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# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import os
import warnings
from contextlib import nullcontext
from typing import Optional, Tuple
import accelerate
import torch
from accelerate.hooks import add_hook_to_module, remove_hook_from_module
from accelerate.utils import is_npu_available, is_xpu_available
from huggingface_hub import file_exists
from huggingface_hub.utils import EntryNotFoundError, HFValidationError
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from ..import_utils import is_auto_gptq_available, is_torch_tpu_available
from .constants import (
CONFIG_NAME,
EMBEDDING_LAYER_NAMES,
INCLUDE_LINEAR_LAYERS_SHORTHAND,
SAFETENSORS_WEIGHTS_NAME,
TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING,
TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING,
TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING,
TRANSFORMERS_MODELS_TO_LNTUNING_TARGET_MODULES_MAPPING,
TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,
TRANSFORMERS_MODELS_TO_VERA_TARGET_MODULES_MAPPING,
WEIGHTS_NAME,
bloom_model_postprocess_past_key_value,
starcoder_model_postprocess_past_key_value,
)
mlu_available = False
if version.parse(accelerate.__version__) >= version.parse("0.29.0"):
from accelerate.utils import is_mlu_available
mlu_available = is_mlu_available()
__all__ = [
"CONFIG_NAME",
"EMBEDDING_LAYER_NAMES",
"SAFETENSORS_WEIGHTS_NAME",
"TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING",
"TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING",
"TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING",
"TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING",
"TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING",
"TRANSFORMERS_MODELS_TO_LNTUNING_TARGET_MODULES_MAPPING",
"TRANSFORMERS_MODELS_TO_VERA_TARGET_MODULES_MAPPING",
"WEIGHTS_NAME",
"INCLUDE_LINEAR_LAYERS_SHORTHAND",
"bloom_model_postprocess_past_key_value",
"starcoder_model_postprocess_past_key_value",
]
# Get current device name based on available devices
def infer_device() -> str:
if torch.cuda.is_available():
return "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
return "mps"
elif mlu_available:
return "mlu"
elif is_xpu_available():
return "xpu"
elif is_npu_available():
return "npu"
return "cpu"
def prepare_model_for_kbit_training(model, use_gradient_checkpointing=True, gradient_checkpointing_kwargs=None):
r"""
Note this method only works for `transformers` models.
This method wraps the entire protocol for preparing a model before running a training. This includes:
1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm
head to fp32
Args:
model (`transformers.PreTrainedModel`):
The loaded model from `transformers`
use_gradient_checkpointing (`bool`, *optional*, defaults to `True`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
gradient_checkpointing_kwargs (`dict`, *optional*, defaults to `None`):
Keyword arguments to pass to the gradient checkpointing function, please refer to the documentation of
`torch.utils.checkpoint.checkpoint` for more details about the arguments that you can pass to that method.
Note this is only available in the latest transformers versions (> 4.34.1).
"""
loaded_in_kbit = getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False)
is_gptq_quantized = getattr(model, "quantization_method", None) == "gptq"
is_aqlm_quantized = getattr(model, "quantization_method", None) == "aqlm"
is_eetq_quantized = getattr(model, "quantization_method", None) == "eetq"
is_hqq_quantized = getattr(model, "quantization_method", None) == "hqq" or getattr(model, "hqq_quantized", False)
if gradient_checkpointing_kwargs is None:
gradient_checkpointing_kwargs = {}
for name, param in model.named_parameters():
# freeze base model's layers
param.requires_grad = False
if not is_gptq_quantized and not is_aqlm_quantized and not is_eetq_quantized and not is_hqq_quantized:
# cast all non INT8 parameters to fp32
for param in model.parameters():
if (
(param.dtype == torch.float16) or (param.dtype == torch.bfloat16)
) and param.__class__.__name__ != "Params4bit":
param.data = param.data.to(torch.float32)
if (
loaded_in_kbit or is_gptq_quantized or is_aqlm_quantized or is_eetq_quantized or is_hqq_quantized
) and use_gradient_checkpointing:
# When having `use_reentrant=False` + gradient_checkpointing, there is no need for this hack
if "use_reentrant" not in gradient_checkpointing_kwargs or gradient_checkpointing_kwargs["use_reentrant"]:
# For backward compatibility
if hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()
else:
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
# To support older transformers versions, check if the model supports gradient_checkpointing_kwargs
_supports_gc_kwargs = "gradient_checkpointing_kwargs" in list(
inspect.signature(model.gradient_checkpointing_enable).parameters
)
if not _supports_gc_kwargs and len(gradient_checkpointing_kwargs) > 0:
warnings.warn(
"gradient_checkpointing_kwargs is not supported in this version of transformers. The passed kwargs will be ignored."
