text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant)
)
is_sharded = True
# At this stage we don't have a weight file so we will raise an error.
elif not use_safetensors and (
os.path.isfile(os.path... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
):
raise EnvironmentError(
f"Error no file named {_add_variant(WEIGHTS_NAME, variant)} found in directory"
f" {pretrained_model_name_or_path} but there is a file ... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
f" {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME + '.index'} or {FLAX_WEIGHTS_NAME} found in directory"
f" {pretrained_model_name_or_path}."
)
elif os.path.isfile(os.path.join(subfolder, pretrained_model_name_or_path)):
archive_file = pretrained_model_name_... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
resolved_archive_file = download_url(pretrained_model_name_or_path)
else:
# set correct filename
if from_tf:
filename = TF2_WEIGHTS_NAME
elif from_flax:
filename = FLAX_WEIGHTS_NAME
elif use_safetensors i... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
try:
# Load from URL or cache if already cached
cached_file_kwargs = {
"cache_dir": cache_dir,
"force_download": force_download,
"proxies": proxies,
"resume_download": resume_download,... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None
# result when internet is up, the repo and revision exist, but the file does not.
if resolved_archive_file is None and filename == _add_variant(SAFE_WEIGHTS_NAME, variant):
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
pretrained_model_name_or_path, **cached_file_kwargs
)
cached_file_kwargs["revision"] = revision
if resolved_archive_file is None:
raise EnvironmentError(
f"{pretrai... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
filename = _add_variant(WEIGHTS_NAME, variant)
resolved_archive_file = cached_file(
pretrained_model_name_or_path, filename, **cached_file_kwargs
)
if resolved_archive_file is None and filename == _add_variant(WE... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if filename in [WEIGHTS_NAME, WEIGHTS_INDEX_NAME]:
# If the PyTorch file was found, check if there is a safetensors file on the repository
# If there is no safetensors file on the repositories, start an auto conversion
safe_... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
"resume_download": resume_download,
"local_files_only": local_files_only,
"user_agent": user_agent,
"subfolder": subfolder,
"_raise_exceptions_for_gated_repo": False,
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
name="Thread-auto_conversion",
).start()
else:
# Otherwise, no PyTorch file was found, maybe there is a TF or Flax model file.
# We try those to give a helpful error message.
h... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
f" {_add_variant(WEIGHTS_NAME, variant)} but there is a file for TensorFlow weights."
" Use `from_tf=True` to load this model from those weights."
)
elif has_file(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME, **has_file_... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named"
f" {_add_variant(WEIGHTS_NAME, variant)} but there is a file without the variant"
f" {variant}. Use `variant=None` t... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
except EnvironmentError:
# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
# to the original exception.
raise
except Exception as e:
# For any other exception, we throw a gene... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if is_local:
logger.info(f"loading weights file {archive_file}")
resolved_archive_file = archive_file
else:
logger.info(f"loading weights file {filename} from cache at {resolved_archive_file}")
elif gguf_file:
from .modeling_gguf_pytorch_ut... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Case 1: the GGUF file is present locally
if os.path.isfile(gguf_file):
gguf_path = gguf_file
# Case 2: The GGUF path is a location on the Hub
# Load from URL or cache if already cached
else:
cached_file_kwargs = {
"cac... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
gguf_path = cached_file(pretrained_model_name_or_path, gguf_file, **cached_file_kwargs)
# we need a dummy model to help rename state_dict
with torch.device("meta"):
dummy_model = cls(config)
state_dict = load_gguf_checkpoint(gguf_path, return_tensors=True, model_to_l... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# We'll need to download and cache each checkpoint shard if the checkpoint is sharded.
if is_sharded:
