From 6f297800624f0e3ae5e7e6e440b1445be88a745c Mon Sep 17 00:00:00 2001 From: Andrei Kashchikhin Date: Fri, 16 Aug 2024 15:23:41 +0100 Subject: [PATCH 01/23] [CI] [GHA] Add check for `FASTER_BUILD=ON` on Windows (#26088) ### Tickets: - *149827* --- .github/workflows/windows_conditional_compilation.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/windows_conditional_compilation.yml b/.github/workflows/windows_conditional_compilation.yml index 641ee2289c8532..b94da30fdf95a3 100644 --- a/.github/workflows/windows_conditional_compilation.yml +++ b/.github/workflows/windows_conditional_compilation.yml @@ -169,6 +169,7 @@ jobs: -DCMAKE_COMPILE_WARNING_AS_ERROR=ON ` -DENABLE_PROFILING_ITT=ON ` -DSELECTIVE_BUILD=COLLECT ` + -DENABLE_FASTER_BUILD=ON ` -DCMAKE_DISABLE_FIND_PACKAGE_PkgConfig=ON ` -DCMAKE_TOOLCHAIN_FILE=${{ env.ONECORE_TOOLCHAIN }} ` -S ${{ env.OPENVINO_REPO }} ` From 2b6bcf182dda006b626931c05d8a8289cbaae610 Mon Sep 17 00:00:00 2001 From: Evgeny Kotov Date: Fri, 16 Aug 2024 17:01:45 +0200 Subject: [PATCH 02/23] Rope testing in pre-commit (#25943) ### Details: - add precommit tests to check if RoPE fusion works ### Tickets: - 146982 --------- Co-authored-by: Ivan Tikhonov --- .../workflows/job_pytorch_models_tests.yml | 9 ++ .../models_hub_common/utils.py | 60 +++++++----- .../models/transformations-models-precommit | 27 +++++ .../test_transformations.py | 98 +++++++++++++++++++ 4 files changed, 167 insertions(+), 27 deletions(-) create mode 100644 tests/model_hub_tests/transformation_tests/models/transformations-models-precommit create mode 100644 tests/model_hub_tests/transformation_tests/test_transformations.py diff --git a/.github/workflows/job_pytorch_models_tests.yml b/.github/workflows/job_pytorch_models_tests.yml index 381b0a51eb49df..43e1d46319b089 100644 --- a/.github/workflows/job_pytorch_models_tests.yml +++ b/.github/workflows/job_pytorch_models_tests.yml @@ -160,6 +160,15 @@ jobs: TEST_DEVICE: CPU USE_SYSTEM_CACHE: False + - name: RoPE Test + if: ${{ inputs.model_scope == 'precommit' }} + run: | + export PYTHONPATH=${MODEL_HUB_TESTS_INSTALL_DIR}:$PYTHONPATH + python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/transformation_tests/test_transformations.py -m precommit --html=${INSTALL_TEST_DIR}/TEST-torch_rope_tests.html --self-contained-html -v --tb=short -n 2 + env: + TEST_DEVICE: CPU + USE_SYSTEM_CACHE: False + - name: StatefulToStateless Test if: ${{ inputs.model_scope == 'precommit' }} run: | diff --git a/tests/model_hub_tests/models_hub_common/utils.py b/tests/model_hub_tests/models_hub_common/utils.py index b014c8dc64c2d8..428802c58463fc 100644 --- a/tests/model_hub_tests/models_hub_common/utils.py +++ b/tests/model_hub_tests/models_hub_common/utils.py @@ -11,39 +11,45 @@ from models_hub_common.constants import test_device -def get_models_list(file_name: str): - models = [] - with open(file_name) as f: - for model_info in f: +def parse_list_file(file_name: str): + with open(file_name, 'r') as f_in: + for model_info in f_in: + if not model_info: + continue model_info = model_info.strip() # skip comment in model scope file - if model_info.startswith('#'): + if not model_info or model_info.startswith('#'): continue - mark = None - reason = None - assert len(model_info.split(',')) == 2 or len(model_info.split(',')) == 4, \ - "Incorrect model info `{}`. It must contain either 2 or 4 fields.".format(model_info) - if len(model_info.split(',')) == 2: - model_name, model_link = model_info.split(',') - elif len(model_info.split(',')) == 4: - model_name, model_link, mark, reason = model_info.split(',') - models.append((model_name, model_link, mark, reason)) + yield model_info.split(',') - return models +def get_models_list(file_name: str): + models = [] + for line_items in parse_list_file(file_name): + if len(line_items) == 2: + model_name, model_link = line_items + models.append((model_name, model_link, None, None)) + elif len(line_items) == 4: + model_name, model_link, mark, reason = line_items + models.append((model_name, model_link, mark, reason)) + elif len(line_items) > 4: + model_name, model_link, mark, reason = line_items[:4] + if not mark: + mark = None + if not reason: + reason = None + other = line_items[4:] + transformations = [item[8:] for item in other if item.startswith('ts_name:')] + layers = [item[6:] for item in other if item.startswith('layer:')] + models.append((model_name, model_link, mark, reason, transformations, layers)) + else: + items = ','.join(line_items) + assert False, \ + f'Incorrect model info fields {items}. It must contain either 2 or 4 or more than 4 fields.' + return models -def get_skipped_model_links(filename: str): - links = set() - if not os.path.exists(filename): - return links - with open(filename) as f: - for model_info in f: - model_info = model_info.strip() - if not model_info: - continue - model_name, model_link = model_info.split(',') - links.add(model_link) - return links +def get_skipped_model_links(file_name: str): + return {line_items[1] for line_items in parse_list_file(file_name)} def get_models_list_not_skipped(model_list_file: str, skip_list_file: str): diff --git a/tests/model_hub_tests/transformation_tests/models/transformations-models-precommit b/tests/model_hub_tests/transformation_tests/models/transformations-models-precommit new file mode 100644 index 00000000000000..b3068ffbb52840 --- /dev/null +++ b/tests/model_hub_tests/transformation_tests/models/transformations-models-precommit @@ -0,0 +1,27 @@ +hf-internal-testing/tiny-random-LlamaForCausalLM,https://huggingface.co/trl-internal-testing/tiny-random-LlamaForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-GPTJForCausalLM,https://huggingface.co/trl-internal-testing/tiny-random-GPTJForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-GPTNeoXForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-GPTNeoXForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-MistralForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-MistralForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-CodeGenForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-CodeGenForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/Mixtral-tiny,https://huggingface.co/hf-internal-testing/Mixtral-tiny,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-Starcoder2ForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-Starcoder2ForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-PhiForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-PhiForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-StableLmForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-StableLmForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-PersimmonForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-PersimmonForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +hf-internal-testing/tiny-random-FalconForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-FalconForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-xverse,https://huggingface.co/katuni4ka/tiny-random-xverse,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-qwen,https://huggingface.co/katuni4ka/tiny-random-qwen,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-aquilachat,https://huggingface.co/katuni4ka/tiny-random-aquilachat,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-aquila2,https://huggingface.co/katuni4ka/tiny-random-aquila2,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-qwen1.5-moe,https://huggingface.co/katuni4ka/tiny-random-qwen1.5-moe,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-olmo-hf,https://huggingface.co/katuni4ka/tiny-random-olmo-hf,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-baichuan2,https://huggingface.co/katuni4ka/tiny-random-baichuan2,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-internlm,https://huggingface.co/katuni4ka/tiny-random-internlm,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-internlm2,https://huggingface.co/katuni4ka/tiny-random-internlm2,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-minicpm,https://huggingface.co/katuni4ka/tiny-random-minicpm,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-falcon-40b,https://huggingface.co/katuni4ka/tiny-random-falcon-40b,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +katuni4ka/tiny-random-dbrx,https://huggingface.co/katuni4ka/tiny-random-dbrx,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +fxmarty/tiny-random-GemmaForCausalLM,https://huggingface.co/fxmarty/tiny-random-GemmaForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +fxmarty/tiny-dummy-qwen2,https://huggingface.co/fxmarty/tiny-dummy-qwen2,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +fxmarty/really-tiny-falcon-testing,https://huggingface.co/fxmarty/really-tiny-falcon-testing,,,ts_name:ov::pass::RoPEFusion,layer:RoPE +Xenova/tiny-random-Phi3ForCausalLM,https://huggingface.co/Xenova/tiny-random-Phi3ForCausalLM,,,ts_name:ov::pass::RoPEFusion,layer:RoPE diff --git a/tests/model_hub_tests/transformation_tests/test_transformations.py b/tests/model_hub_tests/transformation_tests/test_transformations.py new file mode 100644 index 00000000000000..235fb2cf10b6f3 --- /dev/null +++ b/tests/model_hub_tests/transformation_tests/test_transformations.py @@ -0,0 +1,98 @@ +# Copyright (C) 2018-2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + + +from optimum.intel import OVModelForCausalLM +import models_hub_common.utils as utils +import pytest +import os +import openvino as ov +import tempfile +from collections import deque +import csv + + +def parse_transformations_log(file_name): + with open(file_name, 'r') as f_in: + csv_reader = csv.reader(f_in, delimiter=';') + for line in csv_reader: + if line[0] != 't': + continue + ts_name = line[1] + status = line[4] + yield ts_name, status + + +def check_transformations(file_name, ts_names): + not_executed = deque(ts_names) + false_executed = set() + for ts_name, status in parse_transformations_log(file_name): + if not not_executed and not false_executed: + break + for _ in range(len(not_executed)): + not_executed_name = not_executed.popleft() + if not_executed_name == ts_name: + if status != '1': + false_executed.add(not_executed_name) + break + not_executed.append(not_executed_name) + if ts_name in false_executed and status == '1': + false_executed.remove(ts_name) + if not_executed or false_executed: + fail_text = '' + if not_executed: + not_executed_names = ','.join(not_executed) + fail_text = f'transformation(s) {not_executed_names} not executed' + if false_executed: + false_executed_names = ','.join(false_executed) + if bool(fail_text): + fail_text += '; ' + fail_text += f'transformation(s) {false_executed_names} executed with false return' + pytest.fail(fail_text) + + +def check_operations(actual_layer_types, expected_layer_types): + not_found = [layer for layer in expected_layer_types if layer not in actual_layer_types] + if not_found: + names = ','.join(not_found) + pytest.fail(f'operation(s) {names} not found in compiled model') + + +class EnvVar: + def __init__(self, env_vars): + self.__vars = env_vars + + def __enter__(self): + for name, value in self.__vars.items(): + os.environ[name] = value + + def __exit__(self, exc_type, exc_val, exc_tb): + for name in self.__vars: + del os.environ[name] + + +def run_test(model_id, ie_device, ts_names, expected_layer_types): + model = OVModelForCausalLM.from_pretrained(model_id, export=True, trust_remote_code=True) + + with tempfile.NamedTemporaryFile(delete=True) as temp_file, \ + EnvVar({'OV_ENABLE_PROFILE_PASS': temp_file.name}): + core = ov.Core() + compiled = core.compile_model(model.model, ie_device) + check_transformations(temp_file.name, ts_names) + ov_model = compiled.get_runtime_model() + type_names = {op.get_rt_info()["layerType"] for op in ov_model.get_ordered_ops()} + check_operations(type_names, expected_layer_types) + + +@pytest.mark.precommit +@pytest.mark.parametrize("model_name, model_link, mark, reason, ts_names, layer_types", utils.get_models_list(os.path.join(os.path.dirname(__file__), "models", "transformations-models-precommit"))) +def test_transformations_precommit(tmp_path, model_name, model_link, mark, reason, ie_device, ts_names, layer_types): + assert mark is None or mark == 'skip' or mark == 'xfail', \ + "Incorrect test case: {}, {}".format(model_name, model_link) + if mark == 'skip': + pytest.skip(reason) + elif mark == 'xfail': + pytest.xfail(reason) + if not ts_names and not layer_types: + return + run_test(model_name, ie_device, ts_names, layer_types) From 9ae8c658bcb6b8cccc1d0f91a1590a2794578b35 Mon Sep 17 00:00:00 2001 From: David Pava Date: Fri, 16 Aug 2024 22:49:55 +0300 Subject: [PATCH 03/23] [NPU] Adding a model set property test for Workload Type (#26070) ### Details: - Adding a test in which we set the WorkloadType property for a compiled model and validate that it changed. ### Tickets: - EISW-134862 --- .../internal/overload/compile_and_infer.hpp | 23 +++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/src/plugins/intel_npu/tests/functional/internal/overload/compile_and_infer.hpp b/src/plugins/intel_npu/tests/functional/internal/overload/compile_and_infer.hpp index d35f0b393c02e5..03df711402c8e4 100644 --- a/src/plugins/intel_npu/tests/functional/internal/overload/compile_and_infer.hpp +++ b/src/plugins/intel_npu/tests/functional/internal/overload/compile_and_infer.hpp @@ -183,6 +183,29 @@ TEST_P(OVCompileAndInferRequest, CompiledModelWorkloadTypeDelayedExecutor) { } } +TEST_P(OVCompileAndInferRequest, CompiledModelWorkloadTypeUpdateAfterCompilation) { + if (isCommandQueueExtSupported()) { + configuration[workload_type.name()] = WorkloadType::DEFAULT; + OV_ASSERT_NO_THROW(execNet = core->compile_model(function, target_device, configuration)); + + ASSERT_EQ(execNet.get_property(workload_type.name()).as(), WorkloadType::DEFAULT); + ov::AnyMap modelConfiguration; + modelConfiguration[workload_type.name()] = WorkloadType::EFFICIENT; + OV_ASSERT_NO_THROW(execNet.set_property(modelConfiguration)); + ASSERT_EQ(execNet.get_property(workload_type.name()).as(), WorkloadType::EFFICIENT); + ov::InferRequest req; + OV_ASSERT_NO_THROW(req = execNet.create_infer_request()); + bool is_called = false; + OV_ASSERT_NO_THROW(req.set_callback([&](std::exception_ptr exception_ptr) { + ASSERT_EQ(exception_ptr, nullptr); + is_called = true; + })); + OV_ASSERT_NO_THROW(req.start_async()); + OV_ASSERT_NO_THROW(req.wait()); + ASSERT_TRUE(is_called); + } +} + using OVCompileAndInferRequestTurbo = OVCompileAndInferRequest; TEST_P(OVCompileAndInferRequestTurbo, CompiledModelTurbo) { From 3692cf8fcb92343814c6bf7edd902dcaa6dfe015 Mon Sep 17 00:00:00 2001 From: Xuejun Zhai Date: Sun, 18 Aug 2024 18:05:11 +0800 Subject: [PATCH 04/23] Fix PVS-Studio static analyzer issues (#25897) ### Details: - Static analyzer issues: https://pvs-studio.com/en/blog/posts/cpp/1137/ - *...* ### Tickets: - https://github.com/openvinotoolkit/openvino/issues/25247 --------- Signed-off-by: Zhai, Xuejun --- .../shuffle_channels_fusion.cpp | 2 +- .../op_conversions/convert_pad_to_group_conv.cpp | 2 +- src/core/src/pattern/matcher.cpp | 2 +- .../frontend/src/detail/subgraph_extraction.cpp | 2 +- .../emitters/plugin/x64/jit_eltwise_emitters.cpp | 8 ++++---- src/plugins/intel_cpu/src/node.cpp | 14 ++++++++------ src/plugins/intel_cpu/src/nodes/bin_conv.cpp | 2 +- src/plugins/intel_cpu/src/nodes/fake_quantize.cpp | 2 +- .../src/nodes/kernels/x64/grid_sample.cpp | 6 +++--- src/plugins/template/src/variable_state.hpp | 4 ++-- 10 files changed, 23 insertions(+), 21 deletions(-) diff --git a/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp index 6b6d3081fe434c..8b2407f4d99d84 100644 --- a/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp +++ b/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp @@ -112,7 +112,7 @@ ov::pass::ShuffleChannelsFusion::ShuffleChannelsFusion(const bool reshape_consta std::dynamic_pointer_cast(pattern_map.at(reshape_after_pattern).get_node_shared_ptr()); auto reshape_after_constant = std::dynamic_pointer_cast( pattern_map.at(reshape_after_const_pattern).get_node_shared_ptr()); - if (!reshape_after || !transpose || !reshape_after || !reshape_before_constant || !reshape_after_constant) { + if (!reshape_after || !transpose || !reshape_before || !reshape_before_constant || !reshape_after_constant) { return false; } diff --git a/src/common/transformations/src/transformations/op_conversions/convert_pad_to_group_conv.cpp b/src/common/transformations/src/transformations/op_conversions/convert_pad_to_group_conv.cpp index be3233a45ad811..c7d01d732b60b7 100644 --- a/src/common/transformations/src/transformations/op_conversions/convert_pad_to_group_conv.cpp +++ b/src/common/transformations/src/transformations/op_conversions/convert_pad_to_group_conv.cpp @@ -64,7 +64,7 @@ ov::pass::ConvertPadToGroupConvolution::ConvertPadToGroupConvolution() { return a < 0; }; if (std::any_of(pad_begin.begin(), pad_begin.end(), pred) || - std::any_of(pad_begin.begin(), pad_begin.end(), pred)) { + std::any_of(pad_end.begin(), pad_end.end(), pred)) { return false; } diff --git a/src/core/src/pattern/matcher.cpp b/src/core/src/pattern/matcher.cpp index 0ab96573f010bb..c01f7b58d9c1e5 100644 --- a/src/core/src/pattern/matcher.cpp +++ b/src/core/src/pattern/matcher.cpp @@ -44,7 +44,7 @@ MatcherState::~MatcherState() { m_matcher->m_matched_list.end()); } - if (!m_pattern_value_maps.empty()) { + if (!m_matcher->m_pattern_value_maps.empty()) { m_matcher->m_pattern_value_maps.erase(m_pattern_value_maps.begin() + m_capture_size, m_pattern_value_maps.end()); } diff --git a/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp b/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp index 7822e47d54b70d..65fed5f1fe70a2 100644 --- a/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp +++ b/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp @@ -297,7 +297,7 @@ void SubgraphExtractor::add_new_inputs(const std::vector& new_inputs, input_consumers = 0; // get index of the current edge from new_inputs in order to pass it to append_new_graph_input() - auto it = std::find_if(new_inputs.begin(), new_inputs.begin(), [&](const InputEdge& input_edge) { + auto it = std::find_if(new_inputs.begin(), new_inputs.end(), [&](const InputEdge& input_edge) { return get_input_tensor_name(m_onnx_graph, input_edge) == input.first; }); index = static_cast(std::distance(new_inputs.begin(), it)); diff --git a/src/plugins/intel_cpu/src/emitters/plugin/x64/jit_eltwise_emitters.cpp b/src/plugins/intel_cpu/src/emitters/plugin/x64/jit_eltwise_emitters.cpp index 4f1ca1c4185132..1c90af2d48e85a 100644 --- a/src/plugins/intel_cpu/src/emitters/plugin/x64/jit_eltwise_emitters.cpp +++ b/src/plugins/intel_cpu/src/emitters/plugin/x64/jit_eltwise_emitters.cpp @@ -702,7 +702,7 @@ void jit_power_dynamic_emitter::emit_isa(const std::vector &in_vec_idxs, // caller obligation to save k-regs as callee may use them size_t n_k_regs_to_save = 8; - if (isa == x64::avx512_core || isa == x64::avx512_core) { + if (isa == x64::avx512_core) { h->sub(h->rsp, n_k_regs_to_save * k_mask_size); for (size_t i = 0; i < n_k_regs_to_save; ++i) { if (x64::mayiuse(x64::avx512_core)) @@ -751,7 +751,7 @@ void jit_power_dynamic_emitter::emit_isa(const std::vector &in_vec_idxs, h->add(h->rsp, (get_max_vecs_count() + 2) * get_vec_length()); // restore k registers - if (isa == x64::avx512_core || isa == x64::avx512_core) { + if (isa == x64::avx512_core) { for (int i = n_k_regs_to_save - 1; i >= 0; --i) { if (x64::mayiuse(x64::avx512_core)) h->kmovq(Opmask(i), h->ptr[h->rsp + i * k_mask_size]); @@ -1606,7 +1606,7 @@ void jit_power_static_emitter::emit_isa(const std::vector &in_vec_idxs, // caller obligation to save k-regs as callee may use them size_t n_k_regs_to_save = 8; - if (isa == x64::avx512_core || isa == x64::avx512_core) { + if (isa == x64::avx512_core) { h->sub(h->rsp, n_k_regs_to_save * k_mask_size); for (size_t i = 0; i < n_k_regs_to_save; ++i) { if (x64::mayiuse(x64::avx512_core)) @@ -1655,7 +1655,7 @@ void jit_power_static_emitter::emit_isa(const std::vector &in_vec_idxs, h->add(h->rsp, (get_max_vecs_count() + 2) * get_vec_length()); // restore k registers - if (isa == x64::avx512_core || isa == x64::avx512_core) { + if (isa == x64::avx512_core) { for (int i = n_k_regs_to_save - 1; i >= 0; --i) { if (x64::mayiuse(x64::avx512_core)) h->kmovq(Opmask(i), h->ptr[h->rsp + i * k_mask_size]); diff --git a/src/plugins/intel_cpu/src/node.cpp b/src/plugins/intel_cpu/src/node.cpp index 326b3e907dcc8f..5f9ca8f02ee4ce 100644 --- a/src/plugins/intel_cpu/src/node.cpp +++ b/src/plugins/intel_cpu/src/node.cpp @@ -396,9 +396,10 @@ void Node::resolveInPlaceEdges(Edge::LOOK look) { MemoryDescPtr Node::getBaseMemDescAtInputPort(size_t portNum) const { if (auto primDesc = getSelectedPrimitiveDescriptor()) { const auto& inConfs = primDesc->getConfig().inConfs; - if (inConfs.size() < portNum) { - OPENVINO_THROW("Can't get input memory desc at port: ", portNum, ", incorrect port number"); - } + OPENVINO_ASSERT(portNum < inConfs.size(), + "Can't get input memory desc at port: ", + portNum, + ", incorrect port number"); return inConfs[portNum].getMemDesc(); } OPENVINO_THROW("Can't get input memory desc, primitive descriptor is not selected"); @@ -407,9 +408,10 @@ MemoryDescPtr Node::getBaseMemDescAtInputPort(size_t portNum) const { MemoryDescPtr Node::getBaseMemDescAtOutputPort(size_t portNum) const { if (auto primDesc = getSelectedPrimitiveDescriptor()) { const auto& outConfs = primDesc->getConfig().outConfs; - if (outConfs.size() < portNum) { - OPENVINO_THROW("Can't get output memory desc at port: ", portNum, ", incorrect port number"); - } + OPENVINO_ASSERT(portNum < outConfs.size(), + "Can't get output memory desc at port: ", + portNum, + ", incorrect port number"); return outConfs[portNum].getMemDesc(); } OPENVINO_THROW("Can't get output memory desc, primitive descriptor is not selected"); diff --git a/src/plugins/intel_cpu/src/nodes/bin_conv.cpp b/src/plugins/intel_cpu/src/nodes/bin_conv.cpp index a54c4b76ea8feb..f2de2d20a17528 100644 --- a/src/plugins/intel_cpu/src/nodes/bin_conv.cpp +++ b/src/plugins/intel_cpu/src/nodes/bin_conv.cpp @@ -1085,7 +1085,7 @@ void BinaryConvolution::createPrimitive() { int r_pad_no_tail = nstl::max(0, (jcp.ow - jcp.ur_w_tail - 1) * jcp.stride_w + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1)); - bool args_ok = jcp.l_pad <= jcp.ur_w && (r_pad_no_tail <= jcp.ur_w) && (jcp.l_pad <= jcp.ur_w) && + bool args_ok = (jcp.l_pad <= jcp.ur_w) && (r_pad_no_tail <= jcp.ur_w) && IMPLICATION(jcp.kw > 7, (jcp.t_pad == 0 && jcp.l_pad == 0) || (jcp.stride_w == 1 && jcp.stride_h == 1)); if (!args_ok) OPENVINO_THROW("BinaryConvolution with name '", getName(), "' has unsupported parameters"); diff --git a/src/plugins/intel_cpu/src/nodes/fake_quantize.cpp b/src/plugins/intel_cpu/src/nodes/fake_quantize.cpp index fa823d79c3c208..f12ab40cf5643b 100644 --- a/src/plugins/intel_cpu/src/nodes/fake_quantize.cpp +++ b/src/plugins/intel_cpu/src/nodes/fake_quantize.cpp @@ -1298,7 +1298,7 @@ void FakeQuantize::getSupportedDescriptors() { if (getChildEdges().empty()) OPENVINO_THROW(errorPrefix, "has incorrect number of output edges: ", getChildEdges().size()); - if (getInputShapeAtPort(0).getRank() != getInputShapeAtPort(0).getRank()) { + if (getInputShapeAtPort(0).getRank() != getOutputShapeAtPort(0).getRank()) { OPENVINO_THROW(errorPrefix, "has different ranks for input and output tensors"); } diff --git a/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp b/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp index e56de2dd892bea..d91688689b86c0 100644 --- a/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp +++ b/src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp @@ -922,7 +922,7 @@ void GridSampleKernel::reflectionPadding(const Vmm& vCoordDst, const Vmm& v mov(rAux, ptr[regParams + GET_OFF(srcWidthMul2Sub1F)]); uni_vmovups(vAux1, ptr[rAux]); } - } else if (coord::h) { + } else if (dim == coord::h) { if (vSrcHeightMul2Sub1F.isInitialized()) { vMul2Sub1 = vSrcHeightMul2Sub1F; } else { @@ -956,7 +956,7 @@ void GridSampleKernel::reflectionPadding(const Vmm& vCoordDst, const Vmm& v mov(rAux, ptr[regParams + GET_OFF(srcWidthMul2F)]); uni_vmovups(vAux1, ptr[rAux]); } - } else if (coord::h) { + } else if (dim == coord::h) { if (vSrcHeightMul2F.isInitialized()) { vMul2 = vSrcHeightMul2F; } else { @@ -987,7 +987,7 @@ void GridSampleKernel::reflectionPadding(const Vmm& vCoordDst, const Vmm& v mov(rAux, ptr[regParams + GET_OFF(srcWidthMul2Sub1F)]); uni_vsubps(vAux0, vCoordDst, ptr[rAux]); } - } else if (coord::h) { + } else if (dim == coord::h) { if (vSrcHeightMul2Sub1F.isInitialized()) { uni_vsubps(vAux0, vCoordDst, vSrcHeightMul2Sub1F); } else { diff --git a/src/plugins/template/src/variable_state.hpp b/src/plugins/template/src/variable_state.hpp index 22517b5e3e9694..8227a22c0fe93c 100644 --- a/src/plugins/template/src/variable_state.hpp +++ b/src/plugins/template/src/variable_state.hpp @@ -20,8 +20,8 @@ class VariableState : public ov::IVariableState { } void set_state(const ov::SoPtr& state) override { OPENVINO_ASSERT(state->get_shape() == m_state->get_shape(), "Wrong tensor shape."); - OPENVINO_ASSERT(state->get_element_type() == state->get_element_type(), "Wrong tensor type."); - OPENVINO_ASSERT(state->get_byte_size() == state->get_byte_size(), "Blob size of tensors are not equal."); + OPENVINO_ASSERT(state->get_element_type() == m_state->get_element_type(), "Wrong tensor type."); + OPENVINO_ASSERT(state->get_byte_size() == m_state->get_byte_size(), "Blob size of tensors are not equal."); std::memcpy(m_state->data(), state->data(), state->get_byte_size()); } From 70b729a5c5e45785c889756910d8eae8a776fdc7 Mon Sep 17 00:00:00 2001 From: River Li Date: Mon, 19 Aug 2024 13:13:59 +0800 Subject: [PATCH 05/23] Fix Coverity Scan issue (#26099) ### Details: - Fix CS issues: Performance inefficiencies (AUTO_CAUSES_COPY) Using the "auto" keyword without an "&" causes the copy of an object of type "ov::PartialShape". ### Tickets: - *ticket-id* --- .../transformations/decompose_reduce_scalar_output.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/plugins/intel_gpu/src/plugin/transformations/decompose_reduce_scalar_output.cpp b/src/plugins/intel_gpu/src/plugin/transformations/decompose_reduce_scalar_output.cpp index 7bca11f9143393..99503babd5750e 100644 --- a/src/plugins/intel_gpu/src/plugin/transformations/decompose_reduce_scalar_output.cpp +++ b/src/plugins/intel_gpu/src/plugin/transformations/decompose_reduce_scalar_output.cpp @@ -33,14 +33,14 @@ ov::intel_gpu::DecomposeReduceForScalarOutput::DecomposeReduceForScalarOutput() { auto check_reduce_shape = [=](Output output) -> bool { const auto reduce = ov::as_type_ptr(output.get_node_shared_ptr()); - const auto input_shape = reduce->input_value(0).get_partial_shape(); - const auto reduce_shape = reduce->input_value(1).get_partial_shape(); + auto& input_shape = reduce->input_value(0).get_partial_shape(); + auto& reduce_shape = reduce->input_value(1).get_partial_shape(); if (reduce_shape.is_dynamic() || reduce_shape.size() != 1) { return false; } else if (reduce_shape.to_shape()[0] <= 1 || reduce_shape.to_shape()[0] != input_shape.size()) { return false; } - const auto output_shape = reduce->get_output_partial_shape(0); + auto& output_shape = reduce->get_output_partial_shape(0); if (output_shape.is_static() && input_shape.is_static()) { // Output size decides at most how many EU threads can be used for this node execution, // less than 4 EU threads to execute a primitive will lead to poor performance. @@ -69,8 +69,8 @@ ov::intel_gpu::DecomposeReduceForScalarOutput::DecomposeReduceForScalarOutput() if (!reduce_orig || transformation_callback(reduce_orig)) return false; - const auto input_shape = reduce_orig->input_value(0).get_partial_shape(); - const auto output_shape = reduce_orig->get_output_partial_shape(0); + auto& input_shape = reduce_orig->input_value(0).get_partial_shape(); + auto& output_shape = reduce_orig->get_output_partial_shape(0); bool dynamic_shape = input_shape.is_dynamic() || output_shape.is_dynamic(); std::shared_ptr reduce_new = nullptr; if (!dynamic_shape) { From 0a48811933f89ff6d2e02a88355798177894bbc1 Mon Sep 17 00:00:00 2001 From: Aleksandr Voron Date: Mon, 19 Aug 2024 07:24:27 +0200 Subject: [PATCH 06/23] [CPU][ARM] Disable ACL MVN executor for NHWC and `across_channels` = false (#26089) ### Details: - Reverts PR https://github.com/openvinotoolkit/openvino/pull/25905 - Fixed layout check in `isSupported` method to cover both 4D and 5D cases - Removed workaround that skips unsupported cases (NHWC\NDHWC + `across_channels` = false) on ARM - Moved tests with unsupported cases from common to x64 dir ### Tickets: - *ticket-id* --- .../src/nodes/executors/acl/acl_mvn.cpp | 39 ++++++ .../src/nodes/executors/acl/acl_mvn.hpp | 32 +---- .../custom/single_layer_tests/classes/mvn.cpp | 52 +++++--- .../custom/single_layer_tests/classes/mvn.hpp | 8 +- .../instances/common/mvn.cpp | 122 ++++++++++-------- .../single_layer_tests/instances/x64/mvn.cpp | 89 +++++++++---- 6 files changed, 215 insertions(+), 127 deletions(-) diff --git a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_mvn.cpp b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_mvn.cpp index 120e957740ae41..6fde4bb0db5604 100644 --- a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_mvn.cpp +++ b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_mvn.cpp @@ -72,5 +72,44 @@ void AclMVNExecutor::exec(const std::vector& src, const std::vector< dstTensor.allocator()->free(); } +bool AclMVNExecutorBuilder::isSupported(const MVNAttrs& mvnAttrs, + const std::vector& srcDescs, + const std::vector& dstDescs) const { + if ((srcDescs[0]->getPrecision() != ov::element::f32 && + srcDescs[0]->getPrecision() != ov::element::f16) || + srcDescs[0]->getPrecision() != dstDescs[0]->getPrecision()) { + DEBUG_LOG("NEMeanStdDevNormalizationLayer does not support precisions:", + " src[0]=", srcDescs[0]->getPrecision(), + " dst[0]=", dstDescs[0]->getPrecision()); + return false; + } + + if (!(srcDescs[0]->hasLayoutType(LayoutType::ncsp) && + dstDescs[0]->hasLayoutType(LayoutType::ncsp)) && + !(srcDescs[0]->hasLayoutType(LayoutType::nspc) && + dstDescs[0]->hasLayoutType(LayoutType::nspc))) { + DEBUG_LOG("NEMeanStdDevNormalizationLayer does not support layout:", + " src: ", srcDescs[0]->serializeFormat(), + " dst: ", dstDescs[0]->serializeFormat()); + return false; + } + + if (mvnAttrs.epsMode_ == MVNEpsMode::OUTSIDE_SQRT) { + DEBUG_LOG("NEMeanStdDevNormalizationLayer does not support OUTSIDE_SQRT mode"); + return false; + } + if (!mvnAttrs.normalizeVariance_) { + DEBUG_LOG("NEMeanStdDevNormalizationLayer supports normalize_variance=true only"); + return false; + } + if (!mvnAttrs.initAcrossChannels_ && + srcDescs[0]->hasLayoutType(LayoutType::nspc)) { + DEBUG_LOG("initAcrossChannels = false is not supported by ACL for NHWC layout"); + return false; + } + + return true; + } + } // namespace intel_cpu } // namespace ov diff --git a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_mvn.hpp b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_mvn.hpp index f8c5603d3e529e..7ba445253f8d02 100644 --- a/src/plugins/intel_cpu/src/nodes/executors/acl/acl_mvn.hpp +++ b/src/plugins/intel_cpu/src/nodes/executors/acl/acl_mvn.hpp @@ -40,37 +40,7 @@ class AclMVNExecutorBuilder : public MVNExecutorBuilder { public: bool isSupported(const MVNAttrs& mvnAttrs, const std::vector& srcDescs, - const std::vector& dstDescs) const override { - if ((srcDescs[0]->getPrecision() != ov::element::f32 && - srcDescs[0]->getPrecision() != ov::element::f16) || - srcDescs[0]->getPrecision() != dstDescs[0]->getPrecision()) { - DEBUG_LOG("NEMeanStdDevNormalizationLayer does not support precisions:", - " src[0]=", srcDescs[0]->getPrecision(), - " dst[0]=", dstDescs[0]->getPrecision()); - return false; - } - - if (!(srcDescs[0]->hasLayoutType(LayoutType::ncsp) && - dstDescs[0]->hasLayoutType(LayoutType::ncsp)) && - !(srcDescs[0]->hasLayoutType(LayoutType::nspc) && - dstDescs[0]->hasLayoutType(LayoutType::nspc))) { - DEBUG_LOG("NEMeanStdDevNormalizationLayer does not support layout:", - " src: ", srcDescs[0]->serializeFormat(), - " dst: ", dstDescs[0]->serializeFormat()); - return false; - } - - if (mvnAttrs.epsMode_ == MVNEpsMode::OUTSIDE_SQRT) { - DEBUG_LOG("NEMeanStdDevNormalizationLayer does not support OUTSIDE_SQRT mode"); - return false; - } - if (!mvnAttrs.normalizeVariance_) { - DEBUG_LOG("NEMeanStdDevNormalizationLayer supports normalize_variance=true only"); - return false; - } - - return true; - } + const std::vector& dstDescs) const override; MVNExecutorPtr makeExecutor(const ExecutorContext::CPtr context) const override { return std::make_shared(context); diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.cpp index 598447f8ec8363..2537fb4e87a5db 100644 --- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.cpp +++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.cpp @@ -61,17 +61,6 @@ std::string MvnLayerCPUTest::getTestCaseName(testing::TestParamInfo& epsilon() { return epsilon; } +const std::vector& cpuParams_4D() { + static const std::vector cpuParams_4D = { + CPUSpecificParams({nchw}, {nchw}, {}, {}), + CPUSpecificParams({nhwc}, {nhwc}, {}, {}), + }; + return cpuParams_4D; +} + +const std::vector& cpuParams_5D() { + static const std::vector cpuParams_5D = { + CPUSpecificParams({ncdhw}, {ncdhw}, {}, {}), + CPUSpecificParams({ndhwc}, {ndhwc}, {}, {}), + }; + return cpuParams_5D; +} + +const std::vector& inpPrc() { + static const std::vector inpPrc = { + ElementType::i8, + ElementType::f32, + }; + return inpPrc; +} + +const std::vector& outPrc() { + static const std::vector outPrc = { + ElementType::f32, + }; + return outPrc; +} + +const std::vector& fusingParamsSet() { + static const std::vector fusingParamsSet = { + emptyFusingSpec, + }; + return fusingParamsSet; +} } // namespace MVN } // namespace test } // namespace ov diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.hpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.hpp index daa002fe559660..6c76360bc12420 100644 --- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.hpp +++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.hpp @@ -35,7 +35,6 @@ class MvnLayerCPUTest : public testing::WithParamInterface obj); - bool isSupportedTestCase(); protected: void SetUp() override; private: @@ -58,6 +57,13 @@ namespace MVN { const std::vector& acrossChannels(); const std::vector& epsilon(); + const std::vector& cpuParams_4D(); + const std::vector& cpuParams_5D(); + + const std::vector& inpPrc(); + const std::vector& outPrc(); + + const std::vector& fusingParamsSet(); const std::vector& additionalConfig(); } // namespace MVN } // namespace test diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/mvn.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/mvn.cpp index 30cfdacde5291e..1140beeb1db26a 100644 --- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/mvn.cpp +++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/mvn.cpp @@ -17,26 +17,12 @@ const std::vector normalizeVariance = { true }; -std::vector inpPrc = { - ElementType::i8, - ElementType::f32, -}; -std::vector outPrc = { - ElementType::f32, -}; - -std::vector cpuParams_4D = { - CPUSpecificParams({nchw}, {nchw}, {}, {}), - CPUSpecificParams({nhwc}, {nhwc}, {}, {}), -}; - -std::vector cpuParams_5D = { - CPUSpecificParams({ncdhw}, {ncdhw}, {}, {}), - CPUSpecificParams({ndhwc}, {ndhwc}, {}, {}), +const std::vector cpuParams_4D_ncsp = { + CPUSpecificParams({nchw}, {nchw}, {}, {}) }; -std::vector fusingParamsSet { - emptyFusingSpec, +const std::vector cpuParams_5D_ncsp = { + CPUSpecificParams({ncdhw}, {ncdhw}, {}, {}) }; std::vector fusingParamsSetStaticShape { @@ -52,44 +38,76 @@ const auto Mvn3D = ::testing::Combine( ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), - ::testing::ValuesIn(fusingParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(fusingParamsSet()), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn3D, MvnLayerCPUTest, Mvn3D, MvnLayerCPUTest::getTestCaseName); -const auto Mvn4D = ::testing::Combine( +const auto Mvn4D_across_channels = ::testing::Combine( ::testing::Combine( ::testing::ValuesIn(inputShapes_4D()), ::testing::Values(ElementType::f32), ::testing::ValuesIn(emptyReductionAxes()), - ::testing::ValuesIn(acrossChannels()), + ::testing::Values(true), ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), - ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)), - ::testing::ValuesIn(fusingParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())), + ::testing::ValuesIn(fusingParamsSet()), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), + ::testing::ValuesIn(additionalConfig())); + +INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn4D_across_channels, MvnLayerCPUTest, Mvn4D_across_channels, MvnLayerCPUTest::getTestCaseName); + +const auto Mvn4D_no_across_channels_ncsp = ::testing::Combine( + ::testing::Combine( + ::testing::ValuesIn(inputShapes_4D()), + ::testing::Values(ElementType::f32), + ::testing::ValuesIn(emptyReductionAxes()), + ::testing::Values(false), + ::testing::Values(true), + ::testing::ValuesIn(epsilon())), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_ncsp)), + ::testing::ValuesIn(fusingParamsSet()), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); -INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn4D, MvnLayerCPUTest, Mvn4D, MvnLayerCPUTest::getTestCaseName); +INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn4D_no_across_channels_ncsp, MvnLayerCPUTest, Mvn4D_no_across_channels_ncsp, MvnLayerCPUTest::getTestCaseName); -const auto Mvn5D = ::testing::Combine( +const auto Mvn5D_across_channels = ::testing::Combine( ::testing::Combine( ::testing::ValuesIn(inputShapes_5D()), ::testing::Values(ElementType::f32), ::testing::ValuesIn(emptyReductionAxes()), - ::testing::ValuesIn(acrossChannels()), + ::testing::Values(true), ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), - ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D)), - ::testing::ValuesIn(fusingParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D())), + ::testing::ValuesIn(fusingParamsSet()), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), + ::testing::ValuesIn(additionalConfig())); + +INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn5D_across_channels, MvnLayerCPUTest, Mvn5D_across_channels, MvnLayerCPUTest::getTestCaseName); + +const auto Mvn5D_no_across_channels_ncsp = ::testing::Combine( + ::testing::Combine( + ::testing::ValuesIn(inputShapes_5D()), + ::testing::Values(ElementType::f32), + ::testing::ValuesIn(emptyReductionAxes()), + ::testing::Values(false), + ::testing::Values(true), + ::testing::ValuesIn(epsilon())), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_ncsp)), + ::testing::ValuesIn(fusingParamsSet()), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); -INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn5D, MvnLayerCPUTest, Mvn5D, MvnLayerCPUTest::getTestCaseName); +INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn5D_no_across_channels_ncsp, MvnLayerCPUTest, Mvn5D_no_across_channels_ncsp, MvnLayerCPUTest::getTestCaseName); // 1D 2D case std::vector fusingUnaryEltwiseParamsSet { @@ -106,8 +124,8 @@ const auto Mvn1D = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingUnaryEltwiseParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn1D, MvnLayerCPUTest, Mvn1D, MvnLayerCPUTest::getTestCaseName); @@ -122,9 +140,9 @@ const auto Mvn2D = ::testing::Combine( ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), - ::testing::ValuesIn(fusingParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(fusingParamsSet()), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn2D, MvnLayerCPUTest, Mvn2D, MvnLayerCPUTest::getTestCaseName); @@ -140,8 +158,8 @@ const auto Mvn2DTrans = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingUnaryEltwiseParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn2DTrans, MvnLayerCPUTest, Mvn2DTrans, MvnLayerCPUTest::getTestCaseName); @@ -156,8 +174,8 @@ const auto Mvn2DStatic = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingParamsSetStaticShape), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); const auto Mvn3DStatic = ::testing::Combine( @@ -170,8 +188,8 @@ const auto Mvn3DStatic = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingParamsSetStaticShape), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn3D_Static, MvnLayerCPUTest, Mvn3DStatic, MvnLayerCPUTest::getTestCaseName); @@ -184,10 +202,10 @@ const auto Mvn4DStatic = ::testing::Combine( ::testing::Values(true), ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), - ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())), ::testing::ValuesIn(fusingParamsSetStaticShape), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn4D_Static, MvnLayerCPUTest, Mvn4DStatic, MvnLayerCPUTest::getTestCaseName); @@ -200,10 +218,10 @@ const auto Mvn5DStatic = ::testing::Combine( ::testing::Values(true), ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), - ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D)), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D())), ::testing::ValuesIn(fusingParamsSetStaticShape), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn5D_Static, MvnLayerCPUTest, Mvn5DStatic, MvnLayerCPUTest::getTestCaseName); diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/mvn.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/mvn.cpp index 25893c59c0a96c..e3edc36741cac5 100644 --- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/mvn.cpp +++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/mvn.cpp @@ -19,18 +19,11 @@ const std::vector normalizeVariance = { false }; -std::vector inpPrc = { - ElementType::bf16 -}; -std::vector outPrc = { - ElementType::bf16 -}; - -std::vector cpuParams_4D = { +std::vector cpuParams_4D_blocked = { CPUSpecificParams({nChw16c}, {nChw16c}, {}, {}) }; -std::vector cpuParams_5D = { +std::vector cpuParams_5D_blocked = { CPUSpecificParams({nCdhw16c}, {nCdhw16c}, {}, {}) }; @@ -64,8 +57,8 @@ const auto Mvn3D = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::Values(ElementType::bf16), + ::testing::Values(ElementType::bf16), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn3D, MvnLayerCPUTest, Mvn3D, MvnLayerCPUTest::getTestCaseName); @@ -89,8 +82,8 @@ const auto Mvn1D = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingUnaryEltwiseParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::Values(ElementType::bf16), + ::testing::Values(ElementType::bf16), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn1D, MvnLayerCPUTest, Mvn1D, MvnLayerCPUTest::getTestCaseName); @@ -106,8 +99,8 @@ const auto Mvn2D = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::Values(ElementType::bf16), + ::testing::Values(ElementType::bf16), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn2D, MvnLayerCPUTest, Mvn2D, MvnLayerCPUTest::getTestCaseName); @@ -123,8 +116,8 @@ const auto Mvn2DTrans = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingUnaryEltwiseParamsSet), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::Values(ElementType::bf16), + ::testing::Values(ElementType::bf16), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn2DTrans, MvnLayerCPUTest, Mvn2DTrans, MvnLayerCPUTest::getTestCaseName); @@ -139,8 +132,8 @@ const auto Mvn2DStatic = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingParamsSetStaticShape), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::Values(ElementType::bf16), + ::testing::Values(ElementType::bf16), ::testing::ValuesIn(additionalConfig())); const auto Mvn3DStatic = ::testing::Combine( @@ -153,8 +146,8 @@ const auto Mvn3DStatic = ::testing::Combine( ::testing::ValuesIn(epsilon())), ::testing::Values(emptyCPUSpec), ::testing::ValuesIn(fusingParamsSetStaticShape), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::Values(ElementType::bf16), + ::testing::Values(ElementType::bf16), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn3D_Static, MvnLayerCPUTest, Mvn3DStatic, MvnLayerCPUTest::getTestCaseName); @@ -167,10 +160,10 @@ const auto Mvn4DStatic = ::testing::Combine( ::testing::Values(false), ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), - ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_blocked)), ::testing::ValuesIn(fusingParamsSetStaticShape), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::Values(ElementType::bf16), + ::testing::Values(ElementType::bf16), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn4D_Static, MvnLayerCPUTest, Mvn4DStatic, MvnLayerCPUTest::getTestCaseName); @@ -198,7 +191,7 @@ const auto Mvn4DStaticCTails = ::testing::Combine( ::testing::Values(false), ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), - ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_blocked)), ::testing::Values(emptyFusingSpec), ::testing::Values(ElementType::f32), ::testing::Values(ElementType::f32), @@ -215,10 +208,10 @@ const auto Mvn5DStatic = ::testing::Combine( ::testing::Values(false), ::testing::ValuesIn(normalizeVariance), ::testing::ValuesIn(epsilon())), - ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D)), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_blocked)), ::testing::ValuesIn(fusingParamsSetStaticShape), - ::testing::ValuesIn(inpPrc), - ::testing::ValuesIn(outPrc), + ::testing::Values(ElementType::bf16), + ::testing::Values(ElementType::bf16), ::testing::ValuesIn(additionalConfig())); INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn5D_Static, MvnLayerCPUTest, Mvn5DStatic, MvnLayerCPUTest::getTestCaseName); @@ -247,6 +240,46 @@ const auto MvnSmallSpatial = ::testing::Combine( INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_MvnSmallSpatial, MvnLayerCPUTest, MvnSmallSpatial, MvnLayerCPUTest::getTestCaseName); +const std::vector cpuParams_4D_nspc = { + CPUSpecificParams({nhwc}, {nhwc}, {}, {}) +}; + +const std::vector cpuParams_5D_nspc = { + CPUSpecificParams({ndhwc}, {ndhwc}, {}, {}) +}; + +const auto Mvn4D_no_across_channels_nspc = ::testing::Combine( + ::testing::Combine( + ::testing::ValuesIn(inputShapes_4D()), + ::testing::Values(ElementType::f32), + ::testing::ValuesIn(emptyReductionAxes()), + ::testing::Values(false), + ::testing::ValuesIn(normalizeVariance), + ::testing::ValuesIn(epsilon())), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D_nspc)), + ::testing::Values(emptyFusingSpec), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), + ::testing::ValuesIn(additionalConfig())); + +INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn4D_no_across_channels_nspc, MvnLayerCPUTest, Mvn4D_no_across_channels_nspc, MvnLayerCPUTest::getTestCaseName); + +const auto Mvn5D_no_across_channels_nspc = ::testing::Combine( + ::testing::Combine( + ::testing::ValuesIn(inputShapes_5D()), + ::testing::Values(ElementType::f32), + ::testing::ValuesIn(emptyReductionAxes()), + ::testing::Values(false), + ::testing::ValuesIn(normalizeVariance), + ::testing::ValuesIn(epsilon())), + ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D_nspc)), + ::testing::Values(emptyFusingSpec), + ::testing::ValuesIn(inpPrc()), + ::testing::ValuesIn(outPrc()), + ::testing::ValuesIn(additionalConfig())); + +INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_Mvn5D_no_across_channels_nspc, MvnLayerCPUTest, Mvn5D_no_across_channels_nspc, MvnLayerCPUTest::getTestCaseName); + } // namespace } // namespace MVN } // namespace test From f06365af7e85f546e713e9c3a0e35ad7b3c84e67 Mon Sep 17 00:00:00 2001 From: Andrew Kwangwoong Park Date: Mon, 19 Aug 2024 17:05:16 +0900 Subject: [PATCH 07/23] [GPU] Fix accuracy issue for StableDiffusion3 (#26032) ### Details: - Fix accuracy issue from runtime buffer fusing for crop(https://github.com/openvinotoolkit/openvino/pull/25060) - If user is MVN and dynamic padding exists, output padding propagation is not supported in the base mode reshape - oneDNN supports padded input of outer axis only for buffer fusing on static shape ### Tickets: - [CVS-149472](https://jira.devtools.intel.com/browse/CVS-149472) --------- Signed-off-by: Andrew Park --- .../src/graph/include/reshape_inst.h | 9 +- .../intel_gpu/src/graph/primitive_inst.cpp | 11 ++- .../passes/prepare_buffer_fusing_test.cpp | 82 +++++++++++++++++++ 3 files changed, 98 insertions(+), 4 deletions(-) diff --git a/src/plugins/intel_gpu/src/graph/include/reshape_inst.h b/src/plugins/intel_gpu/src/graph/include/reshape_inst.h index 7ae1d4792879eb..d5a60138a50777 100644 --- a/src/plugins/intel_gpu/src/graph/include/reshape_inst.h +++ b/src/plugins/intel_gpu/src/graph/include/reshape_inst.h @@ -8,6 +8,7 @@ #include "openvino/core/partial_shape.hpp" #include "crop_inst.h" #include "rope_inst.h" +#include "mvn_inst.h" #include "primitive_inst.h" #include @@ -42,8 +43,12 @@ struct typed_program_node : public typed_program_node_base { if (!input().is_type()) return false; - // TODO: If user is RoPE and dynamic padding exists, ouput padding propagation is not supported in the base mode - if (get_users().size() == 1 && get_users().front()->is_type()) + // oneDNN supports padded input of outer axis only for buffer fusing on static shape + if (!has_outer_padding_offset() && get_users().front()->get_preferred_impl_type() == impl_types::onednn) + return false; + + // TODO: If user is RoPE or MVN and dynamic padding exists, ouput padding propagation is not supported in the base mode + if (get_users().size() == 1 && (get_users().front()->is_type() || get_users().front()->is_type())) return false; auto axis = input().as().get_primitive()->axis; diff --git a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp index 281700ba8770b1..03c2444042623e 100644 --- a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp +++ b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp @@ -1494,12 +1494,19 @@ void primitive_inst::do_runtime_in_place_crop() { if (!crop_in_place_optimization::match(u->get_node(), *u->_impl_params, pred_layout, true)) { u->set_can_be_optimized(false); GPU_DEBUG_TRACE_DETAIL << "[In place crop] " << u->id() << " cannot be optimized " << std::endl; - return; + continue; } auto crop_axis = u->_impl_params->typed_desc()->axis; auto offsets = u->_impl_params->input_offsets[0]; if (crop_in_place_optimization::can_crop_be_optimized_along_feature(crop_layout, pred_layout)) { + // TODO: If crop is optimized out w/ data padding along feature and crop's user is reshape + // manual dynamic padding update to reshape output layout is not currently supported + if (user_info.first) { + u->set_can_be_optimized(false); + GPU_DEBUG_TRACE_DETAIL << "[In place crop] " << u->id() << " cannot be optimized " << std::endl; + continue; + } crop_in_place_optimization::update_in_place_crop_padding_along_feature(u->get_node(), crop_layout, pred_layout, offsets, crop_axis, true); } else if (crop_in_place_optimization::can_crop_be_optimized_simple_data_format(crop_layout, pred_layout)) { crop_in_place_optimization::update_in_place_crop_padding_simple_data_format(crop_layout, pred_layout, user_info, offsets, crop_axis, true); @@ -1511,7 +1518,7 @@ void primitive_inst::do_runtime_in_place_crop() { } else { u->set_can_be_optimized(false); GPU_DEBUG_TRACE_DETAIL << "[In place crop] " << u->id() << " cannot be optimized " << std::endl; - return; + continue; } u->_impl_params->output_layouts[0] = crop_layout; u->set_can_be_optimized(true); diff --git a/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp b/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp index 668003aac77192..c1ccb7bbf0ff7a 100644 --- a/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp +++ b/src/plugins/intel_gpu/tests/unit/passes/prepare_buffer_fusing_test.cpp @@ -20,6 +20,7 @@ #include "shape_of_inst.h" #include "gather_inst.h" #include "strided_slice_inst.h" +#include "mvn_inst.h" #include "intel_gpu/graph/network.hpp" #include "pass_manager.h" #include "to_string_utils.h" @@ -941,6 +942,87 @@ TEST(prepare_buffer_fusing, in_place_crop_dynamic_split_lengths) { ASSERT_EQ(output_ptr_3[i], out3[i]); } +TEST(prepare_buffer_fusing, in_place_crop_dynamic_mvn) { + auto& engine = get_test_engine(); + + auto in_layout = layout{ ov::PartialShape{-1, -1, 4}, data_types::f32, format::bfyx}; + auto input_mem = engine.allocate_memory({ {1, 2, 4}, data_types::f32, format::bfyx }); + auto weights_mem = engine.allocate_memory({ {8, 4}, data_types::u8, format::bfyx }); + auto bias_mem = engine.allocate_memory({ {1, 1, 8}, data_types::f32, format::bfyx }); + auto scale_mem = engine.allocate_memory({ {8, 1}, data_types::f32, format::bfyx }); + auto zp_mem = engine.allocate_memory({ {8, 1}, data_types::f32, format::bfyx }); + auto axis_mem = engine.allocate_memory({ {}, data_types::i64, format::bfyx }); + auto splits_length_mem = engine.allocate_memory({ {2}, data_types::i64, format::bfyx }); + + int64_t axis = 2; + set_values(input_mem, { -0.5f, 2.0f, 0.5f, 1.0f, + 0.5f, -2.0f, -0.5f, -1.0f }); + set_values(axis_mem, {axis}); + set_values(splits_length_mem, { 2, 6 }); + set_values(weights_mem, { 1, 2, 3, 4, + 5, 6, 7, 8, + 9, 10, 11, 12, + 13, 14, 15, 0, + 15, 14, 13, 12, + 11, 10, 9, 8, + 7, 6, 5, 4, + 3, 2, 1, 0}); + set_values(bias_mem, { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, 2.0f }); + set_values(scale_mem, { 2.0f, 4.0f, -2.0f, -4.0f, 0.5f, -0.5f, 2.0f, 2.0f }); + set_values(zp_mem, { 1.0f, 2.0f, 2.0f, 1.0f, 4.0f, 1.0f, 6.0f, 2.0f }); + + std::vector out1 = { 13.f, 58.f, -11.f, -62.f }; + std::vector out2 = { -24.0833f, -81.0833f, 45.4167f, 8.91667f, 27.9167f, 22.9167f, 27.75f, 70.75f, -37.75f, -23.25f, -16.25f, -21.25f }; + std::vector out3 = { 13.f, 58.f, -51.f, -108.f, 18.5f, -18.f, 1.f, -4.f, -11.f, -62.f, 57.f, 100.f, -8.5f, 6.f, 13.f, 8.f }; + + cldnn::crop_ngraph_op_mode op_mode = cldnn::crop_ngraph_op_mode::variadic_split; + topology topology( + input_layout("input", in_layout), + data("axis", axis_mem), + data("splits_length", splits_length_mem), + data("weights", weights_mem), + data("bias", bias_mem), + data("scale", scale_mem), + data("zp", zp_mem), + fully_connected("fc", input_info("input"), "weights", "bias", "scale", "zp", data_types::f32, 3, 2), + crop("crop1", { input_info("fc"), input_info("axis"), input_info("splits_length") }, cldnn::tensor(1), cldnn::tensor(0), op_mode, 0, axis), + reorder("output1", input_info("crop1"), format::bfyx, data_types::f32), + crop("crop2", { input_info("fc"), input_info("axis"), input_info("splits_length") }, cldnn::tensor(1), cldnn::tensor(0), op_mode, 1, axis), + reshape("reshape", input_info("crop2"), true, std::vector{0, 0, 3, 2}, ov::PartialShape{-1, -1, 3, 2}, cldnn::reshape::reshape_mode::base), + mvn("mvn", input_info("reshape"), false, 1e-10f, false, {2, 3}), + reorder("output2", input_info("mvn"), format::bfyx, data_types::f32, std::vector(), reorder_mean_mode::subtract, padding(), true), + reorder("output3", input_info("fc"), format::bfyx, data_types::f32) + ); + + auto config = get_test_default_config(engine); + config.set_property(ov::intel_gpu::allow_new_shape_infer(true)); + config.set_property(ov::intel_gpu::optimize_data(true)); + network network(engine, topology, config); + + network.set_input_data("input", input_mem); + + auto outputs = network.execute(); + + auto output = outputs.at("output1").get_memory(); + cldnn::mem_lock output_ptr(output, get_test_stream()); + + for (size_t i = 0; i < out1.size(); i++) + ASSERT_EQ(output_ptr[i], out1[i]); + + auto output_2 = outputs.at("output2").get_memory(); + cldnn::mem_lock output_ptr_2(output_2, get_test_stream()); + + for (size_t i = 0; i < out2.size(); i++) { + ASSERT_NEAR(output_ptr_2[i], out2[i], 0.0001); + } + + auto output_3 = outputs.at("output3").get_memory(); + cldnn::mem_lock output_ptr_3(output_3, get_test_stream()); + + for (size_t i = 0; i < out3.size(); i++) + ASSERT_EQ(output_ptr_3[i], out3[i]); +} + // Testing for implicit crop along batch axis and outer padding optimzing. // Outer padding opt includes opt out of reshape and reorder which has padded input only in batch axis // This optimzing also includes offset(outer axis padded input) handling of oneDNN primitive. From dcf9605f7fba5ed7a79835d91d3fdedecc73779b Mon Sep 17 00:00:00 2001 From: Sergey Shlyapnikov Date: Mon, 19 Aug 2024 12:35:50 +0400 Subject: [PATCH 08/23] [GPU] Fix accuracy loss in Range due to high value generation for attention mask (#26093) ### Details: - Fixed accuracy loss in the Range operation due to high value generation for the attention mask in cases of long input sequences ### Tickets: - [CVS-146283](https://jira.devtools.intel.com/browse/CVS-146283) --- .../src/transformations/convert_precision.cpp | 2 +- ...k_subgraphs_to_keep_in_mixed_precision.cpp | 71 ++++++++++++++++++ ...bgraph_to_keep_in_mixed_precision_test.cpp | 72 +++++++++++++++++++ 3 files changed, 144 insertions(+), 1 deletion(-) diff --git a/src/common/transformations/src/transformations/convert_precision.cpp b/src/common/transformations/src/transformations/convert_precision.cpp index 3c819d481bacee..ef1e815ab2fad1 100644 --- a/src/common/transformations/src/transformations/convert_precision.cpp +++ b/src/common/transformations/src/transformations/convert_precision.cpp @@ -560,7 +560,7 @@ bool fuse_type_to_range_v4(const std::shared_ptr& node, const precisio return false; const auto& to = it->second; if (auto range = ov::as_type_ptr(node)) { - if (to.is_integral_number() || to.is_real()) { + if (!fp16_compression_is_disabled(node) && (to.is_integral_number() || to.is_real())) { range->set_output_type(to); return true; } diff --git a/src/common/transformations/src/transformations/fp16_compression/mark_subgraphs_to_keep_in_mixed_precision.cpp b/src/common/transformations/src/transformations/fp16_compression/mark_subgraphs_to_keep_in_mixed_precision.cpp index ef367571642eb2..769fa3c72870e9 100644 --- a/src/common/transformations/src/transformations/fp16_compression/mark_subgraphs_to_keep_in_mixed_precision.cpp +++ b/src/common/transformations/src/transformations/fp16_compression/mark_subgraphs_to_keep_in_mixed_precision.cpp @@ -13,13 +13,18 @@ #include "openvino/op/divide.hpp" #include "openvino/op/exp.hpp" #include "openvino/op/fake_quantize.hpp" +#include "openvino/op/greater.hpp" +#include "openvino/op/greater_eq.hpp" #include "openvino/op/interpolate.hpp" +#include "openvino/op/less.hpp" +#include "openvino/op/less_eq.hpp" #include "openvino/op/max_pool.hpp" #include "openvino/op/maximum.hpp" #include "openvino/op/multiply.hpp" #include "openvino/op/mvn.hpp" #include "openvino/op/normalize_l2.hpp" #include "openvino/op/power.hpp" +#include "openvino/op/range.hpp" #include "openvino/op/reduce_max.hpp" #include "openvino/op/reduce_mean.hpp" #include "openvino/op/reduce_min.hpp" @@ -78,6 +83,19 @@ void erase_fq_path(const std::shared_ptr& node) { rt_info.erase("fq_path"); } +void mark_range_path(const std::shared_ptr& node) { + node->get_rt_info().emplace("range_path", true); +} + +bool is_range_path(const std::shared_ptr& node) { + return node->get_rt_info().count("range_path"); +} + +void erase_range_path(const std::shared_ptr& node) { + auto& rt_info = node->get_rt_info(); + rt_info.erase("range_path"); +} + // Marking continues to propagate through these ops. const std::shared_ptr propagate_through_ops = pattern::wrap_type(); + + matcher_pass_callback callback = [=](pattern::Matcher& m) { + const auto& node = m.get_match_root(); + if (!node) + return false; + + auto range = as_type_ptr(node); + if (range && range->get_output_type().is_real()) { + mark_range_path(node); + disable_fp16_compression(node); + return true; + } + + bool is_changed = false; + + for (const auto& in_node_output : node->input_values()) { + auto input_node = in_node_output.get_node_shared_ptr(); + if (is_range_path(input_node)) { + mark_range_path(node); + disable_fp16_compression(node); + is_changed = true; + } + } + + return is_changed; + }; + auto m = make_shared(range_propagating_nodes, matcher_name); + register_matcher(m, callback); + } +}; + class PropagateMarkUpReduceOpPath : public pass::MatcherPass { public: OPENVINO_RTTI("PropagateMarkUpReduceOpPath", "0"); @@ -426,6 +495,7 @@ bool MarkSugraphsToKeepInMixedPrecision::run_on_model(const shared_ptrget_ops()) { erase_reduceop_path(node); erase_fq_path(node); + erase_range_path(node); } return false; // no need to revalidate diff --git a/src/common/transformations/tests/common_optimizations/mark_subgraph_to_keep_in_mixed_precision_test.cpp b/src/common/transformations/tests/common_optimizations/mark_subgraph_to_keep_in_mixed_precision_test.cpp index 95dfeae604e247..669d79ffbf6335 100644 --- a/src/common/transformations/tests/common_optimizations/mark_subgraph_to_keep_in_mixed_precision_test.cpp +++ b/src/common/transformations/tests/common_optimizations/mark_subgraph_to_keep_in_mixed_precision_test.cpp @@ -1087,6 +1087,78 @@ TEST(TransformationTests, MarkDivWithEpsToKeepInMixedPrecision_MinimalPatternUnc ASSERT_TRUE(result.valid) << result.message; } +TEST(TransformationTests, MarkFloatingPointRange) { + shared_ptr model, model_ref; + pass::Manager manager; + { + auto begin = Constant::create(element::i64, Shape{}, {0}); + auto step = Constant::create(element::i64, Shape{}, {1}); + + auto end = make_shared(element::i64, Shape{}); + + auto range_1 = make_shared(begin, end, step, element::f32); + auto range_2 = make_shared(begin, end, step, element::f32); + + auto convert_1 = make_shared(range_1, element::i64); + auto convert_2 = make_shared(convert_1, element::f32); + + auto unsqueeze_const = Constant::create(element::i64, Shape{2}, {-1, 1}); + auto unsqueeze = make_shared(range_2, unsqueeze_const); + + auto greater = make_shared(convert_2, unsqueeze); + auto convert = make_shared(greater, element::f32); + + auto multiply_const = Constant::create(element::f32, Shape{}, {1.f}); + auto multiply = make_shared(convert, multiply_const); + + model = make_shared(NodeVector{convert}, ParameterVector{end}); + + manager.register_pass(); + manager.run_passes(model); + } + + { + auto begin = Constant::create(element::i64, Shape{}, {0}); + auto step = Constant::create(element::i64, Shape{}, {1}); + + auto end = make_shared(element::i64, Shape{}); + + auto range_1 = make_shared(begin, end, step, element::f32); + auto range_2 = make_shared(begin, end, step, element::f32); + + auto convert_1 = make_shared(range_1, element::i64); + auto convert_2 = make_shared(convert_1, element::f32); + + auto unsqueeze_const = Constant::create(element::i64, Shape{2}, {-1, 1}); + auto unsqueeze = make_shared(range_2, unsqueeze_const); + + auto greater = make_shared(convert_2, unsqueeze); + auto convert = make_shared(greater, element::f32); + + auto multiply_const = Constant::create(element::f32, Shape{}, {1.f}); + auto multiply = make_shared(convert, multiply_const); + + // marking nodes to be kept in fp32 for mixed precision + disable_fp16_compression(range_1); + disable_fp16_compression(range_2); + disable_fp16_compression(convert_1); + disable_fp16_compression(convert_2); + disable_fp16_compression(unsqueeze); + disable_fp16_compression(greater); + disable_fp16_compression(convert); + + model_ref = make_shared(NodeVector{convert}, ParameterVector{end}); + } + + const FunctionsComparator func_comparator = + FunctionsComparator::with_default().enable(FunctionsComparator::RUNTIME_KEYS); + // need to compare twice to ensure that no extra nodes are marked + FunctionsComparator::Result result = func_comparator(model_ref, model); + ASSERT_TRUE(result.valid) << result.message; + result = func_comparator(model, model_ref); + ASSERT_TRUE(result.valid) << result.message; +} + TEST(TransformationTests, MarkDivWithEpsToKeepInMixedPrecision_InL2NormWithSqrtAndWithMax) { const float eps_value = 1.e-12f; shared_ptr model, model_ref; From 17fa918f1868c4c80e8395149d4b8a98ff8d0f5e Mon Sep 17 00:00:00 2001 From: Damian Kurek Date: Mon, 19 Aug 2024 11:32:03 +0200 Subject: [PATCH 09/23] [GPU] Improve Group Normalization BFYX OPT reductions (#26098) ### Details: - Improve Group Normalization BFYX OPT kernel performance - Simplify code - Use work group functions - Drop usage of local memory which is not needed anymore - Removed SLM_SIZE for improved host performance and smaller code size ### Tickets: - 149951 --- .../group_normalization_gpu_bfyx_opt.cl | 28 +++---------------- .../group_normalization_kernel_bfyx_opt.cpp | 2 -- 2 files changed, 4 insertions(+), 26 deletions(-) diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/group_normalization_gpu_bfyx_opt.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/group_normalization_gpu_bfyx_opt.cl index ed943c65d5a67e..498b5c8fb80339 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/group_normalization_gpu_bfyx_opt.cl +++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/group_normalization_gpu_bfyx_opt.cl @@ -36,8 +36,6 @@ KERNEL(calc_mean_per_feature)( const uint x_base = get_local_id(0); const uint x_leftover = INPUT0_SIZE_X - x_num_workers * x_block_size; - __local ACCUMULATOR_TYPE mean_per_feature[SLM_SIZE]; - ACCUMULATOR_TYPE mean = ACCUMULATOR_VAL_ZERO; #if INPUT0_DIMS == 5 @@ -120,18 +118,10 @@ KERNEL(calc_mean_per_feature)( #endif const uint worker_idx = get_local_linear_id(); - mean_per_feature[worker_idx] = mean; - uint reduce_add_level = 1; - while (num_local_workers > reduce_add_level) { - barrier(CLK_LOCAL_MEM_FENCE); - if (worker_idx % (reduce_add_level * 2) == 0 && (worker_idx + reduce_add_level) < num_local_workers) { - mean_per_feature[worker_idx] += mean_per_feature[worker_idx + reduce_add_level]; - } - reduce_add_level *= 2; - } + mean = work_group_reduce_add(mean); if (worker_idx == 0) { - mean = mean_per_feature[0] / TO_ACCUMULATOR_TYPE(INPUT0_SIZE_Z * INPUT0_SIZE_Y * INPUT0_SIZE_X); + mean = mean / TO_ACCUMULATOR_TYPE(INPUT0_SIZE_Z * INPUT0_SIZE_Y * INPUT0_SIZE_X); internal_mean[bf] = mean; } } @@ -189,8 +179,6 @@ KERNEL(calc_var_per_feature)( const uint x_base = get_local_id(0); const uint x_leftover = INPUT0_SIZE_X - x_num_workers * x_block_size; - __local ACCUMULATOR_TYPE var_per_feature[SLM_SIZE]; - const ACCUMULATOR_TYPE mean = internal_mean[bf]; ACCUMULATOR_TYPE variance = ACCUMULATOR_VAL_ZERO; @@ -284,18 +272,10 @@ KERNEL(calc_var_per_feature)( #endif const uint worker_idx = get_local_linear_id(); - var_per_feature[worker_idx] = variance; - uint reduce_add_level = 1; - while (num_local_workers > reduce_add_level) { - barrier(CLK_LOCAL_MEM_FENCE); - if (worker_idx % (reduce_add_level * 2) == 0 && (worker_idx + reduce_add_level) < num_local_workers) { - var_per_feature[worker_idx] += var_per_feature[worker_idx + reduce_add_level]; - } - reduce_add_level *= 2; - } + variance = work_group_reduce_add(variance); if (worker_idx == 0) { - variance = var_per_feature[0] / TO_ACCUMULATOR_TYPE(INPUT0_SIZE_Z * INPUT0_SIZE_Y * INPUT0_SIZE_X); + variance = variance / TO_ACCUMULATOR_TYPE(INPUT0_SIZE_Z * INPUT0_SIZE_Y * INPUT0_SIZE_X); internal_variance[bf] = variance; } } diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_bfyx_opt.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_bfyx_opt.cpp index 069e2abbe9a1cf..806fdfa77755d2 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_bfyx_opt.cpp +++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/group_normalization/group_normalization_kernel_bfyx_opt.cpp @@ -125,7 +125,6 @@ JitConstants GroupNormalizationKernelBfyx::GetJitConstants(const group_normaliza if (params.has_dynamic_tensors()) { jit.AddConstants({ - MakeJitConstant("SLM_SIZE", params.engineInfo.maxWorkGroupSize), MakeJitConstant("GWS0", "get_global_size(0)"), MakeJitConstant("LWS0", "get_local_size(0)"), MakeJitConstant("LWS1", "get_local_size(1)"), @@ -133,7 +132,6 @@ JitConstants GroupNormalizationKernelBfyx::GetJitConstants(const group_normaliza }); } else { jit.AddConstants({ - MakeJitConstant("SLM_SIZE", (dispatchData.lws[0] * dispatchData.lws[1] * dispatchData.lws[2])), MakeJitConstant("GWS0", dispatchData.gws[0]), MakeJitConstant("LWS0", dispatchData.lws[0]), MakeJitConstant("LWS1", dispatchData.lws[1]), From 54b79bb5289cd00c2b0d71eef9b4df42d8877256 Mon Sep 17 00:00:00 2001 From: Ivan Novoselov Date: Mon, 19 Aug 2024 12:14:48 +0100 Subject: [PATCH 10/23] [Snippets][TPP] Fix ov_cpu_unit_tests build & status for LIBXSMM (#26095) ### Details: - *Fixes `Linux CPU Plugin Snippets with LIBXSMM` GHA job* - *Fixes BrgemmBlocking unit tests when the `ENABLE_SNIPPETS_LIBXSMM_TPP` is set* ### Tickets: - *-* --- .../dev_cpu_linux_snippets_libxsmm.yml | 2 ++ .../intel_cpu/tests/unit/CMakeLists.txt | 7 +++++ .../x64/lowered/brgemm_blocking.cpp | 29 ++++++++++++------- 3 files changed, 28 insertions(+), 10 deletions(-) diff --git a/.github/workflows/dev_cpu_linux_snippets_libxsmm.yml b/.github/workflows/dev_cpu_linux_snippets_libxsmm.yml index a259c8e5fb33e4..17c40b94265b4f 100644 --- a/.github/workflows/dev_cpu_linux_snippets_libxsmm.yml +++ b/.github/workflows/dev_cpu_linux_snippets_libxsmm.yml @@ -11,6 +11,8 @@ on: - 'src/plugins/intel_cpu/src/emitters/tpp/**' - 'src/plugins/intel_cpu/src/transformations/snippets/**' - 'src/plugins/intel_cpu/src/transformations/tpp/**' + - 'src/plugins/intel_cpu/tests/unit/snippets_transformations/**' + - 'src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/**' concurrency: group: ${{ github.event_name == 'push' && github.run_id || github.ref }}-linux-cpu-dev diff --git a/src/plugins/intel_cpu/tests/unit/CMakeLists.txt b/src/plugins/intel_cpu/tests/unit/CMakeLists.txt index 24a0b490e76936..a3cf33c347ffb8 100644 --- a/src/plugins/intel_cpu/tests/unit/CMakeLists.txt +++ b/src/plugins/intel_cpu/tests/unit/CMakeLists.txt @@ -66,6 +66,13 @@ ov_add_test_target( OV UNIT CPU ) +if (ENABLE_SNIPPETS_LIBXSMM_TPP) + add_definitions(-DSNIPPETS_LIBXSMM_TPP -DLIBXSMM_DEFAULT_CONFIG) + target_compile_definitions(xsmm PRIVATE __BLAS=0) + target_link_libraries(${TARGET_NAME} PRIVATE xsmm) + target_include_directories(${TARGET_NAME} SYSTEM PRIVATE $) +endif() + # LTO set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO}) diff --git a/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/lowered/brgemm_blocking.cpp b/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/lowered/brgemm_blocking.cpp index dfae3be2a73946..a0ec6c931a9420 100644 --- a/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/lowered/brgemm_blocking.cpp +++ b/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/lowered/brgemm_blocking.cpp @@ -3,11 +3,12 @@ // #include "transformations/snippets/x64/pass/lowered/brgemm_cpu_blocking.hpp" -#include "transformations/tpp/x64/pass/lowered/brgemm_tpp_blocking.hpp" +#ifdef SNIPPETS_LIBXSMM_TPP + #include "transformations/tpp/x64/pass/lowered/brgemm_tpp_blocking.hpp" +#endif #include "lir_test_utils.hpp" #include "openvino/opsets/opset10.hpp" -#include "snippets/lowered/linear_ir.hpp" #include "snippets/lowered/loop_info.hpp" #include "snippets/snippets_isa.hpp" #include "transformations/snippets/x64/op/brgemm_copy_b.hpp" @@ -24,14 +25,21 @@ using namespace ov::snippets; using BRGEMM_TYPE = intel_cpu::brgemm_utils::BRGEMM_TYPE; namespace { - -SpecificIterationHandlers get_k_loop_handlers(size_t work_amount, size_t block_size) { +enum class BACKEND_TYPE{CPU, TPP}; +SpecificIterationHandlers get_k_loop_handlers(size_t work_amount, size_t block_size, BACKEND_TYPE backend = BACKEND_TYPE::CPU) { auto handlers = BrgemmBlockingBase::get_default_blocking_loop_handlers(work_amount, block_size); + switch (backend) { #ifdef SNIPPETS_LIBXSMM_TPP - handlers.register_pass(); -#else - handlers.register_pass(); + case BACKEND_TYPE::TPP: + handlers.register_pass(); + break; #endif + case BACKEND_TYPE::CPU: + handlers.register_pass(); + break; + default: + OPENVINO_THROW("Unsupported code generator backend type"); + } return handlers; } @@ -39,7 +47,8 @@ void create_brgemm_loop_infos(const LinearIRPtr& linear_ir, const ExpressionPtr& brgemm_expr, size_t m = 0, size_t m_blk = 0, size_t k = 0, size_t k_blk = 0, - size_t n = 0, size_t n_blk = 0) { + size_t n = 0, size_t n_blk = 0, + BACKEND_TYPE backend = BACKEND_TYPE::CPU) { const bool k_block = k != 0 && k_blk != 0; const bool n_block = k != 0 && k_blk != 0; const bool m_block = m != 0 && m_blk != 0; @@ -49,7 +58,7 @@ void create_brgemm_loop_infos(const LinearIRPtr& linear_ir, std::vector{LoopPort(brgemm_expr->get_input_port(0)), LoopPort(brgemm_expr->get_input_port(1), true, 1)}, std::vector{LoopPort(brgemm_expr->get_output_port(0), false)}, - get_k_loop_handlers(k, k_block)); + get_k_loop_handlers(k, k_block, backend)); linear_ir->get_loop_manager()->add_loop_info(loop_info); } if (n_block) { @@ -328,7 +337,7 @@ TEST_F(BrgemmTPPBlockingTest, TPPFloating) { layout_a, layout_b, layout_c); const auto& brgemm_expr = *brgemm.first; init_expr_descriptors(brgemm_expr, {{m_blk, k_blk}, {k_blk, n_blk}, {m_blk, n_blk}}, {layout_a, layout_b, layout_c}); - create_brgemm_loop_infos(linear_ir_ref, brgemm_expr, 384, m_blk, 1024, k_blk, 384, n_blk); + create_brgemm_loop_infos(linear_ir_ref, brgemm_expr, 384, m_blk, 1024, k_blk, 384, n_blk, BACKEND_TYPE::TPP); brgemm_expr->set_loop_ids({2, 1, 0}); auto result = linear_ir_ref->push_node(brgemm.second); } From 2b919e180198f663273b26c8eb0cb185a393ae1e Mon Sep 17 00:00:00 2001 From: Sofya Balandina Date: Mon, 19 Aug 2024 12:26:26 +0100 Subject: [PATCH 11/23] Update IR version for conformance tests (#25984) ### Details: - *Version of IR package is moved to [2023.3.0](https://storage.openvinotoolkit.org/test_data/conformance_ir/2023.3.0-13657-d5b0f4d2d73.tar) for actions tests* --- .../layer_tests_summary/data/models.lst | 2 +- .../github/cache/CPU/test_cache_OP.lst | 10694 ++++++++-------- .../skip_configs/CPU/expected_failures_OP.csv | 445 +- 3 files changed, 5262 insertions(+), 5879 deletions(-) diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/models.lst b/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/models.lst index 3f517d20061dbb..6ae099e9196e92 100644 --- a/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/models.lst +++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/models.lst @@ -1 +1 @@ -https://storage.openvinotoolkit.org/test_data/conformance_ir/2023.3.0-13657-d5b0f4d2d73.tar \ No newline at end of file +https://storage.openvinotoolkit.org/test_data/conformance_ir/2024.3.0-15913-e4cfd8f2edd.tar \ No newline at end of file diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/cache/CPU/test_cache_OP.lst b/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/cache/CPU/test_cache_OP.lst index 7e00aa09e2d998..180b256128960c 100644 --- a/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/cache/CPU/test_cache_OP.lst +++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/cache/CPU/test_cache_OP.lst @@ -1,5631 +1,5069 @@ -849647:conformance_MaxPool/ReadIRTest.Inference/Op=MaxPool.8_Type=f32_Shape=dynamic_IR=5ae2e8ce34957ac812bd04943714d0b0ca6e2098c46caccfd775620d7f373cbf_Device=CPU_Config=() -731668:conformance_ReduceSum/ReadIRTest.Inference/Op=ReduceSum.1_Type=f32_Shape=dynamic_IR=2f842d4b64513c6df5748c54a1166a3f14436dc1ca59b7a28530bcafcdcde2f6_Device=CPU_Config=() -635397:conformance_ReduceSum/ReadIRTest.Inference/Op=ReduceSum.1_Type=f32_Shape=dynamic_IR=d11097e7fa04dc0b540bf3b963cde252591b39b7dcbfae66e64ed19cd2b3b06e_Device=CPU_Config=() -456010:conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=dynamic_IR=c20603ac895226554bc910680f6be82589e053503b3067b3074bcc210f4d0ef2_Device=CPU_Config=() -446637:conformance_Interpolate/ReadIRTest.Inference/Op=Interpolate.4_Type=f32_Shape=static_IR=214e4e8f7de64e9cc8c77c67d214172905cfb4b9fde65e2ef3d32bb7b4ed93f1_Device=CPU_Config=() -441629:conformance_Softmax/ReadIRTest.Inference/Op=Softmax.8_Type=f32_Shape=dynamic_IR=0b30cc6cee9ce5400085a0e78b44763bc169eeea93357f22fd716564f20226db_Device=CPU_Config=() -396559:conformance_GroupConvolution/ReadIRTest.Inference/Op=GroupConvolution.1_Type=f32_Shape=dynamic_IR=72c58b462f61521af4eab9c890e568b5676c7a3194c4e35f8e04f98596013c47_Device=CPU_Config=() -396011:conformance_GroupConvolution/ReadIRTest.Inference/Op=GroupConvolution.1_Type=f32_Shape=dynamic_IR=a9b0552d84d057a656080c8e302afa30962dc02105abe7136cfd77f0433eec18_Device=CPU_Config=() -395765:conformance_GroupConvolution/ReadIRTest.Inference/Op=GroupConvolution.1_Type=f32_Shape=dynamic_IR=3d24c272ca88d4ee24f437a310abc05340e110f8596beb6a1ef96dd18818ebbe_Device=CPU_Config=() 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-417:conformance_LSTMSequence/ReadIRTest.ImportExport/Op=LSTMSequence.5_Type=f32_Shape=static_IR=b8e32896d2ab304fb4fdca3924e0110852da92be25307f30709cd7d897c2f038_Device=CPU_Config=() -417:conformance_Concat/ReadIRTest.QueryModel/Op=Concat.1_Type=f32_Shape=static_IR=03e7b025285b1369ca39bcf887783a843fe06ea29f7f394efc8201d1b7ad3a09_Device=CPU_Config=() -417:conformance_Add/ReadIRTest.Inference/Op=Add.1_Type=f32_Shape=static_IR=586094b4ff6617c08c87a53c7be1ca26aae40657c8d964d81eda731dbb27e848_Device=CPU_Config=() -416:conformance_subgraph/ReadIRTest.ImportExport/Extractor=repeat_pattern_Shape=static_IR=c5637c5151109c002830514b8b1450092dc52df14146ecee467dc54469a77718_Device=CPU_Config=() -416:conformance_Unsqueeze/ReadIRTest.Inference/Op=Unsqueeze.1_Type=f32_Shape=static_IR=e8df0b3ab9e127c1d37881f4c250ca0fd0dd2ec822cd24bf95e7860484fe9b8a_Device=CPU_Config=() 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--1:conformance_PriorBox/ReadIRTest.Inference/Op=PriorBox.1_Type=f32_Shape=static_IR=f110ef35c9642ecd941cd85a67a12b616353d4a8cd33f9770d532759e2846255_Device=CPU_Config=() --1:conformance_PriorBox/ReadIRTest.Inference/Op=PriorBox.1_Type=f32_Shape=static_IR=f0edc45979b98d4401eea2c345bbcb794721dd3cdbfb3963be5a2842b27ccc5b_Device=CPU_Config=() --1:conformance_PriorBox/ReadIRTest.Inference/Op=PriorBox.1_Type=f32_Shape=static_IR=e4baf41ae9a77441993eb0f95c3d7335e9a719e5eac8b1ffaf60d8f515f769a1_Device=CPU_Config=() --1:conformance_PriorBox/ReadIRTest.Inference/Op=PriorBox.1_Type=f32_Shape=static_IR=e48a363cfdabe0b62509e21641bb1cc88edaaa7d2eb82bf3ce747cab8355ff3b_Device=CPU_Config=() --1:conformance_PriorBox/ReadIRTest.Inference/Op=PriorBox.1_Type=f32_Shape=static_IR=e3a5a7f1a73793457fae9520ae122c6bbbfa92f1daac0ef214e47a2ec7ea18e2_Device=CPU_Config=() 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+354:conformance/OpImplCheckTest.checkPluginImplementation/Function=Divide_opset1_Device=CPU_Config=() +348:conformance_Concat/ReadIRTest.QueryModel/Op=Concat.1_Type=f32_Shape=static_IR=e8a26a33d6dbe0bb560820295fb6b8aafc3da0d2b78e29199d2f09e952722efe_Device=CPU_Config=() +345:conformance_Convert/ReadIRTest.ImportExport/Op=Convert.1_Type=i32_Shape=dynamic_IR=60bd170e816e0c2345a1658fd88459775fe8b7cce5de31a16e4e6cdea199f264_Device=CPU_Config=() +342:conformance_Convert/ReadIRTest.Inference/Op=Convert.1_Type=f32_Shape=dynamic_IR=e306da3fedc4369302fb21159f2bbbe65849661eabe5bb83efdad3e83f64fd68_Device=CPU_Config=() +314:conformance/OpImplCheckTest.checkPluginImplementation/Function=Abs_opset1_Device=CPU_Config=() +277:conformance/OpImplCheckTest.checkPluginImplementation/Function=Acosh_opset4_Device=CPU_Config=() +276:conformance/OpImplCheckTest.checkPluginImplementation/Function=AdaptiveMaxPool_opset8_Device=CPU_Config=() +248:conformance/OpImplCheckTest.checkPluginImplementation/Function=Acos_opset1_Device=CPU_Config=() \ No newline at end of file diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_OP.csv b/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_OP.csv index 857d92d1ddd718..cc638e28267751 100644 --- a/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_OP.csv +++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_OP.csv @@ -1,251 +1,196 @@ Test Name,Fix Priority -conformance_FakeQuantize/ReadIRTest.Inference/Op=FakeQuantize.1_Type=f32_Shape=static_IR=935369702948a57e71d169e75218162f370b48f344fe819f11112c011b6626fc_Device=CPU_Config=() -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=bf235091db192c86756347e70771b4b00a6ac2c8852b93079749ba718d57d022_Device=CPU_Config=(),1.0 -conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=77e1c522d9ea4975c3071869b7b485038bb4035c9aae6f5d44291f60ae253a0e_Device=CPU_Config=(),0.575008 -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=bee11d430236dcbd0fb5efbae712d8d89d84beeb89e0ee60e0ba3ba9512079f8_Device=CPU_Config=(),0.301142 -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=1be99c00176df777bd8cdbd9f74ff064237f55053dc7490050d692274182182d_Device=CPU_Config=(),0.273069 -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=dynamic_IR=df8ed5b481f6b03ca63572f2059d20911d3a7757f4c032455bef9933f2c1dc35_Device=CPU_Config=(),0.204151 -conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=eb966d8fd7e1301280e6ef709dd785d210a35a1346eb88c3f38379bd96036ce4_Device=CPU_Config=(),0.188153 -conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=dynamic_IR=90f882a97d637e527900edfb1b7c277b65544832793d08efdf8454be21a2f496_Device=CPU_Config=(),0.136236 -conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=0ffc7d7836be264b6d9f26daa71a8c7100ae6bc6fa1af23614a2736226fbdf0f_Device=CPU_Config=(),0.133748 -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=351e48db45e09ca6c4bc54a271eda4cb2ddd69ba43f361b9915a6588913768b0_Device=CPU_Config=(),0.0716183 -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=ab760f0d90b0fef133a0555cb2a5d40fb525aef88e6568c5387a87d7e82f67f8_Device=CPU_Config=(),0.0684052 -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=afc2cb913bcb4e4badd203c9cdf491ea1e6ed4f1cd835e7507889a9bba25b958_Device=CPU_Config=(),0.053864 -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=0a16d9d3d8d553c2c747b2c68b12eee3dcc016e29e597992cad8f83aff0aa759_Device=CPU_Config=(),0.0497053 -conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=0f623457008d91f7fcaead549e4a3f90a5ca77dd7c52fba19906f559c34b333b_Device=CPU_Config=(),0.0350581 -conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=c9352ef8b6aae01025051f9c73f023e7b5a13f8987f81bfff4ce0ff9725c21b5_Device=CPU_Config=(),0.0338749 -conformance_Reshape/ReadIRTest.Inference/Op=Reshape.1_Type=f32_Shape=dynamic_IR=469d09b261b88011c82288ea622dde06d63805eb41dc256c901b0d206ac5780b_Device=CPU_Config=(),0.0227255 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+conformance_subgraph/ReadIRTest.Inference/Extractor=fused_names_Shape=static_IR=df303191a733f2e33492260b5a1c6aea1db7c5d0063f1516f49de923a176a64e_Device=CPU_Config=(),9.28838e-06 +conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=0a2b1efb810d1dcf7897c3671f1eef0c36bcdca679e24b8e86f078128b381833_Device=CPU_Config=(),7.81362e-06 +conformance_Broadcast/ReadIRTest.Inference/Op=Broadcast.3_Type=f32_Shape=dynamic_IR=7562536120d473cca837bb2ad1e3969484868111954ac0b168a5c2805264a689_Device=CPU_Config=(),6.92301e-06 +conformance_Unsqueeze/ReadIRTest.Inference/Op=Unsqueeze.1_Type=f32_Shape=dynamic_IR=bda73cc94d837df9fb535743febd300cf0baf7fdf48ff538c079a4a7ca291592_Device=CPU_Config=(),5.36728e-06 +conformance_ReduceSum/ReadIRTest.Inference/Op=ReduceSum.1_Type=f32_Shape=dynamic_IR=d11097e7fa04dc0b540bf3b963cde252591b39b7dcbfae66e64ed19cd2b3b06e_Device=CPU_Config=(),5.36728e-06 +conformance_Multiply/ReadIRTest.Inference/Op=Multiply.1_Type=f32_Shape=dynamic_IR=287a7562757ef0295cc38442e3d775cff0fb1ea9b27e6897bd456f01ce82d455_Device=CPU_Config=(),5.36728e-06 +conformance_Add/ReadIRTest.Inference/Op=Add.1_Type=f32_Shape=dynamic_IR=f86f86769ec214942eaf1fdcd312a29e26308676419d8fbd98fdc485c2de0815_Device=CPU_Config=(),5.36728e-06 +conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=cfa4ea6d340205f9fc4db4ec912037243299168e9bfa4e248e530c98a0f8a8ab_Device=CPU_Config=(),4.71135e-06 +conformance_subgraph/ReadIRTest.Inference/Extractor=repeat_pattern_Shape=static_IR=264ac1f5ebe805838ff6e76fca75305ba3ecb27f84e44ae7b44e9c01d613df98_Device=CPU_Config=(),1.68808e-06 From f0f4c5ab52d7c7a88d3037c9a4c84def0067bccf Mon Sep 17 00:00:00 2001 From: Edward Shogulin Date: Mon, 19 Aug 2024 12:53:32 +0100 Subject: [PATCH 12/23] [LPT] Slice transformation (#26091) ### Details: - *Slice transformation (per-tensor quantization)* ### Tickets: - *CVS-149909* --- .../include/low_precision/slice.hpp | 34 +++++++++ .../src/low_precision.cpp | 2 + .../src/slice.cpp | 67 +++++++++++++++++ .../slice_transformation.cpp | 64 +++++++++++++++++ .../slice_transformation.hpp | 40 +++++++++++ .../slice_transformation.cpp | 72 +++++++++++++++++++ .../include/ov_lpt_models/slice.hpp | 30 ++++++++ .../ov_helpers/ov_lpt_models/src/slice.cpp | 49 +++++++++++++ 8 files changed, 358 insertions(+) create mode 100644 src/common/low_precision_transformations/include/low_precision/slice.hpp create mode 100644 src/common/low_precision_transformations/src/slice.cpp create mode 100644 src/plugins/intel_cpu/tests/functional/shared_tests_instances/low_precision_transformations/slice_transformation.cpp create mode 100644 src/tests/functional/plugin/shared/include/low_precision_transformations/slice_transformation.hpp create mode 100644 src/tests/functional/plugin/shared/src/low_precision_transformations/slice_transformation.cpp create mode 100644 src/tests/ov_helpers/ov_lpt_models/include/ov_lpt_models/slice.hpp create mode 100644 src/tests/ov_helpers/ov_lpt_models/src/slice.cpp diff --git a/src/common/low_precision_transformations/include/low_precision/slice.hpp b/src/common/low_precision_transformations/include/low_precision/slice.hpp new file mode 100644 index 00000000000000..206a07d7bca9c7 --- /dev/null +++ b/src/common/low_precision_transformations/include/low_precision/slice.hpp @@ -0,0 +1,34 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include + +#include "layer_transformation.hpp" + +namespace ov { +namespace pass { +namespace low_precision { + +/** + * @ingroup ov_transformation_common_api + * @brief SliceTransformation propagates dequantization operations through Slice operation. + * + * For more details about the transformation, refer to + * [SliceTransformation](@ref openvino_docs_OV_UG_lpt_SliceTransformation) page + * in the OpenVINO Developer Guide. + */ +class LP_TRANSFORMATIONS_API SliceTransformation : public LayerTransformation { +public: + OPENVINO_RTTI("SliceTransformation", "0"); + SliceTransformation(const Params& params = Params()); + bool transform(TransformationContext& context, ov::pass::pattern::Matcher& m) override; + bool canBeTransformed(const TransformationContext& context, std::shared_ptr op) const override; + bool isPrecisionPreserved(std::shared_ptr layer) const noexcept override; +}; + +} // namespace low_precision +} // namespace pass +} // namespace ov diff --git a/src/common/low_precision_transformations/src/low_precision.cpp b/src/common/low_precision_transformations/src/low_precision.cpp index e58374ed3e2b1a..52479a5c2dc1fa 100644 --- a/src/common/low_precision_transformations/src/low_precision.cpp +++ b/src/common/low_precision_transformations/src/low_precision.cpp @@ -68,6 +68,7 @@ #include "low_precision/relu.hpp" #include "low_precision/squeeze.hpp" #include "low_precision/subtract.hpp" +#include "low_precision/slice.hpp" #include "low_precision/space_to_batch.hpp" #include "low_precision/split.hpp" #include "low_precision/shuffle_channels.hpp" @@ -267,6 +268,7 @@ bool ov::pass::low_precision::LowPrecision::run_on_model(const std::shared_ptr + +#include "low_precision/slice.hpp" + +#include "itt.hpp" +#include "openvino/util/log.hpp" +#include "openvino/pass/pattern/op/wrap_type.hpp" +#include "openvino/opsets/opset8.hpp" + +#include "low_precision/network_helper.hpp" + +namespace ov { +namespace pass { +namespace low_precision { + +SliceTransformation::SliceTransformation(const Params& params) : LayerTransformation(params) { + MATCHER_SCOPE(SliceTransformation); + auto matcher = ov::pass::pattern::wrap_type(); + + ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) { + auto op = m.get_match_root(); + if (transformation_callback(op)) { + return false; + } + return transform(*context, m); + }; + + auto m = std::make_shared(matcher, matcher_name); + this->register_matcher(m, callback); +} + +bool SliceTransformation::transform(TransformationContext& context, ov::pass::pattern::Matcher& m) { + if (!SliceTransformation::canBeTransformed(context, m.get_match_root())) { + return false; + } + + const auto strided_slice = NetworkHelper::separateInStandaloneBranch(m.get_match_root(), defaultPrecisions); + auto dequantization = NetworkHelper::getDequantization(strided_slice, defaultPrecisions); + const auto newOperation = moveDequantizationAfter(context, strided_slice, NetworkHelper::getDequantization(strided_slice, defaultPrecisions)); + + OPENVINO_DEBUG("LPT: done: ", newOperation); + return true; +} + +bool SliceTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr operation) const { + if (!LayerTransformation::canBeTransformed(context, operation)) { + return false; + } + + if (!ov::is_type(operation)) { + return false; + } + + const auto dequantization = NetworkHelper::getDequantization(operation); + return dequantization.isPerTensor(); +} + +bool SliceTransformation::isPrecisionPreserved(std::shared_ptr layer) const noexcept { + return true; +} +} // namespace low_precision +} // namespace pass +} // namespace ov diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/low_precision_transformations/slice_transformation.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/low_precision_transformations/slice_transformation.cpp new file mode 100644 index 00000000000000..2696d6f14f8ed8 --- /dev/null +++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/low_precision_transformations/slice_transformation.cpp @@ -0,0 +1,64 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include "low_precision_transformations/slice_transformation.hpp" + + +using namespace LayerTestsDefinitions; + +namespace { +const std::vector netPrecisions = { + ov::element::f32 +}; + +const std::vector trasformationParamValues = { + LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams() +}; + +const std::vector params = { + { + { + 256ul, + ov::Shape{ 1, 1, 1, 1 }, + { 0.f }, + { 25.5f }, + { 0.f }, + { 12.8f } + }, + { 0 }, // start + { 2147483647 }, // end + { 2 }, // step + { 2 }, // axes + "u8" + }, + { + { + 256ul, + ov::Shape{ 1, 3, 1, 1 }, + { 0.f, 0.f, 0.f }, + { 255.f / 1.f, 255.f / 2.f, 255.f / 3.f }, + { 0.f, 0.f, 0.f }, + { 255.f / 1.f, 255.f / 2.f, 255.f / 3.f } + }, + { 0 }, // start + { 2147483647 }, // end + { 2 }, // step + { 2 }, // axes + "f32" + }, +}; + +INSTANTIATE_TEST_SUITE_P(smoke_LPT, SliceTransformation, + ::testing::Combine( + ::testing::ValuesIn(netPrecisions), + ::testing::Values(ov::PartialShape({ 1, 3, 24, 24 })), + ::testing::Values(ov::test::utils::DEVICE_CPU), + ::testing::ValuesIn(trasformationParamValues), + ::testing::ValuesIn(params)), + SliceTransformation::getTestCaseName); + +} // namespace diff --git a/src/tests/functional/plugin/shared/include/low_precision_transformations/slice_transformation.hpp b/src/tests/functional/plugin/shared/include/low_precision_transformations/slice_transformation.hpp new file mode 100644 index 00000000000000..783f42c6b1967e --- /dev/null +++ b/src/tests/functional/plugin/shared/include/low_precision_transformations/slice_transformation.hpp @@ -0,0 +1,40 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "shared_test_classes/base/low_precision_transformations/layer_transformation.hpp" +#include "ov_lpt_models/common/fake_quantize_on_data.hpp" +#include "ov_lpt_models/common/dequantization_operations.hpp" + +namespace LayerTestsDefinitions { +class SliceTransformationParam { +public: + ov::builder::subgraph::FakeQuantizeOnData fakeQuantize; + std::vector start; + std::vector stop; + std::vector step; + std::vector axes; + std::string expectedPrecision; +}; + +typedef std::tuple< + ov::element::Type, + ov::PartialShape, + std::string, + ov::pass::low_precision::LayerTransformation::Params, + SliceTransformationParam +> SliceTransformationParams; + +class SliceTransformation : + public testing::WithParamInterface, + public LayerTestsUtils::LayerTransformation { +public: + static std::string getTestCaseName(const testing::TestParamInfo& obj); + +protected: + void SetUp() override; +}; + +} // namespace LayerTestsDefinitions diff --git a/src/tests/functional/plugin/shared/src/low_precision_transformations/slice_transformation.cpp b/src/tests/functional/plugin/shared/src/low_precision_transformations/slice_transformation.cpp new file mode 100644 index 00000000000000..78dd8d1550f090 --- /dev/null +++ b/src/tests/functional/plugin/shared/src/low_precision_transformations/slice_transformation.cpp @@ -0,0 +1,72 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "low_precision_transformations/slice_transformation.hpp" +#include +#include +#include + +#include "ov_lpt_models/slice.hpp" + +namespace LayerTestsDefinitions { + +inline std::ostream& operator<<(std::ostream& os, const std::vector& values) { + os << "{ "; + for (size_t i = 0; i < values.size(); ++i) { + os << values[i]; + if (i != (values.size() - 1ul)) { + os << ", "; + } + } + os << " }"; + return os; +} + +std::string SliceTransformation::getTestCaseName(const testing::TestParamInfo& obj) { + ov::element::Type netPrecision; + ov::PartialShape inputShape; + std::string targetDevice; + ov::pass::low_precision::LayerTransformation::Params params; + SliceTransformationParam param;; + std::tie(netPrecision, inputShape, targetDevice, params, param) = obj.param; + + std::ostringstream result; + result << get_test_case_name_by_params(netPrecision, inputShape, targetDevice, params) << "_" << + param.fakeQuantize << "_" << + param.start << "_" << + param.stop << "_" << + param.step << "_" << + param.axes; + return result.str(); +} + +void SliceTransformation::SetUp() { + ov::element::Type netPrecision; + ov::PartialShape inputShape; + ov::pass::low_precision::LayerTransformation::Params params; + SliceTransformationParam param; + std::tie(netPrecision, inputShape, targetDevice, params, param) = this->GetParam(); + + init_input_shapes(inputShape); + + function = ov::builder::subgraph::SliceFunction::get( + netPrecision, + inputShape, + param.fakeQuantize, + param.start, + param.stop, + param.step, + param.axes); +} + +TEST_P(SliceTransformation, CompareWithRefImpl) { + SKIP_IF_CURRENT_TEST_IS_DISABLED(); + run(); + + const auto params = std::get<4>(GetParam()); + const auto& actualPrecision = get_runtime_precision_by_type("StridedSlice"); + EXPECT_EQ(actualPrecision, params.expectedPrecision); +}; + +} // namespace LayerTestsDefinitions diff --git a/src/tests/ov_helpers/ov_lpt_models/include/ov_lpt_models/slice.hpp b/src/tests/ov_helpers/ov_lpt_models/include/ov_lpt_models/slice.hpp new file mode 100644 index 00000000000000..0a8d7b0161743b --- /dev/null +++ b/src/tests/ov_helpers/ov_lpt_models/include/ov_lpt_models/slice.hpp @@ -0,0 +1,30 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include +#include "ov_lpt_models/common/builders.hpp" +#include "ov_lpt_models/common/dequantization_operations.hpp" + +namespace ov { +namespace builder { +namespace subgraph { + +class SliceFunction { +public: + static std::shared_ptr get( + const ov::element::Type inputPrecision, + const ov::PartialShape& inputShape, + const ov::builder::subgraph::FakeQuantizeOnData& fakeQuantize, + const std::vector& start, + const std::vector& stop, + const std::vector& step, + const std::vector& axes); +}; + +} // namespace subgraph +} // namespace builder +} // namespace ov diff --git a/src/tests/ov_helpers/ov_lpt_models/src/slice.cpp b/src/tests/ov_helpers/ov_lpt_models/src/slice.cpp new file mode 100644 index 00000000000000..6701c62898fec9 --- /dev/null +++ b/src/tests/ov_helpers/ov_lpt_models/src/slice.cpp @@ -0,0 +1,49 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "ov_lpt_models/slice.hpp" +#include "openvino/opsets/opset8.hpp" + +using namespace ov::pass::low_precision; + +namespace ov { +namespace builder { +namespace subgraph { + +std::shared_ptr SliceFunction::get( + const ov::element::Type inputPrecision, + const ov::PartialShape& inputShape, + const ov::builder::subgraph::FakeQuantizeOnData& fakeQuantize, + const std::vector& start, + const std::vector& stop, + const std::vector& step, + const std::vector& axes) { + const auto input = std::make_shared(inputPrecision, inputShape); + input->set_friendly_name("input"); + const auto fqOnData = makeFakeQuantize(input, inputPrecision, fakeQuantize); + + const auto start_constant = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{ start.size() }, start); + start_constant->set_friendly_name("start"); + const auto stop_constant = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{ stop.size() }, stop); + stop_constant->set_friendly_name("stop"); + const auto step_constant = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{ step.size() }, step); + step_constant->set_friendly_name("step"); + const auto axes_constant = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{ axes.size() }, axes); + axes_constant->set_friendly_name("axes "); + + const auto stridedSlice = std::make_shared(fqOnData, start_constant, stop_constant, step_constant, axes_constant); + stridedSlice->set_friendly_name("slice"); + + const auto res = std::make_shared(stridedSlice); + const auto function = std::make_shared( + ov::ResultVector{ res }, + ov::ParameterVector{ input }, + "SliceTransformation"); + + return function; +} + +} // namespace subgraph +} // namespace builder +} // namespace ov From 4c5a13cd201b199aa1da19e7dd367a8b53548370 Mon Sep 17 00:00:00 2001 From: Jorge Ragde Date: Mon, 19 Aug 2024 05:07:20 -0700 Subject: [PATCH 13/23] [TF FE] Implemented RGBToHSV operation (#25318) ### Details: - Implemented and registered loader for RGBToHSV - Created unit test test_tf_RGBToHSV.py - Slightly modified helper to accept ov::Output instead of shared_ptr ### Tickets: - #24792 --------- Co-authored-by: Roman Kazantsev --- .../tensorflow/docs/supported_ops.md | 2 +- src/frontends/tensorflow/src/op_table.cpp | 1 + .../include/common_op_table.hpp | 1 + .../tensorflow_common/include/utils.hpp | 2 +- .../tensorflow_common/src/op/rgb_to_hsv.cpp | 38 +++++++++++++ src/frontends/tensorflow_common/src/utils.cpp | 2 +- .../tensorflow_tests/test_tf_RGBToHSV.py | 54 +++++++++++++++++++ 7 files changed, 97 insertions(+), 3 deletions(-) create mode 100644 src/frontends/tensorflow_common/src/op/rgb_to_hsv.cpp create mode 100644 tests/layer_tests/tensorflow_tests/test_tf_RGBToHSV.py diff --git a/src/frontends/tensorflow/docs/supported_ops.md b/src/frontends/tensorflow/docs/supported_ops.md index aa96ddb4fa1c3f..9f6af0f7670ae0 100644 --- a/src/frontends/tensorflow/docs/supported_ops.md +++ b/src/frontends/tensorflow/docs/supported_ops.md @@ -829,7 +829,7 @@ A "supported operation" is one that TensorFlow Frontend can convert to the OpenV | RFFT | YES | | | RFFT2D | YES | | | RFFT3D | YES | | -| RGBToHSV | NO | | +| RGBToHSV | YES | | | RaggedBincount | NO | | | RaggedCountSparseOutput | NO | | | RaggedCross | NO | | diff --git a/src/frontends/tensorflow/src/op_table.cpp b/src/frontends/tensorflow/src/op_table.cpp index 67a8ecfb68288c..8cd0c4a9b15016 100644 --- a/src/frontends/tensorflow/src/op_table.cpp +++ b/src/frontends/tensorflow/src/op_table.cpp @@ -363,6 +363,7 @@ const std::map get_supported_ops() { {"RFFT", CreatorFunction(translate_rfft_op)}, {"RFFT2D", CreatorFunction(translate_rfft_op)}, {"RFFT3D", CreatorFunction(translate_rfft_op)}, + {"RGBToHSV", CreatorFunction(translate_rgb_to_hsv_op)}, {"Rint", CreatorFunction(translate_rint_op)}, {"Roll", CreatorFunction(translate_roll_op)}, {"Round", CreatorFunction(translate_round_op)}, diff --git a/src/frontends/tensorflow_common/include/common_op_table.hpp b/src/frontends/tensorflow_common/include/common_op_table.hpp index 3332122edee0c8..b85e8b094f2d63 100644 --- a/src/frontends/tensorflow_common/include/common_op_table.hpp +++ b/src/frontends/tensorflow_common/include/common_op_table.hpp @@ -139,6 +139,7 @@ OP_CONVERTER(translate_reverse_op); OP_CONVERTER(translate_reverse_v2_op); OP_CONVERTER(translate_reverse_sequence_op); OP_CONVERTER(translate_rfft_op); +OP_CONVERTER(translate_rgb_to_hsv_op); OP_CONVERTER(translate_rint_op); OP_CONVERTER(translate_roll_op); OP_CONVERTER(translate_round_op); diff --git a/src/frontends/tensorflow_common/include/utils.hpp b/src/frontends/tensorflow_common/include/utils.hpp index 0ed5bbf9c22b8b..b47c9f97cceaaf 100644 --- a/src/frontends/tensorflow_common/include/utils.hpp +++ b/src/frontends/tensorflow_common/include/utils.hpp @@ -156,7 +156,7 @@ ov::Output get_data_slice(const ov::Output& data, ov::Output compute_broadcast_args(const ov::Output& shape1, const ov::Output& shape2); std::shared_ptr, std::shared_ptr, std::shared_ptr>> rgb_to_hsv( - const std::shared_ptr& images); + const ov::Output& images); std::shared_ptr hsv_to_rgb(const ov::Output& h, const ov::Output& s, diff --git a/src/frontends/tensorflow_common/src/op/rgb_to_hsv.cpp b/src/frontends/tensorflow_common/src/op/rgb_to_hsv.cpp new file mode 100644 index 00000000000000..45a0f3598c05bc --- /dev/null +++ b/src/frontends/tensorflow_common/src/op/rgb_to_hsv.cpp @@ -0,0 +1,38 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "common_op_table.hpp" +#include "openvino/op/concat.hpp" +#include "utils.hpp" + +using namespace std; +using namespace ov; +using namespace ov::op; + +namespace ov { +namespace frontend { +namespace tensorflow { +namespace op { + +OutputVector translate_rgb_to_hsv_op(const NodeContext& node) { + default_op_checks(node, 1, {"RGBToHSV"}); + auto images = node.get_input(0); + auto node_name = node.get_name(); + + auto hsv_components = rgb_to_hsv(images); + + auto hh = get<0>(*hsv_components); + auto ss = get<1>(*hsv_components); + auto vv = get<2>(*hsv_components); + + auto rgb = make_shared(NodeVector{hh, ss, vv}, -1); + + set_node_name(node_name, rgb); + return {rgb}; +} + +} // namespace op +} // namespace tensorflow +} // namespace frontend +} // namespace ov diff --git a/src/frontends/tensorflow_common/src/utils.cpp b/src/frontends/tensorflow_common/src/utils.cpp index ab5ecd94f4d2b0..2b79a2de8bac5a 100644 --- a/src/frontends/tensorflow_common/src/utils.cpp +++ b/src/frontends/tensorflow_common/src/utils.cpp @@ -431,7 +431,7 @@ Output compute_broadcast_args(const Output& shape1, const Outputoutput(0); } -shared_ptr, shared_ptr, shared_ptr>> rgb_to_hsv(const shared_ptr& images) { +shared_ptr, shared_ptr, shared_ptr>> rgb_to_hsv(const ov::Output& images) { // image format conversion based on // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/adjust_saturation_op.cc auto const_zero_f_ = create_same_type_const_scalar(images, 0.0f); diff --git a/tests/layer_tests/tensorflow_tests/test_tf_RGBToHSV.py b/tests/layer_tests/tensorflow_tests/test_tf_RGBToHSV.py new file mode 100644 index 00000000000000..e7c03893b4df05 --- /dev/null +++ b/tests/layer_tests/tensorflow_tests/test_tf_RGBToHSV.py @@ -0,0 +1,54 @@ +# Copyright (C) 2018-2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import numpy as np +import platform +import pytest +import tensorflow as tf +from common.tf_layer_test_class import CommonTFLayerTest + +rng = np.random.default_rng(3476123) + + +class TestRGBToHSV(CommonTFLayerTest): + def _prepare_input(self, inputs_info): + assert 'images:0' in inputs_info + if self.special_case == 'Black Image': + images_shape = inputs_info['images:0'] + inputs_data = {} + inputs_data['images:0'] = np.zeros(images_shape).astype(self.input_type) + elif self.special_case == 'Grayscale Image': + images_shape = inputs_info['images:0'] + inputs_data = {} + inputs_data['images:0'] = np.ones(images_shape).astype(self.input_type) * rng.random() + else: + images_shape = inputs_info['images:0'] + inputs_data = {} + inputs_data['images:0'] = rng.random(images_shape).astype(self.input_type) + + return inputs_data + + def create_rgb_to_hsv_net(self, input_shape, input_type, special_case): + self.special_case = special_case + self.input_type = input_type + tf.compat.v1.reset_default_graph() + # Create the graph and model + with tf.compat.v1.Session() as sess: + images = tf.compat.v1.placeholder(input_type, input_shape, 'images') + tf.raw_ops.RGBToHSV(images=images) + tf.compat.v1.global_variables_initializer() + tf_net = sess.graph_def + + return tf_net, None + + @pytest.mark.parametrize('input_shape', [[2, 3], [5, 6, 3], [2, 5, 10, 3]]) + @pytest.mark.parametrize('input_type', [np.float32, np.float64]) + @pytest.mark.parametrize('special_case', [None, 'Black Image', 'Grayscale Image']) + @pytest.mark.precommit + @pytest.mark.nightly + def test_adjust_hue_basic(self, input_shape, input_type, special_case, + ie_device, precision, ir_version, temp_dir, + use_legacy_frontend): + self._test(*self.create_rgb_to_hsv_net(input_shape, input_type, special_case), + ie_device, precision, ir_version, temp_dir=temp_dir, + use_legacy_frontend=use_legacy_frontend) From 8281fcc5d0cb33ec015eb927db3ea1bc45d578a9 Mon Sep 17 00:00:00 2001 From: Sergey Shlyapnikov Date: Mon, 19 Aug 2024 17:24:57 +0400 Subject: [PATCH 14/23] [GPU] Fix segmentation fault during remote tensor destruction in the Python API (#26109) ### Details: This patch updates `create_host_tensor()` and `create_tensor()` of `RemoteContext` to update _so to std::shared_ptr of the created remote tensor, preventing the dynamically loaded library from unloading before all remote tensors are destroyed --- src/inference/src/cpp/remote_context.