" if you want to use that feature, please upgrade to the latest version of transformers.",
FutureWarning,
)
gc_enable_kwargs = (
{} if not _supports_gc_kwargs else {"gradient_checkpointing_kwargs": gradient_checkpointing_kwargs}
)
# enable gradient checkpointing for memory efficiency
model.gradient_checkpointing_enable(**gc_enable_kwargs)
return model
# copied from transformers.models.bart.modeling_bart
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): input ids
pad_token_id (`int`): The id of the `padding` token.
decoder_start_token_id (`int`): The id of the `start` token.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
class ModulesToSaveWrapper(torch.nn.Module):
def __init__(self, module_to_save, adapter_name):
super().__init__()
self.original_module = module_to_save
self.modules_to_save = torch.nn.ModuleDict({})
self._active_adapter = adapter_name
self._disable_adapters = False
self.update(adapter_name)
self.check_module()
def check_module(self):
"""Perform some sanity checks on the module to ensure that it works"""
# Try to anticipate some modules that users could try to target that would not work.
# Note: It's not possible to check hasattr(module, "forward"), since that returns True for ModuleDict and
# ModuleList, even though their forward methods cannot be called
forbidden_classes = (torch.nn.ModuleDict, torch.nn.ModuleList, torch.nn.ParameterDict, torch.nn.ParameterList)
if isinstance(self.original_module, forbidden_classes):
cls_name = self.original_module.__class__.__name__
raise TypeError(f"modules_to_save cannot be applied to modules of type {cls_name}")
@property
def disable_adapters(self) -> bool:
# use a property to ensure that disable_adapters is not set directly, instead use the enable_adapters method
return self._disable_adapters
@property
def active_adapter(self) -> str:
# use a property to ensure that active_adapter is not set directly, instead use the set_adapter method
return self._active_adapter
@property
def weight(self):
if self.active_adapter not in self.modules_to_save:
return self.original_module.weight
return self.modules_to_save[self.active_adapter].weight
def update(self, adapter_name):
context_manager = nullcontext()
for _, param in self.original_module.named_parameters():
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
import deepspeed
context_manager = deepspeed.zero.GatheredParameters(self.original_module.parameters(), modifier_rank=0)
break
with context_manager:
self.modules_to_save.update(torch.nn.ModuleDict({adapter_name: copy.deepcopy(self.original_module)}))
if hasattr(self.modules_to_save[adapter_name], "_hf_hook"):
old_hook = self.modules_to_save[adapter_name]._hf_hook
new_hook = self._create_new_hook(old_hook)
remove_hook_from_module(self.modules_to_save[adapter_name])
add_hook_to_module(self.modules_to_save[adapter_name], new_hook)
self.original_module.requires_grad_(False)
if adapter_name == self.active_adapter:
self.modules_to_save[adapter_name].requires_grad_(True)
def _create_new_hook(self, old_hook):
r"""
Creates a new hook based on the old hook. Use it only if you know what you are doing !
