Fix FA2 integration (huggingface#28142)

* fix fa2

* fix FA2 for popular models

* improve warning and add Younes as co-author

Co-Authored-By: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/modeling_utils.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fix the warning

* Add Tip

* typo fix

* nit

---------

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

docs/source/en/model_doc/llama2.md

@@ -67,6 +67,8 @@ come in several checkpoints they each contain a part of each weight of the model
 
 - The LLaMA tokenizer is a BPE model based on [sentencepiece](https://github.com/google/sentencepiece). One quirk of sentencepiece is that when decoding a sequence, if the first token is the start of the word (e.g. "Banana"), the tokenizer does not prepend the prefix space to the string.
 
+- When using Flash Attention 2 via `attn_implementation="flash_attention_2"`, don't pass `torch_dtype` to the `from_pretrained` class method and use Automatic Mixed-Precision training. When using `Trainer`, it is simply specifying either `fp16` or `bf16` to `True`. Otherwise, make sure you are using `torch.autocast`. This is required because the Flash Attention only support `fp16` and `bf16` data type.
+
 
 ## Resources
 

src/transformers/modeling_utils.py

@@ -1419,9 +1419,9 @@ def _check_and_enable_flash_attn_2(
                 "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]:
-            raise ValueError(
-                f"Flash Attention 2.0 only supports torch.float16 and torch.bfloat16 dtypes. You passed {torch_dtype}, this might lead to"
-                " unexpected behaviour."
+            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."
             )
 
         # The check `torch.empty(0).device.type != "cuda"` is needed as the model may be initialized after `torch.set_default_device` has been called,

src/transformers/models/falcon/modeling_falcon.py

@@ -620,6 +620,8 @@ def forward(
             # Handle the case where the model is quantized
             if hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             else:
                 target_dtype = self.query_key_value.weight.dtype
 

src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py

@@ -378,6 +378,8 @@ def forward(
             # Handle the case where the model is quantized
             if hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             else:
                 target_dtype = self.c_attn.weight.dtype
 

src/transformers/models/llama/modeling_llama.py

@@ -531,6 +531,8 @@ def forward(
             # Handle the case where the model is quantized
             if hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             else:
                 target_dtype = self.q_proj.weight.dtype
 

src/transformers/models/mistral/modeling_mistral.py

@@ -431,6 +431,8 @@ def forward(
             # Handle the case where the model is quantized
             if hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             else:
                 target_dtype = self.q_proj.weight.dtype
 

src/transformers/models/mixtral/modeling_mixtral.py

@@ -479,6 +479,8 @@ def forward(
             # Handle the case where the model is quantized
             if hasattr(self.config, "_pre_quantization_dtype"):
                 target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             else:
                 target_dtype = self.q_proj.weight.dtype