Make CosmosPredict2 work with optimized_attention_override

comfy/ldm/cosmos/predict2.py

@@ -44,7 +44,7 @@ class GPT2FeedForward(nn.Module):
         return x
 
 
-def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor) -> torch.Tensor:
+def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
     """Computes multi-head attention using PyTorch's native implementation.
 
     This function provides a PyTorch backend alternative to Transformer Engine's attention operation.
@@ -71,7 +71,7 @@ def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H
     q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1])
     k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
     v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
-    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True)
+    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options)
 
 
 class Attention(nn.Module):
@@ -180,8 +180,8 @@ class Attention(nn.Module):
 
         return q, k, v
 
-    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
+    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
-        result = self.attn_op(q, k, v)  # [B, S, H, D]
+        result = self.attn_op(q, k, v, transformer_options=transformer_options)  # [B, S, H, D]
         return self.output_dropout(self.output_proj(result))
 
     def forward(
@@ -189,6 +189,7 @@ class Attention(nn.Module):
         x: torch.Tensor,
         context: Optional[torch.Tensor] = None,
         rope_emb: Optional[torch.Tensor] = None,
+        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Args:
@@ -196,7 +197,7 @@ class Attention(nn.Module):
             context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
         """
         q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)
-        return self.compute_attention(q, k, v)
+        return self.compute_attention(q, k, v, transformer_options=transformer_options)
 
 
 class Timesteps(nn.Module):
@@ -459,6 +460,7 @@ class Block(nn.Module):
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_T_3D: Optional[torch.Tensor] = None,
         extra_per_block_pos_emb: Optional[torch.Tensor] = None,
+        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         if extra_per_block_pos_emb is not None:
             x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
@@ -512,6 +514,7 @@ class Block(nn.Module):
                 rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                 None,
                 rope_emb=rope_emb_L_1_1_D,
+                transformer_options=transformer_options,
             ),
             "b (t h w) d -> b t h w d",
             t=T,
@@ -525,6 +528,7 @@ class Block(nn.Module):
             layer_norm_cross_attn: Callable,
             _scale_cross_attn_B_T_1_1_D: torch.Tensor,
             _shift_cross_attn_B_T_1_1_D: torch.Tensor,
+            transformer_options: Optional[dict] = {},
         ) -> torch.Tensor:
             _normalized_x_B_T_H_W_D = _fn(
                 _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D
@@ -534,6 +538,7 @@ class Block(nn.Module):
                     rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                     crossattn_emb,
                     rope_emb=rope_emb_L_1_1_D,
+                    transformer_options=transformer_options,
                 ),
                 "b (t h w) d -> b t h w d",
                 t=T,
@@ -547,6 +552,7 @@ class Block(nn.Module):
             self.layer_norm_cross_attn,
             scale_cross_attn_B_T_1_1_D,
             shift_cross_attn_B_T_1_1_D,
+            transformer_options=transformer_options,
         )
         x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
 
@@ -865,6 +871,7 @@ class MiniTrainDIT(nn.Module):
             "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0),
             "adaln_lora_B_T_3D": adaln_lora_B_T_3D,
             "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
+            "transformer_options": kwargs.get("transformer_options", {}),
         }
         for block in self.blocks:
             x_B_T_H_W_D = block(