[Feature] Support Flash Comm V1 for VL models (with MLA) (#7390)

## Summary

Flash Comm V1 (flashcomm1) was previously blocked for all VL models.

**Root cause:** For VL models, `inputs_embeds` at layer 0 originates
from the vision encoder as a full `[N, H]` tensor — it has **not** been
reduce-scattered across TP ranks. The original MLA forward path assumed
inputs were already scattered, producing wrong output shapes under TP >
1.

**Fix:**
- Detect at init time (statically, not via runtime shape checks) whether
a layer is the first layer of a VL model (`is_vl_first_layer`) so dynamo
treats the branch as a constant.
- In `AscendMultiHeadLatentAttention.forward`, when `flashcomm1 + TP > 1
+ is_vl_first_layer`, set `need_gather_q_kv=False` and pre-allocate
output as `[N//tp_size, H]`.
- Remove the platform-level assertion that prevented VL models from
enabling Flash Comm V1.

**Other improvements:**
- `is_vl_model()` now uses vllm's canonical detection (`hf_config is not
hf_text_config`) instead of fragile key-name checks, with the old checks
kept as fallback.
- Added `parse_layer_idx(prefix)` utility.
- Added `maybe_chunk_residual` call in `AscendRMSNorm` before the
add-rms-norm op.
- Removed unnecessary CPU/fp32 round-trip in
  `AscendLearnable2DInterpPosEmbDivided_fixed.forward()`.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Co-authored-by: LoganJane <loganJane73@hotmail.com>

vllm_ascend/utils.py

@@ -23,6 +23,7 @@ import atexit
 import functools
 import math
 import os
+import re
 from contextlib import nullcontext
 from enum import Enum
 from functools import lru_cache
@@ -842,15 +843,29 @@ def _is_contain_expert(config: Any):
 
 
 def is_vl_model(vllm_config: VllmConfig):
-    """Checks if the model is a VL model by config"""
+    """Checks if the model is a VL model by config.
+
+    Uses the same criterion as vllm itself (model_config.py): a model is
+    multimodal when its top-level hf_config differs from its hf_text_config
+    (i.e. there is a separate vision sub-config).  The legacy key-name checks
+    are kept as fallbacks for configs that override get_text_config() to return
+    self (rare but possible).
+    """
     global _IS_VL_MODEL
     if _IS_VL_MODEL is None and vllm_config and vllm_config.model_config:
-        hf_config = vllm_config.model_config.hf_config.to_dict()
-        if "thinker_config" in hf_config:
-            # Qwen-Omni-thinker models
+        model_config = vllm_config.model_config
+        # Primary: vllm's own VL detection — hf_config is the top-level
+        # (multimodal) config; hf_text_config is the language-model sub-config.
+        # They are the same object for pure-text models.
+        if model_config.hf_config is not model_config.hf_text_config:
             _IS_VL_MODEL = True
         else:
-            _IS_VL_MODEL = "vision_config" in hf_config
+            # Fallback: check well-known config keys
+            hf_config = model_config.hf_config.to_dict()
+            if "thinker_config" in hf_config or "vision_config" in hf_config:
+                _IS_VL_MODEL = True
+            else:
+                _IS_VL_MODEL = False
     return _IS_VL_MODEL
 
 
@@ -1244,3 +1259,9 @@ def trans_nd_to_nz(cache_tensor: torch.Tensor):
     cache_tensor = cache_tensor.reshape(nz_shape[:-4] + [m1, m0, n1, n0])
     cache_tensor = cache_tensor.permute(*array_trans)
     return cache_tensor
+
+
+def parse_layer_idx(prefix: str) -> int | None:
+    """Extract the layer index from a module prefix string like 'model.layers.0.self_attn'."""
+    match = re.search(r"layers\.(\d+)", prefix)
+    return int(match.group(1)) if match else None