[WIP] Hard error when ignoring tensors. (#27484)

* [WIP] Hard error when ignoring tensors.

* Better selection/error when saving a checkpoint.

- Find all names we should normally drop (those are in the transformers
  config)
- Find all disjoint tensors (for those we can safely trigger a copy to
  get rid of the sharing before saving)
- Clone those disjoint tensors getting rid of the issue
- Find all identical names (those should be declared in the config
  but we try to find them all anyway.)
- For all identical names:
  - If they are in the config, just ignore them everything is fine
  - If they are not, warn about them.
- For all remainder tensors which are shared yet neither identical NOR
  disjoint. raise a hard error.

* Adding a failing test on `main` that passes here.

* We don't need to keep the subfolder logic in this test.

* Apply suggestions from code review

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

src/transformers/modeling_utils.py

@@ -29,7 +29,7 @@
 from contextlib import contextmanager
 from dataclasses import dataclass
 from functools import partial, wraps
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 from zipfile import is_zipfile
 
 import torch
@@ -570,6 +570,65 @@ def set_initialized_submodules(model, state_dict_keys):
     return not_initialized_submodules
 
 
+def _end_ptr(tensor: torch.Tensor) -> int:
+    # extract the end of the pointer if the tensor is a slice of a bigger tensor
+    if tensor.nelement():
+        stop = tensor.view(-1)[-1].data_ptr() + tensor.element_size()
+    else:
+        stop = tensor.data_ptr()
+    return stop
+
+
+def _find_disjoint(tensors: List[Set[str]], state_dict: Dict[str, torch.Tensor]) -> Tuple[List[Set[str]], Set[str]]:
+    filtered_tensors = []
+    for shared in tensors:
+        if len(shared) < 2:
+            filtered_tensors.append(shared)
+            continue
+
+        areas = []
+        for name in shared:
+            tensor = state_dict[name]
+            areas.append((tensor.data_ptr(), _end_ptr(tensor), name))
+        areas.sort()
+
+        _, last_stop, last_name = areas[0]
+        filtered_tensors.append({last_name})
+        for start, stop, name in areas[1:]:
+            if start >= last_stop:
+                filtered_tensors.append({name})
+            else:
+                filtered_tensors[-1].add(name)
+            last_stop = stop
+    disjoint_tensors = []
+    shared_tensors = []
+    for tensors in filtered_tensors:
+        if len(tensors) == 1:
+            disjoint_tensors.append(tensors.pop())
+        else:
+            shared_tensors.append(tensors)
+    return shared_tensors, disjoint_tensors
+
+
+def _find_identical(tensors: List[Set[str]], state_dict: Dict[str, torch.Tensor]) -> Tuple[List[Set[str]], Set[str]]:
+    shared_tensors = []
+    identical = []
+    for shared in tensors:
+        if len(shared) < 2:
+            continue
+
+        areas = collections.defaultdict(set)
+        for name in shared:
+            tensor = state_dict[name]
+            area = (tensor.device, tensor.data_ptr(), _end_ptr(tensor))
+            areas[area].add(name)
+        if len(areas) == 1:
+            identical.append(shared)
+        else:
+            shared_tensors.append(shared)
+    return shared_tensors, identical
+
+
 def _load_state_dict_into_model(model_to_load, state_dict, start_prefix):
     # Convert old format to new format if needed from a PyTorch state_dict
     old_keys = []
@@ -2382,6 +2441,8 @@ def save_pretrained(
             # These are all the pointers of shared tensors.
             shared_ptrs = {ptr: names for ptr, names in ptrs.items() if len(names) > 1}
             warn_names = set()
+            error_names = set()
+            to_delete_names = set()
             for names in shared_ptrs.values():
                 # Removing the keys which are declared as known duplicates on
                 # load. This allows to make sure the name which is kept is consistent.
@@ -2392,25 +2453,42 @@ def save_pretrained(
                         if matches_pattern and name in state_dict:
                             found += 1
                             if found < len(names):
-                                del state_dict[name]
-
-                # When not all duplicates have been cleaned, still remove those keys, but put a clear warning.
-                # If the link between tensors was done at runtime then `from_pretrained` will not get
-                # the key back leading to random tensor. A proper warning will be shown
-                # during reload (if applicable), but since the file is not necessarily compatible with
-                # the config, better show a proper warning.
-                found = 0
-                for name in names:
-                    if name in state_dict:
-                        found += 1
-                        if found > 1:
-                            del state_dict[name]
-                            warn_names.add(name)
+                                to_delete_names.add(name)
+            # We are entering a place where the weights and the transformers configuration do NOT match.
+            shared_names, disjoint_names = _find_disjoint(shared_ptrs.values(), state_dict)
+            # Those are actually tensor sharing but disjoint from each other, we can safely clone them
+            # Reloaded won't have the same property, but it shouldn't matter in any meaningful way.
+            for name in disjoint_names:
+                state_dict[name] = state_dict[name].clone()
+
+            # When not all duplicates have been cleaned, still remove those keys, but put a clear warning.
+            # If the link between tensors was done at runtime then `from_pretrained` will not get
+            # the key back leading to random tensor. A proper warning will be shown
+            # during reload (if applicable), but since the file is not necessarily compatible with
+            # the config, better show a proper warning.
+            shared_names, identical_names = _find_identical(shared_names, state_dict)
+            # delete tensors that have identical storage
+            for inames in identical_names:
+                known = inames.intersection(to_delete_names)
+                for name in known:
+                    del state_dict[name]
+                unknown = sorted(inames.difference(to_delete_names))
+                for name in unknown[1:]:
+                    del state_dict[name]
+                    warn_names.add(name)
+
+            error_names.update(shared_names)
+
             if len(warn_names) > 0:
                 logger.warning_once(
                     f"Removed shared tensor {warn_names} while saving. This should be OK, but check by verifying that you don't receive any warning while reloading",
                 )
 
+            if len(error_names) > 0:
+                raise RuntimeError(
+                    f"The weights trying to be saved contained shared tensors {error_names} that are mismatching the transformers base configuration. Try saving using `safe_serialization=False` or remove this tensor sharing.",
+                )
+
         # Shard the model if it is too big.
         if not _hf_peft_config_loaded:
             weights_name = SAFE_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME

tests/test_modeling_utils.py

@@ -257,6 +257,26 @@ def test_model_from_pretrained_subfolder(self):
 
         self.assertTrue(check_models_equal(model, model_loaded))
 
+    def test_model_manually_shared_disjointed_tensors_optimum(self):
+        config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
+        model = BertModel(config)
+
+        # Let's fuse qkv
+        attn = model.encoder.layer[0].attention.self
+        q = attn.query.weight
+        k = attn.key.weight
+        v = attn.value.weight
+        # Force some shared storage
+        qkv = torch.stack([q, k, v], dim=0)
+        attn.query.weight = torch.nn.Parameter(qkv[0])
+        attn.key.weight = torch.nn.Parameter(qkv[1])
+        attn.value.weight = torch.nn.Parameter(qkv[2])
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(tmp_dir)
+            model_loaded = BertModel.from_pretrained(tmp_dir)
+
+        self.assertTrue(check_models_equal(model, model_loaded))
+
     def test_model_from_pretrained_subfolder_sharded(self):
         config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
         model = BertModel(config)