use meta device

timm/models/_builder.py

@@ -2,8 +2,10 @@
 import logging
 import os
 from copy import deepcopy
-from typing import Any, Callable, Dict, List, Optional, Tuple
+from typing import Any, Callable, Dict, Optional, Tuple
+from contextlib import nullcontext
 
+import torch
 from torch import nn as nn
 from torch.hub import load_state_dict_from_url
 
@@ -411,10 +413,13 @@ def build_model_with_cfg(
             feature_cfg['feature_cls'] = kwargs.pop('feature_cls')
 
     # Instantiate the model
-    if model_cfg is None:
-        model = model_cls(**kwargs)
-    else:
-        model = model_cls(cfg=model_cfg, **kwargs)
+    with torch.device("meta") if pretrained else nullcontext():
+        if model_cfg is None:
+            model = model_cls(**kwargs)
+        else:
+            model = model_cls(cfg=model_cfg, **kwargs)
+    if pretrained:
+        model.to_empty(device="cpu")
     model.pretrained_cfg = pretrained_cfg
     model.default_cfg = model.pretrained_cfg  # alias for backwards compat
 

timm/models/vision_transformer.py

@@ -539,7 +539,8 @@ def __init__(
             self.patch_drop = nn.Identity()
         self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
 
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        with torch.device("cpu"):
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
         self.blocks = nn.Sequential(*[
             block_fn(
                 dim=embed_dim,