|
||||
for (auto& e : edge_mapping) {
|
||||
hash = hash_combine(hash, AttrType::edge);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::from_layer), e.from_layer);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::from_port), e.from_port);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::to_layer), e.to_layer);
|
||||
hash = hash_combine(hash_combine(hash, AttrType::to_port), e.to_port);
|
||||
}
|
||||
|
||||
// Serialize rt info
|
||||
hash = hash_combine(hash, AttrType::rt_info);
|
||||
for (const auto& it : model.get_rt_info()) {
|
||||
hash_rt_info(hash, it.first, it.second);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace
|
||||
|
||||
bool Hash::run_on_model(const std::shared_ptr<ov::Model>& f) {
|
||||
uint64_t seed = 0;
|
||||
std::string name = "net";
|
||||
SnippetsHasher visitor(seed, name);
|
||||
std::shared_ptr<ov::Model> m(f); // for complilation error, on_attribute don't accept f
|
||||
visitor.on_attribute(name, m);
|
||||
m_hash = seed;
|
||||
// Return false because we didn't change OpenVINO Model
|
||||
return false;
|
||||
}
|
||||
|
||||
Hash::Hash(uint64_t& output_hash_value) : m_hash(output_hash_value) {}
|
||||
|
||||
} // namespace pass
|
||||
} // namespace snippets
|
||||
} // namespace ov
|
||||
@ -34,6 +34,8 @@
|
||||
#include "transformations/cpu_opset/common/pass/convert_to_swish_cpu.hpp"
|
||||
#include "transformations/defs.hpp"
|
||||
#include "shape_inference/custom/subgraph.hpp"
|
||||
#include <common/primitive_hashing_utils.hpp>
|
||||
#include "snippets/pass/hash.hpp"
|
||||
|
||||
using namespace InferenceEngine;
|
||||
using namespace dnnl::impl::utils;
|
||||
@ -44,35 +46,138 @@ using namespace Xbyak;
|
||||
namespace ov {
|
||||
namespace intel_cpu {
|
||||
namespace node {
|
||||
namespace {
|
||||
|
||||
struct SnippetKey {
|
||||
Snippet::SnippetAttrs attrs;
|
||||
|
||||
size_t hash() const;
|
||||
bool operator==(const SnippetKey& rhs) const;
|
||||
};
|
||||
|
||||
size_t SnippetKey::hash() const {
|
||||
using namespace dnnl::impl;
|
||||
using namespace dnnl::impl::primitive_hashing;
|
||||
|
||||
size_t seed = 0;
|
||||
for (const auto& blockedDim : attrs.inMemBlockedDims)
|
||||
seed = get_vector_hash(seed, blockedDim);
|
||||
for (const auto& order : attrs.inMemOrders)
|
||||
seed = get_vector_hash(seed, order);
|
||||
for (const auto& prec : attrs.inMemPrecs)
|
||||
seed = hash_combine(seed, prec.getPrecVal());
|
||||
|
||||
for (const auto& blockedDim : attrs.outMemBlockedDims)
|
||||
seed = get_vector_hash(seed, blockedDim);
|
||||
for (const auto& order : attrs.outMemOrders)
|
||||
seed = get_vector_hash(seed, order);
|
||||
for (const auto& prec : attrs.outMemPrecs)
|
||||
seed = hash_combine(seed, prec.getPrecVal());
|
||||
|
||||
seed = hash_combine(seed, attrs.bodyHash);
|
||||
|
||||
return seed;
|
||||
}
|
||||
|
||||
bool SnippetKey::operator==(const SnippetKey& rhs) const {
|
||||
if (attrs.bodyHash != rhs.attrs.bodyHash)
|
||||
return false;
|
||||
if (attrs.inMemBlockedDims.size() != rhs.attrs.inMemBlockedDims.size() ||
|
||||
attrs.inMemOrders.size() != rhs.attrs.inMemOrders.size() ||
|
||||
attrs.inMemPrecs.size() != rhs.attrs.inMemPrecs.size())
|
||||
return false;
|
||||
if (attrs.outMemBlockedDims.size() != rhs.attrs.outMemBlockedDims.size() ||
|
||||
attrs.outMemOrders.size() != rhs.attrs.outMemOrders.size() ||
|
||||
attrs.outMemPrecs.size() != rhs.attrs.outMemPrecs.size())
|
||||
return false;
|
||||
|
||||
for (size_t i = 0; i < attrs.inMemBlockedDims.size(); i++) {
|
||||
if (!(attrs.inMemBlockedDims[i] == rhs.attrs.inMemBlockedDims[i]))
|
||||
return false;
|
||||
}
|
||||
for (size_t i = 0; i < attrs.outMemBlockedDims.size(); i++) {
|
||||
if (!(attrs.outMemBlockedDims[i] == rhs.attrs.outMemBlockedDims[i]))
|
||||
return false;
|
||||
}
|
||||
for (size_t i = 0; i < attrs.inMemOrders.size(); i++) {
|
||||
if (!(attrs.inMemOrders[i] == rhs.attrs.inMemOrders[i]))
|
||||
return false;
|
||||
}
|
||||
for (size_t i = 0; i < attrs.outMemOrders.size(); i++) {
|
||||
if (!(attrs.outMemOrders[i] == rhs.attrs.outMemOrders[i]))
|
||||
return false;
|
||||
}
|
||||
for (size_t i = 0; i < attrs.inMemPrecs.size(); i++) {
|
||||
if (!(attrs.inMemPrecs[i] == rhs.attrs.inMemPrecs[i]))
|
||||
return false;
|
||||
}
|
||||
for (size_t i = 0; i < attrs.outMemPrecs.size(); i++) {
|
||||
if (!(attrs.outMemPrecs[i] == rhs.attrs.outMemPrecs[i]))
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
snippets::op::Subgraph::BlockedShapeVector getBlockedShapes(const std::vector<std::vector<size_t>>& memBlockedDims,
|
||||
const std::vector<std::vector<size_t>>& memOrders, const std::vector<InferenceEngine::Precision>& memPrecs) {
|
||||
size_t numShapes = memBlockedDims.size();
|
||||
if (memOrders.size() != numShapes || memPrecs.size() != numShapes)
|
||||
IE_THROW(Unexpected) << "Number of shapes is mismacthed for dimensions, orders and precisions";
|
||||
snippets::op::Subgraph::BlockedShapeVector blockedShapes(numShapes);
|
||||
for (size_t i = 0; i < numShapes; i++) {
|
||||
size_t dimSize = memBlockedDims[i].size();
|
||||
std::vector<Dimension> dims(dimSize);
|
||||
for (size_t j = 0; j < dimSize; j++) {
|
||||
dims[j] = memBlockedDims[i][j];
|
||||
}
|
||||
ov::PartialShape shape(dims);
|
||||
ov::AxisVector order(memOrders[i]);
|
||||
ov::element::Type precision = InferenceEngine::details::convertPrecision(memPrecs[i]);
|
||||
|
||||
blockedShapes[i] = snippets::op::Subgraph::BlockedShape{shape, order, precision};
|
||||
}
|
||||
|
||||
return blockedShapes;
|
||||
}
|
||||
} // namespace
|
||||
|
||||
Snippet::Snippet(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr context)
|
||||
: Node(op, context, SnippetShapeInferFactory(this)) {
|
||||
: Node(op, context, SnippetShapeInferFactory(op)) {
|
||||
host_isa = dnnl::impl::cpu::x64::mayiuse(dnnl::impl::cpu::x64::avx512_core) ?
|
||||
dnnl::impl::cpu::x64::avx512_core : dnnl::impl::cpu::x64::avx2;
|
||||
original_snippet = ov::as_type_ptr<snippets::op::Subgraph>(op);
|
||||
if (!original_snippet) {
|
||||
IE_THROW(NotImplemented) << "Node is not an instance of snippets::op::Subgraph";
|
||||
}
|
||||
init_body_hash();
|
||||
is_dynamic = isDynamicNgraphNode(op);
|
||||
}
|
||||
|
||||
void Snippet::copy_snippet() {
|
||||
void Snippet::copy_snippet() const {
|
||||
ov::OutputVector subgraph_node_inputs;
|
||||
for (const auto &input : original_snippet->input_values()) {
|
||||
auto new_input = std::make_shared<ov::opset1::Parameter>(input.get_element_type(), input.get_partial_shape());
|
||||
subgraph_node_inputs.push_back(new_input);
|
||||
}
|
||||
std::shared_ptr<ov::Model> new_body = original_snippet->body_ptr()->clone();
|
||||
snippet = std::make_shared<snippets::op::Subgraph>(subgraph_node_inputs, new_body);
|
||||
ov::copy_runtime_info(original_snippet, snippet);
|
||||
snippet->set_friendly_name(original_snippet->get_friendly_name());
|
||||
snippetAttrs.snippet = std::make_shared<snippets::op::Subgraph>(subgraph_node_inputs, new_body);
|
||||
ov::copy_runtime_info(original_snippet, snippetAttrs.snippet);
|
||||
snippetAttrs.snippet->set_friendly_name(original_snippet->get_friendly_name());
|
||||
#if defined(OPENVINO_ARCH_X86_64)
|
||||
snippet->set_generator(std::make_shared<CPUGenerator>(host_isa));
|
||||
isa_num_lanes = snippet->get_generator()->get_target_machine()->get_lanes();
|
||||
snippetAttrs.snippet->set_generator(std::make_shared<CPUGenerator>(host_isa));
|
||||
#else
|
||||
IE_THROW(NotImplemented) << "CPU plugin: code-generation is not supported on non-x64 platforms";
|
||||
#endif // OPENVINO_ARCH_X86_64
|
||||
}
|
||||
|
||||
void Snippet::init_body_hash() {
|
||||
uint64_t seed = 0;
|
||||
ov::snippets::pass::Hash hash_function(seed);
|
||||
hash_function.run_on_model(original_snippet->body_ptr());
|
||||
snippetAttrs.bodyHash = seed;
|
||||
}
|
||||
|
||||
void Snippet::initSupportedPrimitiveDescriptors() {
|
||||
copy_snippet();
|
||||
if (!supportedPrimitiveDescriptors.empty())
|
||||
@ -90,12 +195,12 @@ void Snippet::initSupportedPrimitiveDescriptors() {
|
||||
|
||||
const size_t ndims = outputShapes[0].getRank();
|
||||
// Domain sensitive operations support only Planar layout
|
||||
const bool isOnlyPlanarApplicable = snippet->has_domain_sensitive_ops();
|
||||
const bool isOnlyPlanarApplicable = snippetAttrs.snippet->has_domain_sensitive_ops();
|
||||
const bool isChannelsFirstApplicable = dnnl::impl::utils::one_of(ndims, 1u, 2u, 3u, 4u, 5u) && dimRanksAreEqual && !isOnlyPlanarApplicable;
|
||||
// Todo: Snippets currently don't support per-channel broadcasting of Blocked descriptors because
|
||||
// canonicalization can't distinguish between <N, C, H, W, c> and <N, C, D, H, W> cases.
|
||||
// See snippets::op::Subgraph::canonicalize for details.
|
||||
bool isBlockedApplicable = dnnl::impl::utils::one_of(ndims, 4u, 5u) && dimRanksAreEqual && !isOnlyPlanarApplicable;
|
||||
bool isBlockedApplicable = dnnl::impl::utils::one_of(ndims, 3u, 4u, 5u) && dimRanksAreEqual && !isOnlyPlanarApplicable;
|
||||
|
||||
for (const auto& inShape : inputShapes) {
|
||||
if (isDynamic && inShape.getRank() != 1)
|
||||
@ -153,7 +258,7 @@ void Snippet::initSupportedPrimitiveDescriptors() {
|
||||
const auto originalInputPrecision = getOriginalInputPrecisionAtPort(i);
|
||||
const auto precision = ((originalInputPrecision == InferenceEngine::Precision::FP32) &&
|
||||
context->getConfig().inferencePrecision == ov::element::bf16 &&
|
||||
snippet->has_domain_sensitive_ops()) ?
|
||||
snippetAttrs.snippet->has_domain_sensitive_ops()) ?
|
||||
static_cast<InferenceEngine::Precision>(InferenceEngine::Precision::BF16) :
|
||||
originalInputPrecision;
|
||||
if (supportedPrecisions.count(precision) == 0)
|
||||
@ -208,6 +313,33 @@ void Snippet::initSupportedPrimitiveDescriptors() {
|
||||
void Snippet::selectOptimalPrimitiveDescriptor() {
|
||||
selectPreferPrimitiveDescriptor(getImplPriority(), true);
|
||||
}
|
||||
|
||||
void Snippet::initOptimalPrimitiveDescriptor() {
|
||||
Node::initOptimalPrimitiveDescriptor();
|
||||
// memory order and precision is determined now, there is no need to prepare for each dynamic shapes.
|
||||
const auto config = getSelectedPrimitiveDescriptor()->getConfig();
|
||||
inputNum = config.inConfs.size();
|
||||
snippetAttrs.inMemPrecs.resize(inputNum);
|
||||
snippetAttrs.inMemOrders.resize(inputNum);
|
||||
for (size_t i = 0; i < inputNum; i++) {
|
||||
const auto& memDesc = config.inConfs[i].getMemDesc();
|
||||
snippetAttrs.inMemPrecs[i] = memDesc->getPrecision();
|
||||
snippetAttrs.inMemOrders[i] = memDesc->as<BlockedMemoryDesc>()->getOrder();
|
||||
}
|
||||
outputNum = config.outConfs.size();
|
||||
snippetAttrs.outMemPrecs.resize(outputNum);
|
||||
snippetAttrs.outMemOrders.resize(outputNum);
|
||||
for (size_t i = 0; i < outputNum; i++) {
|
||||
snippetAttrs.outMemPrecs[i] = config.outConfs[i].getMemDesc()->getPrecision();
|
||||
snippetAttrs.outMemOrders[i] = config.outConfs[i].getMemDesc()->as<BlockedMemoryDesc>()->getOrder();
|
||||
}
|
||||
// reserve fixed size.
|
||||
snippetAttrs.inMemBlockedDims.resize(inputNum);
|
||||
snippetAttrs.outMemBlockedDims.resize(outputNum);
|
||||
srcMemPtrs.resize(inputNum);
|
||||
dstMemPtrs.resize(outputNum);
|
||||
}
|
||||
|
||||
InferenceEngine::Precision Snippet::getRuntimePrecision() const {
|
||||
std::vector<InferenceEngine::Precision> inputPrecisions;
|
||||
for (size_t i = 0; i < getParentEdges().size(); i++) {
|
||||
@ -220,7 +352,280 @@ InferenceEngine::Precision Snippet::getRuntimePrecision() const {
|
||||
return getMaxPrecision(inputPrecisions);
|
||||
}
|
||||
|
||||
bool Snippet::optimizeExecDomain(std::vector<VectorDims>& inputShapes, std::vector<VectorDims>& outputShapes,
|
||||
void Snippet::prepareParams() {
|
||||
for (size_t i = 0; i < inputNum; i++)
|
||||
snippetAttrs.inMemBlockedDims[i] = getParentEdgesAtPort(i)[0]->getMemory().getDescWithType<BlockedMemoryDesc>()->getBlockDims();
|
||||
for (size_t i = 0; i < outputNum; i++)
|
||||
snippetAttrs.outMemBlockedDims[i] = getChildEdgesAtPort(i)[0]->getMemory().getDescWithType<BlockedMemoryDesc>()->getBlockDims();
|
||||
|
||||
SnippetKey key = {snippetAttrs};
|
||||
|
||||
auto builder = [this](const SnippetKey& key) -> std::shared_ptr<SnippetExecutor> {
|
||||
std::shared_ptr<SnippetExecutor> executor = std::make_shared<SnippetJitExecutor>(key.attrs, is_canonicalized,
|
||||
is_dynamic, context->getConfig().inferencePrecision == ov::element::bf16);
|
||||
is_canonicalized = true;
|
||||
return executor;
|
||||
};
|
||||
|
||||
auto cache = context->getParamsCache();
|
||||
auto result = cache->getOrCreate(key, builder);
|
||||
execPtr = result.first;
|
||||
if (!execPtr) {
|
||||
IE_THROW() << "Executor is not created for node " << getName() << ".";
|
||||
}
|
||||
}
|
||||
|
||||
bool Snippet::needPrepareParams() const {
|
||||
auto jit_executor = dynamic_cast<SnippetJitExecutor*>(execPtr.get());
|
||||
return inputShapesModified() || (jit_executor && !jit_executor->schedule_created());
|
||||
}
|
||||
|
||||
bool Snippet::canBeInPlace() const {
|
||||
if (isDynamic || getParentEdgesAtPort(0)[0]->getParent()->getType() == Type::Input) {
|
||||
return false;
|
||||
}
|
||||
if (getChildEdges().size() != 1) {
|
||||
return false;
|
||||
}
|
||||
|
||||
for (auto& parentEdge : getParentEdges()) {
|
||||
auto parent = parentEdge.lock()->getParent();
|
||||
if (parent->getChildEdges().size() != 1)
|
||||
return false;
|
||||
|
||||
// WA to prevent memory corruption caused by inplace feature
|
||||
if (parent->getType() == Type::Concatenation) {
|
||||
for (auto& parentParentEdge : parent->getParentEdges()) {
|
||||
auto parentParent = parentParentEdge.lock()->getParent();
|
||||
if (parentParent->getChildEdges().size() != 1)
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
return getInputShapeAtPort(0) == getOutputShapeAtPort(0);
|
||||
}
|
||||
|
||||
bool Snippet::created() const {
|
||||
return getType() == Type::Subgraph;
|
||||
}
|
||||
|
||||
void Snippet::execute(dnnl::stream strm) {
|
||||
if (!execPtr) {
|
||||
IE_THROW() << "Can't execute Subgraph node. Primitive didn't created";
|
||||
}
|
||||
for (size_t i = 0; i < inputNum; i++)
|
||||
srcMemPtrs[i] = getParentEdgeAt(i)->getMemoryPtr();
|
||||
for (size_t i = 0; i < outputNum; i++)
|
||||
dstMemPtrs[i] = getChildEdgeAt(i)->getMemoryPtr();
|
||||
|
||||
execPtr->exec(srcMemPtrs, dstMemPtrs);
|
||||
}
|
||||
|
||||
void Snippet::executeDynamicImpl(dnnl::stream strm) {
|
||||
execute(strm);
|
||||
}
|
||||
|
||||
void Snippet::SnippetJitExecutor::exec(const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs) {
|
||||
if (schedule.ptr == nullptr) {
|
||||
IE_THROW() << "Snippet can't use Optimized implementation and can't fallback to reference";
|
||||
}
|
||||
auto initStartMemoryOffsets = [this, &inMemPtrs, &outMemPtrs]() {
|
||||
for (size_t i = 0; i < numInput; i++) {
|
||||
start_offset_in[i] = inMemPtrs[i]->getDescWithType<BlockedMemoryDesc>()->getOffsetPadding() * dataSize[i];
|
||||
}
|
||||
for (size_t i = 0; i < numOutput; i++) {
|
||||
start_offset_out[i] = outMemPtrs[i]->getDescWithType<BlockedMemoryDesc>()->getOffsetPadding() * dataSize[i + numInput];
|
||||
}
|
||||
};
|
||||
// initialize start offsets to src and dst memory
|
||||
// Needs to be done for every set of infer, as data memory ptrs could've updated
|
||||
initStartMemoryOffsets();
|
||||
|
||||
if (tensorRank == rank6D) {
|
||||
schedule_6d(inMemPtrs, outMemPtrs);
|
||||
} else {
|
||||
schedule_nt(inMemPtrs, outMemPtrs);
|
||||
}
|
||||
}
|
||||
|
||||
void Snippet::SnippetJitExecutor::update_ptrs(jit_snippets_call_args& call_args,
|
||||
const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs) {
|
||||
for (size_t i = 0; i < inMemPtrs.size(); i++)
|
||||
call_args.src_ptrs[i] = reinterpret_cast<const uint8_t*>(inMemPtrs[i]->getData()) + start_offset_in[i];
|
||||
|
||||
for (size_t i = 0; i < outMemPtrs.size(); i++)
|
||||
call_args.dst_ptrs[i] = reinterpret_cast<uint8_t*>(outMemPtrs[i]->getData()) + start_offset_out[i];
|
||||
|
||||
if (buffer_scratchpad_size > 0) {
|
||||
call_args.buffer_scratchpad_ptr =
|
||||
reinterpret_cast<uint8_t*>(buffer_scratchpad.data()) + parallel_get_thread_num() * buffer_scratchpad_size;
|
||||
}
|
||||
}
|
||||
|
||||
void Snippet::SnippetJitExecutor::schedule_6d(const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs) {
|
||||
const auto& dom = exec_domain;
|
||||
// < N, C, H, W > < 1, 1, N, C*H*W>
|
||||
parallel_for5d(dom[0], dom[1], dom[2], dom[3], dom[4],
|
||||
[&](int64_t d0, int64_t d1, int64_t d2, int64_t d3, int64_t d4) {
|
||||
int64_t indexes[] = {d0, d1, d2, d3, d4};
|
||||
jit_snippets_call_args call_args;
|
||||
update_ptrs(call_args, inMemPtrs, outMemPtrs);
|
||||
|
||||
schedule.get_callable<kernel>()(indexes, &call_args);
|
||||
});
|
||||
}
|
||||
|
||||
void Snippet::SnippetJitExecutor::schedule_nt(const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs) {
|
||||
const auto& work_size = exec_domain;
|
||||
parallel_nt(0, [&](const int ithr, const int nthr) {
|
||||
jit_snippets_call_args call_args;
|
||||
update_ptrs(call_args, inMemPtrs, outMemPtrs);
|
||||
|
||||
size_t start = 0, end = 0;
|
||||
splitter(harnessWorkAmount, nthr, ithr, start, end);
|
||||
|
||||
std::vector<int64_t> indexes(work_size.size() - 1, 0);
|
||||
for (size_t iwork = start; iwork < end; ++iwork) {
|
||||
size_t tmp = iwork;
|
||||
for (ptrdiff_t j = work_size.size() - 2; j >= 0; j--) {
|
||||
indexes[j] = tmp % work_size[j];
|
||||
tmp /= work_size[j];
|
||||
}
|
||||
|
||||
schedule.get_callable<kernel>()(indexes.data(), &call_args);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
Snippet::SnippetExecutor::SnippetExecutor(const SnippetAttrs& attrs, bool is_canonicalized, bool is_dynamic, bool enforceBF16)
|
||||
: snippetAttrs(attrs), is_canonicalized(is_canonicalized), is_dynamic(is_dynamic), enforceBF16(enforceBF16) {}
|
||||
|
||||
Snippet::SnippetJitExecutor::SnippetJitExecutor(const SnippetAttrs& attrs, bool is_canonicalized, bool is_dynamic, bool enforceBF16) :
|
||||
SnippetExecutor(attrs, is_canonicalized, is_dynamic, enforceBF16) {
|
||||
numInput = snippetAttrs.inMemBlockedDims.size();
|
||||
numOutput = snippetAttrs.outMemBlockedDims.size();
|
||||
start_offset_in.resize(numInput);
|
||||
start_offset_out.resize(numOutput);
|
||||
auto local_copy = [this]() {
|
||||
ov::OutputVector subgraph_node_inputs;
|
||||
for (size_t i = 0; i < numInput; i++) {
|
||||
const auto paramShape = snippetAttrs.snippet->body_ptr()->get_parameters()[i]->get_shape();
|
||||
const auto paramType = snippetAttrs.snippet->body_ptr()->get_parameters()[i]->get_element_type();
|
||||
auto new_input = std::make_shared<ov::opset1::Parameter>(paramType, paramShape);
|
||||
subgraph_node_inputs.push_back(new_input);
|
||||
}
|
||||
std::shared_ptr<ov::Model> new_body = snippetAttrs.snippet->body_ptr()->clone();
|
||||
|
||||
snippet_for_generation = std::make_shared<ov::snippets::op::Subgraph>(subgraph_node_inputs, new_body);
|
||||
ov::copy_runtime_info(snippetAttrs.snippet, snippet_for_generation);
|
||||
snippet_for_generation->set_friendly_name(snippetAttrs.snippet->get_friendly_name());
|
||||
auto host_isa = dnnl::impl::cpu::x64::mayiuse(dnnl::impl::cpu::x64::avx512_core) ?