"""
old_hook_cls = getattr(accelerate.hooks, old_hook.__class__.__name__)
old_hook_attr = old_hook.__dict__
filtered_old_hook_attr = {}
old_hook_init_signature = inspect.signature(old_hook_cls.__init__)
for k in old_hook_attr.keys():
if k in old_hook_init_signature.parameters:
filtered_old_hook_attr[k] = old_hook_attr[k]
new_hook = old_hook_cls(**filtered_old_hook_attr)
return new_hook
def forward(self, *args, **kwargs):
if self.disable_adapters or (self.active_adapter not in self.modules_to_save):
return self.original_module(*args, **kwargs)
return self.modules_to_save[self.active_adapter](*args, **kwargs)
def enable_adapters(self, enabled: bool):
"""Toggle the enabling and disabling of adapters
Takes care of setting the requires_grad flag for the adapter weights.
Args:
enabled (bool): True to enable adapters, False to disable adapters
"""
if self._disable_adapters is not enabled:
# already in the desired state, do nothing
return
if enabled:
self.original_module.requires_grad_(False)
self.modules_to_save[self.active_adapter].requires_grad_(True)
self._disable_adapters = False
else:
self.original_module.requires_grad_(True)
self.modules_to_save.requires_grad_(False)
self._disable_adapters = True
def set_adapter(self, adapter_name: str):
"""Set the active adapter
Additionally, this function will set the specified adapter to trainable (i.e., requires_grad=True). If this is
not desired, use the following code.
```py
>>> for name, param in model_peft.named_parameters():
... if ...: # some check on name (ex. if 'lora' in name)
... param.requires_grad = False
```
Args:
adapter_name (str): The name of the adapter to set as active
"""
if adapter_name not in self.modules_to_save:
raise ValueError(f"Adapter {adapter_name} not found in {self.modules_to_save.keys()}")
self.modules_to_save[self.active_adapter].requires_grad_(False)
self.modules_to_save[adapter_name].requires_grad_(True)
self._active_adapter = adapter_name
def _get_submodules(model, key):
parent = model.get_submodule(".".join(key.split(".")[:-1]))
target_name = key.split(".")[-1]
target = model.get_submodule(key)
return parent, target, target_name
def _freeze_adapter(model, adapter_name):
for n, p in model.named_parameters():
if adapter_name in n:
p.requires_grad = False
def _set_trainable(model, adapter_name):
key_list = [key for key, _ in model.named_modules()]
for key in key_list:
target_module_found = any(key.endswith(target_key) for target_key in model.modules_to_save)
if target_module_found:
parent, target, target_name = _get_submodules(model, key)
if isinstance(target, ModulesToSaveWrapper):
target.update(adapter_name)
target.set_adapter(target.active_adapter)
else:
new_module = ModulesToSaveWrapper(target, adapter_name)
new_module.set_adapter(adapter_name)
setattr(parent, target_name, new_module)
def _set_adapter(model, adapter_name):
def check_adapter_name(adapter_name):
if isinstance(adapter_name, str):
return adapter_name
# adapter_name is a list of str
if len(adapter_name) > 1:
raise ValueError("Only one adapter can be set at a time for modules_to_save")
elif len(adapter_name) == 0:
raise ValueError("Please specify at least one adapter to set")
adapter_name = adapter_name[0]
return adapter_name
for module in model.modules():
if isinstance(module, ModulesToSaveWrapper):
# only check the adapter_name if we actually encounter a ModulesToSaveWrapper, otherwise we don't care
adapter_name = check_adapter_name(adapter_name)
# if the adapter is found in this module, set it as the active adapter, else disable the adapters of this
# module
if adapter_name in module.modules_to_save:
module.set_adapter(adapter_name)
else:
module.enable_adapters(False)
def _prepare_prompt_learning_config(peft_config, model_config):
