Quick patch to scope xformer imports until a better solution

requirements.txt

@@ -32,5 +32,4 @@ sentencepiece
 huggingface_hub
 peft
 gradio
-python-slugify
-xformers
+python-slugify

toolkit/models/pixtral_vision.py

@@ -1,15 +1,16 @@
 import math
-from typing import List, Optional, Tuple, Any, Union
+from typing import List, Optional, Tuple, Any, Union, TYPE_CHECKING
 import os
 import torch
 import torch.nn as nn
 from dataclasses import dataclass
-from xformers.ops.fmha.attn_bias import BlockDiagonalMask
-from xformers.ops.fmha import memory_efficient_attention
 from huggingface_hub import snapshot_download
 from safetensors.torch import load_file
 import json
 
+if TYPE_CHECKING:
+    from xformers.ops.fmha.attn_bias import BlockDiagonalMask
+
 
 class RMSNorm(torch.nn.Module):
     def __init__(self, dim: int, eps: float = 1e-6):
@@ -86,8 +87,9 @@ class Attention(nn.Module):
             x: torch.Tensor,
             freqs_cis: torch.Tensor,
             cache: Optional[Any] = None,
-            mask: Optional[BlockDiagonalMask] = None,
+            mask: Optional['BlockDiagonalMask'] = None,
     ) -> torch.Tensor:
+        from xformers.ops.fmha import memory_efficient_attention
         assert mask is None or cache is None
         seqlen_sum, _ = x.shape
 
@@ -155,7 +157,7 @@ class TransformerBlock(nn.Module):
             x: torch.Tensor,
             freqs_cis: torch.Tensor,
             cache: Optional[Any] = None,
-            mask: Optional[BlockDiagonalMask] = None,
+            mask: Optional['BlockDiagonalMask'] = None,
     ) -> torch.Tensor:
         r = self.attention.forward(self.attention_norm(x), freqs_cis, cache)
         h = x + r
@@ -317,6 +319,7 @@ class PixtralVisionEncoder(nn.Module):
             self,
             images: List[torch.Tensor],
     ) -> torch.Tensor:
+        from xformers.ops.fmha.attn_bias import BlockDiagonalMask
         """
         Args:
             images: list of N_img images of variable sizes, each of shape (C, H, W)
@@ -387,7 +390,7 @@ class VisionTransformerBlocks(nn.Module):
     def forward(
             self,
             x: torch.Tensor,
-            mask: BlockDiagonalMask,
+            mask: 'BlockDiagonalMask',
             freqs_cis: Optional[torch.Tensor],
     ) -> torch.Tensor:
         for layer in self.layers: