Add support to load safetensors weights

avg_checkpoints.py

@@ -17,21 +17,26 @@ import os
 import glob
 import hashlib
 from timm.models import load_state_dict
+import safetensors.torch
+
+DEFAULT_OUTPUT = "./average.pth"
+DEFAULT_SAFE_OUTPUT = "./average.safetensors"
 
 parser = argparse.ArgumentParser(description='PyTorch Checkpoint Averager')
 parser.add_argument('--input', default='', type=str, metavar='PATH',
                     help='path to base input folder containing checkpoints')
 parser.add_argument('--filter', default='*.pth.tar', type=str, metavar='WILDCARD',
                     help='checkpoint filter (path wildcard)')
-parser.add_argument('--output', default='./averaged.pth', type=str, metavar='PATH',
+parser.add_argument('--output', default=DEFAULT_OUTPUT, type=str, metavar='PATH',
-                    help='output filename')
+                    help=f'Output filename. Defaults to {DEFAULT_SAFE_OUTPUT} when passing --safetensors.')
 parser.add_argument('--no-use-ema', dest='no_use_ema', action='store_true',
                     help='Force not using ema version of weights (if present)')
 parser.add_argument('--no-sort', dest='no_sort', action='store_true',
                     help='Do not sort and select by checkpoint metric, also makes "n" argument irrelevant')
 parser.add_argument('-n', type=int, default=10, metavar='N',
                     help='Number of checkpoints to average')
-
+parser.add_argument('--safetensors', action='store_true',
+                    help='Save weights using safetensors instead of the default torch way (pickle).')
 
 def checkpoint_metric(checkpoint_path):
     if not checkpoint_path or not os.path.isfile(checkpoint_path):
@@ -55,6 +60,15 @@ def main():
     # by default sort by checkpoint metric (if present) and avg top n checkpoints
     args.sort = not args.no_sort
 
+    if args.safetensors and args.output == DEFAULT_OUTPUT:
+        # Default path changes if using safetensors
+        args.output = DEFAULT_SAFE_OUTPUT
+    if args.safetensors and not args.output.endswith(".safetensors"):
+        print(
+            "Warning: saving weights as safetensors but output file extension is not "
+            f"set to '.safetensors': {args.output}"
+        )
+
     if os.path.exists(args.output):
         print("Error: Output filename ({}) already exists.".format(args.output))
         exit(1)
@@ -107,10 +121,13 @@ def main():
         v = v.clamp(float32_info.min, float32_info.max)
         final_state_dict[k] = v.to(dtype=torch.float32)
 
-    try:
+    if args.safetensors:
-        torch.save(final_state_dict, args.output, _use_new_zipfile_serialization=False)
+        safetensors.torch.save_file(final_state_dict, args.output)
-    except:
+    else:
-        torch.save(final_state_dict, args.output)
+        try:
+            torch.save(final_state_dict, args.output, _use_new_zipfile_serialization=False)
+        except:
+            torch.save(final_state_dict, args.output)
 
     with open(args.output, 'rb') as f:
         sha_hash = hashlib.sha256(f.read()).hexdigest()

clean_checkpoint.py

@@ -11,8 +11,8 @@ import torch
 import argparse
 import os
 import hashlib
+import safetensors.torch
 import shutil
-from collections import OrderedDict
 from timm.models import load_state_dict
 
 parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner')
@@ -24,6 +24,8 @@ parser.add_argument('--no-use-ema', dest='no_use_ema', action='store_true',
                     help='use ema version of weights if present')
 parser.add_argument('--clean-aux-bn', dest='clean_aux_bn', action='store_true',
                     help='remove auxiliary batch norm layers (from SplitBN training) from checkpoint')
+parser.add_argument('--safetensors', action='store_true',
+                    help='Save weights using safetensors instead of the default torch way (pickle).')
 
