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>

tests/ut/torchair/models/test_torchair_deepseek_v2.py

@@ -13,7 +13,7 @@
 # This file is a part of the vllm-ascend project.
 #
 from types import SimpleNamespace
-from unittest.mock import Mock, patch
+from unittest.mock import MagicMock, Mock, patch
 
 import pytest
 import torch
@@ -100,6 +100,11 @@ def mock_distributed():
     pp_group.rank_in_group = 0
     pp_group.world_size = 1
 
+    dcp_group = MagicMock(spec=GroupCoordinator)
+    dcp_group.rank_in_group = 0
+    dcp_group.world_size = 1
+    dcp_group.device_group = MagicMock()
+
     mlp_tp_group = Mock(spec=GroupCoordinator)
     mlp_tp_group.rank_in_group = 0
     mlp_tp_group.world_size = 1
@@ -117,6 +122,9 @@ def mock_distributed():
             patch("vllm_ascend.torchair.models.torchair_deepseek_v2.get_pp_group", return_value=pp_group), \
             patch("vllm_ascend.torchair.models.torchair_deepseek_v2.get_pp_group",
                   return_value=Mock(is_first_rank=False, is_last_rank=False)), \
+            patch('vllm.distributed.parallel_state.get_dcp_group', return_value=dcp_group), \
+            patch('vllm.distributed.parallel_state._DCP', new_callable=lambda: MagicMock(spec=GroupCoordinator)), \
+            patch("vllm.distributed.get_decode_context_model_parallel_world_size", return_value=1),\
             patch("vllm_ascend.torchair.ops.torchair_fused_moe.get_current_vllm_config", return_value=mock_vllm_config), \
             patch.dict("vllm.distributed.parallel_state.__dict__", _TP=tp_group, _EP=ep_group, _DP=dp_group,
                        _PP=pp_group), \