[Core] support saving and loading of sharded checkpoints (#7830)

* feat: support saving a model in sharded checkpoints.

* feat: make loading of sharded checkpoints work.

* add tests

* cleanse the loading logic a bit more.

* more resilience while loading from the Hub.

* parallelize shard downloads by using snapshot_download()/

* default to a shard size.

* more fix

* Empty-Commit

* debug

* fix

* uality

* more debugging

* fix more

* initial comments from Benjamin

* move certain methods to loading_utils

* add test to check if the correct number of shards are present.

* add a test to check if loading of sharded checkpoints from the Hub is okay

* clarify the unit when passed as an int.

* use hf_hub for sharding.

* remove unnecessary code

* remove unnecessary function

* lucain's comments.

* fixes

* address high-level comments.

* fix test

* subfolder shenanigans./

* Update src/diffusers/utils/hub_utils.py

Co-authored-by: Lucain <l...

setup.py

@@ -101,7 +101,7 @@ _deps = [
     "filelock",
     "flax>=0.4.1",
     "hf-doc-builder>=0.3.0",
-    "huggingface-hub>=0.20.2",
+    "huggingface-hub>=0.23.2",
     "requests-mock==1.10.0",
     "importlib_metadata",
     "invisible-watermark>=0.2.0",

src/diffusers/dependency_versions_table.py

@@ -9,7 +9,7 @@ deps = {
     "filelock": "filelock",
     "flax": "flax>=0.4.1",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
-    "huggingface-hub": "huggingface-hub>=0.20.2",
+    "huggingface-hub": "huggingface-hub>=0.23.2",
     "requests-mock": "requests-mock==1.10.0",
     "importlib_metadata": "importlib_metadata",
     "invisible-watermark": "invisible-watermark>=0.2.0",

src/diffusers/models/model_loading_utils.py

@@ -18,13 +18,19 @@ import importlib
 import inspect
 import os
 from collections import OrderedDict
+from pathlib import Path
 from typing import List, Optional, Union
 
 import safetensors
 import torch
+from huggingface_hub.utils import EntryNotFoundError
 
 from ..utils import (
+    SAFE_WEIGHTS_INDEX_NAME,
     SAFETENSORS_FILE_EXTENSION,
+    WEIGHTS_INDEX_NAME,
+    _add_variant,
+    _get_model_file,
     is_accelerate_available,
     is_torch_version,
     logging,
@@ -175,3 +181,52 @@ def _load_state_dict_into_model(model_to_load, state_dict: OrderedDict) -> List[
     load(model_to_load)
 
     return error_msgs
+
+
+def _fetch_index_file(
+    is_local,
+    pretrained_model_name_or_path,
+    subfolder,
+    use_safetensors,
+    cache_dir,
+    variant,
+    force_download,
+    resume_download,
+    proxies,
+    local_files_only,
+    token,
+    revision,
+    user_agent,
+    commit_hash,
+):
+    if is_local:
+        index_file = Path(
+            pretrained_model_name_or_path,
+            subfolder or "",
+            _add_variant(SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME, variant),
+        )
+    else:
+        index_file_in_repo = Path(
+            subfolder or "",
+            _add_variant(SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME, variant),
+        ).as_posix()
+        try:
+            index_file = _get_model_file(
+                pretrained_model_name_or_path,
+                weights_name=index_file_in_repo,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                subfolder=subfolder,
+                user_agent=user_agent,
+                commit_hash=commit_hash,
+            )
+            index_file = Path(index_file)
+        except (EntryNotFoundError, EnvironmentError):
+            index_file = None
+
+    return index_file

src/diffusers/models/modeling_utils.py

@@ -16,6 +16,7 @@
 
 import inspect
 import itertools
+import json
 import os
 import re
 from collections import OrderedDict
@@ -25,7 +26,7 @@ from typing import Any, Callable, List, Optional, Tuple, Union
 
 import safetensors
 import torch
-from huggingface_hub import create_repo
+from huggingface_hub import create_repo, split_torch_state_dict_into_shards
 from huggingface_hub.utils import validate_hf_hub_args
 from torch import Tensor, nn
 