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/inference/src/cpp/remote_context.cpp b/src/inference/src/cpp/remote_context.cpp index 3f75b4e402139e..ea3bd38b558a4b 100644 --- a/src/inference/src/cpp/remote_context.cpp +++ b/src/inference/src/cpp/remote_context.cpp @@ -67,6 +67,8 @@ std::string RemoteContext::get_device_name() const { RemoteTensor RemoteContext::create_tensor(const element::Type& type, const Shape& shape, const AnyMap& params) { OV_REMOTE_CONTEXT_STATEMENT({ auto tensor = _impl->create_tensor(type, shape, params); + if (!tensor._so) + tensor._so = _so; return make_tensor(tensor).as(); }); } @@ -74,6 +76,8 @@ RemoteTensor RemoteContext::create_tensor(const element::Type& type, const Shape Tensor RemoteContext::create_host_tensor(const element::Type element_type, const Shape& shape) { OV_REMOTE_CONTEXT_STATEMENT({ auto tensor = _impl->create_host_tensor(element_type, shape); + if (!tensor._so) + tensor._so = _so; return make_tensor(tensor); }); } From 00d9800aa2df83879ddf774bd98eb4d2a66930d9 Mon Sep 17 00:00:00 2001 From: Alexandra Sidorova Date: Mon, 19 Aug 2024 20:54:09 +0400 Subject: [PATCH 15/23] [Snippets] Added SplitLoops support for dynamic loops (#25957) ### Details: - *Introduced the class `InnerSplittedUnifiedLoopInfo` which describes inner splitted Loop after `SplitLoops`. Work amount of thus Loop is equal to increment of outer splitted loop. Removed the attribute `is_work_amount_const` from `LoopInfo`* - *Enabled `SplitLoops` optimization for dynamic loops/* - *Fixed finalization offset calculation for dynamic case* - *Fixed `increment = 1` for dynamic innermost Loops with only Eltwise ops inside - now all dynamic `Last` Loop iterations has `increment = work_amount` (`evaluate_once = true`) by default. If there is innermost Loop with only Eltiwse ops, the pass `SetLoopIncrementOne` will be registered in last iteration handler by default* - *Fixed LoopManager cloning: now all LoopInfo are cloned recursively and fully - with UnifiedLoopInfo in ExpandedLoopInfo. Before we didn't clone them.* - *Fixed `ExtractLoopInvariants` for splitted loops (which may have the same `dim_idx`) - now we separately create vector of ordered loop IDs by execution and iterate through this vector.* - *Fixed `ValidateExpandedLoopInfo` to support validation of loops iterations of which are executed not consistently (like outer splitted loop iterations)* - *Fixed buffer scratchpad size calculation in RuntimeConfigurator: no need to calculate allocation size of Buffers which are in Loops with `work_amount = 0`* - *Added workaround in `ComputeBufferAllocationSize::get_allocation_size` (the ticket 149219) to handle Buffers in cloned OuterSplittedLoops after `InsertSpecificIterations`* - *Added debug method `to_string()` for `RuntimeConfig`* ### Tickets: - *[141735](https://jira.devtools.intel.com/browse/CVS-141735)* ### Prerequisites: - [x] https://github.com/openvinotoolkit/openvino/pull/25772 --- .../include/snippets/lowered/loop_info.hpp | 141 +++++++++++++----- .../include/snippets/lowered/loop_manager.hpp | 16 +- .../snippets/lowered/pass/iter_handler.hpp | 28 +--- .../snippets/lowered/pass/split_loops.hpp | 18 ++- .../lowered/specific_loop_iter_handlers.hpp | 2 +- .../pass/analyze_broadcastable_inputs.hpp | 2 +- .../include/snippets/runtime_configurator.hpp | 7 +- src/common/snippets/src/generator.cpp | 6 +- src/common/snippets/src/lowered/loop_info.cpp | 124 +++++++++++---- .../snippets/src/lowered/loop_manager.cpp | 18 ++- .../src/lowered/pass/brgemm_blocking.cpp | 1 - .../pass/compute_buffer_allocation_size.cpp | 35 ++++- .../lowered/pass/extract_loop_invariants.cpp | 46 ++++-- .../snippets/src/lowered/pass/fuse_loops.cpp | 9 +- .../snippets/src/lowered/pass/init_loops.cpp | 9 +- .../pass/insert_specific_iterations.cpp | 8 +- .../src/lowered/pass/iter_handler.cpp | 73 +-------- .../pass/optimize_loop_single_evaluation.cpp | 3 +- .../snippets/src/lowered/pass/split_loops.cpp | 135 +++++++++++++---- .../lowered/pass/validate_expanded_loops.cpp | 62 ++++---- .../lowered/specific_loop_iter_handlers.cpp | 22 ++- .../snippets/src/runtime_configurator.cpp | 79 ++++++++-- .../pass/extracted_loop_invariants.cpp | 78 +++++++++- .../snippets/tests/src/lowered/pass/loop.cpp | 135 +++++++++-------- .../snippets/cpu_runtime_configurator.cpp | 21 +++ .../snippets/cpu_runtime_configurator.hpp | 4 + .../snippets/x64/jit_loop_emitters.cpp | 7 +- 27 files changed, 736 insertions(+), 353 deletions(-) diff --git a/src/common/snippets/include/snippets/lowered/loop_info.hpp b/src/common/snippets/include/snippets/lowered/loop_info.hpp index 5563dc240c2818..cc66f5f6ffcc95 100644 --- a/src/common/snippets/include/snippets/lowered/loop_info.hpp +++ b/src/common/snippets/include/snippets/lowered/loop_info.hpp @@ -12,28 +12,40 @@ namespace ov { namespace snippets { namespace lowered { +class LoopInfo; +using LoopInfoMap = std::unordered_map>; +using LoopInfoSet = std::unordered_set; +using LoopInfoPtr = std::shared_ptr; + /** * @interface LoopInfo * @brief The base class that contains the common information about a Loop in Linear Intermediate Representation (Linear IR): * work amount of the Loop, step of loop counter increment, input and output ports of the Loop. * @ingroup snippets */ -class LoopInfo { +class LoopInfo : public std::enable_shared_from_this { public: enum {UNDEFINED_DIM_IDX = std::numeric_limits::max()}; LoopInfo() = default; - LoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, bool is_wa_const = false); - LoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, - bool is_wa_const = false); + LoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits); + LoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits); virtual ~LoopInfo() = default; /** - * @brief Clone LoopInfo with new expressions + * @brief Clone LoopInfo with new Expressions * @param expr_map map of new and old expressions + * @param loop_map map of new and old LoopInfo. * @return the copy */ - virtual std::shared_ptr clone_with_new_expr(const ExpressionMap& expr_map) const = 0; + virtual std::shared_ptr clone_with_new_expr(const ExpressionMap& expr_map, LoopInfoMap& loop_map) const = 0; + + /** + * @brief Apply the passed function to the current LoopInfo + * @param func function for applying + * @param applied_loops set of already updated loops + */ + virtual void apply(const std::function& func, LoopInfoSet& applied_loops) = 0; /** * @brief Check if some parameters of Loop are dynamic (undefined) @@ -62,7 +74,7 @@ class LoopInfo { * @brief Returns work amount of the Loop. * @return m_work_amount */ - size_t get_work_amount() const; + virtual size_t get_work_amount() const; /** * @brief Returns step of loop counter increment. * @return m_increment @@ -83,17 +95,12 @@ class LoopInfo { * @return m_output_ports */ const std::vector& get_output_ports() const; - /** - * @brief Returns True if `work_amount` cannot be rewritten/updated by passes. - * @return m_is_work_amount_const - */ - bool is_work_amount_const() const; /** * @brief Set m_work_amount value * @param work_amount - work amount of the loop */ - void set_work_amount(size_t work_amount); + virtual void set_work_amount(size_t work_amount); /** * @brief Set m_increment value * @param increment - step of loop counter increment @@ -104,13 +111,8 @@ class LoopInfo { * @param dim_idx - index */ void set_dim_idx(size_t dim_idx); - /** - * @brief Sets `value` to `m_is_work_amount_const` - * @param value - value of the attribute - */ - void set_work_amount_const(bool value); -/** + /** * @brief Replace the current LoopPort `actual_port` with new `target_ports` * @param actual_port actual port * @param target_ports new ports. The ports order is important. Can contain `actual_port` @@ -188,12 +190,7 @@ class LoopInfo { // Note: Scalars aren't input expressions but can be before first input expr in Linear IR std::vector m_input_ports = {}; std::vector m_output_ports = {}; - - // TODO [143394] : All static values in compilation stage should be `is_const=True` (not only `work_amount`) - // If True, no one pass can rewrite the value of `m_work_amount` - bool m_is_work_amount_const = false; }; -using LoopInfoPtr = std::shared_ptr; /** * @interface UnifiedLoopInfo @@ -227,20 +224,28 @@ class UnifiedLoopInfo : public LoopInfo { UnifiedLoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, const std::vector& in_descs, const std::vector& out_descs, - const SpecificIterationHandlers& handlers = SpecificIterationHandlers(), bool is_wa_const = false); + const SpecificIterationHandlers& handlers = SpecificIterationHandlers()); UnifiedLoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, - const SpecificIterationHandlers& handlers = SpecificIterationHandlers(), bool is_wa_const = false); + const SpecificIterationHandlers& handlers = SpecificIterationHandlers()); UnifiedLoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, - const SpecificIterationHandlers& handlers = SpecificIterationHandlers(), bool is_wa_const = false); + const SpecificIterationHandlers& handlers = SpecificIterationHandlers()); /** - * @brief Clone LoopInfo with new expressions + * @brief Clone LoopInfo with new Expressions * @param expr_map map of new and old expressions + * @param loop_map map of new and old LoopInfo. * @return the copy */ - std::shared_ptr clone_with_new_expr(const ExpressionMap& expr_map) const override; + std::shared_ptr clone_with_new_expr(const ExpressionMap& expr_map, LoopInfoMap& loop_map) const override; + + /** + * @brief Apply the passed function on the current LoopInfo. + * @param func function for applying + * @param applied_loops set of already updated loops + */ + void apply(const std::function& func, LoopInfoSet& applied_loops) override; /** * @brief Check if some parameters of Loop are dynamic (undefined) @@ -362,7 +367,7 @@ class UnifiedLoopInfo : public LoopInfo { caller(m_output_ports[i], m_output_port_descs[i]); } -private: +protected: /** * @brief Clone LoopPortDesc[actual_port_idx] `new_count` times and insert on the place of current desc * @param actual_port_idx index of the current descriptor/port @@ -377,6 +382,65 @@ class UnifiedLoopInfo : public LoopInfo { }; using UnifiedLoopInfoPtr = std::shared_ptr; +/** + * @interface InnerSplittedUnifiedLoopInfo + * @brief The structure describes inner splitted Loop after `SplitLoops`. + * Contains pointer to outer splitted loop info. WorkAmount is equal to increment of outer splitted loop info. + * @ingroup snippets + */ +class InnerSplittedUnifiedLoopInfo : public UnifiedLoopInfo { +public: + OPENVINO_RTTI("InnerSplittedUnifiedLoopInfo", "0", UnifiedLoopInfo) + + InnerSplittedUnifiedLoopInfo() = default; + InnerSplittedUnifiedLoopInfo(size_t increment, const std::vector& entries, const std::vector& exits, + const std::vector& in_descs, const std::vector& out_descs, + const SpecificIterationHandlers& handlers, LoopInfoPtr outer_splitted_loop_info); + + /** + * @brief Clone LoopInfo with new Expressions + * @param expr_map map of new and old expressions + * @param loop_map map of new and old LoopInfo. + * If `loop_map` contains cloned outer splitted loop -info, we take it from there. + * Otherwise we manually clone it and add to this map. + * @return the copy + */ + std::shared_ptr clone_with_new_expr(const ExpressionMap& expr_map, LoopInfoMap& loop_map) const override; + + /** + * @brief Apply the passed function on OuterSplittedLoopInfo and then on the current LoopInfo. + * @param func function for applying + * @param applied_loops set of already updated loops + */ + void apply(const std::function& func, LoopInfoSet& applied_loops) override; + + /** + * @brief Returns work amount of the Loop. + * @return m_work_amount + */ + size_t get_work_amount() const override; + /** + * @brief Returns OuterSplittedLoopInfo + * @return m_outer_splitted_loop_info + */ + LoopInfoPtr get_outer_splitted_loop_info() const; + + /** + * @brief Set m_work_amount value + * @param work_amount - work amount of the loop + */ + void set_work_amount(size_t work_amount) override; + /** + * @brief Set m_outer_splitted_loop_info value + * @param outer - OuterSplittedLoopInfo + */ + void set_outer_splitted_loop_info(LoopInfoPtr outer); + +private: + LoopInfoPtr m_outer_splitted_loop_info = nullptr; +}; +using InnerSplittedUnifiedLoopInfoPtr = std::shared_ptr; + /** * @interface ExpandedLoopInfo * @brief The structure describes expanded Loop (specific iterations) after unified loop decomposition into specific loop iterations. @@ -390,14 +454,23 @@ class ExpandedLoopInfo : public LoopInfo { ExpandedLoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, std::vector ptr_increments, std::vector final_offsets, std::vector data_sizes, - SpecificLoopIterType type, std::shared_ptr unified_loop_info, bool is_wa_const = false, - bool evaluate_once = false); + SpecificLoopIterType type, UnifiedLoopInfoPtr unified_loop_info, bool evaluate_once = false); /** - * @brief Clone LoopInfo with new expressions + * @brief Clone LoopInfo with new Expressions * @param expr_map map of new and old expressions + * @param loop_map map of new and old LoopInfo. + * If `loop_map` contains cloned unified loop -info, we take it from there. + * Otherwise we manually clone it and add to this map. * @return the copy */ - std::shared_ptr clone_with_new_expr(const ExpressionMap& expr_map) const override; + std::shared_ptr clone_with_new_expr(const ExpressionMap& expr_map, LoopInfoMap& loop_map) const override; + + /** + * @brief Apply the passed function on UnifiedLoopInfo and then on the current LoopInfo. + * @param func function for applying + * @param applied_loops set of already updated loops + */ + void apply(const std::function& func, LoopInfoSet& applied_loops) override; /** * @brief Check if some parameters of Loop are dynamic (undefined) diff --git a/src/common/snippets/include/snippets/lowered/loop_manager.hpp b/src/common/snippets/include/snippets/lowered/loop_manager.hpp index ab4db81a8e2e6b..099d7ff634924d 100644 --- a/src/common/snippets/include/snippets/lowered/loop_manager.hpp +++ b/src/common/snippets/include/snippets/lowered/loop_manager.hpp @@ -98,13 +98,12 @@ class LoopManager { size_t increment, const std::vector& entries, const std::vector& exits, - bool set_default_handlers = true, - bool is_work_amount_const = false) { + bool set_default_handlers = true) { const auto normalized_increment = utils::is_dynamic_value(work_amount) || work_amount == 0 ? increment : std::min(increment, work_amount); - const auto& handlers = set_default_handlers - ? SpecificIterationHandlers(work_amount, normalized_increment) - : SpecificIterationHandlers(); - const auto loop_info = std::make_shared(work_amount, normalized_increment, entries, exits, handlers, is_work_amount_const); + const auto loop_info = std::make_shared(work_amount, normalized_increment, entries, exits); + if (set_default_handlers) + loop_info->set_handlers(SpecificIterationHandlers(work_amount, normalized_increment, loop_info->get_dim_idx())); + const auto loop_id = this->add_loop_info(loop_info); for (auto expr_it = loop_begin_pos; expr_it != loop_end_pos; ++expr_it) { insert_loop_id(*expr_it, loop_id); @@ -131,9 +130,8 @@ class LoopManager { size_t dim_idx, const std::vector& entries, const std::vector& exits, - bool set_default_handlers = true, - bool is_work_amount_const = false) { - const auto loop_id = mark_loop(loop_begin_pos, loop_end_pos, work_amount, increment, entries, exits, set_default_handlers, is_work_amount_const); + bool set_default_handlers = true) { + const auto loop_id = mark_loop(loop_begin_pos, loop_end_pos, work_amount, increment, entries, exits, set_default_handlers); const auto loop_info = get_loop_info(loop_id); loop_info->set_dim_idx(dim_idx); return loop_id; diff --git a/src/common/snippets/include/snippets/lowered/pass/iter_handler.hpp b/src/common/snippets/include/snippets/lowered/pass/iter_handler.hpp index b7eb4e7176f3c1..2587ffbd546dfa 100644 --- a/src/common/snippets/include/snippets/lowered/pass/iter_handler.hpp +++ b/src/common/snippets/include/snippets/lowered/pass/iter_handler.hpp @@ -48,32 +48,14 @@ class SetFillOffset : public pass::RangedPass { }; /** - * @interface TransformInnerSplitLoop - * @brief The pass updates finalization offsets, work amount and increment of inner Loop basing on tail_size of the current Loop - * @param m_tail_size - tail_size of the current Loop + * @interface SetLoopIncrementOne + * @brief The pass set `increment = 1` to ExpandedLoopInfo which is mapped on LoopEnd in the passed iterator `end` and to this LoopEnd. * @ingroup snippets */ -class TransformInnerSplitLoop : public pass::RangedPass { +class SetLoopIncrementOne : public snippets::lowered::pass::RangedPass { public: - TransformInnerSplitLoop(size_t tail_size); - OPENVINO_RTTI("TransformInnerSplitLoop", "RangedPass") - bool run(LinearIR& linear_ir, LinearIR::constExprIt begin, LinearIR::constExprIt end) override; - std::shared_ptr merge(const std::shared_ptr& other) override; - -private: - size_t m_tail_size; -}; - -/** - * @interface SetEvaluateOnce - * @brief The pass set `evaluate once = true` only to ExpandedLoopInfo which is mapped on LoopEnd in the passed iterator `end`. - * The pointer arithmetic should be updated in the separate optimization `OptimizeLoopSingleEvaluation` - * @ingroup snippets - */ -class SetEvaluateOnce : public snippets::lowered::pass::RangedPass { -public: - SetEvaluateOnce() = default; - OPENVINO_RTTI("SetEvaluateOnce", "RangedPass") + SetLoopIncrementOne() = default; + OPENVINO_RTTI("SetLoopIncrementOne", "RangedPass") bool run(snippets::lowered::LinearIR& linear_ir, snippets::lowered::LinearIR::constExprIt begin, snippets::lowered::LinearIR::constExprIt end) override; diff --git a/src/common/snippets/include/snippets/lowered/pass/split_loops.hpp b/src/common/snippets/include/snippets/lowered/pass/split_loops.hpp index c186e132d455e3..953f20bbe56c3d 100644 --- a/src/common/snippets/include/snippets/lowered/pass/split_loops.hpp +++ b/src/common/snippets/include/snippets/lowered/pass/split_loops.hpp @@ -32,11 +32,27 @@ namespace pass { class SplitLoops : public RangedPass { public: OPENVINO_RTTI("SplitLoops", "RangedPass") - SplitLoops(); + SplitLoops() = default; bool run(LinearIR& linear_ir, lowered::LinearIR::constExprIt begin, lowered::LinearIR::constExprIt end) override; private: static bool can_be_split(const UnifiedLoopInfoPtr& current, const UnifiedLoopInfoPtr& target); + + static void split(LinearIR& linear_ir, size_t loop_to_split_id, size_t outer_increment); + + /** + * @interface TransformInnerSplitLoop + * @brief The pass replace existing inner splitted LoopInfo with new InnerSplittedUnifiedLoopInfo and + * update the corresponding LoopInfo + * @ingroup snippets + */ + class TransformInnerSplitLoop : public pass::RangedPass { + public: + TransformInnerSplitLoop() = default; + OPENVINO_RTTI("TransformInnerSplitLoop", "RangedPass") + bool run(LinearIR& linear_ir, LinearIR::constExprIt begin, LinearIR::constExprIt end) override; + std::shared_ptr merge(const std::shared_ptr& other) override; + }; }; } // namespace pass diff --git a/src/common/snippets/include/snippets/lowered/specific_loop_iter_handlers.hpp b/src/common/snippets/include/snippets/lowered/specific_loop_iter_handlers.hpp index c94ac6561bce4d..f91595bf86bd26 100644 --- a/src/common/snippets/include/snippets/lowered/specific_loop_iter_handlers.hpp +++ b/src/common/snippets/include/snippets/lowered/specific_loop_iter_handlers.hpp @@ -15,7 +15,7 @@ namespace lowered { class SpecificIterationHandlers { public: SpecificIterationHandlers() = default; - SpecificIterationHandlers(size_t loop_work_amount, size_t loop_increment); + SpecificIterationHandlers(size_t loop_work_amount, size_t loop_increment, size_t processing_dim_idx); SpecificIterationHandlers(pass::PassPipeline first_iter_handlers, pass::PassPipeline main_body_handlers, pass::PassPipeline last_iter_handlers); diff --git a/src/common/snippets/include/snippets/pass/analyze_broadcastable_inputs.hpp b/src/common/snippets/include/snippets/pass/analyze_broadcastable_inputs.hpp index 1cb8122685bde6..4367567c9df1c7 100644 --- a/src/common/snippets/include/snippets/pass/analyze_broadcastable_inputs.hpp +++ b/src/common/snippets/include/snippets/pass/analyze_broadcastable_inputs.hpp @@ -12,7 +12,7 @@ namespace pass { /** * @interface AnalyzeBroadcastableInputs - * @brief Analyzes body parameters which affects inputs of broadcastable operations (If needed, `Broadcast` op should be inserted there).s + * @brief Analyzes body parameters which affects inputs of broadcastable operations (If needed, `Broadcast` op should be inserted there). * Also the pass initializes special map `BroadcastableInputsMap` * Notes: * - Must be called after Canonicalization pass diff --git a/src/common/snippets/include/snippets/runtime_configurator.hpp b/src/common/snippets/include/snippets/runtime_configurator.hpp index af01757cb0194f..169d63ee4baa92 100644 --- a/src/common/snippets/include/snippets/runtime_configurator.hpp +++ b/src/common/snippets/include/snippets/runtime_configurator.hpp @@ -37,6 +37,10 @@ class RuntimeConfig { return get_type_info().name; } +#ifdef SNIPPETS_DEBUG_CAPS + virtual std::string to_string() const; +#endif + size_t tensor_rank = 0; size_t tile_rank = 0; @@ -214,7 +218,8 @@ class RuntimeConfigurator { std::vector m_io_descs = {}; std::vector m_io_data_sizes = {}; // [cluster_id -> buffer expressions ] - std::map> m_dynamic_buffer_clusters; + std::map> m_dynamic_buffer_clusters = {}; + std::vector m_ordered_loop_ids = {}; std::vector m_latest_shapes = {}; }; diff --git a/src/common/snippets/src/generator.cpp b/src/common/snippets/src/generator.cpp index 3629245cc978bd..7ba5e830fd3362 100644 --- a/src/common/snippets/src/generator.cpp +++ b/src/common/snippets/src/generator.cpp @@ -51,9 +51,11 @@ LoweringResult Generator::generate(const lowered::LinearIRPtr& linear_ir, const } result.compiled_snippet = target->get_snippet(); result.kernel_executor_table = target->get_runtime_configurator()->get_kernel_executor_table(); - // Some kernel executors might've been registered during code emission. + // In static case some kernel executors might've been registered during code emission. // We need to update them, so appropriate kernels will be compiled. - result.kernel_executor_table->update_state(linear_ir); + // In dynamic case it should be handled by RuntimeConfigurator + if (!linear_ir->is_dynamic()) + result.kernel_executor_table->update_state(linear_ir); return result; } diff --git a/src/common/snippets/src/lowered/loop_info.cpp b/src/common/snippets/src/lowered/loop_info.cpp index 092d222bde86ec..534159020e4b4f 100644 --- a/src/common/snippets/src/lowered/loop_info.cpp +++ b/src/common/snippets/src/lowered/loop_info.cpp @@ -11,12 +11,11 @@ namespace ov { namespace snippets { namespace lowered { -LoopInfo::LoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, bool is_wa_const) - : m_work_amount(work_amount), m_increment(increment), m_input_ports(entries), m_output_ports(exits), m_is_work_amount_const(is_wa_const) {} +LoopInfo::LoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits) + : m_work_amount(work_amount), m_increment(increment), m_input_ports(entries), m_output_ports(exits) {} -LoopInfo::LoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, - bool is_wa_const) - : m_work_amount(work_amount), m_increment(increment), m_is_work_amount_const(is_wa_const) { +LoopInfo::LoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits) + : m_work_amount(work_amount), m_increment(increment) { m_input_ports.reserve(entries.size()); m_output_ports.reserve(exits.size()); for (const auto& port : entries) @@ -73,10 +72,6 @@ const std::vector& LoopInfo::get_output_ports() const { return m_output_ports; } -bool LoopInfo::is_work_amount_const() const { - return m_is_work_amount_const; -} - void LoopInfo::set_work_amount(size_t work_amount) { m_work_amount = work_amount; } @@ -91,10 +86,6 @@ void LoopInfo::set_dim_idx(size_t dim_idx) { std::for_each(m_output_ports.begin(), m_output_ports.end(), setter); } -void LoopInfo::set_work_amount_const(bool value) { - m_is_work_amount_const = value; -} - template<> std::vector::iterator LoopInfo::find_loop_port(const LoopPort& loop_port) { auto& ports = loop_port.expr_port->get_type() == ExpressionPort::Input ? m_input_ports : m_output_ports; @@ -187,16 +178,16 @@ bool UnifiedLoopInfo::LoopPortDesc::is_dynamic() const { UnifiedLoopInfo::UnifiedLoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, - const SpecificIterationHandlers& handlers, bool is_wa_const) - : LoopInfo(work_amount, increment, entries, exits, is_wa_const), m_handlers(handlers), + const SpecificIterationHandlers& handlers) + : LoopInfo(work_amount, increment, entries, exits), m_handlers(handlers), m_input_port_descs(std::vector(entries.size())), m_output_port_descs(std::vector(exits.size())) { sort_ports(); } UnifiedLoopInfo::UnifiedLoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, - const SpecificIterationHandlers& handlers, bool is_wa_const) - : LoopInfo(work_amount, increment, entries, exits, is_wa_const), m_handlers(handlers), + const SpecificIterationHandlers& handlers) + : LoopInfo(work_amount, increment, entries, exits), m_handlers(handlers), m_input_port_descs(std::vector(entries.size())), m_output_port_descs(std::vector(exits.size())) { sort_ports(); } @@ -204,17 +195,27 @@ UnifiedLoopInfo::UnifiedLoopInfo(size_t work_amount, size_t increment, UnifiedLoopInfo::UnifiedLoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, const std::vector& in_shifts, const std::vector& out_shifts, - const SpecificIterationHandlers& handlers, bool is_wa_const) - : LoopInfo(work_amount, increment, entries, exits, is_wa_const), m_handlers(handlers), m_input_port_descs(in_shifts), m_output_port_descs(out_shifts) { + const SpecificIterationHandlers& handlers) + : LoopInfo(work_amount, increment, entries, exits), m_handlers(handlers), m_input_port_descs(in_shifts), m_output_port_descs(out_shifts) { sort_ports(); } -std::shared_ptr UnifiedLoopInfo::clone_with_new_expr(const ExpressionMap& expr_map) const { - const auto& new_input_ports = clone_loop_ports(expr_map, m_input_ports); - const auto& new_output_ports = clone_loop_ports(expr_map, m_output_ports); +std::shared_ptr UnifiedLoopInfo::clone_with_new_expr(const ExpressionMap& expr_map, LoopInfoMap& loop_map) const { + if (loop_map.count(this) == 0) { + const auto& new_input_ports = clone_loop_ports(expr_map, m_input_ports); + const auto& new_output_ports = clone_loop_ports(expr_map, m_output_ports); - return std::make_shared(m_work_amount, m_increment, new_input_ports, new_output_ports, - m_input_port_descs, m_output_port_descs, m_handlers, m_is_work_amount_const); + loop_map[this] = std::make_shared(m_work_amount, m_increment, new_input_ports, new_output_ports, + m_input_port_descs, m_output_port_descs, m_handlers); + } + return loop_map.at(this); +} + +void UnifiedLoopInfo::apply(const std::function& func, LoopInfoSet& applied_loops) { + if (applied_loops.count(this) == 0) { + func(shared_from_this()); + applied_loops.insert(this); + } } bool UnifiedLoopInfo::is_dynamic() const { @@ -364,24 +365,83 @@ void UnifiedLoopInfo::replace_with_new_ports(const ExpressionPort& actual_port, sort_ports(); } +InnerSplittedUnifiedLoopInfo::InnerSplittedUnifiedLoopInfo(size_t increment, const std::vector& entries, const std::vector& exits, + const std::vector& in_descs, const std::vector& out_descs, + const SpecificIterationHandlers& handlers, LoopInfoPtr outer_splitted_loop_info) + : UnifiedLoopInfo(utils::get_dynamic_value(), increment, entries, exits, in_descs, out_descs, handlers), + m_outer_splitted_loop_info(std::move(outer_splitted_loop_info)) { + OPENVINO_ASSERT(m_outer_splitted_loop_info != nullptr, "Outer Splitted Loop Info is missed!"); +} + +std::shared_ptr InnerSplittedUnifiedLoopInfo::clone_with_new_expr(const ExpressionMap& expr_map, LoopInfoMap& loop_map) const { + if (loop_map.count(this) == 0) { + auto cloned_outer_splitted_loop_info = m_outer_splitted_loop_info->clone_with_new_expr(expr_map, loop_map); + const auto& new_input_ports = clone_loop_ports(expr_map, m_input_ports); + const auto& new_output_ports = clone_loop_ports(expr_map, m_output_ports); + + loop_map[this] = std::make_shared(m_increment, new_input_ports, new_output_ports, + m_input_port_descs, m_output_port_descs, m_handlers, + std::move(cloned_outer_splitted_loop_info)); + } + return loop_map.at(this); +} + +void InnerSplittedUnifiedLoopInfo::apply(const std::function& func, LoopInfoSet& applied_loops) { + if (applied_loops.count(this) == 0) { + m_outer_splitted_loop_info->apply(func, applied_loops); + func(shared_from_this()); + applied_loops.insert(this); + } +} + +size_t InnerSplittedUnifiedLoopInfo::get_work_amount() const { + return get_outer_splitted_loop_info()->get_increment(); +} + +LoopInfoPtr InnerSplittedUnifiedLoopInfo::get_outer_splitted_loop_info() const { + OPENVINO_ASSERT(m_outer_splitted_loop_info, "Outer Splitted loop info is nullptr!"); + return m_outer_splitted_loop_info; +} + +void InnerSplittedUnifiedLoopInfo::set_work_amount(size_t work_amount) { + OPENVINO_THROW("InnerSplittedUnifiedLoopInfo doesn't support `set_work_amount`"); +} + +void InnerSplittedUnifiedLoopInfo::set_outer_splitted_loop_info(LoopInfoPtr outer) { + OPENVINO_ASSERT(outer, "Outer Splitted loop info cannot be nullptr!"); + m_outer_splitted_loop_info = std::move(outer); +} + ExpandedLoopInfo::ExpandedLoopInfo(size_t work_amount, size_t increment, const std::vector& entries, const std::vector& exits, std::vector ptr_increments, std::vector final_offsets, std::vector data_sizes, - SpecificLoopIterType type, std::shared_ptr unified_loop_info, bool is_wa_const, bool evaluate_once) - : LoopInfo(work_amount, increment, entries, exits, is_wa_const), + SpecificLoopIterType type, std::shared_ptr unified_loop_info, bool evaluate_once) + : LoopInfo(work_amount, increment, entries, exits), m_ptr_increments(std::move(ptr_increments)), m_finalization_offsets(std::move(final_offsets)), m_data_sizes(std::move(data_sizes)), m_type(type), m_unified_loop_info(std::move(unified_loop_info)), m_evaluate_once(evaluate_once) { OPENVINO_ASSERT(m_unified_loop_info, "Failed to create ExpandedLoopInfo: unified loop info is nullptr!"); sort_ports(); } -std::shared_ptr ExpandedLoopInfo::clone_with_new_expr(const ExpressionMap& expr_map) const { - const auto& new_input_ports = clone_loop_ports(expr_map, m_input_ports); - const auto& new_output_ports = clone_loop_ports(expr_map, m_output_ports); +std::shared_ptr ExpandedLoopInfo::clone_with_new_expr(const ExpressionMap& expr_map, LoopInfoMap& loop_map) const { + if (loop_map.count(this) == 0) { + auto cloned_unified_loop_info = ov::as_type_ptr(m_unified_loop_info->clone_with_new_expr(expr_map, loop_map)); + const auto& new_input_ports = clone_loop_ports(expr_map, m_input_ports); + const auto& new_output_ports = clone_loop_ports(expr_map, m_output_ports); - return std::make_shared(m_work_amount, m_increment, new_input_ports, new_output_ports, - m_ptr_increments, m_finalization_offsets, m_data_sizes, m_type, - m_unified_loop_info, m_is_work_amount_const, m_evaluate_once); + loop_map[this] = std::make_shared(m_work_amount, m_increment, new_input_ports, new_output_ports, + m_ptr_increments, m_finalization_offsets, m_data_sizes, m_type, + std::move(cloned_unified_loop_info), m_evaluate_once); + } + return loop_map.at(this); +} + +void ExpandedLoopInfo::apply(const std::function& func, LoopInfoSet& applied_loops) { + if (applied_loops.count(this) == 0) { + m_unified_loop_info->apply(func, applied_loops); + func(shared_from_this()); + applied_loops.insert(this); + } } bool ExpandedLoopInfo::is_dynamic() const { diff --git a/src/common/snippets/src/lowered/loop_manager.cpp b/src/common/snippets/src/lowered/loop_manager.cpp index 15634edb2c34c7..21f4ecc83c57b0 100644 --- a/src/common/snippets/src/lowered/loop_manager.cpp +++ b/src/common/snippets/src/lowered/loop_manager.cpp @@ -20,8 +20,12 @@ namespace lowered { std::shared_ptr LoopManager::clone_with_new_expr(const ExpressionMap& expr_map) const { auto new_loop_manager = std::make_shared(); + // To fully cloned all LoopInfo we have to create this map [old LoopInfo -> cloned LoopInfo], + // because some LoopInfo types contains pointer to another LoopInfo + // so we should recurrently make a cloning of LoopInfos' + LoopInfoMap loop_info_map; // [ old - > cloned ] for (const auto& id_info : m_map) - new_loop_manager->m_map.insert({id_info.first, id_info.second->clone_with_new_expr(expr_map)}); + new_loop_manager->m_map.insert({id_info.first, id_info.second->clone_with_new_expr(expr_map, loop_info_map)}); new_loop_manager->next_id = next_id; return new_loop_manager; } @@ -273,14 +277,22 @@ void LoopManager::fuse_loops(LinearIR::constExprIt loop_begin_target, LinearIR:: const auto work_amount = std::max(loop_info_upper->get_work_amount(), loop_info_lower->get_work_amount()); const auto increment = std::max(loop_info_upper->get_increment(), loop_info_lower->get_increment()); const auto handlers = SpecificIterationHandlers::merge_handlers(loop_info_upper->get_handlers(), loop_info_lower->get_handlers()); - const auto is_work_amount_const = loop_info_upper->is_work_amount_const() || loop_info_lower->is_work_amount_const(); auto new_entries = std::move(input_ports_upper); new_entries.insert(new_entries.end(), input_ports_lower.begin(), input_ports_lower.end()); auto new_exits = std::move(output_ports_upper); new_exits.insert(new_exits.end(), output_ports_lower.begin(), output_ports_lower.end()); - m_map[to] = std::make_shared(work_amount, increment, new_entries, new_exits, handlers, is_work_amount_const); + m_map[to] = std::make_shared(work_amount, increment, new_entries, new_exits, handlers); + + // Need to handle InnerSplittedLoopInfo - update outer splitted loop info if it was fused + for (const auto& p : m_map) { + if (const auto inner_splitted_loop_info = ov::as_type_ptr(p.second)) { + const auto outer = inner_splitted_loop_info->get_outer_splitted_loop_info(); + if (utils::one_of(outer, loop_info_upper, loop_info_lower)) + inner_splitted_loop_info->set_outer_splitted_loop_info(m_map[to]); + } + } for (auto it = loop_begin_target; it != loop_end_target; ++it) { const auto& expr = *it; diff --git a/src/common/snippets/src/lowered/pass/brgemm_blocking.cpp b/src/common/snippets/src/lowered/pass/brgemm_blocking.cpp index a7336c14454319..d689b183456bc1 100644 --- a/src/common/snippets/src/lowered/pass/brgemm_blocking.cpp +++ b/src/common/snippets/src/lowered/pass/brgemm_blocking.cpp @@ -26,7 +26,6 @@ snippets::lowered::SpecificIterationHandlers BrgemmBlockingBase::get_default_blo const auto tail_size = snippets::utils::is_dynamic_value(work_amount) ? snippets::utils::get_dynamic_value() : work_amount % block_size; if (tail_size != 0) handlers.register_pass(tail_size); - handlers.register_pass(); return handlers; } diff --git a/src/common/snippets/src/lowered/pass/compute_buffer_allocation_size.cpp b/src/common/snippets/src/lowered/pass/compute_buffer_allocation_size.cpp index 3abb254092268a..85bbed324a9865 100644 --- a/src/common/snippets/src/lowered/pass/compute_buffer_allocation_size.cpp +++ b/src/common/snippets/src/lowered/pass/compute_buffer_allocation_size.cpp @@ -27,9 +27,13 @@ std::vector get_parent_inner_loops(const std::vector& parent_loo // Ticket: 113744 // TODO: This logic covers only several specific cases so it should be generalized. size_t ComputeBufferAllocationSize::get_allocation_size(const LoopManagerPtr& loop_manager, const ExpressionPtr& buffer_expr, size_t allocation_rank) { + const auto& current_buffer = ov::as_type_ptr(buffer_expr->get_node()); + OPENVINO_ASSERT(current_buffer, "`get_allocation_size` expected Buffer"); + // Note: Buffer expressions can have more than one parent after the loops splitting transformation, but only the last parent // can be used to access valid loop ports. More info in the ticket: 146646 - const auto& parent_port = buffer_expr->get_input_port_connector(buffer_expr->get_input_count() - 1)->get_source(); + const auto buffer_in_idx = buffer_expr->get_input_count() - 1; + const auto& parent_port = buffer_expr->get_input_port_connector(buffer_in_idx)->get_source(); const auto& parent_loop_ids = get_parent_inner_loops(parent_port.get_expr()->get_loop_ids(), buffer_expr->get_loop_ids()); const auto planar_shape = utils::get_preordered_vdims(parent_port); @@ -38,13 +42,38 @@ size_t ComputeBufferAllocationSize::get_allocation_size(const LoopManagerPtr& lo const auto& subtensor = ov::snippets::utils::get_projected_subtensor(parent_port); + auto hard_equal = [&parent_port](const LoopPort& port) { + return *port.expr_port == parent_port; + }; + auto soft_equal = [&](const LoopPort& loop_port) { + const auto& port = *loop_port.expr_port; + // Check semantic of LoopPort + if (parent_port.get_index() != port.get_index() || + port.get_expr()->get_node()->get_type_info() != parent_port.get_expr()->get_node()->get_type_info()) + return false; + // Check that this LoopPort is connected to the same by semantic Buffer + const auto consumers = port.get_connected_ports(); + for (const auto& consumer : consumers) { + if (const auto buffer_consumer = ov::as_type_ptr(consumer.get_expr()->get_node())) { + if (buffer_consumer->get_cluster_id() == current_buffer->get_cluster_id() && consumer.get_index() == buffer_in_idx) + return true; + } + } + return false; + }; + size_t allocation_size = 1; std::set processed_dim_idxs; for (const auto& parent_loop : parent_loop_ids) { const auto loop_info = loop_manager->get_loop_info(parent_loop); const auto& output_ports = loop_info->get_output_ports(); - auto it = std::find_if(output_ports.begin(), output_ports.end(), [&parent_port](const LoopPort& port) { return *port.expr_port == parent_port; }); - OPENVINO_ASSERT(it != output_ports.end(), "compute_allocation_shape: output port of parent loop can not be found"); + auto it = std::find_if(output_ports.begin(), output_ports.end(), hard_equal); + // [149219] : Try to find original loop port if this LoopInfo is cloned after InsertSpecificIterations + // and ports are not mapped on the original ExpressionPorts + if (it == output_ports.end()) { + it = std::find_if(output_ports.begin(), output_ports.end(), soft_equal); + OPENVINO_ASSERT(it != output_ports.end(), "compute_allocation_shape: output port of parent loop can not be found"); + } const auto& loop_port = *it; const auto& dim_idx = loop_port.dim_idx; if (loop_port.is_incremented && dim_idx < rank) { diff --git a/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp b/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp index 6053451be9fafb..f20ace893df463 100644 --- a/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp +++ b/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp @@ -14,6 +14,34 @@ namespace snippets { namespace lowered { namespace pass { namespace { + +// Sort Loop IDs by execution order of these Loops +std::vector get_reordered_loop_ids(const LoopManagerPtr& loop_manager) { + const auto& loop_map = loop_manager->get_map(); + std::vector loop_ids_need_extract; + loop_ids_need_extract.reserve(loop_map.size()); + for (const auto& p : loop_map) + loop_ids_need_extract.push_back(p.first); + + auto sorter = [&](size_t lhs, size_t rhs) { + const auto lhs_last_expr = loop_manager->get_loop_info(lhs)->get_output_ports().back().expr_port->get_expr(); + const auto rhs_last_expr = loop_manager->get_loop_info(rhs)->get_output_ports().back().expr_port->get_expr(); + // If last output loop ports are the same expressions - first executive Loop has inner ID in expression loop IDs. + if (lhs_last_expr == rhs_last_expr) { + for (const auto& id : lhs_last_expr->get_loop_ids()) { + if (id == lhs) return false; + if (id == rhs) return true; + } + OPENVINO_THROW("Incorrect Loop IDs"); + } else { + return lhs_last_expr->get_exec_num() < rhs_last_expr->get_exec_num(); + } + }; + + std::sort(loop_ids_need_extract.begin(), loop_ids_need_extract.end(), sorter); + return loop_ids_need_extract; +} + void remove_last_loop_id(const std::shared_ptr& expr) { auto loop_ids = expr->get_loop_ids(); OPENVINO_ASSERT(!loop_ids.empty(), "Expr loop_ids should not be empty when remove last loop id."); @@ -179,22 +207,10 @@ bool ExtractLoopInvariants::run(LinearIR& linear_ir, lowered::LinearIR::constExp OV_ITT_SCOPED_TASK(ov::pass::itt::domains::SnippetsTransform, "Snippets::ExtractLoopInvariants") bool modified = false; - const auto& loop_depth = linear_ir.get_config().m_loop_depth; - std::vector> loop_ids_need_extract(loop_depth); - const auto& loop_map = linear_ir.get_loop_manager()->get_map(); - for (const auto& loop : loop_map) { - const auto& loop_dim = loop.second->get_dim_idx(); - if (loop_dim != LoopInfo::UNDEFINED_DIM_IDX) { - OPENVINO_ASSERT(loop_dim < loop_depth, "dim_idx of loop should be smaller than loop_depth"); - loop_ids_need_extract[loop_dim].insert(loop.first); - } - } // move invariant expr to top(outside) of current loop - for (size_t d = 0; d < loop_depth; d++) { - const auto& loops_in_this_depth = loop_ids_need_extract[d]; - for (const auto& loop_id : loops_in_this_depth) { - modified |= extract_from_loop(loop_id, linear_ir); - } + const auto loop_ids_need_extract = get_reordered_loop_ids(linear_ir.get_loop_manager()); + for (const auto& loop_id : loop_ids_need_extract) { + modified |= extract_from_loop(loop_id, linear_ir); } return modified; diff --git a/src/common/snippets/src/lowered/pass/fuse_loops.cpp b/src/common/snippets/src/lowered/pass/fuse_loops.cpp index 1673e4ddf712c0..3708896f5abf39 100644 --- a/src/common/snippets/src/lowered/pass/fuse_loops.cpp +++ b/src/common/snippets/src/lowered/pass/fuse_loops.cpp @@ -65,13 +65,18 @@ bool FuseLoops::can_be_fused(const UnifiedLoopInfoPtr& loop_upper, const Unified (work_amount_upper == work_amount_lower) && increment_upper == increment_lower; const bool bcastable_upper = work_amount_upper == 1 && increment_upper == 1; const bool bcastable_lower = work_amount_lower == 1 && increment_lower == 1; - const auto is_const_wa_equal = loop_upper->is_work_amount_const() == loop_lower->is_work_amount_const(); // WA: we can't fuse 2 loops if one of them has first iteration handler but second hasn't, // because in this case Main/Tail body handlers of the loop wo first iter handler must be reset with new parameters // (e.g. tail size). This logic is not implemented for now, so fusion for such loops is skipped. const bool first_iter_handlers_match = loop_upper->get_handlers().get_passes().empty() == loop_lower->get_handlers().get_passes().empty(); - return first_iter_handlers_match && is_const_wa_equal && (is_dynamic_case || equal_parameters || bcastable_upper || bcastable_lower); + // InnerSplittedUnifiedLoops can be fused only if they point to the same outer loop + const auto& ispl_loop_upper = ov::as_type_ptr(loop_upper); + const auto& ispl_loop_lower = ov::as_type_ptr(loop_lower); + const auto& inner_splitted_loop_compatible = + (!ispl_loop_upper && !ispl_loop_lower) || + (ispl_loop_upper && ispl_loop_lower && ispl_loop_upper->get_outer_splitted_loop_info() == ispl_loop_lower->get_outer_splitted_loop_info()); + return first_iter_handlers_match && inner_splitted_loop_compatible && (is_dynamic_case || equal_parameters || bcastable_upper || bcastable_lower); } void FuseLoops::move(LinearIR& linear_ir, const LoopManagerPtr& loop_manager, size_t loop_id, diff --git a/src/common/snippets/src/lowered/pass/init_loops.cpp b/src/common/snippets/src/lowered/pass/init_loops.cpp index 18575131aad306..8e9b62d8fab825 100644 --- a/src/common/snippets/src/lowered/pass/init_loops.cpp +++ b/src/common/snippets/src/lowered/pass/init_loops.cpp @@ -88,8 +88,11 @@ inline int64_t get_ptr_increment(const LoopPort& loop_port, size_t work_amount, } inline int64_t get_finalization_offset(size_t work_amount, int64_t ptr_increment) { - return utils::is_dynamic_value(work_amount) || utils::is_dynamic_value(ptr_increment) ? utils::get_dynamic_value() - : -1 * ptr_increment * work_amount; + if (ptr_increment == 0 || work_amount == 0) + return 0; + if (utils::is_dynamic_value(work_amount) || utils::is_dynamic_value(ptr_increment)) + return utils::get_dynamic_value(); + return -1 * ptr_increment * work_amount; } inline int64_t get_data_size(const LoopPort& loop_port) { @@ -145,7 +148,7 @@ void InitLoops::update_data_pointer_shifts(const UnifiedLoopInfoPtr& loop_info) void InitLoops::update_runtime_parameters(const UnifiedLoopInfoPtr& loop_info) { OPENVINO_ASSERT(loop_info != nullptr, "UnifiedLoopInfo is nullptr, nothing to update"); - if (!loop_info->is_work_amount_const()) + if (!ov::is_type(loop_info)) init_work_amount(loop_info); update_data_pointer_shifts(loop_info); } diff --git a/src/common/snippets/src/lowered/pass/insert_specific_iterations.cpp b/src/common/snippets/src/lowered/pass/insert_specific_iterations.cpp index dcff90015d28f2..badf4b0477759c 100644 --- a/src/common/snippets/src/lowered/pass/insert_specific_iterations.cpp +++ b/src/common/snippets/src/lowered/pass/insert_specific_iterations.cpp @@ -91,14 +91,13 @@ size_t InsertSpecificIterations::get_decomposed_loop_increment(const UnifiedLoop size_t remaining_work_amount) { OPENVINO_ASSERT(unified_loop_info, "UnifiedLoopInfo is missed!"); const auto increment = unified_loop_info->get_increment(); - const auto is_dynamic = utils::is_dynamic_value(remaining_work_amount); switch (type) { case (SpecificLoopIterType::FIRST_ITER): case (SpecificLoopIterType::MAIN_BODY): return increment; case(SpecificLoopIterType::LAST_ITER): - return is_dynamic ? 