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/attention/attention_v1.py

@@ -252,7 +252,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
         query: torch.Tensor,
         key: torch.Tensor,
         value: torch.Tensor,
-        kv_cache: torch.Tensor,
+        kv_cache: Tuple[torch.Tensor],
         attn_metadata: AscendMetadata,
         output: Optional[torch.Tensor] = None,
         trace_flag: bool = True,
@@ -262,8 +262,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
             query: shape = [batch_size, seq_len, num_heads * head_size]
             key: shape = [batch_size, seq_len, num_kv_heads * head_size]
             value: shape = [batch_size, seq_len, num_kv_heads * head_size]
-            kv_cache: shape = [2, num_blocks, block_size,
-                               num_kv_heads, head_size]
+            kv_cache: shape = [key_cache, value_cache]
                       key_cache = [num_blocks, block_size,
                                    num_kv_heads, head_size]
                       value_cache = [num_blocks, block_size,
@@ -273,8 +272,8 @@ class AscendAttentionBackendImpl(AttentionImpl):
             shape = [batch_size * seq_len, num_heads, head_size]
         """
         num_tokens = query.shape[0]
-        use_kv_cache_int8 = kv_cache.numel(
-        ) > 0 and kv_cache[0].dtype == torch.int8
+        use_kv_cache_int8 = len(
+            kv_cache) > 0 and kv_cache[0].dtype == torch.int8
         if output is None:
             output = torch.empty(num_tokens,
                                  self.num_heads,
@@ -314,7 +313,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
             # TODO: Remove this contiguous in the future.
             value = value.contiguous()
 
-            if kv_cache.numel() > 0:
+            if len(kv_cache) > 1:
                 if self.key_cache is None:
                     self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
                 slots = attn_metadata.slot_mapping