FP32 KV Cache #28840
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1348
## This PR introduces basic (Paged ) KV-Cache to use on CPU
### Summary:
1. To ensure proper gemm-prepacking,
1.1. The Page Size of Key Cache is currently hardcoded as `FAST_GEMM_F32_NR`(which is 8, 12 or 16 depending on CPU architecture)
1.2. The Page Size of Values Cache is hardcoded as `FAST_GEMM_F32_PACKED_STRIDE_K`
2. there are two phases supported - prefill & generate.
2.1. prefill grows cache by `N` tokens and is allowed **only** for empty cache
2.2. generate grows cache by 1 token.
2.3. **Improtant**: it is currently not allowed to grow non-empty cache by more than one token at a time (thisbehaviour is sufficient for normal LLM querying, but should be extended if we want to implement speculative decoding)
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CPU Kernels for fp32 KV Cache #28524
The kernels are:
- pagedAttnQKGemmKernel
- pagedAttnAVGemmKernel
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AttentionOnnxAiLayer #27988
Implements https://onnx.ai/onnx/operators/onnx__Attention.html#attention-23
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RMSNorm: reference cpu impl #28104https://onnx.ai/onnx/operators/onnx__RMSNormalization.html
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Resolving failing tests of rotary embedding layer on win32 #28080
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Onnx importer2 dispatch map #28058
I have noticed that PR[28032]( https://github.com/opencv/opencv/pull/28032) was incomplete - it fixed only the new ONNX importer. Also, building the dispatch map based on the parsed opset version hypotetically can cause trouble if the graph simplifier inserts a node with a different opset version which is not included in the dispatch map. So I removed this parameter in `buildDispatchMap_COM_MICROSOFT` and `buildDispatchMap_ONNX_AI` for now. It was marked as `CV_UNUSED` anyway.
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Rotary position layer #28031
Implemented https://onnx.ai/onnx/operators/onnx__RotaryEmbedding.html#rotaryembedding-23
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Onnx importer2 dispatch map #28032
in the new onnx_importer all domains in the dispatch map should be included per default.
See https://github.com/opencv/opencv/pull/27988#issuecomment-3521140872
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Trilu layer #27527
Trilu layer https://onnx.ai/onnx/operators/onnx__Trilu.html is needed for importing paligemma
Merged with https://github.com/opencv/opencv_extra/pull/1264
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Onnx multifile import #27449
Merge with https://github.com/opencv/opencv_extra/pull/1259
LLMs are larger than 2GB and don't fit into single file onnx. this patch adds support for importing large onnx models with external data
updated `opencv-onnx.proto` to version 1.18.0 [(https://github.com/onnx/onnx/releases/tag/v1.18.0](https://github.com/onnx/onnx/blob/v1.18.0/onnx/onnx.proto)
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Test gemm 3inputs #27102
Merge with test data: https://github.com/opencv/opencv_extra/pull/1245
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determine gemm op mode at runtime #27017
See https://github.com/opencv/opencv/issues/26209
TODOs:
- [x] Determine OP mode on runtime
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nklskyoy