Load sharded pt to flax

src/transformers/modeling_flax_pytorch_utils.py

@@ -38,7 +38,9 @@
 #####################
 
 
-def load_pytorch_checkpoint_in_flax_state_dict(flax_model, pytorch_checkpoint_path, allow_missing_keys=False):
+def load_pytorch_checkpoint_in_flax_state_dict(
+    flax_model, pytorch_checkpoint_path, is_sharded, allow_missing_keys=False
+):
     """Load pytorch checkpoints in a flax model"""
     try:
         import torch  # noqa: F401
@@ -50,14 +52,17 @@ def load_pytorch_checkpoint_in_flax_state_dict(flax_model, pytorch_checkpoint_pa
         )
         raise
 
-    pt_path = os.path.abspath(pytorch_checkpoint_path)
-    logger.info(f"Loading PyTorch weights from {pt_path}")
+    if not is_sharded:
+        pt_path = os.path.abspath(pytorch_checkpoint_path)
+        logger.info(f"Loading PyTorch weights from {pt_path}")
 
-    pt_state_dict = torch.load(pt_path, map_location="cpu")
-    logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters.")
-
-    flax_state_dict = convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model)
+        pt_state_dict = torch.load(pt_path, map_location="cpu")
+        logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters.")
 
+        flax_state_dict = convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model)
+    else:
+        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
+        flax_state_dict = convert_pytorch_sharded_state_dict_to_flax(pytorch_checkpoint_path, flax_model)
     return flax_state_dict
 
 
@@ -156,6 +161,61 @@ def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model):
     return unflatten_dict(flax_state_dict)
 
 
+############################
+# Sharded Pytorch => Flax #
+############################
+
+
+def convert_pytorch_sharded_state_dict_to_flax(shard_filenames, flax_model):
+    import torch
+
+    # Load the index
+    flax_state_dict = {}
+    for shard_file in shard_filenames:
+        # load using msgpack utils
+        pt_state_dict = torch.load(shard_file)
+        pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
+
+        model_prefix = flax_model.base_model_prefix
+        random_flax_state_dict = flatten_dict(flax_model.params)
+
+        load_model_with_head_into_base_model = (model_prefix not in flax_model.params) and (
+            model_prefix in set([k.split(".")[0] for k in pt_state_dict.keys()])
+        )
+        load_base_model_into_model_with_head = (model_prefix in flax_model.params) and (
+            model_prefix not in set([k.split(".")[0] for k in pt_state_dict.keys()])
+        )
+        # Need to change some parameters name to match Flax names
+        for pt_key, pt_tensor in pt_state_dict.items():
+
+            pt_tuple_key = tuple(pt_key.split("."))
+
+            # remove base model prefix if necessary
+            has_base_model_prefix = pt_tuple_key[0] == model_prefix
+            if load_model_with_head_into_base_model and has_base_model_prefix:
+                pt_tuple_key = pt_tuple_key[1:]
+
+            # Correctly rename weight parameters
+            flax_key, flax_tensor = rename_key_and_reshape_tensor(
+                pt_tuple_key, pt_tensor, random_flax_state_dict, model_prefix
+            )
+            # add model prefix if necessary
+            require_base_model_prefix = (model_prefix,) + flax_key in random_flax_state_dict
+            if load_base_model_into_model_with_head and require_base_model_prefix:
+                flax_key = (model_prefix,) + flax_key
+
+            if flax_key in random_flax_state_dict:
+                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
+                    raise ValueError(
+                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
+                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
+                    )
+
+            # also add unexpected weight so that warning is thrown
+            flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
+    return unflatten_dict(flax_state_dict)
+
+
 #####################
 # Flax => PyTorch #
 #####################

src/transformers/modeling_flax_utils.py

@@ -40,6 +40,7 @@
 from .utils import (
     FLAX_WEIGHTS_INDEX_NAME,
     FLAX_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
     WEIGHTS_NAME,
     PushToHubMixin,
     add_code_sample_docstrings,
@@ -650,6 +651,10 @@ def from_pretrained(
                 if from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
                     # Load from a PyTorch checkpoint
                     archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
+                elif from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME)):
+                    # Load from a sharded pytorch checkpoint
+                    archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME)
+                    is_sharded = True
                 elif os.path.isfile(os.path.join(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME)):
                     # Load from a Flax checkpoint
                     archive_file = os.path.join(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME)
@@ -700,6 +705,13 @@ def from_pretrained(
                         )
                         if resolved_archive_file is not None:
                             is_sharded = True
+                    # Maybe the checkpoint is pytorch sharded, we try to grab the pytorch index name in this case.
+                    elif resolved_archive_file is None and from_pt:
+                        resolved_archive_file = cached_file(
+                            pretrained_model_name_or_path, WEIGHTS_INDEX_NAME, **cached_file_kwargs
+                        )
+                        if resolved_archive_file is not None:
+                            is_sharded = True
                     if resolved_archive_file is None:
                         # Otherwise, maybe there is a TF or Flax model file.  We try those to give a helpful error
                         # message.
@@ -714,6 +726,12 @@ def from_pretrained(
                                 f" {FLAX_WEIGHTS_NAME} but there is a file for PyTorch weights. Use `from_pt=True` to"
                                 " load this model from those weights."
                             )
+                        elif has_file(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME, **has_file_kwargs):
+                            raise EnvironmentError(
+                                f"{pretrained_model_name_or_path} does not appear to have a file named"
+                                f" {FLAX_WEIGHTS_INDEX_NAME} but there is a sharded file for PyTorch weights. Use"
+                                " `from_pt=True` to load this model from those weights."
+                            )
                         else:
                             raise EnvironmentError(
                                 f"{pretrained_model_name_or_path} does not appear to have a file named"
@@ -761,7 +779,7 @@ def from_pretrained(
         model = cls(config, *model_args, _do_init=_do_init, **model_kwargs)
 
         if from_pt:
-            state = load_pytorch_checkpoint_in_flax_state_dict(model, resolved_archive_file)
+            state = load_pytorch_checkpoint_in_flax_state_dict(model, resolved_archive_file, is_sharded)
         else:
 
             if is_sharded:

tests/test_modeling_flax_common.py

@@ -1099,6 +1099,14 @@ def test_checkpoint_sharding_local(self):
                 for p1, p2 in zip(flatten_dict(model.params).values(), flatten_dict(new_model.params).values()):
                     self.assertTrue(np.allclose(np.array(p1), np.array(p2)))
 
+    @is_pt_flax_cross_test
+    def test_from_sharded_pt(self):
+        model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded", from_pt=True)
+        ref_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-fx-only")
+        for key, ref_val in flatten_dict(ref_model.params).items():
+            val = flatten_dict(model.params)[key]
+            assert np.allclose(np.array(val), np.array(ref_val))
+
     def test_gradient_checkpointing(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()