Fix duplicate & unnecessary flash attention warnings (#28557)

* fix duplicate & unnecessary flash warnings

* trigger ci

* warning_once

* if/else order

---------
Co-authored-by: Your Name <you@example.com>

src/transformers/modeling_utils.py

@@ -1321,7 +1321,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         config = copy.deepcopy(config)  # We do not want to modify the config inplace in _from_config.
         config._attn_implementation = kwargs.pop("attn_implementation", None)
         config = cls._autoset_attn_implementation(
-            config, use_flash_attention_2=use_flash_attention_2, check_device_map=False
+            config,
+            use_flash_attention_2=use_flash_attention_2,
+            check_device_map=False,
+            torch_dtype=torch_dtype,
         )
 
         if is_deepspeed_zero3_enabled():
@@ -1396,7 +1399,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         elif requested_attn_implementation in [None, "sdpa"]:
             # use_flash_attention_2 takes priority over SDPA, hence SDPA treated in this elif.
             config = cls._check_and_enable_sdpa(
-                config, hard_check_only=False if requested_attn_implementation is None else True
+                config,
+                hard_check_only=False if requested_attn_implementation is None else True,
             )
         else:
             config._attn_implementation = "eager"
@@ -1503,20 +1507,21 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             )
 
         if torch_dtype is None:
-            logger.warning(
+            logger.warning_once(
                 "You are attempting to use Flash Attention 2.0 without specifying a torch dtype. This might lead to unexpected behaviour"
             )
         elif torch_dtype is not None and torch_dtype not in [torch.float16, torch.bfloat16]:
-            logger.warning(
-                "Flash Attention 2.0 only supports torch.float16 and torch.bfloat16 dtypes. "
-                "No dtype was provided, you should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator."
+            logger.warning_once(
+                "Flash Attention 2.0 only supports torch.float16 and torch.bfloat16 dtypes, but"
+                f" the current dype in {cls.__name__} is {torch_dtype}. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator,"
+                ' or load the model with the `torch_dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", torch_dtype=torch.float16)`'
             )
 
         # The check `torch.empty(0).device.type != "cuda"` is needed as the model may be initialized after `torch.set_default_device` has been called,
         # or the model may be initialized under the context manager `with torch.device("cuda"):`.
         if check_device_map and device_map is None and torch.empty(0).device.type != "cuda":
             if torch.cuda.is_available():
-                logger.warning(
+                logger.warning_once(
                     "You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU"
                     " after initializing it on CPU with `model.to('cuda')`."
                 )