For nz unset in bf16&fp16

vllm_ascend/attention/mla_v1.py

@@ -652,7 +652,7 @@ def get_and_maybe_dequant_weights(layer: LinearBase):
 
         # Function `get_and_maybe_dequant_weights` will cast the weights to
         # FRACTAL_AND. So we need to cast to FRACTAL_NZ again.
-        if is_enable_nz():
+        if is_enable_nz(self.kv_b_proj.weight.data.dtype):
             self.kv_b_proj.weight.data = torch_npu.npu_format_cast(
                 self.kv_b_proj.weight.data, ACL_FORMAT_FRACTAL_NZ)
 

vllm_ascend/models/qwen2_5_vl.py

@@ -284,7 +284,7 @@ def pad_qkv_weight(self, data):
             dim=2)
         qkv_weight_final = qkv_weight_padded.reshape(-1, self.hidden_size)
 
-        if is_enable_nz():
+        if is_enable_nz(qkv_weight_final.dtype):
             qkv_weight_final_copy = torch.empty_like(qkv_weight_final).copy_(
                 qkv_weight_final)
             qkv_weight_final_copy = torch_npu.npu_format_cast(
@@ -300,7 +300,7 @@ def pad_proj_weight(self, data):
             (0, self.half_pad_hidden_size_per_attention_head, 0, 0)).reshape(
                 self.hidden_size, -1)
 
-        if is_enable_nz():
+        if is_enable_nz(out_weight.dtype):
             out_weight_copy = torch.empty_like(out_weight).copy_(out_weight)
             out_weight_copy = torch_npu.npu_format_cast(
                 out_weight_copy, ACL_FORMAT_FRACTAL_ND)

vllm_ascend/models/qwen2_vl.py

@@ -268,7 +268,7 @@ def pad_qkv_weight(self, data):
             dim=2)
         qkv_weight_final = qkv_weight_padded.reshape(-1, self.hidden_size)
 
-        if is_enable_nz():
+        if is_enable_nz(qkv_weight_final.dtype):
             qkv_weight_final_copy = torch.empty_like(qkv_weight_final).copy_(
                 qkv_weight_final)
             qkv_weight_final_copy = torch_npu.npu_format_cast(
@@ -284,7 +284,7 @@ def pad_proj_weight(self, data):
             (0, self.half_pad_hidden_size_per_attention_head, 0, 0)).reshape(
                 self.hidden_size, -1)
 
-        if is_enable_nz():
+        if is_enable_nz(out_weight.dtype):
             out_weight_copy = torch.empty_like(out_weight).copy_(out_weight)
             out_weight_copy = torch_npu.npu_format_cast(
                 out_weight_copy, ACL_FORMAT_FRACTAL_ND)

vllm_ascend/ops/common_fused_moe.py

@@ -76,7 +76,7 @@ def process_weights_after_loading(self, layer):
             w2_data = self._maybe_pad_weight(layer.w2_weight.data)
             layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
 
-        if not is_310p() and is_enable_nz():
+        if not is_310p() and is_enable_nz(layer.w13_weight.data.dtype):
             layer.w13_weight.data = torch_npu.npu_format_cast(
                 layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
             layer.w2_weight.data = torch_npu.npu_format_cast(

vllm_ascend/ops/linear.py

@@ -45,8 +45,7 @@ class AscendUnquantizedLinearMethod(UnquantizedLinearMethod):
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         super().process_weights_after_loading(layer)
-        if (is_enable_nz() and layer.weight.data.dtype
-                in [torch.float16, torch.bfloat16]):
+        if (is_enable_nz(layer.weight.data.dtype)):
             layer.weight.data = torch_npu.npu_format_cast(
                 layer.weight.data, ACL_FORMAT_FRACTAL_NZ)
 

vllm_ascend/torchair/torchair_sfa.py

@@ -842,7 +842,7 @@ def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
         wd_qkv = wd_qkv.t().contiguous()
         wd_qkv = transdata(wd_qkv,
                            block_size=(16, 32)).unsqueeze(0).contiguous()
-        if is_enable_nz():
+        if is_enable_nz(wd_qkv.dtype):
             self.wd_qkv = torch_npu.npu_format_cast(wd_qkv, 29)
 
         kv_a_proj_deq_scl = self.kv_a_proj_with_mqa.deq_scale.clone()
@@ -876,7 +876,7 @@ def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
             self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim),
             -1)
         wu_q = transdata(wu_q, block_size=(16, 32)).unsqueeze(0).contiguous()
-        if is_enable_nz():
+        if is_enable_nz(wu_q.dtype):
             self.wu_q = torch_npu.npu_format_cast(wu_q, 29)
 
         qb_deq_scl = self.q_proj.deq_scale.data.clone()

vllm_ascend/torchair/utils.py

@@ -146,7 +146,7 @@ def converting_weight_acl_format(model, format):
             if torch_npu.get_npu_format(module.w13_weight.data) == format:
                 return
             if format == ACL_FORMAT_FRACTAL_NZ \
-                    and not is_enable_nz():
+                    and not is_enable_nz(module.w13_weight.data.dtype):
                 return
             module.w13_weight.data = torch_npu.npu_format_cast(
                 module.w13_weight.data, format)

vllm_ascend/utils.py

@@ -71,13 +71,16 @@ def is_310p():
     return _IS_310P
 
 
-def is_enable_nz(vllm_config: Optional[VllmConfig] = None) -> bool:
+def is_enable_nz(dtype: Optional[torch.dtype] = torch.int8,
+                 vllm_config: Optional[VllmConfig] = None) -> bool:
     global _ENABLE_NZ
     if _ENABLE_NZ is None:
         if not vllm_config:
             raise ValueError(
                 "vllm_config must be provided when _ENABLE_NZ is None")
         _ENABLE_NZ = envs_ascend.VLLM_ASCEND_ENABLE_NZ and vllm_config.model_config.hf_config.model_type != "qwen3_next"
+    if dtype in [torch.float16, torch.bfloat16]:
+        return False
     return _ENABLE_NZ
 
 

vllm_ascend/worker/model_runner_v1.py

@@ -2676,7 +2676,7 @@ def load_model(self) -> None:
 
     def _convert_torch_format(self, tensor):
         if ACL_FORMAT == ACL_FORMAT_FRACTAL_NZ \
-                and not is_enable_nz():
+                and not is_enable_nz(tensor.dtype):
             return tensor
         tensor = torch_npu.npu_format_cast(tensor, ACL_FORMAT)
         return tensor

vllm_ascend/worker/worker_v1.py

@@ -81,7 +81,7 @@ def __init__(
         # register patch for vllm
         from vllm_ascend.utils import adapt_patch
         adapt_patch()
-        is_enable_nz(vllm_config)
+        is_enable_nz(vllm_config=vllm_config)
         # Register ops when worker init.
         from vllm_ascend import ops
         ops.register_dummy_fusion_op()