|
||||
dnnl::impl::cpu::x64::avx512_core : dnnl::impl::cpu::x64::avx2;
|
||||
snippet_for_generation->set_generator(std::make_shared<CPUGenerator>(host_isa));
|
||||
};
|
||||
|
||||
// is_canonicalized is ture means just reshape canonicalized graph with new input shapes, and get updated master shape,
|
||||
// false means canonicalization, determine master_shape on snippetAttrs.snippet.
|
||||
ov::PartialShape canonicalShape = canonicalizeBody(is_canonicalized);
|
||||
|
||||
if (is_dynamic) {
|
||||
// we need a local snippets for generation, which will be adjusted based on input shapes possibily.
|
||||
// The adjustment may be not compatible with new input shape in dynamic node, such as broadcastMove inserted.
|
||||
local_copy();
|
||||
} else {
|
||||
snippet_for_generation = snippetAttrs.snippet;
|
||||
}
|
||||
|
||||
// initialize by maximum output dimension. Dimensions of outputs should be broadcastable
|
||||
tensorRank = std::max(static_cast<size_t>(rank6D), canonicalShape.size());
|
||||
auto initDataSizes = [this]() {
|
||||
dataSize.resize(numInput + numOutput);
|
||||
for (size_t i = 0; i < numInput; i++)
|
||||
dataSize[i] = snippetAttrs.inMemPrecs[i].size();
|
||||
for (size_t i = 0; i < numOutput; i++)
|
||||
dataSize[i + numInput] = snippetAttrs.outMemPrecs[i].size();
|
||||
};
|
||||
initDataSizes();
|
||||
|
||||
if (canonicalShape.is_dynamic())
|
||||
IE_THROW() << "Snippets: Canonicalization returned dynamic shape in static pipeline";
|
||||
masterShape = canonicalShape.get_shape();
|
||||
const auto &body = snippet_for_generation->body_ptr();
|
||||
normInputShapes.clear();
|
||||
for (const auto& p : body->get_parameters())
|
||||
normInputShapes.emplace_back(p->get_output_shape(0));
|
||||
normOutputShapes.clear();
|
||||
for (const auto& r : body->get_results())
|
||||
normOutputShapes.emplace_back(r->get_input_shape(0));
|
||||
|
||||
// prepare
|
||||
masterShape = getNormalizedDimsBySize(masterShape, tensorRank);
|
||||
std::vector<size_t> original_input_shape_ranks;
|
||||
for (auto& pshape : normInputShapes) {
|
||||
original_input_shape_ranks.push_back(pshape.size());
|
||||
pshape = getNormalizedDimsBySize(pshape, tensorRank);
|
||||
}
|
||||
for (auto& pshape : normOutputShapes)
|
||||
pshape = getNormalizedDimsBySize(pshape, tensorRank);
|
||||
|
||||
tileRank = 1;
|
||||
bool dims_collapsed = false;
|
||||
fullWorkAmount = std::accumulate(masterShape.begin(), masterShape.end(), 1, std::multiplies<size_t>());
|
||||
if (snippet_for_generation->has_domain_sensitive_ops()) {
|
||||
tileRank = 2;
|
||||
} else {
|
||||
dims_collapsed = optimizeExecDomain(normInputShapes, normOutputShapes, masterShape, tileRank);
|
||||
}
|
||||
exec_domain = masterShape;
|
||||
|
||||
std::vector<size_t> scheduler_work_amounts;
|
||||
// rename schedulerWorkAmount to harnessWorkAmount?
|
||||
harnessWorkAmount = fullWorkAmount;
|
||||
const auto rank = exec_domain.size();
|
||||
for (auto i = rank - tileRank; i < rank; i++) {
|
||||
auto& dim = exec_domain[i];
|
||||
harnessWorkAmount /= dim;
|
||||
scheduler_work_amounts.push_back(dim);
|
||||
dim = 1;
|
||||
}
|
||||
|
||||
if (dims_collapsed) {
|
||||
std::vector<ov::Shape> new_shapes;
|
||||
for (size_t i = 0; i < normInputShapes.size(); i++) {
|
||||
const auto norm_shape = normInputShapes[i];
|
||||
size_t ndims_to_skip = norm_shape.size() - original_input_shape_ranks[i];
|
||||
new_shapes.emplace_back(norm_shape.begin() + ndims_to_skip, norm_shape.end());
|
||||
}
|
||||
snippet_for_generation->reshape_body(new_shapes);
|
||||
}
|
||||
snippet_for_generation->set_master_shape(ov::PartialShape(masterShape));
|
||||
snippet_for_generation->set_tile_rank(tileRank);
|
||||
|
||||
// generate
|
||||
jit_snippets_compile_args jcp;
|
||||
jcp.master_shape = masterShape;
|
||||
jcp.tile_rank = tileRank;
|
||||
generate(&jcp);
|
||||
buffer_scratchpad_size = snippet_for_generation->get_buffer_scratchpad_size();
|
||||
buffer_scratchpad.resize(buffer_scratchpad_size * parallel_get_max_threads(), 0);
|
||||
}
|
||||
|
||||
ov::PartialShape Snippet::SnippetJitExecutor::canonicalizeBody(bool reshape) {
|
||||
ov::snippets::op::Subgraph::BlockedShapeVector input_blocked_shapes = getBlockedShapes(
|
||||
snippetAttrs.inMemBlockedDims, snippetAttrs.inMemOrders, snippetAttrs.inMemPrecs);
|
||||
if (reshape) {
|
||||
const auto& canonicalShape = snippetAttrs.snippet->canonicalized_body_shape_infer(input_blocked_shapes);
|
||||
return canonicalShape;
|
||||
} else {
|
||||
ov::snippets::op::Subgraph::BlockedShapeVector output_blocked_shapes = getBlockedShapes(
|
||||
snippetAttrs.outMemBlockedDims, snippetAttrs.outMemOrders, snippetAttrs.outMemPrecs);
|
||||
|
||||
const auto& canonicalShape = snippetAttrs.snippet->canonicalize(output_blocked_shapes, input_blocked_shapes);
|
||||
return canonicalShape;
|
||||
}
|
||||
}
|
||||
|
||||
bool Snippet::SnippetJitExecutor::optimizeExecDomain(std::vector<VectorDims>& inputShapes, std::vector<VectorDims>& outputShapes,
|
||||
VectorDims &domain, size_t& TileRank) const {
|
||||
const size_t minimalConcurrency = parallel_get_max_threads();
|
||||
const size_t minimalJitWorkAmount = 256;
|
||||
@ -287,240 +692,11 @@ bool Snippet::optimizeExecDomain(std::vector<VectorDims>& inputShapes, std::vect
|
||||
};
|
||||
return findDimsToCollapse();
|
||||
}
|
||||
ov::PartialShape Snippet::canonicalizeBody() {
|
||||
auto edgeToBlockedShape = [](const EdgePtr& edge) {
|
||||
const auto blockedDesc = edge->getMemory().getDescWithType<BlockedMemoryDesc>();
|
||||
std::vector<Dimension> dims;
|
||||
// if blockDim == Shape::UNDEFINED_DIM, then it's a dynamic dimension, and we need to recreate a proper dynamic Dim
|
||||
for (const auto& d : blockedDesc->getBlockDims())
|
||||
dims.emplace_back(d == Shape::UNDEFINED_DIM ? -1 : d);
|
||||
ov::PartialShape shape(dims);
|
||||
ov::AxisVector blocking(blockedDesc->getOrder());
|
||||
ov::element::Type precision = InferenceEngine::details::convertPrecision(blockedDesc->getPrecision());
|
||||
return snippets::op::Subgraph::BlockedShape{shape, blocking, precision};
|
||||
};
|
||||
inputShapeIsBlocked.resize(inputShapes.size(), false);
|
||||
masterShapeIsBlocked = false;
|
||||
snippets::op::Subgraph::BlockedShapeVector input_blocked_shapes;
|
||||
for (size_t i = 0; i < inputShapes.size(); i++) {
|
||||
auto blockedShape = edgeToBlockedShape(getParentEdgesAtPort(i)[0]);
|
||||
inputShapeIsBlocked[i] = std::get<0>(blockedShape).size() != std::get<1>(blockedShape).size();
|
||||
masterShapeIsBlocked = masterShapeIsBlocked || inputShapeIsBlocked[i];
|
||||
input_blocked_shapes.push_back(blockedShape);
|
||||
}
|
||||
|
||||
outputShapeIsBlocked.resize(outputShapes.size(), false);
|
||||
ov::snippets::op::Subgraph::BlockedShapeVector output_blocked_shapes;
|
||||
for (size_t i = 0; i < outputShapes.size(); i++) {
|
||||
auto blockedShape = edgeToBlockedShape(getChildEdgesAtPort(i)[0]);
|
||||
outputShapeIsBlocked[i] = std::get<0>(blockedShape).size() != std::get<1>(blockedShape).size();
|
||||
output_blocked_shapes.push_back(blockedShape);
|
||||
}
|
||||
|
||||
const auto& canonicalShape = snippet->canonicalize(output_blocked_shapes, input_blocked_shapes);
|
||||
return canonicalShape;
|
||||
}
|
||||
void Snippet::createPrimitive() {
|
||||
// determine canonicalize, determine master_shape and prepend up to 6D
|
||||
// NB! normInputShapes are updated, so body reshape might be needed
|
||||
const auto& canonicalShape = canonicalizeBody();
|
||||
// initialize by maximum output dimension. Dimensions of outputs should be broadcastable
|
||||
tensorRank = std::max(static_cast<size_t>(rank6D), canonicalShape.size());
|
||||
|
||||
const auto config = getSelectedPrimitiveDescriptor()->getConfig();
|
||||
auto initDataSizes = [this, config]() {
|
||||
const size_t numInputs = inputShapes.size();
|
||||
const size_t numOutputs = outputShapes.size();
|
||||
dataSize.resize(numInputs + numOutputs);
|
||||
for (size_t i = 0; i < numInputs; i++)
|
||||
dataSize[i] = config.inConfs[i].getMemDesc()->getPrecision().size();
|
||||
for (size_t i = 0; i < numOutputs; i++)
|
||||
dataSize[i + numInputs] = config.outConfs[i].getMemDesc()->getPrecision().size();
|
||||
};
|
||||
initDataSizes();
|
||||
|
||||
jit_snippets_compile_args jcp;
|
||||
if (canonicalShape.is_dynamic())
|
||||
IE_THROW() << "Snippets: Canonicalization returned dynamic shape in static pipeline";
|
||||
masterShape = canonicalShape.get_shape();
|
||||
const auto &body = snippet->body_ptr();
|
||||
for (const auto& p : body->get_parameters())
|
||||
normInputShapes.emplace_back(p->get_output_shape(0));
|
||||
for (const auto& r : body->get_results())
|
||||
normOutputShapes.emplace_back(r->get_input_shape(0));
|
||||
|
||||
prepareParams();
|
||||
jcp.master_shape = masterShape;
|
||||
jcp.tile_rank = tileRank;
|
||||
generate(&jcp);
|
||||
buffer_scratchpad_size = snippet->get_buffer_scratchpad_size();
|
||||
buffer_scratchpad.resize(buffer_scratchpad_size * parallel_get_max_threads(), 0);
|
||||
}
|
||||
|
||||
std::vector<VectorDims> Snippet::shapeInfer() {
|
||||
// todo: it's very strange that we don't have broadcast_merge_into for cpu shapes
|
||||
auto broadcast_merge = [](VectorDims& dst, const VectorDims& src){
|
||||
// Ranks are both static.
|
||||
auto dst_rank = dst.size();
|
||||
auto src_rank = src.size();
|
||||
const auto new_rank = std::max(dst_rank, src_rank);
|
||||
dst.insert(dst.begin(), new_rank - dst_rank, 1);
|
||||
std::vector<Dimension> dims(new_rank);
|
||||
bool success = true;
|
||||
for (size_t i = 0; i < new_rank; i++) {
|
||||
auto dsti = i < (new_rank - dst_rank) ? 1 : dst[i - (new_rank - dst_rank)];
|
||||
auto srci = i < (new_rank - src_rank) ? 1 : src[i - (new_rank - src_rank)];
|
||||
if (dsti != srci && srci != Shape::UNDEFINED_DIM) {
|
||||
if (dsti == 1 || dsti == Shape::UNDEFINED_DIM) {
|
||||
dsti = srci;
|
||||
} else {
|
||||
success = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
return success;
|
||||
};
|
||||
for (size_t i = 0; i < getParentEdges().size(); i++) {
|
||||
VectorDims inDims {getParentEdgesAtPort(i)[0]->getMemory().getShape().getDims()};
|
||||
if (masterShapeIsBlocked && !inputShapeIsBlocked[i])
|
||||
inDims.insert(inDims.end(), 1);
|
||||
// todo: this is a simple master_shape inference for shape-agnostic operations,
|
||||
// we'll need to account for body operations semantics in the future
|
||||
if (i == 0)
|
||||
masterShape = inDims;
|
||||
else
|
||||
broadcast_merge(masterShape, inDims);
|
||||
normInputShapes[i] = std::move(inDims);
|
||||
}
|
||||
if (std::any_of(masterShape.begin(), masterShape.end(), [](const Dim& d){ return d == Shape::UNDEFINED_DIM;})) {
|
||||
std::ostringstream errorMessage;
|
||||
errorMessage << "Can't compute static master shape for Snippet node with name: " << getName();
|
||||
errorMessage << ". Input shapes = ( ";
|
||||
for (size_t i = 0; i < getParentEdges().size(); i++) {
|
||||
errorMessage << i << " port = " << getParentEdgesAtPort(i)[0]->getMemory().getShape().toString() << ", ";
|
||||
}
|
||||
errorMessage << "). Master shape = ( " << Shape(masterShape).toString() << " )";
|
||||
IE_THROW() << errorMessage.str();
|
||||
}
|
||||
|
||||
if (normOutputShapes.size() == 1) {
|
||||
normOutputShapes[0] = masterShape;
|
||||
return {masterShape};
|
||||
}
|
||||
std::vector<VectorDims> outputDims;
|
||||
std::vector<ov::Shape> new_shapes;
|
||||
for (const auto& s : normInputShapes)
|
||||
new_shapes.emplace_back(s);
|
||||
const auto& outputShapes = snippet->reshape_body(new_shapes);
|
||||
for (size_t i = 0; i < outputShapes.size(); i++)
|
||||
normOutputShapes[i] = outputShapes[i];
|
||||
return normOutputShapes;
|
||||
}
|
||||
|
||||
void Snippet::prepareParams() {
|
||||
masterShape = getNormalizedDimsBySize(masterShape, tensorRank);
|
||||
std::vector<size_t> original_input_shape_ranks;
|
||||
for (auto& pshape : normInputShapes) {
|
||||
original_input_shape_ranks.push_back(pshape.size());
|
||||
pshape = getNormalizedDimsBySize(pshape, tensorRank);
|
||||
}
|
||||
for (auto& pshape : normOutputShapes)
|
||||
pshape = getNormalizedDimsBySize(pshape, tensorRank);
|
||||
|
||||
tileRank = 1;
|
||||
bool dims_collapsed = false;
|
||||
fullWorkAmount = std::accumulate(masterShape.begin(), masterShape.end(), 1, std::multiplies<size_t>());
|
||||
if (snippet->has_domain_sensitive_ops()) {
|
||||
tileRank = 2;
|
||||
} else {
|
||||
dims_collapsed = optimizeExecDomain(normInputShapes, normOutputShapes, masterShape, tileRank);
|
||||
}
|
||||
exec_domain = masterShape;
|
||||
|
||||
auto initStartMemoryOffsets = [this]() {
|
||||
const auto config = getSelectedPrimitiveDescriptor()->getConfig();
|
||||
const size_t numInputs = inputShapes.size();
|
||||
start_offset_in.resize(numInputs);
|
||||
srcMemPtrs.resize(numInputs);
|
||||
for (size_t i = 0; i < numInputs; i++) {
|
||||
const auto memPtr = getParentEdgeAt(i)->getMemoryPtr();
|
||||
srcMemPtrs[i] = memPtr;
|
||||
start_offset_in[i] = memPtr->getDescWithType<BlockedMemoryDesc>()->getOffsetPadding() * dataSize[i];
|
||||
}
|
||||
const size_t numOutputs = outputShapes.size();
|
||||
start_offset_out.resize(numOutputs);
|
||||
dstMemPtrs.resize(numOutputs);
|
||||
for (size_t i = 0; i < numOutputs; i++) {
|
||||
const auto memPtr = getChildEdgeAt(i)->getMemoryPtr();
|
||||
dstMemPtrs[i] = memPtr;
|
||||
start_offset_out[i] = memPtr->getDescWithType<BlockedMemoryDesc>()->getOffsetPadding() * dataSize[i + numInputs];
|
||||
}
|
||||
};
|
||||
// initialize start offsets to src and dst memory
|
||||
// Needs to be done for every set of input shapes sce memory ptrs could've updated
|
||||
initStartMemoryOffsets();
|
||||
std::vector<size_t> scheduler_work_amounts;
|
||||
// rename schedulerWorkAmount to harnessWorkAmount?