1 : remaining_work_amount; + return remaining_work_amount; default: OPENVINO_THROW("Unknown SpecificLoopIterType!"); } @@ -167,10 +166,9 @@ bool InsertSpecificIterations::decompose(LinearIR& linear_ir, LinearIR::constExp if (is_decomposed_loop_needed(unified_loop_info, iter_type, remaining_work_amount)) { const auto work_amount = get_decomposed_loop_work_amount(unified_loop_info, iter_type, remaining_work_amount); const auto increment = get_decomposed_loop_increment(unified_loop_info, iter_type, remaining_work_amount); - const auto evaluate_once = !utils::is_dynamic_value(work_amount) && work_amount == increment; // Update remaining Loop work amount // Note: if work_amount is unknown and increment = 1, it means that a loop will iterate by whole work_amount - if (!is_wa_dynamic || increment == 1) { + if (!is_wa_dynamic || increment == 1 || iter_type == SpecificLoopIterType::LAST_ITER) { remaining_work_amount -= work_amount; } @@ -200,7 +198,7 @@ bool InsertSpecificIterations::decompose(LinearIR& linear_ir, LinearIR::constExp const auto decomposed_loop_info = std::make_shared(work_amount, increment, decomposed_loop_entry_ports, decomposed_loop_exit_ports, decomposed_ptr_increments, decomposed_finalization_offsets, - decomposed_data_sizes, iter_type, unified_loop_info, false, evaluate_once); + decomposed_data_sizes, iter_type, unified_loop_info); init_decomposed_loop(linear_ir, decomposed_loop_begin_it, decomposed_loop_end_it, decomposed_loop_info, loop_id, decomposed_loop_end); decomposed = true; diff --git a/src/common/snippets/src/lowered/pass/iter_handler.cpp b/src/common/snippets/src/lowered/pass/iter_handler.cpp index a3ee577338a691..3e035628df476f 100644 --- a/src/common/snippets/src/lowered/pass/iter_handler.cpp +++ b/src/common/snippets/src/lowered/pass/iter_handler.cpp @@ -80,78 +80,17 @@ std::shared_ptr SetFillOffset::merge(const std::shared_ptrget_node(); - const auto loop_end = ov::as_type_ptr(node); - OPENVINO_ASSERT(loop_end, "the last operation in range must be LoopEnd"); - - const auto& loop_manager = linear_ir.get_loop_manager(); - const auto& loop_info = loop_manager->get_loop_info(loop_end->get_id()); - const auto current_dim_idx = loop_info->get_dim_idx(); - OPENVINO_ASSERT(current_dim_idx != LoopInfo::UNDEFINED_DIM_IDX, - "Outer splitted loop unexpectedly iterates by several dimension indices"); - - bool modified = false; - for (auto it = begin; it != end; ++it) { - const auto& expr = *it; - const auto inner_loop_end = ov::as_type_ptr(expr->get_node()); - if (!inner_loop_end) - continue; - // There is already ExpandedLoopInfo - const auto inner_loop_info = loop_manager->get_loop_info(inner_loop_end->get_id()); - const auto inner_dim_idx = inner_loop_info->get_dim_idx(); - if (inner_dim_idx != current_dim_idx) - continue; - // TODO [141735] : At the moment Splitted loops are not supported in dynamic case - OPENVINO_ASSERT(!inner_loop_end->has_dynamic_params(), "inner loop must be static in TransformInnerSplitLoop"); - const auto inner_loop_begin = inner_loop_end->get_loop_begin(); - const auto inner_loop_work_amount = static_cast(inner_loop_end->get_work_amount()); - const auto inner_loop_increment = inner_loop_end->get_increment(); - auto inner_finalization_offsets = inner_loop_end->get_finalization_offsets(); - for (auto& offset : inner_finalization_offsets) { - offset = offset / inner_loop_work_amount * static_cast(m_tail_size); - } - inner_loop_end->set_work_amount(m_tail_size); - // Since the loop has work amount equal to increment of outer loop, not broadcasted dimension, - // we should set `work_amount_const = true` to avoid rewriting in common loop intiialization passes (for example, `InitLoops`) - inner_loop_info->set_work_amount_const(true); - // TODO: if m_tail_size more than inner loop increment, - // handlers of the inner loop must be reset with new tail size - inner_loop_end->set_increment(std::min(inner_loop_increment, m_tail_size)); - inner_loop_end->set_finalization_offsets(inner_finalization_offsets); - const auto inner_loop_begin_it = std::find(begin, it, linear_ir.get_expr_by_node(inner_loop_begin)); - const auto inner_loop_end_it = std::next(it); - OPENVINO_ASSERT(inner_loop_begin_it != it, "LoopBegin has not been found!"); - const auto& last_iter_handlers = inner_loop_info->get_unified_loop_info()->get_handlers().get_passes(); - last_iter_handlers.run(linear_ir, std::next(inner_loop_begin_it), inner_loop_end_it); - modified = true; - } - return modified; -} - -std::shared_ptr TransformInnerSplitLoop::merge(const std::shared_ptr& other) { - const auto merged_pass = std::make_shared(m_tail_size); - if (other == nullptr) - return merged_pass; - const auto casted_pass = ov::as_type_ptr(other); - if (!casted_pass || m_tail_size != casted_pass->m_tail_size) - return nullptr; - return merged_pass; -} - -bool SetEvaluateOnce::run(LinearIR& linear_ir, LinearIR::constExprIt begin, LinearIR::constExprIt end) { +bool SetLoopIncrementOne::run(LinearIR& linear_ir, LinearIR::constExprIt begin, LinearIR::constExprIt end) { const auto& loop_end = ov::as_type_ptr(end->get()->get_node()); - OPENVINO_ASSERT(loop_end, "SetEvaluateOnce expected LoopEnd node in iterator `end`."); + OPENVINO_ASSERT(loop_end, "SetLoopIncrementOne expected LoopEnd node in iterator `end`."); const auto& loop_info = linear_ir.get_loop_manager()->get_loop_info(loop_end->get_id()); - loop_info->set_evaluate_once(true); + loop_info->set_increment(1); + loop_end->set_increment(1); return true; } -std::shared_ptr SetEvaluateOnce::merge(const std::shared_ptr& other) { - return !other || ov::is_type(other) ? std::make_shared() : nullptr; +std::shared_ptr SetLoopIncrementOne::merge(const std::shared_ptr& other) { + return !other || ov::is_type(other) ? std::make_shared() : nullptr; } } // namespace pass diff --git a/src/common/snippets/src/lowered/pass/optimize_loop_single_evaluation.cpp b/src/common/snippets/src/lowered/pass/optimize_loop_single_evaluation.cpp index c6255d90106e77..24ba946c92382b 100644 --- a/src/common/snippets/src/lowered/pass/optimize_loop_single_evaluation.cpp +++ b/src/common/snippets/src/lowered/pass/optimize_loop_single_evaluation.cpp @@ -24,7 +24,7 @@ bool OptimizeLoopSingleEvaluation::run(lowered::LinearIR& linear_ir, lowered::Li const auto& expr = *expr_it; if (auto loop_end = ov::as_type_ptr(expr->get_node())) { const auto& loop_info = loop_manager->get_loop_info(loop_end->get_id()); - if (loop_info->is_evaluate_once()) { + if (loop_info->get_work_amount() == loop_info->get_increment()) { auto new_finalization_offsets = loop_end->get_finalization_offsets(); const auto& ptr_increments = loop_end->get_ptr_increments(); const auto work_amount_incr = static_cast(loop_end->get_increment()); @@ -39,6 +39,7 @@ bool OptimizeLoopSingleEvaluation::run(lowered::LinearIR& linear_ir, lowered::Li // Update the corresponding ExpandedLoopInfo loop_info->update_ptr_increments(loop_end->get_ptr_increments()); loop_info->update_finalization_offsets(loop_end->get_finalization_offsets()); + loop_info->set_evaluate_once(true); is_modified = true; } diff --git a/src/common/snippets/src/lowered/pass/split_loops.cpp b/src/common/snippets/src/lowered/pass/split_loops.cpp index 348347c35dd975..e6c59d502d4804 100644 --- a/src/common/snippets/src/lowered/pass/split_loops.cpp +++ b/src/common/snippets/src/lowered/pass/split_loops.cpp @@ -5,6 +5,7 @@ #include "snippets/lowered/pass/split_loops.hpp" #include "snippets/lowered/pass/fuse_loops.hpp" +#include "snippets/lowered/pass/init_loops.hpp" #include "snippets/lowered/linear_ir.hpp" #include "snippets/lowered/loop_manager.hpp" #include "snippets/lowered/pass/iter_handler.hpp" @@ -16,8 +17,6 @@ namespace snippets { namespace lowered { namespace pass { -SplitLoops::SplitLoops() : RangedPass() {} - bool SplitLoops::can_be_split(const UnifiedLoopInfoPtr& loop_to_split, const UnifiedLoopInfoPtr& loop_to_fuse) { OPENVINO_ASSERT(loop_to_split != nullptr && loop_to_fuse != nullptr, "LoopInfo is nullptr!"); const auto current_dim_idx = loop_to_split->get_dim_idx(); @@ -26,9 +25,7 @@ bool SplitLoops::can_be_split(const UnifiedLoopInfoPtr& loop_to_split, const Uni const bool equal_dim_idxes = current_dim_idx != LoopInfo::UNDEFINED_DIM_IDX && current_dim_idx == parent_dim_idx; const bool only_main_body = handlers.get_passes().empty() && handlers.get_passes().empty(); - // TODO [141735] : At the moment Splitted loops are not supported in dynamic case - const auto are_static = !loop_to_split->is_dynamic() && !loop_to_fuse->is_dynamic(); - return are_static && loop_to_split->get_work_amount() == loop_to_fuse->get_work_amount() && + return loop_to_split->get_work_amount() == loop_to_fuse->get_work_amount() && loop_to_split->get_increment() != loop_to_fuse->get_increment() && equal_dim_idxes && only_main_body; } @@ -70,32 +67,8 @@ bool SplitLoops::run(LinearIR& linear_ir, lowered::LinearIR::constExprIt begin, const auto& loop_to_fuse = !split_parent ? parent_loop : loop; // We don't split loop which are not compatible with parent loop because such loops will not be fused if (FuseLoops::can_be_fused(upper_loop, lower_loop) && can_be_split(loop_to_split, loop_to_fuse)) { + split(linear_ir, split_parent ? parent_loop_id : loop_id, loop_to_fuse->get_increment()); loop_was_split = true; - loop_to_split->set_work_amount(loop_to_fuse->get_increment()); - // Since the loop has work amount equal to increment of outer loop, not broadcasted dimension, - // we should set `work_amount_const = true` to avoid rewriting in common loop intiialization passes (for example, `InitLoops`) - loop_to_split->set_work_amount_const(true); - - const auto& loop_to_split_id = split_parent ? parent_loop_id : loop_id; - const auto loop_bounds = LoopManager::get_loop_bounds(linear_ir, loop_to_split_id, - loop_to_split->get_input_ports(), - loop_to_split->get_output_ports()); - const auto split_loop_id = loop_manager->mark_loop(loop_bounds.first, - loop_bounds.second, - loop_to_fuse->get_work_amount(), - loop_to_fuse->get_increment(), - loop_to_split->get_dim_idx(), - loop_to_split->get_input_ports(), - loop_to_split->get_output_ports()); - const auto& new_loop_info = loop_manager->get_loop_info(split_loop_id); - const auto work_amount = loop_to_fuse->get_work_amount(); - const auto increment = loop_to_fuse->get_increment(); - const auto tail_size = work_amount % increment; - auto new_handlers = loop_to_split->get_handlers(); - if (tail_size != 0) { - new_handlers.register_pass(tail_size); - } - new_loop_info->set_handlers(new_handlers); break; } } @@ -107,6 +80,108 @@ bool SplitLoops::run(LinearIR& linear_ir, lowered::LinearIR::constExprIt begin, FuseLoops().run(linear_ir, begin, end); return loop_was_split; } + +void SplitLoops::split(LinearIR& linear_ir, size_t loop_to_split_id, size_t outer_increment) { + const auto& loop_manager = linear_ir.get_loop_manager(); + + const auto& inner_loop_info = loop_manager->get_loop_info(loop_to_split_id); + const auto loop_bounds = LoopManager::get_loop_bounds(linear_ir, loop_to_split_id, + inner_loop_info->get_input_ports(), + inner_loop_info->get_output_ports()); + const auto outer_loop_id = loop_manager->mark_loop(loop_bounds.first, loop_bounds.second, inner_loop_info->get_work_amount(), + outer_increment, inner_loop_info->get_dim_idx(), + inner_loop_info->get_input_ports(), inner_loop_info->get_output_ports(), false); + const auto& outer_loop_info = loop_manager->get_loop_info(outer_loop_id); + + const auto& inner_splitted_loop_info = + std::make_shared(inner_loop_info->get_increment(), inner_loop_info->get_input_ports(), + inner_loop_info->get_output_ports(), inner_loop_info->get_input_port_descs(), + inner_loop_info->get_output_port_descs(), inner_loop_info->get_handlers(), + outer_loop_info); + loop_manager->replace_with_new_loop(linear_ir, loop_bounds.first, loop_bounds.second, inner_splitted_loop_info, loop_to_split_id); + + if (!outer_loop_info->get_handlers().get_passes().empty()) { + outer_loop_info->register_pass_to_handler(); + } + outer_loop_info->register_pass_to_handler(); + outer_loop_info->register_pass_to_handler(); +} + +namespace { +InnerSplittedUnifiedLoopInfoPtr make_own_inner_splitted_unified_loop_info(const ExpandedLoopInfoPtr& inner_expanded, + const ExpandedLoopInfoPtr& outer_expanded, + const InnerSplittedUnifiedLoopInfoPtr& existing_inner_unified) { + const auto loop_info = + std::make_shared(inner_expanded->get_increment(), inner_expanded->get_input_ports(), + inner_expanded->get_output_ports(), existing_inner_unified->get_input_port_descs(), + existing_inner_unified->get_output_port_descs(), existing_inner_unified->get_handlers(), + outer_expanded); + InitLoops::update_runtime_parameters(loop_info); + return loop_info; +} +ExpandedLoopInfoPtr make_own_inner_splitted_expanded_loop_info(const ExpandedLoopInfoPtr& inner_expanded, + const InnerSplittedUnifiedLoopInfoPtr& inner_unified) { + return std::make_shared(inner_unified->get_work_amount(), inner_unified->get_increment(), + inner_unified->get_input_ports(), inner_unified->get_output_ports(), + inner_unified->get_ptr_increments(), + inner_unified->get_finalization_offsets(), + inner_unified->get_data_sizes(), inner_expanded->get_type(), + inner_unified, inner_expanded->is_evaluate_once()); +} +} // namespace + +bool SplitLoops::TransformInnerSplitLoop::run(LinearIR& linear_ir, LinearIR::constExprIt begin, LinearIR::constExprIt end) { + OPENVINO_ASSERT(end != linear_ir.cend(), "Incorrect LinearIR range for processing"); + const auto& expr = *end; + const auto node = expr->get_node(); + const auto loop_end = ov::as_type_ptr(node); + OPENVINO_ASSERT(loop_end, "the last operation in range must be LoopEnd"); + + const auto& loop_manager = linear_ir.get_loop_manager(); + const auto& outer_loop_info = loop_manager->get_loop_info(loop_end->get_id()); + const auto current_dim_idx = outer_loop_info->get_dim_idx(); + OPENVINO_ASSERT(current_dim_idx != LoopInfo::UNDEFINED_DIM_IDX, + "Outer splitted loop unexpectedly iterates by several dimension indices"); + + bool modified = false; + for (auto it = begin; it != end; ++it) { + const auto& expr = *it; + const auto inner_loop_end = ov::as_type_ptr(expr->get_node()); + if (!inner_loop_end) + continue; + + // There is already ExpandedLoopInfo + const auto& inner_expanded_loop_info = loop_manager->get_loop_info(inner_loop_end->get_id()); + const auto inner_unified_loop_info = ov::as_type_ptr(inner_expanded_loop_info->get_unified_loop_info()); + if (!inner_unified_loop_info || inner_unified_loop_info->get_outer_splitted_loop_info() != outer_loop_info->get_unified_loop_info()) + continue; + + OPENVINO_ASSERT(current_dim_idx == inner_unified_loop_info->get_dim_idx(), "Incorrect processing dim index of splitted loops"); + OPENVINO_ASSERT(inner_expanded_loop_info->get_type() == SpecificLoopIterType::MAIN_BODY, "InnerSplittedLoop must be Main Body of loop"); + + // We have to make a new UnifiedLoopInfo to distinguish it from other unified loops in other specific iterations of outer loop. + const auto inner_splitted_unified_loop_info = make_own_inner_splitted_unified_loop_info(inner_expanded_loop_info, outer_loop_info, + inner_unified_loop_info); + + // We have to replace existing ExpandedLoopInfo with new one to have the own InnerSplittedUnifiedLoopInfo and + // distinguish it from other expanded loops in other specific iterations of outer loop. + const auto new_expanded_inner_loop_info = make_own_inner_splitted_expanded_loop_info(inner_expanded_loop_info, inner_splitted_unified_loop_info); + const auto inner_begin = linear_ir.find_before(it, linear_ir.get_expr_by_node(inner_loop_end->get_loop_begin())); + const auto new_id = loop_manager->replace_with_new_loop(linear_ir, inner_begin, std::next(it), new_expanded_inner_loop_info, inner_loop_end->get_id()); + + // [147894] : Update inner LoopEnd expression + inner_loop_end->set_id(new_id); + inner_loop_end->set_work_amount(new_expanded_inner_loop_info->get_work_amount()); + inner_loop_end->set_increment(new_expanded_inner_loop_info->get_increment()); + inner_loop_end->set_finalization_offsets(new_expanded_inner_loop_info->get_finalization_offsets()); + } + return modified; +} + +std::shared_ptr SplitLoops::TransformInnerSplitLoop::merge(const std::shared_ptr& other) { + return !other || ov::is_type(other) ? std::make_shared() : nullptr; +} + } // namespace pass } // namespace lowered } // namespace snippets diff --git a/src/common/snippets/src/lowered/pass/validate_expanded_loops.cpp b/src/common/snippets/src/lowered/pass/validate_expanded_loops.cpp index cc598b4e768cd4..ec77d75fe3ff43 100644 --- a/src/common/snippets/src/lowered/pass/validate_expanded_loops.cpp +++ b/src/common/snippets/src/lowered/pass/validate_expanded_loops.cpp @@ -43,49 +43,51 @@ void ValidateExpandedLoops::validate_loop_information(const LinearIR& linear_ir) const auto& loop_manager = linear_ir.get_loop_manager(); const auto& loop_map = loop_manager->get_map(); - UnifiedLoopInfoPtr current_unified_loop_info = nullptr; - std::vector total_finalization_offsets; - size_t current_work_amount = 0; - size_t num_ports = 0; + // Initialized UnifiedLoopInfo + struct CurrentUnifiedLoopInfo { + size_t work_amount = 0; + size_t num_ports = 0; + size_t id = 0; + std::vector finalization_offsets; + }; + std::unordered_map initializated_info_map; for (const auto& p : loop_map) { const auto& expanded_loop_info = ov::as_type_ptr(p.second); INFORMATIVE_ASSERT(expanded_loop_info, "expects only ExpandedLoopInfo in LoopManager"); - const auto& unified_loop_info = expanded_loop_info->get_unified_loop_info(); - INFORMATIVE_ASSERT(unified_loop_info, "expects non nullptr UnifiedLoopInfo in ExpandedLoopInfo"); + const auto& current_unified_loop_info = expanded_loop_info->get_unified_loop_info(); + INFORMATIVE_ASSERT(current_unified_loop_info, "expects non nullptr UnifiedLoopInfo in ExpandedLoopInfo"); - if (unified_loop_info != current_unified_loop_info) { - // If there is `current_unified_loop_info` - the previos loop is finished and need to validate total information - if (current_unified_loop_info) { - INFORMATIVE_ASSERT(current_work_amount == current_unified_loop_info->get_work_amount(), - "total work amount of expanded loops is not equal to work amount of undefined loop"); - INFORMATIVE_ASSERT(total_finalization_offsets == current_unified_loop_info->get_finalization_offsets(), - "total finalization offsets are not equal to finalization offsets of undefined loop"); - } - - current_unified_loop_info = unified_loop_info; - - INFORMATIVE_ASSERT(current_unified_loop_info->get_input_count() == expanded_loop_info->get_input_count() && - current_unified_loop_info->get_output_count() == expanded_loop_info->get_output_count(), - "incompatible loop ports with UnifiedLoopInfo"); - - current_work_amount = 0; - num_ports = expanded_loop_info->get_input_count() + expanded_loop_info->get_output_count(); - total_finalization_offsets.clear(); - total_finalization_offsets.resize(num_ports, 0); + auto& current_info = initializated_info_map[current_unified_loop_info]; + if (current_info.num_ports == 0) { // the info was just default constructed + current_info.num_ports = current_unified_loop_info->get_input_count() + current_unified_loop_info->get_output_count(); + current_info.finalization_offsets.resize(current_info.num_ports, 0); } - current_work_amount = utils::dynamic_safe_add(current_work_amount, expanded_loop_info->get_work_amount()); - INFORMATIVE_ASSERT(current_unified_loop_info, "expects non nullptr current UnifiedLoopInfo"); + INFORMATIVE_ASSERT(current_unified_loop_info->get_input_count() == expanded_loop_info->get_input_count() && + current_unified_loop_info->get_output_count() == expanded_loop_info->get_output_count(), + "incompatible loop ports with UnifiedLoopInfo"); + + current_info.work_amount = utils::dynamic_safe_add(current_info.work_amount, expanded_loop_info->get_work_amount()); INFORMATIVE_ASSERT(current_unified_loop_info->get_ptr_increments() == expanded_loop_info->get_ptr_increments(), "incompatible pointer increments with UnifiedLoopInfo"); const auto& finalization_offsets = expanded_loop_info->get_finalization_offsets(); - INFORMATIVE_ASSERT(finalization_offsets.size() == total_finalization_offsets.size(), + INFORMATIVE_ASSERT(finalization_offsets.size() == current_info.finalization_offsets.size(), "incompatible finalization offset count"); - for (size_t i = 0; i < num_ports; ++i) - total_finalization_offsets[i] = utils::dynamic_safe_add(total_finalization_offsets[i], finalization_offsets[i]); + for (size_t i = 0; i < current_info.num_ports; ++i) + current_info.finalization_offsets[i] = utils::dynamic_safe_add(current_info.finalization_offsets[i], finalization_offsets[i]); + } + + // Validation of total information + for (const auto& p : initializated_info_map) { + const auto loop_info = p.first; + const auto total_info = p.second; + INFORMATIVE_ASSERT(total_info.work_amount == loop_info->get_work_amount(), + "total work amount of expanded loops is not equal to work amount of undefined loop with ID: " + std::to_string(total_info.id)); + INFORMATIVE_ASSERT(total_info.finalization_offsets == loop_info->get_finalization_offsets(), + "total finalization offsets are not equal to finalization offsets of undefined loop with ID: " + std::to_string(total_info.id)); } } diff --git a/src/common/snippets/src/lowered/specific_loop_iter_handlers.cpp b/src/common/snippets/src/lowered/specific_loop_iter_handlers.cpp index ffc00cdd08e50a..c1302084aa68f7 100644 --- a/src/common/snippets/src/lowered/specific_loop_iter_handlers.cpp +++ b/src/common/snippets/src/lowered/specific_loop_iter_handlers.cpp @@ -13,11 +13,23 @@ namespace ov { namespace snippets { namespace lowered { -SpecificIterationHandlers::SpecificIterationHandlers(size_t loop_work_amount, size_t loop_increment) { - const auto tail_size = utils::is_dynamic_value(loop_work_amount) ? 1lu : loop_work_amount % loop_increment; - if (tail_size != 0) { - m_last_iter_handlers.register_pass(tail_size); - m_last_iter_handlers.register_pass(tail_size); +SpecificIterationHandlers::SpecificIterationHandlers(size_t loop_work_amount, size_t loop_increment, size_t processing_dim_idx) { + // The following handlers are set only for Last Iter processing + if (loop_increment > 1) { + size_t last_iter_increment = utils::get_dynamic_value(); + if (!utils::is_dynamic_value(loop_work_amount)) { + last_iter_increment = loop_work_amount % loop_increment; + } else if (utils::is_dynamic_value(loop_work_amount) && processing_dim_idx == 0) { + // [149935] : Last Iterations of Loop processed last dimensions with Eltwise nodes inside should have increment = 1 + last_iter_increment = 1; + } + if (last_iter_increment != 0) { + m_last_iter_handlers.register_pass(last_iter_increment); + m_last_iter_handlers.register_pass(last_iter_increment); + // Last Iterations of Loop processed last dimensions with Eltwise nodes inside should have increment = 1 + if (last_iter_increment == 1) + m_last_iter_handlers.register_pass(); + } } } diff --git a/src/common/snippets/src/runtime_configurator.cpp b/src/common/snippets/src/runtime_configurator.cpp index e22e4b3bee57fb..552455b89f5529 100644 --- a/src/common/snippets/src/runtime_configurator.cpp +++ b/src/common/snippets/src/runtime_configurator.cpp @@ -17,6 +17,24 @@ namespace snippets { using namespace ov::snippets::pass; using namespace ov::snippets::lowered; +#ifdef SNIPPETS_DEBUG_CAPS +std::string RuntimeConfig::to_string() const { + std::stringstream out; + out << " ========== RuntimeConfig state ==========\n" << + "tensor_rank: " << tensor_rank << "\n" << + "tile_rank: " << tile_rank << "\n" << + "master_shape: " << ov::Shape(master_shape) << "\n"; + out << "io_data_offsets: " << "\n"; + for (size_t i = 0; i < io_data_offsets.size(); ++i) + out << "\t[" << i << "]" << ov::Shape(io_data_offsets[i]) << "\n"; + out << "buffer_scratchpad_size: " << buffer_scratchpad_size << "\n"; + out << "buffer_cluster_offsets: " << "\n"; + for (size_t i = 0; i < buffer_cluster_offsets.size(); ++i) + out << "\t[" << i << "]" << buffer_cluster_offsets[i] << "\n"; + return out.str(); +} +#endif + RuntimeConfigurator::RuntimeConfigurator(std::shared_ptr c) : m_config(std::move(c)) { OPENVINO_ASSERT(m_config, "Runtime config is nullptr!"); @@ -45,6 +63,21 @@ void RuntimeConfigurator::initialization(const lowered::LinearIRCPtr& linear_ir) m_config->io_data_offsets.resize(m_io_num); m_config->tile_rank = linear_ir->get_config().m_loop_depth; m_optimizer.init(linear_ir, m_io_descs, m_in_num); + + // InnerSplittedLoops should be inited after OuterSplittedLoops + const auto& loop_map = linear_ir->get_loop_manager()->get_map(); + m_ordered_loop_ids.clear(); + m_ordered_loop_ids.reserve(loop_map.size()); + std::vector loops_must_be_last; + for (const auto& p : loop_map) { + const auto loop_id = p.first; + const auto& expanded_loop_info = ov::as_type_ptr(p.second); + OPENVINO_ASSERT(expanded_loop_info, "UpdateLoopInfo expects ExpandedLoopInfo in LoopManager"); + const auto& unified_loop_info = expanded_loop_info->get_unified_loop_info(); + auto& collection = ov::is_type(unified_loop_info) ? loops_must_be_last : m_ordered_loop_ids; + collection.push_back(loop_id); + } + m_ordered_loop_ids.insert(m_ordered_loop_ids.end(), loops_must_be_last.cbegin(), loops_must_be_last.cend()); } void RuntimeConfigurator::update(const lowered::LinearIRCPtr& linear_ir) { @@ -152,18 +185,17 @@ void RuntimeConfigurator::init_buffer_info(const lowered::LinearIRCPtr& linear_i void RuntimeConfigurator::update_loop_info(const lowered::LinearIRCPtr& linear_ir, LoopInfoRuntimeParamsMap& initializated_info_map) const { - const auto& loop_map = linear_ir->get_loop_manager()->get_map(); - for (const auto& p : loop_map) { - const auto& expanded_loop_info = ov::as_type_ptr(p.second); - OPENVINO_ASSERT(expanded_loop_info, "UpdateLoopInfo expects ExpandedLoopInfo in LoopManager"); + auto update_unified_loop_info = [&](const lowered::UnifiedLoopInfoPtr& unified_loop_info) { + if (initializated_info_map.count(unified_loop_info) == 0) { + lowered::pass::InitLoops::update_runtime_parameters(unified_loop_info); + initializated_info_map[unified_loop_info] = compute_runtime_params(unified_loop_info); + } + }; - // First visiting of unified (whole) loop + auto update_expanded_loop_info = [&](const lowered::ExpandedLoopInfoPtr& expanded_loop_info) { const auto& current_unified_loop_info = expanded_loop_info->get_unified_loop_info(); - if (initializated_info_map.count(current_unified_loop_info) == 0) { - lowered::pass::InitLoops::update_runtime_parameters(current_unified_loop_info); - initializated_info_map[current_unified_loop_info] = compute_runtime_params(current_unified_loop_info); - } + OPENVINO_ASSERT(initializated_info_map.count(current_unified_loop_info) > 0, "UnifiedLoopInfo must be updated before ExpandedLoopInfo"); auto& initializated_info = initializated_info_map.at(current_unified_loop_info); auto& current_work_amount = initializated_info.work_amount; const auto& ptr_increments = initializated_info.ptr_increments; @@ -176,7 +208,7 @@ void RuntimeConfigurator::update_loop_info(const lowered::LinearIRCPtr& linear_i expanded_loop_info->set_work_amount(0); if (expanded_loop_info->is_evaluate_once()) expanded_loop_info->set_increment(0); - continue; + return; } const auto work_amount = @@ -196,6 +228,22 @@ void RuntimeConfigurator::update_loop_info(const lowered::LinearIRCPtr& linear_i expanded_loop_info->update_ptr_increments(ptr_increments); } expanded_loop_info->update_finalization_offsets(updated_finalization_offsets); + }; + + auto update_loop_info = [&](const lowered::LoopInfoPtr& loop_info) { + if (const auto unified_loop_info = ov::as_type_ptr(loop_info)) { + update_unified_loop_info(unified_loop_info); + } else if (const auto expanded_loop_info = ov::as_type_ptr(loop_info)) { + update_expanded_loop_info(expanded_loop_info); + } else { + OPENVINO_THROW("Failed to update loop info: unknown type!"); + } + }; + + lowered::LoopInfoSet updated_loops; + const auto& loop_map = linear_ir->get_loop_manager()->get_map(); + for (const auto& p : loop_map) { + p.second->apply(update_loop_info, updated_loops); } } @@ -203,6 +251,12 @@ void RuntimeConfigurator::update_buffer_scratchpad_size(const lowered::LinearIRC const auto& loop_manager = linear_ir->get_loop_manager(); m_config->buffer_scratchpad_size = linear_ir->get_static_buffer_scratchpad_size(); + auto is_not_executed = [&loop_manager](const lowered::ExpressionPtr& buffer_expr) { + const auto& loop_ids = buffer_expr->get_loop_ids(); + return std::any_of(loop_ids.cbegin(), loop_ids.cend(), + [&loop_manager](size_t loop_id) { return loop_manager->get_loop_info(loop_id)->get_work_amount() == 0; }); + }; + for (const auto& p : m_dynamic_buffer_clusters) { const auto& cluster_id = p.first; const auto& cluster = p.second; @@ -212,13 +266,16 @@ void RuntimeConfigurator::update_buffer_scratchpad_size(const lowered::LinearIRC size_t additional_size = 0; for (const auto& buffer_expr : cluster) { + // No need to calculate allocation size of Buffers which are in Loops with `work_amount = 0` - they won't be executed + if (is_not_executed(buffer_expr)) + continue; const auto& allocation_size = lowered::pass::ComputeBufferAllocationSize::get_allocation_size(loop_manager, buffer_expr, m_config->tile_rank); + OPENVINO_ASSERT(!utils::is_dynamic_value(allocation_size), "Buffer scratchpad size must be defined!"); additional_size = std::max(allocation_size * buffer_expr->get_node()->get_element_type().size(), additional_size); } cluster_offset = m_config->buffer_scratchpad_size; OPENVINO_ASSERT(!utils::is_dynamic_value(cluster_offset), "Offset of the cluster must be defined!"); - OPENVINO_ASSERT(!utils::is_dynamic_value(additional_size), "Buffer scratchpad size must be defined!"); m_config->buffer_scratchpad_size += additional_size; } diff --git a/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp b/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp index db58805a8f023b..3e148d3c1cf329 100644 --- a/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp +++ b/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp @@ -7,12 +7,8 @@ #include "openvino/opsets/opset10.hpp" #include "snippets/lowered/pass/extract_loop_invariants.hpp" #include "snippets/lowered/pass/normalize_loop_ids.hpp" -#include "snippets/op/broadcastmove.hpp" -#include "snippets/op/scalar.hpp" -#include "snippets/op/vector_buffer.hpp" -#include "snippets/op/horizon_max.hpp" -#include "snippets/op/horizon_sum.hpp" -#include "snippets/op/powerstatic.hpp" +#include "snippets/lowered/pass/split_loops.hpp" +#include "snippets/snippets_isa.hpp" namespace ov { namespace test { @@ -292,6 +288,76 @@ TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsFromInnermostToLoopOuts } } +TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsSplitLoops) { + size_t vector_size = 16; + size_t block_size = 32; + const auto input_precision = ov::element::f32; + const ov::Shape input_shape_0{128, 512}; + const ov::Shape input_shape_1{512, 64}; + const ov::Shape input_shape_2{1, 1}; + const ov::snippets::VectorDims layout{0, 1}; + const ov::snippets::VectorDims subtensor{1, vector_size}; + /* + * Params Param2(1,1) + * \ / + * MatMul Broadcast + * \ / + * Add + * | + * Result + */ + { + const auto param0 = linear_ir->push_node(input_precision, input_shape_0); + const auto param1 = linear_ir->push_node(input_precision, input_shape_1); + const auto param2 = linear_ir->push_node(input_precision, input_shape_2); + const auto matmul = linear_ir->push_node(param0.second, param1.second); + const auto broadcastmove = linear_ir->push_node(param2.second, input_shape_1.back()); + init_expr_descriptors(*broadcastmove.first, {{1, 1}, subtensor}, {layout, layout}); + const auto add = linear_ir->push_node(matmul.second, broadcastmove.second); + init_expr_descriptors(*add.first, {subtensor, subtensor, subtensor}, {layout, layout, layout}); + const auto result = linear_ir->push_node(add.second); + const auto& loop_manager = linear_ir->get_loop_manager(); + loop_manager->mark_loop(matmul.first, broadcastmove.first, 128, block_size, 1, + std::vector{LoopPort((*matmul.first)->get_input_port(0)), + LoopPort((*matmul.first)->get_input_port(1), false)}, + std::vector{LoopPort((*matmul.first)->get_output_port(0))}); + loop_manager->mark_loop(broadcastmove.first, result.first, 64, vector_size, 0, + std::vector{LoopPort((*broadcastmove.first)->get_input_port(0)), + LoopPort((*add.first)->get_input_port(0))}, + std::vector{LoopPort((*add.first)->get_output_port(0))}); + loop_manager->mark_loop(broadcastmove.first, result.first, 128, 1, 1, + std::vector{LoopPort((*broadcastmove.first)->get_input_port(0)), + LoopPort((*add.first)->get_input_port(0))}, + std::vector{LoopPort((*add.first)->get_output_port(0))}); + ov::snippets::lowered::pass::SplitLoops().run(*linear_ir, linear_ir->begin(), linear_ir->end()); + } + { + const auto param0 = linear_ir_ref->push_node(input_precision, input_shape_0); + const auto param1 = linear_ir_ref->push_node(input_precision, input_shape_1); + const auto param2 = linear_ir_ref->push_node(input_precision, input_shape_2); + auto broadcastmove = linear_ir_ref->push_node(param2.second, input_shape_1.back()); + init_expr_descriptors(*broadcastmove.first, {{1, 1}, subtensor}, {layout, layout}); + const auto matmul = linear_ir_ref->push_node(param0.second, param1.second); + const auto add = linear_ir_ref->push_node(matmul.second, broadcastmove.second); + init_expr_descriptors(*add.first, {subtensor, subtensor, subtensor}, {layout, layout, layout}); + auto result = linear_ir_ref->push_node(add.second); + const auto& loop_manager = linear_ir_ref->get_loop_manager(); + loop_manager->mark_loop(matmul.first, add.first, 128, block_size, 1, + std::vector{LoopPort((*matmul.first)->get_input_port(0)), + LoopPort((*matmul.first)->get_input_port(1), false)}, + std::vector{LoopPort((*matmul.first)->get_output_port(0))}); + loop_manager->mark_loop(add.first, result.first, 64, vector_size, 0, + std::vector{LoopPort((*add.first)->get_input_port(0)), + LoopPort((*add.first)->get_input_port(1))}, + std::vector{LoopPort((*add.first)->get_output_port(0))}); + loop_manager->mark_loop(add.first, result.first, 128, 1, 1, + std::vector{LoopPort((*add.first)->get_input_port(0)), + LoopPort((*add.first)->get_input_port(1))}, + std::vector{LoopPort((*add.first)->get_output_port(0))}); + ov::snippets::lowered::pass::SplitLoops().run(*linear_ir_ref, linear_ir_ref->begin(), linear_ir_ref->end()); + } +} + class ExtractLoopInvariantsRemoveLoopsTest : public LoweredPassTestsF { public: ExtractLoopInvariantsRemoveLoopsTest() : LoweredPassTestsF() { diff --git a/src/common/snippets/tests/src/lowered/pass/loop.cpp b/src/common/snippets/tests/src/lowered/pass/loop.cpp index a7214c755f5ed6..610b98e558760f 100644 --- a/src/common/snippets/tests/src/lowered/pass/loop.cpp +++ b/src/common/snippets/tests/src/lowered/pass/loop.cpp @@ -2,23 +2,23 @@ // SPDX-License-Identifier: Apache-2.0 // -#include "snippets/op/loop.hpp" - #include +#include "openvino/opsets/opset10.hpp" +#include "snippets/snippets_isa.hpp" #include "snippets/lowered/linear_ir.hpp" #include "snippets/lowered/pass/cleanup_loop_offsets.hpp" #include "snippets/lowered/pass/init_loops.hpp" #include "snippets/lowered/pass/insert_load_store.hpp" #include "snippets/lowered/pass/insert_loops.hpp" #include "snippets/lowered/pass/insert_specific_iterations.hpp" -#include "snippets/lowered/pass/iter_handler.hpp" +#include "snippets/lowered/pass/split_loops.hpp" +#include "snippets/lowered/pass/insert_buffers.hpp" #include "snippets/lowered/pass/optimize_loop_single_evaluation.hpp" #include "snippets/lowered/pass/validate_unified_loops.hpp" #include "snippets/lowered/pass/validate_expanded_loops.hpp" #include "snippets/lowered/pass/normalize_loop_ids.hpp" #include "snippets/shape_inference/shape_inference.hpp" -#include "subgraph_simple.hpp" using Snippets_TailProcessingTransformation = ::testing::Test; // [Inserted Loop number, [ptr_increments, final_offsets] @@ -27,34 +27,32 @@ using namespace ov::snippets::lowered; constexpr static size_t vector_size = 16; -static void init_linear_ir(const std::vector& in_shapes, LinearIR& linear_ir, size_t block_size) { - auto body = ov::test::snippets::AddFunction(in_shapes).getOriginal(); - auto shape_infer_factory = std::make_shared(); - linear_ir = LinearIR(body, shape_infer_factory); - auto expr_it = std::find_if(linear_ir.cbegin(), linear_ir.cend(), - [](const ExpressionPtr& expr) { return ov::is_type(expr->get_node()); }); - ASSERT_TRUE(expr_it != linear_ir.cend()); - const auto add = *expr_it; - const auto loop_input_ports = std::vector{add->get_input_port(0), add->get_input_port(1)}; - const auto loop_output_ports = std::vector{add->get_output_port(0)}; +static void init_linear_ir(const std::vector& in_shapes, LinearIR& linear_ir, size_t block_size) { + Config lir_config; + lir_config.m_manual_build_support = true; + linear_ir = LinearIR(lir_config, std::make_shared()); + + const ov::element::Type input_precision = ov::element::f32; + const auto param0 = linear_ir.push_node(input_precision, in_shapes[0]); + const auto param1 = linear_ir.push_node(input_precision, in_shapes[1]); + const auto param2 = linear_ir.push_node(input_precision, in_shapes[2]); + const auto matmul = linear_ir.push_node(param0.second, param1.second); + const auto add = linear_ir.push_node(matmul.second, param2.second); + const auto result = linear_ir.push_node(add.second); + const auto loop_manager = linear_ir.get_loop_manager(); - const auto in_shape0 = in_shapes[0].get_shape(); - const auto in_shape1 = in_shapes[1].get_shape(); - const auto inner_wa = std::max(*in_shape0.rbegin(), *in_shape1.rbegin()); - const auto inner_inc = std::min(vector_size, inner_wa); - const auto blocked_wa = block_size; - const auto blocked_inc = 1; - const auto outer_wa = std::max(*(in_shape0.rbegin() + 1), *(in_shape1.rbegin() + 1)); - const auto outer_inc = blocked_wa; - loop_manager->mark_loop(expr_it, std::next(expr_it), inner_wa, inner_inc, 0, loop_input_ports, loop_output_ports); - loop_manager->mark_loop(expr_it, std::next(expr_it), blocked_wa, blocked_inc, 1, loop_input_ports, loop_output_ports, true, true); - const auto loop_id = loop_manager->mark_loop(expr_it, std::next(expr_it), outer_wa, outer_inc, 1, loop_input_ports, loop_output_ports); - const auto& outer_loop_info = loop_manager->get_loop_info(loop_id); - const auto outer_tail_size = outer_wa % outer_inc; - if (outer_tail_size != 0) { - outer_loop_info->register_pass_to_handler( - outer_tail_size); - } + linear_ir.get_loop_manager()->mark_loop(matmul.first, add.first, in_shapes[0].front(), block_size, 1, + std::vector{LoopPort((*matmul.first)->get_input_port(0)), + LoopPort((*matmul.first)->get_input_port(1), false)}, + std::vector{LoopPort((*matmul.first)->get_output_port(0))}); + linear_ir.get_loop_manager()->mark_loop(add.first, result.first, in_shapes[2].back(), vector_size, 0, + std::vector{LoopPort((*add.first)->get_input_port(0)), + LoopPort((*add.first)->get_input_port(1))}, + std::vector{LoopPort((*add.first)->get_output_port(0))}); + linear_ir.get_loop_manager()->mark_loop(add.first, result.first, in_shapes[2].front(), 1, 1, + std::vector{LoopPort((*add.first)->get_input_port(0)), + LoopPort((*add.first)->get_input_port(1))}, + std::vector{LoopPort((*add.first)->get_output_port(0))}); } static void apply_transformations(LinearIR& linear_ir, const std::shared_ptr& config) { @@ -64,6 +62,8 @@ static void apply_transformations(LinearIR& linear_ir, const std::shared_ptr(); + pipeline.register_pass(); pipeline.register_pass(vector_size); pipeline.register_pass(); pipeline.register_pass(); @@ -77,25 +77,27 @@ static void apply_transformations(LinearIR& linear_ir, const std::shared_ptr loops; for (const auto& expr : linear_ir) { const auto& node = expr->get_node(); const auto loop_end = ov::as_type_ptr(node); if (!loop_end) continue; + const auto loop_num = loop_end->get_id(); ASSERT_GT(reference.count(loop_num), 0); + loops.insert(loop_num); ASSERT_TRUE(loop_end->get_ptr_increments() == reference.at(loop_num).first); ASSERT_TRUE(loop_end->get_finalization_offsets() == reference.at(loop_num).second); - loop_num++; } - ASSERT_EQ(loop_num, reference.size()); + ASSERT_EQ(loops.size(), reference.size()); } TEST(Snippets_TailProcessingTransformation, BlockedWOTail_OriginalPtrShifts) { LinearIR linear_ir; - ov::Shape inputShape0 = {1, 2, 16, 20}; - ov::Shape inputShape1 = {1, 2, 16, 20}; - init_linear_ir({inputShape0, inputShape1}, linear_ir, 4); + ov::Shape inputShape0 = {12, 16}; + ov::Shape inputShape1 = {16, 20}; + ov::Shape inputShape2 = {12, 20}; + init_linear_ir({inputShape0, inputShape1, inputShape2}, linear_ir, 4); auto config = std::make_shared(); config->disable(); @@ -107,16 +109,17 @@ TEST(Snippets_TailProcessingTransformation, BlockedWOTail_OriginalPtrShifts) { std::map, std::vector>> reference; reference[0] = { std::vector(3, 1), std::vector(3, -20)}; reference[1] = { std::vector(3, 20), std::vector(3, -80)}; - reference[2] = { std::vector(3, 20), std::vector(3, -320)}; + reference[2] = { {16, 0, 20, 20}, {-192, 0, -240, -240}}; validate(linear_ir, reference); } TEST(Snippets_TailProcessingTransformation, BlockedWOTail_CleanUpPtrShifts) { LinearIR linear_ir; - ov::Shape inputShape0 = {1, 2, 16, 20}; - ov::Shape inputShape1 = {1, 2, 16, 20}; - init_linear_ir({inputShape0, inputShape1}, linear_ir, 4); + ov::Shape inputShape0 = {12, 16}; + ov::Shape inputShape1 = {16, 20}; + ov::Shape inputShape2 = {12, 20}; + init_linear_ir({inputShape0, inputShape1, inputShape2}, linear_ir, 4); auto config = std::make_shared(); config->disable(); @@ -126,17 +129,18 @@ TEST(Snippets_TailProcessingTransformation, BlockedWOTail_CleanUpPtrShifts) { // [Inserted Loop number, [ptr_increments, final_offsets] std::map, std::vector>> reference; reference[0] = { std::vector(3, 1), std::vector(3, 0)}; - reference[1] = { std::vector(3, 0), std::vector(3, 0)}; - reference[2] = { std::vector(3, 0), std::vector(3, 0)}; + reference[1] = { std::vector(3, 0), {0, -80, 0}}; // -80 - finalization offset for Buffer ptr + reference[2] = { {16, 0, 0, 0}, std::vector(4, 0)}; validate(linear_ir, reference); } TEST(Snippets_TailProcessingTransformation, BlockedTail_OriginalPtrShifts) { LinearIR linear_ir; - ov::Shape inputShape0 = {1, 2, 18, 20}; - ov::Shape inputShape1 = {1, 2, 18, 20}; - init_linear_ir({inputShape0, inputShape1}, linear_ir, 4); + ov::Shape inputShape0 = {14, 16}; + ov::Shape inputShape1 = {16, 20}; + ov::Shape inputShape2 = {14, 20}; + init_linear_ir({inputShape0, inputShape1, inputShape2}, linear_ir, 4); auto config = std::make_shared(); config->disable(); @@ -144,38 +148,37 @@ TEST(Snippets_TailProcessingTransformation, BlockedTail_OriginalPtrShifts) { // [Inserted Loop number, [ptr_increments, final_offsets] std::map, std::vector>> reference; - reference[0] = { std::vector(3, 0), std::vector(3, 16)}; // Vector Inner - reference[1] = { std::vector(3, 0), std::vector(3, -16)}; // Blocked Inner - reference[2] = { std::vector(3, 20), std::vector(3, -80)}; // Vector Blocked - reference[3] = { std::vector(3, 20), std::vector(3, 0)}; // Vector Outer + reference[0] = { std::vector(3, 0), std::vector(3, 16)}; // Vector Inner + reference[1] = { std::vector(3, 0), std::vector(3, -16)}; // Tail Inner - reference[4] = { std::vector(3, 0), std::vector(3, 16)}; // Vector Inner - reference[5] = { std::vector(3, 0), std::vector(3, -16)}; // Blocked Inner - reference[6] = { std::vector(3, 20), std::vector(3, -40)}; // Tail Blocked - reference[7] = { std::vector(3, 0), std::vector(3, -320)}; // Tail Blocked + reference[2] = { std::vector(3, 20), std::vector(3, -80)}; // Inner Vector Blocked + reference[3] = { {16, 0, 20, 20}, std::vector(4, 0)}; // Outer Vector Blocked + + reference[4] = { std::vector(3, 20), std::vector(3, -40)}; // Inner Tail Blocked + reference[5] = { std::vector(4, 0), {-192, 0, -240, -240}}; // Outer Tail Blocked validate(linear_ir, reference); } TEST(Snippets_TailProcessingTransformation, BlockedTail_CleanUpPtrShifts) { LinearIR linear_ir; - ov::Shape inputShape0 = {1, 2, 18, 20}; - ov::Shape inputShape1 = {1, 2, 18, 20}; - init_linear_ir({inputShape0, inputShape1}, linear_ir, 4); + ov::Shape inputShape0 = {14, 16}; + ov::Shape inputShape1 = {16, 20}; + ov::Shape inputShape2 = {14, 20}; + init_linear_ir({inputShape0, inputShape1, inputShape2}, linear_ir, 4); apply_transformations(linear_ir, std::make_shared()); // [Inserted Loop number, [ptr_increments, final_offsets] std::map, std::vector>> reference; - reference[0] = { std::vector(3, 0), std::vector(3, 16)}; // Vector Inner - reference[1] = { std::vector(3, 0), std::vector(3, 4)}; // Blocked Inner - reference[2] = {std::vector(3, 0), std::vector(3, 0)}; // Vector Blocked - reference[3] = { std::vector(3, 0), std::vector(3, 0)}; // Vector Outer - - reference[4] = { std::vector(3, 0), std::vector(3, 16)}; // Vector Inner - reference[5] = { std::vector(3, 0), std::vector(3, 4)}; // Blocked Inner - reference[6] = { std::vector(3, 0), std::vector(3, 0)}; // Tail Blocked - reference[7] = { std::vector(3, 0), std::vector(3, 0)}; // Tail Blocked + reference[0] = { std::vector(3, 0), std::vector(3, 16)}; // Vector Inner + reference[1] = { std::vector(3, 0), std::vector(3, 4)}; // Tail Inner + + reference[2] = { std::vector(3, 0), {0, -80, 0}}; // Inner Vector Blocked (-80 - finalization offset for Buffer ptr) + reference[3] = { {16, 0, 0, 0}, std::vector(4, 0)}; // Outer Vector Blocked + + reference[4] = { std::vector(3, 0), {0, -40, 0}}; // Inner Tail Blocked (-40 - finalization offset for Buffer ptr) + reference[5] = { std::vector(4, 0), {32, 0, 0, 0}}; // Outer Tail Blocked validate(linear_ir, reference); } diff --git a/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.cpp b/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.cpp index 40da4130b212c9..56c2c75dae9bc2 100644 --- a/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.cpp +++ b/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.cpp @@ -12,6 +12,27 @@ namespace intel_cpu { const size_t CPURuntimeConfigurator::rank6D = 6; +#ifdef SNIPPETS_DEBUG_CAPS +std::string CPURuntimeConfig::to_string() const { + std::stringstream out; + out << RuntimeConfig::to_string(); + out << "Loop Parameters:" << "\n"; + for (size_t i = 0; i < loop_args.size(); ++i) { + const auto& loop = loop_args[i]; + out << "\t[" << i << "] WA: " << loop.m_work_amount << "\n"; + out << "\tPointer Increments: "; + for (int64_t j = 0; j < loop.m_num_data_ptrs; ++j) + out << loop.m_ptr_increments[j] << " "; + out << "\n"; + out << "\tFinalization offsets: "; + for (int64_t j = 0; j < loop.m_num_data_ptrs; ++j) + out << loop.m_finalization_offsets[j] << " "; + out << "\n"; + } + return out.str(); +} +#endif + CPURuntimeConfigurator::CPURuntimeConfigurator() : ov::snippets::RuntimeConfigurator(std::make_shared()) { } diff --git a/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.hpp b/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.hpp index 2104dfbdd42ddb..00bc676678d189 100644 --- a/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.hpp +++ b/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.hpp @@ -17,6 +17,10 @@ class CPURuntimeConfig : public ov::snippets::RuntimeConfig { OPENVINO_RTTI("CPURuntimeConfig", "0", ov::snippets::RuntimeConfig) CPURuntimeConfig() = default; +#ifdef SNIPPETS_DEBUG_CAPS + std::string to_string() const override; +#endif + std::vector loop_args = {}; }; diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_loop_emitters.cpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_loop_emitters.cpp index cb6dfeb741109a..b968786eea8d67 100644 --- a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_loop_emitters.cpp +++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_loop_emitters.cpp @@ -69,7 +69,12 @@ void jit_loop_begin_emitter::emit_impl(const std::vector& in, const std: } // if wa < increment, skip the loop - h->cmp(reg_work_amount, wa_increment); + // Note : If the loop should be evaluated once and increment is dynamic, + // we should manually set `increment = 1` to compare the dynamic work amount + // with `1` at least before loop execution + // (work amount can be zero and we should skip this loop even `evaluate_once = 1`) + auto increment = evaluate_once && snippets::utils::is_dynamic_value(wa_increment) ? 1 : wa_increment; + h->cmp(reg_work_amount, increment); h->jl(*loop_end_label, Xbyak::CodeGenerator::T_NEAR); h->L(*loop_begin_label); From c328ded614fad2b03d598754c3735f44ae2ecb82 Mon Sep 17 00:00:00 2001 From: Peyara Nando Date: Tue, 20 Aug 2024 01:46:18 +0400 Subject: [PATCH 16/23] [TF FE] Implementation of BatchMatrixInverse Support (#26033) Implemented BatchMatrixInverse Registered into OP_loader Implemented associated pytests For Issue #24807 --------- Co-authored-by: Roman Kazantsev --- src/frontends/tensorflow/docs/supported_ops.md | 2 +- src/frontends/tensorflow/src/op_table.cpp | 1 + src/frontends/tensorflow_common/src/op/matrix_inverse.cpp | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/frontends/tensorflow/docs/supported_ops.md b/src/frontends/tensorflow/docs/supported_ops.md index 9f6af0f7670ae0..a851e0f64db932 100644 --- a/src/frontends/tensorflow/docs/supported_ops.md +++ b/src/frontends/tensorflow/docs/supported_ops.md @@ -111,7 +111,7 @@ A "supported operation" is one that TensorFlow Frontend can convert to the OpenV | BatchMatrixDeterminant | NO | | | BatchMatrixDiag | NO | | | BatchMatrixDiagPart | NO | | -| BatchMatrixInverse | NO | | +| BatchMatrixInverse | YES | | | BatchMatrixSetDiag | NO | | | BatchMatrixSolve | NO | | | BatchMatrixSolveLs | NO | | diff --git a/src/frontends/tensorflow/src/op_table.cpp b/src/frontends/tensorflow/src/op_table.cpp index 8cd0c4a9b15016..ad69ff8467994e 100644 --- a/src/frontends/tensorflow/src/op_table.cpp +++ b/src/frontends/tensorflow/src/op_table.cpp @@ -221,6 +221,7 @@ const std::map get_supported_ops() { {"BatchMatMul", CreatorFunction(translate_batch_mat_mul_op)}, {"BatchMatMulV2", CreatorFunction(translate_batch_mat_mul_op)}, {"BatchMatMulV3", CreatorFunction(translate_batch_mat_mul_with_type_op)}, + {"BatchMatrixInverse", CreatorFunction(translate_matrix_inverse_op)}, {"BatchToSpaceND", CreatorFunction(translate_batch_to_space_nd_op)}, {"BroadcastArgs", CreatorFunction(translate_broadcast_args_op)}, {"BroadcastTo", CreatorFunction(translate_broadcast_to_op)}, diff --git a/src/frontends/tensorflow_common/src/op/matrix_inverse.cpp b/src/frontends/tensorflow_common/src/op/matrix_inverse.cpp index f0c051d996ff4f..84a7320a191692 100644 --- a/src/frontends/tensorflow_common/src/op/matrix_inverse.cpp +++ b/src/frontends/tensorflow_common/src/op/matrix_inverse.cpp @@ -13,7 +13,7 @@ namespace frontend { namespace tensorflow { namespace op { OutputVector translate_matrix_inverse_op(const NodeContext& node) { - default_op_checks(node, 1, {"MatrixInverse"}); + default_op_checks(node, 1, {"MatrixInverse", "BatchMatrixInverse"}); // retrieve the input tensor auto input = node.get_input(0); From e0392dcc4691eedcfd9f79fb2702f0575f7c0fd8 Mon Sep 17 00:00:00 2001 From: Maciej Smyk Date: Tue, 20 Aug 2024 00:41:12 +0200 Subject: [PATCH 17/23] [DOCS] Fixing code snippet in OV Tokenizers article for master (#26114) Jira: 149945 Fixed mentioned issues in code snippets in OV Tokenizers article. --- .../learn-openvino/llm_inference_guide/ov-tokenizers.