if peft_config.num_layers is None:
if "num_hidden_layers" in model_config:
num_layers = model_config["num_hidden_layers"]
elif "num_layers" in model_config:
num_layers = model_config["num_layers"]
elif "n_layer" in model_config:
num_layers = model_config["n_layer"]
else:
raise ValueError("Please specify `num_layers` in `peft_config`")
peft_config.num_layers = num_layers
if peft_config.token_dim is None:
if "hidden_size" in model_config:
token_dim = model_config["hidden_size"]
elif "n_embd" in model_config:
token_dim = model_config["n_embd"]
elif "d_model" in model_config:
token_dim = model_config["d_model"]
else:
raise ValueError("Please specify `token_dim` in `peft_config`")
peft_config.token_dim = token_dim
if peft_config.num_attention_heads is None:
if "num_attention_heads" in model_config:
num_attention_heads = model_config["num_attention_heads"]
elif "n_head" in model_config:
num_attention_heads = model_config["n_head"]
elif "num_heads" in model_config:
num_attention_heads = model_config["num_heads"]
elif "encoder_attention_heads" in model_config:
num_attention_heads = model_config["encoder_attention_heads"]
else:
raise ValueError("Please specify `num_attention_heads` in `peft_config`")
peft_config.num_attention_heads = num_attention_heads
if getattr(peft_config, "encoder_hidden_size", None) is None:
setattr(peft_config, "encoder_hidden_size", peft_config.token_dim)
return peft_config
def fsdp_auto_wrap_policy(model):
import functools
import os
from accelerate import FullyShardedDataParallelPlugin
if hasattr(FullyShardedDataParallelPlugin, "get_module_class_from_name"):
get_module_class_from_name = FullyShardedDataParallelPlugin.get_module_class_from_name
else:
from accelerate.utils.dataclasses import get_module_class_from_name
from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy
from ..tuners import PrefixEncoder, PromptEmbedding, PromptEncoder
default_transformer_cls_names_to_wrap = (
",".join(model._no_split_modules) if getattr(model, "_no_split_modules", None) is not None else ""
)
transformer_cls_names_to_wrap = os.environ.get(
"FSDP_TRANSFORMER_CLS_TO_WRAP", default_transformer_cls_names_to_wrap
).split(",")
transformer_cls_to_wrap = {PrefixEncoder, PromptEncoder, PromptEmbedding}
for layer_class in transformer_cls_names_to_wrap:
transformer_cls = get_module_class_from_name(model, layer_class)
if transformer_cls is None:
raise Exception("Could not find the transformer layer class to wrap in the model.")
else:
transformer_cls_to_wrap.add(transformer_cls)
def lambda_policy_fn(module):
if (
len(list(module.named_children())) == 0
and getattr(module, "weight", None) is not None
and module.weight.requires_grad
):
return True
return False
lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn)
transformer_wrap_policy = functools.partial(
transformer_auto_wrap_policy,
transformer_layer_cls=transformer_cls_to_wrap,
)
auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy])
return auto_wrap_policy
def transpose(weight, fan_in_fan_out):
if not fan_in_fan_out:
return weight
if isinstance(weight, torch.nn.Parameter):
return torch.nn.Parameter(weight.T)
return weight.T
def _is_valid_match(key: str, target_key: str):
"""
Helper function to match module names target_key and key. Makes sure that either the key is exactly the target_key
or the target_key is a submodule of key
"""
if key.endswith(target_key):
if len(key) > len(target_key):
return key.endswith("." + target_key) # must be a sub module
return True
return False
def _get_batch_size(input_ids: Optional[torch.Tensor], inputs_embeds: Optional[torch.Tensor]) -> int:
"""Get the batch size based on either input_ids or input_embeds
Raises an ValueError if both are None.