# resolved_archive_file becomes a list of files that point to the different checkpoint shards in this case.
resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if (
is_safetensors_available()
and isinstance(resolved_archive_file, str)
and resolved_archive_file.endswith(".safetensors")
):
with safe_open(resolved_archive_file, framework="pt") as f:
metadata = f.metadata() | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if metadata is None:
# Assume it's a pytorch checkpoint (introduced for timm checkpoints)
pass
elif metadata.get("format") == "pt":
pass
elif metadata.get("format") == "tf":
from_tf = True
logger.info("A TensorFlow s... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# load pt weights early so that we know which dtype to init the model under
if from_pt:
if not is_sharded and state_dict is None:
# Time to load the checkpoint
state_dict = load_state_dict(resolved_archive_file, weights_only=weights_only)
# set dtype to ... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if torch_dtype is not None:
if isinstance(torch_dtype, str):
if torch_dtype == "auto":
if hasattr(config, "torch_dtype") and config.torch_dtype is not None:
torch_dtype = config.torch_dtype
logger.info(f"... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
logger.info(
"Since the `torch_dtype` attribute can't be found in model's config object, "
"will use torch_dtype={torch_dtype} as derived from model's weights"
)
elif hasattr(torch, torch_dtype):
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# main torch dtype for modules that aren't part of any sub-config
torch_dtype = torch_dtype.get("")
config.torch_dtype = torch_dtype
if isinstance(torch_dtype, str) and hasattr(torch, torch_dtype):
torch_dtype = getattr(torch, torch_dty... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
dtype_orig = cls._set_default_torch_dtype(torch_dtype)
else:
# set fp32 as the default dtype for BC
default_dtype = str(torch.get_default_dtype()).split(".")[-1]
config.torch_dtype = default_dtype
for key in config.sub_configs.keys():
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if is_sharded:
loaded_state_dict_keys = sharded_metadata["all_checkpoint_keys"]
else:
loaded_state_dict_keys = list(state_dict.keys())
if (
gguf_path is None
and (low_cpu_mem_usage or (use_keep_in_fp32_modules and is_accelerate_avai... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
logger.info("Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
init_contexts = [
deepspeed.zero.Init(config_dict_or_path=deepspeed_config()),
set_zero3_state(),
] + init_contexts
elif low_cpu_mem_usage:
if not is_accelerate_ava... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Detect the accelerator on the machine. If no accelerator is available, it returns CPU.
device_type = torch._C._get_accelerator().type
device_module = torch.get_device_module(device_type)
# Get device with index assuming equal number of devices per host
tp_device = torch... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
with ContextManagers(init_contexts):
# Let's make sure we don't run the init function of buffer modules
model = cls(config, *model_args, **model_kwargs)
# make sure we use the model's config since the __init__ call might have copied it
config = model.config
# Check firs... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# We store the original dtype for quantized models as we cannot easily retrieve it
# once the weights have been quantized
# Note that once you have loaded a quantized model, you can't change its dtype so this will
# remain a single source of truth
config._pre_quantization... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
no_split_modules = model._get_no_split_modules(device_map)
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
"If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
"'seq... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
device_map_kwargs = {"no_split_module_classes": no_split_modules}
if "special_dtypes" in inspect.signature(infer_auto_device_map).parameters:
device_map_kwargs["special_dtypes"] = special_dtypes
elif len(special_dtypes) > 0:
logger.warning(
"Th... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
max_memory = hf_quantizer.adjust_max_memory(max_memory)
device_map_kwargs["max_memory"] = max_memory | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Make sure tied weights are tied before creating the device map.
model.tie_weights()
device_map = infer_auto_device_map(model, dtype=target_dtype, **device_map_kwargs)
if hf_quantizer is not None:
hf_quantizer.validate_environment(device_map=device_map)
eli... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if from_tf:
if resolved_archive_file.endswith(".index"):
# Load from a TensorFlow 1.X checkpoint - provided by original authors
model = cls.load_tf_weights(model, config, resolved_archive_file[:-6]) # Remove the '.index'
else:
# Load from our Tens... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
model, loading_info = load_tf2_checkpoint_in_pytorch_model(
model, resolved_archive_file, allow_missing_keys=True, output_loading_info=True
)
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, ... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
model = load_flax_checkpoint_in_pytorch_model(model, resolved_archive_file)
except ImportError:
logger.error(
"Loading a Flax model in PyTorch, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthed... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
with ContextManagers(load_contexts):
(
model,
missing_keys,
unexpected_keys,
mismatched_keys,
offload_index,
error_msgs,
) = cls._load_pretrained_model(
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
keep_in_fp32_modules=keep_in_fp32_modules,
gguf_path=gguf_path,
weights_only=weights_only,
) | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# make sure token embedding weights are still tied if needed
model.tie_weights()
# Set model in evaluation mode to deactivate DropOut modules by default
model.eval() | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# If it is a model with generation capabilities, attempt to load the generation config
if model.can_generate() and generation_config is not None:
logger.info("The user-defined `generation_config` will be used to override the default generation config.")
model.generation_config = model.ge... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
_from_pipeline=from_pipeline,
**kwargs,
)
except OSError:
logger.info(
"Generation config file not found, using a generation config created from the model config."