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst b/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst index 1f5d6813cf0ba7..d6e23b3791d001 100644 --- a/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst +++ b/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst @@ -236,8 +236,8 @@ Convert Tokenizers: tokenizer_dir = Path("tokenizer/") core = Core() - ov_tokenizer = core.read_model(tokenizer_dir / "openvino_tokenizer") - ov_detokenizer = core.read_model(tokenizer_dir / "openvino_detokenizer") + ov_tokenizer = core.read_model(tokenizer_dir / "openvino_tokenizer.xml") + ov_detokenizer = core.read_model(tokenizer_dir / "openvino_detokenizer.xml") tokenizer, detokenizer = core.compile_model(ov_tokenizer), core.compile_model(ov_detokenizer) @@ -278,7 +278,7 @@ You can find more information and code snippets in the `OpenVINO Tokenizers Note .. code-block:: python - input numpy as np + import numpy as np text_input = ["Quick brown fox jumped"] From 72516c375a6e04217c04e5df8409d5875b90b6e9 Mon Sep 17 00:00:00 2001 From: Xiuchuan Zhai <110877886+xczhai@users.noreply.github.com> Date: Tue, 20 Aug 2024 13:06:00 +0800 Subject: [PATCH 18/23] [CPU] FC node tensor parallel (#25088) ### Details: - *Enable sub stream to compile model on each socket* - *Limit CPU cores to 32 when TP is enabled* - *Split fc into two sub-fcs at node level instead of graph level* - *Concat fc output in the horizon direction. Drop allgatherv and allreduce for performance reason* - *Supported llm fc int8 and 4bit mode* - *Remove previous version fc TP logic* ### Tickets: - *ticket-id* --------- Co-authored-by: sunxiaoxia2022 Co-authored-by: Shen, Wanglei --- .../runtime/threading/cpu_message.hpp | 58 +++++ .../threading/cpu_streams_executor.hpp | 2 +- .../runtime/threading/istreams_executor.hpp | 50 ++-- .../src/dev/threading/cpu_message.cpp | 96 +++++++ .../dev/threading/cpu_streams_executor.cpp | 100 +------ .../cpu_streams_executor_internal.cpp | 17 +- .../src/dev/threading/istreams_executor.cpp | 27 -- src/inference/tests/unit/cpu_reserve_test.cpp | 5 +- .../intel_cpu/src/async_infer_request.cpp | 10 + .../intel_cpu/src/async_infer_request.h | 13 + src/plugins/intel_cpu/src/compiled_model.cpp | 74 ++++-- src/plugins/intel_cpu/src/compiled_model.h | 19 +- src/plugins/intel_cpu/src/config.h | 4 + src/plugins/intel_cpu/src/cpu_memory.cpp | 121 +++++++++ src/plugins/intel_cpu/src/cpu_memory.h | 3 + .../intel_cpu/src/cpu_streams_calculation.cpp | 70 ++++- .../intel_cpu/src/cpu_streams_calculation.hpp | 12 + src/plugins/intel_cpu/src/graph.cpp | 145 ++--------- src/plugins/intel_cpu/src/graph.h | 5 +- src/plugins/intel_cpu/src/graph_context.cpp | 4 +- src/plugins/intel_cpu/src/graph_context.h | 10 +- src/plugins/intel_cpu/src/infer_request.cpp | 49 ++++ src/plugins/intel_cpu/src/infer_request.h | 2 + src/plugins/intel_cpu/src/node.h | 1 - .../intel_cpu/src/nodes/fullyconnected.cpp | 244 ++++++++++++++++++ .../intel_cpu/src/nodes/fullyconnected.h | 29 +++ src/plugins/intel_cpu/src/plugin.cpp | 9 +- src/plugins/intel_cpu/src/plugin.h | 3 + .../intel_cpu/src/sub_memory_manager.hpp | 57 ++++ .../convert_to_cpu_specific_opset.hpp | 6 +- .../transformation_pipeline.cpp | 6 +- .../transformations/transformation_pipeline.h | 2 - .../unit/streams_info/streams_e2e_test.cpp | 4 +- .../streams_info/streams_info_table_test.cpp | 72 +++--- .../streams_info/streams_rank_table_test.cpp | 125 +++++++++ 35 files changed, 1090 insertions(+), 364 deletions(-) create mode 100644 src/inference/dev_api/openvino/runtime/threading/cpu_message.hpp create mode 100644 src/inference/src/dev/threading/cpu_message.cpp create mode 100644 src/plugins/intel_cpu/src/sub_memory_manager.hpp create mode 100644 src/plugins/intel_cpu/tests/unit/streams_info/streams_rank_table_test.cpp diff --git a/src/inference/dev_api/openvino/runtime/threading/cpu_message.hpp b/src/inference/dev_api/openvino/runtime/threading/cpu_message.hpp new file mode 100644 index 00000000000000..357ea1fc2851e4 --- /dev/null +++ b/src/inference/dev_api/openvino/runtime/threading/cpu_message.hpp @@ -0,0 +1,58 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +/** + * @brief OpenVINO Runtime Executor Manager + * @file openvino/runtime/threading/executor_manager.hpp + */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "openvino/runtime/common.hpp" +#include "openvino/runtime/threading/istreams_executor.hpp" +#include "openvino/runtime/threading/itask_executor.hpp" + + +namespace ov { + +namespace threading { +enum MsgType {CALL_BACK}; + +struct MessageInfo { + MsgType msg_type; +}; + +class OPENVINO_RUNTIME_API MessageManager { +public: + MessageManager(); + + void send_message(const MessageInfo& msg_info); + + void server_wait(); + + ~MessageManager(); + + void set_num_sub_streams(int num_sub_streams); + + int get_num_sub_streams(); +private: + int _num_sub_streams = 0; + std::vector _messageQueue; + std::mutex _msgMutex; + std::condition_variable _msgCondVar; +}; + +OPENVINO_RUNTIME_API std::shared_ptr message_manager(); +} // namespace threading +} // namespace ov \ No newline at end of file diff --git a/src/inference/dev_api/openvino/runtime/threading/cpu_streams_executor.hpp b/src/inference/dev_api/openvino/runtime/threading/cpu_streams_executor.hpp index eaa54701e2b20d..ff21eef7089353 100644 --- a/src/inference/dev_api/openvino/runtime/threading/cpu_streams_executor.hpp +++ b/src/inference/dev_api/openvino/runtime/threading/cpu_streams_executor.hpp @@ -55,7 +55,7 @@ class OPENVINO_RUNTIME_API CPUStreamsExecutor : public IStreamsExecutor { int get_socket_id() override; - void run_sub_stream(Task task, int id) override; + std::vector get_rank() override; private: struct Impl; diff --git a/src/inference/dev_api/openvino/runtime/threading/istreams_executor.hpp b/src/inference/dev_api/openvino/runtime/threading/istreams_executor.hpp index 7a34ea258c43dd..3630c2bb5a6ed7 100644 --- a/src/inference/dev_api/openvino/runtime/threading/istreams_executor.hpp +++ b/src/inference/dev_api/openvino/runtime/threading/istreams_executor.hpp @@ -38,6 +38,19 @@ class OPENVINO_RUNTIME_API IStreamsExecutor : virtual public ITaskExecutor { */ using Ptr = std::shared_ptr; + enum MsgType{ + TP, + START_INFER, + CALL_BACK + }; + + struct MessageInfo{ + MsgType msg_type; + std::vector rank; + void* buf; + Task task; + }; + /** * @brief Defines inference thread binding type */ @@ -95,6 +108,7 @@ class OPENVINO_RUNTIME_API IStreamsExecutor : virtual public ITaskExecutor { std::vector> _streams_info_table = {}; std::vector> _stream_processor_ids; int _sub_streams = 0; + std::vector _rank = {}; /** * @brief Get and reserve cpu ids based on configuration and hardware information, @@ -124,6 +138,7 @@ class OPENVINO_RUNTIME_API IStreamsExecutor : virtual public ITaskExecutor { * @param[in] cpu_reservation @copybrief Config::_cpu_reservation * @param[in] cpu_pinning @copybrief Config::_cpu_pinning * @param[in] streams_info_table @copybrief Config::_streams_info_table + * @param[in] rank @copybrief Config::_rank */ Config(std::string name = "StreamsExecutor", int streams = 1, @@ -131,14 +146,16 @@ class OPENVINO_RUNTIME_API IStreamsExecutor : virtual public ITaskExecutor { ov::hint::SchedulingCoreType thread_preferred_core_type = ov::hint::SchedulingCoreType::ANY_CORE, bool cpu_reservation = false, bool cpu_pinning = false, - std::vector> streams_info_table = {}) + std::vector> streams_info_table = {}, + std::vector rank = {}) : _name{std::move(name)}, _streams{streams}, _threads_per_stream{threads_per_stream}, _thread_preferred_core_type(thread_preferred_core_type), _cpu_reservation{cpu_reservation}, _cpu_pinning{cpu_pinning}, - _streams_info_table{std::move(streams_info_table)} { + _streams_info_table{std::move(streams_info_table)}, + _rank{rank} { update_executor_config(); } @@ -197,6 +214,9 @@ class OPENVINO_RUNTIME_API IStreamsExecutor : virtual public ITaskExecutor { int get_sub_streams() const { return _sub_streams; } + std::vector get_rank() const { + return _rank; + } StreamsMode get_sub_stream_mode() const { const auto proc_type_table = get_proc_type_table(); int sockets = proc_type_table.size() > 1 ? static_cast(proc_type_table.size()) - 1 : 1; @@ -250,31 +270,17 @@ class OPENVINO_RUNTIME_API IStreamsExecutor : virtual public ITaskExecutor { virtual int get_socket_id() = 0; /** - * @brief Execute the task in the current thread using streams executor configuration and constraints - * @param task A task to start + * @brief Return the rank of current stream + * Return {} when current stream has no rank + * @return Rank array, or throws exceptions if called not from stream thread */ - virtual void execute(Task task) = 0; + virtual std::vector get_rank() = 0; /** - * @brief Execute ov::Task inside sub stream of task executor context + * @brief Execute the task in the current thread using streams executor configuration and constraints * @param task A task to start - * @param id Sub stream id - */ - virtual void run_sub_stream(Task task, int id) = 0; - - /** - * @brief Execute all of the tasks and waits for its completion. - * Default run_sub_stream_and_wait() method implementation uses run_sub_stream() pure virtual method - * and higher level synchronization primitives from STL. - * The task is wrapped into std::packaged_task which returns std::future. - * std::packaged_task will call the task and signal to std::future that the task is finished - * or the exception is thrown from task - * Than std::future is used to wait for task execution completion and - * task exception extraction - * @note run_sub_stream_and_wait() does not copy or capture tasks! - * @param tasks A vector of tasks to execute */ - void run_sub_stream_and_wait(const std::vector& tasks); + virtual void execute(Task task) = 0; }; } // namespace threading diff --git a/src/inference/src/dev/threading/cpu_message.cpp b/src/inference/src/dev/threading/cpu_message.cpp new file mode 100644 index 00000000000000..75df25d70d53bf --- /dev/null +++ b/src/inference/src/dev/threading/cpu_message.cpp @@ -0,0 +1,96 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// +#include "openvino/runtime/threading/cpu_message.hpp" + +#include +#include +#include +#include +#include +#include + +namespace ov { +namespace threading { + +MessageManager::MessageManager() { + _num_sub_streams = 0; +} + +MessageManager::~MessageManager() {} + +void MessageManager::send_message(const MessageInfo& msg_info) { + { + std::lock_guard lock(_msgMutex); + _messageQueue.push_back(msg_info); + } + _msgCondVar.notify_all(); +} + +void MessageManager::server_wait() { + assert(_num_sub_streams); + MsgType msg_type; + int count = 0; + bool isStopped = false; + while (!isStopped) { + std::vector msgQueue; + { + std::unique_lock lock(_msgMutex); + _msgCondVar.wait(lock, [&] { + return !_messageQueue.empty(); + }); + std::swap(_messageQueue, msgQueue); + } + + for (auto rec_info : msgQueue) { + msg_type = rec_info.msg_type; + if (msg_type == CALL_BACK) { // CALL_BACK + count++; + if (count == _num_sub_streams) { + count = 0; + isStopped = true; + } + } + } + }; +} + +void MessageManager::set_num_sub_streams(int num_sub_streams) { + _num_sub_streams = num_sub_streams; +} + +int MessageManager::get_num_sub_streams() { + return _num_sub_streams; +} + +namespace { + +class MessageManageHolder { + std::mutex _mutex; + std::weak_ptr _manager; + +public: + MessageManageHolder(const MessageManageHolder&) = delete; + MessageManageHolder& operator=(const MessageManageHolder&) = delete; + + MessageManageHolder() = default; + + std::shared_ptr get() { + std::lock_guard lock(_mutex); + auto manager = _manager.lock(); + if (!manager) { + _manager = manager = std::make_shared(); + } + return manager; + } +}; + +} // namespace + +std::shared_ptr message_manager() { + static MessageManageHolder message_manage; + return message_manage.get(); +} + +} // namespace threading +} // namespace ov diff --git a/src/inference/src/dev/threading/cpu_streams_executor.cpp b/src/inference/src/dev/threading/cpu_streams_executor.cpp index 2df59d27cfe0b2..9e55cd2be18592 100644 --- a/src/inference/src/dev/threading/cpu_streams_executor.cpp +++ b/src/inference/src/dev/threading/cpu_streams_executor.cpp @@ -59,11 +59,6 @@ struct CPUStreamsExecutor::Impl { _streamId = _impl->_streamIdQueue.front(); _impl->_streamIdQueue.pop(); } - if (!_impl->_subStreamIdQueue.empty() && _impl->_subStreamsNum < _impl->_config.get_sub_streams()) { - _sub_stream_id = _impl->_subStreamIdQueue.front(); - _impl->_subStreamIdQueue.pop(); - _impl->_subStreamsNum++; - } } _numaNodeId = _impl->_config.get_streams() @@ -176,8 +171,8 @@ struct CPUStreamsExecutor::Impl { StreamCreateType stream_type; const auto org_proc_type_table = get_org_proc_type_table(); int streams_num = _impl->_config.get_streams(); - const auto stream_id = - streams_num == 0 ? 0 : (_sub_stream_id >= 0 ? streams_num + _sub_stream_id : _streamId % streams_num); + const auto stream_id = streams_num == 0 ? 0 : _streamId % streams_num; + _rank = _impl->_config.get_rank(); get_cur_stream_info(stream_id, _impl->_config.get_cpu_pinning(), org_proc_type_table, @@ -204,7 +199,7 @@ struct CPUStreamsExecutor::Impl { int _numaNodeId = 0; int _socketId = 0; bool _execute = false; - int _sub_stream_id = -1; + std::vector _rank; std::queue _taskQueue; #if OV_THREAD == OV_THREAD_TBB || OV_THREAD == OV_THREAD_TBB_AUTO std::unique_ptr _taskArena; @@ -335,7 +330,6 @@ struct CPUStreamsExecutor::Impl { _exectorMgr = executor_manager(); auto numaNodes = get_available_numa_nodes(); int streams_num = _config.get_streams(); - int sub_streams_num = _config.get_sub_streams(); if (streams_num != 0) { std::copy_n(std::begin(numaNodes), std::min(streams_num, numaNodes.size()), @@ -343,9 +337,6 @@ struct CPUStreamsExecutor::Impl { } else { _usedNumaNodes = std::move(numaNodes); } - if (sub_streams_num > 0) { - _subTaskThread.assign(sub_streams_num, std::make_shared()); - } for (auto streamId = 0; streamId < streams_num; ++streamId) { _threads.emplace_back([this, streamId] { openvino::itt::threadName(_config.get_name() + "_" + std::to_string(streamId)); @@ -368,31 +359,6 @@ struct CPUStreamsExecutor::Impl { }); } _streams.set_thread_ids_map(_threads); - - for (auto subId = 0; subId < sub_streams_num; ++subId) { - _subThreads.emplace_back([this, subId, sub_streams_num] { - openvino::itt::threadName(_config.get_name() + "_subthreads" + "_" + std::to_string(subId)); - for (bool stopped = false; !stopped;) { - Task task; - { _subTaskThread[subId]->que_pop(task, stopped); } - if (task) { - { - std::lock_guard lock{_streamIdMutex}; - if (_subStreamsNum < sub_streams_num) { - _subStreamIdQueue.push(subId); - } else { - std::queue empty; - std::swap(_subStreamIdQueue, empty); - } - } - Execute(task, *(_streams.local())); - } - } - }); - } - if (_subThreads.size() > 0) { - _streams.set_thread_ids_map(_subThreads); - } } void Enqueue(Task task) { @@ -403,10 +369,6 @@ struct CPUStreamsExecutor::Impl { _queueCondVar.notify_one(); } - void Enqueue_sub(Task task, int id) { - _subTaskThread[id]->que_push(std::move(task)); - } - void Execute(const Task& task, Stream& stream) { #if OV_THREAD == OV_THREAD_TBB || OV_THREAD == OV_THREAD_TBB_AUTO auto& arena = stream._taskArena; @@ -467,52 +429,19 @@ struct CPUStreamsExecutor::Impl { } } - struct SubQueue { - std::mutex _subMutex; - std::condition_variable _subQueueCondVar; - bool _isSubStopped = false; - std::queue _subTaskQueue; - - SubQueue() {} - - void que_push(Task task) { - { - std::lock_guard lock(_subMutex); - _subTaskQueue.emplace(std::move(task)); - } - _subQueueCondVar.notify_one(); - } - - void que_pop(Task& task, bool& stopped) { - std::unique_lock lock(_subMutex); - _subQueueCondVar.wait(lock, [&] { - return !_subTaskQueue.empty() || (stopped = _isSubStopped); - }); - if (!_subTaskQueue.empty()) { - task = std::move(_subTaskQueue.front()); - _subTaskQueue.pop(); - } - } - - ~SubQueue() {} - }; - Config _config; std::mutex _streamIdMutex; int _streamId = 0; std::queue _streamIdQueue; - std::queue _subStreamIdQueue; - int _subStreamsNum = 0; std::vector _threads; - std::vector _subThreads; std::mutex _mutex; std::condition_variable _queueCondVar; std::queue _taskQueue; bool _isStopped = false; - std::vector> _subTaskThread; std::vector _usedNumaNodes; CustomThreadLocal _streams; std::shared_ptr _exectorMgr; + bool _isExit = false; }; int CPUStreamsExecutor::get_stream_id() { @@ -534,6 +463,11 @@ int CPUStreamsExecutor::get_socket_id() { return stream->_socketId; } +std::vector CPUStreamsExecutor::get_rank() { + auto stream = _impl->_streams.local(); + return stream->_rank; +} + CPUStreamsExecutor::CPUStreamsExecutor(const IStreamsExecutor::Config& config) : _impl{new Impl{config}} {} CPUStreamsExecutor::~CPUStreamsExecutor() { @@ -547,18 +481,6 @@ CPUStreamsExecutor::~CPUStreamsExecutor() { thread.join(); } } - for (size_t i = 0; i < _impl->_subTaskThread.size(); i++) { - { - std::lock_guard lock(_impl->_subTaskThread[i]->_subMutex); - _impl->_subTaskThread[i]->_isSubStopped = true; - } - _impl->_subTaskThread[i]->_subQueueCondVar.notify_all(); - } - for (auto& thread : _impl->_subThreads) { - if (thread.joinable()) { - thread.join(); - } - } } void CPUStreamsExecutor::execute(Task task) { @@ -573,9 +495,5 @@ void CPUStreamsExecutor::run(Task task) { } } -void CPUStreamsExecutor::run_sub_stream(Task task, int id) { - _impl->Enqueue_sub(std::move(task), id); -} - } // namespace threading } // namespace ov diff --git a/src/inference/src/dev/threading/cpu_streams_executor_internal.cpp b/src/inference/src/dev/threading/cpu_streams_executor_internal.cpp index d994b5d40f5111..547c149c3fc269 100644 --- a/src/inference/src/dev/threading/cpu_streams_executor_internal.cpp +++ b/src/inference/src/dev/threading/cpu_streams_executor_internal.cpp @@ -92,8 +92,15 @@ void reserve_cpu_by_streams_info(const std::vector> _streams_in int num_conditions = 0; int condition_idx = 0; bool last_all_proc = false; + bool sub_stream_enable = false; for (size_t i = 0; i < _streams_info_table.size(); i++) { + if (i > 0 && _streams_info_table[i][NUMBER_OF_STREAMS] < 0 && + _streams_info_table[i - 1][NUMBER_OF_STREAMS] > 0) { + stream_pos.clear(); + num_streams = 0; + sub_stream_enable = true; + } if (_streams_info_table[i][NUMBER_OF_STREAMS] != 0) { stream_pos.push_back(num_streams); } @@ -109,11 +116,15 @@ void reserve_cpu_by_streams_info(const std::vector> _streams_in std::string proc_type = ""; std::string numa_node = ""; std::string socket = ""; - if (_streams_info_table[i][NUMBER_OF_STREAMS] != 0) { + if ((_streams_info_table[i][NUMBER_OF_STREAMS] > 0 && !sub_stream_enable) || + (_streams_info_table[i][NUMBER_OF_STREAMS] < 0 && sub_stream_enable)) { streams_table.push_back(_streams_info_table[i]); if (_streams_info_table[i][NUMBER_OF_STREAMS] < 0) { - streams_table[streams_table.size() - 1][NUMBER_OF_STREAMS] = 1; + streams_table[streams_table.size() - 1][NUMBER_OF_STREAMS] = + std::abs(_streams_info_table[i][NUMBER_OF_STREAMS]); } + } else if (_streams_info_table[i][NUMBER_OF_STREAMS] != 0) { + continue; } if (last_all_proc && _streams_info_table[i][NUMBER_OF_STREAMS] != 0) { last_all_proc = false; @@ -135,7 +146,7 @@ void reserve_cpu_by_streams_info(const std::vector> _streams_in std::make_pair(proc_type + numa_node + socket, _streams_info_table[i][THREADS_PER_STREAM])); threads_status[condition_idx].push_back(0); } - if (_streams_info_table[i][PROC_TYPE] > ALL_PROC && _streams_info_table[i][NUMBER_OF_STREAMS] > 0) { + if (_streams_info_table[i][PROC_TYPE] > ALL_PROC && _streams_info_table[i][NUMBER_OF_STREAMS] != 0) { condition_idx++; } } diff --git a/src/inference/src/dev/threading/istreams_executor.cpp b/src/inference/src/dev/threading/istreams_executor.cpp index 822948e3ef1ab1..53a0c32806c6aa 100644 --- a/src/inference/src/dev/threading/istreams_executor.cpp +++ b/src/inference/src/dev/threading/istreams_executor.cpp @@ -332,32 +332,5 @@ void IStreamsExecutor::Config::set_config_zero_stream() { _cpu_pinning = false; } -void IStreamsExecutor::run_sub_stream_and_wait(const std::vector& tasks) { - std::vector> packagedTasks; - std::vector> futures; - for (std::size_t i = 0; i < tasks.size(); ++i) { - packagedTasks.emplace_back([&tasks, i] { - tasks[i](); - }); - futures.emplace_back(packagedTasks.back().get_future()); - } - for (std::size_t i = 0; i < tasks.size(); ++i) { - run_sub_stream( - [&packagedTasks, i] { - packagedTasks[i](); - }, - static_cast(i)); - } - // std::future::get will rethrow exception from task. - // We should wait all tasks before any exception is thrown. - // So wait() and get() for each future moved to separate loops - for (auto&& future : futures) { - future.wait(); - } - for (auto&& future : futures) { - future.get(); - } -} - } // namespace threading } // namespace ov diff --git a/src/inference/tests/unit/cpu_reserve_test.cpp b/src/inference/tests/unit/cpu_reserve_test.cpp index 327bcff7499688..21c7d374c94cc3 100644 --- a/src/inference/tests/unit/cpu_reserve_test.cpp +++ b/src/inference/tests/unit/cpu_reserve_test.cpp @@ -427,7 +427,7 @@ LinuxCpuReserveTestCase _2sockets_72cores_hyper_1streams = { {68, 1, 1, 68, MAIN_CORE_PROC, 32, -1}, {69, 1, 1, 69, MAIN_CORE_PROC, 33, -1}, {70, 1, 1, 70, MAIN_CORE_PROC, 34, -1}, {71, 1, 1, 71, MAIN_CORE_PROC, 35, -1}, }, - {{1, MAIN_CORE_PROC, 18, 0, 0}, {-1, MAIN_CORE_PROC, 18, 1, 1}}, + {{1, MAIN_CORE_PROC, 36, -1, -1}, {-1, MAIN_CORE_PROC, 18, 0, 0}, {-1, MAIN_CORE_PROC, 18, 1, 1}}, { {36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53}, {54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71}, @@ -789,7 +789,8 @@ LinuxCpuReserveTestCase _2sockets_104cores_1streams = { {204, 3, 1, 102, HYPER_THREADING_PROC, 102, -1}, {205, 3, 1, 102, MAIN_CORE_PROC, 102, -1}, {206, 3, 1, 103, HYPER_THREADING_PROC, 103, -1}, {207, 3, 1, 103, MAIN_CORE_PROC, 103, -1}, }, - {{1, ALL_PROC, 104, -1, 0}, + {{1, ALL_PROC, 208, -1, -1}, + {-1, ALL_PROC, 104, -1, 0}, {0, MAIN_CORE_PROC, 26, 0, 0}, {0, MAIN_CORE_PROC, 26, 1, 0}, {0, HYPER_THREADING_PROC, 26, 0, 0}, diff --git a/src/plugins/intel_cpu/src/async_infer_request.cpp b/src/plugins/intel_cpu/src/async_infer_request.cpp index b77d0059db75ef..8c5d0c8bc5c554 100644 --- a/src/plugins/intel_cpu/src/async_infer_request.cpp +++ b/src/plugins/intel_cpu/src/async_infer_request.cpp @@ -4,6 +4,8 @@ #include "async_infer_request.h" +#include "openvino/runtime/threading/cpu_message.hpp" + ov::intel_cpu::AsyncInferRequest::AsyncInferRequest( const std::shared_ptr& request, const std::shared_ptr& task_executor, @@ -13,9 +15,17 @@ ov::intel_cpu::AsyncInferRequest::AsyncInferRequest( } ov::intel_cpu::AsyncInferRequest::~AsyncInferRequest() { + if (m_has_sub_infers) { + m_sub_infer_requests.clear(); + } stop_and_wait(); } void ov::intel_cpu::AsyncInferRequest::throw_if_canceled() const { check_cancelled_state(); } + +void ov::intel_cpu::AsyncInferRequest::setSubInferRequest( + const std::vector>& requests) { + m_sub_infer_requests = requests; +} diff --git a/src/plugins/intel_cpu/src/async_infer_request.h b/src/plugins/intel_cpu/src/async_infer_request.h index ebf1a210f30a77..0491bb26a80c13 100644 --- a/src/plugins/intel_cpu/src/async_infer_request.h +++ b/src/plugins/intel_cpu/src/async_infer_request.h @@ -17,7 +17,20 @@ class AsyncInferRequest : public ov::IAsyncInferRequest { const std::shared_ptr& callback_executor); ~AsyncInferRequest(); + void setSubInferRequest(const std::vector>& requests); + + std::vector> getSubInferRequest() const { + return m_sub_infer_requests; + } + + void setSubInfer(bool has_sub_infer) { + m_has_sub_infers = has_sub_infer; + } + void throw_if_canceled() const; + + std::vector> m_sub_infer_requests; + bool m_has_sub_infers = false; }; } // namespace intel_cpu diff --git a/src/plugins/intel_cpu/src/compiled_model.cpp b/src/plugins/intel_cpu/src/compiled_model.cpp index 1a9f7f74099188..af8df9657ad34b 100644 --- a/src/plugins/intel_cpu/src/compiled_model.cpp +++ b/src/plugins/intel_cpu/src/compiled_model.cpp @@ -17,6 +17,8 @@ #include "openvino/util/common_util.hpp" #include "openvino/runtime/threading/cpu_streams_executor.hpp" #include "transformations/utils/utils.hpp" +#include "openvino/runtime/threading/cpu_streams_info.hpp" +#include "openvino/runtime/threading/cpu_message.hpp" #include "cpu/x64/cpu_isa_traits.hpp" #include @@ -42,25 +44,33 @@ struct ImmediateSerialExecutor : public ov::threading::ITaskExecutor { CompiledModel::CompiledModel(const std::shared_ptr& model, const std::shared_ptr& plugin, const Config& cfg, - const bool loaded_from_cache) + const bool loaded_from_cache, + const std::shared_ptr sub_memory_manager) : ov::ICompiledModel::ICompiledModel(model, plugin), m_model(model), m_plugin(plugin), m_cfg{cfg}, m_name{model->get_name()}, - m_loaded_from_cache(loaded_from_cache) { + m_loaded_from_cache(loaded_from_cache), + m_sub_memory_manager(sub_memory_manager) { m_mutex = std::make_shared(); const auto& core = m_plugin->get_core(); if (!core) OPENVINO_THROW("Unable to get API version. Core is unavailable"); - ov::threading::IStreamsExecutor::Ptr stream_executor = nullptr; + IStreamsExecutor::Config executor_confg; if (cfg.exclusiveAsyncRequests) { // special case when all InferRequests are muxed into a single queue m_task_executor = m_plugin->get_executor_manager()->get_executor("CPU"); } else { - stream_executor = m_plugin->get_executor_manager()->get_idle_cpu_streams_executor(m_cfg.streamExecutorConfig); - m_task_executor = stream_executor; + executor_confg = m_cfg.numSubStreams > 0 ? IStreamsExecutor::Config{"CPUMainStreamExecutor", + 1, + 1, + ov::hint::SchedulingCoreType::ANY_CORE, + false, + true} + : m_cfg.streamExecutorConfig; + m_task_executor = m_plugin->get_executor_manager()->get_idle_cpu_streams_executor(executor_confg); } if (0 != m_cfg.streamExecutorConfig.get_streams()) { m_callback_executor = m_plugin->get_executor_manager()->get_idle_cpu_streams_executor( @@ -74,11 +84,11 @@ CompiledModel::CompiledModel(const std::shared_ptr& model, if (m_callback_executor) set_callback_executor(m_callback_executor); - int streams = std::max(1, m_cfg.streamExecutorConfig.get_streams()); + int streams = std::max(1, executor_confg.get_streams()); std::vector tasks; tasks.resize(streams); m_graphs.resize(streams); - if (m_cfg.streamExecutorConfig.get_streams() != 0) { + if (executor_confg.get_streams() != 0) { auto all_graphs_ready = [&] { return std::all_of(m_graphs.begin(), m_graphs.end(), [&](Graph& graph) { return graph.IsReady(); @@ -102,15 +112,30 @@ CompiledModel::CompiledModel(const std::shared_ptr& model, } else { CompiledModel::get_graph(); } - // init sub stream threads of executor - int sub_streams = m_cfg.streamExecutorConfig.get_sub_streams(); - if (sub_streams > 0 && stream_executor != nullptr) { - std::vector tasks; - tasks.resize(sub_streams); - for (auto&& task : tasks) { - task = [] {}; + if (m_cfg.numSubStreams > 0) { + m_has_sub_compiled_models = true; + auto sub_cfg = m_cfg; + sub_cfg.numSubStreams = 0; + sub_cfg.enableNodeSplit = true; + auto streams_info_table = m_cfg.streamExecutorConfig.get_streams_info_table(); + auto message = message_manager(); + m_sub_memory_manager = std::make_shared(m_cfg.numSubStreams); + message->set_num_sub_streams(m_cfg.numSubStreams); + for (int i = 0; i < m_cfg.numSubStreams; i++) { + std::vector> sub_streams_table; + sub_streams_table.push_back(streams_info_table[i + 1]); + sub_streams_table[0][NUMBER_OF_STREAMS] = 1; + sub_cfg.streamExecutorConfig = IStreamsExecutor::Config{"CPUStreamsExecutor", + 1, + 1, + ov::hint::SchedulingCoreType::ANY_CORE, + false, + true, + sub_streams_table, + sub_cfg.streamsRankTable[i]}; + m_sub_compiled_models.push_back( + std::make_shared(model, plugin, sub_cfg, loaded_from_cache, m_sub_memory_manager)); } - stream_executor->run_sub_stream_and_wait(tasks); } } @@ -130,11 +155,14 @@ CompiledModel::GraphGuard::Lock CompiledModel::get_graph() const { GraphContext::Ptr ctx; { std::lock_guard lock{*m_mutex.get()}; - auto isQuantizedFlag = - (m_cfg.lpTransformsMode == Config::On) && - ov::pass::low_precision::LowPrecision::isFunctionQuantized(m_model); + auto isQuantizedFlag = (m_cfg.lpTransformsMode == Config::On) && + ov::pass::low_precision::LowPrecision::isFunctionQuantized(m_model); - ctx = std::make_shared(m_cfg, m_socketWeights[socketId], isQuantizedFlag, streamsExecutor); + ctx = std::make_shared(m_cfg, + m_socketWeights[socketId], + isQuantizedFlag, + streamsExecutor, + m_sub_memory_manager); } const std::shared_ptr model = m_model; graphLock._graph.CreateGraph(model, ctx); @@ -165,6 +193,14 @@ std::shared_ptr CompiledModel::create_infer_request() co std::make_shared(std::static_pointer_cast(internal_request), get_task_executor(), get_callback_executor()); + if (m_has_sub_compiled_models) { + std::vector> requests; + for (auto model : m_sub_compiled_models) { + requests.push_back(model->create_infer_request()); + } + async_infer_request->setSubInferRequest(requests); + async_infer_request->setSubInfer(true); + } return async_infer_request; } diff --git a/src/plugins/intel_cpu/src/compiled_model.h b/src/plugins/intel_cpu/src/compiled_model.h index 64caee60aeb0ae..facd9ef3698ca7 100644 --- a/src/plugins/intel_cpu/src/compiled_model.h +++ b/src/plugins/intel_cpu/src/compiled_model.h @@ -14,6 +14,7 @@ #include "openvino/runtime/iplugin.hpp" #include "openvino/runtime/isync_infer_request.hpp" #include "openvino/runtime/threading/thread_local.hpp" +#include "sub_memory_manager.hpp" namespace ov { namespace intel_cpu { @@ -25,7 +26,15 @@ class CompiledModel : public ov::ICompiledModel { CompiledModel(const std::shared_ptr& model, const std::shared_ptr& plugin, const Config& cfg, - const bool loaded_from_cache); + const bool loaded_from_cache, + const std::shared_ptr sub_memory_manager = nullptr); + + ~CompiledModel() { + if (m_has_sub_compiled_models) { + m_sub_compiled_models.clear(); + m_sub_memory_manager->_memorys_table.clear(); + } + } std::shared_ptr create_infer_request() const override; @@ -73,6 +82,14 @@ class CompiledModel : public ov::ICompiledModel { * even from main thread */ GraphGuard::Lock get_graph() const; + + std::vector> get_sub_compiled_models() const { + return m_sub_compiled_models; + } + + std::vector> m_sub_compiled_models; + std::shared_ptr m_sub_memory_manager = nullptr; + bool m_has_sub_compiled_models = false; }; } // namespace intel_cpu diff --git a/src/plugins/intel_cpu/src/config.h b/src/plugins/intel_cpu/src/config.h index 4ca6332c25c3cc..e0bcc6786a85b9 100644 --- a/src/plugins/intel_cpu/src/config.h +++ b/src/plugins/intel_cpu/src/config.h @@ -66,6 +66,7 @@ struct Config { int threadsPerStream = 0; ov::threading::IStreamsExecutor::ThreadBindingType threadBindingType = ov::threading::IStreamsExecutor::ThreadBindingType::NONE; ov::hint::PerformanceMode hintPerfMode = ov::hint::PerformanceMode::LATENCY; + std::vector> streamsRankTable; bool changedHintPerfMode = false; ov::log::Level logLevel = ov::log::Level::NO; uint32_t hintNumRequests = 0; @@ -73,6 +74,9 @@ struct Config { bool changedCpuPinning = false; ov::hint::SchedulingCoreType schedulingCoreType = ov::hint::SchedulingCoreType::ANY_CORE; std::set modelDistributionPolicy = {}; + int streamsRankLevel = 1; + int numSubStreams = 0; + bool enableNodeSplit = false; bool enableHyperThreading = true; bool changedHyperThreading = false; #if defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64) diff --git a/src/plugins/intel_cpu/src/cpu_memory.cpp b/src/plugins/intel_cpu/src/cpu_memory.cpp index ab454382f57d73..fac71e9e5bcc53 100644 --- a/src/plugins/intel_cpu/src/cpu_memory.cpp +++ b/src/plugins/intel_cpu/src/cpu_memory.cpp @@ -6,6 +6,7 @@ #include "memory_desc/cpu_memory_desc_utils.h" #include #include "nodes/reorder.h" +#include "nodes/common/cpu_memcpy.h" #include "utils/debug_capabilities.h" #if defined(__linux__) # include /* Definition of SYS_* constants */ @@ -657,5 +658,125 @@ bool mbind_move(const dnnl::memory mem, int numaNodeID) { return mbind_move(data, size, numaNodeID); } +MemoryPtr split_horizontal(const dnnl::engine& eng, const MemoryPtr src, int dim, int w_rank, int w_size, bool need_fill) { + auto desc = src->getDescPtr(); + auto shape = src->getShape(); + auto dims = shape.getDims(); + auto prec = src->getPrecision(); + if (dim < 0) { + dim += dims.size(); + } + auto split_parts = [](int len, int n) { + int average = len / n; + std::vector parts(n, average); + parts.back() = len - average * (n - 1); + return parts; + }; + if (shape.isDynamic()) { + // if the dim is dynamic, should return a dynamic dim without any change. + // if the dim is static, should split it indeed. + const auto& pshape = shape.toPartialShape(); + if (pshape[dim].is_dynamic()) { + return src; + } + auto new_pshape = pshape; + auto splited_dim_vec = split_parts(new_pshape[dim].get_length(), w_size); + new_pshape[dim] = splited_dim_vec[w_rank]; + + auto new_desc = std::make_shared(prec, Shape{new_pshape}); + MemoryPtr ptr = std::make_shared(eng, new_desc); + return ptr; + } + assert(dims[dim] >= w_size); + auto splited_dim_vec = split_parts(dims[dim], w_size); + + // reference stride + VectorDims stride_dims = dims; + stride_dims[dim] = splited_dim_vec[0]; + size_t stride = std::accumulate(stride_dims.begin(), stride_dims.end(), static_cast(1), std::multiplies()) * prec.size(); + + // create new shape for target memory + VectorDims new_dims = dims; + new_dims[dim] = splited_dim_vec[w_rank]; + + auto new_desc = desc->cloneWithNewDims(new_dims, true); + if (!need_fill) { + MemoryPtr ptr = std::make_shared(eng, new_desc, nullptr); + return ptr; + } + + auto srcPtr = static_cast(src->getData()); + if (prec == ov::element::u4 || prec == ov::element::i4) { + stride /= 2; + } + + MemoryPtr ptr = std::make_shared(eng, new_desc, srcPtr + w_rank * stride); + return ptr; +} + +MemoryPtr split_vertical(const dnnl::engine& eng, const MemoryPtr src, int dim, int w_rank, int w_size, bool need_fill) { + auto desc = src->getDescPtr(); + auto shape = src->getShape(); + auto dims = shape.getDims(); + auto prec = src->getPrecision(); + if (dim < 0) { + dim += dims.size(); + } + auto split_parts = [](int len, int n) { + int average = len / n; + std::vector parts(n, average); + parts.back() = len - average * (n - 1); + return parts; + }; + if (shape.isDynamic()) { + const auto& pshape = shape.toPartialShape(); + if (pshape[dim].is_dynamic()) { + OPENVINO_THROW("Can't split data with dynamic shapes"); + } + auto new_pshape = pshape; + auto splited_dim_vec = split_parts(new_pshape[dim].get_length(), w_size); + new_pshape[dim] = splited_dim_vec[w_rank]; + + auto new_desc = std::make_shared(prec, Shape{new_pshape}); + MemoryPtr ptr = std::make_shared(eng, new_desc); + return ptr; + } + assert(dims[dim] >= w_size); + const auto splited_size = dims[dim] * prec.size(); + auto splited_dim_vec = split_parts(dims[dim], w_size); + auto element_size = prec.size(); + + VectorDims new_dims = dims; + new_dims[dim] = splited_dim_vec[w_rank]; + + auto new_desc = desc->cloneWithNewDims(new_dims, true); + MemoryPtr ptr = std::make_shared(eng, new_desc); + if (!need_fill) { + return ptr; + } + // copy + auto srcPtr = static_cast(src->getData()); + auto dstPtr = static_cast(ptr->getData()); + // selected dim bytes + auto channel_size = dims[dim] * element_size; + // total bytes + auto mem_size = src->getSize(); + // the steps need to copy. + const int step = (mem_size / channel_size); + // bytes of selected dim. + auto strideSize = splited_dim_vec[0] * element_size; + auto copySize = splited_dim_vec[w_rank] * element_size; + if (prec == ov::element::u4 || prec == ov::element::i4) { + strideSize /= 2; + copySize /= 2; + } + parallel_for(step, [&](int i){ + int dst_offset = i * copySize; + int src_offset = i * splited_size + w_rank * strideSize; + cpu_parallel_memcpy(dstPtr + dst_offset, srcPtr + src_offset, copySize); + }); + return ptr; +} + } // namespace intel_cpu } // namespace ov diff --git a/src/plugins/intel_cpu/src/cpu_memory.h b/src/plugins/intel_cpu/src/cpu_memory.h index 69b70cb6e583a3..f62c2f052575f6 100644 --- a/src/plugins/intel_cpu/src/cpu_memory.h +++ b/src/plugins/intel_cpu/src/cpu_memory.h @@ -463,5 +463,8 @@ bool mbind_move(void* data, size_t size, int numaNodeID); bool mbind_move(const MemoryCPtr mem, int numaNodeID); bool mbind_move(const dnnl::memory mem, int numaNodeID); +MemoryPtr split_horizontal(const dnnl::engine& eng, const MemoryPtr src, int dim, int w_rank, int w_size, bool need_fill = true); +MemoryPtr split_vertical(const dnnl::engine& eng, const MemoryPtr src, int dim, int w_rank, int w_size, bool need_fill = true); + } // namespace intel_cpu } // namespace ov diff --git a/src/plugins/intel_cpu/src/cpu_streams_calculation.cpp b/src/plugins/intel_cpu/src/cpu_streams_calculation.cpp index 08ba43f9eff106..fb8294e5ce29fd 100644 --- a/src/plugins/intel_cpu/src/cpu_streams_calculation.cpp +++ b/src/plugins/intel_cpu/src/cpu_streams_calculation.cpp @@ -22,6 +22,7 @@ using namespace ov; using namespace ov::threading; #define INIT_VAL -100 +#define TP_CPU_LIMIT 32 namespace ov { namespace intel_cpu { @@ -101,17 +102,24 @@ std::vector> get_streams_info_table(const int input_streams, const std::vector>& one_proc_table, const int num_threads, const IStreamsExecutor::Config::StreamsMode sub_streams_model) { + stream_info[THREADS_PER_STREAM] = sub_streams_model == IStreamsExecutor::Config::StreamsMode::SUB_STREAMS_NULL + ? num_threads + : std::min(TP_CPU_LIMIT, num_threads); if ((one_proc_info[PROC_NUMA_NODE_ID] < 0) || (one_proc_info[PROC_SOCKET_ID] < 0) || - (((one_proc_info[MAIN_CORE_PROC] > 0) && (one_proc_info[MAIN_CORE_PROC] < num_threads)) || + (((one_proc_info[MAIN_CORE_PROC] > 0) && + (one_proc_info[MAIN_CORE_PROC] < stream_info[THREADS_PER_STREAM])) || ((one_proc_info[MAIN_CORE_PROC] == 0) && (one_proc_info[EFFICIENT_CORE_PROC] > 0) && - (one_proc_info[EFFICIENT_CORE_PROC] < num_threads)))) { - update_mix_stream_info(one_proc_info, one_proc_table, num_threads, sub_streams_model, ALL_PROC); + (one_proc_info[EFFICIENT_CORE_PROC] < stream_info[THREADS_PER_STREAM])))) { + update_mix_stream_info(one_proc_info, + one_proc_table, + stream_info[THREADS_PER_STREAM], + sub_streams_model, + ALL_PROC); } else { stream_info[PROC_TYPE] = - one_proc_info[MAIN_CORE_PROC] >= num_threads ? MAIN_CORE_PROC : EFFICIENT_CORE_PROC; + one_proc_info[MAIN_CORE_PROC] >= stream_info[THREADS_PER_STREAM] ? MAIN_CORE_PROC : EFFICIENT_CORE_PROC; stream_info[NUMBER_OF_STREAMS] = sub_streams_model == IStreamsExecutor::Config::StreamsMode::SUB_STREAMS_NULL ? 1 : -1; - stream_info[THREADS_PER_STREAM] = num_threads; update_ids_method(one_proc_info); streams_info_table.push_back(stream_info); } @@ -371,7 +379,7 @@ std::vector> get_streams_info_table(const int input_streams, create_one_stream(proc_socket_table[current_socket_id], proc_type_table, proc_socket_table[current_socket_id][ALL_PROC], - IStreamsExecutor::Config::StreamsMode::SUB_STREAMS_NULL); + IStreamsExecutor::Config::StreamsMode::SUB_STREAMS_FOR_SOCKET); for (size_t n_node = 0; n_node < proc_socket_table.size(); n_node++) { if (n_node != size_t(current_socket_id)) { create_one_stream(proc_socket_table[n_node], @@ -380,6 +388,23 @@ std::vector> get_streams_info_table(const int input_streams, IStreamsExecutor::Config::StreamsMode::SUB_STREAMS_FOR_SOCKET); } } + stream_info = streams_info_table[0]; + stream_info[NUMBER_OF_STREAMS] = 1; + for (size_t n = 1; n < streams_info_table.size(); n++) { + if (streams_info_table[n][NUMBER_OF_STREAMS] == -1) { + if (stream_info[PROC_TYPE] != streams_info_table[n][PROC_TYPE]) { + stream_info[PROC_TYPE] = ALL_PROC; + } + stream_info[THREADS_PER_STREAM] += streams_info_table[n][THREADS_PER_STREAM]; + if (stream_info[STREAM_NUMA_NODE_ID] != streams_info_table[n][STREAM_NUMA_NODE_ID]) { + stream_info[STREAM_NUMA_NODE_ID] = -1; + } + if (stream_info[STREAM_SOCKET_ID] != streams_info_table[n][STREAM_SOCKET_ID]) { + stream_info[STREAM_SOCKET_ID] = -1; + } + } + } + streams_info_table.insert(streams_info_table.begin(), stream_info); n_streams--; } @@ -462,6 +487,33 @@ std::vector> get_streams_info_table(const int input_streams, return streams_info_table; } +std::vector> get_streams_rank_table(const std::vector>& streams_info_table, + const int input_rank_level, + int& num_sub_streams) { + std::vector> rank_table = {}; + num_sub_streams = 0; + std::vector init_rank = {}; + int rank_level = input_rank_level == 0 ? 1 : input_rank_level; + init_rank.resize(rank_level, 0); + + for (auto& row : streams_info_table) { + if (row[NUMBER_OF_STREAMS] < 0) { + for (int i = 0; i < abs(row[NUMBER_OF_STREAMS]); i++) { + init_rank[rank_level - 1] = num_sub_streams + i; + rank_table.push_back(init_rank); + } + num_sub_streams -= row[NUMBER_OF_STREAMS]; + } + } + if (rank_level == 2) { + for (int i = num_sub_streams / 2; i < num_sub_streams; i++) { + rank_table[i][0] = 1; + rank_table[i][1] -= num_sub_streams / 2; + } + } + return rank_table; +} + int get_model_prefer_threads(const int num_streams, const std::vector> proc_type_table, const std::shared_ptr& model, @@ -608,6 +660,12 @@ std::vector> generate_stream_info(const int streams, ov::util::to_string(config.hintPerfMode), config.modelDistributionPolicy, proc_type_table); + // streams_info_table = {{1, 1, 56, 1, 1}, {-1, 1, 28, 1, 1}, {-1, 1, 28, 0, 0}}; + if (config.modelDistributionPolicy.find(ov::hint::ModelDistributionPolicy::TENSOR_PARALLEL) != + config.modelDistributionPolicy.end()) { + config.streamsRankTable = + get_streams_rank_table(streams_info_table, config.streamsRankLevel, config.numSubStreams); + } auto cpu_pinning = get_cpu_pinning(config.enableCpuPinning, config.changedCpuPinning, proc_type_table, streams_info_table); diff --git a/src/plugins/intel_cpu/src/cpu_streams_calculation.hpp b/src/plugins/intel_cpu/src/cpu_streams_calculation.hpp index 584f9a2d9a0460..e362c0373d8d1d 100644 --- a/src/plugins/intel_cpu/src/cpu_streams_calculation.hpp +++ b/src/plugins/intel_cpu/src/cpu_streams_calculation.hpp @@ -53,6 +53,18 @@ std::vector> get_streams_info_table(const int input_streams, const std::string input_perf_hint, const std::set hint_llm_distribution_policy, const std::vector>& proc_type_table); + +/** + * @brief Generate streams rank table for tensor parallel according to streams info table. + * @param[in] streams_info_table is streams information table for tensor parallel. + * @param[in] input_rank_level is depth of rank nesting. + * @param[out] num_sub_streams is number of sub streams for tensor parallel. + * @return streams rank table which will be used by StreamsExecutor. + */ +std::vector> get_streams_rank_table(const std::vector>& streams_info_table, + const int input_rank_level, + int& num_sub_streams); + /** * @brief Get model_prefer_threads * @param[in] num_streams is target streams set by user via NUM_STREAMS or hints. diff --git a/src/plugins/intel_cpu/src/graph.cpp b/src/plugins/intel_cpu/src/graph.cpp index bfb28f769b95e7..898f69fef766c4 100644 --- a/src/plugins/intel_cpu/src/graph.cpp +++ b/src/plugins/intel_cpu/src/graph.cpp @@ -233,30 +233,6 @@ void Graph::Replicate(const std::shared_ptr &model) { } } -void Graph::GroupParallelNodes() { - std::map> pdomain_nodes; - for (size_t k = 0; k < graphNodes.size(); k++) { - auto& node = graphNodes[k]; - auto& domain = node->getParallelDomain(); - if (domain.empty()) - continue; - if (pdomain_nodes.count(domain) == 0) - pdomain_nodes[domain] = {}; - pdomain_nodes[domain].push_back(node); - } - - // record valid paralell_nodes - for (auto& pn : pdomain_nodes) { - auto& node_ptrs = pn.second; - if (node_ptrs.size() > 1) { - // parallelWith includes pointer to itself - for (auto& pnode : node_ptrs) { - pnode->parallelWith = node_ptrs; - } - } - } -} - static std::vector IdentifySyncPoints(const std::vector& graphNodes) { OV_ITT_SCOPE(FIRST_INFERENCE, itt::domains::intel_cpu_LT, "Graph::IdentifySyncPoints"); std::vector syncNodesInds; @@ -352,8 +328,6 @@ void Graph::InitGraph(bool optimize) { optimizer.ApplyImplSpecificGraphOptimizations(*this); - GroupParallelNodes(); - SortTopologically(); const bool hasDynNodes = ProcessDynNodes(); @@ -1382,87 +1356,27 @@ void Graph::InferDynamic(SyncInferRequest* request, UpdateStrategy&& update) { } inline void Graph::ExecuteNode(const NodePtr& node, const dnnl::stream& stream) const { - if (!node->parallelWith.empty()) { - // run nodes in parallel - auto& parallelNodes = node->parallelWith; - if (node == parallelNodes[0]) { - if (const auto& cpuExecutor = context->getCPUStreamExecutor()) { - auto num_parallel_nodes = parallelNodes.size(); - ParalleMtNuma(num_parallel_nodes, cpuExecutor, [&](int subStreamID, size_t i) { - auto& n = parallelNodes[i]; - - if (n->isDynamicNode()) { - n->executeDynamic(stream, subStreamID); - } else { - n->executeStatic(stream, subStreamID); - } - }); - } else { - // fallback to serialize executor - for (auto& node : parallelNodes) { - if (node->isDynamicNode()) { - node->executeDynamic(stream); - } else { - node->executeStatic(stream); - } - } - } - } + DUMP(node, getConfig().debugCaps, infer_count); + OV_ITT_SCOPED_TASK(itt::domains::intel_cpu, node->profiling.execute); + DEBUG_LOG(*node); + // TODO: 132954 workaround for latency + int numaID = GetNumaNodeId(); + if (node->isDynamicNode()) { + node->executeDynamic(stream, numaID); } else { - DUMP(node, getConfig().debugCaps, infer_count); - OV_ITT_SCOPED_TASK(itt::domains::intel_cpu, node->profiling.execute); - DEBUG_LOG(*node); - // TODO: 132954 workaround for latency - int subStreamID = -1; -#if defined(__x86_64__) && defined(__linux__) - if ((getGraphContext()->getCPUStreamExecutor()) && (getConfig().hintPerfMode == ov::hint::PerformanceMode::LATENCY)) { - subStreamID = getGraphContext()->getCPUStreamExecutor()->get_numa_node_id(); - } -#endif - if (node->isDynamicNode()) { - node->executeDynamic(stream, subStreamID); - } else { - node->executeStatic(stream, subStreamID); - } + node->executeStatic(stream, numaID); } } -void Graph::ParalleMtNuma(size_t num_nodes, - ov::threading::CPUStreamsExecutor::Ptr executor, - const std::function& func) const { - OPENVINO_ASSERT(num_nodes > 1, "Parallel Nodes must be more than 1. But now got ", - num_nodes, - " Nodes, which shouldn't invoke multi nodes parallel."); - std::atomic nodes_remain(num_nodes); - int cur_numa_id = executor->get_numa_node_id(); - // enqueue (nsockets-1) sub stream tasks - int sub_stream_id = 0; - for (size_t socket_id = 0; socket_id < num_nodes; socket_id++) { - if (socket_id != static_cast(cur_numa_id)) { - size_t i0{0}, i1{0}; - splitter(num_nodes, num_nodes, socket_id, i0, i1); - executor->run_sub_stream( - [socket_id, i0, i1, &func, &nodes_remain]() { - for (size_t i = i0; i < i1; i++) { - func(socket_id, i); - nodes_remain--; - } - }, - sub_stream_id); - sub_stream_id++; - } - } - // run in main stream (current socket) - { - size_t i0{0}, i1{0}; - splitter(num_nodes, num_nodes, static_cast(cur_numa_id), i0, i1); - for (size_t i = i0; i < i1; i++) { - func(cur_numa_id, i); - nodes_remain--; - } +int Graph::GetNumaNodeId() const { + int numaNodeId = -1; +#if defined(__x86_64__) && defined(__linux__) + if ((getGraphContext()->getCPUStreamExecutor()) && + (getConfig().hintPerfMode == ov::hint::PerformanceMode::LATENCY)) { + numaNodeId = getGraphContext()->getCPUStreamExecutor()->get_numa_node_id(); } - // wait and sync - while (nodes_remain.load() > 0) {} +#endif + return numaNodeId; } void Graph::Infer(SyncInferRequest* request) { @@ -1504,29 +1418,12 @@ void Graph::SortTopologically() { if (node->execIndex >= 0) return; // already visited - if (!node->parallelWith.empty()) { - for (auto& n : node->parallelWith) { - for (size_t i = 0; i < n->getParentEdges().size(); i++) { - visit(n->getParentEdgeAt(i)->getParent()); - } - } - - // parallel nodes has same execIndex - // so they can provide correct start/end time point for - // their input and output memory object - ++execIndexCnt; - for (auto& n : node->parallelWith) { - sorted.push_back(n); - n->execIndex = execIndexCnt; - } - } else { - for (size_t i = 0; i < node->getParentEdges().size(); i++) { - visit(node->getParentEdgeAt(i)->getParent()); - } - - sorted.push_back(node); - node->execIndex = ++execIndexCnt; + for (size_t i = 0; i < node->getParentEdges().size(); i++) { + visit(node->getParentEdgeAt(i)->getParent()); } + + sorted.push_back(node); + node->execIndex = ++execIndexCnt; }; // First execute MemoryInput because it will change the memory pointer of diff --git a/src/plugins/intel_cpu/src/graph.h b/src/plugins/intel_cpu/src/graph.h index 6816dcc75c1a67..f91275f399ca95 100644 --- a/src/plugins/intel_cpu/src/graph.h +++ b/src/plugins/intel_cpu/src/graph.h @@ -223,7 +223,6 @@ class Graph { void ResolveEdgeConflicts(); void ResolveComplexInplaceConflicts(); bool ProcessDynNodes(); - void GroupParallelNodes(); void Allocate(const std::vector& syncNodesInds); void AllocateWithReuse(const std::vector& syncNodesInds); void ExecuteNode(const NodePtr& node, const dnnl::stream& stream) const; @@ -232,9 +231,6 @@ class Graph { template void InferDynamic(SyncInferRequest* request, UpdateStrategy&& update); - void ParalleMtNuma(size_t num_nodes, - ov::threading::CPUStreamsExecutor::Ptr executor, - const std::function& func) const; friend class intel_cpu::SyncInferRequest; friend std::shared_ptr dump_graph_as_ie_ngraph_net(const Graph &graph); @@ -259,6 +255,7 @@ class Graph { void EnforceBF16(); void insertReorder(EdgePtr& edge, bool isOptimized, std::unordered_set& uniqueLayerNames); void insertConvert(EdgePtr& edge); + int GetNumaNodeId() const; }; using GraphPtr = std::shared_ptr; diff --git a/src/plugins/intel_cpu/src/graph_context.cpp b/src/plugins/intel_cpu/src/graph_context.cpp index c06cb614d03a8c..2699a8854afb80 100644 --- a/src/plugins/intel_cpu/src/graph_context.cpp +++ b/src/plugins/intel_cpu/src/graph_context.cpp @@ -11,11 +11,13 @@ namespace intel_cpu { GraphContext::GraphContext(const Config& config, WeightsSharing::Ptr w_cache, bool isGraphQuantized, - ov::threading::IStreamsExecutor::Ptr streamExecutor) + ov::threading::IStreamsExecutor::Ptr streamExecutor, + std::shared_ptr sub_memory_manager) : config(config), weightsCache(std::move(w_cache)), isGraphQuantizedFlag(isGraphQuantized), streamExecutor(streamExecutor), + subMemoryManager(sub_memory_manager), memoryStatesRegister(std::make_shared()) { rtParamsCache = std::make_shared(config.rtCacheCapacity); // primitive/executors can be shared across sub-stream diff --git a/src/plugins/intel_cpu/src/graph_context.h b/src/plugins/intel_cpu/src/graph_context.h index 0bca410fca3c54..138ccebe0f9a40 100644 --- a/src/plugins/intel_cpu/src/graph_context.h +++ b/src/plugins/intel_cpu/src/graph_context.h @@ -5,6 +5,7 @@ #pragma once #include "openvino/runtime/threading/cpu_streams_executor.hpp" +#include "sub_memory_manager.hpp" #include "cache/multi_cache.h" #include "config.h" #include "dnnl_scratch_pad.h" @@ -25,7 +26,8 @@ class GraphContext { GraphContext(const Config& config, WeightsSharing::Ptr w_cache, bool isGraphQuantized, - ov::threading::IStreamsExecutor::Ptr streamExecutor = nullptr); + ov::threading::IStreamsExecutor::Ptr streamExecutor = nullptr, + std::shared_ptr sub_memory_manager = nullptr); const Config& getConfig() const { return config; @@ -62,6 +64,10 @@ class GraphContext { return cpuStreamExecutor; } + std::shared_ptr getSubMemory() const { + return subMemoryManager; + } + int getNumNumaNodes() const { return numNumaNodes; } @@ -86,6 +92,8 @@ class GraphContext { ov::threading::CPUStreamsExecutor::Ptr cpuStreamExecutor; // cpu stream executor for current graph + std::shared_ptr subMemoryManager; + int numNumaNodes = 1; std::shared_ptr memoryStatesRegister; diff --git a/src/plugins/intel_cpu/src/infer_request.cpp b/src/plugins/intel_cpu/src/infer_request.cpp index 82b72137b6b561..2d382c00508287 100644 --- a/src/plugins/intel_cpu/src/infer_request.cpp +++ b/src/plugins/intel_cpu/src/infer_request.cpp @@ -18,6 +18,7 @@ #include "proxy_mem_mgr.h" #include "utils/general_utils.h" #include "utils/ngraph_utils.hpp" +#include "openvino/runtime/threading/cpu_message.hpp" using OvString = ov::element_type_traits::value_type; @@ -104,8 +105,15 @@ void SyncInferRequest::infer() { OV_ITT_SCOPED_TASK(itt::domains::intel_cpu, m_profiling_task); auto graphLock = m_compiled_model->get_graph(); m_graph = &(graphLock._graph); + auto message = ov::threading::message_manager(); throw_if_canceled(); + if (m_asyncRequest->m_has_sub_infers) { + sub_streams_infer(); + message->server_wait(); + return; + } + convert_batched_tensors(); if (m_batched_tensors.size() > 0) { // batched_tensors will be updated for each infer, external_ptr should be update together @@ -303,6 +311,15 @@ void SyncInferRequest::change_default_ptr() { } std::vector> SyncInferRequest::query_state() const { + if (m_asyncRequest->m_has_sub_infers) { + auto requests = m_asyncRequest->getSubInferRequest(); + std::vector> states; + for (auto request : requests) { + auto cur = request->query_state(); + states.insert(states.end(), cur.begin(), cur.end()); + } + return states; + } return {m_memory_states.begin(), m_memory_states.end()}; } @@ -600,6 +617,38 @@ SyncInferRequest::OutputControlBlock::OutputControlBlock(const ov::element::Type m_tensor = std::make_shared(memory); } +void SyncInferRequest::sub_streams_infer() { + std::map, ov::SoPtr> input_tensors; + auto message = ov::threading::message_manager(); + auto requests = m_asyncRequest->getSubInferRequest(); + auto inputs = get_inputs(); + auto outputs = get_outputs(); + + size_t requests_num = requests.size(); + + if (requests.size() > 0) { + for (const auto& output : outputs) { + auto tensor = requests[0]->get_tensor(output); + set_tensor(output, tensor); + } + for (size_t i = 0; i < requests_num; i++) { + for (auto& input : inputs) { + auto tensor = get_tensor(input); + requests[i]->set_tensor(input, tensor); + } + + requests[i]->set_callback([message](const std::exception_ptr& ptr) { + ov::threading::MessageInfo msg_info; + msg_info.msg_type = ov::threading::MsgType::CALL_BACK; + message->send_message(msg_info); + }); + } + for (size_t i = 0; i < requests_num; i++) { + requests[i]->start_async(); + } + } +} + } // namespace intel_cpu } // namespace ov diff --git a/src/plugins/intel_cpu/src/infer_request.h b/src/plugins/intel_cpu/src/infer_request.h index e3839466f0ce0e..2e097c87673a37 100644 --- a/src/plugins/intel_cpu/src/infer_request.h +++ b/src/plugins/intel_cpu/src/infer_request.h @@ -104,6 +104,8 @@ class SyncInferRequest : public ov::ISyncInferRequest { const ov::Output& get_internal_port(const ov::Output& port) const; + void sub_streams_infer(); + private: std::unordered_map m_outputControlBlocks; diff --git a/src/plugins/intel_cpu/src/node.h b/src/plugins/intel_cpu/src/node.h index eb1f84dc8b9e34..2095907b860508 100644 --- a/src/plugins/intel_cpu/src/node.h +++ b/src/plugins/intel_cpu/src/node.h @@ -658,7 +658,6 @@ class Node { std::vector fusedWith; std::vector mergedWith; - std::vector parallelWith; int curNumaNode = -1; void toNumaNode(int numaID); diff --git a/src/plugins/intel_cpu/src/nodes/fullyconnected.cpp b/src/plugins/intel_cpu/src/nodes/fullyconnected.cpp index da3dcafa4750ef..d7fbd942e124c9 100644 --- a/src/plugins/intel_cpu/src/nodes/fullyconnected.cpp +++ b/src/plugins/intel_cpu/src/nodes/fullyconnected.cpp @@ -9,6 +9,7 @@ #include #include "common/cpu_convert.h" +#include "common/cpu_memcpy.h" #include "dnnl_extension_utils.h" #include "executors/memory_arguments.hpp" #include "graph_context.h" @@ -19,6 +20,7 @@ #include "nodes/executors/executor.hpp" #include "nodes/executors/fullyconnected_config.hpp" #include "openvino/core/type/element_type.hpp" +#include "openvino/runtime/threading/cpu_message.hpp" #include "post_ops.hpp" #include "shape_inference/custom/fullyconnected.hpp" #include "transformations/cpu_opset/common/op/fully_connected.hpp" @@ -58,10 +60,23 @@ bool FullyConnected::isSupportedOperation(const std::shared_ptr& return true; } +void FullyConnected::initTensorParallelConfig(const GraphContext::CPtr context) { + if (context->getCPUStreamExecutor()) { + if (!context->getCPUStreamExecutor()->get_rank().empty()) { + // init tp_cfg.w_rank and tp_cfg.w_size + tp_cfg.w_rank = context->getCPUStreamExecutor()->get_rank()[0]; + tp_cfg.w_size = ov::threading::message_manager()->get_num_sub_streams(); + tp_cfg.enable_tensor_parallel = tp_cfg.w_size > 1 ? true : false; + tp_cfg.sub_memory = context->getSubMemory(); + } + } +} + FullyConnected::FullyConnected(const std::shared_ptr& op, const GraphContext::CPtr context) : Node(op, context, FCShapeInferFactory(op)), errorPrefix("FullyConnected node with name '" + getName() + "'") { std::string errorMessage; + initTensorParallelConfig(context); if (!isSupportedOperation(op, errorMessage)) OPENVINO_THROW_NOT_IMPLEMENTED(errorMessage); } @@ -73,6 +88,36 @@ bool FullyConnected::canBeExecutedInInt8() const { return one_of(srcType, ov::element::u8, ov::element::i8) && weiType == ov::element::i8; } +void FullyConnected::needPrepareParamsForTensorParallel() { + if (tp_cfg.enable_tensor_parallel) { + // must call in dynamic + const auto dstMemoryBuffer = getDstMemoryAtPort(0); + + auto split_parts = [](int len, int n) { + int average = len / n; + std::vector parts(n, average); + parts.back() = len - average * (n - 1); + return parts; + }; + + int dim = -1; + auto dst_shape = dstMemoryBuffer->getShape(); + auto dst_desc = dstMemoryBuffer->getDescPtr(); + auto dims = dst_shape.getDims(); + if (dim < 0) { + dim += dims.size(); + } + assert(dims[dim] >= tp_cfg.w_size); + auto splited_dim_vec = split_parts(dims[dim], tp_cfg.w_size); + + VectorDims new_dims = dims; + new_dims[dim] = splited_dim_vec[tp_cfg.w_rank]; + auto memory_desc = dst_desc->cloneWithNewDims(new_dims, true); + tp_cfg.cached_dst->redefineDesc(memory_desc); + memory[ARG_DST] = tp_cfg.cached_dst; + } +} + ExecutorPtr FullyConnected::createExecutor() { const auto& executor = factory->make(memory); getSelectedPrimitiveDescriptor()->setImplementationType(executor->implType()); @@ -81,11 +126,108 @@ ExecutorPtr FullyConnected::createExecutor() { } void FullyConnected::prepareParams() { + needPrepareParamsForTensorParallel(); executor = createExecutor(); } +void FullyConnected::initTensorParallelSync() { + if (tp_cfg.enable_tensor_parallel) { + tp_cfg.id = tp_cfg.sub_memory->get_memory_id(tp_cfg.w_rank); + tp_cfg.sub_memory->set_memory_used(tp_cfg.id, tp_cfg.w_rank); + while (true) { + std::lock_guard lock(tp_cfg.sub_memory->_flagMutex); + if (tp_cfg.sub_memory->_use_count[tp_cfg.id] == tp_cfg.w_size) { + tp_cfg.sub_memory->_use_count[tp_cfg.id] = 0; + for (int i = 0; i < tp_cfg.w_size; i++) { + tp_cfg.sub_memory->_memorys_table[tp_cfg.id][i].flag = false; + } + } + if (tp_cfg.sub_memory->_use_count[tp_cfg.id] == 0) { + break; + } + } + } +} + +void FullyConnected::execTensorParallelSync() { + if (tp_cfg.enable_tensor_parallel) { + // dst + auto dst = getDstMemoryAtPort(0); + auto dst_ptr = static_cast(dst->getData()); + + auto shape = dst->getShape(); + auto dims = shape.getDims(); + auto prec = dst->getPrecision(); + + // cur dst + auto cur_dst = memory[ARG_DST]; + + auto split_parts = [](int len, int n) { + int average = len / n; + std::vector parts(n, average); + parts.back() = len - average * (n - 1); + return parts; + }; + + const int dim = dims.size() - 1; + // selected dim bytes + auto channel_size = dims[dim] * prec.size(); + // total bytes + auto mem_size = dst->getSize(); + // the steps need to copy. + const size_t count = (mem_size / channel_size); + + auto splited_dim_vec = split_parts(dims[dim], tp_cfg.w_size); + const auto strideSize = splited_dim_vec[0] * prec.size(); + + tp_cfg.sub_memory->_memorys_table[tp_cfg.id][tp_cfg.w_rank].send_buf = cur_dst->getData(); + tp_cfg.sub_memory->_memorys_table[tp_cfg.id][tp_cfg.w_rank].flag = true; + + std::vector wait_list(tp_cfg.w_size, 1); + while (true) { + int wait_size = 0; + for (int idx = 0; idx < tp_cfg.w_size; idx++) { + if (wait_list[idx] > 0 && tp_cfg.sub_memory->_memorys_table[tp_cfg.id][idx].flag) { + auto new_ptr = static_cast(tp_cfg.sub_memory->_memorys_table[tp_cfg.id][idx].send_buf); + const auto copySize = splited_dim_vec[idx] * prec.size(); // bytes of half selected dim. + const size_t unloop = 8; + size_t step = count / unloop; + parallel_for(step, [&](size_t i){ + cpu_memcpy(dst_ptr + idx * strideSize + (i * unloop) * channel_size, new_ptr + (i * unloop) * copySize, copySize); + cpu_memcpy(dst_ptr + idx * strideSize + (i * unloop + 1) * channel_size, new_ptr + (i * unloop + 1) * copySize, copySize); + cpu_memcpy(dst_ptr + idx * strideSize + (i * unloop + 2) * channel_size, new_ptr + (i * unloop + 2) * copySize, copySize); + cpu_memcpy(dst_ptr + idx * strideSize + (i * unloop + 3) * channel_size, new_ptr + (i * unloop + 3) * copySize, copySize); + cpu_memcpy(dst_ptr + idx * strideSize + (i * unloop + 4) * channel_size, new_ptr + (i * unloop + 4) * copySize, copySize); + cpu_memcpy(dst_ptr + idx * strideSize + (i * unloop + 5) * channel_size, new_ptr + (i * unloop + 5) * copySize, copySize); + cpu_memcpy(dst_ptr + idx * strideSize + (i * unloop + 6) * channel_size, new_ptr + (i * unloop + 6) * copySize, copySize); + cpu_memcpy(dst_ptr + idx * strideSize + (i * unloop + 7) * channel_size, new_ptr + (i * unloop + 7) * copySize, copySize); + }); + size_t tail = count & ~(unloop - 1); + for (size_t i = tail; i < count; ++i) { + size_t dst_offset = i * channel_size + idx * strideSize; + size_t src_offset = i * copySize; + cpu_parallel_memcpy(dst_ptr + dst_offset, new_ptr + src_offset, copySize); + } + wait_list[idx] = 0; + } + wait_size += wait_list[idx]; + } + if (wait_size == 0) { + break; + } + } + { + std::lock_guard lock(tp_cfg.sub_memory->_flagMutex); + tp_cfg.sub_memory->_use_count[tp_cfg.id]++; + } + } +} void FullyConnected::execute(dnnl::stream strm) { + initTensorParallelSync(); + executor->execute(memory); + + execTensorParallelSync(); } void FullyConnected::executeDynamicImpl(dnnl::stream strm) { @@ -222,9 +364,29 @@ static bool useSparseWeightsDecompression(const NodePtr& weightsInput, return sparseRate >= minSparseRate; } +void FullyConnected::needUpdateDQScaleForTensorParallel(std::vector& dequantizationScales) { + if (tp_cfg.enable_tensor_parallel) { + auto split_parts = [](int len, int n) { + int average = len / n; + std::vector parts(n, average); + parts.back() = len - average * (n - 1); + return parts; + }; + auto DQScales = getDQScales(); + auto split_lens = split_parts(DQScales.size(), tp_cfg.w_size); + auto split_offset = tp_cfg.w_rank * split_lens[0]; + std::vector newDQScales(split_lens[tp_cfg.w_rank]); + std::copy(DQScales.begin() + split_offset, DQScales.begin() + split_offset + split_lens[tp_cfg.w_rank], newDQScales.begin()); + dequantizationScales = newDQScales; + } +} + void FullyConnected::initSupportedPrimitiveDescriptors() { attrs.withBias = getOriginalInputsNumber() == 3; + attrs.dequantizationScales = getDQScales(); + needUpdateDQScaleForTensorParallel(attrs.dequantizationScales); + attrs.sparseWeights = useSparseWeightsDecompression(getParentEdgeAt(WEIGHTS_ID)->getParent(), getOriginalInputPrecisionAtPort(DATA_ID), context->getConfig().fcSparseWeiDecompressionRate); @@ -259,6 +421,9 @@ void FullyConnected::initSupportedPrimitiveDescriptors() { {ARG_DST, dstDescs[0]}, }; + needUpdateScaleForTensorParallel(); + needUpdateZeroPointForTensorParallel(); + auto executionContext = std::make_shared(context, getImplPriority(), privateWeightCache); factory = std::make_shared>(attrs, postOps, executionContext, descs); const auto nodeDescriptors = factory->getProperMemoryDescriptors(descs); @@ -274,11 +439,55 @@ void FullyConnected::initSupportedPrimitiveDescriptors() { supportedPrimitiveDescriptors.emplace_back(nodeConfig, impl_desc_type::undef); } +void FullyConnected::needSplitMemoryForTensorParallel() { + if (tp_cfg.enable_tensor_parallel) { + auto src = getSrcMemoryAtPort(DATA_ID); + auto wgt = getSrcMemoryAtPort(WEIGHTS_ID); + auto dst = getDstMemoryAtPort(0); + // src + memory[ARG_SRC] = getSrcMemoryAtPort(DATA_ID); + // wgt + // split N direction + tp_cfg.cached_splited_weight = attrs.weightsNonTransposed ? split_vertical(context->getEngine(), wgt, 0, tp_cfg.w_rank, tp_cfg.w_size) + : split_horizontal(context->getEngine(), wgt, 0, tp_cfg.w_rank, tp_cfg.w_size); + memory[ARG_WEI] = tp_cfg.cached_splited_weight; + // bias + if (attrs.withBias) { + auto bias = getSrcMemoryAtPort(BIAS_ID); + auto select_bias = split_horizontal(context->getEngine(), bias, 0, tp_cfg.w_rank, tp_cfg.w_size); + tp_cfg.cached_splited_bias = select_bias; + } else { + tp_cfg.cached_splited_bias = MemoryDescUtils::makeEmptyMemory(context); + } + memory[ARG_BIAS] = tp_cfg.cached_splited_bias; + // dst + memory[ARG_DST] = getDstMemoryAtPort(0); + tp_cfg.cached_dst = split_horizontal(context->getEngine(), dst, -1, tp_cfg.w_rank, tp_cfg.w_size, false); + } +} + +void FullyConnected::needUpdateTensorParalelConfig() { + // tensor parallel should be disabled in two conditions. + // 1. weight shape is dynamic + // 2. last dim can be splited. + if (tp_cfg.enable_tensor_parallel) { + auto shape = getSrcMemoryAtPort(WEIGHTS_ID)->getShape(); + if (shape.isDynamic()) { + tp_cfg.enable_tensor_parallel = false; + } else if (shape.getDims()[0] < static_cast(tp_cfg.w_size)) { + tp_cfg.enable_tensor_parallel = false; + } + } +} void FullyConnected::createPrimitive() { + needUpdateTensorParalelConfig(); + memory[ARG_SRC] = getSrcMemoryAtPort(DATA_ID); memory[ARG_WEI] = getSrcMemoryAtPort(WEIGHTS_ID); memory[ARG_BIAS] = attrs.withBias ? getSrcMemoryAtPort(BIAS_ID) : MemoryDescUtils::makeEmptyMemory(context); memory[ARG_DST] = getDstMemoryAtPort(0); + + needSplitMemoryForTensorParallel(); // @todo should we preconfigure only for dynamic shapes? // Since for static shapes primitive is created in scope of compile_model() anyway factory->preconfigure(memory); @@ -302,12 +511,47 @@ ov::element::Type FullyConnected::getRuntimePrecision() const { return getMaxPrecision(srcTypes); } +void FullyConnected::needUpdateScaleForTensorParallel() { + if (tp_cfg.enable_tensor_parallel && tp_cfg.cached_scale) { + attrs.decompressionMultiplyPtr = tp_cfg.cached_scale; + } +} + +void FullyConnected::needSplitScaleForTensorParallel(const MemoryCPtr& memory) { + if (tp_cfg.enable_tensor_parallel && !tp_cfg.cached_scale) { + auto scale_mem = std::const_pointer_cast(memory); + tp_cfg.cached_scale = attrs.weightsNonTransposed ? split_vertical(context->getEngine(), scale_mem, 0, tp_cfg.w_rank, tp_cfg.w_size) + : split_horizontal(context->getEngine(), scale_mem, 0, tp_cfg.w_rank, tp_cfg.w_size); + } +} + +void FullyConnected::needUpdateZeroPointForTensorParallel() { + if (tp_cfg.enable_tensor_parallel && tp_cfg.cached_zeropoint) { + attrs.decompressionSubtractPtr = tp_cfg.cached_zeropoint; + } +} + +void FullyConnected::needSplitZeroPointForTensorParallel(const MemoryCPtr& memory) { + if (tp_cfg.enable_tensor_parallel && !tp_cfg.cached_zeropoint) { + auto zeropoint_mem = std::const_pointer_cast(memory); + auto element_num = memory->getSize() / memory->getPrecision().size(); + if (element_num == 1) { + tp_cfg.cached_zeropoint = zeropoint_mem; + } else { + tp_cfg.cached_zeropoint = attrs.weightsNonTransposed ? split_vertical(context->getEngine(), zeropoint_mem, 0, tp_cfg.w_rank, tp_cfg.w_size) + : split_horizontal(context->getEngine(), zeropoint_mem, 0, tp_cfg.w_rank, tp_cfg.w_size); + } + } +} + void FullyConnected::fuseDecompressionMultiply(const MemoryCPtr& memory) { attrs.decompressionMultiplyPtr = memory; + needSplitScaleForTensorParallel(memory); } void FullyConnected::fuseDecompressionSubtract(const MemoryCPtr& memory) { attrs.decompressionSubtractPtr = memory; + needSplitZeroPointForTensorParallel(memory); } } // namespace node diff --git a/src/plugins/intel_cpu/src/nodes/fullyconnected.h b/src/plugins/intel_cpu/src/nodes/fullyconnected.h index 06b83d6a784dd9..be29342b851988 100644 --- a/src/plugins/intel_cpu/src/nodes/fullyconnected.h +++ b/src/plugins/intel_cpu/src/nodes/fullyconnected.h @@ -16,11 +16,26 @@ #include "nodes/executors/memory_arguments.hpp" #include "nodes/executors/fullyconnected_config.hpp" #include "post_ops.hpp" +#include "openvino/runtime/threading/cpu_message.hpp" namespace ov { namespace intel_cpu { namespace node { +// tensor parallel config +struct FCTensorParallelConfig { + int w_rank = -1; + int w_size = -1; + int id = 0; + bool enable_tensor_parallel = false; + std::shared_ptr sub_memory = nullptr; + MemoryPtr cached_splited_weight = nullptr; + MemoryPtr cached_splited_bias = nullptr; + MemoryPtr cached_scale = nullptr; + MemoryPtr cached_zeropoint = nullptr; + MemoryPtr cached_dst = nullptr; +}; + class FullyConnected : public Node { public: FullyConnected(const std::shared_ptr& op, const GraphContext::CPtr context); @@ -73,12 +88,26 @@ class FullyConnected : public Node { ExecutorPtr createExecutor(); void fuseDecompressionConstant(const MemoryCPtr& memory, MemoryCPtr& decompressionValuesPtr); + void initTensorParallelConfig(const GraphContext::CPtr context); + void needUpdateTensorParalelConfig(); + void needPrepareParamsForTensorParallel(); + void initTensorParallelSync(); + void execTensorParallelSync(); + void needSplitMemoryForTensorParallel(); + void needSplitScaleForTensorParallel(const MemoryCPtr& memory); + void needUpdateScaleForTensorParallel(); + void needSplitZeroPointForTensorParallel(const MemoryCPtr& memory); + void needUpdateZeroPointForTensorParallel(); + void needUpdateDQScaleForTensorParallel(std::vector& dequantizationScales); + FCAttrs attrs; PostOps postOps; MemoryArgs memory; ExecutorFactoryPtr factory; ExecutorPtr executor = nullptr; std::string errorPrefix; + + FCTensorParallelConfig tp_cfg; }; } // namespace node diff --git a/src/plugins/intel_cpu/src/plugin.cpp b/src/plugins/intel_cpu/src/plugin.cpp index c251f8c619e625..276502a0f62ebd 100644 --- a/src/plugins/intel_cpu/src/plugin.cpp +++ b/src/plugins/intel_cpu/src/plugin.cpp @@ -131,11 +131,13 @@ Plugin::Plugin() : deviceFullName(getDeviceFullName()), specialSetup(new CPUSpec }); auto& ov_version = ov::get_openvino_version(); m_compiled_model_runtime_properties["OV_VERSION"] = std::string(ov_version.buildNumber); + m_msg_manager = ov::threading::message_manager(); } Plugin::~Plugin() { executor_manager()->clear("CPU"); executor_manager()->clear("CPUStreamsExecutor"); + executor_manager()->clear("CPUMainStreamExecutor"); executor_manager()->clear("CPUCallbackExecutor"); } @@ -290,16 +292,11 @@ std::shared_ptr Plugin::compile_model(const std::shared_ptr< conf.readProperties(config, modelType); calculate_streams(conf, cloned_model); - if (conf.streamExecutorConfig.get_sub_stream_mode() == - IStreamsExecutor::Config::StreamsMode::SUB_STREAMS_FOR_SOCKET) { - int num_sub_streams = conf.streamExecutorConfig.get_sub_streams(); - transformations.SetSubStreamNum(num_sub_streams); - } - transformations.PostLpt(); transformations.Snippets(); transformations.CpuSpecificOpSet(); + DEBUG_LOG(PrintableModel(*cloned_model, "cpu_")); if ((cloned_model->inputs().size() != model->inputs().size()) || diff --git a/src/plugins/intel_cpu/src/plugin.h b/src/plugins/intel_cpu/src/plugin.h index 3332d873fdcf71..c2d24e98ee6f98 100644 --- a/src/plugins/intel_cpu/src/plugin.h +++ b/src/plugins/intel_cpu/src/plugin.h @@ -6,6 +6,7 @@ #include "compiled_model.h" #include "cpu_streams_calculation.hpp" +#include "openvino/runtime/threading/cpu_message.hpp" namespace ov { namespace intel_cpu { @@ -43,6 +44,8 @@ class Plugin : public ov::IPlugin { OPENVINO_THROW_NOT_IMPLEMENTED("Not Implemented get_default_context is not supported by CPU plugin!"); }; + std::shared_ptr m_msg_manager; + private: ov::Any get_ro_property(const std::string& name, const ov::AnyMap& options) const; diff --git a/src/plugins/intel_cpu/src/sub_memory_manager.hpp b/src/plugins/intel_cpu/src/sub_memory_manager.hpp new file mode 100644 index 00000000000000..4a0fd7ea0a21b3 --- /dev/null +++ b/src/plugins/intel_cpu/src/sub_memory_manager.hpp @@ -0,0 +1,57 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include +#include +#include +#include +#include "cpu_memory.h" + +namespace ov { +namespace intel_cpu { +class SubMemoryManager { +public: + struct MemoryInfo { + void* send_buf; + bool flag; + bool last_used; + }; + + SubMemoryManager(int num_sub_streams) { + assert(num_sub_streams); + _num_sub_streams = num_sub_streams; + MemoryInfo memory_info; + memory_info.flag = false; + memory_info.last_used = false; + std::vector memorys; + memorys.assign(_num_sub_streams, memory_info); + _memorys_table.assign(2, memorys); + _use_count.assign(2, 0); + } + + int get_memory_id(int sub_stream_id) { + for (int i = 0; i < 2; i++) { + if (!_memorys_table[i][sub_stream_id].last_used) { + return i; + } + } + return -1; + } + + void set_memory_used(int memory_id, int sub_stream_id) { + _memorys_table[memory_id][sub_stream_id].last_used = true; + _memorys_table[(memory_id + 1) % 2][sub_stream_id].last_used = false; + } + + int _num_sub_streams; + std::vector> _memorys_table; + std::vector _use_count; + std::mutex _flagMutex; +}; +} // namespace intel_cpu + +} // namespace ov \ No newline at end of file diff --git a/src/plugins/intel_cpu/src/transformations/cpu_opset/convert_to_cpu_specific_opset.hpp b/src/plugins/intel_cpu/src/transformations/cpu_opset/convert_to_cpu_specific_opset.hpp index 703df1d26cb686..20502f67d3645e 100644 --- a/src/plugins/intel_cpu/src/transformations/cpu_opset/convert_to_cpu_specific_opset.hpp +++ b/src/plugins/intel_cpu/src/transformations/cpu_opset/convert_to_cpu_specific_opset.hpp @@ -26,7 +26,7 @@ namespace ov { namespace intel_cpu { -inline void ConvertToCPUSpecificOpset(std::shared_ptr &model, int subStreamNum) { +inline void ConvertToCPUSpecificOpset(std::shared_ptr &model) { RUN_ON_FUNCTION_SCOPE(ConvertToCPUSpecificOpset); ov::pass::Manager manager("CPU:ConvertToCPUSpecificOpset"); @@ -34,10 +34,6 @@ inline void ConvertToCPUSpecificOpset(std::shared_ptr &model, int sub CPU_REGISTER_PASS_COMMON(manager, ConvertMatMulToFC); CPU_REGISTER_PASS_X64(manager, MoveFCReshapeToWeights); CPU_REGISTER_PASS_X64(manager, ov::pass::Validate); - if (subStreamNum >= 1) { - CPU_REGISTER_PASS_COMMON(manager, SplitFC, subStreamNum); - CPU_REGISTER_PASS_COMMON(manager, ov::pass::Validate); - } CPU_REGISTER_PASS_COMMON(manager, AlignMatMulInputRanks); CPU_REGISTER_PASS_COMMON(manager, ConvertTileToSeqTiles); CPU_REGISTER_PASS_COMMON(manager, ConvertToPowerStatic); diff --git a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp index 726a416fe1c6c5..98da1be4c74876 100644 --- a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp +++ b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp @@ -296,14 +296,10 @@ void Transformations::UpToLpt() { Lpt(defaultPrecisions); } -void Transformations::SetSubStreamNum(int SubStreams) { - subStreamNum = SubStreams; -} - void Transformations::CpuSpecificOpSet(void) { CPU_DEBUG_CAP_TRANSFORMATION_SCOPE(this, Specific); - ConvertToCPUSpecificOpset(model, subStreamNum); + ConvertToCPUSpecificOpset(model); } void Transformations::PreLpt(const std::vector& defaultPrecisions) { diff --git a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.h b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.h index 2c66d3abf1d2c8..420cf102f38fc6 100644 --- a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.h +++ b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.h @@ -37,7 +37,6 @@ class Transformations { void CpuSpecificOpSet(); void PostLpt(); void Snippets(void); - void SetSubStreamNum(int); private: std::shared_ptr model; @@ -45,7 +44,6 @@ class Transformations { const ov::element::Type inferencePrecision; const Config::SnippetsMode snippetsMode; const Config& config; - int subStreamNum = -1; void PreLpt(const std::vector& defaultPrecisions); diff --git a/src/plugins/intel_cpu/tests/unit/streams_info/streams_e2e_test.cpp b/src/plugins/intel_cpu/tests/unit/streams_info/streams_e2e_test.cpp index 15d9098e9faf2a..7fd491e934f336 100644 --- a/src/plugins/intel_cpu/tests/unit/streams_info/streams_e2e_test.cpp +++ b/src/plugins/intel_cpu/tests/unit/streams_info/streams_e2e_test.cpp @@ -411,7 +411,7 @@ StreamGenerateionTestCase generation_latency_2sockets_48cores_10 = { false, ov::hint::PerformanceMode::LATENCY, {{48, 48, 0, 0, -1, -1}, {24, 24, 0, 0, 0, 0}, {24, 24, 0, 0, 1, 1}}, - {{1, MAIN_CORE_PROC, 24, 0, 0}, {-1, MAIN_CORE_PROC, 24, 1, 1}}, + {{1, MAIN_CORE_PROC, 48, -1, -1}, {-1, MAIN_CORE_PROC, 24, 0, 0}, {-1, MAIN_CORE_PROC, 24, 1, 1}}, }; StreamGenerateionTestCase generation_latency_2sockets_48cores_11 = { @@ -434,7 +434,7 @@ StreamGenerateionTestCase generation_latency_2sockets_48cores_11 = { false, ov::hint::PerformanceMode::LATENCY, {{48, 48, 0, 0, -1, -1}, {24, 24, 0, 0, 0, 0}, {24, 24, 0, 0, 1, 1}}, - {{1, MAIN_CORE_PROC, 24, 0, 0}, {-1, MAIN_CORE_PROC, 24, 1, 1}}, + {{1, MAIN_CORE_PROC, 48, -1, -1}, {-1, MAIN_CORE_PROC, 24, 0, 0}, {-1, MAIN_CORE_PROC, 24, 1, 1}}, }; StreamGenerateionTestCase generation_tput_1sockets_14cores_2 = { diff --git a/src/plugins/intel_cpu/tests/unit/streams_info/streams_info_table_test.cpp b/src/plugins/intel_cpu/tests/unit/streams_info/streams_info_table_test.cpp index 73869074bf9779..4cc795eeaf2910 100644 --- a/src/plugins/intel_cpu/tests/unit/streams_info/streams_info_table_test.cpp +++ b/src/plugins/intel_cpu/tests/unit/streams_info/streams_info_table_test.cpp @@ -58,17 +58,14 @@ StreamsCalculationTestCase _2sockets_104cores_latency_auto_1 = { 0, // param[in]: the current socket id of the running thread in this simulation "LATENCY", // param[in]: the performance hint in this simulation {ov::hint::ModelDistributionPolicy::TENSOR_PARALLEL}, // param[in]: the hint of max threads per stream in this - // simulation + // simulation {{208, 104, 0, 104, -1, -1}, {104, 52, 0, 52, 0, 0}, {104, 52, 0, 52, 1, 1}}, // param[in]: the proc_type_table in this simulation - {{1, ALL_PROC, 104, 0, 0}, - {0, MAIN_CORE_PROC, 52, 0, 0}, - {0, HYPER_THREADING_PROC, 52, 0, 0}, - {-1, ALL_PROC, 104, 1, 1}, - {0, MAIN_CORE_PROC, 52, 1, 1}, - {0, HYPER_THREADING_PROC, 52, 1, 1}}, // param[expected out]: the expected result of streams_info_table in this - // simulation + {{1, MAIN_CORE_PROC, 64, -1, -1}, + {-1, MAIN_CORE_PROC, 32, 0, 0}, + {-1, MAIN_CORE_PROC, 32, 1, 1}}, // param[expected out]: the expected result of streams_info_table in this + // simulation }; StreamsCalculationTestCase _2sockets_104cores_latency_auto_2 = { @@ -81,7 +78,7 @@ StreamsCalculationTestCase _2sockets_104cores_latency_auto_2 = { "LATENCY", {ov::hint::ModelDistributionPolicy::TENSOR_PARALLEL}, {{104, 104, 0, 0, -1, -1}, {52, 52, 0, 0, 0, 0}, {52, 52, 0, 0, 1, 1}}, - {{1, MAIN_CORE_PROC, 52, 0, 0}, {-1, MAIN_CORE_PROC, 52, 1, 1}}, + {{1, MAIN_CORE_PROC, 64, -1, -1}, {-1, MAIN_CORE_PROC, 32, 0, 0}, {-1, MAIN_CORE_PROC, 32, 1, 1}}, }; StreamsCalculationTestCase _2sockets_104cores_latency_auto_3 = { 1, @@ -97,16 +94,13 @@ StreamsCalculationTestCase _2sockets_104cores_latency_auto_3 = { {52, 26, 0, 26, 1, 0}, {52, 26, 0, 26, 2, 1}, {52, 26, 0, 26, 3, 1}}, - {{1, ALL_PROC, 104, -1, 0}, + {{1, ALL_PROC, 64, -1, -1}, + {-1, ALL_PROC, 32, -1, 0}, {0, MAIN_CORE_PROC, 26, 0, 0}, - {0, MAIN_CORE_PROC, 26, 1, 0}, - {0, HYPER_THREADING_PROC, 26, 0, 0}, - {0, HYPER_THREADING_PROC, 26, 1, 0}, - {-1, ALL_PROC, 104, -1, 1}, + {0, MAIN_CORE_PROC, 6, 1, 0}, + {-1, ALL_PROC, 32, -1, 1}, {0, MAIN_CORE_PROC, 26, 2, 1}, - {0, MAIN_CORE_PROC, 26, 3, 1}, - {0, HYPER_THREADING_PROC, 26, 2, 1}, - {0, HYPER_THREADING_PROC, 26, 3, 1}}, + {0, MAIN_CORE_PROC, 6, 3, 1}}, }; StreamsCalculationTestCase _2sockets_104cores_latency_auto_4 = { 1, @@ -118,12 +112,13 @@ StreamsCalculationTestCase _2sockets_104cores_latency_auto_4 = { "LATENCY", {ov::hint::ModelDistributionPolicy::TENSOR_PARALLEL}, {{104, 104, 0, 0, -1, -1}, {26, 26, 0, 0, 0, 0}, {26, 26, 0, 0, 1, 0}, {26, 26, 0, 0, 2, 1}, {26, 26, 0, 0, 3, 1}}, - {{1, ALL_PROC, 52, -1, 0}, + {{1, ALL_PROC, 64, -1, -1}, + {-1, ALL_PROC, 32, -1, 0}, {0, MAIN_CORE_PROC, 26, 0, 0}, - {0, MAIN_CORE_PROC, 26, 1, 0}, - {-1, ALL_PROC, 52, -1, 1}, + {0, MAIN_CORE_PROC, 6, 1, 0}, + {-1, ALL_PROC, 32, -1, 1}, {0, MAIN_CORE_PROC, 26, 2, 1}, - {0, MAIN_CORE_PROC, 26, 3, 1}}, + {0, MAIN_CORE_PROC, 6, 3, 1}}, }; StreamsCalculationTestCase _2sockets_104cores_latency_platform_1 = { 1, @@ -2295,9 +2290,10 @@ StreamsCalculationTestCase _2sockets_mock_latency_22 = { "LATENCY", {ov::hint::ModelDistributionPolicy::TENSOR_PARALLEL}, {{60, 30, 0, 30, -1, -1}, {40, 20, 0, 20, 0, 0}, {20, 10, 0, 10, 1, 1}}, - {{1, ALL_PROC, 40, 0, 0}, + {{1, ALL_PROC, 52, -1, -1}, + {-1, ALL_PROC, 32, 0, 0}, {0, MAIN_CORE_PROC, 20, 0, 0}, - {0, HYPER_THREADING_PROC, 20, 0, 0}, + {0, HYPER_THREADING_PROC, 12, 0, 0}, {-1, ALL_PROC, 20, 1, 1}, {0, MAIN_CORE_PROC, 10, 1, 1}, {0, HYPER_THREADING_PROC, 10, 1, 1}}, @@ -2340,16 +2336,13 @@ StreamsCalculationTestCase _2sockets_mock_latency_25 = { {60, 30, 0, 30, 1, 0}, {40, 20, 0, 20, 2, 1}, {20, 10, 0, 10, 3, 1}}, - {{1, ALL_PROC, 140, -1, 0}, - {0, MAIN_CORE_PROC, 40, 0, 0}, - {0, MAIN_CORE_PROC, 30, 1, 0}, - {0, HYPER_THREADING_PROC, 40, 0, 0}, - {0, HYPER_THREADING_PROC, 30, 1, 0}, - {-1, ALL_PROC, 60, -1, 1}, + {{1, ALL_PROC, 64, -1, -1}, + {-1, ALL_PROC, 32, -1, 0}, + {0, MAIN_CORE_PROC, 32, 0, 0}, + {-1, ALL_PROC, 32, -1, 1}, {0, MAIN_CORE_PROC, 20, 2, 1}, {0, MAIN_CORE_PROC, 10, 3, 1}, - {0, HYPER_THREADING_PROC, 20, 2, 1}, - {0, HYPER_THREADING_PROC, 10, 3, 1}}, + {0, HYPER_THREADING_PROC, 2, 2, 1}}, }; StreamsCalculationTestCase _2sockets_mock_latency_26 = { 1, @@ -2405,16 +2398,13 @@ StreamsCalculationTestCase _2sockets_mock_latency_28 = { {60, 30, 0, 30, 1, 0}, {40, 20, 0, 20, 2, 1}, {20, 10, 0, 10, 3, 1}}, - {{1, ALL_PROC, 140, -1, 0}, - {0, MAIN_CORE_PROC, 40, 0, 0}, - {0, MAIN_CORE_PROC, 30, 1, 0}, - {0, HYPER_THREADING_PROC, 40, 0, 0}, - {0, HYPER_THREADING_PROC, 30, 1, 0}, - {-1, ALL_PROC, 60, -1, 1}, + {{1, ALL_PROC, 64, -1, -1}, + {-1, ALL_PROC, 32, -1, 0}, + {0, MAIN_CORE_PROC, 32, 0, 0}, + {-1, ALL_PROC, 32, -1, 1}, {0, MAIN_CORE_PROC, 20, 2, 1}, {0, MAIN_CORE_PROC, 10, 3, 1}, - {0, HYPER_THREADING_PROC, 20, 2, 1}, - {0, HYPER_THREADING_PROC, 10, 3, 1}}, + {0, HYPER_THREADING_PROC, 2, 2, 1}}, }; StreamsCalculationTestCase _2sockets_mock_latency_29 = { 1, @@ -2518,7 +2508,7 @@ StreamsCalculationTestCase _2sockets_mock_latency_34 = { "LATENCY", {ov::hint::ModelDistributionPolicy::TENSOR_PARALLEL}, {{48, 48, 0, 0, -1, -1}, {24, 24, 0, 0, 0, 0}, {24, 24, 0, 0, 1, 1}}, - {{1, MAIN_CORE_PROC, 24, 0, 0}, {-1, MAIN_CORE_PROC, 24, 1, 1}}, + {{1, MAIN_CORE_PROC, 48, -1, -1}, {-1, MAIN_CORE_PROC, 24, 0, 0}, {-1, MAIN_CORE_PROC, 24, 1, 1}}, }; StreamsCalculationTestCase _1sockets_mock_latency_1 = { 1, @@ -2642,7 +2632,7 @@ StreamsCalculationTestCase _2sockets_mock_latency_37 = { "LATENCY", {ov::hint::ModelDistributionPolicy::TENSOR_PARALLEL}, {{48, 48, 0, 0, -1, -1}, {24, 24, 0, 0, 0, 0}, {24, 24, 0, 0, 1, 1}}, - {{1, MAIN_CORE_PROC, 24, 1, 1}, {-1, MAIN_CORE_PROC, 24, 0, 0}}, + {{1, MAIN_CORE_PROC, 48, -1, -1}, {-1, MAIN_CORE_PROC, 24, 1, 1}, {-1, MAIN_CORE_PROC, 24, 0, 0}}, }; TEST_P(StreamsCalculationTests, StreamsCalculation) {} diff --git a/src/plugins/intel_cpu/tests/unit/streams_info/streams_rank_table_test.cpp b/src/plugins/intel_cpu/tests/unit/streams_info/streams_rank_table_test.cpp new file mode 100644 index 00000000000000..ce2dcb324d6837 --- /dev/null +++ b/src/plugins/intel_cpu/tests/unit/streams_info/streams_rank_table_test.cpp @@ -0,0 +1,125 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include "common_test_utils/test_common.hpp" +#include "cpu_streams_calculation.hpp" + +using namespace testing; +using namespace ov; + +namespace { + +struct StreamsRankTestCase { + std::vector> stream_info_table; + int rank_level; + int num_sub_streams; + std::vector> stream_rank_table; +}; + +class StreamsRankTests : public ov::test::TestsCommon, + public testing::WithParamInterface> { +public: + void SetUp() override { + const auto& test_data = std::get<0>(GetParam()); + + int test_num_sub_streams; + std::vector> test_stream_rank_table = + ov::intel_cpu::get_streams_rank_table(test_data.stream_info_table, + test_data.rank_level, + test_num_sub_streams); + + ASSERT_EQ(test_data.stream_rank_table, test_stream_rank_table); + ASSERT_EQ(test_data.num_sub_streams, test_num_sub_streams); + } +}; + +StreamsRankTestCase _2sockets_mock_1 = { + {{1, ALL_PROC, 208, -1, -1}, + {-1, ALL_PROC, 104, 0, 0}, + {0, MAIN_CORE_PROC, 52, 0, 0}, + {0, HYPER_THREADING_PROC, 52, 0, 0}, + {-1, ALL_PROC, 104, 1, 1}, + {0, MAIN_CORE_PROC, 52, 1, 1}, + {0, HYPER_THREADING_PROC, 52, 1, 1}}, // param[in]: the expected result of streams_info_table in this simulation + 1, // param[in]: the number of rank level in this simulation + 2, // param[expected out]: the number of sub stream in this simulation + {{0}, {1}}, // param[expected out]: the expected result of streams_rank_table in thissimulation +}; +StreamsRankTestCase _2sockets_mock_2 = { + {{1, MAIN_CORE_PROC, 104, -1, -1}, {-1, MAIN_CORE_PROC, 52, 0, 0}, {-1, MAIN_CORE_PROC, 52, 1, 1}}, + 1, + 2, + {{0}, {1}}, +}; +StreamsRankTestCase _2sockets_mock_3 = { + {{1, ALL_PROC, 208, -1, -1}, + {-1, ALL_PROC, 104, -1, 0}, + {0, MAIN_CORE_PROC, 26, 0, 0}, + {0, MAIN_CORE_PROC, 26, 1, 0}, + {0, HYPER_THREADING_PROC, 26, 0, 0}, + {0, HYPER_THREADING_PROC, 26, 1, 0}, + {-1, ALL_PROC, 104, -1, 1}, + {0, MAIN_CORE_PROC, 26, 2, 1}, + {0, MAIN_CORE_PROC, 26, 3, 1}, + {0, HYPER_THREADING_PROC, 26, 2, 1}, + {0, HYPER_THREADING_PROC, 26, 3, 1}}, + 1, + 2, + {{0}, {1}}, +}; +StreamsRankTestCase _2sockets_mock_4 = { + {{1, MAIN_CORE_PROC, 104, -1, -1}, + {-1, MAIN_CORE_PROC, 26, 0, 0}, + {-1, MAIN_CORE_PROC, 26, 1, 0}, + {-1, MAIN_CORE_PROC, 26, 2, 1}, + {-1, MAIN_CORE_PROC, 26, 3, 1}}, + 1, + 4, + {{0}, {1}, {2}, {3}}, +}; +StreamsRankTestCase _2sockets_mock_5 = { + {{1, MAIN_CORE_PROC, 104, -1, -1}, + {-1, MAIN_CORE_PROC, 26, 0, 0}, + {-1, MAIN_CORE_PROC, 26, 1, 0}, + {-1, MAIN_CORE_PROC, 26, 2, 1}, + {-1, MAIN_CORE_PROC, 26, 3, 1}}, + 2, + 4, + {{0, 0}, {0, 1}, {1, 0}, {1, 1}}, +}; +StreamsRankTestCase _1sockets_mock_1 = { + {{1, MAIN_CORE_PROC, 16, 0, 0}, {-4, MAIN_CORE_PROC, 4, 0, 0}}, + 1, + 4, + {{0}, {1}, {2}, {3}}, +}; +StreamsRankTestCase _1sockets_mock_2 = { + {{1, MAIN_CORE_PROC, 16, 0, 0}, {-4, MAIN_CORE_PROC, 4, 0, 0}}, + 2, + 4, + {{0, 0}, {0, 1}, {1, 0}, {1, 1}}, +}; +StreamsRankTestCase _1sockets_mock_3 = { + {{1, MAIN_CORE_PROC, 32, 0, 0}, {-8, MAIN_CORE_PROC, 4, 0, 0}}, + 2, + 8, + {{0, 0}, {0, 1}, {0, 2}, {0, 3}, {1, 0}, {1, 1}, {1, 2}, {1, 3}}, +}; + +TEST_P(StreamsRankTests, StreamsRankTest) {} + +INSTANTIATE_TEST_SUITE_P(StreamsRankTable, + StreamsRankTests, + testing::Values(_2sockets_mock_1, + _2sockets_mock_2, + _2sockets_mock_3, + _2sockets_mock_4, + _2sockets_mock_5, + _1sockets_mock_1, + _1sockets_mock_2, + _1sockets_mock_3)); + +} // namespace From 2bfb63046103bb07a5d51fd59b5871ce806f8410 Mon Sep 17 00:00:00 2001 From: Xuejun Zhai Date: Tue, 20 Aug 2024 14:31:43 +0800 Subject: [PATCH 19/23] Don't allow to load library linking to another folder (#25958) ### Details: - *For the security purpose, don't allow to load plugin library linking to another folder* - *Both symbol link and hard link are checked* ### Tickets: - *CVS-142055* --------- Signed-off-by: Zhai, Xuejun --- src/common/util/CMakeLists.txt | 4 ++++ .../util/include/openvino/util/file_util.hpp | 21 +++++++++++++++++++ src/common/util/src/file_util.cpp | 11 ++++++++++ .../dev_api/openvino/core/so_extension.hpp | 3 +++ src/frontends/common/src/manager.cpp | 2 ++ src/inference/src/dev/core_impl.cpp | 3 ++- .../tests/functional/ov_core_test.cpp | 18 ++++++++++++++++ 7 files changed, 61 insertions(+), 1 deletion(-) diff --git a/src/common/util/CMakeLists.txt b/src/common/util/CMakeLists.txt index 6b5cf4b7a64192..efdae4b26837d8 100644 --- a/src/common/util/CMakeLists.txt +++ b/src/common/util/CMakeLists.txt @@ -41,6 +41,10 @@ target_link_libraries(${TARGET_NAME} PRIVATE ${CMAKE_DL_LIBS} PUBLIC openvino::p if (WIN32) target_link_libraries(${TARGET_NAME} PRIVATE Shlwapi) endif() + +if(LINUX) + target_link_libraries(${TARGET_NAME} PRIVATE stdc++fs) +endif() target_include_directories(${TARGET_NAME} PUBLIC $) ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME}) diff --git a/src/common/util/include/openvino/util/file_util.hpp b/src/common/util/include/openvino/util/file_util.hpp index 88b9110abeced8..7b28bda704a98b 100644 --- a/src/common/util/include/openvino/util/file_util.hpp +++ b/src/common/util/include/openvino/util/file_util.hpp @@ -12,6 +12,20 @@ #include "openvino/util/util.hpp" +#if !defined(__EMSCRIPTEN__) && !defined(__ANDROID__) +# if (__cplusplus >= 201703L) || (defined(_MSVC_LANG) && (_MSVC_LANG >= 201703L) && (_MSC_VER >= 1913)) +# if __has_include() +# include +namespace fs = std::filesystem; +# endif +# else +# define _SILENCE_EXPERIMENTAL_FILESYSTEM_DEPRECATION_WARNING +# define _LIBCPP_NO_EXPERIMENTAL_DEPRECATION_WARNING_FILESYSTEM +# include +namespace fs = std::experimental::filesystem; +# endif +#endif + namespace ov { namespace util { @@ -397,5 +411,12 @@ inline std::basic_string make_path(const std::basic_string& folder, const return folder + ov::util::FileTraits::file_separator + file; } +/** + * @brief If the library path is symlink & the symlink file location is not in the real file folder, return true. Others + * return false. + * @param path - Library file path to check + * @return Bool value + */ +bool is_symlink_in_different_path(std::string library_path); } // namespace util } // namespace ov diff --git a/src/common/util/src/file_util.cpp b/src/common/util/src/file_util.cpp index 2846355e9aeb5f..8fa9e5e06b1b97 100644 --- a/src/common/util/src/file_util.cpp +++ b/src/common/util/src/file_util.cpp @@ -704,3 +704,14 @@ const char* ov::util::trim_file_name(const char* const fname) { #endif return fname_trim_ptr; } + +bool ov::util::is_symlink_in_different_path(std::string library_path) { +#if !defined(__EMSCRIPTEN__) && !defined(__ANDROID__) + if (fs::is_symlink(library_path) && ov::util::get_absolute_file_path(ov::util::get_directory(library_path)) != + ov::util::get_directory(ov::util::get_absolute_file_path(library_path))) + return true; + return false; +#else + return false; +#endif +} diff --git a/src/core/dev_api/openvino/core/so_extension.hpp b/src/core/dev_api/openvino/core/so_extension.hpp index 931344b5fc4958..5db9879dc1cb88 100644 --- a/src/core/dev_api/openvino/core/so_extension.hpp +++ b/src/core/dev_api/openvino/core/so_extension.hpp @@ -3,6 +3,7 @@ // #pragma once +#include "openvino/core/except.hpp" #include "openvino/core/extension.hpp" #include "openvino/core/visibility.hpp" #include "openvino/util/file_util.hpp" @@ -40,6 +41,8 @@ inline std::string resolve_extension_path(const std::string& path) { } inline std::vector load_extensions(const std::string& path) { + OPENVINO_ASSERT(!ov::util::is_symlink_in_different_path(path), + "Cannot load extension with symlink path: \"" + path + "\"."); const std::string resolved_path = resolve_extension_path(path); auto so = ov::util::load_shared_object(resolved_path.c_str()); using CreateFunction = void(std::vector&); diff --git a/src/frontends/common/src/manager.cpp b/src/frontends/common/src/manager.cpp index 0a31f9a2c426e9..30bfc0a46c2e86 100644 --- a/src/frontends/common/src/manager.cpp +++ b/src/frontends/common/src/manager.cpp @@ -106,6 +106,8 @@ class FrontEndManager::Impl { } void register_front_end(const std::string& name, const std::string& library_path) { + OPENVINO_ASSERT(!ov::util::is_symlink_in_different_path(library_path), + "Cannot register plugin with symlink path: \"" + library_path + "\"."); auto lib_path = ov::util::from_file_path(ov::util::get_plugin_path(library_path)); PluginInfo plugin; plugin.m_file_path = lib_path; diff --git a/src/inference/src/dev/core_impl.cpp b/src/inference/src/dev/core_impl.cpp index 237c246ab38bdc..f8fcf26b006014 100644 --- a/src/inference/src/dev/core_impl.cpp +++ b/src/inference/src/dev/core_impl.cpp @@ -1189,7 +1189,8 @@ void ov::CoreImpl::register_plugin(const std::string& plugin, if (device_name.find('.') != std::string::npos) { OPENVINO_THROW("Device name must not contain dot '.' symbol"); } - + OPENVINO_ASSERT(!ov::util::is_symlink_in_different_path(plugin), + "Cannot registe plugin with symlink path: \"" + plugin + "\"."); PluginDescriptor desc{ov::util::get_plugin_path(plugin), properties}; register_plugin_in_registry_unsafe(device_name, desc); } diff --git a/src/inference/tests/functional/ov_core_test.cpp b/src/inference/tests/functional/ov_core_test.cpp index 26eb38e3fd13e5..acba5cc49123d3 100644 --- a/src/inference/tests/functional/ov_core_test.cpp +++ b/src/inference/tests/functional/ov_core_test.cpp @@ -95,4 +95,22 @@ TEST(CoreBaseTest, LoadOVFolderOverCWPathPluginXML) { remove_plugin_xml(ov_file_path); } +# if !defined(__EMSCRIPTEN__) && !defined(__ANDROID__) +TEST(CoreBaseTest, AddExtensionwithSymlinkInDiffPlace) { + std::string openvino_template_extension = + ov::util::make_plugin_library_name(ov::test::utils::getExecutableDirectory(), + std::string("openvino_template_extension") + OV_BUILD_POSTFIX); + + // Symlink file & the real file doesn't locale in the diff place. Will throw + fs::create_directory("test_link"); + std::string symlink_for_extension_copy_file = "test_link/symlink_for_extension_copy_file"; + + fs::create_symlink(openvino_template_extension, symlink_for_extension_copy_file); + ov::Core core; + EXPECT_NO_THROW(core.add_extension(openvino_template_extension)); + EXPECT_THROW(core.add_extension(symlink_for_extension_copy_file), std::runtime_error); + + ASSERT_TRUE(fs::remove_all("test_link")); +} +# endif #endif From 82bf2e5c814f91e0da9c372b16c8da467b48a0be Mon Sep 17 00:00:00 2001 From: "Min, Byungil" Date: Tue, 20 Aug 2024 17:21:23 +0900 Subject: [PATCH 20/23] [GPU][Debug] Support dynamic quantized for specified FC layer (#25914) + Added a new debug config OV_GPU_DynamicQuantizeLayers ### Details: - *item1* - *...* ### Tickets: - CVS-149215 --------- Signed-off-by: Min, Byung-il --- .../intel_gpu/runtime/debug_configuration.hpp | 4 +- .../fully_connected_kernel_bf_tiled.cpp | 15 +++++ .../src/runtime/debug_configuration.cpp | 56 +++++++++++++------ 3 files changed, 56 insertions(+), 19 deletions(-) diff --git a/src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp b/src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp index 589b5dd96cb055..348bfea1970a67 100644 --- a/src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp +++ b/src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp @@ -140,7 +140,8 @@ class debug_configuration { int disable_runtime_skip_reorder; // Disable runtime skip reorder int disable_primitive_fusing; // Disable primitive fusing int disable_fake_alignment; // Disable fake alignment - int enable_dynamic_quantize; // Enable Dynamic quantization for fully connected primitive + int enable_dynamic_quantize; // Enable Dynamic quantization for Fully-connected primitive + std::vector dynamic_quantize_layers_without_onednn; // Specify Fully-connected layers which enable Dynamic quantization int disable_horizontal_fc_fusion; // Disable fc horizontal fusion std::set dump_iteration; // Dump n-th execution of network. std::vector load_layers_raw_dump; // List of layers to load dumped raw binary and filenames @@ -151,6 +152,7 @@ class debug_configuration { bool is_layer_for_dumping(const std::string& layerName, bool is_output = false, bool is_input = false) const; bool is_target_iteration(int64_t iteration) const; std::string get_matched_from_filelist(const std::vector& file_names, std::string pattern) const; + bool is_layer_name_matched(const std::string& layer_name, const std::string& pattern) const; struct memory_preallocation_params { bool is_initialized = false; diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp index 1613afec063eb1..61919c10b816ff 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp +++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp @@ -52,9 +52,24 @@ static std::pair get_output_aligned_bf_size(const fully_connecte // DYNAMIC_QUANTIZE static bool should_dynamic_quantize(const fully_connected_params& params) { auto dynamic_quantization_group_size = params.dynamic_quantization_group_size; + GPU_DEBUG_GET_INSTANCE(debug_config); GPU_DEBUG_IF(debug_config->enable_dynamic_quantize) { dynamic_quantization_group_size = quantize_grp_size; + + GPU_DEBUG_IF(!debug_config->dynamic_quantize_layers_without_onednn.empty()) { + auto layers = debug_config->dynamic_quantize_layers_without_onednn; + auto iter = std::find_if(layers.begin(), layers.end(), [&](const std::string& pattern){ + return debug_config->is_layer_name_matched(params.layerID, pattern); + }); + + if (iter != layers.end()) { + dynamic_quantization_group_size = quantize_grp_size; + GPU_DEBUG_COUT << "Found specified Fully-connected layer [" << params.layerID << "]. Enable Dynamic-quantize." << std::endl; + } else { + dynamic_quantization_group_size = 0; + } + } } if (params.inputs[0].GetFirstElementOffset() != 0) diff --git a/src/plugins/intel_gpu/src/runtime/debug_configuration.cpp b/src/plugins/intel_gpu/src/runtime/debug_configuration.cpp index 9a34d77ae47f3e..f0a31f81c2e2bd 100644 --- a/src/plugins/intel_gpu/src/runtime/debug_configuration.cpp +++ b/src/plugins/intel_gpu/src/runtime/debug_configuration.cpp @@ -181,7 +181,9 @@ static void print_help_messages() { message_list.emplace_back("OV_GPU_DisableRuntimeSkipReorder", "Disable runtime skip reorder."); message_list.emplace_back("OV_GPU_DisablePrimitiveFusing", "Disable primitive fusing"); message_list.emplace_back("OV_GPU_DisableFakeAlignment", "Disable fake alignment"); - message_list.emplace_back("OV_GPU_EnableDynamicQuantize", "Enable Dynamic quantization for fully connected primitive"); + message_list.emplace_back("OV_GPU_EnableDynamicQuantize", "Enable Dynamic quantization for Fully connected primitive"); + message_list.emplace_back("OV_GPU_DynamicQuantizeLayersWithoutOnednn", "Enable Dynamic quantization for specified Fully connected layers only, " + "separated by space. Support case-insensitive and regular expression. For example .*fully_connected.*"); message_list.emplace_back("OV_GPU_DisableHorizontalFCFusion", "Disable horizontal fc fusion"); message_list.emplace_back("OV_GPU_DumpIteration", "Dump n-th execution of network, separated by space."); message_list.emplace_back("OV_GPU_MemPreallocationOptions", "Controls buffer pre-allocation feature. Expects 4 values separated by space in " @@ -308,6 +310,8 @@ debug_configuration::debug_configuration() get_gpu_debug_env_var("MemPreallocationOptions", mem_preallocation_params_str); std::string load_dump_raw_bin_str; get_gpu_debug_env_var("LoadDumpRawBinary", load_dump_raw_bin_str); + std::string dynamic_quantize_layers_without_onednn_str; + get_gpu_debug_env_var("DynamicQuantizeLayersWithoutOnednn", dynamic_quantize_layers_without_onednn_str); if (help > 0) { print_help_messages(); @@ -346,6 +350,16 @@ debug_configuration::debug_configuration() } } + if (dynamic_quantize_layers_without_onednn_str.length() > 0) { + // Insert delimiter for easier parsing when used + dynamic_quantize_layers_without_onednn_str = " " + dynamic_quantize_layers_without_onednn_str + " "; + std::stringstream ss(dynamic_quantize_layers_without_onednn_str); + std::string layer; + while (ss >> layer) { + dynamic_quantize_layers_without_onednn.push_back(layer); + } + } + if (forced_impl_types_str.length() > 0) { forced_impl_types_str = " " + forced_impl_types_str + " "; std::stringstream ss(forced_impl_types_str); @@ -502,6 +516,28 @@ std::string debug_configuration::get_name_for_dump(const std::string& file_name) return filename; } +bool debug_configuration::is_layer_name_matched(const std::string& layer_name, const std::string& pattern) const { +#ifdef GPU_DEBUG_CONFIG + auto upper_layer_name = std::string(layer_name.length(), '\0'); + std::transform(layer_name.begin(), layer_name.end(), upper_layer_name.begin(), ::toupper); + auto upper_pattern = std::string(pattern.length(), '\0'); + std::transform(pattern.begin(), pattern.end(), upper_pattern.begin(), ::toupper); + + // Check pattern from exec_graph + size_t pos = upper_layer_name.find(':'); + auto upper_exec_graph_name = upper_layer_name.substr(pos + 1, upper_layer_name.size()); + if (upper_exec_graph_name.compare(upper_pattern) == 0) { + return true; + } + + // Check pattern with regular expression + std::regex re(upper_pattern); + return std::regex_match(upper_layer_name, re); +#else + return false; +#endif +} + bool debug_configuration::is_layer_for_dumping(const std::string& layer_name, bool is_output, bool is_input) const { #ifdef GPU_DEBUG_CONFIG // Dump result layer @@ -518,24 +554,8 @@ bool debug_configuration::is_layer_for_dumping(const std::string& layer_name, bo if (is_input == true && type == "parameter" && dump_layers_input == 1) return true; - auto is_match = [](const std::string& layer_name, const std::string& pattern) -> bool { - auto upper_layer_name = std::string(layer_name.length(), '\0'); - std::transform(layer_name.begin(), layer_name.end(), upper_layer_name.begin(), ::toupper); - auto upper_pattern = std::string(pattern.length(), '\0'); - std::transform(pattern.begin(), pattern.end(), upper_pattern.begin(), ::toupper); - // Check pattern from exec_graph - size_t pos = upper_layer_name.find(':'); - auto upper_exec_graph_name = upper_layer_name.substr(pos + 1, upper_layer_name.size()); - if (upper_exec_graph_name.compare(upper_pattern) == 0) { - return true; - } - // Check pattern with regular expression - std::regex re(upper_pattern); - return std::regex_match(upper_layer_name, re); - }; - auto iter = std::find_if(dump_layers.begin(), dump_layers.end(), [&](const std::string& dl){ - return is_match(layer_name, dl); + return is_layer_name_matched(layer_name, dl); }); return (iter != dump_layers.end()); #else From 23b633d3d2d675a09ecc844aa507b3ed2484c706 Mon Sep 17 00:00:00 2001 From: Maxim Vafin Date: Tue, 20 Aug 2024 10:44:24 +0200 Subject: [PATCH 21/23] Retry torchvision tests on OSError (#26066) ### Details: - *Retry torchvision tests on OSError* ### Tickets: - *CVS-149743* --- tests/model_hub_tests/pytorch/test_torchvision_models.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/model_hub_tests/pytorch/test_torchvision_models.py b/tests/model_hub_tests/pytorch/test_torchvision_models.py index 31aeaedb2366d4..6204d57741eaf5 100644 --- a/tests/model_hub_tests/pytorch/test_torchvision_models.py +++ b/tests/model_hub_tests/pytorch/test_torchvision_models.py @@ -7,7 +7,7 @@ import pytest import torch import torchvision.transforms.functional as F -from models_hub_common.utils import get_models_list +from models_hub_common.utils import get_models_list, retry from torch_utils import TestTorchConvertModel @@ -54,6 +54,7 @@ def prepare_frames_for_raft(name, frames1, frames2): class TestTorchHubConvertModel(TestTorchConvertModel): + @retry(3, exceptions=(OSError,), delay=1) def load_model(self, model_name, model_link): m = torch.hub.load("pytorch/vision", model_name, weights='DEFAULT', skip_validation=True) From 595ce79dab15c3a645927197777b3e8bcb5f0fd3 Mon Sep 17 00:00:00 2001 From: Maxim Vafin Date: Tue, 20 Aug 2024 10:55:07 +0200 Subject: [PATCH 22/23] [PT FE] Code cleanup and coverity fixes (#25888) ### Details: - *Fix several coverity issues* - *Support inplace ops if regular op is present* - *Additional code improvements* ### Tickets: - *ticket-id* --- .../openvino/frontend/pytorch/ts_decoder.py | 152 ++++++++++-------- .../src/openvino/frontend/pytorch/utils.py | 22 +-- ...decompression_convert_constant_folding.cpp | 2 +- .../openvino/frontend/pytorch/frontend.hpp | 3 +- .../frontend/pytorch/node_context.hpp | 15 +- src/frontends/pytorch/src/frontend.cpp | 49 +++--- src/frontends/pytorch/src/input_model.cpp | 18 ++- src/frontends/pytorch/src/input_model.hpp | 1 + src/frontends/pytorch/src/node_context.cpp | 45 +++--- .../pytorch/src/op/adaptive_poolnd.cpp | 2 +- src/frontends/pytorch/src/op/add.cpp | 2 + src/frontends/pytorch/src/op/addmm.cpp | 4 +- src/frontends/pytorch/src/op/any.cpp | 4 +- .../pytorch/src/op/argmax_argmin.cpp | 5 +- src/frontends/pytorch/src/op/as_strided.cpp | 2 +- src/frontends/pytorch/src/op/batch_norm.cpp | 1 - src/frontends/pytorch/src/op/clamp.cpp | 2 +- src/frontends/pytorch/src/op/dim.cpp | 3 +- src/frontends/pytorch/src/op/div.cpp | 2 + src/frontends/pytorch/src/op/pad.cpp | 2 +- src/frontends/pytorch/src/op_table.cpp | 38 +---- src/frontends/pytorch/src/op_table.hpp | 4 +- .../append_list_unpack_replacer.cpp | 11 +- .../src/transforms/aten_getitem_replacer.cpp | 2 +- .../aten_stack_list_construct_replacer.cpp | 12 +- .../src/transforms/einsum_list_construct.cpp | 4 +- .../src/transforms/listconstruct_replacer.cpp | 62 +++---- .../min_max_prim_list_construct_replacer.cpp | 6 +- .../transforms/prim_list_construct_pad.cpp | 4 +- .../prim_list_tuple_construct_replacer.cpp | 2 +- .../transforms/prim_list_unpack_replacer.cpp | 3 +- .../transforms/string_equality_replacer.cpp | 10 +- .../torchfx_gptq_pattern_replacer.cpp | 76 ++++----- .../src/transforms/tuple_unpack_replacer.cpp | 4 +- .../src/transforms/u4_block_repack.cpp | 12 +- .../pytorch/src/translate_session.cpp | 78 +++++---- .../pytorch/src/translate_session.hpp | 6 +- src/frontends/pytorch/src/utils.cpp | 19 +-- src/frontends/pytorch/src/utils_quantize.cpp | 6 +- 39 files changed, 360 insertions(+), 335 deletions(-) diff --git a/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py b/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py index 1853842a8eea26..8e840d111a3b8d 100644 --- a/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py +++ b/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py @@ -7,7 +7,14 @@ from openvino.frontend.pytorch.py_pytorch_frontend import _FrontEndPytorchDecoder as Decoder from openvino.frontend.pytorch.py_pytorch_frontend import _Type as DecoderType from openvino.runtime import op, PartialShape, Type as OVType, OVAny -from openvino.frontend.pytorch.utils import ivalue_to_constant, get_value_from_getattr, pt_to_ov_type_map, prepare_example_inputs_and_model, convert_quantized_tensor, graph_has_ops +from openvino.frontend.pytorch.utils import ( + ivalue_to_constant, + get_value_from_getattr, + pt_to_ov_type_map, + prepare_example_inputs_and_model, + convert_quantized_tensor, + graph_has_ops, +) from openvino.runtime import opset11 as ops from openvino.frontend.pytorch import gptq from openvino.frontend.pytorch import patch_model @@ -15,20 +22,22 @@ import typing import torch +import inspect -class TorchScriptPythonDecoder (Decoder): +class TorchScriptPythonDecoder(Decoder): def __init__( - self, - pt_module, - graph_element=None, - example_input=None, - alias_db=None, - shared_memory=True, - skip_freeze=False, - constant_cache=None, - module_extensions=None): - Decoder.