[Fix] Fix mc2_tokens_capacity-related issues (#3411)

### What this PR does / why we need it?
Replaces the hardcoded `mc2_tokens_capacity` with the max graph capture
size for a more accurate allocation.

This change ensures the capacity is correctly sized relative to the
graph capture configuration, removing a magic number and making the
setup more robust.

This PR fixes two issues:

1. <del>MC2 op restrictions differ between SoCs.</del> @Angazenn This
requires an overhaul, hence removed from this PR, please commit another
PR.
2. The hardcoded value `512` allocates too much buffer for large models.

### Does this PR introduce _any_ user-facing change?
None.

### How was this patch tested?
Tested in daily checks.


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

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/worker/model_runner_v1.py

@@ -458,15 +458,23 @@ class NPUModelRunner(LoRAModelRunnerMixin):
             self.is_kv_producer = vllm_config.kv_transfer_config.is_kv_producer
             self.is_kv_consumer = vllm_config.kv_transfer_config.is_kv_consumer
 
-        # NOTE: Technically, MC2 can have 512 tokens each rank, but this will consume too much memory. The formula is:
-        # ((maxBs * tokenNeedSizeDispatch * ep_worldsize * localMoeExpertNum) + (maxBs * tokenNeedSizeCombine * (k + sharedExpertNum))) * 2
-        # so we have to limit the MC2 tokens to save memory, should fix this in the future.
-        self.mc2_tokens_capacity = 512
+        # NOTE: To be clear, we need to make sure that during graph capture, the number of
+        # tokens is less than or equal to mc2_tokens_capacity. According to _set_cudagraph_sizes,
+        # the max number of tokens in graph is min(max_num_seqs * 2, 512).
+        if self.compilation_config.cudagraph_capture_sizes:
+            max_num_tokens = self.compilation_config.cudagraph_capture_sizes[0]
+        else:
+            max_num_tokens = self.max_num_reqs * self.uniform_decode_query_len
+        tp_size = self.parallel_config.tensor_parallel_size
+        # Use integer arithmetic for ceiling division.
+        num_tokens_per_tp_rank = (max_num_tokens + tp_size - 1) // tp_size
+        self.mc2_tokens_capacity = num_tokens_per_tp_rank * tp_size
         self.reserved_mc2_mask = torch.zeros(
             self.mc2_tokens_capacity,
             dtype=torch.bool,
             device=self.device,
         )
+
         self.dynamic_eplb = self.ascend_config.dynamic_eplb
         if self.dynamic_eplb:
             self.is_eplb_warmuped = False