)
pass | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Dispatch model with hooks on all devices if necessary
if device_map is not None:
device_map_kwargs = {
"device_map": device_map,
"offload_dir": offload_folder,
"offload_index": offload_index,
"offload_buffers": offload_buffers,
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
and hf_quantizer.quantization_config.quant_method == QuantizationMethod.FBGEMM_FP8
and isinstance(device_map, dict)
and ("cpu" in device_map.values() or "disk" in device_map.values())
):
device_map_kwargs["offload_buffers"] = True | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if not is_fsdp_enabled() and not is_deepspeed_zero3_enabled():
dispatch_model(model, **device_map_kwargs)
if hf_quantizer is not None:
hf_quantizer.postprocess_model(model, config=config)
model.hf_quantizer = hf_quantizer
if _adapter_model_path is not None:
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if tp_plan is not None:
assert tp_device is not None, "tp_device not set!"
if not model.supports_tp_plan:
raise NotImplementedError("This model does not have a tensor parallel plan.")
# Assuming sharding the model onto the world
world_size = torch.distribu... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Rename LayerNorm beta & gamma params for some early models ported from Tensorflow (e.g. Bert)
# This rename is logged.
if key.endswith("LayerNorm.beta"):
return key.replace("LayerNorm.beta", "LayerNorm.bias"), True
if key.endswith("LayerNorm.gamma"):
return key.replace(... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Rename weight norm parametrizations to match changes across torch versions.
# Impacts a number of speech/wav2vec models. e.g. Hubert, Wav2Vec2, and others.
# This rename is not logged.
if hasattr(nn.utils.parametrizations, "weight_norm"):
if key.endswith("weight_g"):
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
@classmethod
def _fix_state_dict_keys_on_load(cls, state_dict):
"""Fixes state dict keys by replacing legacy parameter names with their modern equivalents.
Logs if any parameters have been renamed.
"""
renamed_keys = {}
state_dict_keys = list(state_dict.keys())
for k... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if renamed_keys:
warning_msg = f"A pretrained model of type `{cls.__name__}` "
warning_msg += "contains parameters that have been renamed internally (a few are listed below but more are present in the model):\n"
for old_key, new_key in renamed_keys.values():
warning_m... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
def _fix_state_dict_keys_on_save(self, state_dict):
"""
Similar to `_fix_state_dict_keys_on_load` allows to define hook for state dict key renaming on model save.
Apply `_fix_state_dict_key_on_save` to all keys in `state_dict`.
"""
return {self._fix_state_dict_key_on_save(key)[0]... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
@classmethod
def _load_pretrained_model(
cls,
model,
state_dict,
loaded_keys,
resolved_archive_file,
pretrained_model_name_or_path,
ignore_mismatched_sizes=False,
sharded_metadata=None,
_fast_init=True,
low_cpu_mem_usage=False,
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if device_map is not None and "disk" in device_map.values():
archive_file = (
resolved_archive_file[0] if isinstance(resolved_archive_file, (list, tuple)) else resolved_archive_file
)
is_safetensors = archive_file.endswith(".safetensors")
if offload_folder... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# tie the model weights before retrieving the state_dict
model.tie_weights()
# Retrieve missing & unexpected_keys
model_state_dict = model.state_dict()
expected_keys = list(model_state_dict.keys())
prefix = model.base_model_prefix
if hf_quantizer is not None:
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# key re-naming operations are never done on the keys
# that are loaded, but always on the keys of the newly initialized model
remove_prefix_from_model = not has_prefix_module and expects_prefix_module
add_prefix_to_model = has_prefix_module and not expects_prefix_module
if remove_prefi... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Remove nonpersistent buffers from unexpected keys: they are not in the state dict but will be in the model
# buffers
model_buffers = {n for n, _ in model.named_buffers()}
if remove_prefix_from_model:
model_buffers = {key[len(_prefix) :] if key.startswith(_prefix) else key for key i... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
model.tie_weights()
if device_map is None and not is_fsdp_enabled() and not is_deepspeed_zero3_enabled():
ptrs = collections.defaultdict(list)
for name, tensor in model.state_dict().items():
id_tensor = id_tensor_storage(tensor)
ptrs[id_tensor].append(name... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
for group in tied_params:
if remove_prefix_from_model:
group = [key[len(_prefix) :] if key.startswith(_prefix) else key for key in group]
elif add_prefix_to_model:
group = [".".join([prefix, key]) for key in group]
missing_in_group = [k for k in missin... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if cls._keys_to_ignore_on_load_unexpected is not None:
for pat in cls._keys_to_ignore_on_load_unexpected:
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
if hf_quantizer is not None:
missing_keys = hf_quantizer.update_missing_keys(model, missin... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# upcast in fp32 if any
target_dtype = dtype
if (
keep_in_fp32_modules is not None
and dtype == torch.float16
and any(
module_to_keep_in_fp32 in key.split(".") for module_to_keep_in_fp32 in keep_in_fp32_m... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if param.device == torch.device("meta"):
value = torch.empty(*param.size(), dtype=target_dtype)
if (
not is_quantized
or (getattr(hf_quantizer, "requires_parameters_quantization", False))
or not hf_quantizer.... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# retrieve uninitialized modules and initialize before maybe overriding that with the pretrained weights.
