Allow disabling xFormers via environment variable (#180)

Allow disabling the use of xFormers (for inference) by simply setting the XFORMERS_DISABLED environment variable

dinov2/layers/attention.py

@@ -9,6 +9,8 @@
 #   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
 
 import logging
+import os
+import warnings
 
 from torch import Tensor
 from torch import nn
@@ -17,13 +19,19 @@ from torch import nn
 logger = logging.getLogger("dinov2")
 
 
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
 try:
-    from xformers.ops import memory_efficient_attention, unbind, fmha
-
-    XFORMERS_AVAILABLE = True
+    if XFORMERS_ENABLED:
+        from xformers.ops import memory_efficient_attention, unbind
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (Attention)")
+    else:
+        warnings.warn("xFormers is disabled (Attention)")
+        raise ImportError
 except ImportError:
-    logger.warning("xFormers not available")
     XFORMERS_AVAILABLE = False
+    warnings.warn("xFormers is not available (Attention)")
 
 
 class Attention(nn.Module):
@@ -65,7 +73,8 @@ class Attention(nn.Module):
 class MemEffAttention(Attention):
     def forward(self, x: Tensor, attn_bias=None) -> Tensor:
         if not XFORMERS_AVAILABLE:
-            assert attn_bias is None, "xFormers is required for nested tensors usage"
+            if attn_bias is not None:
+                raise AssertionError("xFormers is required for using nested tensors")
             return super().forward(x)
 
         B, N, C = x.shape

dinov2/layers/block.py

@@ -9,7 +9,9 @@
 #   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
 
 import logging
+import os
 from typing import Callable, List, Any, Tuple, Dict
+import warnings
 
 import torch
 from torch import nn, Tensor
@@ -23,15 +25,21 @@ from .mlp import Mlp
 logger = logging.getLogger("dinov2")
 
 
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
 try:
-    from xformers.ops import fmha
-    from xformers.ops import scaled_index_add, index_select_cat
+    if XFORMERS_ENABLED:
+        from xformers.ops import fmha, scaled_index_add, index_select_cat
 
-    XFORMERS_AVAILABLE = True
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (Block)")
+    else:
+        warnings.warn("xFormers is disabled (Block)")
+        raise ImportError
 except ImportError:
-    logger.warning("xFormers not available")
     XFORMERS_AVAILABLE = False
 
+    warnings.warn("xFormers is not available (Block)")
+
 
 class Block(nn.Module):
     def __init__(
@@ -246,7 +254,8 @@ class NestedTensorBlock(Block):
         if isinstance(x_or_x_list, Tensor):
             return super().forward(x_or_x_list)
         elif isinstance(x_or_x_list, list):
-            assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
+            if not XFORMERS_AVAILABLE:
+                raise AssertionError("xFormers is required for using nested tensors")
             return self.forward_nested(x_or_x_list)
         else:
             raise AssertionError

dinov2/layers/swiglu_ffn.py

@@ -4,7 +4,9 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
+import os
 from typing import Callable, Optional
+import warnings
 
 from torch import Tensor, nn
 import torch.nn.functional as F
@@ -33,14 +35,22 @@ class SwiGLUFFN(nn.Module):
         return self.w3(hidden)
 
 
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
 try:
-    from xformers.ops import SwiGLU
+    if XFORMERS_ENABLED:
+        from xformers.ops import SwiGLU
 
-    XFORMERS_AVAILABLE = True
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (SwiGLU)")
+    else:
+        warnings.warn("xFormers is disabled (SwiGLU)")
+        raise ImportError
 except ImportError:
     SwiGLU = SwiGLUFFN
     XFORMERS_AVAILABLE = False
 
+    warnings.warn("xFormers is not available (SwiGLU)")
+
 
 class SwiGLUFFNFused(SwiGLU):
     def __init__(

dinov2/train/ssl_meta_arch.py

@@ -19,13 +19,11 @@ from dinov2.fsdp import get_fsdp_wrapper, ShardedGradScaler, get_fsdp_modules, r
 
 from dinov2.models.vision_transformer import BlockChunk
 
+
 try:
     from xformers.ops import fmha
-
-    XFORMERS_AVAILABLE = True
 except ImportError:
-    XFORMERS_AVAILABLE = False
-assert XFORMERS_AVAILABLE, "xFormers is required for DINOv2 training"
+    raise AssertionError("xFormers is required for training")
 
 
 logger = logging.getLogger("dinov2")