|
||||
harnessWorkAmount = fullWorkAmount;
|
||||
const auto rank = exec_domain.size();
|
||||
for (auto i = rank - tileRank; i < rank; i++) {
|
||||
auto& dim = exec_domain[i];
|
||||
harnessWorkAmount /= dim;
|
||||
scheduler_work_amounts.push_back(dim);
|
||||
dim = 1;
|
||||
}
|
||||
|
||||
if (dims_collapsed) {
|
||||
std::vector<ov::Shape> new_shapes;
|
||||
for (size_t i = 0; i < normInputShapes.size(); i++) {
|
||||
const auto norm_shape = normInputShapes[i];
|
||||
size_t ndims_to_skip = norm_shape.size() - original_input_shape_ranks[i];
|
||||
new_shapes.emplace_back(norm_shape.begin() + ndims_to_skip, norm_shape.end());
|
||||
}
|
||||
snippet->reshape_body(new_shapes);
|
||||
}
|
||||
|
||||
snippet->set_master_shape(ov::PartialShape(masterShape));
|
||||
snippet->set_tile_rank(tileRank);
|
||||
}
|
||||
|
||||
bool Snippet::needPrepareParams() const {
|
||||
return inputShapesModified() || !schedule.ptr;
|
||||
}
|
||||
|
||||
bool Snippet::canBeInPlace() const {
|
||||
if (isDynamic || getParentEdgesAtPort(0)[0]->getParent()->getType() == Type::Input) {
|
||||
return false;
|
||||
}
|
||||
if (getChildEdges().size() != 1) {
|
||||
return false;
|
||||
}
|
||||
|
||||
for (auto& parentEdge : getParentEdges()) {
|
||||
auto parent = parentEdge.lock()->getParent();
|
||||
if (parent->getChildEdges().size() != 1)
|
||||
return false;
|
||||
|
||||
// WA to prevent memory corruption caused by inplace feature
|
||||
if (parent->getType() == Type::Concatenation) {
|
||||
for (auto& parentParentEdge : parent->getParentEdges()) {
|
||||
auto parentParent = parentParentEdge.lock()->getParent();
|
||||
if (parentParent->getChildEdges().size() != 1)
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
return getInputShapeAtPort(0) == getOutputShapeAtPort(0);
|
||||
}
|
||||
|
||||
bool Snippet::created() const {
|
||||
return getType() == Type::Subgraph;
|
||||
}
|
||||
|
||||
void Snippet::generate(const jit_snippets_compile_args* jcp) {
|
||||
void Snippet::SnippetJitExecutor::generate(const jit_snippets_compile_args* jcp) {
|
||||
ov::pass::Manager pre_dialect;
|
||||
pre_dialect.register_pass<ConvertToSwishCPU>();
|
||||
if (context->getConfig().inferencePrecision == ov::element::bf16 && snippet->has_domain_sensitive_ops()) {
|
||||
if (enforceBF16 && snippet_for_generation->has_domain_sensitive_ops()) {
|
||||
// enforce BF16 precisions to supported operations
|
||||
// MatMul has to be decomposed to Brgemm operations before enforcement
|
||||
// Note, MatMul decomposition will be ran later again for case if BF16 enforcement is not happened
|
||||
@ -542,7 +718,7 @@ void Snippet::generate(const jit_snippets_compile_args* jcp) {
|
||||
ov::snippets::lowered::pass::PassPipeline control_flow_pipeline;
|
||||
CPU_REGISTER_PASS_X64(control_flow_pipeline, ov::intel_cpu::pass::FuseLoadStoreConvert);
|
||||
|
||||
schedule = snippet->generate(
|
||||
schedule = snippet_for_generation->generate(
|
||||
pre_dialect,
|
||||
post_dialect,
|
||||
post_precision,
|
||||
@ -551,63 +727,8 @@ void Snippet::generate(const jit_snippets_compile_args* jcp) {
|
||||
reinterpret_cast<const void*>(jcp));
|
||||
}
|
||||
|
||||
void Snippet::update_ptrs(jit_snippets_call_args& call_args) {
|
||||
for (size_t i = 0; i < srcMemPtrs.size(); i++)
|
||||
call_args.src_ptrs[i] = reinterpret_cast<const uint8_t*>(srcMemPtrs[i]->getData()) + start_offset_in[i];
|
||||
|
||||
for (size_t i = 0; i < dstMemPtrs.size(); i++)
|
||||
call_args.dst_ptrs[i] = reinterpret_cast<uint8_t*>(dstMemPtrs[i]->getData()) + start_offset_out[i];
|
||||
|
||||
if (buffer_scratchpad_size > 0) {
|
||||
call_args.buffer_scratchpad_ptr =
|
||||
reinterpret_cast<uint8_t*>(buffer_scratchpad.data()) + parallel_get_thread_num() * buffer_scratchpad_size;
|
||||
}
|
||||
}
|
||||
|
||||
void Snippet::execute(dnnl::stream strm) {
|
||||
if (schedule.ptr == nullptr) {
|
||||
IE_THROW() << "Snippet can't use Optimized implementation and can't fallback to reference";
|
||||
}
|
||||
if (tensorRank == rank6D) {
|
||||
schedule_6d();
|
||||
} else {
|
||||
schedule_nt();
|
||||
}
|
||||
}
|
||||
|
||||
void Snippet::schedule_6d() {
|
||||
const auto& dom = exec_domain;
|
||||
// < N, C, H, W > < 1, 1, N, C*H*W>
|
||||
parallel_for5d(dom[0], dom[1], dom[2], dom[3], dom[4],
|
||||
[&](int64_t d0, int64_t d1, int64_t d2, int64_t d3, int64_t d4) {
|
||||
int64_t indexes[] = {d0, d1, d2, d3, d4};
|
||||
jit_snippets_call_args call_args;
|
||||
update_ptrs(call_args);
|
||||
|
||||
schedule.get_callable<kernel>()(indexes, &call_args);
|
||||
});
|
||||
}
|
||||
|
||||
void Snippet::schedule_nt() {
|
||||
const auto& work_size = exec_domain;
|
||||
parallel_nt(0, [&](const int ithr, const int nthr) {
|
||||
jit_snippets_call_args call_args;
|
||||
update_ptrs(call_args);
|
||||
|
||||
size_t start = 0, end = 0;
|
||||
splitter(harnessWorkAmount, nthr, ithr, start, end);
|
||||
|
||||
std::vector<int64_t> indexes(work_size.size() - 1, 0);
|
||||
for (size_t iwork = start; iwork < end; ++iwork) {
|
||||
size_t tmp = iwork;
|
||||
for (ptrdiff_t j = work_size.size() - 2; j >= 0; j--) {
|
||||
indexes[j] = tmp % work_size[j];
|
||||
tmp /= work_size[j];
|
||||
}
|
||||
|
||||
schedule.get_callable<kernel>()(indexes.data(), &call_args);
|
||||
}
|
||||
});
|
||||
bool Snippet::SnippetJitExecutor::schedule_created() {
|
||||
return schedule.ptr != nullptr;
|
||||
}
|
||||
|
||||
} // namespace node
|
||||
|
||||
@ -30,6 +30,7 @@ public:
|
||||
void getSupportedDescriptors() override {};
|
||||
void initSupportedPrimitiveDescriptors() override;
|
||||
void selectOptimalPrimitiveDescriptor() override;
|
||||
void initOptimalPrimitiveDescriptor() override;
|
||||
InferenceEngine::Precision getRuntimePrecision() const override;
|
||||
|
||||
// to avoid collisions in throughput mode with copy of TypeRelaxed nodes
|
||||
@ -37,9 +38,7 @@ public:
|
||||
void setSharedMutex(const std::shared_ptr<std::mutex>& mutex);
|
||||
|
||||
// Here we convert to canonical for & jit everything
|
||||
void createPrimitive() override;
|
||||
void prepareParams() override;
|
||||
std::vector<VectorDims> shapeInfer();
|
||||
bool needPrepareParams() const override;
|
||||
|
||||
bool canBeInPlace() const override;
|
||||
@ -47,6 +46,19 @@ public:
|
||||
|
||||
// if generator is set, it would execute generated code otherwise it would fallback to nGraph reference
|
||||
void execute(dnnl::stream strm) override;
|
||||
void executeDynamicImpl(dnnl::stream strm) override;
|
||||
|
||||
struct SnippetAttrs {
|
||||
// Local copy of subgraph node for canonization & code generation
|
||||
std::shared_ptr<snippets::op::Subgraph> snippet;
|
||||
uint64_t bodyHash;
|
||||
std::vector<std::vector<size_t>> inMemBlockedDims;
|
||||
std::vector<std::vector<size_t>> inMemOrders;
|
||||
std::vector<InferenceEngine::Precision> inMemPrecs;
|
||||
std::vector<std::vector<size_t>> outMemBlockedDims;
|
||||
std::vector<std::vector<size_t>> outMemOrders;
|
||||
std::vector<InferenceEngine::Precision> outMemPrecs;
|
||||
};
|
||||
|
||||
private:
|
||||
static const size_t rank6D {6};
|
||||
@ -55,61 +67,96 @@ private:
|
||||
|
||||
// Create a deep local copy of the input snippet to perform canonicalization & code generation
|
||||
// TODO: Probably better to implement a proper copy constructor
|
||||
// NOTE: Before call mutex should be initialized
|
||||
void copy_snippet();
|
||||
void copy_snippet() const;
|
||||
void init_body_hash();
|
||||
|
||||
ov::PartialShape canonicalizeBody();
|
||||
// returns true if exec domain was modified
|
||||
bool optimizeExecDomain(std::vector<VectorDims>&, std::vector<VectorDims>&, VectorDims&, size_t&) const;
|
||||
|
||||
void generate(const jit_snippets_compile_args*);
|
||||
inline void update_ptrs(jit_snippets_call_args&);
|
||||
// Evaluates generated snippet using parallel backend
|
||||
void schedule_6d();
|
||||
void schedule_nt();
|
||||
size_t inputNum = 0;
|
||||
size_t outputNum = 0;
|
||||
|
||||
// Original subgraph node
|
||||
std::shared_ptr<snippets::op::Subgraph> original_snippet;
|
||||
// Local copy of subgraph node for canonization & code generation
|
||||
std::shared_ptr<snippets::op::Subgraph> snippet;
|
||||
|
||||
// Holds generated snippet with information about how to schedule it
|
||||
snippets::Schedule schedule;
|
||||
mutable std::shared_ptr<snippets::op::Subgraph> local_snippet;
|
||||
|
||||
// Holds ISA version used is codeGeneration target
|
||||
dnnl::impl::cpu::x64::cpu_isa_t host_isa;
|
||||
size_t isa_num_lanes = 0; // number of elements that fit in vector size
|
||||
|
||||
// Holds index of output used as in execution domain
|
||||
// it should be compatible with a schedule's work size
|
||||
std::vector<size_t> exec_domain = {};
|
||||
|
||||
/// scheduling info
|
||||
size_t tensorRank = 0;
|
||||
size_t tileRank = 1;
|
||||
size_t fullWorkAmount = 0;
|
||||
size_t harnessWorkAmount = 0;
|
||||
const size_t maxTileRank = 2;
|
||||
|
||||
std::vector<MemoryPtr> srcMemPtrs = {};
|
||||
std::vector<MemoryPtr> dstMemPtrs = {};
|
||||
std::vector<size_t> dataSize = {};
|
||||
|
||||
// this is needed for fast shape inference of blocking-invariant prepended shapes
|
||||
std::vector<bool> inputShapeIsBlocked = {}; // we need this info to shape-infer mixed layouts
|
||||
std::vector<bool> outputShapeIsBlocked = {}; // we need this info to shape-infer mixed layouts
|
||||
bool masterShapeIsBlocked = false;
|
||||
mutable SnippetAttrs snippetAttrs;
|
||||
mutable bool is_canonicalized = false;
|
||||
bool is_dynamic = false;
|
||||
|
||||
VectorDims masterShape = {};
|
||||
std::vector<VectorDims> normInputShapes = {};
|
||||
std::vector<VectorDims> normOutputShapes = {};
|
||||
class SnippetExecutor {
|
||||
public:
|
||||
SnippetExecutor(const SnippetAttrs& attrs, bool is_canonicalized, bool is_dynamic, bool enforceBF16);
|
||||
virtual void exec(const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs) = 0;
|
||||
virtual ~SnippetExecutor() = default;
|
||||
|
||||
std::vector<ptrdiff_t> start_offset_in = {};
|
||||
std::vector<ptrdiff_t> start_offset_out = {};
|
||||
protected:
|
||||
SnippetAttrs snippetAttrs;
|
||||
bool is_canonicalized = false;
|
||||
bool is_dynamic = false;
|
||||
bool enforceBF16 = false;
|
||||
};
|
||||
|
||||
// Buffer scratchpad
|
||||
std::vector<uint8_t> buffer_scratchpad = {};
|
||||
size_t buffer_scratchpad_size = 0;
|
||||
std::shared_ptr<SnippetExecutor> execPtr = nullptr;
|
||||
|
||||
class SnippetJitExecutor : public SnippetExecutor {
|
||||
public:
|
||||
SnippetJitExecutor(const SnippetAttrs& attrs, bool is_canonicalized, bool is_dynamic, bool enforceBF16);
|
||||
void exec(const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs) override;
|
||||
|
||||
bool schedule_created();
|
||||
|
||||
private:
|
||||
static const size_t rank6D {6};
|
||||
|
||||
typedef void (*kernel)(const void *, const void *);
|
||||
|
||||
size_t numInput = 0;
|
||||
size_t numOutput = 0;
|
||||
|
||||
ov::PartialShape canonicalizeBody(bool reshape);
|
||||
// returns true if exec domain was modified
|
||||
bool optimizeExecDomain(std::vector<VectorDims>&, std::vector<VectorDims>&, VectorDims&, size_t&) const;
|
||||
|
||||
void generate(const jit_snippets_compile_args*);
|
||||
inline void update_ptrs(jit_snippets_call_args&, const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs);
|
||||
// Evaluates generated snippet using parallel backend
|
||||
void schedule_6d(const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs);
|
||||
void schedule_nt(const std::vector<MemoryPtr>& inMemPtrs, const std::vector<MemoryPtr>& outMemPtrs);
|
||||
|
||||
std::shared_ptr<snippets::op::Subgraph> snippet_for_generation;
|
||||
|
||||
// Holds generated snippet with information about how to schedule it
|
||||
snippets::Schedule schedule;
|
||||
|
||||
// Holds index of output used as in execution domain
|
||||
// it should be compatible with a schedule's work size
|
||||
std::vector<size_t> exec_domain = {};
|
||||
|
||||
/// scheduling info
|
||||
size_t tensorRank = 0;
|
||||
size_t tileRank = 1;
|
||||
size_t fullWorkAmount = 0;
|
||||
size_t harnessWorkAmount = 0;
|
||||
const size_t maxTileRank = 2;
|
||||
|
||||
std::vector<size_t> dataSize = {};
|
||||
|
||||
// master shape is mutable since we need to modify it inside const shapeInfer method
|
||||
mutable VectorDims masterShape = {};
|
||||
mutable std::vector<VectorDims> normInputShapes = {};
|
||||
mutable std::vector<VectorDims> normOutputShapes = {};
|
||||
|
||||
std::vector<ptrdiff_t> start_offset_in = {};
|
||||
std::vector<ptrdiff_t> start_offset_out = {};
|
||||
|
||||
// Buffer scratchpad
|
||||
std::vector<uint8_t> buffer_scratchpad = {};
|
||||
size_t buffer_scratchpad_size = 0;
|
||||
};
|
||||
};
|
||||
|
||||
} // namespace node
|
||||
|
||||
@ -10,15 +10,69 @@ namespace ov {
|
||||
namespace intel_cpu {
|
||||
namespace node {
|
||||
using Result = IShapeInfer::Result;
|
||||
/* This class implementation is a temporal WA
|
||||
TODO: revise the implementation to remove the node reference*/
|
||||
class SnippetShapeInfer : public ShapeInferEmptyPads {
|
||||
public:
|
||||
SnippetShapeInfer(Snippet* node) : m_node(node) {}
|
||||
SnippetShapeInfer(std::shared_ptr<ov::Model> body) : m_body(body) {}
|
||||
Result infer(
|
||||
const std::vector<std::reference_wrapper<const VectorDims>>& input_shapes,
|
||||
const std::unordered_map<size_t, MemoryPtr>& data_dependency) override {
|
||||
return {m_node->shapeInfer(), ShapeInferStatus::success};
|
||||
auto broadcast_merge = [](VectorDims& dst, const VectorDims& src) {
|
||||
// Ranks are both static.
|
||||
auto dst_rank = dst.size();
|
||||
auto src_rank = src.size();
|
||||
const auto new_rank = std::max(dst_rank, src_rank);
|
||||
dst.insert(dst.begin(), new_rank - dst_rank, 1);
|
||||
for (size_t i = 0; i < new_rank; i++) {
|
||||
auto srci = i < (new_rank - src_rank) ? 1 : src[i - (new_rank - src_rank)];
|
||||
if (dst[i] != srci && srci != Shape::UNDEFINED_DIM) {
|
||||
if (dst[i] == 1 || dst[i] == Shape::UNDEFINED_DIM) {
|
||||
dst[i] = srci;
|
||||
} else {
|
||||
if (srci != 1) {
|
||||
IE_THROW() << "Got imcompatible input shapes in snippets shape infer";
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const size_t out_size = m_body->get_output_size();
|
||||
if (out_size == 1) {
|
||||
VectorDims masterShape;
|
||||
for (size_t i = 0; i < input_shapes.size(); i++) {
|
||||
if (i == 0)
|
||||
masterShape = input_shapes[i];
|
||||
else
|
||||
broadcast_merge(masterShape, input_shapes[i]);
|
||||
}
|
||||
size_t output_rank = m_body->get_output_partial_shape(0).rank().get_length();
|
||||
if (output_rank > masterShape.size()) {
|
||||
masterShape.insert(masterShape.begin(), output_rank - masterShape.size(), 1);
|
||||
}
|
||||
return {{masterShape}, ShapeInferStatus::success};
|
||||
} else {
|
||||
std::vector<VectorDims> outputDims;
|
||||
std::vector<ov::Shape> new_shapes;
|
||||
for (const auto& s : input_shapes)
|
||||
new_shapes.emplace_back(s);
|
||||
auto& params = m_body->get_parameters();
|
||||
if (params.size() != input_shapes.size()) {
|
||||
IE_THROW() << "Got invalid number of input shapes to reshape subgraph body";
|
||||
}
|
||||
for (size_t i = 0; i < params.size(); ++i) {
|
||||
params[i]->set_partial_shape(new_shapes[i]);
|
||||
}
|
||||
m_body->validate_nodes_and_infer_types();
|
||||
for (const auto& res : m_body->get_results()) {
|
||||
auto& pshape = res->get_input_partial_shape(0);
|
||||
if (!pshape.is_static()) {
|
||||
IE_THROW() << "Subgraph inferred dynamic output shape during reshape with static inputs";
|
||||
}
|
||||
outputDims.emplace_back(pshape.get_shape());
|
||||
}
|
||||
|
||||
return {outputDims, ShapeInferStatus::success};
|
||||
}
|
||||
}
|
||||
|
||||
port_mask_t get_port_mask() const override {
|
||||
@ -26,18 +80,21 @@ public:
|
||||
}
|
||||
|
||||
private:
|
||||
Snippet* m_node;
|
||||
std::shared_ptr<ov::Model> m_body;
|
||||
};
|
||||
|
||||
class SnippetShapeInferFactory : public ShapeInferFactory {
|
||||
public:
|
||||
SnippetShapeInferFactory(Snippet* node) : m_node(node) {}
|
||||
SnippetShapeInferFactory(const std::shared_ptr<ov::Node>& op) {
|
||||
auto subgraph = ov::as_type_ptr<snippets::op::Subgraph>(op);
|
||||
snippet_body = subgraph->body_ptr()->clone();
|
||||
}
|
||||
ShapeInferPtr makeShapeInfer() const override {
|
||||
return std::make_shared<SnippetShapeInfer>(m_node);
|
||||
return std::make_shared<SnippetShapeInfer>(snippet_body);
|
||||
}
|
||||
|
||||
private:
|
||||
Snippet* m_node;
|
||||
std::shared_ptr<ov::Model> snippet_body = nullptr;
|
||||
};
|
||||
} // namespace node
|
||||
} // namespace intel_cpu
|
||||
|
||||
@ -667,10 +667,14 @@ void Transformations::MainSnippets(void) {
|
||||
}, snippets::pass::ExtractReshapesFromMHA);
|
||||
CPU_SET_CALLBACK_X64(snippetsManager,
|
||||
[](const std::shared_ptr<const ov::Node>& n) -> bool {
|
||||
if (n->is_dynamic())
|
||||
return true;
|
||||
// CPU Plugin support Swish in Subgraph via conversion to SwichCPU which assumes second input to be constant
|
||||
const bool is_unsupported_swish =
|
||||
ov::is_type<const ov::op::v4::Swish>(n) && n->inputs().size() > 1 &&
|
||||
!ov::is_type<const ov::op::v0::Constant>(n->get_input_node_shared_ptr(1));
|
||||
if (is_unsupported_swish)
|
||||
return true;
|
||||
// todo: general tokenization flow is not currently supported for these operations.
