Changing convert logic.

Should be more robust to shared tensors (ok when using
  `from_pretrained). But forcing us to add new checks in our loading
  code (since the chosen key to keep might be different from
  `transformers`).

server/text_generation_server/utils/convert.py

@@ -1,74 +1,43 @@
 import datetime
 import torch
+import os
 
-from collections import defaultdict
 from loguru import logger
 from pathlib import Path
-from safetensors.torch import save_file
-from safetensors import safe_open
-from typing import Dict, List
-
-
-def check_file_size(source_file: Path, target_file: Path):
-    """
-    Check that two files are close in size
-    """
-    source_file_size = source_file.stat().st_size
-    target_file_size = target_file.stat().st_size
-
-    if (source_file_size - target_file_size) / source_file_size > 0.05:
-        raise RuntimeError(
-            f"""The file size different is more than 5%:
-         - {source_file}: {source_file_size}
-         - {target_file}: {target_file_size}
-         """
-        )
-
-
-def remove_shared_pointers(tensors: Dict[str, torch.Tensor]):
-    """
-    For a Dict of tensors, check if two or more tensors point to the same underlying memory and
-    remove them
-    """
-    ptrs = defaultdict(list)
-    for k, v in tensors.items():
-        ptrs[v.data_ptr()].append(k)
-
-    # Iterate over all found memory addresses
-    for ptr, names in ptrs.items():
-        if len(names) > 1:
-            # Multiple tensors are point to the same memory
-            # Only keep the first tensor
-            for name in names[1:]:
-                tensors.pop(name)
+from safetensors.torch import save_file, _remove_duplicate_names, load_file
+from typing import List
 
 
 def convert_file(pt_file: Path, sf_file: Path):
     """
     Convert a pytorch file to a safetensors file
+    This will remove duplicate tensors from the file.
+
+    Unfortunately, this might not respect *transformers* convention.
+    Forcing us to check for potentially different keys during load when looking
+    for specific tensors (making tensor sharing explicit).
     """
-    logger.info(f"Convert {pt_file} to {sf_file}.")
+    loaded = torch.load(pt_file, map_location="cpu")
+    if "state_dict" in loaded:
+        loaded = loaded["state_dict"]
+    to_removes = _remove_duplicate_names(loaded)
 
-    pt_state = torch.load(pt_file, map_location="cpu")
-    if "state_dict" in pt_state:
-        pt_state = pt_state["state_dict"]
+    metadata = {"format": "pt"}
+    for kept_name, to_remove_group in to_removes.items():
+        for to_remove in to_remove_group:
+            if to_remove not in metadata:
+                metadata[to_remove] = kept_name
+            del loaded[to_remove]
+    # Force tensors to be contiguous
+    loaded = {k: v.contiguous() for k, v in loaded.items()}
 
-    remove_shared_pointers(pt_state)
-
-    # Tensors need to be contiguous
-    pt_state = {k: v.contiguous() for k, v in pt_state.items()}
-
-    sf_file.parent.mkdir(parents=True, exist_ok=True)
-    save_file(pt_state, str(sf_file), metadata={"format": "pt"})
-
-    # Check that both files are close in size
-    check_file_size(pt_file, sf_file)
-
-    # Load safetensors state
-    for k in pt_state:
-        pt_tensor = pt_state[k]
-        with safe_open(sf_file, framework="pt") as f:
-            sf_tensor = f.get_tensor(k)
+    dirname = os.path.dirname(sf_file)
+    os.makedirs(dirname, exist_ok=True)
+    save_file(loaded, sf_file, metadata=metadata)
+    reloaded = load_file(sf_file)
+    for k in loaded:
+        pt_tensor = loaded[k]
+        sf_tensor = reloaded[k]
         if not torch.equal(pt_tensor, sf_tensor):
             raise RuntimeError(f"The output tensors do not match for key {k}")