Sdpa dino v2 (#33403)

* add sdpa to dinov2

* fixup

* add dinov2 to sdpa doc

* update doc order

* [run-slow] dinov2

* common to eager

* [run-slow] dinov2

* update attn implementation in common

* update test_modeling_dinov2 to have mask_ration, num_masks and mask_length similar to vit

* [run-slow] dinov2

---------

Co-authored-by: Avishai Elmakies <avishai.elma@cs.huji.ac.il>

docs/source/en/perf_infer_gpu_one.md

@@ -217,6 +217,7 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [data2vec_audio](https://huggingface.co/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioModel)
 * [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
 * [DeiT](https://huggingface.co/docs/transformers/model_doc/deit#transformers.DeiTModel)
+* [Dinov2](https://huggingface.co/docs/transformers/en/model_doc/dinov2)
 * [Dpr](https://huggingface.co/docs/transformers/model_doc/dpr#transformers.DprReader)
 * [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
 * [Gemma](https://huggingface.co/docs/transformers/model_doc/gemma#transformers.GemmaModel)
@@ -275,7 +276,6 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel)
 * [YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos#transformers.YolosModel)
 
-
 <Tip>
 
 FlashAttention can only be used for models with the `fp16` or `bf16` torch type, so make sure to cast your model to the appropriate type first. The memory-efficient attention backend is able to handle `fp32` models.

src/transformers/models/dinov2/modeling_dinov2.py

@@ -231,6 +231,38 @@ class Dinov2SelfAttention(nn.Module):
         return outputs
 
 
+# Copied from transformers.models.vit.modeling_vit.ViTSdpaSelfAttention with ViT->Dinov2
+class Dinov2SdpaSelfAttention(Dinov2SelfAttention):
+    def __init__(self, config: Dinov2Config) -> None:
+        super().__init__(config)
+        self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
+
+    def forward(
+        self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
+        mixed_query_layer = self.query(hidden_states)
+
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        context_layer = torch.nn.functional.scaled_dot_product_attention(
+            query_layer,
+            key_layer,
+            value_layer,
+            head_mask,
+            self.attention_probs_dropout_prob if self.training else 0.0,
+            is_causal=False,
+            scale=None,
+        )
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        return context_layer, None
+
+
 # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Dinov2
 class Dinov2SelfOutput(nn.Module):
     """
@@ -290,6 +322,13 @@ class Dinov2Attention(nn.Module):
         return outputs
 
 
+# Copied from transformers.models.vit.modeling_vit.ViTSdpaAttention with ViT->Dinov2
+class Dinov2SdpaAttention(Dinov2Attention):
+    def __init__(self, config: Dinov2Config) -> None:
+        super().__init__(config)
+        self.attention = Dinov2SdpaSelfAttention(config)
+
+
 class Dinov2LayerScale(nn.Module):
     def __init__(self, config) -> None:
         super().__init__()
@@ -371,6 +410,12 @@ class Dinov2SwiGLUFFN(nn.Module):
         return self.weights_out(hidden)
 
 
+DINOV2_ATTENTION_CLASSES = {
+    "eager": Dinov2Attention,
+    "sdpa": Dinov2SdpaAttention,
+}
+
+
 class Dinov2Layer(nn.Module):
     """This corresponds to the Block class in the original implementation."""
 
@@ -378,7 +423,7 @@ class Dinov2Layer(nn.Module):
         super().__init__()
 
         self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.attention = Dinov2Attention(config)
+        self.attention = DINOV2_ATTENTION_CLASSES[config._attn_implementation](config)
         self.layer_scale1 = Dinov2LayerScale(config)
         self.drop_path = Dinov2DropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()
 
@@ -485,6 +530,7 @@ class Dinov2PreTrainedModel(PreTrainedModel):
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
     _no_split_modules = ["Dinov2SwiGLUFFN"]
+    _supports_sdpa = True
 
     def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
         """Initialize the weights"""

tests/models/dinov2/test_modeling_dinov2.py

@@ -65,6 +65,8 @@ class Dinov2ModelTester:
         type_sequence_label_size=10,
         initializer_range=0.02,
         scope=None,
+        attn_implementation="eager",
+        mask_ratio=0.5,
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -83,10 +85,14 @@ class Dinov2ModelTester:
         self.type_sequence_label_size = type_sequence_label_size
         self.initializer_range = initializer_range
         self.scope = scope
+        self.attn_implementation = attn_implementation
+        self.mask_ratio = mask_ratio
 
         # in Dinov2, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         num_patches = (image_size // patch_size) ** 2
         self.seq_length = num_patches + 1
+        self.num_masks = int(self.mask_ratio * self.seq_length)
+        self.mask_length = num_patches
 
     def prepare_config_and_inputs(self):
         pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
@@ -113,6 +119,7 @@ class Dinov2ModelTester:
             attention_probs_dropout_prob=self.attention_probs_dropout_prob,
             is_decoder=False,
             initializer_range=self.initializer_range,
+            attn_implementation=self.attn_implementation,
         )
 
     def create_and_check_model(self, config, pixel_values, labels):