__init__(self) + self, + pt_module, + graph_element=None, + example_input=None, + alias_db=None, + shared_memory=True, + skip_freeze=False, + constant_cache=None, + module_extensions=None, + ): + super().__init__() # We store every decoder created by this decoder so that all them are not deleted until the first decoder is deleted self.m_decoders = [] self._input_signature = None @@ -37,9 +46,11 @@ def __init__( self.constant_cache = constant_cache if constant_cache is not None else dict() self.module_extensions = module_extensions self.config = None - if hasattr(pt_module, "config"): - self.config = pt_module.config.to_dict() if not isinstance(pt_module.config, dict) else pt_module.config + self.out_debug_name_overwrites = {} if graph_element is None: + if hasattr(pt_module, "config"): + self.config = pt_module.config.to_dict() if not isinstance( + pt_module.config, dict) else pt_module.config try: pt_module = self._get_scripted_model( pt_module, example_input, skip_freeze) @@ -56,7 +67,8 @@ def __init__( f"Couldn't get TorchScript module by {msg}. With exception:\n{e}\n{help_msg} " "You can also provide TorchScript module that you obtained" " yourself, please refer to PyTorch documentation: " - "https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html.") + "https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html." + ) self.graph_element = pt_module.inlined_graph self.alias_db = self.graph_element.alias_db() else: @@ -80,27 +92,27 @@ def __init__( self._transform_tensor_list_constants_to_listconstruct( self.graph_element) self._transform_optional_constants(self.graph_element) - self.out_debug_name_overwrites = {} @staticmethod def _get_preserved_attributes(model) -> list: preserved_attributes = [] for name, module in model.named_modules(): - if hasattr(module, "weight"): - if module.weight is not None and getattr(module.weight, "dtype", None) in [torch.int8, torch.uint8, torch.float16, torch.bfloat16]: - preserved_attributes.append(name) + compressed_types = [torch.int8, torch.uint8, + torch.float16, torch.bfloat16] + if hasattr(module, "weight") and getattr(module.weight, "dtype", None) in compressed_types: + preserved_attributes.append(name) return preserved_attributes def _get_scripted_model(self, pt_module, example_inputs=None, skip_freeze=False): - import torch - import inspect - freeze_by_default = False if isinstance(pt_module, torch.nn.Module): pt_module.eval() input_signature = None input_parameters = None - if isinstance(pt_module, torch.nn.Module) and not isinstance(pt_module, (torch.jit._trace.TopLevelTracedModule, torch.jit._script.RecursiveScriptModule)): + if isinstance(pt_module, torch.nn.Module) and not isinstance( + pt_module, (torch.jit._trace.TopLevelTracedModule, + torch.jit._script.RecursiveScriptModule) + ): # input params is dictionary contains input names and their signature values (type hints and default values if any) input_params = inspect.signature(pt_module.forward if hasattr( pt_module, "forward") else pt_module.__call__).parameters @@ -108,7 +120,8 @@ def _get_scripted_model(self, pt_module, example_inputs=None, skip_freeze=False) if example_inputs is None: if self.module_extensions: - raise RuntimeError("ModuleExtension is not supported for scripting. Please provide valid example_input argument to run tracing.") + raise RuntimeError( + "ModuleExtension is not supported for scripting. Please provide valid example_input argument to run tracing.") scripted = torch.jit.script(pt_module) freeze_by_default = True else: @@ -116,9 +129,10 @@ def _get_scripted_model(self, pt_module, example_inputs=None, skip_freeze=False) example_inputs, input_params, pt_module) # name of attribute in a patched module where the original forward method is kept - orig_forward_name = '_openvino_module_extension_patch_orig_forward' + orig_forward_name = "_openvino_module_extension_patch_orig_forward" if self.module_extensions: - patch_model.patch_model(pt_module, self.module_extensions, orig_forward_name) + patch_model.patch_model( + pt_module, self.module_extensions, orig_forward_name) gptq_patched = False if gptq.detect_gptq_model(pt_module): @@ -127,9 +141,9 @@ def _get_scripted_model(self, pt_module, example_inputs=None, skip_freeze=False) gptq_patched = True except Exception as error: print( - '[ WARNING ] Failed patching of AutoGPTQ model. Error message:\n', error) + "[ WARNING ] Failed patching of AutoGPTQ model. Error message:\n", error) print( - '[ WARNING ] Tracing of the model will likely be unsuccessful or incorrect') + "[ WARNING ] Tracing of the model will likely be unsuccessful or incorrect") gptq.unpatch_model(pt_module) gptq_patched = False @@ -142,10 +156,13 @@ def _get_scripted_model(self, pt_module, example_inputs=None, skip_freeze=False) if self.module_extensions: patch_model.unpatch_model(pt_module, orig_forward_name) - if not freeze_by_default and graph_has_ops(scripted.inlined_graph, ["prim::Uninitialized", "prim::unchecked_cast", "aten::append"]): + have_to_freeze_ops = ["prim::Uninitialized", + "prim::unchecked_cast", "aten::append"] + if not freeze_by_default and graph_has_ops(scripted.inlined_graph, have_to_freeze_ops): # freeze models with unsupported ops freeze_by_default = True - if freeze_by_default and graph_has_ops(scripted.inlined_graph, ["quantized", "aten::as_strided"]): + quantized_hint_ops = ["quantized", "aten::as_strided"] + if freeze_by_default and graph_has_ops(scripted.inlined_graph, quantized_hint_ops): # do not freeze quantized models and can't freeze for aten::as_strided it will result in incorrect inference freeze_by_default = False if freeze_by_default and not skip_freeze: @@ -256,12 +273,14 @@ def get_subgraph_size(self) -> int: def visit_subgraph(self, node_visitor) -> None: # make sure topological order is satisfied for node in self.graph_element.nodes(): - decoder = TorchScriptPythonDecoder(self.pt_module, - node, - alias_db=self.alias_db, - shared_memory=self._shared_memory, - constant_cache=self.constant_cache, - module_extensions=self.module_extensions) + decoder = TorchScriptPythonDecoder( + self.pt_module, + node, + alias_db=self.alias_db, + shared_memory=self._shared_memory, + constant_cache=self.constant_cache, + module_extensions=self.module_extensions, + ) self.m_decoders.append(decoder) node_visitor(decoder) @@ -281,31 +300,32 @@ def get_subgraphs(self) -> list: return list(self.graph_element.blocks()) def get_subgraph_decoder(self, index: int): - decoder = TorchScriptPythonDecoder(self.pt_module, - self.get_subgraphs()[index], - alias_db=self.alias_db, - shared_memory=self._shared_memory, - module_extensions=self.module_extensions) + decoder = TorchScriptPythonDecoder( + self.pt_module, self.get_subgraphs( + )[index], alias_db=self.alias_db, shared_memory=self._shared_memory, module_extensions=self.module_extensions + ) self.m_decoders.append(decoder) return decoder def get_op_type(self) -> str: assert isinstance( self.graph_element, torch.Node), "Function can be called only when self.graph_element is of type torch.Node" - if self.graph_element.kind() == "prim::PythonOp": - if hasattr(self.graph_element, 'pyobj') and callable(self.graph_element.pyobj) and hasattr(self.graph_element.pyobj(), '__self__'): - trampoline = self.graph_element.pyobj().__self__ - if hasattr(trampoline, 'target_extension') and isinstance(trampoline.target_extension, ModuleExtension): - target_op = trampoline.target_extension.target_op - if callable(target_op): - target = target_op(trampoline.original_module) - elif isinstance(target_op, str): - target = target_op - # TODO: Support target as a callable that will play a role of ConversionExtension for an entire module instead of a single op. - # Without supporting target as a callable here, ConversionExtension functionality is still possible to implement - # by combining two extensions: ModuleExtension that use temporary name as a target op and another extension of type ConversionExtension - # that translates that particular temporary name to custom graph. But providing conversion code as a callable `target` is more convenient. - return target + if self.graph_element.kind() == "prim::PythonOp" and callable(getattr(self.graph_element, "pyobj", None)): + pyobj = self.graph_element.pyobj() + trampoline = getattr(pyobj, "__self__", None) + target_extension = getattr(trampoline, "target_extension", None) + + if isinstance(target_extension, ModuleExtension): + target_op = target_extension.target_op + if callable(target_op): + target = target_op(trampoline.original_module) + elif isinstance(target_op, str): + target = target_op + # TODO: Support target as a callable that will play a role of ConversionExtension for an entire module instead of a single op. + # Without supporting target as a callable here, ConversionExtension functionality is still possible to implement + # by combining two extensions: ModuleExtension that use temporary name as a target op and another extension of type ConversionExtension + # that translates that particular temporary name to custom graph. But providing conversion code as a callable `target` is more convenient. + return target return self.graph_element.kind() def get_schema(self) -> str: @@ -345,8 +365,8 @@ def _add_name_to_const_and_cache(self, outputs, name): self.constant_cache[name] = outputs def try_decode_get_attr(self): - pt_value, name = get_value_from_getattr(self.graph_element, - self.pt_module) + pt_value, name = get_value_from_getattr( + self.graph_element, self.pt_module) assert pt_value is not None, "Couldn't retrieve value from prim::GetAttr" if isinstance(pt_value, torch.ScriptObject): # We assume this is __torch__.torch.classes.quantized.Conv2dPackedParamsBase or __torch__.torch.classes.quantized.LinearPackedParamsBase @@ -392,8 +412,8 @@ def try_decode_get_attr(self): if name in self.constant_cache: const = self.constant_cache[name] else: - const = ivalue_to_constant(pt_value, - shared_memory=self._shared_memory) + const = ivalue_to_constant( + pt_value, shared_memory=self._shared_memory) self._add_name_to_const_and_cache(const, name) return const else: @@ -407,12 +427,11 @@ def as_constant(self): pt_value = self._raw_output(0) pt_type = pt_value.type() if isinstance(pt_type, torch.TensorType): - return ivalue_to_constant(pt_value.toIValue(), - shared_memory=self._shared_memory) + return ivalue_to_constant(pt_value.toIValue(), shared_memory=self._shared_memory) if isinstance(pt_type, torch.ListType): return self._as_constant_list(pt_value) - const = ivalue_to_constant(pt_value.toIValue(), - shared_memory=self._shared_memory) + const = ivalue_to_constant( + pt_value.toIValue(), shared_memory=self._shared_memory) if len(const) > 0: # set name corresponding to state_dict name const[0].get_node().set_friendly_name( @@ -466,8 +485,8 @@ def input_is_none(self, index: int) -> bool: else: in_node = r_input.node() if in_node.kind() == "prim::GetAttr": - pt_value, _ = get_value_from_getattr(in_node, - self.pt_module) + pt_value, _ = get_value_from_getattr( + in_node, self.pt_module) return pt_value is None return False @@ -496,7 +515,8 @@ def get_named_input(self, name): def get_rt_info(self): rt_info = {} if self.config is not None and "quantization_config" in self.config and "sym" in self.config["quantization_config"]: - rt_info["symmetric_quantization"] = OVAny(self.config["quantization_config"]["sym"]) + rt_info["symmetric_quantization"] = OVAny( + self.config["quantization_config"]["sym"]) return rt_info @staticmethod diff --git a/src/bindings/python/src/openvino/frontend/pytorch/utils.py b/src/bindings/python/src/openvino/frontend/pytorch/utils.py index 4b94503adb5595..06a58eb80ffed1 100644 --- a/src/bindings/python/src/openvino/frontend/pytorch/utils.py +++ b/src/bindings/python/src/openvino/frontend/pytorch/utils.py @@ -25,7 +25,7 @@ def fetch_attr(self_module, target: str): Return: Any: The value of the attribute. """ - target_atoms = target.split('.') + target_atoms = target.split(".") attr_itr = self_module for i, atom in enumerate(target_atoms): if not hasattr(attr_itr, atom): @@ -91,12 +91,13 @@ def get_value_from_getattr(getattr_node, self_module): node = stack.pop() attr_name = node.s("name") assert hasattr( - module, attr_name), f"No attribute with name \"{attr_name}\" found in module." + module, attr_name), f'No attribute with name "{attr_name}" found in module.' path_name = ".".join([path_name, attr_name]) module = getattr(module, attr_name) return module, path_name -def graph_has_ops(graph, op_types:list) -> bool: + +def graph_has_ops(graph, op_types: list) -> bool: res = False for n in graph.nodes(): if any(kind in n.kind() for kind in op_types): @@ -106,7 +107,7 @@ def graph_has_ops(graph, op_types:list) -> bool: if res: return res return res - + pt_to_ov_type_map = { "float": OVType.f32, @@ -134,7 +135,7 @@ def graph_has_ops(graph, op_types:list) -> bool: "torch.BoolTensor": OVType.boolean, "torch.quint8": OVType.u8, "torch.qint8": OVType.i8, - "torch.qint32": OVType.i32 + "torch.qint32": OVType.i32, } @@ -159,7 +160,7 @@ def process_dict_inputs(inputs, input_params, model): ordered_inputs.append(input_name) input_signature = list(input_params) - if ordered_inputs == input_signature[:len(ordered_inputs)]: + if ordered_inputs == input_signature[: len(ordered_inputs)]: example_inputs = [inputs[input_name] for input_name in ordered_inputs] if all([isinstance(inp, torch.Tensor) for inp in example_inputs]): return {"example_inputs": [inputs[name] for name in ordered_inputs]}, ordered_inputs, model @@ -191,8 +192,8 @@ def process_dict_inputs(inputs, input_params, model): str(input_params[input_name]).replace("NoneType", "None")) input_params_str.append(f"{input_name}={input_name}") - wrapper_class = wrapper_template.format(input_sign=', '.join( - input_sign_str), example_input=', '.join(input_params_str)) + wrapper_class = wrapper_template.format(input_sign=", ".join( + input_sign_str), example_input=", ".join(input_params_str)) result = {} try: exec(wrapper_class, result) @@ -210,7 +211,8 @@ def prepare_example_inputs_and_model(inputs, input_params, model): input_is_list = False input_signature = list(input_params) if isinstance(inputs, dict): - examples, ordered, wrapped = process_dict_inputs(inputs, input_params, model) + examples, ordered, wrapped = process_dict_inputs( + inputs, input_params, model) return examples, ordered, wrapped, input_is_list if isinstance(inputs, list) and len(inputs) == 1 and isinstance(inputs[0], torch.Tensor): if "typing.List" in str(input_params[input_signature[0]].annotation): @@ -219,7 +221,7 @@ def prepare_example_inputs_and_model(inputs, input_params, model): if isinstance(inputs, torch.Tensor): inputs = [inputs] - input_signature = input_signature[:len(inputs)] + input_signature = input_signature[: len(inputs)] return {"example_inputs": inputs}, input_signature, model, input_is_list diff --git a/src/common/transformations/src/transformations/fp16_compression/mark_decompression_convert_constant_folding.cpp b/src/common/transformations/src/transformations/fp16_compression/mark_decompression_convert_constant_folding.cpp index 136ee34877c2dd..7724692be61662 100644 --- a/src/common/transformations/src/transformations/fp16_compression/mark_decompression_convert_constant_folding.cpp +++ b/src/common/transformations/src/transformations/fp16_compression/mark_decompression_convert_constant_folding.cpp @@ -141,7 +141,7 @@ pass::MarkCompressedFloatConstants::MarkCompressedFloatConstants() { mark_as_decompression(convert_node); disable_constant_folding(const_node); disable_constant_folding(convert_node); - return true; + return false; }; auto m = std::make_shared(convert, matcher_name); diff --git a/src/frontends/pytorch/include/openvino/frontend/pytorch/frontend.hpp b/src/frontends/pytorch/include/openvino/frontend/pytorch/frontend.hpp index 2f9ac5432ff975..3b0da6f0a18ebf 100644 --- a/src/frontends/pytorch/include/openvino/frontend/pytorch/frontend.hpp +++ b/src/frontends/pytorch/include/openvino/frontend/pytorch/frontend.hpp @@ -61,7 +61,8 @@ class PYTORCH_API FrontEnd : public ov::frontend::FrontEnd { protected: bool supported_impl(const std::vector& variants) const override; ov::frontend::InputModel::Ptr load_impl(const std::vector& variants) const override; - std::map get_supported_ops(const ov::frontend::InputModel::Ptr& model) const; + std::unordered_map get_supported_ops( + const ov::frontend::InputModel::Ptr& model) const; std::map m_op_extension_translators; std::vector m_conversion_extensions; diff --git a/src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp b/src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp index b1f9153835e2ff..110beedb8b2020 100644 --- a/src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp +++ b/src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp @@ -18,11 +18,11 @@ typedef std::unordered_map> TensorMap; class NodeContext : public frontend::NodeContext { public: - NodeContext(std::shared_ptr decoder, + NodeContext(const std::shared_ptr& decoder, const TensorMap& ext_tensor_map, - std::shared_ptr tensor_map, - std::shared_ptr external_parameters, - std::shared_ptr> mutated_tensors, + const std::shared_ptr& tensor_map, + const std::shared_ptr& external_parameters, + const std::shared_ptr>& mutated_tensors, TranslateSession* translate_session) : frontend::NodeContext(decoder->get_op_type()), m_decoder(decoder), @@ -32,11 +32,12 @@ class NodeContext : public frontend::NodeContext { m_mutated_tensors(mutated_tensors), m_translate_session(translate_session), m_decoder_inputs(decoder->inputs()), - m_decoder_outputs(decoder->outputs()) { + m_decoder_outputs(decoder->outputs()), + m_inputs_is_none(m_decoder_inputs.size(), false) { FRONT_END_GENERAL_CHECK(m_tensor_map != nullptr && m_external_parameters != nullptr && m_mutated_tensors != nullptr && m_translate_session != nullptr); for (size_t i = 0; i < m_decoder_inputs.size(); i++) { - m_inputs_is_none.push_back(decoder->input_is_none(i)); + m_inputs_is_none[i] = decoder->input_is_none(i); } } @@ -140,7 +141,7 @@ class NodeContext : public frontend::NodeContext { return m_translate_session; } - void add_tensor_to_context(size_t index, Output ov_output) const; + void add_tensor_to_context(size_t index, const Output& ov_output) const; Output get_tensor_from_model(size_t index) const { if (m_tensor_map->find(index) != m_tensor_map->end()) { diff --git a/src/frontends/pytorch/src/frontend.cpp b/src/frontends/pytorch/src/frontend.cpp index 2940e385cd4e44..e0b068c4329ee2 100644 --- a/src/frontends/pytorch/src/frontend.cpp +++ b/src/frontends/pytorch/src/frontend.cpp @@ -57,26 +57,28 @@ namespace pytorch { namespace { std::map get_unconverted_types_from_model(const std::shared_ptr& model) { std::map unconverted_ops_types; - for (const auto& node : model->get_ordered_ops()) { + ov::traverse_nodes(model, [&](const std::shared_ptr& node) { if (const auto& fw_node = ov::as_type_ptr(node)) { const auto& attrs = fw_node->get_attrs(); - FRONT_END_GENERAL_CHECK(attrs.find(PtFrameworkNode::op_type_key) != attrs.end(), + auto op_type_it = attrs.find(PtFrameworkNode::op_type_key); + FRONT_END_GENERAL_CHECK(op_type_it != attrs.end(), "FrameworkNode attributes do not contain operation type."); std::string exception_msg; - if (attrs.find(PtFrameworkNode::failed_conversion_key) != attrs.end()) { - exception_msg = attrs.at(PtFrameworkNode::failed_conversion_key); + auto exception_it = attrs.find(PtFrameworkNode::failed_conversion_key); + if (exception_it != attrs.end()) { + exception_msg = exception_it->second; } - if (!unconverted_ops_types.count(attrs.at(PtFrameworkNode::op_type_key))) { - unconverted_ops_types[attrs.at(PtFrameworkNode::op_type_key)] = exception_msg; + if (!unconverted_ops_types.count(op_type_it->second)) { + unconverted_ops_types.emplace(op_type_it->second, std::move(exception_msg)); } } if (const auto& fw_node = ov::as_type_ptr(node)) { - for (size_t i = 0; i < fw_node->get_internal_subgraphs_size(); i++) { + for (size_t i = 0; i < fw_node->get_internal_subgraphs_size(); ++i) { const auto& internal_types = get_unconverted_types_from_model(fw_node->get_function(i)); unconverted_ops_types.insert(internal_types.begin(), internal_types.end()); } } - } + }); return unconverted_ops_types; } @@ -142,7 +144,7 @@ FrontEnd::FrontEnd() {} std::shared_ptr FrontEnd::convert(const ov::frontend::InputModel::Ptr& model) const { auto pt_model = std::dynamic_pointer_cast(model); FRONT_END_GENERAL_CHECK(pt_model, "Invalid input model"); - std::map supported_ops = get_supported_ops(model); + const auto& supported_ops = get_supported_ops(model); std::shared_ptr converted_model; { pt_model->flush_places(); @@ -223,7 +225,7 @@ void FrontEnd::convert(const std::shared_ptr& partiallyConverted) const { std::shared_ptr FrontEnd::convert_partially(const ov::frontend::InputModel::Ptr& model) const { auto pt_model = std::dynamic_pointer_cast(model); FRONT_END_GENERAL_CHECK(pt_model, "Invalid input model"); - std::map supported_ops = get_supported_ops(model); + const auto& supported_ops = get_supported_ops(model); std::shared_ptr partial_model; { pt_model->flush_places(); @@ -247,11 +249,18 @@ std::shared_ptr FrontEnd::decode(const InputModel::Ptr& model) const { void FrontEnd::normalize(const std::shared_ptr& model) const { ov::pass::Manager manager("Frontend:Pytorch:normalize"); - // GPTQ transformations need to be executed before other passes - // Once the GPTQ patterns are modified by other transformations, - // they cannot be captured anymore - manager.register_pass(); - manager.register_pass(); + bool is_fx = false; + if (model->has_rt_info("decoder_type_name")) { + is_fx = model->get_rt_info()["decoder_type_name"].as() == "fx"; + } + // These transformations are only applicable to fx + if (is_fx) { + // GPTQ transformations need to be executed before other passes + // Once the GPTQ patterns are modified by other transformations, + // they cannot be captured anymore + manager.register_pass(); + manager.register_pass(); + } // the following 2 transformations are needed for keypoint detectron2 models to work. // AtenIndexToSelect will be called twice @@ -374,14 +383,16 @@ ov::frontend::InputModel::Ptr FrontEnd::load_impl(const std::vector& va return std::make_shared(tdecoder); } -std::map FrontEnd::get_supported_ops(const ov::frontend::InputModel::Ptr& model) const { - std::map supported_ops; +std::unordered_map FrontEnd::get_supported_ops( + const ov::frontend::InputModel::Ptr& model) const { + std::unordered_map supported_ops; if (std::dynamic_pointer_cast(model)->decoder_type_name() == "fx") supported_ops = get_supported_ops_fx(); else supported_ops = get_supported_ops_ts(); - for (auto i = m_op_extension_translators.begin(); i != m_op_extension_translators.end(); i++) - supported_ops[i->first] = i->second; + for (const auto& ext : m_op_extension_translators) { + supported_ops[ext.first] = ext.second; + } return supported_ops; } diff --git a/src/frontends/pytorch/src/input_model.cpp b/src/frontends/pytorch/src/input_model.cpp index 72140881c89698..52a90d6d1cc7d5 100644 --- a/src/frontends/pytorch/src/input_model.cpp +++ b/src/frontends/pytorch/src/input_model.cpp @@ -11,7 +11,9 @@ namespace ov { namespace frontend { namespace pytorch { -InputModel::InputModel(const std::shared_ptr& model_decoder) : m_model_decoder(model_decoder) { +InputModel::InputModel(const std::shared_ptr& model_decoder) + : m_model_decoder(model_decoder), + m_decoder_type_name(model_decoder->decoder_type_name()) { const auto& inputs = m_model_decoder->inputs(); for (size_t i = 0; i < inputs.size(); ++i) { auto in_place = std::make_shared(*this, inputs[i]); @@ -166,7 +168,7 @@ void InputModel::override_all_inputs(const std::vector& inputs) { }); FRONT_END_GENERAL_CHECK(all_inputs, "Only initial inputs are supported by override_all_inputs."); // We need to add back "self" input if it was in initial inputs - if (m_inputs.size() > 0 && m_inputs[0]) { + if (!m_inputs.empty() && m_inputs[0]) { // We need to remove "self" input to not confuse external users const auto& names = m_inputs[0]->get_names(); if (std::any_of(names.cbegin(), names.cend(), [](const std::string& n) { @@ -180,12 +182,14 @@ void InputModel::override_all_inputs(const std::vector& inputs) { inputs.size()); auto self_place = m_inputs[0]; // Verify that no same place already in vector - auto no_self = std::all_of(inputs.cbegin(), inputs.cend(), [&](const Place::Ptr& p) { - return !p->is_equal(self_place); + auto no_self = std::none_of(inputs.cbegin(), inputs.cend(), [&](const Place::Ptr& p) { + return p->is_equal(self_place); }); FRONT_END_GENERAL_CHECK(no_self, "Unexpected input of 'self' was provided to override_all_inputs."); - m_inputs = std::vector{self_place}; - m_inputs.insert(m_inputs.cend(), inputs.cbegin(), inputs.cend()); + m_inputs.clear(); + m_inputs.reserve(inputs.size() + 1); + m_inputs.push_back(std::move(self_place)); + m_inputs.insert(m_inputs.end(), inputs.cbegin(), inputs.cend()); return; } } @@ -199,7 +203,7 @@ void InputModel::override_all_inputs(const std::vector& inputs) { } const std::string& InputModel::decoder_type_name() const { - return m_model_decoder->decoder_type_name(); + return m_decoder_type_name; } std::shared_ptr InputModel::get_decoder() const { diff --git a/src/frontends/pytorch/src/input_model.hpp b/src/frontends/pytorch/src/input_model.hpp index b647da29465660..bfadc970e6a531 100644 --- a/src/frontends/pytorch/src/input_model.hpp +++ b/src/frontends/pytorch/src/input_model.hpp @@ -52,6 +52,7 @@ class InputModel : public ov::frontend::InputModel { std::vector> m_outputs; std::vector> m_requested_places; std::unordered_map m_descriptors; + const std::string m_decoder_type_name; }; } // namespace pytorch diff --git a/src/frontends/pytorch/src/node_context.cpp b/src/frontends/pytorch/src/node_context.cpp index 5684212f1097f5..efc3bb107eb7f0 100644 --- a/src/frontends/pytorch/src/node_context.cpp +++ b/src/frontends/pytorch/src/node_context.cpp @@ -55,31 +55,23 @@ std::shared_ptr NodeContext::mark_node(std::shared_ptr ov_node) cons void NodeContext::mutate_input(size_t index, Output ov_output) const { FRONT_END_GENERAL_CHECK(!input_is_none(index), "Input is none with index: ", index); auto input_id = m_decoder_inputs.at(index); - FRONT_END_GENERAL_CHECK(m_tensor_map->count(input_id), "No tensor corresponding input: ", input_id, " exist."); + auto tensor_it = m_tensor_map->find(input_id); + FRONT_END_GENERAL_CHECK(tensor_it != m_tensor_map->end(), "No tensor corresponding input: ", input_id, " exist."); m_translate_session->encode_tensor_name(ov_output, input_id, {m_decoder->get_input_debug_name(index)}); - (*m_tensor_map)[input_id] = ov_output; + tensor_it->second = ov_output; m_mutated_tensors->insert(input_id); // Resolve aliases - auto back_input_id = input_id; - auto back_node_input = ov_output; + auto& back_input_id = input_id; + auto& back_node_input = ov_output; while (m_translate_session->m_may_be_alias.count(back_input_id)) { - // Create node to reverseprop data. While loop is needed for the cases when alias to tensor point to another + // Create node to aliased data. While loop is needed for the cases when alias to tensor point to another // alias to tensor. In that case we need to create a chain of reverseprop ops - size_t in_tensor; - std::shared_ptr node; - Output node_converted_output; - std::tie(in_tensor, node, node_converted_output) = m_translate_session->m_may_be_alias.at(back_input_id); + auto& alias_info = m_translate_session->m_may_be_alias.at(back_input_id); + size_t in_tensor = std::get<0>(alias_info); + auto& node = std::get<1>(alias_info); + auto& node_converted_output = std::get<2>(alias_info); auto reverseprop_node = m_translate_session->get_reverseprop_op(node, node_converted_output, back_node_input); - if (m_tensor_map->count(in_tensor)) { - // Tensor is not found in the scope of this body, need to get it from internal context and mark mutated - OPENVINO_DEBUG("Couldn't find in the current body the initial aliased tensor: ", - in_tensor, - " for operation: ", - node->get_op_type(), - " creating new body input."); - get_tensor_from_model_or_create_input(in_tensor); - } m_translate_session->encode_tensor_name(reverseprop_node, in_tensor); (*m_tensor_map)[in_tensor] = reverseprop_node; m_mutated_tensors->insert(in_tensor); @@ -89,17 +81,20 @@ void NodeContext::mutate_input(size_t index, Output ov_output) const { } } -void NodeContext::add_tensor_to_context(size_t index, Output ov_output) const { +void NodeContext::add_tensor_to_context(size_t index, const Output& ov_output) const { +#ifdef ENABLE_OPENVINO_DEBUG if (m_tensor_map->count(index)) { OPENVINO_DEBUG("[ WARNING ] Current context has tensor ", index, ". Assuming mutated output.\n"); } +#endif m_translate_session->encode_tensor_name(ov_output, index); (*m_tensor_map)[index] = ov_output; } Output NodeContext::get_tensor_from_model_or_create_input(size_t index) const { - if (m_tensor_map->find(index) != m_tensor_map->end()) { - return m_tensor_map->at(index); + auto tensor_it = m_tensor_map->find(index); + if (tensor_it != m_tensor_map->end()) { + return tensor_it->second; } else { // nested subgraphs case auto parameter = std::make_shared(element::dynamic, PartialShape::dynamic()); @@ -131,7 +126,7 @@ std::shared_ptr NodeContext::convert_subgraph(size_t index) const { // Extend external context with internal tensors except Parameter nodes, because internal Parameters are created to // link internal context with external TensorMap ext_map(m_ext_tensor_map); - // map::insert does not update elements if their key is already in map; so if we have real tensors in outter scope + // map::insert does not update elements if their key is already in map; so if we have real tensors in outer scope // we will not add Parameters we created in inner scope. ext_map.insert(m_tensor_map->begin(), m_tensor_map->end()); @@ -159,10 +154,12 @@ OutputVector NodeContext::inputs() const { auto inlined_input = m_decoder->inlined_input(i); FRONT_END_GENERAL_CHECK(inlined_input.size() == 1, "Incorrect inlined input with index:", i); res.push_back(inlined_input[0]); + continue; } } - FRONT_END_GENERAL_CHECK(m_tensor_map->count(input), "No tensor corresponding input: ", input, " exist."); - res.push_back(m_tensor_map->at(input)); + auto tensor_it = m_tensor_map->find(input); + FRONT_END_GENERAL_CHECK(tensor_it != m_tensor_map->end(), "No tensor corresponding input: ", input, " exist."); + res.push_back(tensor_it->second); } return res; } diff --git a/src/frontends/pytorch/src/op/adaptive_poolnd.cpp b/src/frontends/pytorch/src/op/adaptive_poolnd.cpp index 1288f1402e778f..7d52386d0edbab 100644 --- a/src/frontends/pytorch/src/op/adaptive_poolnd.cpp +++ b/src/frontends/pytorch/src/op/adaptive_poolnd.cpp @@ -102,7 +102,7 @@ OutputVector translate_adaptive_max_pool_base(const NodeContext& context, pooled_indices = context.mark_node(std::make_shared(pooled_indices, output_shape, false)); // aten::adaptive_max_pool{n}d always returns tuple with 2 tensors: pooled tensor and indicies // output selecting only first or preserve both made outside of operation by return_indices flag - return {pooled_tensor, pooled_indices}; + return {std::move(pooled_tensor), std::move(pooled_indices)}; }; } // namespace diff --git a/src/frontends/pytorch/src/op/add.cpp b/src/frontends/pytorch/src/op/add.cpp index eada7871b8739a..dd72222a9ec943 100644 --- a/src/frontends/pytorch/src/op/add.cpp +++ b/src/frontends/pytorch/src/op/add.cpp @@ -18,6 +18,7 @@ namespace op { using namespace ov::op; +namespace { OutputVector translate_add_common(const NodeContext& context, bool inplace) { num_inputs_check(context, 2, 3); Output lhs; @@ -67,6 +68,7 @@ OutputVector translate_add_common(const NodeContext& context, bool inplace) { context.mutate_input(0, add); return {add}; }; +} // namespace OutputVector translate_add(const NodeContext& context) { return translate_add_common(context, false); diff --git a/src/frontends/pytorch/src/op/addmm.cpp b/src/frontends/pytorch/src/op/addmm.cpp index 522c5237ecd11b..4ecfd403afc6dd 100644 --- a/src/frontends/pytorch/src/op/addmm.cpp +++ b/src/frontends/pytorch/src/op/addmm.cpp @@ -46,7 +46,7 @@ OutputVector translate_addmm(const NodeContext& context) { if (!context.input_is_none(4)) { alpha = context.get_input(4); } - return {translate_addmm_common(context, beta, alpha)}; + return {translate_addmm_common(context, std::move(beta), std::move(alpha))}; }; OutputVector translate_addmm_fx(const NodeContext& context) { @@ -60,7 +60,7 @@ OutputVector translate_addmm_fx(const NodeContext& context) { if (context.has_attribute("alpha")) { alpha = context.get_input("alpha"); } - return {translate_addmm_common(context, beta, alpha)}; + return {translate_addmm_common(context, std::move(beta), std::move(alpha))}; }; OutputVector translate_conv1d_ext(const NodeContext& context) { diff --git a/src/frontends/pytorch/src/op/any.cpp b/src/frontends/pytorch/src/op/any.cpp index fb36c4268cf939..a17b8777e5f916 100644 --- a/src/frontends/pytorch/src/op/any.cpp +++ b/src/frontends/pytorch/src/op/any.cpp @@ -55,7 +55,7 @@ OutputVector translate_any(const NodeContext& context) { out_id = 3; } - auto result = translate_any_common(context, input_tensor, axes, keep_dims); + const auto& result = translate_any_common(context, input_tensor, axes, keep_dims); if (!context.input_is_none(out_id)) { context.mutate_input(out_id, result); } @@ -75,7 +75,7 @@ OutputVector translate_any_fx(const NodeContext& context) { bool keep_dims = false; if (!context.input_is_none(2)) keep_dims = context.const_input(2); - auto any = translate_any_common(context, x, dims, keep_dims); + const auto& any = translate_any_common(context, x, dims, keep_dims); return {any}; }; diff --git a/src/frontends/pytorch/src/op/argmax_argmin.cpp b/src/frontends/pytorch/src/op/argmax_argmin.cpp index e3b0b9d20680c2..9d6360609f0a57 100644 --- a/src/frontends/pytorch/src/op/argmax_argmin.cpp +++ b/src/frontends/pytorch/src/op/argmax_argmin.cpp @@ -46,8 +46,7 @@ OutputVector create_argmax_argmin_op(const NodeContext& context, TopKMode mode) std::make_shared(flatten_input, k, axis, mode, TopKSortType::SORT_VALUES, element::i64, true)); indices = topk->output(1); if (keep_dims) { - auto input_shape = context.mark_node(std::make_shared(input, element::i32)); - auto input_rank = context.mark_node(std::make_shared(input_shape, element::i32)); + auto input_rank = std::get<1>(get_shape_rank(context, input)); auto new_shape = context.mark_node(std::make_shared(k, input_rank)); indices = context.mark_node(std::make_shared(indices, new_shape, BroadcastType::BIDIRECTIONAL)); @@ -56,7 +55,7 @@ OutputVector create_argmax_argmin_op(const NodeContext& context, TopKMode mode) indices = context.mark_node(std::make_shared(indices, zero)); } } - return {indices}; + return {std::move(indices)}; } }; // namespace diff --git a/src/frontends/pytorch/src/op/as_strided.cpp b/src/frontends/pytorch/src/op/as_strided.cpp index 01bad08af60b44..20efb3ba5cb684 100644 --- a/src/frontends/pytorch/src/op/as_strided.cpp +++ b/src/frontends/pytorch/src/op/as_strided.cpp @@ -33,7 +33,7 @@ OutputVector translate_as_strided(const NodeContext& context) { auto const_neg_1 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1})); auto input_strides = decoder->get_input_strides(0); PYTORCH_OP_CONVERSION_CHECK(input_strides.size() != 0, - "aten::as_strided: Couldn't retrive input stride information from torchscript."); + "aten::as_strided: Couldn't retrieve input stride information from torchscript."); std::vector idxs(input_strides.size()); iota(idxs.begin(), idxs.end(), 0); diff --git a/src/frontends/pytorch/src/op/batch_norm.cpp b/src/frontends/pytorch/src/op/batch_norm.cpp index 7f31d0894e4af6..03c6f8a75d2ae5 100644 --- a/src/frontends/pytorch/src/op/batch_norm.cpp +++ b/src/frontends/pytorch/src/op/batch_norm.cpp @@ -63,7 +63,6 @@ OutputVector make_batch_norm(const NodeContext& context, } else { b = context.mark_node(std::make_shared(b, input)); } - auto zero = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0})); auto zero_1d = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0})); auto one = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1})); auto two = context.mark_node(v0::Constant::create(element::i32, Shape{}, {2})); diff --git a/src/frontends/pytorch/src/op/clamp.cpp b/src/frontends/pytorch/src/op/clamp.cpp index 67002dd61049c3..691a2d95008fa2 100644 --- a/src/frontends/pytorch/src/op/clamp.cpp +++ b/src/frontends/pytorch/src/op/clamp.cpp @@ -28,7 +28,7 @@ OutputVector translate_clamp(const NodeContext& context) { max_clip = context.mark_node(std::make_shared(max_clip, x)); x = context.mark_node(std::make_shared(x, max_clip)); } - return {x}; + return {std::move(x)}; }; } // namespace op diff --git a/src/frontends/pytorch/src/op/dim.cpp b/src/frontends/pytorch/src/op/dim.cpp index 443a171733ea5c..1d15e0b9e4d7a2 100644 --- a/src/frontends/pytorch/src/op/dim.cpp +++ b/src/frontends/pytorch/src/op/dim.cpp @@ -14,8 +14,7 @@ using namespace ov::op; OutputVector translate_dim(const NodeContext& context) { num_inputs_check(context, 1, 1); - Output rank; - std::tie(std::ignore, rank) = get_shape_rank(context, context.get_input(0), true); + Output rank = std::get<1>(get_shape_rank(context, context.get_input(0), true)); return {rank}; }; diff --git a/src/frontends/pytorch/src/op/div.cpp b/src/frontends/pytorch/src/op/div.cpp index 7c091f2c2cb8da..55ef9508dec5fc 100644 --- a/src/frontends/pytorch/src/op/div.cpp +++ b/src/frontends/pytorch/src/op/div.cpp @@ -17,6 +17,7 @@ namespace frontend { namespace pytorch { namespace op { +namespace { OutputVector translate_div_common(const NodeContext& context, const Output& lhs, const Output& rhs, @@ -56,6 +57,7 @@ OutputVector translate_div_common(const NodeContext& context, context.mutate_input(0, res); return {res}; }; +} // namespace OutputVector translate_div(const NodeContext& context) { num_inputs_check(context, 2, 3); diff --git a/src/frontends/pytorch/src/op/pad.cpp b/src/frontends/pytorch/src/op/pad.cpp index 2a8365de8a6d0d..c8f35a0afab71a 100644 --- a/src/frontends/pytorch/src/op/pad.cpp +++ b/src/frontends/pytorch/src/op/pad.cpp @@ -85,7 +85,7 @@ OutputVector translate_pad_common(const NodeContext& context, return {cur}; } auto pad_value_ = context.mark_node(std::make_shared(pad_value, data)); - const std::map pt_to_ov_pad{ + static const std::map pt_to_ov_pad{ {"constant", PadMode::CONSTANT}, {"reflect", PadMode::REFLECT}, {"replicate", PadMode::EDGE}, diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp index 83ff923efcd89e..b3c606f3f05c98 100644 --- a/src/frontends/pytorch/src/op_table.cpp +++ b/src/frontends/pytorch/src/op_table.cpp @@ -318,7 +318,7 @@ OP_CONVERTER(translate_zeros_like_fx); } // namespace op // Supported ops for TorchScript -const std::map get_supported_ops_ts() { +const std::unordered_map get_supported_ops_ts() { return { {"aten::__and__", op::translate_bitwise_and}, {"aten::__derive_index", op::translate_derive_index}, @@ -398,17 +398,12 @@ const std::map get_supported_ops_ts() { {"aten::ceil", op::optional_out, 1>}, {"aten::ceil_", op::inplace_op>}, {"aten::celu", op::translate_celu}, - {"aten::celu_", op::inplace_op}, {"aten::channel_shuffle", op::translate_channel_shuffle}, // aten::chunk - Supported in limited set of patterns {"aten::clamp", op::translate_clamp}, - {"aten::clamp_", op::inplace_op}, {"aten::clamp_max", op::translate_1to1_match_2_inputs_align_types}, - {"aten::clamp_max_", op::inplace_op>}, {"aten::clamp_min", op::translate_1to1_match_2_inputs_align_types}, - {"aten::clamp_min_", op::inplace_op>}, {"aten::clip", op::translate_clamp}, - {"aten::clip_", op::inplace_op}, {"aten::clone", op::skip_node}, // ignore clone operators that are inserted by PyTorch autograd {"aten::col2im", op::translate_col2im}, // aten::complex - Supported in limited set of patterns @@ -440,22 +435,18 @@ const std::map get_supported_ops_ts() { {"aten::dropout_", op::skip_node}, // aten::einsum - Supported in limited set of patterns {"aten::elu", op::translate_elu}, - {"aten::elu_", op::inplace_op}, {"aten::embedding", op::translate_embedding}, {"aten::embedding_bag", op::translate_embedding_bag}, {"aten::empty", op::translate_empty}, {"aten::empty_like", op::translate_empty_like}, {"aten::eq", op::translate_1to1_match_2_inputs_align_types}, {"aten::erf", op::translate_erf}, - {"aten::erf_", op::inplace_op}, {"aten::erfc", op::translate_erfc}, - {"aten::erfc_", op::inplace_op}, {"aten::exp", op::optional_out, 1>}, {"aten::exp_", op::inplace_op>}, {"aten::expand", op::translate_expand}, {"aten::expand_as", op::translate_expand_as}, {"aten::expm1", op::translate_expm1}, - {"aten::expm1_", op::inplace_op}, {"aten::eye", op::translate_eye}, {"aten::fake_quantize_per_channel_affine", op::translate_fake_quantize_per_channel_affine}, {"aten::fake_quantize_per_tensor_affine", op::translate_fake_quantize_per_tensor_affine}, @@ -463,14 +454,12 @@ const std::map get_supported_ops_ts() { // aten::fft_irfftn - Supported in limited set of patterns // aten::fft_rfftn - Supported in limited set of patterns {"aten::fill", op::translate_fill}, - {"aten::fill_", op::inplace_op}, - {"aten::fill_diagonal_", op::inplace_op}, + {"aten::fill_diagonal", op::translate_fill_diagonal}, {"aten::flatten", op::quantizable_op}, {"aten::flip", op::translate_flip}, {"aten::floor", op::optional_out, 1>}, {"aten::floor_", op::inplace_op>}, {"aten::floor_divide", op::translate_floor_divide}, - {"aten::floor_divide_", op::inplace_op}, {"aten::floordiv", op::translate_floor_divide}, {"aten::fmod", op::translate_fmod}, {"aten::frobenius_norm", op::translate_frobenius_norm}, @@ -487,19 +476,14 @@ const std::map get_supported_ops_ts() { {"aten::gt", op::translate_1to1_match_2_inputs_align_types}, {"aten::hann_window", op::translate_hann_window}, {"aten::hardsigmoid", op::quantizable_op>}, - {"aten::hardsigmoid_", - op::quantizable_op>>}, {"aten::hardswish", op::quantizable_op>}, - {"aten::hardswish_", op::quantizable_op>>}, {"aten::hardtanh", op::quantizable_op}, - {"aten::hardtanh_", op::inplace_op>}, {"aten::im2col", op::translate_im2col}, // aten::imag - Supported in limited set of patterns // aten::index - Supported in limited set of patterns {"aten::index_copy_", op::inplace_op}, {"aten::index_put_", op::inplace_op}, {"aten::index_add", op::translate_index_add}, - {"aten::index_add_", op::inplace_op}, {"aten::index_select", op::translate_index_select}, {"aten::instance_norm", op::translate_instance_norm}, {"aten::inverse", op::translate_inverse}, @@ -514,7 +498,6 @@ const std::map get_supported_ops_ts() { {"aten::layer_norm", op::translate_layer_norm}, {"aten::le", op::translate_1to1_match_2_inputs_align_types}, {"aten::leaky_relu", op::translate_1to1_match_2_inputs}, - {"aten::leaky_relu_", op::inplace_op>}, {"aten::len", op::translate_len}, // lift op is torchscript specific op responsible for tensors coping with guarantee of new memory allocation {"aten::lift", op::skip_node}, @@ -544,9 +527,7 @@ const std::map get_supported_ops_ts() { {"aten::lstm", op::translate_lstm}, {"aten::lt", op::translate_1to1_match_2_inputs_align_types}, {"aten::masked_fill", op::translate_masked_fill}, - {"aten::masked_fill_", op::inplace_op}, {"aten::masked_scatter", op::translate_masked_scatter}, - {"aten::masked_scatter_", op::inplace_op}, {"aten::matmul", op::translate_1to1_match_2_inputs}, {"aten::max", op::translate_max}, {"aten::mv", op::translate_1to1_match_2_inputs}, @@ -562,7 +543,6 @@ const std::map get_supported_ops_ts() { {"aten::min", op::translate_min}, {"aten::minimum", op::translate_minimum}, {"aten::mish", op::translate_1to1_match_1_inputs_with_fp32_type_alignment}, - {"aten::mish_", op::inplace_op>}, {"aten::mm", op::translate_1to1_match_2_inputs}, {"aten::movedim", op::translate_movedim}, {"aten::mul", op::translate_mul}, @@ -611,7 +591,6 @@ const std::map get_supported_ops_ts() { {"aten::relu", op::optional_out, 1>}, {"aten::relu_", op::inplace_op>}, {"aten::relu6", op::translate_relu6}, - {"aten::relu6_", op::inplace_op}, {"aten::remainder", op::translate_remainder}, {"aten::repeat", op::translate_1to1_match_2_inputs}, {"aten::repeat_interleave", op::translate_repeat_interleave}, @@ -632,20 +611,15 @@ const std::map get_supported_ops_ts() { {"aten::ScalarImplicit", op::skip_node}, {"aten::scaled_dot_product_attention", op::translate_scaled_dot_product_attention}, {"aten::scatter", op::translate_scatter}, - {"aten::scatter_", op::inplace_op}, {"aten::scatter_add", op::translate_scatter_add}, - {"aten::scatter_add_", op::inplace_op}, {"aten::scatter_reduce", op::translate_scatter_reduce}, - {"aten::scatter_reduce_", op::inplace_op}, {"aten::select", op::quantizable_op}, {"aten::selu", op::translate_selu}, - {"aten::selu_", op::inplace_op}, {"aten::sigmoid", op::optional_out, 1>}, {"aten::sigmoid_", op::inplace_op>}, {"aten::sign", op::translate_sign}, {"aten::silu", op::translate_1to1_match_1_inputs}, - {"aten::silu_", op::inplace_op>}, {"aten::sin", op::optional_out, 1>}, {"aten::sin_", op::inplace_op>}, {"aten::sinh", op::optional_out, 1>}, @@ -669,7 +643,6 @@ const std::map get_supported_ops_ts() { {"aten::sum", op::translate_sum}, {"aten::swapaxes", op::quantizable_op}, {"aten::t", op::translate_t}, - {"aten::t_", op::inplace_op}, {"aten::take_along_dim", op::translate_take_along_dim}, {"aten::tan", op::optional_out, 1>}, {"aten::tan_", op::inplace_op>}, @@ -682,16 +655,13 @@ const std::map get_supported_ops_ts() { {"aten::topk", op::translate_topk}, {"aten::transpose", op::quantizable_op}, {"aten::tril", op::translate_tril}, - {"aten::tril_", op::inplace_op}, {"aten::triu", op::translate_triu}, - {"aten::triu_", op::inplace_op}, {"aten::type_as", op::translate_1to1_match_2_inputs}, // TODO: overflow semantics is different // aten::unbind - Supported in limited set of patterns {"aten::unflatten", op::translate_unflatten}, {"aten::unfold", op::translate_unfold}, {"aten::unsqueeze", op::quantizable_op>}, - {"aten::unsqueeze_", op::quantizable_op>>}, {"aten::upsample_bicubic2d", op::translate_upsample_bicubic2d}, {"aten::upsample_bilinear2d", op::translate_upsample_bilinear2d}, {"aten::upsample_linear1d", op::translate_upsample_linear1d}, @@ -704,7 +674,7 @@ const std::map get_supported_ops_ts() { {"aten::view", op::quantizable_op}, {"aten::view_as", op::translate_reshape_as}, {"aten::where", op::translate_where}, - {"aten::zero_", op::inplace_op}, + {"aten::zero", op::translate_zeros_like}, {"aten::zeros", op::translate_zeros}, {"aten::zeros_like", op::translate_zeros_like}, {"ov_ext::embedding", op::translate_embedding_ext}, @@ -742,7 +712,7 @@ const std::map get_supported_ops_ts() { }; }; -const std::map get_supported_ops_fx() { +const std::unordered_map get_supported_ops_fx() { return { {"", op::translate_add}, {"", op::translate_floor_divide}, diff --git a/src/frontends/pytorch/src/op_table.hpp b/src/frontends/pytorch/src/op_table.hpp index f7f1a51b571cb7..721146a426a12d 100644 --- a/src/frontends/pytorch/src/op_table.hpp +++ b/src/frontends/pytorch/src/op_table.hpp @@ -10,8 +10,8 @@ namespace ov { namespace frontend { namespace pytorch { -const std::map get_supported_ops_ts(); -const std::map get_supported_ops_fx(); +const std::unordered_map get_supported_ops_ts(); +const std::unordered_map get_supported_ops_fx(); } // namespace pytorch } // namespace frontend diff --git a/src/frontends/pytorch/src/transforms/append_list_unpack_replacer.cpp b/src/frontends/pytorch/src/transforms/append_list_unpack_replacer.cpp index fc5689639ec353..6263e769639e6b 100644 --- a/src/frontends/pytorch/src/transforms/append_list_unpack_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/append_list_unpack_replacer.cpp @@ -44,20 +44,23 @@ AppendListUnpackReplacer::AppendListUnpackReplacer() { while (auto append_node = cast_fw_node(input_node, "aten::append")) { rt_copy_from.push_back(append_node); - tmp_inputs.push_back(append_node->input(1).get_source_output()); + tmp_inputs.emplace_back(append_node->input(1).get_source_output()); input_node = append_node->input(0).get_source_output().get_node_shared_ptr(); } OutputVector inputs; - auto list_construct_node = cast_fw_node(input_node, "prim::ListConstruct"); + auto list_construct_node = cast_fw_node(std::move(input_node), "prim::ListConstruct"); if (!list_construct_node) { return false; } + inputs.reserve(list_construct_node->inputs().size() + tmp_inputs.size()); rt_copy_from.push_back(list_construct_node); for (auto& input : list_construct_node->inputs()) { inputs.push_back(input.get_source_output()); } - inputs.insert(inputs.end(), tmp_inputs.rbegin(), tmp_inputs.rend()); + inputs.insert(inputs.end(), + std::make_move_iterator(tmp_inputs.rbegin()), + std::make_move_iterator(tmp_inputs.rend())); if (getitem_node) { // If aten::__getitem__, expect inputs to be equivalent of pytorch Tensor[][]. // Tensor selected by aten::__getitem__ index needs to be splitted in axis 0. @@ -81,7 +84,7 @@ AppendListUnpackReplacer::AppendListUnpackReplacer() { to_copy_rt.push_back(squeeze); res.push_back(squeeze); } - copy_runtime_info_and_name(list_unpack, to_copy_rt, rt_copy_from); + copy_runtime_info_and_name(list_unpack, std::move(to_copy_rt), rt_copy_from); replace_node(list_unpack, res); return true; } else { diff --git a/src/frontends/pytorch/src/transforms/aten_getitem_replacer.cpp b/src/frontends/pytorch/src/transforms/aten_getitem_replacer.cpp index 4249363a8ef801..5d371e0d65f725 100644 --- a/src/frontends/pytorch/src/transforms/aten_getitem_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/aten_getitem_replacer.cpp @@ -47,7 +47,7 @@ AtenGetItemReplacer::AtenGetItemReplacer() { return false; ov::pass::NodeRegistry rg; - auto input_node = getitem->input_value(0).get_node_shared_ptr(); + const auto& input_node = getitem->input_value(0).get_node_shared_ptr(); if (auto torch_split = cast_fw_node(input_node, "aten::split")) { auto