@@ -33,9 +34,12 @@ from .. import __version__
 from ..utils import (
     CONFIG_NAME,
     FLAX_WEIGHTS_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
     SAFETENSORS_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
     WEIGHTS_NAME,
     _add_variant,
+    _get_checkpoint_shard_files,
     _get_model_file,
     deprecate,
     is_accelerate_available,
@@ -49,6 +53,7 @@ from ..utils.hub_utils import (
 )
 from .model_loading_utils import (
     _determine_device_map,
+    _fetch_index_file,
     _load_state_dict_into_model,
     load_model_dict_into_meta,
     load_state_dict,
@@ -57,6 +62,8 @@ from .model_loading_utils import (
 
 logger = logging.get_logger(__name__)
 
+_REGEX_SHARD = re.compile(r"(.*?)-\d{5}-of-\d{5}")
+
 
 if is_torch_version(">=", "1.9.0"):
     _LOW_CPU_MEM_USAGE_DEFAULT = True
@@ -263,6 +270,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         save_function: Optional[Callable] = None,
         safe_serialization: bool = True,
         variant: Optional[str] = None,
+        max_shard_size: Union[int, str] = "5GB",
         push_to_hub: bool = False,
         **kwargs,
     ):
@@ -285,6 +293,10 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                 Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
             variant (`str`, *optional*):
                 If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
+            max_shard_size (`int` or `str`, defaults to `"5GB"`):
+                The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
+                lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5GB"`).
+                If expressed as an integer, the unit is bytes.
             push_to_hub (`bool`, *optional*, defaults to `False`):
                 Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
@@ -296,6 +308,14 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
 
+        weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
+        weights_name = _add_variant(weights_name, variant)
+        weight_name_split = weights_name.split(".")
+        if len(weight_name_split) in [2, 3]:
+            weights_name_pattern = weight_name_split[0] + "{suffix}." + ".".join(weight_name_split[1:])
+        else:
+            raise ValueError(f"Invalid {weights_name} provided.")
+
         os.makedirs(save_directory, exist_ok=True)
 
         if push_to_hub:
@@ -317,18 +337,58 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         # Save the model
         state_dict = model_to_save.state_dict()
 
-        weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
-        weights_name = _add_variant(weights_name, variant)
-
         # Save the model
-        if safe_serialization:
-            safetensors.torch.save_file(
-                state_dict, Path(save_directory, weights_name).as_posix(), metadata={"format": "pt"}
+        state_dict_split = split_torch_state_dict_into_shards(
+            state_dict, max_shard_size=max_shard_size, filename_pattern=weights_name_pattern
+        )
+
+        # Clean the folder from a previous save
+        if is_main_process:
+            for filename in os.listdir(save_directory):
+                if filename in state_dict_split.filename_to_tensors.keys():
+                    continue
+                full_filename = os.path.join(save_directory, filename)
+                if not os.path.isfile(full_filename):
+                    continue
+                weights_without_ext = weights_name_pattern.replace(".bin", "").replace(".safetensors", "")
+                weights_without_ext = weights_without_ext.replace("{suffix}", "")
+                filename_without_ext = filename.replace(".bin", "").replace(".safetensors", "")
+                # make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005
+                if (
+                    filename.startswith(weights_without_ext)
+                    and _REGEX_SHARD.fullmatch(filename_without_ext) is not None
+                ):
+                    os.remove(full_filename)
+
+        for filename, tensors in state_dict_split.filename_to_tensors.items():
+            shard = {tensor: state_dict[tensor] for tensor in tensors}
+            filepath = os.path.join(save_directory, filename)
+            if safe_serialization:
+                # At some point we will need to deal better with save_function (used for TPU and other distributed
+                # joyfulness), but for now this enough.
+                safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})
+            else:
+                torch.save(shard, filepath)
+
+        if state_dict_split.is_sharded:
+            index = {
+                "metadata": state_dict_split.metadata,
+                "weight_map": state_dict_split.tensor_to_filename,
+            }
+            save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME
+            save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))
+            # Save the index as well
+            with open(save_index_file, "w", encoding="utf-8") as f:
+                content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+                f.write(content)
+            logger.info(
+                f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
+                f"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the "
+                f"index located at {save_index_file}."
             )
         else:
-            torch.save(state_dict, Path(save_directory, weights_name).as_posix())
-
-        logger.info(f"Model weights saved in {Path(save_directory, weights_name).as_posix()}")
+            path_to_weights = os.path.join(save_directory, weights_name)
+            logger.info(f"Model weights saved in {path_to_weights}")
 