|
||||
// they can be tokenized only as a part of complex patterns
|
||||
const bool is_disabled_tokenization = (ov::is_type<const ov::op::v1::Softmax>(n) ||
|
||||
@ -679,6 +683,8 @@ void Transformations::MainSnippets(void) {
|
||||
ov::is_type<const ov::op::v1::Transpose>(n) ||
|
||||
ov::is_type<const ov::op::v1::Broadcast>(n) ||
|
||||
ov::is_type<const ov::op::v3::Broadcast>(n));
|
||||
if (is_disabled_tokenization)
|
||||
return true;
|
||||
const auto& inputs = n->inputs();
|
||||
// todo: clarify whether we can evaluate snippets on const paths
|
||||
const bool has_only_const_inputs = std::all_of(inputs.begin(), inputs.end(),
|
||||
@ -686,6 +692,8 @@ void Transformations::MainSnippets(void) {
|
||||
return ov::is_type<ov::op::v0::Constant>(
|
||||
in.get_source_output().get_node_shared_ptr());
|
||||
});
|
||||
if (has_only_const_inputs)
|
||||
return true;
|
||||
// todo: clarify whether we can evaluate snippets on inputs with larger ranks
|
||||
auto rank_is_too_large = [](const ov::descriptor::Tensor& t) {
|
||||
// callback is called has_supported_in_out(), so it's safe to assume that the shapes are static
|
||||
@ -695,13 +703,17 @@ void Transformations::MainSnippets(void) {
|
||||
[&](const ov::Input<const ov::Node>& in) {
|
||||
return rank_is_too_large(in.get_tensor());
|
||||
});
|
||||
if (bad_input_rank)
|
||||
return true;
|
||||
const auto& outputs = n->outputs();
|
||||
const bool bad_output_rank = std::any_of(outputs.begin(), outputs.end(),
|
||||
[&](const ov::Output<const ov::Node>& out) {
|
||||
return rank_is_too_large(out.get_tensor());
|
||||
});
|
||||
return has_only_const_inputs || bad_input_rank || bad_output_rank || is_unsupported_swish ||
|
||||
is_disabled_tokenization;
|
||||
if (bad_output_rank)
|
||||
return true;
|
||||
|
||||
return false;
|
||||
},
|
||||
snippets::pass::TokenizeSnippets);
|
||||
}
|
||||
|
||||
@ -180,7 +180,7 @@ std::vector<std::string> disabledTestPatterns() {
|
||||
// Issue: 111412
|
||||
R"(.*smoke_Proposal_(Static|Dynamic)_Test_Case1/ProposalLayerCPUTest.*)",
|
||||
// Issue: 111418
|
||||
R"(.*smoke_Snippets_ConvertStub/ConvertStub\.CompareWithRefImpl/IS=.*_OT=\(bf16\)_#N=2_#S=2_targetDevice=CPU.*)",
|
||||
R"(.*smoke_Snippets_ConvertStub/ConvertStub\.CompareWithRefImpl/IS.*_OT=\(bf16\)_#N=2_#S=2_targetDevice=CPU.*)",
|
||||
// Issue: 111944
|
||||
R"(.*smoke_DefConvLayoutTest6.*)",
|
||||
// Issue: 106939
|
||||
|
||||
@ -11,11 +11,17 @@ namespace snippets {
|
||||
|
||||
|
||||
namespace {
|
||||
|
||||
namespace snippets_static_1 {
|
||||
// ===================================Add=========================================================//
|
||||
// These inputs are needed to test static Loop optimizations (emit the whole tile, body with increments, set WA etc)
|
||||
std::vector<ov::Shape> inShapesStatic1{{1, 16, 29, 1}, {1, 16, 29, 7}, {1, 16, 29, 8}, {1, 16, 29, 15}, {1, 16, 29, 16}, {1, 16, 29, 31}};
|
||||
std::vector<ov::Shape> inShapesStatic2{{1, 16, 29, 1}, {1, 16, 1, 1}, {1, 1, 1, 1}};
|
||||
std::vector<ov::test::InputShape> inShapesStatic1{{{}, {{1, 16, 29, 1}}},
|
||||
{{}, {{1, 16, 29, 7}}},
|
||||
{{}, {{1, 16, 29, 8}}},
|
||||
{{}, {{1, 16, 29, 15}}},
|
||||
{{}, {{1, 16, 29, 16}}},
|
||||
{{}, {{1, 16, 29, 31}}}};
|
||||
std::vector<ov::test::InputShape> inShapesStatic2{{{}, {{1, 16, 29, 1}}},
|
||||
{{}, {{1, 16, 1, 1}}},
|
||||
{{}, {{1, 1, 1, 1}}}};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, Add,
|
||||
::testing::Combine(
|
||||
@ -26,41 +32,79 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, Add,
|
||||
::testing::Values(1), // Subgraph is created, since the inputs are followed by converts
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
Add::getTestCaseName);
|
||||
|
||||
// DS
|
||||
std::vector<InputShape> inShapesDynamic1{
|
||||
{
|
||||
{{ov::Dimension::dynamic(), ov::Dimension::dynamic(), ov::Dimension::dynamic(), ov::Dimension::dynamic()},
|
||||
{{1, 3, 1, 10}, {1, 3, 10, 10}, {1, 3, 1, 10}}},
|
||||
}
|
||||
};
|
||||
std::vector<InputShape> inShapesDynamic2{
|
||||
{
|
||||
{{ov::Dimension::dynamic(), ov::Dimension::dynamic(), ov::Dimension::dynamic(), ov::Dimension::dynamic()},
|
||||
{{1, 3, 10, 1}, {1, 3, 1, 1}, {1, 3, 10, 1}}},
|
||||
}
|
||||
};
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise_Add, Add,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn(inShapesDynamic1),
|
||||
::testing::ValuesIn(inShapesDynamic2),
|
||||
::testing::Values(ov::element::f32),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1), // Subgraph is created, since the inputs are followed by converts
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
Add::getTestCaseName);
|
||||
|
||||
// ===================================AddPair=========================================================//
|
||||
// test cross-tile (vector vs scalar) optimizations in the absence of vector tile
|
||||
std::vector<std::vector<ov::Shape>> inShapesStatic{
|
||||
{{1, 128, 1, 1}, {1, 128, 1, 1}},
|
||||
{{1, 128, 1, 9}, {1, 128, 1, 9}},
|
||||
{{1, 128, 1, 16}, {1, 128, 1, 16}},
|
||||
{{1, 128, 1, 17}, {1, 128, 1, 17}},
|
||||
{{1, 128, 1, 29}, {1, 128, 1, 29}},
|
||||
{{1, 128, 1, 33}, {1, 128, 1, 33}},
|
||||
{{1, 128, 9, 30}, {1, 128, 1, 30}},
|
||||
{{1, 128, 9, 1}, {1, 128, 1, 30}},
|
||||
{{1, 128, 9, 16}, {1, 128, 9, 1}},
|
||||
std::vector<std::vector<InputShape>> inShapesAddPair {
|
||||
{{{}, {{1, 128, 1, 1}}}, {{}, {{1, 128, 1, 1}}}},
|
||||
{{{}, {{1, 128, 1, 9}}}, {{}, {{1, 128, 1, 9}}}},
|
||||
{{{}, {{1, 128, 1, 16}}}, {{}, {{1, 128, 1, 16}}}},
|
||||
{{{}, {{1, 128, 1, 17}}}, {{}, {{1, 128, 1, 17}}}},
|
||||
{{{}, {{1, 128, 1, 29}}}, {{}, {{1, 128, 1, 29}}}},
|
||||
{{{}, {{1, 128, 1, 33}}}, {{}, {{1, 128, 1, 33}}}},
|
||||
{{{}, {{1, 128, 9, 30}}}, {{}, {{1, 128, 1, 30}}}},
|
||||
{{{}, {{1, 128, 9, 1}}}, {{}, {{1, 128, 1, 30}}}},
|
||||
{{{}, {{1, 128, 9, 16}}}, {{}, {{1, 128, 9, 1}}}},
|
||||
// DS
|
||||
{{{1, -1, {1, 10}, {1, 33}}, {{1, 128, 1, 1}, {1, 128, 1, 9}, {1, 128, 1, 17}, {1, 128, 1, 29}, {1, 128, 9, 1}, {1, 128, 1, 1}}},
|
||||
{{{1, 1}, {128, 128}, {1, 10}, {1, 33}}, {{1, 128, 1, 1}, {1, 128, 1, 9}, {1, 128, 1, 17}, {1, 128, 1, 29}, {1, 128, 1, 30}, {1, 128, 1, 1}}}},
|
||||
{{{1, -1, 1, {1, 32}}, {{1, 16, 1, 32}, {1, 16, 1, 32}, {1, 16, 1, 32}, {1, 16, 1, 32}}},
|
||||
{{1, -1, 1, {1, 32}}, {{1, 16, 1, 32}, {1, 16, 1, 32}, {1, 16, 1, 32}, {1, 16, 1, 32}}}},
|
||||
};
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, AddPair,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn(inShapesStatic),
|
||||
::testing::ValuesIn(inShapesAddPair),
|
||||
::testing::Values(ov::element::f32),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1), // Subgraph is created, since the inputs are followed by converts
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
AddPair::getTestCaseName);
|
||||
|
||||
} // namespace snippets_static_1
|
||||
// ===================================AddConst, AddRollConst=========================================================//
|
||||
std::vector<ov::test::InputShape> inShapesAddConst{{{}, {{1, 2, 3, 32}}},
|
||||
{{}, {{1, 3, 17, 33}}},
|
||||
{{-1, -1, -1, -1}, {{1, 3, 17, 33}, {1, 2, 1, 65}, {1, 3, 17, 33}}},
|
||||
{{1, {1, 10}, {1, 8}, {1, 4}}, {{1, 2, 8, 4}, {1, 8, 1, 1}, {1, 2, 8, 4}}}};
|
||||
std::vector<PartialShape> inShapesConstAddConst{{1, 1, 1, 1}};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, AddConst,
|
||||
::testing::Combine(
|
||||
::testing::Values(ov::Shape {1, 42, 16, 64}),
|
||||
::testing::ValuesIn(inShapesAddConst),
|
||||
::testing::ValuesIn(inShapesConstAddConst),
|
||||
::testing::Values(ov::element::f32),
|
||||
::testing::Values(1), // Add
|
||||
::testing::Values(1), // Subgraph is created, since the inputs are followed by converts
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
AddConst::getTestCaseName);
|
||||
|
||||
// ===================================AddRollConst=========================================================//
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, AddRollConst,
|
||||
::testing::Combine(
|
||||
::testing::Values(ov::Shape {1, 42, 16, 64}),
|
||||
::testing::ValuesIn(inShapesAddConst),
|
||||
::testing::ValuesIn(inShapesConstAddConst),
|
||||
::testing::Values(ov::element::f32),
|
||||
::testing::Values(2), // Add + roll after inputs
|
||||
::testing::Values(1), // Subgraph is created, since the inputs are followed by converts
|
||||
@ -69,13 +113,15 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, AddRollConst,
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise_BF16, AddRollConst,
|
||||
::testing::Combine(
|
||||
::testing::Values(ov::Shape {1, 2, 3, 32}),
|
||||
::testing::ValuesIn(inShapesAddConst),
|
||||
::testing::ValuesIn(inShapesConstAddConst),
|
||||
::testing::Values(ov::element::bf16),
|
||||
::testing::Values(3), // Add + reorder + roll after inputs
|
||||
::testing::Values(1), // Subgraph is created, since the inputs are followed by converts
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
AddRollConst::getTestCaseName);
|
||||
} // namespace
|
||||
|
||||
} // namespace
|
||||
} // namespace snippets
|
||||
} // namespace test
|
||||
} // namespace ov
|
||||
@ -21,13 +21,26 @@ const std::vector<ov::element::Type> input_types = {
|
||||
const std::vector<CheckBroadcastTestCaseParams> test_cases = {
|
||||
// broadcast is neccessary
|
||||
{
|
||||
{{1, 3, 4, 4}, {4, 4}},
|
||||
{{{}, {{1, 3, 4, 4}}}, {{}, {{4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::PDPD, -1),
|
||||
1,
|
||||
0
|
||||
},
|
||||
{
|
||||
{{1, 3, 4, 4}, {4, 4}},
|
||||
{{{}, {{1, 3, 4, 4}}}, {{}, {{4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::PDPD, 2),
|
||||
1,
|
||||
0
|
||||
},
|
||||
// DS
|
||||
{
|
||||
{{{1, 3, -1, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}, {{-1, {1, 4}}, {{4, 4}, {1, 3}, {4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::PDPD, -1),
|
||||
1,
|
||||
0
|
||||
},
|
||||
{
|
||||
{{{1, 3, -1, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}, {{-1, {1, 4}}, {{4, 4}, {1, 3}, {4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::PDPD, 2),
|
||||
1,
|
||||
0
|
||||
@ -35,13 +48,26 @@ const std::vector<CheckBroadcastTestCaseParams> test_cases = {
|
||||
|
||||
// broadcast is not neccessary
|
||||
{
|
||||
{{1, 3, 4, 4}, {1, 3, 4, 4}},
|
||||
{{{}, {{1, 3, 4, 4}}}, {{}, {{1, 3, 4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::PDPD, -1),
|
||||
1,
|
||||
1
|
||||
},
|
||||
{
|
||||
{{1, 3, 4, 4}, {1, 3, 4, 4}},
|
||||
{{{}, {{1, 3, 4, 4}}}, {{}, {{1, 3, 4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::PDPD, 0),
|
||||
1,
|
||||
1
|
||||
},
|
||||
// DS
|
||||
{
|
||||
{{{1, 3, -1, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}, {{-1, 3, {1, 4}, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::PDPD, -1),
|
||||
1,
|
||||
1
|
||||
},
|
||||
{
|
||||
{{{1, 3, -1, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}, {{-1, 3, {1, 4}, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::PDPD, 0),
|
||||
1,
|
||||
1
|
||||
@ -49,19 +75,38 @@ const std::vector<CheckBroadcastTestCaseParams> test_cases = {
|
||||
|
||||
// any other PDPD
|
||||
{
|
||||
{{1, 3, 4, 4}, {4, 4}},
|
||||
{{{}, {{1, 3, 4, 4}}}, {{}, {{4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::NUMPY, -1),
|
||||
1,
|
||||
1
|
||||
},
|
||||
{
|
||||
{{1, 3, 4, 4}, {4, 4}},
|
||||
{{{}, {{1, 3, 4, 4}}}, {{}, {{4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::NUMPY, 0),
|
||||
1,
|
||||
1
|
||||
},
|
||||
{
|
||||
{{1, 3, 4, 4}, {4, 4}},
|
||||
{{{}, {{1, 3, 4, 4}}}, {{}, {{4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::NUMPY, 2),
|
||||
1,
|
||||
1
|
||||
},
|
||||
// DS
|
||||
{
|
||||
{{{1, 3, -1, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}, {{-1, {1, 4}}, {{4, 4}, {1, 3}, { 4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::NUMPY, -1),
|
||||
1,
|
||||
1
|
||||
},
|
||||
{
|
||||
{{{1, 3, -1, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}, {{-1, {1, 4}}, {{4, 4}, {1, 3}, {4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::NUMPY, 0),
|
||||
1,
|
||||
1
|
||||
},
|
||||
{
|
||||
{{{1, 3, -1, {1, 4}}, {{1, 3, 4, 4}, {1, 3, 1, 3}, {1, 3, 4, 4}}}, {{-1, {1, 4}}, {{4, 4}, {1, 3}, {4, 4}}}},
|
||||
ov::op::AutoBroadcastSpec(ov::op::AutoBroadcastType::NUMPY, 2),
|
||||
1,
|
||||
1
|
||||
|
||||
@ -13,15 +13,42 @@ namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
namespace {
|
||||
std::vector<InputShape> inShapes0{
|
||||
{{}, {{1, 38, 130}}},
|
||||
{{}, {{1, 1, 130}}},
|
||||
};
|
||||
std::vector<InputShape> inShapes1{
|
||||
{{}, {{1, 38, 130}}},
|
||||
{{}, {{1, 38, 1}}},
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(NoReshape, CodegenGelu,
|
||||
::testing::Combine(
|
||||
::testing::Values(ov::element::f32),
|
||||
::testing::Values(ov::Shape {1, 384, 4096}),
|
||||
::testing::Values(true, false),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
CodegenGelu::getTestCaseName);
|
||||
} // namespace
|
||||
INSTANTIATE_TEST_SUITE_P(NoReshapeAndReshape, CodegenGelu,
|
||||
::testing::Combine(
|
||||
::testing::Values(ov::element::f32),
|
||||
::testing::ValuesIn(inShapes0),
|
||||
::testing::ValuesIn(inShapes1),
|
||||
::testing::Values(true, false),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
CodegenGelu::getTestCaseName);
|
||||
|
||||
// DS
|
||||
std::vector<InputShape> inShapesDynamic0{
|
||||
{{-1, -1, -1}, {{1, 12, 128}, {1, 12, 1}, {1, 12, 128}}},
|
||||
};
|
||||
std::vector<InputShape> inShapesDynamic1{
|
||||
{{-1, -1, -1}, {{1, 12, 128}, {1, 1, 128}, {1, 12, 128}}},
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(NoReshapeAndReshapeDynamic, CodegenGelu,
|
||||
::testing::Combine(
|
||||
::testing::Values(ov::element::f32),
|
||||
::testing::ValuesIn(inShapesDynamic0),
|
||||
::testing::ValuesIn(inShapesDynamic1),
|
||||
::testing::Values(true, false),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
CodegenGelu::getTestCaseName);
|
||||
|
||||
} // namespace
|
||||
} // namespace snippets
|
||||
} // namespace test
|
||||
} // namespace ov
|
||||
|
||||
@ -30,10 +30,14 @@ const std::vector<std::pair<std::vector<ov::element::Type>, std::vector<ov::elem
|
||||
{ { ov::element::u8 }, { ov::element::i8 } },
|
||||
};
|
||||
|
||||
const std::vector<std::vector<ov::PartialShape>> inputShapes_Convert = {
|
||||
{ ov::PartialShape{2, 16} },
|
||||
{ ov::PartialShape{5, 5} },
|
||||
{ ov::PartialShape{2, 12, 1} }
|
||||
const std::vector<std::vector<ov::test::InputShape>> inputShapes_Convert = {
|
||||
{ {{}, {{2, 16}}} },
|
||||
{ {{}, {{5, 5}}} },
|
||||
{ {{}, {{2, 12, 1}}} },
|
||||
// DS(dynamic shape)
|
||||
{ {{-1, -1}, {{2, 16}, {2, 8}, {2, 16}}} },
|
||||
{ {{{1, 5}, 5}, {{5, 5}, {1, 5}, {5, 5}}} },
|
||||
{ {{{1, 10}, {4, 12}, {1, 2}}, {{2, 12, 1}, {4, 4, 2}, {2, 12, 1}}} }
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Convert, Convert,
|
||||
@ -57,10 +61,14 @@ const std::vector<std::pair<std::vector<ov::element::Type>, std::vector<ov::elem
|
||||
{ { ov::element::u8 }, { ov::element::bf16 } },
|
||||
};
|
||||
|
||||
const std::vector<std::vector<ov::PartialShape>> inputShapes_ConvertInput = {
|
||||
{ ov::PartialShape{2, 16}, ov::PartialShape{1, 16} },
|
||||
{ ov::PartialShape{5, 18}, ov::PartialShape{5, 1} },
|
||||
{ ov::PartialShape{3, 1}, ov::PartialShape{3, 21} }
|
||||
const std::vector<std::vector<ov::test::InputShape>> inputShapes_ConvertInput = {
|
||||
{ {{}, {{2, 16}}}, {{}, {{1, 16}}} },
|
||||
{ {{}, {{5, 18}}}, {{}, {{5, 1}}} },
|
||||
{ {{}, {{3, 1}}}, {{}, {{3, 21}}} },
|
||||
// DS
|
||||
{ {{-1, -1}, {{2, 16}, {1, 16}, {2, 16}}}, {{-1, -1}, {{1, 16}, {2, 16}, {1, 16}}} },
|
||||
{ {{5, -1}, {{5, 18}, {5, 1}, {5, 18}}}, {{-1, 1}, {{5, 1}, {1, 1}, {5, 1}}} },
|
||||
{ {{{1, 4}, {1, 8}}, {{3, 1}, {4, 8}, {3, 1}}}, {{{1, 4}, {8, 21}}, {{3, 21}, {1, 8}, {3, 21}}} }
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_ConvertInput, ConvertInput,
|
||||
@ -94,10 +102,12 @@ const std::vector<std::pair<std::vector<ov::element::Type>, std::vector<ov::elem
|
||||
{ { ov::element::i8, ov::element::i8, ov::element::f32 }, { ov::element::f32, ov::element::i8 } },
|
||||
};
|
||||
|
||||
const std::vector<std::vector<ov::PartialShape>> inputShapes_ConvertPartialInputsAndResults = {
|
||||
{ ov::PartialShape{2, 16}, ov::PartialShape{1, 16}, ov::PartialShape{1, 1} },
|
||||
{ ov::PartialShape{5, 18}, ov::PartialShape{5, 1}, ov::PartialShape{1, 18} },
|
||||
{ ov::PartialShape{3, 1}, ov::PartialShape{3, 21}, ov::PartialShape{3, 1} }
|
||||
const std::vector<std::vector<ov::test::InputShape>> inputShapes_ConvertPartialInputsAndResults = {
|
||||
{ {{}, {{2, 16}}}, {{}, {{1, 16}}}, {{}, {{1, 1}}} },
|
||||
{ {{}, {{5, 18}}}, {{}, {{5, 1}}}, {{}, {{1, 18}}} },
|
||||
{ {{}, {{3, 1}}}, {{}, {{3, 21}}}, {{}, {{3, 1}}} },
|
||||
// DS
|
||||
{ {{-1, -1}, {{3, 1}, {2, 4}, {3, 1}}}, {{{1, 3}, -1}, {{3, 21}, {2, 1}, {3, 21}}}, {{{1, 3}, {1, 2}}, {{3, 1}, {1, 1}, {3, 1}}} },
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_ConvertPartialInputsAndResults, ConvertPartialInputsAndResults,
|
||||
@ -115,9 +125,15 @@ const std::vector<std::pair<std::vector<ov::element::Type>, std::vector<ov::elem
|
||||
{ { ov::element::f32, ov::element::f32, ov::element::i8, ov::element::i8 }, {} },
|
||||
};
|
||||
|
||||
const std::vector<std::vector<ov::test::InputShape>> inputShapes_ConvertManyOnInputs = {
|
||||
{ {{}, {{5, 5, 5, 5}}} },
|
||||
// DS
|
||||
{ {{-1, -1, -1, -1}, {{5, 5, 5, 5}, {3, 3, 3, 3}, {5, 5, 5, 5}}} }
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_ConvertManyOnInputs, ConvertManyOnInputs,
|
||||
::testing::Combine(
|
||||
::testing::Values(std::vector<ov::PartialShape>{{5, 5, 5, 5}}),
|
||||
::testing::ValuesIn(inputShapes_ConvertManyOnInputs),
|
||||
::testing::ValuesIn(types_ConvertMany),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1),
|
||||
@ -126,7 +142,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_ConvertManyOnInputs, ConvertManyOnInputs
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_ConvertManyOnOutputs, ConvertManyOnOutputs,
|
||||
::testing::Combine(
|
||||
::testing::Values(std::vector<ov::PartialShape>{{5, 5, 5, 5}}),
|
||||
::testing::ValuesIn(inputShapes_ConvertManyOnInputs),
|
||||
::testing::ValuesIn(types_ConvertMany),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1),
|
||||
@ -140,7 +156,7 @@ const std::vector<std::pair<std::vector<ov::element::Type>, std::vector<ov::elem
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_ConvertManyOnInputOutput, ConvertManyOnInputOutput,
|
||||
::testing::Combine(
|
||||
::testing::Values(std::vector<ov::PartialShape>{{5, 5, 5, 5}}),
|
||||
::testing::ValuesIn(inputShapes_ConvertManyOnInputs),
|
||||
::testing::ValuesIn(types_ConvertManyIO),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1),
|
||||
|
||||
@ -10,16 +10,25 @@ namespace test {
|
||||
namespace snippets {
|
||||
namespace {
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, EltwiseTwoResults,
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise_TwoResults, EltwiseTwoResults,
|
||||
::testing::Combine(
|
||||
::testing::Values(ov::Shape {1, 64, 10, 10}),
|
||||
::testing::Values(ov::Shape {1, 64, 10, 1}),
|
||||
::testing::Values(InputShape {{}, {{1, 64, 10, 10}}}),
|
||||
::testing::Values(InputShape {{}, {{1, 64, 10, 1}}}),
|
||||
::testing::Values(2),
|
||||
::testing::Values(2),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
EltwiseTwoResults::getTestCaseName);
|
||||
|
||||
} // namespace
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise_TwoResults_Dynamic, EltwiseTwoResults,
|
||||
::testing::Combine(
|
||||
::testing::Values(InputShape {{-1, -1, -1, -1}, {{1, 64, 10, 10}, {2, 8, 2, 1}, {1, 64, 10, 10}}}),
