fix FA2 when using quantization (huggingface#28203)

src/transformers/models/falcon/modeling_falcon.py

@@ -617,11 +617,11 @@ def forward(
         # cast them back in float16 just to be sure everything works as expected.
         input_dtype = query_layer.dtype
         if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
+            elif 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

@@ -375,11 +375,11 @@ def forward(
         # cast them back in float16 just to be sure everything works as expected.
         input_dtype = query.dtype
         if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
+            elif 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

@@ -528,11 +528,11 @@ def forward(
 
         input_dtype = query_states.dtype
         if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
+            elif 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

@@ -428,11 +428,11 @@ def forward(
         # cast them back in float16 just to be sure everything works as expected.
         input_dtype = query_states.dtype
         if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
+            elif 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

@@ -477,11 +477,11 @@ def forward(
         # cast them back in float16 just to be sure everything works as expected.
         input_dtype = query_states.dtype
         if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
             # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
+            elif 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