flash attn

src/model/dino.py

@@ -5,6 +5,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from flash_attn import flash_attn_func
 from transformers.models.dinov3_vit.configuration_dinov3_vit import DINOv3ViTConfig
 
 
@@ -194,53 +195,54 @@ class DINOv3ViTAttention(nn.Module):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
+        other: torch.Tensor | None = None,
+        attention_mask: Optional[torch.Tensor] = None,  # wont work rn
         position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
-        assert position_embeddings is not None
+        # assert position_embeddings is not None
 
         batch_size, patches, _ = hidden_states.size()
 
         query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
+        key_states = self.k_proj(hidden_states) if other is None else self.k_proj(other)
+        value_states = (
+            self.v_proj(hidden_states) if other is None else self.v_proj(other)
+        )
 
         query_states = query_states.view(
-            batch_size, patches, self.num_heads, self.head_dim
+            batch_size, -1, self.num_heads, self.head_dim
         ).transpose(1, 2)
         key_states = key_states.view(
-            batch_size, patches, self.num_heads, self.head_dim
+            batch_size, -1, self.num_heads, self.head_dim
         ).transpose(1, 2)
         value_states = value_states.view(
-            batch_size, patches, self.num_heads, self.head_dim
+            batch_size, -1, self.num_heads, self.head_dim
         ).transpose(1, 2)
 
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(
-            query_states, key_states, cos, sin
-        )
-
-        attn_weights = (
-            torch.matmul(query_states, key_states.transpose(-1, -2)) * self.scaling
-        )
-        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
-            query_states.dtype
-        )
-
-        if self.training:
-            attn_weights = F.dropout(
-                attn_weights, p=self.dropout, training=self.training
+        if position_embeddings is not None:
+            cos, sin = position_embeddings
+            query_states, key_states = apply_rotary_pos_emb(
+                query_states, key_states, cos, sin
             )
 
-        if attention_mask is not None:
-            attn_weights = attn_weights * attention_mask
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        dropout_p = self.dropout if self.training else 0.0
 
-        attn_output = torch.matmul(attn_weights, value_states)
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(batch_size, patches, -1).contiguous()
+        attn_output = flash_attn_func(
+            query_states,
+            key_states,
+            value_states,
+            dropout_p=dropout_p,
+            softmax_scale=None,  # Will use default 1/sqrt(headdim)
+            causal=False,
+        )
+
+        attn_output = attn_output.reshape(batch_size, patches, -1).contiguous()  # type: ignore
         attn_output = self.o_proj(attn_output)
 
-        return attn_output, attn_weights
+        return attn_output
 
 
 class DINOv3ViTLayerScale(nn.Module):
@@ -350,7 +352,7 @@ class DINOv3ViTLayer(nn.Module):
 
         residual = hidden_states
         hidden_states = self.norm1(hidden_states)
-        hidden_states, _ = self.attention(
+        hidden_states = self.attention(
             hidden_states,
             attention_mask=attention_mask,
             position_embeddings=position_embeddings,

src/model/utransformer.py

@@ -8,6 +8,7 @@ from torch import nn
 from transformers.models.dinov3_vit.configuration_dinov3_vit import DINOv3ViTConfig
 
 from src.model.dino import (
+    DINOv3ViTAttention,
     DINOv3ViTEmbeddings,
     DINOv3ViTLayer,
     DINOv3ViTLayerScale,
@@ -81,12 +82,7 @@ class DinoConditionedLayer(DINOv3ViTLayer):
         self.is_encoder = is_encoder
 
         self.norm_cond = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.cond = nn.MultiheadAttention(
-            config.hidden_size,
-            config.num_attention_heads,
-            config.drop_path_rate,
-            batch_first=True,
-        )
+        self.cond = DINOv3ViTAttention(config)
         self.layer_scale_cond = DINOv3ViTLayerScale(config)
 
         # no init zeros!
@@ -114,7 +110,7 @@ class DinoConditionedLayer(DINOv3ViTLayer):
 
         residual = hidden_states
         hidden_states = self.norm1(hidden_states)
-        hidden_states, _ = self.attention(
+        hidden_states = self.attention(
             hidden_states,
             attention_mask=attention_mask,
             position_embeddings=position_embeddings,
@@ -125,8 +121,9 @@ class DinoConditionedLayer(DINOv3ViTLayer):
         if do_condition:
             residual = hidden_states
             hidden_states = self.norm_cond(hidden_states)
-            hidden_states, _ = self.cond(
-                hidden_states, conditioning_input, conditioning_input
+            hidden_states = self.cond(
+                hidden_states,
+                conditioning_input,
             )
             hidden_states = self.layer_scale_cond(hidden_states)
             hidden_states = self.drop_path(hidden_states) + residual
@@ -188,9 +185,7 @@ class DinoV3ViTDecoder(nn.Module):
         self.patch_size = config.patch_size
 
         self.projection = nn.Linear(
-            config.hidden_size,
-            config.num_channels * (self.patch_size**2),
-            bias=True,
+            config.hidden_size, config.num_channels * (self.patch_size**2), bias=True
         )
         self.pixel_shuffle = nn.PixelShuffle(self.patch_size)
 
@@ -209,11 +204,8 @@ class DinoV3ViTDecoder(nn.Module):
         w_grid = image_size[1] // p
 
         assert x.shape[1] == h_grid * w_grid
-
         x = self.projection(x)
-
         x = x.reshape(batch_size, h_grid, w_grid, -1).permute(0, 3, 1, 2)
-
         x = self.pixel_shuffle(x)
 
         return x