         if push_to_hub:
             # Create a new empty model card and eventually tag it
@@ -566,6 +626,32 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             **kwargs,
         )
 
+        # Determine if we're loading from a directory of sharded checkpoints.
+        is_sharded = False
+        index_file = None
+        is_local = os.path.isdir(pretrained_model_name_or_path)
+        index_file = _fetch_index_file(
+            is_local=is_local,
+            pretrained_model_name_or_path=pretrained_model_name_or_path,
+            subfolder=subfolder or "",
+            use_safetensors=use_safetensors,
+            cache_dir=cache_dir,
+            variant=variant,
+            force_download=force_download,
+            resume_download=resume_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            token=token,
+            revision=revision,
+            user_agent=user_agent,
+            commit_hash=commit_hash,
+        )
+        if index_file is not None and index_file.is_file():
+            is_sharded = True
+
+        if is_sharded and from_flax:
+            raise ValueError("Loading of sharded checkpoints is not supported when `from_flax=True`.")
+
         # load model
         model_file = None
         if from_flax:
@@ -590,7 +676,21 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
 
             model = load_flax_checkpoint_in_pytorch_model(model, model_file)
         else:
-            if use_safetensors:
+            if is_sharded:
+                sharded_ckpt_cached_folder, sharded_metadata = _get_checkpoint_shard_files(
+                    pretrained_model_name_or_path,
+                    index_file,
+                    cache_dir=cache_dir,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    local_files_only=local_files_only,
+                    token=token,
+                    user_agent=user_agent,
+                    revision=revision,
+                    subfolder=subfolder or "",
+                )
+
+            elif use_safetensors and not is_sharded:
                 try:
                     model_file = _get_model_file(
                         pretrained_model_name_or_path,
@@ -606,11 +706,16 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                         user_agent=user_agent,
                         commit_hash=commit_hash,
                     )
+
                 except IOError as e:
+                    logger.error(f"An error occurred while trying to fetch {pretrained_model_name_or_path}: {e}")
                     if not allow_pickle:
-                        raise e
-                    pass
-            if model_file is None:
+                        raise
+                    logger.warning(
+                        "Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead."
+                    )
+
+            if model_file is None and not is_sharded:
                 model_file = _get_model_file(
                     pretrained_model_name_or_path,
                     weights_name=_add_variant(WEIGHTS_NAME, variant),
@@ -632,7 +737,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     model = cls.from_config(config, **unused_kwargs)
 
                 # if device_map is None, load the state dict and move the params from meta device to the cpu
-                if device_map is None:
+                if device_map is None and not is_sharded:
                     param_device = "cpu"
                     state_dict = load_state_dict(model_file, variant=variant)
                     model._convert_deprecated_attention_blocks(state_dict)
@@ -670,7 +775,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     try:
                         accelerate.load_checkpoint_and_dispatch(
                             model,
-                            model_file,
+                            model_file if not is_sharded else sharded_ckpt_cached_folder,
                             device_map,
                             max_memory=max_memory,
                             offload_folder=offload_folder,

