[main] flashcomm_v1 optim in Qwen Dense Models (#2802)

### What this PR does / why we need it?
Flashcomm_v1 optim in Qwen Dense Models.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.10.1.1
- vLLM main:
5e537f45b4

Co-authored-by: 1024daniel <xxltju324@gmail.com>

vllm_ascend/ascend_forward_context.py

@@ -83,6 +83,7 @@ def set_ascend_forward_context(
         forward_context = get_forward_context()
         forward_context.moe_comm_method_name = moe_comm_method + "commimpl"
         forward_context.with_prefill = with_prefill
+        tp_world_size = get_tensor_model_parallel_world_size()
         ep_size = (get_ep_group().world_size if
                    vllm_config.parallel_config.enable_expert_parallel else 1)
 
@@ -103,6 +104,21 @@ def set_ascend_forward_context(
         # due to multiple warmups before actual capturing
         forward_context.capturing = False
 
+        # set for flashcomm_v1, 1000 is the batchsize concurrency threshold for enabling the flashcomm_v1 feature.
+        # Currently, it is an empirical value. In normal scenarios, if the concurrency exceeds this threshold,
+        # the performance benefits can be maximized. Conversely, if the concurrency is below the threshold,
+        # the performance may degrade due to the switching of communication methods.
+        flashcomm_v1_enabled = envs_ascend.VLLM_ASCEND_ENABLE_FLASHCOMM and \
+            tp_world_size > 1 and \
+            num_tokens is not None and num_tokens > 1000
+
+        if flashcomm_v1_enabled:
+            pad_size = (tp_world_size -
+                        (num_tokens % tp_world_size)) % tp_world_size
+            forward_context.pad_size = pad_size
+
+        forward_context.flashcomm_v1_enabled = flashcomm_v1_enabled
+
         if num_tokens is None and attn_metadata is not None:
             num_tokens = attn_metadata.num_actual_tokens
 
@@ -118,7 +134,6 @@ def set_ascend_forward_context(
         if num_tokens is not None:
             if num_actual_tokens is None:
                 num_actual_tokens = num_tokens
-            tp_world_size = get_tensor_model_parallel_world_size()
             # NOTE: token num which need to pad to when mc2
             forward_context.padded_num_tokens = math.ceil(
                 max_tokens_across_dp / tp_world_size) * tp_world_size