feat: support chunkprefill and prefill cuda graph

[Simon12345777]

Summary

C++ 端引入 chunked-prefill 编译路径：新增 csrc/engine/compiler/chunk_prefill_compiler.{hpp,cpp}；GeneralCompiler 拼装 ChunkPrefillCompiler 并在 get_compiled() 中先于 decode 匹配；新增 enable_chunk_prefill_graph 开关贯通 InferEngine → RankWorker → GeneralCompiler。
pybind11 与 Python InferEngine 包装层（csrc/pybind11/engine/engine.hpp、python/infinilm/infer_engine.py）将 enable_chunk_prefill_graph 透传到底层。
脚本层移植：把 InfiniGraph/InfiniLM/scripts 中 infer_task.py 与 launch_server.py 的 chunked-prefill 调度逻辑（setup_chunked_prefill / advance_prefill_chunk、--chunk-size、优先级 worker_loop）原样移植到 hds/InfiniLM/scripts/，并保留 hds 既有的 KVCache.init 签名以兼容 jiuge*/deepseek/qwen3vl 的 create_kv_cache()。
Python 服务侧 chunked-prefill 调度（接通 enable_chunk_prefill_graph）：
llm/request.py 给 InferenceRequest 增加 chunk_size、chunk_prefill_offset 与 is_chunking() / chunk_is_last()。
llm/scheduler.py 新增 chunking_queue，schedule() 采用三级优先级：running（decode）> chunking（续片）> waiting（新请求）；长 prompt 进入 chunking 时以 batch=1 单请求返回，匹配 C++ 侧 (batch_size, chunk_size) 预编图签名。
processors/basic_llm_processor.py 的 paged 分支按 chunk_prefill_offset / chunk_size 切片 input_ids / position_ids / slot_mapping，并设置正确的 past_kv_lengths / total_kv_lengths。
llm/llm.py::_update_requests 识别 chunk 中间步骤：不消费采样 token、不触发 reset_req_blocks，仅推进 offset 后 requeue_chunking；最后一片走正常路径并清零 chunk 状态。
配置链路：EngineConfig / LLM / LLMEngine / AsyncLLMEngine / InferenceServer 增加 chunk_size；BaseConfig 增加 --chunk-size（默认 512）与 --enable-chunk-prefill-graph。

Motivation

InfiniGraph 分支上 csrc 已经有 chunk-prefill 图编译路径与配套的 infer_task.py / launch_server.py 分片调度，但 hds 分支只有传统 prefill。本 PR 把整条链路移到 hds：底层补齐 ChunkPrefillCompiler 与 enable_chunk_prefill_graph 开关，并在 inference_server.py 这条新链路里以原生方式实现 chunked-prefill 调度，使开关一打开就能命中预编图，从而：

降低首 token 延迟时的瞬时显存压力（长 prompt 不必一次性进入 forward）；
与 paged KV cache 一致地复用 C++ 侧预编 (batch_size, chunk_size) 图。

Closes #

Type of Change

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[wooway777]

这个文件在哪里

[wooway777]

请补充启动指令和测试截图，请支持flash attn后端