openvino

5d00a842 - [GPU] Add the capability for KV cache to update past KV (#33114)

[GPU] Add the capability for KV cache to update past KV (#33114) ### Details: This PR is to recognize the pattern of ScatterElementUpdate+Slice node(blue nodes in the picture below) and fuse them into multi-stages KVCache node. Besides, past_seq_len from onnx GQA which serves for correcting the length of KV Cache is missing in decomposition of onnx operator, it is added in the PR to make sure it is benefited from the new capability of KVCache. After fusion, two related changes happened. 1. ScatteElementUpdate is handled by adding reorder_stage to execute ScatteElementUpdate kernel 2. Slice is handled by in-place crop by updating the data padding of variableState. The picture below shows the graph changes before and after fusion. <img width="2503" height="1183" alt="image" src="https://github.com/user-attachments/assets/4743da6b-edf9-4fe9-b7e2-158c81fc7abd" /> ### Motivation and Context The target application leverages tree-based speculative decoding to accelerate LLM inference. This technique requires frequent manipulation of past KV cache states (e.g. trimming, reordering). This is because only a single branch of the speculative draft tree is accepted after verification. The current KV Cache API available is OV is very slow which cannot meet customer requirements. Details in [CVS-174809](https://jira.devtools.intel.com/browse/CVS-174809). As OV team suggested, the only way to support reorder feature is to add specific nodes in the original graph. This PR is to recognize the pattern of added nodes and fuse them into multi-stages KVCache node to be more performant. ### Tickets: [CVS-176367](https://jira.devtools.intel.com/browse/CVS-176367) ### Related PR #32708 --------- Co-authored-by: Dvoretckii, Mikhail <mikhail.dvoretckii@intel.com> Co-authored-by: czekun <chen.zekun@intel.com>