src/diffusers/utils/__init__.py

@@ -28,9 +28,11 @@ from .constants import (
     MIN_PEFT_VERSION,
     ONNX_EXTERNAL_WEIGHTS_NAME,
     ONNX_WEIGHTS_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
     SAFETENSORS_FILE_EXTENSION,
     SAFETENSORS_WEIGHTS_NAME,
     USE_PEFT_BACKEND,
+    WEIGHTS_INDEX_NAME,
     WEIGHTS_NAME,
 )
 from .deprecation_utils import deprecate
@@ -40,6 +42,7 @@ from .export_utils import export_to_gif, export_to_obj, export_to_ply, export_to
 from .hub_utils import (
     PushToHubMixin,
     _add_variant,
+    _get_checkpoint_shard_files,
     _get_model_file,
     extract_commit_hash,
     http_user_agent,

src/diffusers/utils/constants.py

@@ -28,9 +28,11 @@ _CHECK_PEFT = os.environ.get("_CHECK_PEFT", "1") in ENV_VARS_TRUE_VALUES
 
 CONFIG_NAME = "config.json"
 WEIGHTS_NAME = "diffusion_pytorch_model.bin"
+WEIGHTS_INDEX_NAME = "diffusion_pytorch_model.bin.index.json"
 FLAX_WEIGHTS_NAME = "diffusion_flax_model.msgpack"
 ONNX_WEIGHTS_NAME = "model.onnx"
 SAFETENSORS_WEIGHTS_NAME = "diffusion_pytorch_model.safetensors"
+SAFE_WEIGHTS_INDEX_NAME = "diffusion_pytorch_model.safetensors.index.json"
 SAFETENSORS_FILE_EXTENSION = "safetensors"
 ONNX_EXTERNAL_WEIGHTS_NAME = "weights.pb"
 HUGGINGFACE_CO_RESOLVE_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://huggingface.co")

src/diffusers/utils/hub_utils.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 
+import json
 import os
 import re
 import sys
@@ -29,6 +30,8 @@ from huggingface_hub import (
     ModelCardData,
     create_repo,
     hf_hub_download,
+    model_info,
+    snapshot_download,
     upload_folder,
 )
 from huggingface_hub.constants import HF_HUB_CACHE, HF_HUB_DISABLE_TELEMETRY, HF_HUB_OFFLINE
@@ -393,6 +396,109 @@ def _get_model_file(
             )
 