|
||||
::testing::Values(InputShape {{{1, 2}, {1, 64}, {1, 10}, 1}, {{1, 64, 10, 1}, {2, 1, 1, 1}, {1, 64, 10, 1}}}),
|
||||
::testing::Values(2),
|
||||
::testing::Values(2),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
EltwiseTwoResults::getTestCaseName);
|
||||
|
||||
} // namespace
|
||||
} // namespace snippets
|
||||
} // namespace test
|
||||
} // namespace ov
|
||||
@ -10,8 +10,19 @@ namespace test {
|
||||
namespace snippets {
|
||||
namespace {
|
||||
// Note that we need these shapes to cover all cases of code emission (none/one/multiple of scalar/vector tiles)
|
||||
std::vector<ov::Shape> input_shapes {{1, 64, 10, 10}, {1, 1, 17, 37}, {1, 1, 1, 1}, {1, 1, 1, 7},
|
||||
{1, 1, 1, 128}, {1, 1, 1, 14}, {1, 1, 1, 16}, {1, 1, 1, 30}};
|
||||
std::vector<InputShape> input_shapes {{{}, {{1, 64, 10, 10}}},
|
||||
{{}, {{1, 1, 17, 37}}},
|
||||
{{}, {{1, 1, 1, 1}}},
|
||||
{{}, {{1, 1, 1, 7}}},
|
||||
{{}, {{1, 1, 1, 128}}},
|
||||
{{}, {{1, 1, 1, 14}}},
|
||||
{{}, {{1, 1, 1, 16}}},
|
||||
{{}, {{1, 1, 1, 30}}},
|
||||
// DS
|
||||
{{-1, -1, -1, -1}, {{1, 64, 10, 10}, {1, 1, 17, 37}, {1, 64, 10, 10}}},
|
||||
{{1, {1, 64}, {10, 20}, -1}, {{1, 64, 10, 10}, {1, 1, 17, 37}, {1, 64, 10, 10}}},
|
||||
{{1, 1, 1, {1, 128}}, {{1, 1, 1, 1}, {1, 1, 1, 7}, {1, 1, 1, 128}, {1, 1, 1, 14}, {1, 1, 1, 16}, {1, 1, 1, 1}}}};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, MaxNumParamsEltwise,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn(input_shapes),
|
||||
@ -20,7 +31,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, MaxNumParamsEltwise,
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
MaxNumParamsEltwise::getTestCaseName);
|
||||
|
||||
} // namespace
|
||||
} // namespace
|
||||
} // namespace snippets
|
||||
} // namespace test
|
||||
} // namespace ov
|
||||
@ -13,8 +13,11 @@ namespace snippets {
|
||||
|
||||
namespace {
|
||||
|
||||
const std::vector<std::vector<ov::PartialShape>> input_shapes = {
|
||||
{{ 1, 3, 16, 16 }, { 1, 1, 1, 16 }},
|
||||
const std::vector<std::vector<ov::test::InputShape>> input_shapes = {
|
||||
{ {{}, {{1, 3, 16, 16}}}, {{}, {{1, 1, 1, 16}}} },
|
||||
// DS
|
||||
{ {{-1, -1, -1, -1}, {{1, 3, 16, 16}, {1, 1, 1, 16}, {1, 3, 16, 16}}}, {{-1, -1, -1, -1}, {{1, 3, 16, 16}, {1, 1, 1, 16}, {1, 3, 16, 16}}} },
|
||||
{ {{1, 16, -1, {1, 16}}, {{1, 16, 32, 1}, {1, 16, 1, 16}, {1, 16, 32, 1}}}, {{1, 1, -1, {1, 20}}, {{1, 1, 1, 16}, {1, 1, 8, 16}, {1, 1, 1, 16}}} }
|
||||
};
|
||||
|
||||
const std::vector<std::vector<float>> fake_quantize_intervals = {
|
||||
|
||||
@ -12,31 +12,71 @@ namespace snippets {
|
||||
|
||||
namespace {
|
||||
|
||||
//============================Select=======================================//
|
||||
std::vector<ov::test::InputShape> inShapes_a{{{}, {{1, 5, 5, 35}}}};
|
||||
std::vector<ov::test::InputShape> inShapes_b{{{}, {{1}}}};
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Select, Select,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn({ov::Shape{1, 5, 5, 35}, }),
|
||||
::testing::ValuesIn({ov::Shape{1, 5, 5, 35}, }),
|
||||
::testing::ValuesIn({ov::Shape{1}}),
|
||||
::testing::ValuesIn(inShapes_a),
|
||||
::testing::ValuesIn(inShapes_a),
|
||||
::testing::ValuesIn(inShapes_b),
|
||||
::testing::ValuesIn({ov::element::f32, ov::element::i8}),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
Select::getTestCaseName);
|
||||
|
||||
// DS
|
||||
std::vector<ov::test::InputShape> inShapesDynamic_a{{{1, {1, 5}, -1, 35}, {{1, 5, 5, 35}, {1, 1, 1, 35}, {1, 5, 5, 35}}}};
|
||||
std::vector<ov::test::InputShape> inShapesDynamic_b{{{-1}, {{1}, {1}, {1}}}};
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Select_Dynamic, Select,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn(inShapesDynamic_a),
|
||||
::testing::ValuesIn(inShapesDynamic_a),
|
||||
::testing::ValuesIn(inShapesDynamic_b),
|
||||
::testing::ValuesIn({ov::element::f32, ov::element::i8}),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
Select::getTestCaseName);
|
||||
|
||||
//============================BroadcastSelect=======================================//
|
||||
std::vector<ov::test::InputShape> inShapes0{{{}, {{1, 8, 2, 1}}}, {{}, {{1, 1, 1, 1}}}};
|
||||
std::vector<ov::test::InputShape> inShapes1{{{}, {{1, 8, 2, 10}}}, {{}, {{1, 8, 2, 1}}}};
|
||||
std::vector<ov::test::InputShape> inShapes2{{{}, {{1, 8, 2, 10}}}, {{}, {{1, 1, 1, 1}}}};
|
||||
std::vector<ov::PartialShape> inShapes3{{1, 8, 2, 1}, {1, 8, 2, 10}};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_BroadcastSelect, BroadcastSelect,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn({Shape{1, 8, 2, 1}, Shape{1, 1, 1, 1}}),
|
||||
::testing::ValuesIn({Shape{1, 8, 2, 10}, Shape{1, 8, 2, 1}}),
|
||||
::testing::ValuesIn({Shape{1, 8, 2, 10}, Shape{1, 1, 1, 1}}),
|
||||
::testing::ValuesIn({Shape{1, 8, 2, 1}, Shape{1, 8, 2, 10}}),
|
||||
::testing::ValuesIn(inShapes0),
|
||||
::testing::ValuesIn(inShapes1),
|
||||
::testing::ValuesIn(inShapes2),
|
||||
::testing::ValuesIn(inShapes3),
|
||||
::testing::ValuesIn({ov::element::f32, ov::element::i8}),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
BroadcastSelect::getTestCaseName);
|
||||
|
||||
// DS
|
||||
std::vector<ov::test::InputShape> inShapes0_d{{{-1, -1, -1, -1}, {{1, 8, 2, 1}, {1, 1, 1, 1}, {1, 8, 2, 1}}}};
|
||||
std::vector<ov::test::InputShape> inShapes1_d{{{1, -1, -1, -1}, {{1, 8, 2, 10}, {1, 8, 2, 10}, {1, 8, 2, 10}}}};
|
||||
std::vector<ov::test::InputShape> inShapes2_d{{{1, {1, 8}, {1, 2}, {1, 10}}, {{1, 8, 2, 10}, {1, 1, 2, 1}, {1, 8, 2, 10}}}};
|
||||
std::vector<ov::PartialShape> inShapes3_d{{1, 8, 2, 1}, {1, 8, 2, 10}};
|
||||
|
||||
} // namespace
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_BroadcastSelect_Dynamic, BroadcastSelect,
|
||||
::testing::Combine(
|
||||
::testing::ValuesIn(inShapes0_d),
|
||||
::testing::ValuesIn(inShapes1_d),
|
||||
::testing::ValuesIn(inShapes2_d),
|
||||
::testing::ValuesIn(inShapes3_d),
|
||||
::testing::ValuesIn({ov::element::f32, ov::element::i8}),
|
||||
::testing::Values(1),
|
||||
::testing::Values(1),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
BroadcastSelect::getTestCaseName);
|
||||
|
||||
} // namespace
|
||||
} // namespace snippets
|
||||
} // namespace test
|
||||
} // namespace ov
|
||||
@ -12,31 +12,34 @@ namespace snippets {
|
||||
|
||||
namespace {
|
||||
|
||||
const std::vector<ov::Shape> inputShape = {
|
||||
ov::Shape{1, 16},
|
||||
ov::Shape{1, 32},
|
||||
ov::Shape{1, 1},
|
||||
ov::Shape{1, 9},
|
||||
ov::Shape{1, 17},
|
||||
ov::Shape{1, 19},
|
||||
ov::Shape{1, 49},
|
||||
ov::Shape{1, 50},
|
||||
ov::Shape{5, 16},
|
||||
ov::Shape{5, 32},
|
||||
ov::Shape{5, 1},
|
||||
ov::Shape{5, 9},
|
||||
ov::Shape{5, 17},
|
||||
ov::Shape{5, 19},
|
||||
ov::Shape{5, 49},
|
||||
ov::Shape{5, 50},
|
||||
ov::Shape{1, 3, 128, 128},
|
||||
ov::Shape{1, 3, 128, 129},
|
||||
ov::Shape{1, 3, 128, 130},
|
||||
ov::Shape{1, 3, 128, 1},
|
||||
ov::Shape{1, 3, 128, 9},
|
||||
ov::Shape{1, 3, 128, 16},
|
||||
ov::Shape{1, 3, 128, 17},
|
||||
ov::Shape{1, 3, 128, 20},
|
||||
const std::vector<InputShape> inputShape = {
|
||||
{{}, {{1, 16}}},
|
||||
{{}, {{1, 32}}},
|
||||
{{}, {{1, 1}}},
|
||||
{{}, {{1, 9}}},
|
||||
{{}, {{1, 17}}},
|
||||
{{}, {{1, 19}}},
|
||||
{{}, {{1, 49}}},
|
||||
{{}, {{1, 50}}},
|
||||
{{}, {{5, 16}}},
|
||||
{{}, {{5, 32}}},
|
||||
{{}, {{5, 1}}},
|
||||
{{}, {{5, 9}}},
|
||||
{{}, {{5, 17}}},
|
||||
{{}, {{5, 19}}},
|
||||
{{}, {{5, 49}}},
|
||||
{{}, {{5, 50}}},
|
||||
{{}, {{1, 3, 128, 128}}},
|
||||
{{}, {{1, 3, 128, 129}}},
|
||||
{{}, {{1, 3, 128, 130}}},
|
||||
{{}, {{1, 3, 128, 1}}},
|
||||
{{}, {{1, 3, 128, 9}}},
|
||||
{{}, {{1, 3, 128, 16}}},
|
||||
{{}, {{1, 3, 128, 17}}},
|
||||
{{}, {{1, 3, 128, 20}}},
|
||||
// DS
|
||||
{{-1, -1}, {{1, 16}, {1, 32}, {1, 1}, {1, 9}, {1, 17}, {1, 19}, {1, 49}, {1, 50}, {5, 16}, {1, 16}, {1, 9}}},
|
||||
{{-1, -1, -1, -1}, {{1, 3, 128, 128}, {1, 3, 128, 129}, {1, 3, 128, 130}, {1, 3, 128, 1}, {1, 3, 128, 16}, {1, 3, 128, 1}}}
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Softmax, Softmax,
|
||||
@ -48,13 +51,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Softmax, Softmax,
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
Softmax::getTestCaseName);
|
||||
|
||||
const std::vector<std::pair<ov::Shape, ov::Shape>> inputShapesPair = {
|
||||
std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 5, 16, 35}, ov::Shape{1, 5, 16, 35}},
|
||||
std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 5, 16, 1}, ov::Shape{1, 5, 16, 35}},
|
||||
std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 5, 16, 35}, ov::Shape{1, 5, 1, 1}},
|
||||
std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 5, 16, 1}, ov::Shape{1, 5, 16, 1}},
|
||||
std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 5, 16, 35}, ov::Shape{1, 5, 1, 35}},
|
||||
std::pair<ov::Shape, ov::Shape>{ov::Shape{1, 5, 1, 35}, ov::Shape{1, 5, 1, 35}},
|
||||
const std::vector<std::pair<InputShape, InputShape>> inputShapesPair = {
|
||||
{{{}, {{1, 5, 16, 35}}}, {{}, {{1, 5, 16, 35}}}},
|
||||
{{{}, {{1, 5, 16, 1}}}, {{}, {{1, 5, 16, 35}}}},
|
||||
{{{}, {{1, 5, 16, 35}}}, {{}, {{1, 5, 1, 1}}}},
|
||||
{{{}, {{1, 5, 16, 1}}}, {{}, {{1, 5, 16, 1}}}},
|
||||
{{{}, {{1, 5, 16, 35}}}, {{}, {{1, 5, 1, 35}}}},
|
||||
{{{}, {{1, 5, 1, 35}}}, {{}, {{1, 5, 1, 35}}}},
|
||||
// DS
|
||||
{{{-1, -1, -1, -1}, {{1, 5, 16, 35}, {1, 5, 16, 1}, {1, 5, 16, 35}}}, {{-1, -1, -1, -1}, {{1, 5, 16, 35}, {1, 5, 16, 35}, {1, 5, 16, 35}}}},
|
||||
{{{-1, {1, 8}, {1, 16}, {1, 16}}, {{1, 3, 1, 8}, {1, 8, 16, 16}, {1, 3, 1, 8}}}, {{-1, {1, 8}, -1, {1, 8}}, {{1, 3, 2, 8}, {2, 1, 1, 1}, {1, 3, 2, 8}}}}
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_AddSoftmax, AddSoftmax,
|
||||
|
||||
@ -12,15 +12,25 @@ namespace {
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, ThreeInputsEltwise,
|
||||
::testing::Combine(
|
||||
::testing::Values(ov::Shape {1, 64, 10, 10}),
|
||||
::testing::Values(ov::Shape {1, 64, 10, 1}),
|
||||
::testing::Values(ov::Shape {1, 1, 1, 10}),
|
||||
::testing::Values(InputShape {{}, {{1, 64, 10, 10}}}),
|
||||
::testing::Values(InputShape {{}, {{1, 64, 10, 1}}}),
|
||||
::testing::Values(InputShape {{}, {{1, 1, 1, 10}}}),
|
||||
::testing::Values(1), // eltwises fuse only for non-broadcasted shapes
|
||||
::testing::Values(1),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
ThreeInputsEltwise::getTestCaseName);
|
||||
|
||||
} // namespace
|
||||
// DS
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise_Dynamic, ThreeInputsEltwise,
|
||||
::testing::Combine(
|
||||
::testing::Values(InputShape {{-1, -1, -1, -1}, {{1, 64, 10, 10}, {2, 3, 1, 8}, {1, 64, 10, 10}}}),
|
||||
::testing::Values(InputShape {{1, -1, {1, 10}, 1}, {{1, 64, 10, 1}, {1, 3, 2, 1}, {1, 64, 10, 1}}}),
|
||||
::testing::Values(InputShape {{1, 1, 1, {1, 10}}, {{1, 1, 1, 10}, {1, 1, 1, 8}, {1, 1, 1, 10}}}),
|
||||
::testing::Values(1), // eltwises fuse only for non-broadcasted shapes
|
||||
::testing::Values(1),
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU)),
|
||||
ThreeInputsEltwise::getTestCaseName);
|
||||
} // namespace
|
||||
} // namespace snippets
|
||||
} // namespace test
|
||||
} // namespace ov
|
||||
@ -10,24 +10,32 @@ namespace test {
|
||||
namespace snippets {
|
||||
namespace {
|
||||
|
||||
const std::vector<std::vector<ov::PartialShape>> input_shapes = {
|
||||
{ {5, 5, 256, 1}, {5, 5, 256, 1} },
|
||||
{ {5, 5, 16, 35}, {5, 5, 16, 35} },
|
||||
{ {5, 5, 256, 1}, {5, 5, 256, 35} },
|
||||
{ {5, 5, 256, 1}, {5, 5, 1, 1} },
|
||||
const std::vector<std::vector<InputShape>> input_shapes = {
|
||||
{ {{}, {{5, 5, 256, 1}}}, {{}, {{5, 5, 256, 1}}} },
|
||||
{ {{}, {{5, 5, 16, 35}}}, {{}, {{5, 5, 16, 35}}} },
|
||||
{ {{}, {{5, 5, 256, 1}}}, {{}, {{5, 5, 256, 35}}} },
|
||||
{ {{}, {{5, 5, 256, 1}}}, {{}, {{5, 5, 1, 1}}} },
|
||||
|
||||
{ {5, 5, 16, 35}, {5, 5, 1, 1} },
|
||||
{ {5, 5, 16, 35}, {5, 5, 16, 1} },
|
||||
{ {5, 5, 5, 35}, {5, 5, 1, 35} },
|
||||
{ {5, 5, 16, 1}, {5, 5, 1, 35} },
|
||||
{ {{}, {{5, 5, 16, 35}}}, {{}, {{5, 5, 1, 1}}} },
|
||||
{ {{}, {{5, 5, 16, 35}}}, {{}, {{5, 5, 16, 1}}} },
|
||||
{ {{}, {{5, 5, 5, 35}}}, {{}, {{5, 5, 1, 35}}} },
|
||||
{ {{}, {{5, 5, 16, 1}}}, {{}, {{5, 5, 1, 35}}} },
|
||||
|
||||
{ {5, 5, 35, 16}, {5, 5, 35, 16} },
|
||||
{ {5, 5, 35, 16}, {5, 5, 1, 16} },
|
||||
{ {{}, {{5, 5, 35, 16}}}, {{}, {{5, 5, 35, 16}}} },
|
||||
{ {{}, {{5, 5, 35, 16}}}, {{}, {{5, 5, 1, 16}}} },
|
||||
|
||||
{ {5, 5, 35, 17}, {5, 5, 35, 17} },
|
||||
{ {5, 5, 35, 17}, {5, 5, 1, 17} },
|
||||
{ {5, 5, 35, 18}, {5, 5, 35, 18} },
|
||||
{ {5, 5, 35, 18}, {5, 5, 1, 18} },
|
||||
{ {{}, {{5, 5, 35, 17}}}, {{}, {{5, 5, 35, 17}}} },
|
||||
{ {{}, {{5, 5, 35, 17}}}, {{}, {{5, 5, 1, 17}}} },
|
||||
{ {{}, {{5, 5, 35, 18}}}, {{}, {{5, 5, 35, 18}}} },
|
||||
{ {{}, {{5, 5, 35, 18}}}, {{}, {{5, 5, 1, 18}}} },
|
||||
|
||||
// DS
|
||||
{ {{-1, -1, -1, -1}, {{5, 5, 256, 1}, {3, 3, 8, 15}, {5, 5, 256, 1}}},
|
||||
{{-1, -1, -1, -1}, {{5, 5, 256, 1}, {1, 1, 8, 1}, {5, 5, 256, 1}}} },
|
||||
{ {{{1, 5}, {2, 6}, {3, 7}, {4, 8}}, {{1, 2, 3, 4}, {5, 6, 7, 8}, {1, 2, 3, 4}}},
|
||||
{{1, 1, 1, -1}, {{1, 1, 1, 4}, {1, 1, 1, 8}, {1, 1, 1, 4}}} },
|
||||
{ {{1, -1, {1, 10}, {4, 12}}, {{1, 5, 3, 4}, {1, 10, 8, 12}, {1, 5, 3, 4}}},
|
||||
{{1, 1, -1, {1, 12}}, {{1, 1, 3, 1}, {1, 1, 8, 12}, {1, 1, 3, 1}}} }
|
||||
};
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_Snippets_Eltwise, TwoInputsAndOutputs,
|
||||
|
||||
@ -7,6 +7,7 @@
|
||||
#include "openvino/core/type/element_type.hpp"
|
||||
#include "openvino/runtime/properties.hpp"
|
||||
#include "test_utils/cpu_test_utils.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
using namespace InferenceEngine;
|
||||
using namespace CPUTestUtils;
|
||||
@ -19,13 +20,15 @@ std::string EltwiseLayerCPUTest::getTestCaseName(testing::TestParamInfo<EltwiseL
|
||||
subgraph::EltwiseTestParams basicParamsSet;
|
||||
CPUSpecificParams cpuParams;
|
||||
fusingSpecificParams fusingParams;
|
||||
std::tie(basicParamsSet, cpuParams, fusingParams) = obj.param;
|
||||
bool enforceSnippets;
|
||||
std::tie(basicParamsSet, cpuParams, fusingParams, enforceSnippets) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << subgraph::EltwiseLayerTest::getTestCaseName(testing::TestParamInfo<subgraph::EltwiseTestParams>(
|
||||
basicParamsSet, 0));
|
||||
result << CPUTestsBase::getTestCaseName(cpuParams);
|
||||
result << CpuTestWithFusing::getTestCaseName(fusingParams);
|
||||
result << "_enforceSnippets=" << enforceSnippets;
|
||||
|
||||
return result.str();
|
||||
}
|
||||
@ -78,7 +81,8 @@ void EltwiseLayerCPUTest::SetUp() {
|
||||
subgraph::EltwiseTestParams basicParamsSet;
|
||||
CPUSpecificParams cpuParams;
|
||||
fusingSpecificParams fusingParams;
|
||||
std::tie(basicParamsSet, cpuParams, fusingParams) = this->GetParam();
|
||||
bool enforceSnippets;
|
||||
std::tie(basicParamsSet, cpuParams, fusingParams, enforceSnippets) = this->GetParam();
|
||||
std::vector<InputShape> shapes;
|
||||
ElementType netType;
|
||||
ngraph::helpers::InputLayerType secondaryInputType;
|
||||
@ -126,6 +130,13 @@ void EltwiseLayerCPUTest::SetUp() {
|
||||
}
|
||||
#endif
|
||||
|
||||
if (enforceSnippets) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
} else {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::DISABLE});
|
||||
}
|
||||
ov::ParameterVector parameters{std::make_shared<ov::op::v0::Parameter>(netType, inputDynamicShapes.front())};
|
||||
std::shared_ptr<ngraph::Node> secondaryInput;
|
||||
if (secondaryInputType == ngraph::helpers::InputLayerType::PARAMETER) {
|
||||
@ -426,5 +437,10 @@ const std::vector<CPUSpecificParams>& cpuParams_5D_1D_constant() {
|
||||
return cpuParams_5D_1D_constant;
|
||||
}
|
||||
|
||||
const std::vector<bool>& enforceSnippets() {
|
||||
static const std::vector<bool> enforceSnippets = { false, true };
|
||||
return enforceSnippets;
|
||||
}
|
||||
|
||||
} // namespace Eltwise
|
||||
} // namespace CPULayerTestsDefinitions
|
||||
|
||||
@ -20,7 +20,8 @@ namespace CPULayerTestsDefinitions {
|
||||
typedef std::tuple<
|
||||
subgraph::EltwiseTestParams,
|
||||
CPUSpecificParams,
|
||||
fusingSpecificParams> EltwiseLayerCPUTestParamsSet;
|
||||
fusingSpecificParams,
|
||||
bool> EltwiseLayerCPUTestParamsSet;
|
||||
|
||||
class EltwiseLayerCPUTest : public testing::WithParamInterface<EltwiseLayerCPUTestParamsSet>,
|
||||
virtual public SubgraphBaseTest, public CPUTestUtils::CpuTestWithFusing {
|
||||
@ -67,5 +68,7 @@ const std::vector<CPUSpecificParams>& cpuParams_5D_1D_parameter();
|
||||
|
||||
const std::vector<ngraph::helpers::EltwiseTypes>& eltwiseOpTypesI32();
|
||||
|
||||
const std::vector<bool>& enforceSnippets();
|
||||
|
||||
} // namespace Eltwise
|
||||
} // namespace CPULayerTestsDefinitions
|
||||
|
||||
@ -27,7 +27,8 @@ const auto params_4D = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_MemOrder, EltwiseLayerCPUTest, params_4D, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -43,7 +44,8 @@ const auto params_4D_emptyCPUSpec = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::Values(emptyCPUSpec),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_emptyCPUSpec, EltwiseLayerCPUTest, params_4D_emptyCPUSpec, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -59,7 +61,8 @@ const auto params_5D = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_MemOrder, EltwiseLayerCPUTest, params_5D, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -75,7 +78,8 @@ const auto params_5D_emptyCPUSpec = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::Values(emptyCPUSpec),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D, EltwiseLayerCPUTest, params_5D_emptyCPUSpec, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -91,7 +95,8 @@ const auto params_4D_1D_constant_mode = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_1D_Constant_mode())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_1D_Constant, EltwiseLayerCPUTest, params_4D_1D_constant_mode, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -107,7 +112,8 @@ const auto params_4D_1D_parameter_mode = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_1D_Parameter_mode())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_1D_Parameter, EltwiseLayerCPUTest, params_4D_1D_parameter_mode, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -123,7 +129,8 @@ const auto params_5D_1D_constant = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_1D_constant())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_1D_Constant, EltwiseLayerCPUTest, params_5D_1D_constant, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -139,7 +146,8 @@ const auto params_5D_1D_parameter = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_1D_parameter())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_1D_Parameter, EltwiseLayerCPUTest, params_5D_1D_parameter, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -155,7 +163,8 @@ const auto params_4D_dyn_const = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_MemOrder_dyn_const, EltwiseLayerCPUTest, params_4D_dyn_const, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -171,7 +180,8 @@ const auto params_4D_dyn_param = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_MemOrder_dyn_param, EltwiseLayerCPUTest, params_4D_dyn_param, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -187,7 +197,8 @@ const auto params_5D_dyn_const = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_MemOrder_dyn_const, EltwiseLayerCPUTest, params_5D_dyn_const, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -203,7 +214,8 @@ const auto params_5D_dyn_param = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::Values(false));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_MemOrder_dyn_param, EltwiseLayerCPUTest, params_5D_dyn_param, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
