[300I][Bugfix] fix unquant model weight nd2nz error (#6851)

### What this PR does / why we need it?
- This PR fixes an issue with weight format conversion for unquantized
models running on Ascend 310P devices.

- The changes refactor the logic for converting weights to the
FRACTAL_NZ format. Previously, this was handled in a 310P-specific
linear layer implementation (`AscendUnquantizedLinearMethod310`). This
implementation has been removed, and the logic is now centralized in the
`maybe_trans_nz` utility function. This function now checks if the
device is a 310P and applies the NZ format cast accordingly for
`float16`/`bfloat16` weights.

- This refactoring simplifies the code by removing platform-specific
duplication and ensures correct weight handling for unquantized models
on 310P.

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
ut and local test
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1

---------

Signed-off-by: Tflowers-0129 <2906339855@qq.com>

vllm_ascend/utils.py

@@ -134,22 +134,35 @@ def _unregister_print_streams_on_exit():
 atexit.register(_unregister_print_streams_on_exit)
 
 
-def maybe_trans_nz(weight: torch.Tensor):
+def _should_trans_nz(weight: torch.Tensor) -> bool:
+    # FP32 cannot use NZ.
+    if weight.dtype == torch.float32:
+        return False
+
+    # 310P always converts to NZ.
+    if is_310p():
+        return True
+
+    # NZ is disabled on non-310P.
     if not envs_ascend.VLLM_ASCEND_ENABLE_NZ:
-        # NZ is not enabled
+        return False
+
+    # BF16/FP16 convert only when enable_nz == 2.
+    if weight.dtype in {torch.bfloat16, torch.float16}:
+        return envs_ascend.VLLM_ASCEND_ENABLE_NZ == 2
+
+    # Quantized or other supported dtypes convert by default.
+    return True
+
+
+# NZ conversion policy:
+# - 310P: always convert supported weights to FRACTAL_NZ
+# - non-310P: follow VLLM_ASCEND_ENABLE_NZ
+# - FP32: never convert
+def maybe_trans_nz(weight: torch.Tensor) -> torch.Tensor:
+    if not _should_trans_nz(weight):
         return weight
-    if weight.dtype == torch.float:
-        # fp32 can not support NZ
-        return weight
-    elif weight.dtype in {torch.bfloat16, torch.float16}:
-        # bf16/fp16 will trans nz when VLLM_ASCEND_ENABLE_NZ is 2
-        if envs_ascend.VLLM_ASCEND_ENABLE_NZ == 2:
-            return torch_npu.npu_format_cast(weight, ACL_FORMAT_FRACTAL_NZ)
-        else:
-            return weight
-    else:
-        # quant weight will trans nz by default
-        return torch_npu.npu_format_cast(weight, ACL_FORMAT_FRACTAL_NZ)
+    return torch_npu.npu_format_cast(weight, ACL_FORMAT_FRACTAL_NZ)
 
 
 def _round_up(x: int, align: int):
@@ -631,6 +644,10 @@ def register_ascend_customop(vllm_config: VllmConfig | None = None):
         from vllm_ascend._310p.ops.activation import AscendSiluAndMul310
         from vllm_ascend._310p.ops.layernorm import AscendGemmaRMSNorm310, AscendRMSNorm310
         from vllm_ascend._310p.ops.rotary_embedding import AscendRotaryEmbedding310
+        from vllm_ascend._310p.ops.vocab_parallel_embedding import (
+            AscendParallelLMHead310,
+            AscendVocabParallelEmbedding310,
+        )
 
         REGISTERED_ASCEND_OPS.update(
             {
@@ -640,6 +657,8 @@ def register_ascend_customop(vllm_config: VllmConfig | None = None):
                 "GemmaRMSNorm": AscendGemmaRMSNorm310,
                 "FusedMoE": AscendFusedMoE310,
                 "SharedFusedMoE": AscendSharedFusedMoE310,
+                "ParallelLMHead": AscendParallelLMHead310,
+                "VocabParallelEmbedding": AscendVocabParallelEmbedding310,
             }
         )