 
+# Adapted from
+# https://github.com/huggingface/transformers/blob/1360801a69c0b169e3efdbb0cd05d9a0e72bfb70/src/transformers/utils/hub.py#L976
+# Differences are in parallelization of shard downloads and checking if shards are present.
+
+
+def _check_if_shards_exist_locally(local_dir, subfolder, original_shard_filenames):
+    shards_path = os.path.join(local_dir, subfolder)
+    shard_filenames = [os.path.join(shards_path, f) for f in original_shard_filenames]
+    for shard_file in shard_filenames:
+        if not os.path.exists(shard_file):
+            raise ValueError(
+                f"{shards_path} does not appear to have a file named {shard_file} which is "
+                "required according to the checkpoint index."
+            )
+
+
+def _get_checkpoint_shard_files(
+    pretrained_model_name_or_path,
+    index_filename,
+    cache_dir=None,
+    proxies=None,
+    resume_download=False,
+    local_files_only=False,
+    token=None,
+    user_agent=None,
+    revision=None,
+    subfolder="",
+):
+    """
+    For a given model:
+
+    - download and cache all the shards of a sharded checkpoint if `pretrained_model_name_or_path` is a model ID on the
+      Hub
+    - returns the list of paths to all the shards, as well as some metadata.
+
+    For the description of each arg, see [`PreTrainedModel.from_pretrained`]. `index_filename` is the full path to the
+    index (downloaded and cached if `pretrained_model_name_or_path` is a model ID on the Hub).
+    """
+    if not os.path.isfile(index_filename):
+        raise ValueError(f"Can't find a checkpoint index ({index_filename}) in {pretrained_model_name_or_path}.")
+
+    with open(index_filename, "r") as f:
+        index = json.loads(f.read())
+
+    original_shard_filenames = sorted(set(index["weight_map"].values()))
+    sharded_metadata = index["metadata"]
+    sharded_metadata["all_checkpoint_keys"] = list(index["weight_map"].keys())
+    sharded_metadata["weight_map"] = index["weight_map"].copy()
+    shards_path = os.path.join(pretrained_model_name_or_path, subfolder)
+
+    # First, let's deal with local folder.
+    if os.path.isdir(pretrained_model_name_or_path):
+        _check_if_shards_exist_locally(
+            pretrained_model_name_or_path, subfolder=subfolder, original_shard_filenames=original_shard_filenames
+        )
+        return pretrained_model_name_or_path, sharded_metadata
+
+    # At this stage pretrained_model_name_or_path is a model identifier on the Hub
+    allow_patterns = original_shard_filenames
+    ignore_patterns = ["*.json", "*.md"]
+    if not local_files_only:
+        # `model_info` call must guarded with the above condition.
+        model_files_info = model_info(pretrained_model_name_or_path)
+        for shard_file in original_shard_filenames:
+            shard_file_present = any(shard_file in k.rfilename for k in model_files_info.siblings)
+            if not shard_file_present:
+                raise EnvironmentError(
+                    f"{shards_path} does not appear to have a file named {shard_file} which is "
+                    "required according to the checkpoint index."
+                )
+
+    try:
+        # Load from URL
+        cached_folder = snapshot_download(
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+            resume_download=resume_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            token=token,
+            revision=revision,
+            allow_patterns=allow_patterns,
+            ignore_patterns=ignore_patterns,
+            user_agent=user_agent,
+        )
+
+    # We have already dealt with RepositoryNotFoundError and RevisionNotFoundError when getting the index, so
+    # we don't have to catch them here. We have also dealt with EntryNotFoundError.
+    except HTTPError as e:
+        raise EnvironmentError(
+            f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load {pretrained_model_name_or_path}. You should try"
+            " again after checking your internet connection."
+        ) from e
+
+    # If `local_files_only=True`, `cached_folder` may not contain all the shard files.
+    if local_files_only:
+        _check_if_shards_exist_locally(
+            local_dir=cache_dir, subfolder=subfolder, original_shard_filenames=original_shard_filenames
+        )
+
+    return cached_folder, sharded_metadata
+
+
 class PushToHubMixin:
     """
     A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub.

src/diffusers/utils/import_utils.py

@@ -131,7 +131,6 @@ try:
 except importlib_metadata.PackageNotFoundError:
     _unidecode_available = False
 
-
 _onnxruntime_version = "N/A"
 _onnx_available = importlib.util.find_spec("onnxruntime") is not None
 if _onnx_available:

tests/models/test_modeling_common.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 
 import inspect
+import json
+import os
 import tempfile
 import traceback
 import unittest
@@ -37,7 +39,7 @@ from diffusers.models.attention_processor import (
     XFormersAttnProcessor,
 )
 from diffusers.training_utils import EMAModel
-from diffusers.utils import is_torch_npu_available, is_xformers_available, logging
+from diffusers.utils import SAFE_WEIGHTS_INDEX_NAME, is_torch_npu_available, is_xformers_available, logging
 from diffusers.utils.testing_utils import (
     CaptureLogger,
     get_python_version,
@@ -129,7 +131,9 @@ class ModelUtilsTest(unittest.TestCase):
                 )
 
             download_requests = [r.method for r in m.request_history]
-            assert download_requests.count("HEAD") == 2, "2 HEAD requests one for config, one for model"
+            assert (
+                download_requests.count("HEAD") == 3
+            ), "3 HEAD requests one for config, one for model, and one for shard index file."
             assert download_requests.count("GET") == 2, "2 GET requests one for config, one for model"
 
             with requests_mock.mock(real_http=True) as m:
@@ -142,8 +146,8 @@ class ModelUtilsTest(unittest.TestCase):
 
             cache_requests = [r.method for r in m.request_history]
             assert (
-                "HEAD" == cache_requests[0] and len(cache_requests) == 1
-            ), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
+                "HEAD" == cache_requests[0] and len(cache_requests) == 2
+            ), "We should call only `model_info` to check for commit hash and  knowing if shard index is present."
 