rank = torch_split->input(1).get_partial_shape().rank(); if (rank.is_dynamic()) { diff --git a/src/frontends/pytorch/src/transforms/aten_stack_list_construct_replacer.cpp b/src/frontends/pytorch/src/transforms/aten_stack_list_construct_replacer.cpp index b58c42b17912c1..aad973c3769246 100644 --- a/src/frontends/pytorch/src/transforms/aten_stack_list_construct_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/aten_stack_list_construct_replacer.cpp @@ -23,19 +23,19 @@ using namespace ov::op; using namespace ov::pass::pattern; AtenStackListConstructReplacer::AtenStackListConstructReplacer() { - auto list_construct = wrap_type(); - auto axis = wrap_type(); + const auto& list_construct = wrap_type(); + const auto& axis = wrap_type(); // We search for a pattern: ListConstruct -> aten::stack <- Constant - auto stack = wrap_type({list_construct, axis}); + const auto& stack = wrap_type({list_construct, axis}); - ov::matcher_pass_callback callback = [=](Matcher& m) { + ov::matcher_pass_callback callback = [list_construct, axis](Matcher& m) { auto stack = cast_fw_node(m.get_match_root(), "aten::stack"); if (!stack) { return false; } const auto& pattern_map = m.get_pattern_value_map(); - auto input_node = pattern_map.at(list_construct).get_node_shared_ptr(); + const auto& input_node = pattern_map.at(list_construct).get_node_shared_ptr(); auto axis_node = pattern_map.at(axis).get_node_shared_ptr(); auto axis_const = std::dynamic_pointer_cast(axis_node); auto axis = axis_const->cast_vector(); @@ -47,7 +47,7 @@ AtenStackListConstructReplacer::AtenStackListConstructReplacer() { if (auto list_construct_node = cast_fw_node(input_node, "prim::ListConstruct")) { const auto& list_inputs = list_construct_node->input_values(); std::shared_ptr node; - if (auto compression = u4_compression_stack(list_inputs, axis[0])) { + if (const auto& compression = u4_compression_stack(list_inputs, axis[0])) { node = compression; } else { OutputVector node_vector; diff --git a/src/frontends/pytorch/src/transforms/einsum_list_construct.cpp b/src/frontends/pytorch/src/transforms/einsum_list_construct.cpp index d0873e8479e671..a8f43d8f52cd50 100644 --- a/src/frontends/pytorch/src/transforms/einsum_list_construct.cpp +++ b/src/frontends/pytorch/src/transforms/einsum_list_construct.cpp @@ -28,8 +28,8 @@ AtenEinsumListConstructReplacer::AtenEinsumListConstructReplacer() { if (!einsum_op) { return false; } - auto equation_input = einsum_op->input_value(0).get_node_shared_ptr(); - auto tensor_list = einsum_op->input_value(1).get_node_shared_ptr(); + const auto& equation_input = einsum_op->input_value(0).get_node_shared_ptr(); + const auto& tensor_list = einsum_op->input_value(1).get_node_shared_ptr(); std::string equation; if (const auto& fw_node_mode = cast_fw_node(equation_input, "prim::Constant")) { const auto& attrs = fw_node_mode->get_attrs(); diff --git a/src/frontends/pytorch/src/transforms/listconstruct_replacer.cpp b/src/frontends/pytorch/src/transforms/listconstruct_replacer.cpp index c7db874634ba58..caa8d3cc18a3e6 100644 --- a/src/frontends/pytorch/src/transforms/listconstruct_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/listconstruct_replacer.cpp @@ -38,48 +38,48 @@ using namespace ov::op; ListConstructReplacer::ListConstructReplacer() { // Transformation for torch operators for cases where prim::ListConstruct can be replaced with Concat. - auto list = pattern::wrap_type(); + const auto& list = pattern::wrap_type(); // Both aten::view and aten::reshape are using same translation returning Reshape operator. - auto reshape_op = pattern::wrap_type({pattern::any_input(), list}); - auto roll_op = pattern::wrap_type({pattern::any_input(), list, pattern::any_input()}); - auto broadcast_op = pattern::wrap_type({pattern::any_input(), list}); - auto adapool_op = pattern::wrap_type({pattern::any_input(), list}); + const auto& reshape_op = pattern::wrap_type({pattern::any_input(), list}); + const auto& roll_op = pattern::wrap_type({pattern::any_input(), list, pattern::any_input()}); + const auto& broadcast_op = pattern::wrap_type({pattern::any_input(), list}); + const auto& adapool_op = pattern::wrap_type({pattern::any_input(), list}); // replace list construct for aten::expand(tensor, prim::ListConstruct(shapes)) old decomposition - // shape_of + broadcast + equal + select - auto shape_of_op = pattern::wrap_type({list}); - auto equal_op = pattern::wrap_type({list, pattern::any_input()}); - auto select_op = pattern::wrap_type({pattern::any_input(), pattern::any_input(), list}); + // shape_of + broadcast + equal + select + const auto& shape_of_op = pattern::wrap_type({list}); + const auto& equal_op = pattern::wrap_type({list, pattern::any_input()}); + const auto& select_op = pattern::wrap_type({pattern::any_input(), pattern::any_input(), list}); // replace list construct for aten::expand(tensor, prim::ListConstruct(shapes)) new decomposition - auto abs_op = pattern::wrap_type({list}); - auto expand_op = pattern::wrap_type({pattern::any_input(), abs_op}); + const auto& abs_op = pattern::wrap_type({list}); + const auto& expand_op = pattern::wrap_type({pattern::any_input(), abs_op}); // replace list construct for aten::repeat(tensor, prim::ListConstruct(shapes))) // shape_of + broadcast + tile - auto tile_op = pattern::wrap_type({pattern::any_input(), list}); + const auto& tile_op = pattern::wrap_type({pattern::any_input(), list}); // replace aten::permute(tensor, prim::ListConstruct) - auto transpose_op = pattern::wrap_type({pattern::any_input(), list}); + const auto& transpose_op = pattern::wrap_type({pattern::any_input(), list}); // aten::split_with_sizes case - auto vsplit_op = pattern::wrap_type({pattern::any_input(), pattern::any_input(), list}); + const auto& vsplit_op = pattern::wrap_type({pattern::any_input(), pattern::any_input(), list}); // aten::upsample... case inside the body when body was removed - auto interpolate_convert_op = pattern::wrap_type({list}); - auto interpolate_mul_op = pattern::wrap_type({interpolate_convert_op, pattern::any_input()}); - auto interpolate_op = + const auto& interpolate_convert_op = pattern::wrap_type({list}); + const auto& interpolate_mul_op = pattern::wrap_type({interpolate_convert_op, pattern::any_input()}); + const auto& interpolate_op = pattern::wrap_type({pattern::any_input(), interpolate_mul_op, pattern::any_input()}); // aten::randint case - auto rand_op = pattern::wrap_type({list, pattern::any_input(), pattern::any_input()}); - auto lc_pattern = std::make_shared(OutputVector{reshape_op, - roll_op, - broadcast_op, - adapool_op, - shape_of_op, - equal_op, - select_op, - expand_op, - tile_op, - transpose_op, - vsplit_op, - interpolate_op, - rand_op}); + const auto& rand_op = pattern::wrap_type({list, pattern::any_input(), pattern::any_input()}); + const auto& lc_pattern = std::make_shared(OutputVector{reshape_op, + roll_op, + broadcast_op, + adapool_op, + shape_of_op, + equal_op, + select_op, + expand_op, + tile_op, + transpose_op, + vsplit_op, + interpolate_op, + rand_op}); ov::matcher_pass_callback callback = [=](pattern::Matcher& m) { auto& pattern_map = m.get_pattern_value_map(); diff --git a/src/frontends/pytorch/src/transforms/min_max_prim_list_construct_replacer.cpp b/src/frontends/pytorch/src/transforms/min_max_prim_list_construct_replacer.cpp index f2e092d316c875..8c056067b6b038 100644 --- a/src/frontends/pytorch/src/transforms/min_max_prim_list_construct_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/min_max_prim_list_construct_replacer.cpp @@ -26,12 +26,12 @@ namespace pass { using namespace ov::op; MinMaxPrimListConstructReplacer::MinMaxPrimListConstructReplacer() { - auto op = ov::pass::pattern::wrap_type(); + const auto& op = ov::pass::pattern::wrap_type(); ov::matcher_pass_callback callback = [](ov::pass::pattern::Matcher& m) { bool is_min = false; - auto max_op = cast_fw_node(m.get_match_root(), "prim::max"); - auto min_op = cast_fw_node(m.get_match_root(), "prim::min"); + const auto& max_op = cast_fw_node(m.get_match_root(), "prim::max"); + const auto& min_op = cast_fw_node(m.get_match_root(), "prim::min"); std::shared_ptr op; if (!max_op && !min_op) { return false; diff --git a/src/frontends/pytorch/src/transforms/prim_list_construct_pad.cpp b/src/frontends/pytorch/src/transforms/prim_list_construct_pad.cpp index a8483cd882903f..480d37d01681c8 100644 --- a/src/frontends/pytorch/src/transforms/prim_list_construct_pad.cpp +++ b/src/frontends/pytorch/src/transforms/prim_list_construct_pad.cpp @@ -59,7 +59,7 @@ const std::unordered_map PAD_MODES = {{"constant", PadMode PrimListConstructPadReplacer::PrimListConstructPadReplacer() { // transformation for case aten::pad + prim::ListConstruct as paddings - auto pad_op = ov::pass::pattern::wrap_type(); + const auto& pad_op = ov::pass::pattern::wrap_type(); ov::matcher_pass_callback callback = [](ov::pass::pattern::Matcher& m) { Output input_node; Output padding; @@ -70,7 +70,7 @@ PrimListConstructPadReplacer::PrimListConstructPadReplacer() { mode = "constant"; input_node = pad_op->input_value(0); padding = pad_op->input_value(1); - auto mode_node = pad_op->input_value(2).get_node_shared_ptr(); + const auto& mode_node = pad_op->input_value(2).get_node_shared_ptr(); if (const auto& fw_node_mode = cast_fw_node(mode_node, "prim::Constant")) { const auto& attrs = fw_node_mode->get_attrs(); if (attrs.find("string_value") != attrs.end()) { diff --git a/src/frontends/pytorch/src/transforms/prim_list_tuple_construct_replacer.cpp b/src/frontends/pytorch/src/transforms/prim_list_tuple_construct_replacer.cpp index 44f771887285e0..0c3f75360cb7f6 100644 --- a/src/frontends/pytorch/src/transforms/prim_list_tuple_construct_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/prim_list_tuple_construct_replacer.cpp @@ -52,7 +52,7 @@ bool DecomposeListTupleResults::run_on_model(const std::shared_ptr& model } auto names = out.get_names(); out.set_names({}); - for (auto name : names) { + for (auto& name : names) { if (!is_index(name)) { // Set first found non-index name as output name. If such name exist it will be debug name out.set_names({name}); diff --git a/src/frontends/pytorch/src/transforms/prim_list_unpack_replacer.cpp b/src/frontends/pytorch/src/transforms/prim_list_unpack_replacer.cpp index cec60b6a1a2e49..27d1245d475c56 100644 --- a/src/frontends/pytorch/src/transforms/prim_list_unpack_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/prim_list_unpack_replacer.cpp @@ -156,7 +156,8 @@ PrimListUnpackReplacer::PrimListUnpackReplacer() { auto range = rg.make(const_0_scalar, list_num_outs_scalar, const_1_scalar, element::i32); auto range_plus_1 = rg.make(range, const_1); - auto sections = rg.make(OutputVector{const_0, indices_or_sections, const_max}, 0); + auto sections = + rg.make(OutputVector{const_0, std::move(indices_or_sections), const_max}, 0); auto starts_tensor = rg.make(sections, const_0, const_neg_1, const_1, const_0); auto starts = diff --git a/src/frontends/pytorch/src/transforms/string_equality_replacer.cpp b/src/frontends/pytorch/src/transforms/string_equality_replacer.cpp index 028de354665fec..60a12b802f2a5f 100644 --- a/src/frontends/pytorch/src/transforms/string_equality_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/string_equality_replacer.cpp @@ -24,12 +24,12 @@ using namespace ov::pass; using namespace ov::op; StringEqualityReplacer::StringEqualityReplacer() { - auto framework_node_lhs = pattern::wrap_type(); - auto framework_node_rhs = pattern::wrap_type(); - auto equal_op = pattern::wrap_type({framework_node_lhs, framework_node_rhs}); - auto not_equal_op = pattern::wrap_type({framework_node_lhs, framework_node_rhs}); + const auto& framework_node_lhs = pattern::wrap_type(); + const auto& framework_node_rhs = pattern::wrap_type(); + const auto& equal_op = pattern::wrap_type({framework_node_lhs, framework_node_rhs}); + const auto& not_equal_op = pattern::wrap_type({framework_node_lhs, framework_node_rhs}); - auto string_equality_pattern = std::make_shared(OutputVector{equal_op, not_equal_op}); + const auto& string_equality_pattern = std::make_shared(OutputVector{equal_op, not_equal_op}); ov::matcher_pass_callback callback = [=](pattern::Matcher& m) { auto& pattern_map = m.get_pattern_value_map(); diff --git a/src/frontends/pytorch/src/transforms/torchfx_gptq_pattern_replacer.cpp b/src/frontends/pytorch/src/transforms/torchfx_gptq_pattern_replacer.cpp index 95682cd784f67c..3108765b21e651 100644 --- a/src/frontends/pytorch/src/transforms/torchfx_gptq_pattern_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/torchfx_gptq_pattern_replacer.cpp @@ -52,28 +52,28 @@ void write_u4_data(void* array, size_t index, uint32_t data) { }; GPTQDecompressionReplacer::GPTQDecompressionReplacer() { - auto const_1 = wrap_type(); - auto const_2 = wrap_type(); - auto unsqueeze_1 = wrap_type({const_1, const_2}); - auto const_abs = wrap_type(); - auto abs = wrap_type({const_abs}); - auto broadcast_3 = wrap_type({unsqueeze_1, abs}); - auto const_6 = wrap_type(); - auto const_7 = wrap_type(); - auto unsqueeze_2 = wrap_type({const_6, const_7}); - auto bitwise_right_shift = wrap_type({broadcast_3, unsqueeze_2}); - auto convert_1 = wrap_type({bitwise_right_shift}); - auto const_8 = wrap_type(); - auto convert_2 = wrap_type({const_8}); - auto bitwise_and = wrap_type({convert_1, convert_2}); - - ov::matcher_pass_callback callback = [=](Matcher& m) { + const auto& const_1 = wrap_type(); + const auto& const_2 = wrap_type(); + const auto& unsqueeze_1 = wrap_type({const_1, const_2}); + const auto& const_abs = wrap_type(); + const auto& abs = wrap_type({const_abs}); + const auto& broadcast_3 = wrap_type({unsqueeze_1, abs}); + const auto& const_6 = wrap_type(); + const auto& const_7 = wrap_type(); + const auto& unsqueeze_2 = wrap_type({const_6, const_7}); + const auto& bitwise_right_shift = wrap_type({broadcast_3, unsqueeze_2}); + const auto& convert_1 = wrap_type({bitwise_right_shift}); + const auto& const_8 = wrap_type(); + const auto& convert_2 = wrap_type({const_8}); + const auto& bitwise_and = wrap_type({convert_1, convert_2}); + + ov::matcher_pass_callback callback = [unsqueeze_1](Matcher& m) { auto bitwise_and = m.get_match_root(); if (!bitwise_and) { return false; } const auto& pattern_map = m.get_pattern_value_map(); - auto input_node = pattern_map.at(unsqueeze_1).get_node_shared_ptr(); + const auto& input_node = pattern_map.at(unsqueeze_1).get_node_shared_ptr(); auto weights_u32 = std::dynamic_pointer_cast(input_node->get_input_node_shared_ptr(0)); auto axis = std::dynamic_pointer_cast(input_node->get_input_node_shared_ptr(1)); auto axis_data = axis->get_data_ptr(); @@ -118,7 +118,7 @@ GPTQDecompressionReplacer::GPTQDecompressionReplacer() { } } - copy_runtime_info_and_name(weights_u32, {new_const}, {weights_u32, bitwise_and, bitwise_right_shift}); + copy_runtime_info_and_name(weights_u32, {new_const}, {weights_u32, bitwise_and}); auto new_convert = std::make_shared(new_const, bitwise_and->get_output_element_type(0)); copy_runtime_info_and_name(bitwise_and, {new_convert}, {input_node}); @@ -131,22 +131,22 @@ GPTQDecompressionReplacer::GPTQDecompressionReplacer() { }; GPTQMultPatternReplacer::GPTQMultPatternReplacer() { - auto const_1_1 = wrap_type(); - auto convert_1 = wrap_type({const_1_1}); - auto const_1_2 = wrap_type(); - auto convert_2 = wrap_type({const_1_2}); - auto add = wrap_type({convert_1, convert_2}); - auto const_2 = wrap_type(); - auto reshape_1 = wrap_type({add, const_2}); - auto const_3_1 = wrap_type(); - auto convert_4 = wrap_type({const_3_1}); - auto const_3_2 = wrap_type(); - auto reshape_2 = wrap_type({convert_4, const_3_2}); - auto subtract = wrap_type({reshape_2, reshape_1}); - auto convert_3 = wrap_type({subtract}); - auto const_4_1 = wrap_type(); - auto const_4_2 = wrap_type(); - auto reshape_3 = wrap_type({const_4_1, const_4_2}); + const auto& const_1_1 = wrap_type(); + const auto& convert_1 = wrap_type({const_1_1}); + const auto& const_1_2 = wrap_type(); + const auto& convert_2 = wrap_type({const_1_2}); + const auto& add = wrap_type({convert_1, convert_2}); + const auto& const_2 = wrap_type(); + const auto& reshape_1 = wrap_type({add, const_2}); + const auto& const_3_1 = wrap_type(); + const auto& convert_4 = wrap_type({const_3_1}); + const auto& const_3_2 = wrap_type(); + const auto& reshape_2 = wrap_type({convert_4, const_3_2}); + const auto& subtract = wrap_type({reshape_2, reshape_1}); + const auto& convert_3 = wrap_type({subtract}); + const auto& const_4_1 = wrap_type(); + const auto& const_4_2 = wrap_type(); + const auto& reshape_3 = wrap_type({const_4_1, const_4_2}); auto mult = wrap_type({reshape_3, convert_3}); @@ -174,8 +174,8 @@ GPTQMultPatternReplacer::GPTQMultPatternReplacer() { auto add_in1_ptr = convert_2_input_const->get_data_ptr(); if (!add_in1_ptr) return false; - auto add_in0_shape = add_input0_const->get_shape(); - auto static_shape_1 = reshape_node->get_shape(); + const auto& add_in0_shape = add_input0_const->get_shape(); + const auto& static_shape_1 = reshape_node->get_shape(); size_t add_in0_size = shape_size(add_in0_shape); auto add_replace_const = std::make_shared(element::f32, static_shape_1); auto add_replace_ptr = const_cast(reinterpret_cast(add_replace_const->get_data_ptr())); @@ -191,7 +191,7 @@ GPTQMultPatternReplacer::GPTQMultPatternReplacer() { add_replace_ptr[i] = (float)val; } - auto static_shape_2 = reshape2_node->get_shape(); + const auto& static_shape_2 = reshape2_node->get_shape(); auto reshape2_in0_const = std::dynamic_pointer_cast(convert_4_node->get_input_node_shared_ptr(0)); auto sub_replace_const = std::make_shared(reshape2_in0_const->get_element_type(), static_shape_2, @@ -199,7 +199,7 @@ GPTQMultPatternReplacer::GPTQMultPatternReplacer() { auto new_convert_node = std::make_shared(sub_replace_const, element::f32); auto new_sub_node = std::make_shared(new_convert_node, add_replace_const); - auto static_shape_3 = reshape3_node->get_shape(); + const auto& static_shape_3 = reshape3_node->get_shape(); auto reshape3_in0_const = std::dynamic_pointer_cast(reshape3_node->get_input_node_shared_ptr(0)); auto mult_scale_const = std::make_shared(reshape3_in0_const->get_element_type(), static_shape_3, diff --git a/src/frontends/pytorch/src/transforms/tuple_unpack_replacer.cpp b/src/frontends/pytorch/src/transforms/tuple_unpack_replacer.cpp index a9690c8a37c436..7ea557fa81682d 100644 --- a/src/frontends/pytorch/src/transforms/tuple_unpack_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/tuple_unpack_replacer.cpp @@ -145,8 +145,8 @@ bool TupleUnpackInBodyReplacer::run_on_model(const std::shared_ptr& model int else_p_idx = inputs_mapping[j].second; if (else_p_idx > else_body_idx && else_body_idx != -1) else_p_idx--; - auto then_p = then_p_idx == -1 ? nullptr : then_body->get_parameters()[then_p_idx]; - auto else_p = else_p_idx == -1 ? nullptr : else_body->get_parameters()[else_p_idx]; + const auto& then_p = then_p_idx == -1 ? nullptr : then_body->get_parameters()[then_p_idx]; + const auto& else_p = else_p_idx == -1 ? nullptr : else_body->get_parameters()[else_p_idx]; if (then_p || else_p) new_if->set_invariant_inputs(if_op->input_value(j), {then_p, else_p}); } diff --git a/src/frontends/pytorch/src/transforms/u4_block_repack.cpp b/src/frontends/pytorch/src/transforms/u4_block_repack.cpp index 8d268d111d3653..9fc4d2c8b104bb 100644 --- a/src/frontends/pytorch/src/transforms/u4_block_repack.cpp +++ b/src/frontends/pytorch/src/transforms/u4_block_repack.cpp @@ -22,22 +22,22 @@ using namespace ov::op; using namespace ov::pass::pattern; U4BlockRepack::U4BlockRepack(bool is_symmetrical) { - const auto m_constant = ov::pass::pattern::wrap_type(); - const auto m_reshape1 = ov::pass::pattern::wrap_type({m_constant, any_input()}); - const auto m_transpose = ov::pass::pattern::wrap_type({m_reshape1, any_input()}); - const auto m_reshape2 = ov::pass::pattern::wrap_type({m_transpose, any_input()}); + const auto& m_constant = ov::pass::pattern::wrap_type(); + const auto& m_reshape1 = ov::pass::pattern::wrap_type({m_constant, any_input()}); + const auto& m_transpose = ov::pass::pattern::wrap_type({m_reshape1, any_input()}); + const auto& m_reshape2 = ov::pass::pattern::wrap_type({m_transpose, any_input()}); auto pack_byte = [](uint8_t lo, uint8_t hi) -> uint8_t { return (hi << 4) | (lo & 0x0F); }; // swap halfs because Convert op assumes this layout - std::function get_u4 = [](const uint8_t* src, size_t idx) { + const std::function& get_u4 = [](const uint8_t* src, size_t idx) { const size_t byte_idx = idx / 2; const uint8_t bit_shift = 4 * (idx % 2); return (src[byte_idx] >> bit_shift) & 0xF; }; - std::function get_i4 = [get_u4](const uint8_t* src, size_t idx) { + const std::function& get_i4 = [get_u4](const uint8_t* src, size_t idx) { // by flipping first bit we get same effect as subtracting 8 return get_u4(src, idx) ^ 0b1000; }; diff --git a/src/frontends/pytorch/src/translate_session.cpp b/src/frontends/pytorch/src/translate_session.cpp index eb7c6c08137ad7..a17e1b7d03b27d 100644 --- a/src/frontends/pytorch/src/translate_session.cpp +++ b/src/frontends/pytorch/src/translate_session.cpp @@ -21,7 +21,7 @@ namespace pytorch { using namespace ov::op; TranslateSession::TranslateSession(const ov::frontend::InputModel::Ptr& input_model, - const std::map& translator_map, + const std::unordered_map& translator_map, const std::shared_ptr& telemetry) : m_input_model(input_model), m_translator_map(translator_map), @@ -70,9 +70,10 @@ std::shared_ptr TranslateSession::translate_graph(const ov::frontend: // process model rt_info auto rt_info = pytorch_model->get_decoder()->get_rt_info(); - for (auto item : rt_info) { + for (const auto& item : rt_info) { model->set_rt_info(item.second, item.first); } + model->set_rt_info("decoder_type_name", pytorch_model->decoder_type_name()); return model; } @@ -99,10 +100,10 @@ std::shared_ptr TranslateSession::convert_pytorch_model( element::Type type = pytorch_place->get_element_type(); PartialShape pshape = pytorch_place->get_partial_shape(); auto parameter = std::make_shared(type, pshape); - if (pytorch_place->get_names().size() > 0) - parameter->set_friendly_name(pytorch_place->get_names().at(0)); + if (!pytorch_place->get_names().empty()) + parameter->set_friendly_name(pytorch_place->get_names().front()); encode_tensor_name(parameter->output(0), tensor_id); - parameters->push_back(parameter); + parameters->emplace_back(parameter); (*tensor_map)[tensor_id] = parameter; } } else { @@ -119,7 +120,7 @@ std::shared_ptr TranslateSession::convert_pytorch_model( auto parameter = std::make_shared(type, pshape); parameter->set_friendly_name(pytorch_model->get_input_signature_name(i)); encode_tensor_name(parameter->output(0), inputs.at(i), {pytorch_model->get_input_debug_name(i)}); - parameters->push_back(parameter); + parameters->emplace_back(parameter); (*tensor_map)[inputs.at(i)] = parameter; } } @@ -176,15 +177,17 @@ std::shared_ptr TranslateSession::convert_pytorch_model( converted_outputs.size(), " respectively."); + const bool has_inputs = !node->inputs().empty(); + const size_t in_tensor_id = has_inputs ? node->inputs().at(0) : 0; for (size_t i = 0; i < fw_outputs.size(); ++i) { size_t fw_tensor_id = node->output(i); - if (node->inputs().size() > 0 && node->may_produce_alias(0, i)) { + if (has_inputs && node->may_produce_alias(0, i)) { + auto alias_iter = m_may_be_alias.find(fw_tensor_id); // TODO: do we need to check other inputs, not only 0? - auto in_tensor_id = node->inputs().at(0); - if (m_may_be_alias.count(fw_tensor_id)) { + if (alias_iter != m_may_be_alias.end()) { size_t recorded_in_tensor_id; std::shared_ptr recorded_node; - std::tie(recorded_in_tensor_id, recorded_node, std::ignore) = m_may_be_alias.at(fw_tensor_id); + std::tie(recorded_in_tensor_id, recorded_node, std::ignore) = alias_iter->second; FRONT_END_GENERAL_CHECK(recorded_in_tensor_id == in_tensor_id, "Operation ", context.get_op_type(), @@ -199,14 +202,16 @@ std::shared_ptr TranslateSession::convert_pytorch_model( OPENVINO_DEBUG("Registered alias: ", fw_tensor_id, " of tensor: ", - node->inputs().at(0), + in_tensor_id, " of operation: ", context.get_op_type()); } FRONT_END_GENERAL_CHECK(tensor_map->find(fw_tensor_id) == tensor_map->end(), "Duplicated producer for PT value with unique ID: ", fw_tensor_id); - auto out_type = context.get_output_type(i); + +#ifdef ENABLE_OPENVINO_DEBUG + const auto out_type = context.get_output_type(i); if (out_type.is()) { if (!converted_outputs[i].get_element_type().compatible(out_type.as())) { OPENVINO_DEBUG("[WARNING] Produced output type for operation ", @@ -219,6 +224,7 @@ std::shared_ptr TranslateSession::convert_pytorch_model( out_type.as()); } } +#endif (*tensor_map)[fw_tensor_id] = converted_outputs[i]; encode_tensor_name(converted_outputs[i], fw_tensor_id, {node->get_output_debug_name(i)}); } @@ -236,7 +242,7 @@ std::shared_ptr TranslateSession::convert_pytorch_model( FRONT_END_GENERAL_CHECK(pytorch_place, "Only place produced by PyTorch Frontend is supported."); auto tensor_id = pytorch_place->get_tensor_index(); auto ov_output = tensor_map->at(tensor_id); - FRONT_END_GENERAL_CHECK(ov_output.get_names().size() > 0, + FRONT_END_GENERAL_CHECK(!ov_output.get_names().empty(), "Tensor doesn't have name, while it should have name: ", tensor_id); auto result = std::make_shared(ov_output); @@ -245,18 +251,18 @@ std::shared_ptr TranslateSession::convert_pytorch_model( } else { for (size_t i = 0; i < pytorch_model->num_of_outputs(); ++i) { size_t id = pytorch_model->output(i); - if (tensor_map->find(id) == tensor_map->end()) { + auto it = tensor_map->find(id); + if (it == tensor_map->end()) { // Not found in this scope, adding Parameter to connect to external scope auto parameter = std::make_shared(element::dynamic, PartialShape::dynamic()); encode_tensor_name(parameter->output(0), id); parameters->push_back(parameter); - (*tensor_map)[id] = parameter; + it = tensor_map->emplace(id, parameter).first; } - auto ov_output = tensor_map->at(id); - FRONT_END_GENERAL_CHECK(ov_output.get_names().size() > 0, + FRONT_END_GENERAL_CHECK(!it->second.get_names().empty(), "Tensor doesn't have name, while it should have name: ", id); - auto result = std::make_shared(ov_output); + auto result = std::make_shared(it->second); results.push_back(result); } } @@ -307,11 +313,22 @@ std::shared_ptr TranslateSession::convert_pytorch_model( OutputVector TranslateSession::convert_node(const NodeContext& context) { std::string exception; try { - auto it = m_translator_map.find(context.get_op_type()); + const auto& op_type = context.get_op_type(); + auto it = m_translator_map.find(op_type); if (it != m_translator_map.end()) { return it->second(context); + } else if (op_type.back() == '_') { + // inplace op case + std::string op_type_cut = op_type.substr(0, op_type.size() - 1); + auto it = m_translator_map.find(op_type_cut); + if (it != m_translator_map.end()) { + const auto& res = it->second(context); + FRONT_END_OP_CONVERSION_CHECK(res.size() == 1, "inplace op must have single output."); + context.mutate_input(0, res[0]); + return res; + } } - OPENVINO_DEBUG("No translator found for: ", context.get_op_type(), "\n"); + OPENVINO_DEBUG("No translator found for: ", op_type, "\n"); } catch (std::exception& e) { exception = e.what(); } catch (...) { @@ -332,7 +349,7 @@ OutputVector TranslateSession::convert_node(const NodeContext& context) { void TranslateSession::encode_tensor_name(Output output, size_t tensor_idx, - std::vector additional_names) { + const std::vector& additional_names) { if (!output.get_names().empty()) { OPENVINO_DEBUG("Tensor names already exist: ", output.get_any_name(), @@ -342,22 +359,21 @@ void TranslateSession::encode_tensor_name(Output output, return; } auto name = std::to_string(tensor_idx); - std::unordered_set names; - names.insert(name); - if (additional_names.size() > 0) { + std::unordered_set names = {name}; + if (!additional_names.empty()) { names.insert(additional_names.begin(), additional_names.end()); } - if (m_counter_map.count(tensor_idx)) { - auto&& pair = m_counter_map[tensor_idx]; + auto it = m_counter_map.find(tensor_idx); + if (it != m_counter_map.end()) { + auto& pair = it->second; auto new_name = name + '_' + std::to_string(++pair.first); - pair.second.set_names({new_name}); + pair.second.set_names({std::move(new_name)}); pair.second = output; - output.set_names(names); } else { - m_counter_map[tensor_idx] = {0, output}; - output.set_names(names); + m_counter_map.emplace(tensor_idx, std::make_pair(0, output)); } + output.set_names(std::move(names)); } namespace { @@ -424,7 +440,7 @@ using ReversepropCreatorFunction = std::function(const Outp Output TranslateSession::get_reverseprop_op(const std::shared_ptr& node, const Output& direct_op_output, const Output& value) { - std::map backprop_map = { + static const std::map backprop_map = { {"aten::slice", slice_reverseprop}, {"aten::select", select_reverseprop}, }; diff --git a/src/frontends/pytorch/src/translate_session.hpp b/src/frontends/pytorch/src/translate_session.hpp index 723e94e25116a8..df669dbabe1fae 100644 --- a/src/frontends/pytorch/src/translate_session.hpp +++ b/src/frontends/pytorch/src/translate_session.hpp @@ -18,7 +18,7 @@ namespace pytorch { class TranslateSession { public: TranslateSession(const frontend::InputModel::Ptr& input_model, - const std::map& translator_map, + const std::unordered_map& translator_map, const std::shared_ptr& telemetry); ~TranslateSession(); std::shared_ptr get_converted_model(); @@ -42,7 +42,7 @@ class TranslateSession { /// \brief Writes pytorch tensor index into openvino tensor void encode_tensor_name(Output tensor_desc, size_t tensor_idx, - std::vector additional_names = {}); + const std::vector& additional_names = {}); /// \brief Gets pytorch tensor index from openvino tensor size_t decode_tensor_name(const Output& tensor_desc); @@ -55,7 +55,7 @@ class TranslateSession { OutputVector convert_node(const NodeContext& context); const frontend::InputModel::Ptr m_input_model; - const std::map& m_translator_map; + const std::unordered_map& m_translator_map; std::shared_ptr m_telemetry; std::shared_ptr m_ov_model; diff --git a/src/frontends/pytorch/src/utils.cpp b/src/frontends/pytorch/src/utils.cpp index a2c4b35529de1f..b096da45f25437 100644 --- a/src/frontends/pytorch/src/utils.cpp +++ b/src/frontends/pytorch/src/utils.cpp @@ -130,9 +130,7 @@ std::shared_ptr get_node_axes_range(const NodeContext& context, const Outp Output normalize_axis(const NodeContext& context, const Output& axis, const Output& rank) { auto axis_rank = context.mark_node(std::make_shared(axis, rank)); - auto is_less = context.mark_node(std::make_shared(axis_rank, rank)); - auto new_axis = context.mark_node(std::make_shared(is_less, axis_rank, axis)); - return new_axis; + return context.mark_node(std::make_shared(axis_rank, rank)); } std::shared_ptr numel(const NodeContext& context, const Output& x, element::Type output_type) { @@ -217,14 +215,13 @@ std::shared_ptr create_fw_node_with_exception(const NodeContext bool skip_subgraphs = false) { auto fw_node = std::make_shared(context.get_decoder(), inputs, num_outputs, false, skip_subgraphs); context.mark_node(fw_node); - auto attrs = fw_node->get_attrs(); - std::string message(exception_message); - if (!message.empty()) { - message = "Exception happened during conversion of operation " + fw_node->get_friendly_name() + - " with schema " + context.get_schema() + '\n' + message; + if (!exception_message.empty()) { + auto attrs = fw_node->get_attrs(); + std::string message = "Exception happened during conversion of operation " + fw_node->get_friendly_name() + + " with schema " + context.get_schema() + '\n' + exception_message; + attrs[PtFrameworkNode::failed_conversion_key] = std::move(message); + fw_node->set_attrs(attrs); } - attrs[PtFrameworkNode::failed_conversion_key] = message; - fw_node->set_attrs(attrs); return fw_node; } } // namespace @@ -537,7 +534,7 @@ void copy_runtime_info_and_name(const std::shared_ptr& from, } else { unique_names.insert(new_name); } - op->set_friendly_name(new_name); + op->set_friendly_name(std::move(new_name)); } } copy_runtime_info(from, to); diff --git a/src/frontends/pytorch/src/utils_quantize.cpp b/src/frontends/pytorch/src/utils_quantize.cpp index ad61dc38a9580f..ccf7f20785b09b 100644 --- a/src/frontends/pytorch/src/utils_quantize.cpp +++ b/src/frontends/pytorch/src/utils_quantize.cpp @@ -256,9 +256,9 @@ std::shared_ptr u4_compression_stack(const OutputVector& list_elems, int64 // Pattern detected, weights_u8 is target u8 packed constant with weights // Part 2: Form u4 constant by repacking of the original weights_u8 - // Repacking transformes half of lanes to interleaved representation. + // Repacking transforms half of lanes to interleaved representation. - auto u8_shape = weights_u8->get_shape(); + const auto& u8_shape = weights_u8->get_shape(); size_t full_size = shape_size(u8_shape); auto src = weights_u8->get_data_ptr(); @@ -268,7 +268,7 @@ std::shared_ptr u4_compression_stack(const OutputVector& list_elems, int64 auto dst = const_cast(reinterpret_cast(new_const->get_data_ptr())); std::copy(src, src + full_size, dst); // TODO: Avoid copying, reuse the same constant - copy_runtime_info_and_name(weights_u8, {new_const}, {weights_u8, bitwise_and, bitwise_shift}); + copy_runtime_info_and_name(weights_u8, {new_const}, {weights_u8, std::move(bitwise_and), bitwise_shift}); return new_const; } From db22d0a0080701bdef58d09c98b1f9582231b6ff Mon Sep 17 00:00:00 2001 From: Roman Kazantsev Date: Tue, 20 Aug 2024 15:31:44 +0400 Subject: [PATCH 23/23] [TF FE][Tokenizers] Avoid dependency from TF FE in tokenizers (#26131) **Details:** All required routines (Variable, HashTable) are moved to common FE API **Ticket:** 148101 --------- Signed-off-by: Kazantsev, Roman --- .../include/openvino/frontend/hash_table.hpp | 135 ++++++++++++++++++ .../openvino/frontend/node_context.hpp | 5 + .../include/openvino/frontend/variable.hpp | 117 +++++++++++++++ src/frontends/common/src/hash_table.cpp | 9 ++ src/frontends/common/src/variable.cpp | 9 ++ .../frontend/tensorflow/hash_table.hpp | 117 +-------------- .../frontend/tensorflow/node_context.hpp | 2 +- .../openvino/frontend/tensorflow/variable.hpp | 100 +------------ 8 files changed, 280 insertions(+), 214 deletions(-) create mode 100644 src/frontends/common/include/openvino/frontend/hash_table.hpp create mode 100644 src/frontends/common/include/openvino/frontend/variable.hpp create mode 100644 src/frontends/common/src/hash_table.cpp create mode 100644 src/frontends/common/src/variable.cpp diff --git a/src/frontends/common/include/openvino/frontend/hash_table.hpp b/src/frontends/common/include/openvino/frontend/hash_table.hpp new file mode 100644 index 00000000000000..b60159fd381eda --- /dev/null +++ b/src/frontends/common/include/openvino/frontend/hash_table.hpp @@ -0,0 +1,135 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "openvino/core/node_output.hpp" +#include "openvino/core/type/element_type.hpp" +#include "openvino/frontend/variable.hpp" +#include "openvino/frontend/visibility.hpp" + +namespace ov { +namespace frontend { + +/// \brief HashTable is a special type of Variable that has a complex value including keys and values. +/// Keys and values are represented with two separate graph at each time step +class FRONTEND_API HashTable : public Variable { +public: + using Ptr = std::shared_ptr; + OPENVINO_OP("HashTable", "ov::frontend", Variable); + + HashTable(const std::string& name, + const ov::element::Type& key_type, + const ov::element::Type& value_type, + const std::shared_ptr& decoder = nullptr) + : Variable(name, decoder), + m_key_type(key_type), + m_value_type(value_type) { + validate_and_infer_types(); + } + + HashTable(const HashTable& other, const ov::Output& keys, const ov::Output& values) + : HashTable(other) { + m_keys = keys; + m_values = values; + m_is_initialized = true; + ++m_init_counter; + } + + // it must be used only for cloning + // other ways are illegal + HashTable(const std::string& name, + const ov::element::Type& key_type, + const ov::element::Type& value_type, + const ov::Output& keys, + const ov::Output& values, + bool is_initialized, + uint64_t init_counter, + const std::shared_ptr& decoder = nullptr) + : Variable(name, decoder), + m_key_type(key_type), + m_value_type(value_type), + m_keys(keys), + m_values(values) { + m_init_counter = init_counter; + m_is_initialized = is_initialized; + validate_and_infer_types(); + } + + void validate_and_infer_types() override { + // this is a type of resource so its shape and type is not applicable + // its output serves to store a reference to a resource + set_output_type(0, ov::element::dynamic, ov::PartialShape::dynamic()); + // these two outputs serves to store keys and values of a resource + // keys and values are 1D tensors + set_output_type(1, m_key_type, ov::PartialShape::dynamic(1)); + set_output_type(2, m_value_type, ov::PartialShape::dynamic(1)); + } + + std::shared_ptr clone_with_new_inputs(const OutputVector& inputs) const override { + auto hash_table_node = std::make_shared(m_name, + m_key_type, + m_value_type, + m_keys, + m_values, + m_is_initialized, + m_init_counter, + m_decoder); + hash_table_node->set_attrs(get_attrs()); + return hash_table_node; + } + + ov::Output get_value() override { + return output(0); + } + + /// \brief Returns a value corresponding keys of hash table + ov::Output get_keys() { + if (m_is_initialized) { + return m_keys; + } else if (m_other_keys.size() > 0) { + return *(m_other_keys.begin()); + } + + return output(1); + } + + /// \brief Returns a value corresponding values of hash table + ov::Output get_values() { + if (m_is_initialized) { + return m_values; + } else if (m_other_values.size() > 0) { + return *(m_other_values.begin()); + } + + return output(2); + } + + ov::element::Type get_key_type() const { + return m_key_type; + } + + ov::element::Type get_value_type() const { + return m_value_type; + } + + void add_other_keys_values(const ov::Output& other_key, const ov::Output& other_value) { + m_other_keys.insert(other_key); + m_other_values.insert(other_value); + } + + virtual ~HashTable(); + +private: + ov::element::Type m_key_type; + ov::element::Type m_value_type; + ov::Output m_keys; + ov::Output m_values; + + std::set> m_other_keys; + std::set> m_other_values; +}; + +} // namespace frontend +} // namespace ov diff --git a/src/frontends/common/include/openvino/frontend/node_context.hpp b/src/frontends/common/include/openvino/frontend/node_context.hpp index c18e6b77baa5ea..98243fab0e9c63 100644 --- a/src/frontends/common/include/openvino/frontend/node_context.hpp +++ b/src/frontends/common/include/openvino/frontend/node_context.hpp @@ -48,6 +48,11 @@ class FRONTEND_API NodeContext { FRONT_END_NOT_IMPLEMENTED(get_input); } + /// \brief Returns the input by reference. The reference value can be changed by consuming operation + virtual Output get_input_by_reference(int idx) const { + FRONT_END_NOT_IMPLEMENTED(get_input_by_reference); + } + /// \brief Returns values from Constant input with the given index as ov::Any. /// Throws an exception if the input cannot be represented as Constant. virtual Any get_values_from_const_input(int idx) const { diff --git a/src/frontends/common/include/openvino/frontend/variable.hpp b/src/frontends/common/include/openvino/frontend/variable.hpp new file mode 100644 index 00000000000000..79415bcddd2390 --- /dev/null +++ b/src/frontends/common/include/openvino/frontend/variable.hpp @@ -0,0 +1,117 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "openvino/frontend/decoder.hpp" +#include "openvino/frontend/visibility.hpp" +#include "openvino/op/util/framework_node.hpp" + +namespace ov { +namespace frontend { + +/// \brief Variable is a special node used in a conversion step +/// It can have several values (or states) during the conversion. +/// Variable value at some time step is represented with a graph. +class FRONTEND_API Variable : public ov::op::util::FrameworkNode { +public: + using Ptr = std::shared_ptr; + OPENVINO_OP("Variable", "ov::frontend", ov::op::util::FrameworkNode); + + Variable(const std::string& name, const std::shared_ptr& decoder) + : ov::op::util::FrameworkNode(ov::OutputVector{}, 1), + m_name(name), + m_shape(ov::Shape{}), + m_type(ov::element::dynamic), + m_decoder(decoder), + m_is_initialized(false), + m_init_counter(0) { + validate_and_infer_types(); + } + + Variable(const std::string& name, + const ov::Shape& shape, + const ov::element::Type& type, + const std::shared_ptr& decoder) + : ov::op::util::FrameworkNode(ov::OutputVector{}, 1), + m_name(name), + m_shape(shape), + m_type(type), + m_decoder(decoder), + m_is_initialized(false), + m_init_counter(0) { + validate_and_infer_types(); + } + + Variable(const std::string& name, + const ov::Shape& shape, + const ov::element::Type& type, + const ov::Output& value, + const std::shared_ptr& decoder) + : Variable(name, shape, type, decoder) { + m_value = value; + // reset names of tensor corresponding to variable value + // that is because variable can have multiple values during inference + m_value.set_names({}); + m_is_initialized = true; + ++m_init_counter; + } + + Variable(const Variable& other, const ov::Output& value) : Variable(other) { + m_value = value; + // reset names of tensor corresponding to variable value + // that is because variable can have multiple values during inference + m_value.set_names({}); + m_is_initialized = true; + ++m_init_counter; + } + + void validate_and_infer_types() override { + set_output_type(0, m_type, m_shape); + } + + /// \brief Checks if variable is initialized with some value + bool is_initialized() const { + return m_is_initialized; + } + + /// \brief Returns a value at the current step of conversion + virtual ov::Output get_value() { + FRONT_END_GENERAL_CHECK(m_is_initialized, "internal error: get_value() is called for uninitialized variable"); + return m_value; + } + + std::string get_name() const { + return m_name; + } + + /// \brief Returns a counter value (a number of values that have assigned to this variable) + uint64_t get_init_counter() const { + return m_init_counter; + } + + std::shared_ptr get_decoder() const { + return m_decoder; + } + + std::shared_ptr clone_with_new_inputs(const OutputVector& inputs) const override { + auto new_variable = std::make_shared(*this); + new_variable->set_attrs(get_attrs()); + return new_variable; + } + + virtual ~Variable(); + +protected: + std::string m_name; + ov::Shape m_shape; + ov::element::Type m_type; + std::shared_ptr m_decoder; + bool m_is_initialized; + ov::Output m_value; + uint64_t m_init_counter; +}; + +} // namespace frontend +} // namespace ov diff --git a/src/frontends/common/src/hash_table.cpp b/src/frontends/common/src/hash_table.cpp new file mode 100644 index 00000000000000..87991938fbfc3b --- /dev/null +++ b/src/frontends/common/src/hash_table.cpp @@ -0,0 +1,9 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "openvino/frontend/hash_table.hpp" + +using namespace ov::frontend; + +HashTable::~HashTable(){}; diff --git a/src/frontends/common/src/variable.cpp b/src/frontends/common/src/variable.cpp new file mode 100644 index 00000000000000..dc3b2a692f328c --- /dev/null +++ b/src/frontends/common/src/variable.cpp @@ -0,0 +1,9 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "openvino/frontend/variable.hpp" + +using namespace ov::frontend; + +Variable::~Variable(){}; diff --git a/src/frontends/tensorflow/include/openvino/frontend/tensorflow/hash_table.hpp b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/hash_table.hpp index beecb75e733f56..f2e39df79443fb 100644 --- a/src/frontends/tensorflow/include/openvino/frontend/tensorflow/hash_table.hpp +++ b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/hash_table.hpp @@ -4,9 +4,7 @@ #pragma once -#include "openvino/core/node_output.hpp" -#include "openvino/core/type/element_type.hpp" -#include "openvino/frontend/tensorflow/variable.hpp" +#include "openvino/frontend/hash_table.hpp" namespace ov { namespace frontend { @@ -16,118 +14,7 @@ namespace tensorflow { // tf.raw_ops.MutableHashTable and stores a dictionary of keys mapped to values // Objects of this class is fed to Lookup* operations for initialization and searching values by keys // Types of keys and values can be different -class HashTable : public Variable { -public: - using Ptr = std::shared_ptr; - OPENVINO_OP("TFHashTable", "ov::frontend::tensorflow", Variable); - - HashTable(const std::string& name, - const ov::element::Type& key_type, - const ov::element::Type& value_type, - const std::shared_ptr& decoder = nullptr) - : Variable(name, decoder), - m_key_type(key_type), - m_value_type(value_type) { - validate_and_infer_types(); - } - - HashTable(const HashTable& other, const ov::Output& keys, const ov::Output& values) - : HashTable(other) { - m_keys = keys; - m_values = values; - m_is_initialized = true; - ++m_init_counter; - } - - // it must be used only for cloning - // other ways are illegal - HashTable(const std::string& name, - const ov::element::Type& key_type, - const ov::element::Type& value_type, - const ov::Output& keys, - const ov::Output& values, - bool is_initialized, - uint64_t init_counter, - const std::shared_ptr& decoder = nullptr) - : Variable(name, decoder), - m_key_type(key_type), - m_value_type(value_type), - m_keys(keys), - m_values(values) { - m_init_counter = init_counter; - m_is_initialized = is_initialized; - validate_and_infer_types(); - } - - void validate_and_infer_types() override { - // this is a type of resource so its shape and type is not applicable - // its output serves to store a reference to a resource - set_output_type(0, ov::element::dynamic, ov::PartialShape::dynamic()); - // these two outputs serves to store keys and values of a resource - // keys and values are 1D tensors - set_output_type(1, m_key_type, ov::PartialShape::dynamic(1)); - set_output_type(2, m_value_type, ov::PartialShape::dynamic(1)); - } - - std::shared_ptr clone_with_new_inputs(const OutputVector& inputs) const override { - auto hash_table_node = std::make_shared(m_name, - m_key_type, - m_value_type, - m_keys, - m_values, - m_is_initialized, - m_init_counter, - m_decoder); - hash_table_node->set_attrs(get_attrs()); - return hash_table_node; - } - - ov::Output get_value() override { - return output(0); - } - - ov::Output get_keys() { - if (m_is_initialized) { - return m_keys; - } else if (m_other_keys.size() > 0) { - return *(m_other_keys.begin()); - } - - return output(1); - } - - ov::Output get_values() { - if (m_is_initialized) { - return m_values; - } else if (m_other_values.size() > 0) { - return *(m_other_values.begin()); - } - - return output(2); - } - - ov::element::Type get_key_type() const { - return m_key_type; - } - - ov::element::Type get_value_type() const { - return m_value_type; - } - - void add_other_keys_values(const ov::Output& other_key, const ov::Output& other_value) { - m_other_keys.insert(other_key); - m_other_values.insert(other_value); - } - -private: - ov::element::Type m_key_type; - ov::element::Type m_value_type; - ov::Output m_keys; - ov::Output m_values; - - std::set> m_other_keys; - std::set> m_other_values; -}; +using ov::frontend::HashTable; } // namespace tensorflow } // namespace frontend diff --git a/src/frontends/tensorflow/include/openvino/frontend/tensorflow/node_context.hpp b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/node_context.hpp index 65f2c7a7bf9f0a..f2dba04b49dca7 100644 --- a/src/frontends/tensorflow/include/openvino/frontend/tensorflow/node_context.hpp +++ b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/node_context.hpp @@ -37,7 +37,7 @@ class NodeContext : public ov::frontend::NodeContext { } /// Retrieve the input by reference. It is needed for operations working with reference inputs - Output get_input_by_reference(int port_index) const { + Output get_input_by_reference(int port_index) const override { auto input = m_inputs.at(port_index); auto ref_node = ov::as_type_ptr(input.get_node_shared_ptr()); if (ref_node) { diff --git a/src/frontends/tensorflow/include/openvino/frontend/tensorflow/variable.hpp b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/variable.hpp index 5f54249d6e926f..14b2692bc3c00c 100644 --- a/src/frontends/tensorflow/include/openvino/frontend/tensorflow/variable.hpp +++ b/src/frontends/tensorflow/include/openvino/frontend/tensorflow/variable.hpp @@ -4,109 +4,13 @@ #pragma once -#include "openvino/frontend/tensorflow/decoder.hpp" -#include "openvino/op/util/framework_node.hpp" +#include "openvino/frontend/variable.hpp" namespace ov { namespace frontend { namespace tensorflow { -class Variable : public ov::op::util::FrameworkNode { -public: - using Ptr = std::shared_ptr; - OPENVINO_OP("TFVariable", "ov::frontend::tensorflow", ::ov::op::util::FrameworkNode); - - Variable(const std::string& name, const std::shared_ptr& decoder) - : ov::op::util::FrameworkNode(ov::OutputVector{}, 1), - m_name(name), - m_shape(ov::Shape{}), - m_type(ov::element::dynamic), - m_decoder(decoder), - m_is_initialized(false), - m_init_counter(0) { - validate_and_infer_types(); - } - - Variable(const std::string& name, - const ov::Shape& shape, - const ov::element::Type& type, - const std::shared_ptr& decoder) - : ov::op::util::FrameworkNode(ov::OutputVector{}, 1), - m_name(name), - m_shape(shape), - m_type(type), - m_decoder(decoder), - m_is_initialized(false), - m_init_counter(0) { - validate_and_infer_types(); - } - - Variable(const std::string& name, - const ov::Shape& shape, - const ov::element::Type& type, - const ov::Output& value, - const std::shared_ptr& decoder) - : Variable(name, shape, type, decoder) { - m_value = value; - // reset names of tensor corresponding to variable value - // that is because variable can have multiple values during inference - m_value.set_names({}); - m_is_initialized = true; - ++m_init_counter; - } - - Variable(const Variable& other, const ov::Output& value) : Variable(other) { - m_value = value; - // reset names of tensor corresponding to variable value - // that is because variable can have multiple values during inference - m_value.set_names({}); - m_is_initialized = true; - ++m_init_counter; - } - - void validate_and_infer_types() override { - set_output_type(0, m_type, m_shape); - } - - bool is_initialized() const { - return m_is_initialized; - } - - virtual ov::Output get_value() { - FRONT_END_GENERAL_CHECK( - m_is_initialized, - "[TensorFlow Frontend] internal error: get_value() is called for uninitialized variable"); - return m_value; - } - - std::string get_name() const { - return m_name; - } - - uint64_t get_init_counter() const { - return m_init_counter; - } - - std::shared_ptr get_decoder() const { - return m_decoder; - } - - std::shared_ptr clone_with_new_inputs(const OutputVector& inputs) const override { - auto new_variable = std::make_shared(*this); - new_variable->set_attrs(get_attrs()); - return new_variable; - } - -protected: - std::string m_name; - ov::Shape m_shape; - ov::element::Type m_type; - std::shared_ptr m_decoder; - bool m_is_initialized; - ov::Output m_value; - // this member is used to select the latest state of Variable - uint64_t m_init_counter; -}; +using ov::frontend::Variable; } // namespace tensorflow } // namespace frontend