|
||||
@ -167,7 +167,8 @@ const auto params_4D_Blocked_Blocked = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Blocked_Blocked())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_MemOrder_Blocked_Blocked, EltwiseLayerCPUTest, params_4D_Blocked_Blocked,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -184,7 +185,8 @@ const auto params_4D_fusing = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())),
|
||||
::testing::ValuesIn(fusingParamsSet_x64));
|
||||
::testing::ValuesIn(fusingParamsSet_x64),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_Fusing, EltwiseLayerCPUTest, params_4D_fusing, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -200,7 +202,8 @@ const auto params_4D_fusing_blocked_blocked = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Blocked_Blocked())),
|
||||
::testing::ValuesIn(fusingParamsSet_x64));
|
||||
::testing::ValuesIn(fusingParamsSet_x64),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_Fusing_Blocked_Blocked, EltwiseLayerCPUTest, params_4D_fusing_blocked_blocked,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -217,7 +220,8 @@ const auto params_4D_blocked_blocked_fusing = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Blocked_Blocked())),
|
||||
::testing::ValuesIn(fusingParamsSet_x64));
|
||||
::testing::ValuesIn(fusingParamsSet_x64),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_Blocked_Blocked_Fusing, EltwiseLayerCPUTest, params_4D_blocked_blocked_fusing,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -234,7 +238,8 @@ const auto params_4D_emptyCPUSpec = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::Values(emptyCPUSpec),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_emptyCPUSpec_x64, EltwiseLayerCPUTest, params_4D_emptyCPUSpec, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -250,7 +255,8 @@ const auto params_5D_Blocked_Blocked = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_Blocked_Blocked())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_MemOrder_Blocked_Blocked, EltwiseLayerCPUTest, params_5D_Blocked_Blocked,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -271,7 +277,8 @@ const auto params_5D_emptyCPUSpec_I32 = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::Values(emptyCPUSpec),
|
||||
::testing::ValuesIn(fusingParamsSet_I32));
|
||||
::testing::ValuesIn(fusingParamsSet_I32),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_I32, EltwiseLayerCPUTest, params_5D_emptyCPUSpec_I32, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -287,7 +294,8 @@ const auto params_4D_Blocked_Planar = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Blocked_Planar())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_Blocked_Planar, EltwiseLayerCPUTest, params_4D_Blocked_Planar, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -303,7 +311,8 @@ const auto params_4D_Planar_Blocked = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Planar_Blocked())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_Planar_Blocked, EltwiseLayerCPUTest, params_4D_Planar_Blocked, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -319,7 +328,8 @@ const auto params_5D_Blocked_Planar = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_Blocked_Planar())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_Blocked_Planar, EltwiseLayerCPUTest, params_5D_Blocked_Planar, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -335,7 +345,8 @@ const auto params_5D_Planar_Blocked = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_Planar_Blocked())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_Planar_Blocked_x64, EltwiseLayerCPUTest, params_5D_Planar_Blocked, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -351,7 +362,8 @@ const auto params_4D_1D_constant_mode = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_1D_Constant_mode_x64())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_1D_Constant_x64, EltwiseLayerCPUTest, params_4D_1D_constant_mode, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -367,7 +379,8 @@ const auto params_4D_1D_parameter_mode = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_1D_Parameter_mode())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_1D_Parameter_x64, EltwiseLayerCPUTest, params_4D_1D_parameter_mode, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -383,7 +396,8 @@ const auto params_5D_1D_constant = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_1D_constant())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_1D_Constant_x64, EltwiseLayerCPUTest, params_5D_1D_constant, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -399,7 +413,8 @@ const auto params_5D_1D_parameter = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_1D_parameter())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_1D_Parameter_x64, EltwiseLayerCPUTest, params_5D_1D_parameter, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -417,7 +432,8 @@ const auto params_4D_dyn_const = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_MemOrder_dyn_const_x64, EltwiseLayerCPUTest, params_4D_dyn_const, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -433,7 +449,8 @@ const auto params_4D_blocked_blocked_dyn_const = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Blocked_Blocked())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_Blocked_Blocked_MemOrder_dyn_const_x64, EltwiseLayerCPUTest, params_4D_blocked_blocked_dyn_const,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -450,7 +467,8 @@ const auto params_4D_dyn_param = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_MemOrder_dyn_param_x64, EltwiseLayerCPUTest, params_4D_dyn_param, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -466,7 +484,8 @@ const auto params_4D_blocked_blocked_dyn_param = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Blocked_Blocked())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_Blocked_Blocked_MemOrder_dyn_param_x64, EltwiseLayerCPUTest, params_4D_blocked_blocked_dyn_param,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -483,7 +502,8 @@ const auto params_4D_dyn_param_fusing = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())),
|
||||
::testing::ValuesIn(fusingParamsSet_x64));
|
||||
::testing::ValuesIn(fusingParamsSet_x64),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_dyn_param_fusing, EltwiseLayerCPUTest, params_4D_dyn_param_fusing, EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -499,7 +519,8 @@ const auto params_4D_dyn_param_fusing_Blocked_Blocked = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Blocked_Blocked())),
|
||||
::testing::ValuesIn(fusingParamsSet_x64));
|
||||
::testing::ValuesIn(fusingParamsSet_x64),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_dyn_param_fusing_Blocked_Blocked, EltwiseLayerCPUTest, params_4D_dyn_param_fusing_Blocked_Blocked,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -516,7 +537,8 @@ const auto params_4D_blocked_blocked_dyn_param_fusing = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_Blocked_Blocked())),
|
||||
::testing::ValuesIn(fusingParamsSet_x64));
|
||||
::testing::ValuesIn(fusingParamsSet_x64),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_blocked_blocked_dyn_param_fusing, EltwiseLayerCPUTest, params_4D_blocked_blocked_dyn_param_fusing,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -535,7 +557,8 @@ const auto params_5D_dyn_const_Blocked_Blocked = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_Blocked_Blocked())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_MemOrder_dyn_const_Blocked_Blocked, EltwiseLayerCPUTest, params_5D_dyn_const_Blocked_Blocked,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
@ -552,7 +575,8 @@ const auto params_5D_dyn_param_Blocked_Blocked = ::testing::Combine(
|
||||
::testing::Values(ov::test::utils::DEVICE_CPU),
|
||||
::testing::ValuesIn(additional_config())),
|
||||
::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_Blocked_Blocked())),
|
||||
::testing::Values(emptyFusingSpec));
|
||||
::testing::Values(emptyFusingSpec),
|
||||
::testing::ValuesIn(enforceSnippets()));
|
||||
|
||||
INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_5D_MemOrder_dyn_param_Blocked_Blocked, EltwiseLayerCPUTest, params_5D_dyn_param_Blocked_Blocked,
|
||||
EltwiseLayerCPUTest::getTestCaseName);
|
||||
|
||||
@ -86,7 +86,7 @@ protected:
|
||||
|
||||
TEST_P(SelectLayerCPUTest, CompareWithRefs) {
|
||||
run();
|
||||
CheckPluginRelatedResults(compiledModel, "Eltwise");
|
||||
CheckPluginRelatedResults(compiledModel, std::set<std::string>{"Eltwise", "Subgraph"});
|
||||
}
|
||||
|
||||
const std::vector<ElementType> precisions = {
|
||||
|
||||
@ -8,6 +8,7 @@
|
||||
#include "shared_test_classes/base/ov_subgraph.hpp"
|
||||
#include "ngraph_functions/utils/ngraph_helpers.hpp"
|
||||
#include "ngraph_functions/builders.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
using namespace CPUTestUtils;
|
||||
using namespace InferenceEngine;
|
||||
@ -127,6 +128,11 @@ public:
|
||||
function = makeNgraphFunction(getNetType(), inputParams, sum, "ConvolutionSumBroadcast");
|
||||
|
||||
targetDevice = ov::test::utils::DEVICE_CPU;
|
||||
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::DISABLE});
|
||||
}
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
@ -13,6 +13,7 @@
|
||||
#include <common_test_utils/ov_tensor_utils.hpp>
|
||||
#include "test_utils/cpu_test_utils.hpp"
|
||||
#include <openvino/opsets/opset1.hpp>
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
using namespace CPUTestUtils;
|
||||
using namespace ov::test;
|
||||
@ -205,6 +206,11 @@ protected:
|
||||
std::tie(inputShapes, data_et, idces_et, k) = this->GetParam();
|
||||
init_input_shapes(inputShapes);
|
||||
function = initNgram(inputDynamicShapes, data_et, idces_et, k);
|
||||
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::DISABLE});
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
@ -11,8 +11,8 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
ov::Shape, // Input 0 Shape
|
||||
ov::Shape, // Input 1 Shape
|
||||
InputShape, // Input 0 Shape
|
||||
InputShape, // Input 1 Shape
|
||||
ov::element::Type, // Element type
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
@ -20,21 +20,22 @@ typedef std::tuple<
|
||||
> AddParams;
|
||||
|
||||
typedef std::tuple<
|
||||
std::vector<ov::Shape>, // Input 0, Input 1 Shape
|
||||
ov::element::Type, // Element type
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
std::string // Target Device
|
||||
> AddParamsPair;
|
||||
|
||||
typedef std::tuple<
|
||||
ov::Shape, // Input 0 Shape
|
||||
InputShape, // Input 0 Shape
|
||||
PartialShape, // const shape
|
||||
ov::element::Type, // Element type
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
std::string // Target Device
|
||||
> AddConstParams;
|
||||
|
||||
typedef std::tuple<
|
||||
std::vector<InputShape>, // Input 0, Input 1 Shape
|
||||
ov::element::Type, // Element type
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
std::string // Target Device
|
||||
> AddParamsPair;
|
||||
|
||||
class Add : public testing::WithParamInterface<ov::test::snippets::AddParams>,
|
||||
virtual public ov::test::SnippetsTestsCommon {
|
||||
public:
|
||||
@ -57,7 +58,6 @@ protected:
|
||||
void SetUp() override;
|
||||
};
|
||||
|
||||
// repack AddPair input shapes into shape vector to cover some cases easier
|
||||
class AddPair : public testing::WithParamInterface<ov::test::snippets::AddParamsPair>,
|
||||
virtual public ov::test::SnippetsTestsCommon {
|
||||
public:
|
||||
|
||||
@ -12,7 +12,7 @@ namespace snippets {
|
||||
|
||||
class CheckBroadcastTestCaseParams {
|
||||
public:
|
||||
std::pair<ov::PartialShape, ov::PartialShape> input_shapes;
|
||||
std::pair<InputShape, InputShape> input_shapes;
|
||||
ov::op::AutoBroadcastSpec broadcast;
|
||||
size_t num_nodes;
|
||||
size_t num_subgraphs;
|
||||
|
||||
@ -4,15 +4,7 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
#include <string>
|
||||
#include <memory>
|
||||
|
||||
#include "shared_test_classes/base/layer_test_utils.hpp"
|
||||
#include "ngraph_functions/utils/ngraph_helpers.hpp"
|
||||
#include "ngraph_functions/builders.hpp"
|
||||
// todo: Rewrite this test using Snippets test infrastructure. See add_convert or conv_eltwise for example
|
||||
#include "shared_test_classes/base/snippets_test_utils.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
@ -20,13 +12,14 @@ namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
ov::element::Type_t, // Network Precision
|
||||
ov::Shape, // Input Shape,
|
||||
InputShape, // Input1 Shape,
|
||||
InputShape, // Input2 Shape,
|
||||
bool,
|
||||
std::string // Target Device
|
||||
std::string // Target Device
|
||||
> CodegenGeluParams;
|
||||
|
||||
class CodegenGelu : public testing::WithParamInterface<ov::test::snippets::CodegenGeluParams>,
|
||||
virtual public LayerTestsUtils::LayerTestsCommon {
|
||||
virtual public ov::test::SnippetsTestsCommon {
|
||||
public:
|
||||
static std::string getTestCaseName(testing::TestParamInfo<ov::test::snippets::CodegenGeluParams> obj);
|
||||
|
||||
|
||||
@ -11,7 +11,7 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
std::vector<ov::PartialShape>, // InputShapes
|
||||
std::vector<InputShape>, // InputShapes
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>>, // Input and Output data types for Converts
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
|
||||
@ -11,8 +11,8 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
ov::Shape, // Input 0 Shape
|
||||
ov::Shape, // Input 1 Shape
|
||||
InputShape, // Input 0 Shape
|
||||
InputShape, // Input 1 Shape
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
std::string // Target Device
|
||||
|
||||
@ -11,7 +11,7 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
ov::Shape, // Input Shape All shapes are replicated
|
||||
InputShape, // Input Shape All shapes are replicated
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
std::string // Target Device
|
||||
|
||||
@ -11,7 +11,7 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
std::vector<ov::PartialShape>, // Input shapes
|
||||
std::vector<InputShape>, // Input shapes
|
||||
std::vector<float>, // FakeQuantize intervals
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
|
||||
@ -11,9 +11,9 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
ov::Shape, // Input 0 Shape
|
||||
ov::Shape, // Input 1 Shape
|
||||
ov::Shape, // Input 2 Shape
|
||||
InputShape, // Input 0 Shape
|
||||
InputShape, // Input 1 Shape
|
||||
InputShape, // Input 2 Shape
|
||||
ov::element::Type, // Element type
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
@ -21,10 +21,10 @@ typedef std::tuple<
|
||||
> SelectParams;
|
||||
|
||||
typedef std::tuple<
|
||||
ov::Shape, // Input 0 Shape
|
||||
ov::Shape, // Input 1 Shape
|
||||
ov::Shape, // Input 2 Shape
|
||||
ov::Shape, // Input 3 Shape
|
||||
InputShape, // Input 0 Shape
|
||||
InputShape, // Input 1 Shape
|
||||
InputShape, // Input 2 Shape
|
||||
ov::PartialShape, // Input 3 Shape
|
||||
ov::element::Type, // Element type
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
@ -53,7 +53,6 @@ protected:
|
||||
void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override;
|
||||
};
|
||||
|
||||
|
||||
} // namespace snippets
|
||||
} // namespace test
|
||||
} // namespace ov
|
||||
|
||||
@ -11,7 +11,7 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
ov::Shape, // Input 0 Shape
|
||||
InputShape, // Input 0 Shape
|
||||
int, // Axis
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
@ -19,7 +19,7 @@ typedef std::tuple<
|
||||
> SoftmaxParams;
|
||||
|
||||
typedef std::tuple<
|
||||
std::pair<ov::Shape, ov::Shape>, // Input Shapes
|
||||
std::pair<InputShape, InputShape>,// Input Shapes
|
||||
int, // Axis
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
|
||||
@ -11,23 +11,14 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
ov::Shape, // Input 0 Shape
|
||||
ov::Shape, // Input 1 Shape
|
||||
ov::Shape, // Input 2 Shape
|
||||
InputShape, // Input 0 Shape
|
||||
InputShape, // Input 1 Shape
|
||||
InputShape, // Input 2 Shape
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
std::string // Target Device
|
||||
> ThreeInputsEltwiseParams;
|
||||
|
||||
typedef std::tuple<
|
||||
InputShape, // Input 0 Shape
|
||||
InputShape, // Input 1 Shape
|
||||
InputShape, // Input 2 Shape
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
std::string // Target Device
|
||||
> ThreeInputsEltwiseDynamicParams;
|
||||
|
||||
class ThreeInputsEltwise : public testing::WithParamInterface<ov::test::snippets::ThreeInputsEltwiseParams>,
|
||||
virtual public ov::test::SnippetsTestsCommon {
|
||||
public:
|
||||
|
||||
@ -11,7 +11,7 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
typedef std::tuple<
|
||||
std::vector<ov::PartialShape>, // Input Shape All shapes
|
||||
std::vector<InputShape>, // Input Shape All shapes
|
||||
size_t, // Expected num nodes
|
||||
size_t, // Expected num subgraphs
|
||||
std::string // Target Device
|
||||
|
||||
@ -7,21 +7,30 @@
|
||||
#include "subgraph_simple.hpp"
|
||||
#include "ngraph_functions/builders.hpp"
|
||||
#include "functional_test_utils/skip_tests_config.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string Add::getTestCaseName(testing::TestParamInfo<ov::test::snippets::AddParams> obj) {
|
||||
ov::Shape inputShapes0, inputShapes1, newInputShapes;
|
||||
ov::test::InputShape inputShapes0, inputShapes1;
|
||||
ov::element::Type type;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes0, inputShapes1, type, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes0) << "_";
|
||||
result << "IS[1]=" << ov::test::utils::vec2str(inputShapes1) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({inputShapes0.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : inputShapes0.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[1]=" << ov::test::utils::partialShape2str({inputShapes1.first}) << "_";
|
||||
result << "TS[1]=";
|
||||
for (const auto& shape : inputShapes1.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "T=" << type << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
@ -30,25 +39,34 @@ std::string Add::getTestCaseName(testing::TestParamInfo<ov::test::snippets::AddP
|
||||
}
|
||||
|
||||
void Add::SetUp() {
|
||||
ov::Shape inputShape0, inputShape1;
|
||||
ov::test::InputShape inputShape0, inputShape1;
|
||||
ov::element::Type type;
|
||||
std::tie(inputShape0, inputShape1, type, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({{{}, {inputShape0, }}, {{}, {inputShape1, }}});
|
||||
|
||||
auto f = ov::test::snippets::AddFunction({inputShape0, inputShape1});
|
||||
init_input_shapes({inputShape0, inputShape1});
|
||||
auto f = ov::test::snippets::AddFunction(inputDynamicShapes);
|
||||
function = f.getOriginal();
|
||||
setInferenceType(type);
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
std::string AddConst::getTestCaseName(testing::TestParamInfo<ov::test::snippets::AddConstParams> obj) {
|
||||
ov::Shape inputShapes, newInputShapes;
|
||||
InputShape inputShapes;
|
||||
PartialShape constShape;
|
||||
ov::element::Type type;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes, type, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
std::tie(inputShapes, constShape, type, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({inputShapes.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : inputShapes.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS_ConstShape=" << ov::test::utils::partialShape2str({constShape}) << "_";
|
||||
result << "T=" << type << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
