Pass attn_implementation when using AutoXXX.from_config

src/transformers/models/blip_2/modeling_blip_2.py

@@ -1194,9 +1194,13 @@ def __init__(self, config: Blip2Config):
 
         self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
         if config.use_decoder_only_language_model:
-            language_model = AutoModelForCausalLM.from_config(config.text_config)
+            language_model = AutoModelForCausalLM.from_config(
+                config.text_config, attn_implementation=config._attn_implementation
+            )
         else:
-            language_model = AutoModelForSeq2SeqLM.from_config(config.text_config)
+            language_model = AutoModelForSeq2SeqLM.from_config(
+                config.text_config, attn_implementation=config._attn_implementation
+            )
 
         # Update _tied_weights_keys using the base model used.
         if language_model._tied_weights_keys is not None:
@@ -1549,9 +1553,13 @@ def __init__(self, config: Blip2Config):
 
         self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
         if config.use_decoder_only_language_model:
-            language_model = AutoModelForCausalLM.from_config(config.text_config)
+            language_model = AutoModelForCausalLM.from_config(
+                config.text_config, attn_implementation=config._attn_implementation
+            )
         else:
-            language_model = AutoModelForSeq2SeqLM.from_config(config.text_config)
+            language_model = AutoModelForSeq2SeqLM.from_config(
+                config.text_config, attn_implementation=config._attn_implementation
+            )
 
         # Update _tied_weights_keys using the base model used.
         if language_model._tied_weights_keys is not None:

src/transformers/models/depth_anything/modeling_depth_anything.py

@@ -367,7 +367,9 @@ class DepthAnythingForDepthEstimation(DepthAnythingPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
 
-        self.backbone = AutoBackbone.from_config(config.backbone_config)
+        self.backbone = AutoBackbone.from_config(
+            config.backbone_config, attn_implementation=config._attn_implementation
+        )
         self.neck = DepthAnythingNeck(config)
         self.head = DepthAnythingDepthEstimationHead(config)
 

src/transformers/models/encoder_decoder/modeling_encoder_decoder.py

@@ -209,12 +209,12 @@ def __init__(
         if encoder is None:
             from ..auto.modeling_auto import AutoModel
 
-            encoder = AutoModel.from_config(config.encoder)
+            encoder = AutoModel.from_config(config.encoder, attn_implementation=config._attn_implementation)
 
         if decoder is None:
             from ..auto.modeling_auto import AutoModelForCausalLM
 
-            decoder = AutoModelForCausalLM.from_config(config.decoder)
+            decoder = AutoModelForCausalLM.from_config(config.decoder, attn_implementation=config._attn_implementation)
 
         self.encoder = encoder
         self.decoder = decoder

src/transformers/models/fuyu/modeling_fuyu.py

@@ -149,7 +149,9 @@ def __init__(self, config: FuyuConfig):
         super().__init__(config)
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
-        self.language_model = AutoModelForCausalLM.from_config(config.text_config)
+        self.language_model = AutoModelForCausalLM.from_config(
+            config.text_config, attn_implementation=config._attn_implementation
+        )
 
         self.vision_embed_tokens = nn.Linear(
             config.patch_size * config.patch_size * config.num_channels, config.hidden_size

src/transformers/models/grounding_dino/modeling_grounding_dino.py

@@ -2113,7 +2113,9 @@ def __init__(self, config: GroundingDinoConfig):
             )
 
         # Create text backbone
-        self.text_backbone = AutoModel.from_config(config.text_config, add_pooling_layer=False)
+        self.text_backbone = AutoModel.from_config(
+            config.text_config, add_pooling_layer=False, attn_implementation=config._attn_implementation
+        )
         self.text_projection = nn.Linear(config.text_config.hidden_size, config.d_model)
 
         if config.embedding_init_target or not config.two_stage:

src/transformers/models/idefics2/modeling_idefics2.py

@@ -1476,7 +1476,7 @@ def __init__(self, config: Idefics2Config):
 
         self.vision_model = Idefics2VisionTransformer(config.vision_config)
         self.connector = Idefics2Connector(config)
-        self.text_model = AutoModel.from_config(config.text_config)
+        self.text_model = AutoModel.from_config(config.text_config, attn_implementation=config._attn_implementation)
 
         self.image_seq_len = config.perceiver_config.resampler_n_latents
         self.image_token_id = self.config.image_token_id

src/transformers/models/instructblip/modeling_instructblip.py

@@ -1251,9 +1251,13 @@ def __init__(self, config: InstructBlipConfig):
         self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
 
         if config.use_decoder_only_language_model:
-            language_model = AutoModelForCausalLM.from_config(config.text_config)
+            language_model = AutoModelForCausalLM.from_config(
+                config.text_config, attn_implementation=config._attn_implementation
+            )
         else:
-            language_model = AutoModelForSeq2SeqLM.from_config(config.text_config)
+            language_model = AutoModelForSeq2SeqLM.from_config(
+                config.text_config, attn_implementation=config._attn_implementation
+            )
 
         if language_model._no_split_modules is not None:
             self._no_split_modules.extend(language_model._no_split_modules)

src/transformers/models/rag/modeling_rag.py

@@ -506,12 +506,16 @@ def __init__(
         if question_encoder is None:
             from ..auto.modeling_auto import AutoModel
 
-            question_encoder = AutoModel.from_config(config.question_encoder)
+            question_encoder = AutoModel.from_config(
+                config.question_encoder, attn_implementation=config._attn_implementation
+            )
 
         if generator is None:
             from ..auto.modeling_auto import AutoModelForSeq2SeqLM
 
-            generator = AutoModelForSeq2SeqLM.from_config(config.generator)
+            generator = AutoModelForSeq2SeqLM.from_config(
+                config.generator, attn_implementation=config._attn_implementation
+            )
 
         self.retriever = retriever
         if self.retriever is not None:

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -212,10 +212,10 @@ def __init__(
         super().__init__(config)
 
         if encoder is None:
-            encoder = AutoModel.from_config(config.encoder)
+            encoder = AutoModel.from_config(config.encoder, attn_implementation=config._attn_implementation)
 
         if decoder is None:
-            decoder = AutoModelForCausalLM.from_config(config.decoder)
+            decoder = AutoModelForCausalLM.from_config(config.decoder, attn_implementation=config._attn_implementation)
 
         self.encoder = encoder
         self.decoder = decoder

src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py

@@ -190,10 +190,10 @@ def __init__(
         super().__init__(config)
 
         if encoder is None:
-            encoder = AutoModel.from_config(config.encoder)
+            encoder = AutoModel.from_config(config.encoder, attn_implementation=config._attn_implementation)
 
         if decoder is None:
-            decoder = AutoModelForCausalLM.from_config(config.decoder)
+            decoder = AutoModelForCausalLM.from_config(config.decoder, attn_implementation=config._attn_implementation)
 
         self.encoder = encoder
         self.decoder = decoder

src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py

@@ -185,10 +185,12 @@ def __init__(
             if isinstance(config.vision_config, CLIPVisionConfig):
                 vision_model = CLIPVisionModel(config.vision_config)
             else:
-                vision_model = AutoModel.from_config(config.vision_config)
+                vision_model = AutoModel.from_config(
+                    config.vision_config, attn_implementation=config._attn_implementation
+                )
 
         if text_model is None:
-            text_model = AutoModel.from_config(config.text_config)
+            text_model = AutoModel.from_config(config.text_config, attn_implementation=config._attn_implementation)
 
         self.vision_model = vision_model
         self.text_model = text_model