[Misc] rename torch_dtype to dtype (#26695)

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm/model_executor/models/longcat_flash.py

@@ -114,7 +114,7 @@ class FlashConfig(PretrainedConfig):
         attention_dropout=0.0,
         mla_scale_q_lora=False,
         mla_scale_kv_lora=False,
-        torch_dtype="bfloat16",
+        dtype="bfloat16",
         params_dtype="bfloat16",
         router_dtype="float32",
         router_bias=False,
@@ -130,7 +130,7 @@ class FlashConfig(PretrainedConfig):
             bos_token_id=bos_token_id,
             eos_token_id=eos_token_id,
             tie_word_embeddings=tie_word_embeddings,
-            torch_dtype=torch_dtype,
+            dtype=dtype,
             params_dtype=params_dtype,
             router_dtype=router_dtype,
             topk_method=topk_method,

vllm/model_executor/models/nano_nemotron_vl.py

@@ -987,7 +987,7 @@ class NemotronH_Nano_VL_V2(
             prefix=maybe_prefix(prefix, "language_model"),
         )
         self.vision_model = self.get_vit_model_from_radio_config(config).to(
-            self.language_model.config.torch_dtype
+            self.language_model.config.dtype
         )
 
         # Construct the vision projection.
@@ -1008,7 +1008,7 @@ class NemotronH_Nano_VL_V2(
             ReLUSquaredActivation(),
             nn.Linear(vision_projection_hidden_size, llm_hidden_size, bias=False),
         )
-        self.mlp1 = self.mlp1.to(self.language_model.config.torch_dtype)
+        self.mlp1 = self.mlp1.to(self.language_model.config.dtype)
 
         self.config = config
         self.model_config = vllm_config.model_config

vllm/model_executor/models/qwen3_next.py

@@ -338,7 +338,7 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
             group_size=None,
             norm_before_gate=True,
             device=current_platform.current_device(),
-            dtype=config.torch_dtype,
+            dtype=config.dtype,
         )
 
         self.out_proj = RowParallelLinear(
@@ -847,7 +847,7 @@ class Qwen3NextDecoderLayer(nn.Module):
                     1,
                     1,
                     config.hidden_size,
-                    dtype=config.torch_dtype,
+                    dtype=config.dtype,
                 ),
             )
             self.ffn_layer_scale = torch.nn.Parameter(
@@ -855,7 +855,7 @@ class Qwen3NextDecoderLayer(nn.Module):
                     1,
                     1,
                     config.hidden_size,
-                    dtype=config.torch_dtype,
+                    dtype=config.dtype,
                 ),
             )
 

vllm/model_executor/models/transformers.py

@@ -530,7 +530,7 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
         with init_on_device_without_buffers("meta"):
             self.model: PreTrainedModel = AutoModel.from_config(
                 self.config,
-                torch_dtype=self.model_config.dtype,
+                dtype=self.model_config.dtype,
                 trust_remote_code=self.model_config.trust_remote_code,
             )
 

vllm/model_executor/models/transformers_pooling.py

@@ -157,7 +157,7 @@ class TransformersForSequenceClassification(TransformersPoolingBase):
         with torch.device("meta"):
             seq_cls_model = AutoModelForSequenceClassification.from_config(
                 self.config,
-                torch_dtype=self.model_config.dtype,
+                dtype=self.model_config.dtype,
                 trust_remote_code=self.model_config.trust_remote_code,
             )
 

vllm/platforms/cuda.py

@@ -500,8 +500,8 @@ class CudaPlatformBase(Platform):
         return supported
 
     @classmethod
-    def check_if_supports_dtype(cls, torch_dtype: torch.dtype):
-        if torch_dtype == torch.bfloat16:  # noqa: SIM102
+    def check_if_supports_dtype(cls, dtype: torch.dtype):
+        if dtype == torch.bfloat16:  # noqa: SIM102
             if not cls.has_device_capability(80):
                 capability = cls.get_device_capability()
                 gpu_name = cls.get_device_name()