@ -57,29 +75,37 @@ std::string AddConst::getTestCaseName(testing::TestParamInfo<ov::test::snippets:
|
||||
}
|
||||
|
||||
void AddConst::SetUp() {
|
||||
ov::Shape inputShape;
|
||||
InputShape inputShape;
|
||||
PartialShape constShape;
|
||||
ov::element::Type type;
|
||||
std::tie(inputShape, type, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({{{}, {inputShape, }}});
|
||||
|
||||
auto f = ov::test::snippets::AddConstFunction({inputShape});
|
||||
std::tie(inputShape, constShape, type, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({{inputShape}});
|
||||
auto f = ov::test::snippets::AddConstFunction({inputDynamicShapes}, constShape);
|
||||
function = f.getOriginal();
|
||||
setInferenceType(type);
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
void AddRollConst::SetUp() {
|
||||
ov::Shape inputShape;
|
||||
InputShape inputShape;
|
||||
PartialShape constShape;
|
||||
ov::element::Type type;
|
||||
std::tie(inputShape, type, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({{{}, {inputShape, }}});
|
||||
|
||||
auto f = ov::test::snippets::AddRollConstFunction({inputShape});
|
||||
std::tie(inputShape, constShape, type, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({inputShape});
|
||||
auto f = ov::test::snippets::AddRollConstFunction({inputDynamicShapes}, constShape);
|
||||
function = f.getOriginal();
|
||||
setInferenceType(type);
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
std::string AddPair::getTestCaseName(testing::TestParamInfo<ov::test::snippets::AddParamsPair> obj) {
|
||||
std::vector<ov::Shape> input_shapes;
|
||||
std::vector<InputShape> input_shapes;
|
||||
ov::element::Type type;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
@ -87,8 +113,16 @@ std::string AddPair::getTestCaseName(testing::TestParamInfo<ov::test::snippets::
|
||||
if (input_shapes.size() != 2)
|
||||
IE_THROW() << "Invalid input shapes vector size";
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(input_shapes[0]) << "_";
|
||||
result << "IS[1]=" << ov::test::utils::vec2str(input_shapes[1]) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({input_shapes[0].first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : input_shapes[0].second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[1]=" << ov::test::utils::partialShape2str({input_shapes[1].first}) << "_";
|
||||
result << "TS[1]=";
|
||||
for (const auto& shape : input_shapes[1].second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "T=" << type << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
@ -97,17 +131,17 @@ std::string AddPair::getTestCaseName(testing::TestParamInfo<ov::test::snippets::
|
||||
}
|
||||
|
||||
void AddPair::SetUp() {
|
||||
std::vector<ov::Shape> input_shapes;
|
||||
std::vector<InputShape> input_shapes;
|
||||
ov::element::Type type;
|
||||
std::tie(input_shapes, type, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
std::vector<InputShape> is;
|
||||
for (const auto& s : input_shapes) {
|
||||
is.emplace_back(InputShape {{}, {s, }});
|
||||
}
|
||||
init_input_shapes(is);
|
||||
auto f = ov::test::snippets::AddFunction({input_shapes[0], input_shapes[1]});
|
||||
init_input_shapes(input_shapes);
|
||||
auto f = ov::test::snippets::AddFunction(inputDynamicShapes);
|
||||
function = f.getOriginal();
|
||||
setInferenceType(type);
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(Add, CompareWithRefImpl) {
|
||||
|
||||
@ -7,6 +7,7 @@
|
||||
#include "common_test_utils/common_utils.hpp"
|
||||
#include "subgraph_converts.hpp"
|
||||
#include "common_test_utils/ov_tensor_utils.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
@ -49,8 +50,16 @@ std::string CheckBroadcast::getTestCaseName(testing::TestParamInfo<CheckBroadcas
|
||||
std::tie(input_type, test_case_params, target_device) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS=" << test_case_params.input_shapes.first.get_shape() << "_" <<
|
||||
test_case_params.input_shapes.second.get_shape() << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({test_case_params.input_shapes.first.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : test_case_params.input_shapes.first.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[1]=" << ov::test::utils::partialShape2str({test_case_params.input_shapes.second.first}) << "_";
|
||||
result << "TS[1]=";
|
||||
for (const auto& shape : test_case_params.input_shapes.second.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IT=" << input_type << "_";
|
||||
result << "BCT=" << test_case_params.broadcast.m_type << "_";
|
||||
result << "BCA=" << test_case_params.broadcast.m_axis << "_";
|
||||
@ -68,15 +77,17 @@ void CheckBroadcast::SetUp() {
|
||||
ref_num_nodes = test_case_params.num_nodes;
|
||||
ref_num_subgraphs = test_case_params.num_subgraphs;
|
||||
|
||||
init_input_shapes(static_partial_shapes_to_test_representation({
|
||||
test_case_params.input_shapes.first,
|
||||
test_case_params.input_shapes.second}));
|
||||
init_input_shapes({test_case_params.input_shapes.first, test_case_params.input_shapes.second});
|
||||
|
||||
function = CheckBroadcastFunction::get(
|
||||
test_case_params.input_shapes.first,
|
||||
test_case_params.input_shapes.second,
|
||||
inputDynamicShapes[0],
|
||||
inputDynamicShapes[1],
|
||||
input_type,
|
||||
test_case_params.broadcast);
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(CheckBroadcast, CompareWithRefImpl) {
|
||||
|
||||
@ -3,41 +3,36 @@
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
//
|
||||
|
||||
#include <memory>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
#include <string>
|
||||
|
||||
#include <ie_core.hpp>
|
||||
|
||||
#include "common_test_utils/common_utils.hpp"
|
||||
#include "functional_test_utils/plugin_cache.hpp"
|
||||
#include "shared_test_classes/base/layer_test_utils.hpp"
|
||||
#include "functional_test_utils/blob_utils.hpp"
|
||||
|
||||
#include "ngraph_functions/pass/convert_prc.hpp"
|
||||
|
||||
#include "snippets/codegen_gelu.hpp"
|
||||
|
||||
#include <ngraph/pass/constant_folding.hpp>
|
||||
#include <ngraph/pass/visualize_tree.hpp>
|
||||
#include "common_test_utils/common_utils.hpp"
|
||||
#include "snippets/codegen_gelu.hpp"
|
||||
#include "subgraph_simple.hpp"
|
||||
#include "ngraph_functions/builders.hpp"
|
||||
#include "functional_test_utils/skip_tests_config.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
#include <transformations/init_node_info.hpp>
|
||||
#include <transformations/utils/utils.hpp>
|
||||
// todo: Rewrite this test using Snippets test infrastructure. See add_convert or conv_eltwise for example
|
||||
namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string CodegenGelu::getTestCaseName(testing::TestParamInfo<ov::test::snippets::CodegenGeluParams> obj) {
|
||||
ov::element::Type_t netPrecision;
|
||||
ov::Shape inputShapes0, newInputShapes;
|
||||
InputShape inputShapes0, inputShapes1;
|
||||
bool useSubgraph;
|
||||
std::string targetDevice;
|
||||
std::tie(netPrecision, inputShapes0, useSubgraph, targetDevice) = obj.param;
|
||||
std::tie(netPrecision, inputShapes0, inputShapes1, useSubgraph, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes0) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({inputShapes0.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : inputShapes0.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[1]=" << ov::test::utils::partialShape2str({inputShapes1.first}) << "_";
|
||||
result << "TS[1]=";
|
||||
for (const auto& shape : inputShapes1.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "netPRC=" << netPrecision << "_";
|
||||
result << "overSnippet=" << (useSubgraph ? "yes" : "no") << "_";
|
||||
result << "targetDevice=" << targetDevice;
|
||||
@ -46,13 +41,15 @@ namespace snippets {
|
||||
|
||||
// Gelu from bert-large-uncased-whole-word-masking-squad-fp32-onnx-0001
|
||||
void CodegenGelu::SetUp() {
|
||||
ov::Shape inputShape0;
|
||||
InputShape inputShape0, inputShapes1;
|
||||
ov::element::Type_t netPrecision;
|
||||
bool useSubgraph;
|
||||
std::tie(netPrecision, inputShape0, useSubgraph, targetDevice) = this->GetParam();
|
||||
std::tie(netPrecision, inputShape0, inputShapes1, useSubgraph, targetDevice) = this->GetParam();
|
||||
|
||||
auto input0 = std::make_shared<ngraph::opset1::Parameter>(netPrecision, ngraph::Shape{inputShape0});
|
||||
auto input1 = std::make_shared<ngraph::opset1::Parameter>(netPrecision, ngraph::Shape{inputShape0});
|
||||
init_input_shapes({inputShape0, inputShapes1});
|
||||
|
||||
auto input0 = std::make_shared<ngraph::opset1::Parameter>(netPrecision, inputDynamicShapes[0]);
|
||||
auto input1 = std::make_shared<ngraph::opset1::Parameter>(netPrecision, inputDynamicShapes[1]);
|
||||
auto add = std::make_shared<ngraph::opset1::Add>(input0, input1);
|
||||
|
||||
auto gelu = std::make_shared<ngraph::opset2::Gelu>(add);
|
||||
@ -67,10 +64,14 @@ namespace snippets {
|
||||
ov::pass::InitNodeInfo().run_on_model(function);
|
||||
ngraph::pass::ConstantFolding().run_on_model(function);
|
||||
}
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(CodegenGelu, CompareWithRefImpl) {
|
||||
Run();
|
||||
run();
|
||||
};
|
||||
|
||||
|
||||
|
||||
@ -6,22 +6,27 @@
|
||||
#include "snippets/convert.hpp"
|
||||
#include "subgraph_converts.hpp"
|
||||
#include "common_test_utils/ov_tensor_utils.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string Convert::getTestCaseName(testing::TestParamInfo<ov::test::snippets::ConvertParams> obj) {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShape, types, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS=";
|
||||
for (const auto& sh : inputShape)
|
||||
result << ov::test::utils::vec2str(sh.get_shape()) << "_";
|
||||
for (size_t i = 0; i < inputShape.size(); ++i) {
|
||||
result << "IS[" << i << "]=" << ov::test::utils::partialShape2str({inputShape[i].first}) << "_";
|
||||
result << "TS[" << i << "]=";
|
||||
for (const auto& shape : inputShape[i].second) {
|
||||
result << ov::test::utils::vec2str(shape) << "_";
|
||||
}
|
||||
}
|
||||
result << "IT=" << ov::test::utils::vec2str(types.first) << "_";
|
||||
result << "OT=" << ov::test::utils::vec2str(types.second) << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
@ -31,13 +36,17 @@ std::string Convert::getTestCaseName(testing::TestParamInfo<ov::test::snippets::
|
||||
}
|
||||
|
||||
void Convert::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::tie(inputShape, types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
auto f = ov::test::snippets::ConvertFunction(inputShape, types.first[0], types.second[0]);
|
||||
init_input_shapes(inputShape);
|
||||
auto f = ov::test::snippets::ConvertFunction(inputDynamicShapes, types.first[0], types.second[0]);
|
||||
function = f.getOriginal();
|
||||
output_type = types.second.front();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
parameters Convert::generate_params_random() const {
|
||||
@ -84,12 +93,16 @@ void Convert::generate_inputs(const std::vector<ov::Shape>& targetInputStaticSha
|
||||
}
|
||||
|
||||
void ConvertInput::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::tie(inputShape, types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
auto f = ov::test::snippets::ConvertInputFunction(inputShape, types.first[0], types.second[0]);
|
||||
init_input_shapes(inputShape);
|
||||
auto f = ov::test::snippets::ConvertInputFunction(inputDynamicShapes, types.first[0], types.second[0]);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
parameters ConvertInput::generate_params_random() const {
|
||||
@ -123,65 +136,89 @@ parameters ConvertInput::generate_params_random() const {
|
||||
}
|
||||
|
||||
void ConvertOutput::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::tie(inputShape, types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
init_input_shapes(inputShape);
|
||||
|
||||
auto f = ov::test::snippets::ConvertOutputFunction(inputShape, types.first[0], types.second[0]);
|
||||
auto f = ov::test::snippets::ConvertOutputFunction(inputDynamicShapes, types.first[0], types.second[0]);
|
||||
function = f.getOriginal();
|
||||
output_type = types.second.front();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
void ConvertStub::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::tie(inputShape, types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
init_input_shapes(inputShape);
|
||||
|
||||
auto f = ov::test::snippets::ConvertStubFunction(inputShape, types.first[0], types.second[0]);
|
||||
auto f = ov::test::snippets::ConvertStubFunction(inputDynamicShapes, types.first[0], types.second[0]);
|
||||
function = f.getOriginal();
|
||||
output_type = types.second.front();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
void ConvertPartialInputsAndResults::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::tie(inputShape, types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
init_input_shapes(inputShape);
|
||||
|
||||
auto f = ov::test::snippets::ConvertPartialInputsAndResultsFunction(inputShape, types.first, types.second);
|
||||
auto f = ov::test::snippets::ConvertPartialInputsAndResultsFunction(inputDynamicShapes, types.first, types.second);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
void ConvertManyOnInputs::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::tie(inputShape, types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
init_input_shapes(inputShape);
|
||||
|
||||
auto f = ov::test::snippets::ConvertManyOnInputsFunction(inputShape, types.first);
|
||||
auto f = ov::test::snippets::ConvertManyOnInputsFunction(inputDynamicShapes, types.first);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
void ConvertManyOnOutputs::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::tie(inputShape, types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
init_input_shapes(inputShape);
|
||||
|
||||
auto f = ov::test::snippets::ConvertManyOnOutputsFunction(inputShape, types.first);
|
||||
auto f = ov::test::snippets::ConvertManyOnOutputsFunction(inputDynamicShapes, types.first);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
void ConvertManyOnInputOutput::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::pair<std::vector<ov::element::Type>, std::vector<ov::element::Type>> types;
|
||||
std::tie(inputShape, types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
init_input_shapes(inputShape);
|
||||
|
||||
auto f = ov::test::snippets::ConvertManyOnInputOutputFunction(inputShape, types.first, types.second);
|
||||
auto f = ov::test::snippets::ConvertManyOnInputOutputFunction(inputDynamicShapes, types.first, types.second);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(Convert, CompareWithRefImpl) {
|
||||
|
||||
@ -5,20 +5,30 @@
|
||||
#include "common_test_utils/common_utils.hpp"
|
||||
#include "snippets/eltwise_two_results.hpp"
|
||||
#include "subgraph_simple.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string EltwiseTwoResults::getTestCaseName(testing::TestParamInfo<ov::test::snippets::EltwiseTwoResultsParams> obj) {
|
||||
ov::Shape inputShapes0, inputShapes1;
|
||||
InputShape inputShapes0, inputShapes1;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes0, inputShapes1, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes0) << "_";
|
||||
result << "IS[1]=" << ov::test::utils::vec2str(inputShapes1) << "_";
|
||||
result << "IS=" << ov::test::utils::partialShape2str({inputShapes0.first}) << "_"
|
||||
<< ov::test::utils::partialShape2str({inputShapes1.first}) << "_";
|
||||
|
||||
result << "TS[0]=";
|
||||
for (const auto& item : inputShapes0.second) {
|
||||
result << ov::test::utils::vec2str(item) << "_";
|
||||
}
|
||||
result << "TS[1]=";
|
||||
for (const auto& item : inputShapes1.second) {
|
||||
result << ov::test::utils::vec2str(item) << "_";
|
||||
}
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
result << "targetDevice=" << targetDevice;
|
||||
@ -26,12 +36,16 @@ std::string EltwiseTwoResults::getTestCaseName(testing::TestParamInfo<ov::test::
|
||||
}
|
||||
|
||||
void EltwiseTwoResults::SetUp() {
|
||||
ov::Shape inputShape0, inputShape1;
|
||||
InputShape inputShape0, inputShape1;
|
||||
std::tie(inputShape0, inputShape1, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({{{}, {inputShape0, }}, {{}, {inputShape1, }}});
|
||||
init_input_shapes({inputShape0, inputShape1});
|
||||
|
||||
auto f = ov::test::snippets::EltwiseTwoResultsFunction({inputShape0, inputShape1});
|
||||
auto f = ov::test::snippets::EltwiseTwoResultsFunction({inputDynamicShapes[0], inputDynamicShapes[1]});
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(EltwiseTwoResults, CompareWithRefImpl) {
|
||||
|
||||
@ -5,19 +5,25 @@
|
||||
#include "common_test_utils/common_utils.hpp"
|
||||
#include "snippets/max_num_params_eltwise.hpp"
|
||||
#include "subgraph_simple.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string MaxNumParamsEltwise::getTestCaseName(testing::TestParamInfo<ov::test::snippets::MaxNumParamsEltwiseParams> obj) {
|
||||
ov::Shape inputShapes;
|
||||
ov::test::InputShape inputShapes;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({inputShapes.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& item : inputShapes.second) {
|
||||
result << ov::test::utils::vec2str(item) << "_";
|
||||
}
|
||||
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
result << "targetDevice=" << targetDevice;
|
||||
@ -25,18 +31,17 @@ std::string MaxNumParamsEltwise::getTestCaseName(testing::TestParamInfo<ov::test
|
||||
}
|
||||
|
||||
void MaxNumParamsEltwise::SetUp() {
|
||||
ov::Shape inputShape;
|
||||
ov::test::InputShape inputShape;
|
||||
std::tie(inputShape, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
std::vector<ov::PartialShape> expandedShapes(10, inputShape);
|
||||
std::vector<InputShape> input_shapes;
|
||||
for (const auto& s : expandedShapes) {
|
||||
input_shapes.emplace_back(InputShape {{}, {s.get_shape(), }});
|
||||
}
|
||||
std::vector<ov::test::InputShape> expandedShapes(10, inputShape);
|
||||
init_input_shapes(expandedShapes);
|
||||
|
||||
init_input_shapes(input_shapes);
|
||||
|
||||
auto f = ov::test::snippets::EltwiseMaxNumParamsFunction(expandedShapes);
|
||||
auto f = ov::test::snippets::EltwiseMaxNumParamsFunction(inputDynamicShapes);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(MaxNumParamsEltwise, CompareWithRefImpl) {
|
||||
|
||||
@ -6,21 +6,27 @@
|
||||
|
||||
#include "common_test_utils/common_utils.hpp"
|
||||
#include "precision_propagation_convertion_function.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string PrecisionPropagationConvertion::getTestCaseName(testing::TestParamInfo<PrecisionPropagationParams> obj) {
|
||||
std::vector<ov::PartialShape> input_shapes;
|
||||
std::vector<InputShape> input_shapes;
|
||||
std::vector<float> fake_quantize_intervals;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(input_shapes, fake_quantize_intervals, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
for (size_t i = 0; i < input_shapes.size(); ++i)
|
||||
result << "IS[" << i << "]=" << input_shapes[i] << "_";
|
||||
for (size_t i = 0; i < input_shapes.size(); ++i) {
|
||||
result << "IS[" << i << "]=" << ov::test::utils::partialShape2str({input_shapes[i].first}) << "_";
|
||||
result << "TS[" << i << "}=";
|
||||
for (const auto& shape : input_shapes[i].second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
}
|
||||
for (size_t i = 0; i < fake_quantize_intervals.size(); ++i)
|
||||
result << "FQ[" << i << "]=" << fake_quantize_intervals[i] << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
@ -30,12 +36,16 @@ std::string PrecisionPropagationConvertion::getTestCaseName(testing::TestParamIn
|
||||
}
|
||||
|
||||
void PrecisionPropagationConvertion::SetUp() {
|
||||
std::vector<ov::PartialShape> input_shapes;