     def test_weight_overwrite(self):
         with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(ValueError) as error_context:
@@ -866,6 +870,41 @@ class ModelTesterMixin:
 
                 self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
 
+    @require_torch_gpu
+    def test_sharded_checkpoints(self):
+        config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**config).eval()
+        if model._no_split_modules is None:
+            return
+        model = model.to(torch_device)
+
+        torch.manual_seed(0)
+        base_output = model(**inputs_dict)
+
+        model_size = compute_module_sizes(model)[""]
+        max_shard_size = int((model_size * 0.75) / (2**10))  # Convert to KB as these test models are small.
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.cpu().save_pretrained(tmp_dir, max_shard_size=f"{max_shard_size}KB")
+            self.assertTrue(os.path.exists(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
+
+            # Now check if the right number of shards exists. First, let's get the number of shards.
+            # Since this number can be dependent on the model being tested, it's important that we calculate it
+            # instead of hardcoding it.
+            with open(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)) as f:
+                weight_map_dict = json.load(f)["weight_map"]
+                first_key = list(weight_map_dict.keys())[0]
+                weight_loc = weight_map_dict[first_key]  # e.g., diffusion_pytorch_model-00001-of-00002.safetensors
+                expected_num_shards = int(weight_loc.split("-")[-1].split(".")[0])
+
+            actual_num_shards = len([file for file in os.listdir(tmp_dir) if file.endswith(".safetensors")])
+            self.assertTrue(actual_num_shards == expected_num_shards)
+
+            new_model = self.model_class.from_pretrained(tmp_dir, device_map="auto")
+
+            torch.manual_seed(0)
+            new_output = new_model(**inputs_dict)
+            self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
+
 
 @is_staging_test
 class ModelPushToHubTester(unittest.TestCase):

tests/models/unets/test_models_unet_2d_condition.py

@@ -21,6 +21,7 @@ import unittest
 from collections import OrderedDict
 
 import torch
+from huggingface_hub import snapshot_download
 from parameterized import parameterized
 from pytest import mark
 
@@ -1034,6 +1035,25 @@ class UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.Test
         assert sample2.allclose(sample5, atol=1e-4, rtol=1e-4)
         assert sample2.allclose(sample6, atol=1e-4, rtol=1e-4)
 
+    @require_torch_gpu
+    def test_load_sharded_checkpoint_from_hub(self):
+        _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        loaded_model = self.model_class.from_pretrained("hf-internal-testing/unet2d-sharded-dummy", device_map="auto")
+        new_output = loaded_model(**inputs_dict)
+
+        assert loaded_model
+        assert new_output.sample.shape == (4, 4, 16, 16)
+
+    @require_torch_gpu
+    def test_load_sharded_checkpoint_from_hub_local(self):
+        _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        ckpt_path = snapshot_download("hf-internal-testing/unet2d-sharded-dummy")
+        loaded_model = self.model_class.from_pretrained(ckpt_path, local_files_only=True, device_map="auto")
+        new_output = loaded_model(**inputs_dict)
+
+        assert loaded_model
+        assert new_output.sample.shape == (4, 4, 16, 16)
+
     @require_peft_backend
     def test_lora(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

tests/pipelines/test_pipelines.py

@@ -29,6 +29,7 @@ import PIL.Image
 import requests_mock
 import safetensors.torch
 import torch
+import torch.nn as nn
 from parameterized import parameterized
 from PIL import Image
 from requests.exceptions import HTTPError
@@ -135,6 +136,7 @@ def _test_from_save_pretrained_dynamo(in_queue, out_queue, timeout):
 class CustomEncoder(ModelMixin, ConfigMixin):
     def __init__(self):
         super().__init__()
+        self.linear = nn.Linear(3, 3)
 
 
 class CustomPipeline(DiffusionPipeline):