vllm/platforms/interface.py

@@ -563,7 +563,7 @@ class Platform:
         return False
 
     @classmethod
-    def check_if_supports_dtype(cls, torch_dtype: torch.dtype):
+    def check_if_supports_dtype(cls, dtype: torch.dtype):
         """
         Check if the dtype is supported by the current platform.
         """

vllm/platforms/rocm.py

@@ -484,8 +484,8 @@ class RocmPlatform(Platform):
         return True
 
     @classmethod
-    def check_if_supports_dtype(cls, torch_dtype: torch.dtype):
-        if torch_dtype == torch.bfloat16:  # noqa: SIM102
+    def check_if_supports_dtype(cls, dtype: torch.dtype):
+        if dtype == torch.bfloat16:  # noqa: SIM102
             if not cls.has_device_capability(80):
                 capability = cls.get_device_capability()
                 gpu_name = cls.get_device_name()

vllm/platforms/xpu.py

@@ -236,8 +236,8 @@ class XPUPlatform(Platform):
         return torch.xpu.device_count()
 
     @classmethod
-    def check_if_supports_dtype(cls, torch_dtype: torch.dtype):
-        if torch_dtype == torch.bfloat16:  # noqa: SIM102
+    def check_if_supports_dtype(cls, dtype: torch.dtype):
+        if dtype == torch.bfloat16:  # noqa: SIM102
             device_name = cls.get_device_name().lower()
             # client gpu a770
             if device_name.count("a770") > 0:

vllm/utils/__init__.py

@@ -806,7 +806,7 @@ def create_kv_caches_with_random_flash(
 
     current_platform.seed_everything(seed)
 
-    torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
+    dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
     generic_kv_cache_shape = (num_blocks, 2, block_size, num_heads, head_size)
     assert cache_layout in ("NHD", "HND")
     stride_order = (0, 1, 2, 3, 4) if cache_layout == "NHD" else (0, 1, 3, 2, 4)
@@ -819,7 +819,7 @@ def create_kv_caches_with_random_flash(
 
     for _ in range(num_layers):
         key_value_cache = torch.empty(
-            size=kv_cache_allocation_shape, dtype=torch_dtype, device=device
+            size=kv_cache_allocation_shape, dtype=dtype, device=device
         ).permute(*stride_order)
         if cache_dtype in ["auto", "half", "bfloat16", "float"]:
             key_value_cache.uniform_(-scale, scale)
@@ -851,14 +851,14 @@ def create_kv_caches_with_random(
 
     current_platform.seed_everything(seed)
 
-    torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
+    dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
 
     scale = head_size**-0.5
-    x = 16 // torch.tensor([], dtype=torch_dtype).element_size()
+    x = 16 // torch.tensor([], dtype=dtype).element_size()
     key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
     key_caches: list[torch.Tensor] = []
     for _ in range(num_layers):
-        key_cache = torch.empty(size=key_cache_shape, dtype=torch_dtype, device=device)
+        key_cache = torch.empty(size=key_cache_shape, dtype=dtype, device=device)
         if cache_dtype in ["auto", "half", "bfloat16", "float"]:
             key_cache.uniform_(-scale, scale)
         elif cache_dtype == "fp8":
@@ -870,9 +870,7 @@ def create_kv_caches_with_random(
     value_cache_shape = (num_blocks, num_heads, head_size, block_size)
     value_caches: list[torch.Tensor] = []
     for _ in range(num_layers):
-        value_cache = torch.empty(
-            size=value_cache_shape, dtype=torch_dtype, device=device
-        )
+        value_cache = torch.empty(size=value_cache_shape, dtype=dtype, device=device)
         if cache_dtype in ["auto", "half", "bfloat16", "float"]:
             value_cache.uniform_(-scale, scale)
         elif cache_dtype == "fp8":