if _fast_init:
if not ignore_mismatched_sizes:
if remove_prefix_from_model:
_loaded_keys = [f"{prefix}.{k}" for k in loaded_keys]
elif add_pre... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if output_embeddings is not None:
# Still need to initialize if there is a bias term since biases are not tied.
if not hasattr(output_embeddings, "bias") or output_embeddings.bias is None:
output_embeddings._is_hf_initialized = True
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
not_initialized_parameters = list(
set(
itertools.chain.from_iterable(
submodule.parameters(recurse=False) for submodule in not_initialized_submodules.values()
)
)
)
with d... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Make sure we are able to load base models as well as derived models (with heads)
start_prefix = ""
model_to_load = model
if len(cls.base_model_prefix) > 0 and not hasattr(model, cls.base_model_prefix) and has_prefix_module:
start_prefix = cls.base_model_prefix + "."
if len(... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
device_map = {k.replace(f"{cls.base_model_prefix}.", ""): v for k, v in device_map.items()} | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
def _find_mismatched_keys(
state_dict,
model_state_dict,
loaded_keys,
original_loaded_keys,
add_prefix_to_model,
remove_prefix_from_model,
ignore_mismatched_sizes,
):
mismatched_keys = []
if ignore_mismat... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
model_key = ".".join(model_key.split(".")[1:]) | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if (
model_key in model_state_dict
and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
):
if (
state_dict[checkpoint_key].shape[-1] == 1
and state_dic... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
del state_dict[checkpoint_key]
return mismatched_keys | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if resolved_archive_file is not None:
folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
else:
folder = None
if device_map is not None and is_safetensors:
param_device_map = expand_device_map(device_map, original_loaded_keys, start_prefix)
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
for p, f in weight_map.items()
if p.startswith(start_prefix) and param_device_map[p[len(start_prefix) :]] == "disk"
}
else:
offload_index = None | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if state_dict is not None:
# Whole checkpoint
mismatched_keys = _find_mismatched_keys(
state_dict,
model_state_dict,
loaded_keys,
original_loaded_keys,
add_prefix_to_model,
remove_prefix_from_model,
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# For GGUF models `state_dict` is never set to None as the state dict is always small
if gguf_path or low_cpu_mem_usage:
fixed_state_dict = cls._fix_state_dict_keys_on_load(state_dict)
error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Sharded checkpoint or whole but low_cpu_mem_usage==True
assign_to_params_buffers = check_support_param_buffer_assignment(
model_to_load, state_dict, start_prefix
)
fixed_state_dict = cls._fix_state_dict_keys_on_load(state_dict)
error_... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
else:
# This should always be a list but, just to be sure.
if not isinstance(resolved_archive_file, list):
resolved_archive_file = [resolved_archive_file]
error_msgs = []
mismatched_keys = []
if not is_safetensors:
offload_inde... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if len(resolved_archive_file) > 1:
resolved_archive_file = logging.tqdm(resolved_archive_file, desc="Loading checkpoint shards")
assign_to_params_buffers = None
for shard_file in resolved_archive_file:
# Skip the load for shards that only contain disk-offloaded we... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
shard_file, is_quantized=is_quantized, map_location=map_location, weights_only=weights_only
) | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Mistmatched keys contains tuples key/shape1/shape2 of weights in the checkpoint that have a shape not
