[FEAT] Model loading refactor

src/diffusers/loaders/single_file_model.py

@@ -362,17 +362,18 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
 
         if is_accelerate_available():
             param_device = torch.device(device) if device else torch.device("cpu")
-            named_buffers = model.named_buffers()
-            unexpected_keys = load_model_dict_into_meta(
+            unexpected_keys = [
+                param_name for param_name in diffusers_format_checkpoint if param_name not in model.state_dict()
+            ]
+            load_model_dict_into_meta(
                 model,
                 diffusers_format_checkpoint,
                 dtype=torch_dtype,
-                device=param_device,
+                device_map={"": param_device},
                 hf_quantizer=hf_quantizer,
                 keep_in_fp32_modules=keep_in_fp32_modules,
-                named_buffers=named_buffers,
+                unexpected_keys=unexpected_keys,
             )
-
         else:
             _, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False)
 

src/diffusers/loaders/single_file_utils.py

@@ -1588,18 +1588,9 @@ def create_diffusers_clip_model_from_ldm(
         raise ValueError("The provided checkpoint does not seem to contain a valid CLIP model.")
 
     if is_accelerate_available():
-        unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
+        load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
     else:
-        _, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False)
-
-    if model._keys_to_ignore_on_load_unexpected is not None:
-        for pat in model._keys_to_ignore_on_load_unexpected:
-            unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
-
-    if len(unexpected_keys) > 0:
-        logger.warning(
-            f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
-        )
+        model.load_state_dict(diffusers_format_checkpoint, strict=False)
 
     if torch_dtype is not None:
         model.to(torch_dtype)
@@ -2056,16 +2047,7 @@ def create_diffusers_t5_model_from_checkpoint(
     diffusers_format_checkpoint = convert_sd3_t5_checkpoint_to_diffusers(checkpoint)
 
     if is_accelerate_available():
-        unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
-        if model._keys_to_ignore_on_load_unexpected is not None:
-            for pat in model._keys_to_ignore_on_load_unexpected:
-                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
-
-        if len(unexpected_keys) > 0:
-            logger.warning(
-                f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
-            )
-
+        load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
     else:
         model.load_state_dict(diffusers_format_checkpoint)
 

src/diffusers/models/model_loading_utils.py

@@ -20,7 +20,8 @@
 from array import array
 from collections import OrderedDict
 from pathlib import Path
-from typing import Dict, Iterator, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Union
+from zipfile import is_zipfile
 
 import safetensors
 import torch
@@ -55,7 +56,7 @@
 
 if is_accelerate_available():
     from accelerate import infer_auto_device_map
-    from accelerate.utils import get_balanced_memory, get_max_memory, set_module_tensor_to_device
+    from accelerate.utils import get_balanced_memory, get_max_memory, offload_weight, set_module_tensor_to_device
 
 
 # Adapted from `transformers` (see modeling_utils.py)
@@ -132,17 +133,29 @@ def _fetch_remapped_cls_from_config(config, old_class):
         return old_class
 
 
+def _check_archive_and_maybe_raise_error(checkpoint_file, format_list):
+    """
+    Check format of the archive
+    """
+    with safetensors.safe_open(checkpoint_file, framework="pt") as f:
+        metadata = f.metadata()
+        if metadata is not None and metadata.get("format") not in format_list:
+            raise OSError(
+                f"The safetensors archive passed at {checkpoint_file} does not contain the valid metadata. Make sure "
+                "you save your model with the `save_pretrained` method."
+            )
+
+
 def load_state_dict(
     checkpoint_file: Union[str, os.PathLike],
-    variant: Optional[str] = None,
     dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
     disable_mmap: bool = False,
+    map_location: Optional[Union[str, torch.device]] = None,
 ):
     """
     Reads a checkpoint file, returning properly formatted errors if they arise.
     """
-    # TODO: We merge the sharded checkpoints in case we're doing quantization. We can revisit this change
-    # when refactoring the _merge_sharded_checkpoints() method later.
+    # TODO: maybe refactor a bit this part where we pass a dict here
     if isinstance(checkpoint_file, dict):
         return checkpoint_file
     try:
@@ -152,19 +165,28 @@ def load_state_dict(
                 # tensors are loaded on cpu
                 with dduf_entries[checkpoint_file].as_mmap() as mm:
                     return safetensors.torch.load(mm)
+            _check_archive_and_maybe_raise_error(checkpoint_file, format_list=["pt", "flax"])
             if disable_mmap:
                 return safetensors.torch.load(open(checkpoint_file, "rb").read())
             else:
-                return safetensors.torch.load_file(checkpoint_file, device="cpu")
+                return safetensors.torch.load_file(checkpoint_file, device=map_location)
         elif file_extension == GGUF_FILE_EXTENSION:
             return load_gguf_checkpoint(checkpoint_file)
         else:
+            if map_location is None:
+                map_location = "cpu"
+            extra_args = {}
             weights_only_kwarg = {"weights_only": True} if is_torch_version(">=", "1.13") else {}
-            return torch.load(
-                checkpoint_file,
-                map_location="cpu",
-                **weights_only_kwarg,
-            )
+            # mmap can only be used with files serialized with zipfile-based format.
+            if (
+                isinstance(checkpoint_file, str)
+                and map_location != "meta"
+                and is_torch_version(">=", "2.1.0")
+                and is_zipfile(checkpoint_file)
+                and not disable_mmap
+            ):
+                extra_args = {"mmap": True}
+            return torch.load(checkpoint_file, map_location="cpu", **weights_only_kwarg, **extra_args)
     except Exception as e:
         try:
             with open(checkpoint_file) as f:
@@ -188,23 +210,25 @@ def load_state_dict(
 def load_model_dict_into_meta(
     model,
     state_dict: OrderedDict,
-    device: Optional[Union[str, torch.device]] = None,
     dtype: Optional[Union[str, torch.dtype]] = None,
     model_name_or_path: Optional[str] = None,
     hf_quantizer=None,
     keep_in_fp32_modules=None,
-    named_buffers: Optional[Iterator[Tuple[str, torch.Tensor]]] = None,
+    device_map=None,
+    unexpected_keys=None,
+    offload_folder=None,
+    offload_index=None,
+    state_dict_index=None,
+    state_dict_folder=None,
 ) -> List[str]:
-    if device is not None and not isinstance(device, (str, torch.device)):
-        raise ValueError(f"Expected device to have type `str` or `torch.device`, but got {type(device)=}.")
-    if hf_quantizer is None:
-        device = device or torch.device("cpu")
-    dtype = dtype or torch.float32
-    is_quantized = hf_quantizer is not None
+    """
+    This is somewhat similar to `_load_state_dict_into_model`, but deals with a model that has some or all of its
+    params on a `meta` device. It replaces the model params with the data from the `state_dict`
+    """
 
