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openvino/openvino-ComputeLibrary-include-string.patch

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Summary of major features and improvements   - More GenAI coverage and framework integrations to minimize code changes * New models supported on CPUs & GPUs: Phi-4, Mistral-7B-Instruct-v0.3, SD-XL Inpainting 0.1, Stable Diffusion 3.5 Large Turbo, Phi-4-reasoning, Qwen3, and Qwen2.5-VL-3B-Instruct. Mistral 7B Instruct v0.3 is also supported on NPUs. * Preview: OpenVINO ™ GenAI introduces a text-to-speech pipeline for the SpeechT5 TTS model, while the new RAG backend offers developers a simplified API that delivers reduced memory usage and improved performance. * Preview: OpenVINO™ GenAI offers a GGUF Reader for seamless integration of llama.cpp based LLMs, with Python and C++ pipelines that load GGUF models, build OpenVINO graphs, and run GPU inference on-the-fly. Validated for popular models: DeepSeek-R1-Distill-Qwen (1.5B, 7B), Qwen2.5 Instruct (1.5B, 3B, 7B) & llama-3.2 Instruct (1B, 3B, 8B). - Broader LLM model support and more model compression techniques * Further optimization of LoRA adapters in OpenVINO GenAI for improved LLM, VLM, and text-to-image model performance on built-in GPUs. Developers can use LoRA adapters to quickly customize models for specialized tasks. * KV cache compression for CPUs is enabled by default for INT8, providing a reduced memory footprint while maintaining accuracy compared to FP16. Additionally, it delivers substantial memory savings for LLMs with INT4 support compared to INT8. * Optimizations for Intel® Core™ Ultra Processor Series 2 built-in GPUs and Intel® Arc™ B Series Graphics with the Intel® XMX systolic platform to enhance the performance of VLM models and hybrid quantized image generation models, as well as improve first-token latency for LLMs through dynamic quantization. - More portability and performance to run AI at the edge, in the cloud, or locally. * Enhanced Linux* support with the latest GPU driver for built-in GPUs on Intel® Core™ Ultra Processor Series 2 (formerly codenamed Arrow Lake H). * Support for INT4 data-free weights compression for ONNX models implemented in the Neural Network Compression Framework (NNCF). * NPU support for FP16-NF4 precision on Intel® Core™ 200V Series processors for models with up to 8B parameters is enabled through symmetrical and channel-wise quantization, improving accuracy while maintaining performance efficiency. Support Change and Deprecation Notices - Discontinued in 2025: * Runtime components: + The OpenVINO property of Affinity API is no longer available. It has been replaced with CPU binding configurations (ov::hint::enable_cpu_pinning). + The openvino-nightly PyPI module has been discontinued. End-users should proceed with the Simple PyPI nightly repo instead. More information in Release Policy. The openvino-nightly PyPI module has been discontinued. End-users should proceed with the Simple PyPI nightly repo instead. More information in Release Policy. * Tools: + The OpenVINO™ Development Tools package (pip install openvino-dev) is no longer available for OpenVINO releases in 2025. + Model Optimizer is no longer available. Consider using the new conversion methods instead. For more details, see the model conversion transition guide. + Intel® Streaming SIMD Extensions (Intel® SSE) are currently not enabled in the binary package by default. They are still supported in the source code form. + Legacy prefixes: l_, w_, and m_ have been removed from OpenVINO archive names. * OpenVINO GenAI: + StreamerBase::put(int64_t token) + The Bool value for Callback streamer is no longer accepted. It must now return one of three values of StreamingStatus enum. + ChunkStreamerBase is deprecated. Use StreamerBase instead. * NNCF create_compressed_model() method is now deprecated. nncf.quantize() method is recommended for Quantization-Aware Training of PyTorch and TensorFlow models. * OpenVINO Model Server (OVMS) benchmark client in C++ using TensorFlow Serving API. - Deprecated and to be removed in the future: * Python 3.9 is now deprecated and will be unavailable after OpenVINO version 2025.4. * openvino.Type.undefined is now deprecated and will be removed with version 2026.0. openvino.Type.dynamic should be used instead. * APT & YUM Repositories Restructure: Starting with release 2025.1, users can switch to the new repository structure for APT and YUM, which no longer uses year-based subdirectories (like “2025”). The old (legacy) structure will still be available until 2026, when the change will be finalized. Detailed instructions are available on the relevant documentation pages: + Installation guide - yum + Installation guide - apt * OpenCV binaries will be removed from Docker images in 2026. * Ubuntu 20.04 support will be deprecated in future OpenVINO releases due to the end of standard support. * “auto shape” and “auto batch size” (reshaping a model in runtime) will be removed in the future. OpenVINO’s dynamic shape models are recommended instead. * MacOS x86 is no longer recommended for use due to the discontinuation of validation. Full support will be removed later in 2025. * The openvino namespace of the OpenVINO Python API has been redesigned, removing the nested openvino.runtime module. The old namespace is now considered deprecated and will be discontinued in 2026.0. OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/openvino?expand=0&rev=37
2025-06-24 04:28:58 +00:00
diff -uNr openvino-2025.2.0.orig/src/plugins/intel_cpu/thirdparty/ComputeLibrary/arm_compute/core/utils/logging/IPrinter.h openvino-2025.2.0/src/plugins/intel_cpu/thirdparty/ComputeLibrary/arm_compute/core/utils/logging/IPrinter.h
--- openvino-2025.2.0.orig/src/plugins/intel_cpu/thirdparty/ComputeLibrary/arm_compute/core/utils/logging/IPrinter.h 2025-06-22 18:14:56.561471325 -0300
+++ openvino-2025.2.0/src/plugins/intel_cpu/thirdparty/ComputeLibrary/arm_compute/core/utils/logging/IPrinter.h 2025-06-22 18:15:23.466678704 -0300
@@ -29,6 +29,7 @@
*/
#include "support/Mutex.h"
+#include <string>
namespace arm_compute
{
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