support cp&dcp (#3260)

### What this PR does / why we need it?
This PR adds the Prefill Context Parallelism (PCP) feature, which
corresponds to DCP. For specific implementation details, please refer to
the RFC https://github.com/vllm-project/vllm/issues/25749.
TL;DR: PCP enhances long-sequence inference capabilities by partitioning
the sequence dimension during the prefill stage.
### Does this PR introduce _any_ user-facing change?
The current implementation primarily includes the following changes:

Modified ModelRunner.py for CP partitioning logic for tokens;
Modified attention_v1.py and mla_v1.py to adapt the GQA/MLA backend to
PCP.
Modified block_tables.py to extend the KV cache storage based on
DCP&PCP;
Added necessary command-line arguments to control parallelism for PCP;
### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: LookAround <lixushi@huawei.com>
Signed-off-by: chenjie <chenjie137@huawei.com>
Signed-off-by: Delphine-Nic <tanwenqin@huawei.com>
Signed-off-by: zhangsicheng5 <zhangsicheng5@huawei.com>
Signed-off-by: Feng Liu <liufeng248@huawei.com>
Signed-off-by: gaojc <1055866782@qq.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Signed-off-by: z50049692 <zhangmingwei11@huawei.com>
Co-authored-by: chenjie <chenjie137@huawei.com>
Co-authored-by: Delphine-Nic <tanwenqin@huawei.com>
Co-authored-by: zhangsicheng5 <zhangsicheng5@huawei.com>
Co-authored-by: Feng Liu <liufeng248@huawei.com>
Co-authored-by: gaojc <1055866782@qq.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: z50049692 <zhangmingwei11@huawei.com>
Co-authored-by: w00896881 <wangzixuan40@huawei.com>

vllm_ascend/distributed/parallel_state.py

@@ -7,6 +7,7 @@ from vllm.distributed.parallel_state import (GroupCoordinator, get_world_group,
 
 import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.utils import prefill_context_parallel_enable
 
 # Currently, mc2 op need their own group coordinator.
 _MC2: Optional[GroupCoordinator] = None
@@ -58,9 +59,15 @@ def init_ascend_model_parallel(parallel_config: ParallelConfig, ):
     # The layout of all ranks: ExternalDP * EP
     # ExternalDP is the data parallel group that is not part of the model,
     # every dp rank can generate independently (in verl integration).
-    all_ranks = torch.arange(world_size).reshape(
-        -1, parallel_config.data_parallel_size *
-        parallel_config.tensor_parallel_size)
+    if prefill_context_parallel_enable():
+        all_ranks = torch.arange(world_size).reshape(
+            -1, parallel_config.data_parallel_size *
+            parallel_config.prefill_context_parallel_size *
+            parallel_config.tensor_parallel_size)
+    else:
+        all_ranks = torch.arange(world_size).reshape(
+            -1, parallel_config.data_parallel_size *
+            parallel_config.tensor_parallel_size)
 
     pd_tp_ratio = get_ascend_config().pd_tp_ratio
     pd_head_ratio = get_ascend_config().pd_head_ratio