Disaggregate prefill for kv cache register style (#950)

### What this PR does / why we need it?
This PR adopt `LLMDataDist` for kv cache register and `pull_blocks`
style disaggregate prefill implementation. The interface implementation
mainly follows the design of NIXL PR
https://github.com/vllm-project/vllm/pull/17751/files#diff-7eaad0b7dee0626bf29d10081b0f0c5e3ea15a4af97e7b182a4e0d35f8346953
.

This PR can be test with the following step:
- Generate the rank table for all machine.
- execute`toy_proxy.py` to launch the disaggregate prefill proxy server,
specify the prefill ip, port and the decode ip, port
- Run the prefill server and decode server.
- send the request to the disaggregate prefill proxy

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.9.2
- vLLM main:
8d0a01a5f2

---------

Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
Signed-off-by: machenglong <machenglong_yewu@cmss.chinamobile.com>
Signed-off-by: liziyu179 <3475441767@qq.com>
Signed-off-by: underfitc <hucong24@huawei.com>
Signed-off-by: zouyida2052 <zouyida@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: underfituu <hzhucong@163.com>
Co-authored-by: machenglong <machenglong_yewu@cmss.chinamobile.com>
Co-authored-by: liziyu179 <3475441767@qq.com>
Co-authored-by: underfitc <hucong24@huawei.com>
Co-authored-by: zouyida2052 <zouyida@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
Co-authored-by: underfituu <hzhucong@163.com>

vllm_ascend/worker/worker_v1.py

@@ -78,6 +78,9 @@ class NPUWorker(WorkerBase):
                          is_driver_worker=is_driver_worker)
 
         # Try to import mindie_turbo to accelerate vLLM inference.
+        local_dp_rank = self.vllm_config.parallel_config.data_parallel_rank_local
+        world_size = self.vllm_config.parallel_config.world_size
+        self.local_rank_across_dp = local_dp_rank * world_size + self.local_rank
         try_register_lib(
             "mindie_turbo",
             "MindIE Turbo is installed. vLLM inference will be accelerated with MindIE Turbo."