 _TEMP_NAME = './_checkpoint.pth'
 
@@ -35,10 +37,10 @@ def main():
         print("Error: Output filename ({}) already exists.".format(args.output))
         exit(1)
 
-    clean_checkpoint(args.checkpoint, args.output, not args.no_use_ema, args.clean_aux_bn)
+    clean_checkpoint(args.checkpoint, args.output, not args.no_use_ema, args.clean_aux_bn, safe_serialization=args.safetensors)
 
 
-def clean_checkpoint(checkpoint, output='', use_ema=True, clean_aux_bn=False):
+def clean_checkpoint(checkpoint, output='', use_ema=True, clean_aux_bn=False, safe_serialization: bool=False):
     # Load an existing checkpoint to CPU, strip everything but the state_dict and re-save
     if checkpoint and os.path.isfile(checkpoint):
         print("=> Loading checkpoint '{}'".format(checkpoint))
@@ -53,10 +55,13 @@ def clean_checkpoint(checkpoint, output='', use_ema=True, clean_aux_bn=False):
             new_state_dict[name] = v
         print("=> Loaded state_dict from '{}'".format(checkpoint))
 
-        try:
+        if safe_serialization:
-            torch.save(new_state_dict, _TEMP_NAME, _use_new_zipfile_serialization=False)
+            safetensors.torch.save_file(new_state_dict, _TEMP_NAME)
-        except:
+        else:
-            torch.save(new_state_dict, _TEMP_NAME)
+            try:
+                torch.save(new_state_dict, _TEMP_NAME, _use_new_zipfile_serialization=False)
+            except:
+                torch.save(new_state_dict, _TEMP_NAME)
 
         with open(_TEMP_NAME, 'rb') as f:
             sha_hash = hashlib.sha256(f.read()).hexdigest()
@@ -67,7 +72,7 @@ def clean_checkpoint(checkpoint, output='', use_ema=True, clean_aux_bn=False):
         else:
             checkpoint_root = ''
             checkpoint_base = os.path.splitext(checkpoint)[0]
-        final_filename = '-'.join([checkpoint_base, sha_hash[:8]]) + '.pth'
+        final_filename = '-'.join([checkpoint_base, sha_hash[:8]]) + ('.safetensors' if safe_serialization else '.pth')
         shutil.move(_TEMP_NAME, os.path.join(checkpoint_root, final_filename))
         print("=> Saved state_dict to '{}, SHA256: {}'".format(final_filename, sha_hash))
         return final_filename

requirements.txt

@@ -2,3 +2,4 @@ torch>=1.7
 torchvision
 pyyaml
 huggingface_hub
+safetensors>=0.2

timm/models/_helpers.py

@@ -7,6 +7,7 @@ import os
 from collections import OrderedDict
 
 import torch
+import safetensors.torch
 
 import timm.models._builder
 
@@ -26,7 +27,12 @@ def clean_state_dict(state_dict):
 
 def load_state_dict(checkpoint_path, use_ema=True):
     if checkpoint_path and os.path.isfile(checkpoint_path):
-        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+        # Check if safetensors or not and load weights accordingly
+        if str(checkpoint_path).endswith(".safetensors"):
+            checkpoint = safetensors.torch.load_file(checkpoint_path, device='cpu')
+        else:
+            checkpoint = torch.load(checkpoint_path, map_location='cpu')
+
         state_dict_key = ''
         if isinstance(checkpoint, dict):
             if use_ema and checkpoint.get('state_dict_ema', None) is not None:

timm/models/_hub.py

@@ -2,19 +2,25 @@ import hashlib
 import json
 import logging
 import os
+import sys
 from functools import partial
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Optional, Union
+from typing import Iterable, Optional, Union
-
 import torch
 from torch.hub import HASH_REGEX, download_url_to_file, urlparse
+import safetensors.torch
 
 try:
     from torch.hub import get_dir
 except ImportError:
     from torch.hub import _get_torch_home as get_dir
 
+if sys.version_info >= (3, 8):
+    from typing import Literal
+else:
+    from typing_extensions import Literal
+
 from timm import __version__
 from timm.models._pretrained import filter_pretrained_cfg
 
@@ -35,6 +41,9 @@ _logger = logging.getLogger(__name__)
 __all__ = ['get_cache_dir', 'download_cached_file', 'has_hf_hub', 'hf_split', 'load_model_config_from_hf',
            'load_state_dict_from_hf', 'save_for_hf', 'push_to_hf_hub']
 
+# Default name for a weights file hosted on the Huggingface Hub.
+HF_WEIGHTS_NAME = "pytorch_model.bin"  # default pytorch pkl
+HF_SAFE_WEIGHTS_NAME = "model.safetensors"  # safetensors version
 
 def get_cache_dir(child_dir=''):
     """
@@ -150,11 +159,23 @@ def load_model_config_from_hf(model_id: str):
     return pretrained_cfg, model_name
 
