Revert "[WIP] Hard error when ignoring tensors." (#28898)

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

This reverts commit 2da28c4.

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, Set, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 from zipfile import is_zipfile
 
 import torch
@@ -570,65 +570,6 @@ 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 = []
@@ -2441,8 +2382,6 @@ 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.
@@ -2453,42 +2392,25 @@ def save_pretrained(
                         if matches_pattern and name in state_dict:
                             found += 1
                             if found < len(names):
-                                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)
-
+                                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)
             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

@@ -257,26 +257,6 @@ 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)