# matching the weights in the model.
mismatched_keys += _find_mismatched_keys(
state_dict,
model_state_dict,
loaded_keys,
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
else:
fixed_state_dict = cls._fix_state_dict_keys_on_load(state_dict)
new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
model_to_load,
fixed_state_dict,
s... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
error_msgs += new_error_msgs
else:
# Sharded checkpoint or whole but low_cpu_mem_usage==True
if assign_to_params_buffers is None:
assign_to_params_buffers = check_support_param_buffer_assignment(
model_to_load, s... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# force memory release
del state_dict
gc.collect()
if offload_index is not None and len(offload_index) > 0:
if model != model_to_load:
# We need to add the prefix of the base model
prefix = cls.base_model_prefix
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if offload_state_dict:
# Load back temporarily offloaded state dict
load_offloaded_weights(model_to_load, state_dict_index, state_dict_folder)
shutil.rmtree(state_dict_folder)
if len(error_msgs) > 0:
error_msg = "\n\t".join(error_msgs)
if ... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if len(unexpected_keys) > 0:
archs = [] if model.config.architectures is None else model.config.architectures
warner = logger.warning if model.__class__.__name__ in archs else logger.info
warner(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
)
else:
logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
if len(missing_keys) > 0:
logger.warning(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if len(mismatched_keys) > 0:
mismatched_warning = "\n".join(
[
f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
for key, shape1, shape2 in mismatched_keys
]
)
logger.warni... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
return model, missing_keys, unexpected_keys, mismatched_keys, offload_index, error_msgs
def retrieve_modules_from_names(self, names, add_prefix=False, remove_prefix=False):
module_keys = {".".join(key.split(".")[:-1]) for key in names}
# torch.nn.ParameterList is a special case where two parameter... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
retrieved_modules = []
# retrieve all modules that has at least one missing weight name
for name, module in self.named_modules():
if remove_prefix:
_prefix = f"{self.base_model_prefix}."
name = name[len(_prefix) :] if name.startswith(_prefix) else name
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
1. save which state_dict keys are available
2. drop state_dict before model is created, since the latter takes 1x model size memory
Here then we continue:
3. switch to the meta device all params/buffers that are going to be replaced from the loaded state_dict
4. load state_dict 2nd tim... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
_move_model_to_meta(model, loaded_state_dict_keys, start_prefix)
state_dict = load_state_dict(resolved_archive_file, weights_only=weights_only)
expected_keys = loaded_state_dict_keys # plug for missing expected_keys. TODO: replace with proper keys
fixed_state_dict = model._fix_state_dict_keys_o... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
Args:
auto_class (`str` or `type`, *optional*, defaults to `"AutoModel"`):
The auto class to register this new model with.
"""
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if ... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
PyTorch's attention fastpath allows to speed up inference through kernel fusions and the use of [nested
tensors](https://pytorch.org/docs/stable/nested.html). Detailed benchmarks can be found in [this blog
post](https://medium.com/pytorch/bettertransformer-out-of-the-box-performance-for-huggingface-tran... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
def reverse_bettertransformer(self):
"""
Reverts the transformation from [`~PreTrainedModel.to_bettertransformer`] so that the original modeling is
used, for example in order to save the model.
Returns:
[`PreTrainedModel`]: The model converted back to the original modeling.
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
def warn_if_padding_and_no_attention_mask(self, input_ids, attention_mask):
"""
Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.
"""
# Skip the check during tracing.
if is_torch_fx_proxy(input_ids) or torch.jit.is_tracing() or ... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# If the pad token is equal to either BOS, EOS, or SEP, we do not know whether the user should use an
# attention_mask or not. In this case, we should still show a warning because this is a rare case.
if (
(self.config.bos_token_id is not None and self.config.bos_token_id == self... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
logger.warning_once(warn_string)
@property
def supports_tp_plan(self):
"""
Returns whether the model has a tensor parallelism plan.
"""
if self._tp_plan is not None:
return True
# Check if base model has a TP plan
if getattr(self.base_model, "_tp_plan... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# Tensor parallelize a nn.Module based on the `_tp_plan` attribute of the module.
# No op if `_tp_plan` attribute does not exist under the module.
# This is a helper function to be used with `model.apply` to recursively
# parallelize a model.
def tplize(mod: torch.nn.Module) -> None:
... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
device_mesh=device_mesh,
parallelize_plan=tp_plan,
) | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
# `apply` is a native method of `nn.Module` that recursively applies a
# function to every submodule.
self.apply(tplize)
@property
def loss_function(self):
loss_type = getattr(self, "loss_type", None)
if loss_type is None or loss_type not in LOSS_MAPPING:
logger.war... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
def get_compiled_call(self, compile_config: CompileConfig):
"""Return a `torch.compile`'d version of `self.__call__`. This is useful to dynamically choose between
non-compiled/compiled `forward` during inference, especially to switch between prefill (where we don't
want to use compiled version t... | 230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
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