 
-def load_state_dict_from_hf(model_id: str, filename: str = 'pytorch_model.bin'):
+def load_state_dict_from_hf(model_id: str, filename: str = HF_WEIGHTS_NAME):
     assert has_hf_hub(True)
-    cached_file = download_from_hf(model_id, filename)
+    hf_model_id, hf_revision = hf_split(model_id)
-    state_dict = torch.load(cached_file, map_location='cpu')
+
-    return state_dict
+    # Look for .safetensors alternatives and load from it if it exists
+    for safe_filename in _get_safe_alternatives(filename):
+        try:
+            cached_safe_file = hf_hub_download(repo_id=hf_model_id, filename=safe_filename, revision=hf_revision)
+            _logger.warning(f"[{model_id}] Safe alternative available for '{filename}' (as '{safe_filename}'). Loading weights using safetensors.")
+            return safetensors.torch.load_file(cached_safe_file, device="cpu")
+        except EntryNotFoundError:
+            pass
+
+    # Otherwise, load using pytorch.load
+    cached_file = hf_hub_download(hf_model_id, filename=filename, revision=hf_revision)
+    _logger.warning(f"[{model_id}] Safe alternative not found for '{filename}'. Loading weights using default pytorch.")
+    return torch.load(cached_file, map_location='cpu')
 
 
 def save_config_for_hf(model, config_path: str, model_config: Optional[dict] = None):
@@ -195,13 +216,22 @@ def save_config_for_hf(model, config_path: str, model_config: Optional[dict] = N
         json.dump(hf_config, f, indent=2)
 
 
-def save_for_hf(model, save_directory: str, model_config: Optional[dict] = None):
+def save_for_hf(
+        model,
+        save_directory: str,
+        model_config: Optional[dict] = None,
+        safe_serialization: Union[bool, Literal["both"]] = False
+    ):
     assert has_hf_hub(True)
     save_directory = Path(save_directory)
     save_directory.mkdir(exist_ok=True, parents=True)
 
-    weights_path = save_directory / 'pytorch_model.bin'
+    # Save model weights, either safely (using safetensors), or using legacy pytorch approach or both.
-    torch.save(model.state_dict(), weights_path)
+    tensors = model.state_dict()
+    if safe_serialization is True or safe_serialization == "both":
+        safetensors.torch.save_file(tensors, save_directory / HF_SAFE_WEIGHTS_NAME)
+    if safe_serialization is False or safe_serialization == "both":
+        torch.save(tensors, save_directory / HF_WEIGHTS_NAME)
 
     config_path = save_directory / 'config.json'
     save_config_for_hf(model, config_path, model_config=model_config)
@@ -217,7 +247,15 @@ def push_to_hf_hub(
     create_pr: bool = False,
     model_config: Optional[dict] = None,
     model_card: Optional[dict] = None,
+    safe_serialization: Union[bool, Literal["both"]] = False
 ):
+    """
+    Arguments:
+        (...)
+        safe_serialization (`bool` or `"both"`, *optional*, defaults to `False`):
+            Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+            Can be set to `"both"` in order to push both safe and unsafe weights.
+    """
     # Create repo if it doesn't exist yet
     repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)
 
@@ -236,7 +274,7 @@ def push_to_hf_hub(
     # Dump model and push to Hub
     with TemporaryDirectory() as tmpdir:
         # Save model weights and config.
-        save_for_hf(model, tmpdir, model_config=model_config)
+        save_for_hf(model, tmpdir, model_config=model_config, safe_serialization=safe_serialization)
 
         # Add readme if it does not exist
         if not has_readme:
@@ -302,3 +340,15 @@ def generate_readme(model_card: dict, model_name: str):
         for c in citations:
             readme_text += f"```bibtex\n{c}\n```\n"
     return readme_text
+
+def _get_safe_alternatives(filename: str) -> Iterable[str]:
+    """Returns potential safetensors alternatives for a given filename.
+
+    Use case:
+        When downloading a model from the Huggingface Hub, we first look if a .safetensors file exists and if yes, we use it.
+        Main use case is filename "pytorch_model.bin" => check for "model.safetensors" or "pytorch_model.safetensors".
+    """
+    if filename == HF_WEIGHTS_NAME:
+        yield HF_SAFE_WEIGHTS_NAME
+    if filename.endswith(".bin"):
+        yield filename[:-4] + ".safetensors"