-    accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())
+    error_msgs = []
+    is_quantized = hf_quantizer is not None
     empty_state_dict = model.state_dict()
-    unexpected_keys = [param_name for param_name in state_dict if param_name not in empty_state_dict]
 
     for param_name, param in state_dict.items():
         if param_name not in empty_state_dict:
@@ -214,7 +238,7 @@ def load_model_dict_into_meta(
         # We convert floating dtypes to the `dtype` passed. We also want to keep the buffers/params
         # in int/uint/bool and not cast them.
         # TODO: revisit cases when param.dtype == torch.float8_e4m3fn
-        if torch.is_floating_point(param):
+        if dtype is not None and torch.is_floating_point(param):
             if (
                 keep_in_fp32_modules is not None
                 and any(
@@ -223,72 +247,93 @@ def load_model_dict_into_meta(
                 and dtype == torch.float16
             ):
                 param = param.to(torch.float32)
-                if accepts_dtype:
-                    set_module_kwargs["dtype"] = torch.float32
+                set_module_kwargs["dtype"] = torch.float32
             else:
                 param = param.to(dtype)
-                if accepts_dtype:
-                    set_module_kwargs["dtype"] = dtype
+                set_module_kwargs["dtype"] = dtype
+
+        # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model, and which
+        # uses `param.copy_(input_param)` that preserves the contiguity of the parameter in the model.
+        # Reference: https://github.com/pytorch/pytorch/blob/db79ceb110f6646523019a59bbd7b838f43d4a86/torch/nn/modules/module.py#L2040C29-L2040C29
+        old_param = model
+        splits = param_name.split(".")
+        for split in splits:
+            old_param = getattr(old_param, split)
+
+        if not isinstance(old_param, (torch.nn.Parameter, torch.Tensor)):
+            old_param = None
+
+        if old_param is not None:
+            if dtype is None:
+                param = param.to(old_param.dtype)
+
+            if old_param.is_contiguous():
+                param = param.contiguous()
+
+        module_name = param_name
+
+        if device_map is None:
+            param_device = "cpu"
+        else:
+            # find next higher level module that is defined in device_map:
+            # bert.lm_head.weight -> bert.lm_head -> bert -> ''
+            while len(module_name) > 0 and module_name not in device_map:
+                module_name = ".".join(module_name.split(".")[:-1])
+            if module_name == "" and "" not in device_map:
+                raise ValueError(f"{param_name} doesn't have any device set.")
+            param_device = device_map[module_name]
 