"""
if (input_ids is None) and (inputs_embeds is None):
raise ValueError("You have to provide either input_ids or inputs_embeds")
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
return batch_size
def get_quantization_config(model: torch.nn.Module, method: str):
"""
Get the quantization config of the related quantization method
"""
if (
hasattr(model, "config")
and hasattr(model.config, "quantization_config")
and (getattr(model, "quantization_method", None) == method)
):
return model.config.quantization_config
return None
def get_auto_gptq_quant_linear(gptq_quantization_config):
"""
Get the right AutoGPTQQuantLinear class based on the quantization config file
"""
if gptq_quantization_config is not None and is_auto_gptq_available():
from auto_gptq.utils.import_utils import dynamically_import_QuantLinear
desc_act = gptq_quantization_config.desc_act
group_size = gptq_quantization_config.group_size
bits = gptq_quantization_config.bits
if hasattr(gptq_quantization_config, "use_exllama"):
use_exllama = gptq_quantization_config.use_exllama
else:
use_exllama = not gptq_quantization_config.disable_exllama
if hasattr(gptq_quantization_config, "exllama_config"):
exllama_version = gptq_quantization_config.exllama_config["version"]
else:
exllama_version = 1
AutoGPTQQuantLinear = dynamically_import_QuantLinear(
use_triton=False,
desc_act=desc_act,
group_size=group_size,
bits=bits,
disable_exllama=not (use_exllama and exllama_version == 1),
disable_exllamav2=not (use_exllama and exllama_version == 2),
)
return AutoGPTQQuantLinear
return None
def id_tensor_storage(tensor: torch.Tensor) -> Tuple[torch.device, int, int]:
"""
Unique identifier to a tensor storage. Multiple different tensors can share the same underlying storage. For
example, "meta" tensors all share the same storage, and thus their identifier will all be equal. This identifier is
guaranteed to be unique and constant for this tensor's storage during its lifetime. Two tensor storages with
non-overlapping lifetimes may have the same id.
This method is the exact same copy of
https://github.com/huggingface/transformers/blob/main/src/transformers/pytorch_utils.py#L282C1-L300C58 but we added
it here manually to avoid import issue with old versions of transformers.
"""
if tensor.device.type == "xla" and is_torch_tpu_available():
# NOTE: xla tensors dont have storage
# use some other unique id to distinguish.
# this is a XLA tensor, it must be created using torch_xla's
# device. So the following import is safe:
import torch_xla
unique_id = torch_xla._XLAC._xla_get_tensor_id(tensor)
else:
unique_id = storage_ptr(tensor)
return tensor.device, unique_id, storage_size(tensor)
def cast_mixed_precision_params(model, dtype):
"""
Cast all non-trainable parameters of the model to the given `dtype`. The `dtype` can be `torch.float16` or
`torch.bfloat16` as per the mixed-precision training you are performing. The trainable parameters are cast to full
precision. This is meant to reduce the GPU memory usage when using PEFT methods by using half-precision dtype for
non-trainable parameters. Having the trainable parameters in full-precision preserves training stability when using
automatic mixed-precision training.
Args:
model (`torch.nn.Module`):
The model to cast the non-trainable parameters of.
dtype (`torch.dtype`):
The dtype to cast the non-trainable parameters to. The `dtype` can be `torch.float16` or
`torch.bfloat16` as per the mixed-precision training you are performing.
"""
for p in model.parameters():
if not p.requires_grad:
p.data = p.to(dtype)
else:
p.data = p.to(torch.float32)
def str_to_bool(value: str) -> int:
"""
Converts a string representation of truth to `True` (1) or `False` (0).
True values are `y`, `yes`, `t`, `true`, `on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;
"""
# same as function as in accelerate.utils, which replaces the deprecated distutils.util.strtobool
value = value.lower()
if value in ("y", "yes", "t", "true", "on", "1"):
return 1
elif value in ("n", "no", "f", "false", "off", "0"):
return 0
else:
raise ValueError(f"invalid truth value {value}")
def check_file_exists_on_hf_hub(repo_id: str, filename: str, **kwargs) -> Optional[bool]:
"""Check if a file exists on HF Hub, if check was not successful returns None instead of erroring.
Respect offline mode if set.
"""
exists: Optional[bool] = None
if str_to_bool(os.environ.get("HF_HUB_OFFLINE", "0")):
# user set offline mode, cannot check
return exists
try:
exists = file_exists(repo_id, filename, **kwargs)
except (HFValidationError, EntryNotFoundError):
# error, exists stays None
pass
except Exception as e:
warnings.warn(
f"Unable to fetch remote file due to the following error {e} - silently ignoring the lookup"
f" for the file {filename} in {repo_id}."
)
return exists