|
||||
std::vector<InputShape> input_shapes;
|
||||
std::vector<float> fake_quantize_intervals;
|
||||
std::tie(input_shapes, fake_quantize_intervals, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(input_shapes));
|
||||
init_input_shapes(input_shapes);
|
||||
|
||||
function = PrecisionPropagationConvertionFunction(input_shapes, ov::element::f32, fake_quantize_intervals).getOriginal();
|
||||
function = PrecisionPropagationConvertionFunction(inputDynamicShapes, ov::element::f32, fake_quantize_intervals).getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(PrecisionPropagationConvertion, CompareWithRefImpl) {
|
||||
|
||||
@ -13,11 +13,12 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
namespace {
|
||||
void generate_data(std::map<std::shared_ptr<ov::Node>, ov::Tensor>& data_inputs, const std::vector<ov::Output<ov::Node>>& model_inputs) {
|
||||
void generate_data(std::map<std::shared_ptr<ov::Node>, ov::Tensor>& data_inputs, const std::vector<ov::Output<ov::Node>>& model_inputs,
|
||||
const std::vector<ngraph::Shape>& targetInputStaticShapes) {
|
||||
data_inputs.clear();
|
||||
auto tensor_bool = ov::test::utils::create_and_fill_tensor(model_inputs[0].get_element_type(), model_inputs[0].get_shape(), 3, -1, 2);
|
||||
auto tensor0 = ov::test::utils::create_and_fill_tensor(model_inputs[1].get_element_type(), model_inputs[1].get_shape(), 10, -10, 2);
|
||||
auto tensor1 = ov::test::utils::create_and_fill_tensor(model_inputs[2].get_element_type(), model_inputs[2].get_shape(), 10, 0, 2);
|
||||
auto tensor_bool = ov::test::utils::create_and_fill_tensor(model_inputs[0].get_element_type(), targetInputStaticShapes[0], 3, -1, 2);
|
||||
auto tensor0 = ov::test::utils::create_and_fill_tensor(model_inputs[1].get_element_type(), targetInputStaticShapes[1], 10, -10, 2);
|
||||
auto tensor1 = ov::test::utils::create_and_fill_tensor(model_inputs[2].get_element_type(), targetInputStaticShapes[2], 10, 0, 2);
|
||||
data_inputs.insert({model_inputs[0].get_node_shared_ptr(), tensor_bool});
|
||||
data_inputs.insert({model_inputs[1].get_node_shared_ptr(), tensor0});
|
||||
data_inputs.insert({model_inputs[2].get_node_shared_ptr(), tensor1});
|
||||
@ -25,16 +26,28 @@ void generate_data(std::map<std::shared_ptr<ov::Node>, ov::Tensor>& data_inputs,
|
||||
} // namespace
|
||||
|
||||
std::string Select::getTestCaseName(testing::TestParamInfo<ov::test::snippets::SelectParams> obj) {
|
||||
ov::Shape inputShapes0, inputShapes1, inputShapes2;
|
||||
InputShape inputShapes0, inputShapes1, inputShapes2;
|
||||
ov::element::Type type;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes0, inputShapes1, inputShapes2, type, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes0) << "_";
|
||||
result << "IS[1]=" << ov::test::utils::vec2str(inputShapes1) << "_";
|
||||
result << "IS[2]=" << ov::test::utils::vec2str(inputShapes2) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({inputShapes0.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : inputShapes0.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[1]=" << ov::test::utils::partialShape2str({inputShapes1.first}) << "_";
|
||||
result << "TS[1]=";
|
||||
for (const auto& shape : inputShapes1.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[2]=" << ov::test::utils::partialShape2str({inputShapes2.first}) << "_";
|
||||
result << "TS[2]=";
|
||||
for (const auto& shape : inputShapes2.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "T=" << type << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
@ -43,12 +56,12 @@ std::string Select::getTestCaseName(testing::TestParamInfo<ov::test::snippets::S
|
||||
}
|
||||
|
||||
void Select::SetUp() {
|
||||
ov::Shape inputShape0, inputShape1, inputShape2;
|
||||
InputShape inputShape0, inputShape1, inputShape2;
|
||||
ov::element::Type type;
|
||||
std::tie(inputShape0, inputShape1, inputShape2, type, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_shapes_to_test_representation({inputShape0, inputShape1, inputShape2}));
|
||||
init_input_shapes({{inputShape0}, {inputShape1}, {inputShape2}});
|
||||
|
||||
auto f = ov::test::snippets::SelectFunction({inputShape0, inputShape1, inputShape2});
|
||||
auto f = ov::test::snippets::SelectFunction(inputDynamicShapes);
|
||||
function = f.getOriginal();
|
||||
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
@ -58,21 +71,34 @@ void Select::SetUp() {
|
||||
}
|
||||
|
||||
void Select::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
|
||||
generate_data(inputs, function->inputs());
|
||||
generate_data(inputs, function->inputs(), targetInputStaticShapes);
|
||||
}
|
||||
|
||||
std::string BroadcastSelect::getTestCaseName(testing::TestParamInfo<ov::test::snippets::BroadcastSelectParams> obj) {
|
||||
ov::Shape inputShapes0, inputShapes1, inputShapes2, broadcastShape;
|
||||
InputShape inputShapes0, inputShapes1, inputShapes2;
|
||||
ov::PartialShape broadcastShape;
|
||||
ov::element::Type type;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes0, inputShapes1, inputShapes2, broadcastShape, type, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes0) << "_";
|
||||
result << "IS[1]=" << ov::test::utils::vec2str(inputShapes1) << "_";
|
||||
result << "IS[2]=" << ov::test::utils::vec2str(inputShapes2) << "_";
|
||||
result << "BS=" << ov::test::utils::vec2str(broadcastShape) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({inputShapes0.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : inputShapes0.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[1]=" << ov::test::utils::partialShape2str({inputShapes1.first}) << "_";
|
||||
result << "TS[1]=";
|
||||
for (const auto& shape : inputShapes1.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[2]=" << ov::test::utils::partialShape2str({inputShapes2.first}) << "_";
|
||||
result << "TS[2]=";
|
||||
for (const auto& shape : inputShapes2.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS_Broadcast=" << ov::test::utils::partialShape2str({broadcastShape}) << "_";
|
||||
result << "T=" << type << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
@ -81,12 +107,13 @@ std::string BroadcastSelect::getTestCaseName(testing::TestParamInfo<ov::test::sn
|
||||
}
|
||||
|
||||
void BroadcastSelect::SetUp() {
|
||||
ov::Shape inputShape0, inputShape1, inputShape2, broadcastShape;
|
||||
InputShape inputShape0, inputShape1, inputShape2;
|
||||
ov::PartialShape broadcastShape;
|
||||
ov::element::Type type;
|
||||
std::tie(inputShape0, inputShape1, inputShape2, broadcastShape, type, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_shapes_to_test_representation({inputShape0, inputShape1, inputShape2}));
|
||||
init_input_shapes({inputShape0, inputShape1, inputShape2});
|
||||
|
||||
auto f = ov::test::snippets::BroadcastSelectFunction({inputShape0, inputShape1, inputShape2}, broadcastShape);
|
||||
auto f = ov::test::snippets::BroadcastSelectFunction({inputDynamicShapes[0], inputDynamicShapes[1], inputDynamicShapes[2]}, broadcastShape);
|
||||
function = f.getOriginal();
|
||||
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
@ -96,7 +123,7 @@ void BroadcastSelect::SetUp() {
|
||||
}
|
||||
|
||||
void BroadcastSelect::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
|
||||
generate_data(inputs, function->inputs());
|
||||
generate_data(inputs, function->inputs(), targetInputStaticShapes);
|
||||
}
|
||||
|
||||
TEST_P(Select, CompareWithRefImpl) {
|
||||
|
||||
@ -14,14 +14,18 @@ namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string Softmax::getTestCaseName(testing::TestParamInfo<ov::test::snippets::SoftmaxParams> obj) {
|
||||
ov::Shape inputShapes;
|
||||
InputShape inputShapes;
|
||||
int axis;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes, axis, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS=" << ov::test::utils::vec2str(inputShapes) << "_";
|
||||
result << "IS=" << ov::test::utils::partialShape2str({inputShapes.first}) << "_";
|
||||
result << "TS=";
|
||||
for (const auto& shape : inputShapes.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "Axis=" << axis << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
@ -30,12 +34,12 @@ std::string Softmax::getTestCaseName(testing::TestParamInfo<ov::test::snippets::
|
||||
}
|
||||
|
||||
void Softmax::SetUp() {
|
||||
ov::Shape inputShape;
|
||||
InputShape inputShape;
|
||||
int axis;
|
||||
std::tie(inputShape, axis, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({{{}, {inputShape, }}});
|
||||
init_input_shapes({inputShape});
|
||||
|
||||
auto f = ov::test::snippets::SoftmaxFunction({inputShape}, axis);
|
||||
auto f = ov::test::snippets::SoftmaxFunction(inputDynamicShapes, axis);
|
||||
function = f.getOriginal();
|
||||
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
@ -45,15 +49,23 @@ void Softmax::SetUp() {
|
||||
}
|
||||
|
||||
std::string AddSoftmax::getTestCaseName(testing::TestParamInfo<ov::test::snippets::AddSoftmaxParams> obj) {
|
||||
std::pair<ov::Shape, ov::Shape> inputShapes;
|
||||
std::pair<InputShape, InputShape> inputShapes;
|
||||
int axis;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes, axis, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes.first) << "_";
|
||||
result << "IS[1]=" << ov::test::utils::vec2str(inputShapes.second) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({inputShapes.first.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : inputShapes.first.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[1]=" << ov::test::utils::partialShape2str({inputShapes.second.first}) << "_";
|
||||
result << "TS[1]=";
|
||||
for (const auto& shape : inputShapes.second.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "Axis=" << axis << "_";
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
@ -62,12 +74,12 @@ std::string AddSoftmax::getTestCaseName(testing::TestParamInfo<ov::test::snippet
|
||||
}
|
||||
|
||||
void AddSoftmax::SetUp() {
|
||||
std::pair<ov::Shape, ov::Shape> inputShapes;
|
||||
std::pair<InputShape, InputShape> inputShapes;
|
||||
int axis;
|
||||
std::tie(inputShapes, axis, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({{{}, {inputShapes.first, }}, {{}, {inputShapes.second, }}});
|
||||
init_input_shapes({inputShapes.first, inputShapes.second});
|
||||
|
||||
auto f = ov::test::snippets::AddSoftmaxFunction({inputShapes.first, inputShapes.second}, axis);
|
||||
auto f = ov::test::snippets::AddSoftmaxFunction(inputDynamicShapes, axis);
|
||||
function = f.getOriginal();
|
||||
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
|
||||
@ -6,22 +6,35 @@
|
||||
#include "snippets/three_inputs_eltwise.hpp"
|
||||
#include "subgraph_simple.hpp"
|
||||
#include "functional_test_utils/skip_tests_config.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string ThreeInputsEltwise::getTestCaseName(testing::TestParamInfo<ov::test::snippets::ThreeInputsEltwiseParams> obj) {
|
||||
ov::Shape inputShapes0, inputShapes1, inputShapes2;
|
||||
InputShape inputShapes0, inputShapes1, inputShapes2;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes0, inputShapes1, inputShapes2,
|
||||
num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
result << "IS[0]=" << ov::test::utils::vec2str(inputShapes0) << "_";
|
||||
result << "IS[1]=" << ov::test::utils::vec2str(inputShapes1) << "_";
|
||||
result << "IS[2]=" << ov::test::utils::vec2str(inputShapes2) << "_";
|
||||
result << "IS[0]=" << ov::test::utils::partialShape2str({inputShapes0.first}) << "_";
|
||||
result << "TS[0]=";
|
||||
for (const auto& shape : inputShapes0.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[1]=" << ov::test::utils::partialShape2str({inputShapes1.first}) << "_";
|
||||
result << "TS[1]=";
|
||||
for (const auto& shape : inputShapes1.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "IS[2]=" << ov::test::utils::partialShape2str({inputShapes2.first}) << "_";
|
||||
result << "TS[2]=";
|
||||
for (const auto& shape : inputShapes2.second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
result << "targetDevice=" << targetDevice;
|
||||
@ -29,13 +42,17 @@ std::string ThreeInputsEltwise::getTestCaseName(testing::TestParamInfo<ov::test:
|
||||
}
|
||||
|
||||
void ThreeInputsEltwise::SetUp() {
|
||||
ov::Shape inputShape0, inputShape1, inputShape2;
|
||||
InputShape inputShape0, inputShape1, inputShape2;
|
||||
std::tie(inputShape0, inputShape1, inputShape2,
|
||||
ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes({{{}, {inputShape0, }}, {{}, {inputShape1, }}, {{}, {inputShape2, }}});
|
||||
init_input_shapes({inputShape0, inputShape1, inputShape2});
|
||||
|
||||
auto f = ov::test::snippets::EltwiseThreeInputsFunction({inputShape0, inputShape1, inputShape2});
|
||||
auto f = ov::test::snippets::EltwiseThreeInputsFunction(inputDynamicShapes);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(ThreeInputsEltwise, CompareWithRefImpl) {
|
||||
|
||||
@ -5,20 +5,26 @@
|
||||
#include "common_test_utils/common_utils.hpp"
|
||||
#include "snippets/two_inputs_and_outputs.hpp"
|
||||
#include "subgraph_simple.hpp"
|
||||
#include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
|
||||
|
||||
namespace ov {
|
||||
namespace test {
|
||||
namespace snippets {
|
||||
|
||||
std::string TwoInputsAndOutputs::getTestCaseName(testing::TestParamInfo<ov::test::snippets::TwoInputsAndOutputsParams> obj) {
|
||||
std::vector<ov::PartialShape> inputShapes;
|
||||
std::vector<InputShape> inputShapes;
|
||||
std::string targetDevice;
|
||||
size_t num_nodes, num_subgraphs;
|
||||
std::tie(inputShapes, num_nodes, num_subgraphs, targetDevice) = obj.param;
|
||||
|
||||
std::ostringstream result;
|
||||
for (auto i = 0; i < inputShapes.size(); i++)
|
||||
result << "IS[" << i << "]=" << ov::test::utils::vec2str(inputShapes[i].get_shape()) << "_";
|
||||
for (size_t i = 0; i < inputShapes.size(); ++i) {
|
||||
result << "IS[" << i << "]=" << ov::test::utils::partialShape2str({inputShapes[i].first}) << "_";
|
||||
result << "TS[" << i << "]=";
|
||||
for (const auto& shape : inputShapes[i].second) {
|
||||
result << "(" << ov::test::utils::vec2str(shape) << ")_";
|
||||
}
|
||||
}
|
||||
result << "#N=" << num_nodes << "_";
|
||||
result << "#S=" << num_subgraphs << "_";
|
||||
result << "targetDevice=" << targetDevice;
|
||||
@ -26,19 +32,27 @@ std::string TwoInputsAndOutputs::getTestCaseName(testing::TestParamInfo<ov::test
|
||||
}
|
||||
|
||||
void TwoInputsAndOutputs::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::tie(inputShape, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
auto f = ov::test::snippets::TwoInputsAndOutputsFunction(inputShape);
|
||||
init_input_shapes(inputShape);
|
||||
auto f = ov::test::snippets::TwoInputsAndOutputsFunction(inputDynamicShapes);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
void TwoInputsAndOutputsWithReversedOutputs::SetUp() {
|
||||
std::vector<ov::PartialShape> inputShape;
|
||||
std::vector<InputShape> inputShape;
|
||||
std::tie(inputShape, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
|
||||
init_input_shapes(static_partial_shapes_to_test_representation(inputShape));
|
||||
auto f = ov::test::snippets::TwoInputsAndOutputsWithReversedOutputsFunction(inputShape);
|
||||
init_input_shapes(inputShape);
|
||||
auto f = ov::test::snippets::TwoInputsAndOutputsWithReversedOutputsFunction(inputDynamicShapes);
|
||||
function = f.getOriginal();
|
||||
if (!configuration.count(InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE)) {
|
||||
configuration.insert({InferenceEngine::PluginConfigInternalParams::KEY_SNIPPETS_MODE,
|
||||
InferenceEngine::PluginConfigInternalParams::IGNORE_CALLBACK});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_P(TwoInputsAndOutputs, CompareWithRefImpl) {
|
||||
|
||||
@ -36,12 +36,14 @@ protected:
|
||||
// todo: remove Sinh once "no subgraph after input" limitation is relaxed
|
||||
class AddConstFunction : public SnippetsFunctionBase {
|
||||
public:
|
||||
explicit AddConstFunction(const std::vector<PartialShape>& inputShapes) : SnippetsFunctionBase(inputShapes) {
|
||||
explicit AddConstFunction(const std::vector<PartialShape>& inputShapes, const PartialShape& constShape) :
|
||||
SnippetsFunctionBase(inputShapes), m_const_shape(constShape) {
|
||||
NGRAPH_CHECK(input_shapes.size() == 1, "Got invalid number of input shapes");
|
||||
NGRAPH_CHECK(input_shapes[0].is_static(), "This test supports only static shapes");
|
||||
NGRAPH_CHECK(m_const_shape.is_static(), "Const shape must be static shape");
|
||||
}
|
||||
protected:
|
||||
std::shared_ptr<ov::Model> initOriginal() const override;
|
||||
PartialShape m_const_shape;
|
||||
// std::shared_ptr<ov::Model> initReference() const override;
|
||||
};
|
||||
// Function is to check for different model precision
|
||||
@ -52,11 +54,12 @@ protected:
|
||||
// Add
|
||||
// Result
|
||||
// The function is needed to check different input element types (model precision change)
|
||||
class AddRollConstFunction : public SnippetsFunctionBase {
|
||||
class AddRollConstFunction : public AddConstFunction {
|
||||
public:
|
||||
explicit AddRollConstFunction(const std::vector<PartialShape>& inputShapes) : SnippetsFunctionBase(inputShapes) {
|
||||
explicit AddRollConstFunction(const std::vector<PartialShape>& inputShapes, const PartialShape& constShape) :
|
||||
AddConstFunction(inputShapes, constShape) {
|
||||
NGRAPH_CHECK(input_shapes.size() == 1, "Got invalid number of input shapes");
|
||||
NGRAPH_CHECK(input_shapes[0].is_static(), "Only static shapes are supported");
|
||||
NGRAPH_CHECK(m_const_shape[0].is_static(), "Const shape must be static shape");
|
||||
}
|
||||
protected:
|
||||
std::shared_ptr<ov::Model> initOriginal() const override;
|
||||
|
||||
@ -28,16 +28,15 @@ std::shared_ptr<ov::Model> AddFunction::initReference() const {
|
||||
}
|
||||
std::shared_ptr<ov::Model> AddConstFunction::initOriginal() const {
|
||||
auto data0 = std::make_shared<op::v0::Parameter>(precision, input_shapes[0]);
|
||||
const std::vector<float> const_values = ov::test::utils::generate_float_numbers(shape_size(input_shapes[0].get_shape()), -10., 10.);
|
||||
auto const_data1 = std::make_shared<op::v0::Constant>(precision, input_shapes[0].get_shape(), const_values);
|
||||
const std::vector<float> const_values = ov::test::utils::generate_float_numbers(shape_size(m_const_shape.get_shape()), -10., 10.);
|
||||
auto const_data1 = std::make_shared<op::v0::Constant>(precision, m_const_shape.get_shape(), const_values);
|
||||
auto add = std::make_shared<op::v1::Add>(data0, const_data1);
|
||||
return std::make_shared<ov::Model>(NodeVector{add}, ParameterVector{data0});
|
||||
}
|
||||
std::shared_ptr<ov::Model> AddRollConstFunction::initOriginal() const {
|
||||
const auto input_shape = input_shapes[0].get_shape();
|
||||
auto data0 = std::make_shared<op::v0::Parameter>(precision, input_shape);
|
||||
const std::vector<float> const_values = ov::test::utils::generate_float_numbers(shape_size(input_shape), -10., 10.);
|
||||
auto const_data1 = std::make_shared<op::v0::Constant>(precision, input_shape, const_values);
|
||||
auto data0 = std::make_shared<op::v0::Parameter>(precision, input_shapes[0]);
|
||||
const std::vector<float> const_values = ov::test::utils::generate_float_numbers(shape_size(m_const_shape.get_shape()), -10., 10.);
|
||||
auto const_data1 = std::make_shared<op::v0::Constant>(precision, m_const_shape.get_shape(), const_values);
|
||||
auto shift = std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, std::vector<float>{1});
|
||||
auto axes = std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, std::vector<float>{0});
|
||||
auto roll0 = std::make_shared<ov::op::v7::Roll>(data0, shift, axes);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user