[bugfix] align max_num_batched_tokens with tp*pcp when using FLASHCOMM1 (#6000)

### What this PR does / why we need it?
Align max_num_batched_tokens with tp*pcp when using FLASHCOMM1 to avoid
assert error in `NPUModelRunner._dummy_run`.

- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

---------

Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>

tests/ut/attention/test_mla_cp.py

@@ -176,12 +176,16 @@ class TestAscendMLAImpl(TestBase):
         vllm_config = MagicMock()
         speculative_config = MagicMock()
         model_config = MagicMock()
+        parallel_config = MagicMock()
+        parallel_config.prefill_context_parallel_size = 1
+        parallel_config.tensor_parallel_size = 2
         speculative_config.num_speculative_tokens = 4
         vllm_config.speculative_config = speculative_config
         model_config.dtype = torch.float16
         vllm_config.model_config = model_config
         get_current_vllm_config.return_value = vllm_config
         vllm_config.additional_config = {"refresh": True}
+        vllm_config.parallel_config = parallel_config
         init_ascend_config(vllm_config)
 
         num_heads = 256

tests/ut/attention/test_mla_v1.py

@@ -757,12 +757,15 @@ class TestAscendMLAImpl(TestBase):
         vllm_config = MagicMock()
         speculative_config = MagicMock()
         model_config = MagicMock()
+        parallel_config = MagicMock()
+        parallel_config.prefill_context_parallel_size = 1
         speculative_config.num_speculative_tokens = 4
         vllm_config.speculative_config = speculative_config
         model_config.dtype = torch.float16
         vllm_config.model_config = model_config
         get_current_vllm_config.return_value = vllm_config
         vllm_config.additional_config = {"refresh": True}
+        vllm_config.parallel_config = parallel_config
         init_ascend_config(vllm_config)
 
         num_heads = 256

tests/ut/attention/test_sfa_v1.py

@@ -5,6 +5,7 @@ import torch
 
 from tests.ut.base import TestBase
 from vllm_ascend.attention.attention_v1 import AscendAttentionState
+from vllm.distributed.parallel_state import GroupCoordinator
 
 if 'torch_npu._inductor' not in sys.modules:
     sys.modules['torch_npu._inductor'] = MagicMock()
@@ -81,7 +82,13 @@ class TestAscendSFAMetadata(TestBase):
 
 class TestAscendSFAMetadataBuilder(TestBase):
 
-    def setUp(self):
+    @patch('vllm.distributed.parallel_state._TP',
+           new_callable=lambda: MagicMock(spec=GroupCoordinator))
+    def setUp(self, mock_tp):
+        mock_tp.world_size = 2
+        mock_tp.rank_in_group = MagicMock()
+        mock_tp.device_group = MagicMock()
+
         self.mock_cfg = MagicMock()
 
         self.mock_cfg.parallel_config = MagicMock()

vllm_ascend/ascend_config.py

@@ -18,6 +18,7 @@ from typing import TYPE_CHECKING
 
 from vllm.logger import logger
 from vllm.triton_utils import HAS_TRITON
+from vllm.utils.math_utils import cdiv
 
 if TYPE_CHECKING:
     from vllm.config import VllmConfig
@@ -62,10 +63,25 @@ class AscendConfig:
             additional_config.get("enable_shared_expert_dp", False)
             and vllm_config.parallel_config.enable_expert_parallel
         )
-        if self.enable_shared_expert_dp:
+        from vllm_ascend.utils import enable_sp
-            from vllm_ascend.utils import enable_sp
 
+        if self.enable_shared_expert_dp:
             assert enable_sp(vllm_config=vllm_config, enable_shared_expert_dp=True)
+
+        if vllm_config.parallel_config.prefill_context_parallel_size > 1 and enable_sp(vllm_config=vllm_config):
+            tp_pcp_size = (
+                vllm_config.parallel_config.tensor_parallel_size
+                * vllm_config.parallel_config.prefill_context_parallel_size
+            )
+            if vllm_config.scheduler_config.max_num_batched_tokens % tp_pcp_size != 0:
+                vllm_config.scheduler_config.max_num_batched_tokens = (
+                    cdiv(vllm_config.scheduler_config.max_num_batched_tokens, tp_pcp_size) * tp_pcp_size
+                )
+                logger.warning_once(
+                    f"When using FLASHCOMM1, the max_num_batched_tokens should be divisible"
+                    f"by tp_size * pcp_size ({tp_pcp_size}). It has been adjusted to"
+                    f"{vllm_config.scheduler_config.max_num_batched_tokens}."
+                )
         self.multistream_overlap_shared_expert = additional_config.get("multistream_overlap_shared_expert", False)
         self.multistream_overlap_gate = additional_config.get("multistream_overlap_gate", False)
         self.recompute_scheduler_enable = additional_config.get("recompute_scheduler_enable", False)