         # bnb params are flattened.
         # gguf quants have a different shape based on the type of quantization applied
         if empty_state_dict[param_name].shape != param.shape:
             if (
                 is_quantized
                 and hf_quantizer.pre_quantized
-                and hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
+                and hf_quantizer.check_if_quantized_param(
+                    model, param, param_name, state_dict, param_device=param_device
+                )
             ):
                 hf_quantizer.check_quantized_param_shape(param_name, empty_state_dict[param_name], param)
             else:
                 model_name_or_path_str = f"{model_name_or_path} " if model_name_or_path is not None else ""
                 raise ValueError(
                     f"Cannot load {model_name_or_path_str} because {param_name} expected shape {empty_state_dict[param_name].shape}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example."
                 )
-
-        if is_quantized and (
-            hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
-        ):
-            hf_quantizer.create_quantized_param(model, param, param_name, device, state_dict, unexpected_keys)
-        else:
-            if accepts_dtype:
-                set_module_tensor_to_device(model, param_name, device, value=param, **set_module_kwargs)
-            else:
-                set_module_tensor_to_device(model, param_name, device, value=param)
-
-    if named_buffers is None:
-        return unexpected_keys
-
-    for param_name, param in named_buffers:
-        if is_quantized and (
-            hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
+        if param_device == "disk":
+            offload_index = offload_weight(param, param_name, offload_folder, offload_index)
+        elif param_device == "cpu" and state_dict_index is not None:
+            state_dict_index = offload_weight(param, param_name, state_dict_folder, state_dict_index)
+        elif is_quantized and (
+            hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=param_device)
         ):
-            hf_quantizer.create_quantized_param(model, param, param_name, device, state_dict, unexpected_keys)
+            hf_quantizer.create_quantized_param(model, param, param_name, param_device, state_dict, unexpected_keys)
         else:
-            if accepts_dtype:
-                set_module_tensor_to_device(model, param_name, device, value=param, **set_module_kwargs)
-            else:
-                set_module_tensor_to_device(model, param_name, device, value=param)
+            set_module_tensor_to_device(model, param_name, param_device, value=param, **set_module_kwargs)
 
-    return unexpected_keys
+    return error_msgs, offload_index, state_dict_index
 
 
-def _load_state_dict_into_model(model_to_load, state_dict: OrderedDict) -> List[str]:
+def _load_state_dict_into_model(
+    model_to_load, state_dict: OrderedDict, assign_to_params_buffers: bool = False
+) -> List[str]:
     # Convert old format to new format if needed from a PyTorch state_dict
     # copy state_dict so _load_from_state_dict can modify it
     state_dict = state_dict.copy()
     error_msgs = []
 
     # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
     # so we need to apply the function recursively.
-    def load(module: torch.nn.Module, prefix: str = ""):
-        args = (state_dict, prefix, {}, True, [], [], error_msgs)
+    def load(module: torch.nn.Module, prefix: str = "", assign_to_params_buffers: bool = False):
+        local_metadata = {}
+        local_metadata["assign_to_params_buffers"] = assign_to_params_buffers
+        args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
         module._load_from_state_dict(*args)
 
         for name, child in module._modules.items():
             if child is not None:
-                load(child, prefix + name + ".")
+                load(child, prefix + name + ".", assign_to_params_buffers)
 
-    load(model_to_load)
+    load(model_to_load, assign_to_params_buffers=assign_to_params_buffers)
 
     return error_msgs
 
@@ -343,46 +388,6 @@ def _fetch_index_file(
     return index_file
 
 
-# Adapted from
-# https://github.com/bghira/SimpleTuner/blob/cea2457ab063f6dedb9e697830ae68a96be90641/helpers/training/save_hooks.py#L64
-def _merge_sharded_checkpoints(
-    sharded_ckpt_cached_folder, sharded_metadata, dduf_entries: Optional[Dict[str, DDUFEntry]] = None
-):
-    weight_map = sharded_metadata.get("weight_map", None)
-    if weight_map is None:
-        raise KeyError("'weight_map' key not found in the shard index file.")
-
-    # Collect all unique safetensors files from weight_map
-    files_to_load = set(weight_map.values())
-    is_safetensors = all(f.endswith(".safetensors") for f in files_to_load)
-    merged_state_dict = {}
-
-    # Load tensors from each unique file
-    for file_name in files_to_load:
-        part_file_path = os.path.join(sharded_ckpt_cached_folder, file_name)
-        if dduf_entries:
-            if part_file_path not in dduf_entries:
-                raise FileNotFoundError(f"Part file {file_name} not found.")
-        else:
-            if not os.path.exists(part_file_path):
-                raise FileNotFoundError(f"Part file {file_name} not found.")
-
-        if is_safetensors:
-            if dduf_entries:
-                with dduf_entries[part_file_path].as_mmap() as mm:
-                    tensors = safetensors.torch.load(mm)
-                    merged_state_dict.update(tensors)
-            else:
-                with safetensors.safe_open(part_file_path, framework="pt", device="cpu") as f:
-                    for tensor_key in f.keys():
-                        if tensor_key in weight_map:
-                            merged_state_dict[tensor_key] = f.get_tensor(tensor_key)
-        else:
-            merged_state_dict.update(torch.load(part_file_path, weights_only=True, map_location="cpu"))
-
-    return merged_state_dict
-
-
 def _fetch_index_file_legacy(
     is_local,
     pretrained_model_name_or_path,