mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-06 10:52:14 +08:00
175 lines
6.7 KiB
Python
175 lines
6.7 KiB
Python
import inspect
|
|
from enum import Enum, unique
|
|
from functools import partial
|
|
from typing import TYPE_CHECKING, Any, Dict, List, Optional
|
|
|
|
import torch
|
|
from transformers import PreTrainedModel
|
|
from transformers.integrations import is_deepspeed_zero3_enabled
|
|
from transformers.utils import cached_file
|
|
from transformers.utils.versions import require_version
|
|
|
|
from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
|
|
from ..extras.logging import get_logger
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
from transformers import PretrainedConfig, PreTrainedTokenizer
|
|
|
|
from ..hparams import ModelArguments
|
|
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
@unique
|
|
class QuantizationMethod(str, Enum):
|
|
r"""
|
|
Borrowed from `transformers.utils.quantization_config.QuantizationMethod`.
|
|
"""
|
|
|
|
BITS_AND_BYTES = "bitsandbytes"
|
|
GPTQ = "gptq"
|
|
AWQ = "awq"
|
|
AQLM = "aqlm"
|
|
QUANTO = "quanto"
|
|
|
|
|
|
def add_z3_leaf_module(model: "PreTrainedModel", module: "torch.nn.Module") -> None:
|
|
r"""
|
|
Sets module as a leaf module to skip partitioning in deepspeed zero3.
|
|
"""
|
|
if is_deepspeed_zero3_enabled():
|
|
require_version("deepspeed>=0.13.0", "To fix: pip install deepspeed>=0.13.0")
|
|
from deepspeed.utils import set_z3_leaf_modules # type: ignore
|
|
|
|
set_z3_leaf_modules(model, [module])
|
|
|
|
|
|
def find_all_linear_modules(model: "PreTrainedModel") -> List[str]:
|
|
r"""
|
|
Finds all available modules to apply lora or galore.
|
|
"""
|
|
quantization_method = getattr(model, "quantization_method", None)
|
|
if quantization_method is None:
|
|
linear_cls = torch.nn.Linear
|
|
elif quantization_method == QuantizationMethod.BITS_AND_BYTES:
|
|
import bitsandbytes as bnb
|
|
|
|
linear_cls = bnb.nn.Linear4bit if getattr(model, "is_loaded_in_4bit", False) else bnb.nn.Linear8bitLt
|
|
else:
|
|
raise ValueError("Finding linear modules for {} models is not supported.".format(quantization_method))
|
|
|
|
output_layer_names = ["lm_head"]
|
|
if model.config.model_type == "chatglm":
|
|
output_layer_names.append("output_layer")
|
|
elif model.config.model_type == "internlm2":
|
|
output_layer_names.append("output")
|
|
|
|
module_names = set()
|
|
for name, module in model.named_modules():
|
|
if isinstance(module, linear_cls) and not any(output_layer in name for output_layer in output_layer_names):
|
|
module_names.add(name.split(".")[-1])
|
|
|
|
logger.info("Found linear modules: {}".format(",".join(module_names)))
|
|
return list(module_names)
|
|
|
|
|
|
def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], num_layer_trainable: int) -> List[str]:
|
|
r"""
|
|
Finds the modules in the expanded blocks to apply lora.
|
|
"""
|
|
num_layers = getattr(model.config, "num_hidden_layers", None)
|
|
if not num_layers:
|
|
raise ValueError("Model was not supported.")
|
|
|
|
if num_layers % num_layer_trainable != 0:
|
|
raise ValueError(
|
|
"`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(num_layers, num_layer_trainable)
|
|
)
|
|
|
|
stride = num_layers // num_layer_trainable
|
|
trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
|
|
trainable_layers = [".{:d}.".format(idx) for idx in trainable_layer_ids]
|
|
module_names = []
|
|
for name, _ in model.named_modules():
|
|
if any(target_module in name for target_module in target_modules) and any(
|
|
trainable_layer in name for trainable_layer in trainable_layers
|
|
):
|
|
module_names.append(name)
|
|
|
|
logger.info("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
|
|
return module_names
|
|
|
|
|
|
def gradient_checkpointing_enable(
|
|
self: "PreTrainedModel", gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
|
|
) -> None:
|
|
r"""
|
|
Activates gradient checkpointing for the current model.
|
|
|
|
Modification of the original method to enable gradient checkpointing for block-wise optimizer.
|
|
"""
|
|
from torch.utils.checkpoint import checkpoint
|
|
|
|
if not self.supports_gradient_checkpointing:
|
|
raise ValueError("{} does not support gradient checkpointing.".format(self.__class__.__name__))
|
|
|
|
if gradient_checkpointing_kwargs is None:
|
|
gradient_checkpointing_kwargs = {"use_reentrant": True}
|
|
|
|
gradient_checkpointing_func = partial(checkpoint, **gradient_checkpointing_kwargs)
|
|
|
|
def custom_gradient_checkpointing_func(func, *args, **kwargs):
|
|
module: "torch.nn.Module" = func.__self__
|
|
|
|
if any(param.requires_grad for param in module.parameters()):
|
|
for arg in args:
|
|
if torch.is_tensor(arg) and torch.is_floating_point(arg):
|
|
arg.requires_grad_(True)
|
|
|
|
return gradient_checkpointing_func(func, *args, **kwargs)
|
|
|
|
if "value" in inspect.signature(self._set_gradient_checkpointing).parameters: # old GC format
|
|
self.apply(partial(self._set_gradient_checkpointing, value=True))
|
|
logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
|
|
else:
|
|
self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=custom_gradient_checkpointing_func)
|
|
|
|
|
|
def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
|
|
r"""
|
|
Loads value head parameters from Hugging Face Hub or local disk.
|
|
|
|
Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
|
|
"""
|
|
kwargs = {"path_or_repo_id": path_or_repo_id, "cache_dir": model_args.cache_dir, "token": model_args.hf_hub_token}
|
|
|
|
try:
|
|
from safetensors import safe_open
|
|
|
|
vhead_file = cached_file(filename=V_HEAD_SAFE_WEIGHTS_NAME, **kwargs)
|
|
with safe_open(vhead_file, framework="pt", device="cpu") as f:
|
|
return {key: f.get_tensor(key) for key in f.keys()}
|
|
except Exception as err:
|
|
logger.info("Failed to load {}: {}".format(V_HEAD_SAFE_WEIGHTS_NAME, str(err)))
|
|
|
|
try:
|
|
vhead_file = cached_file(filename=V_HEAD_WEIGHTS_NAME, **kwargs)
|
|
return torch.load(vhead_file, map_location="cpu")
|
|
except Exception as err:
|
|
logger.info("Failed to load {}: {}".format(V_HEAD_WEIGHTS_NAME, str(err)))
|
|
|
|
logger.info("Provided path ({}) does not contain value head weights.".format(path_or_repo_id))
|
|
logger.info("Ignore these messages if you are not resuming the training of a value head model.")
|
|
return None
|
|
|
|
|
|
def register_autoclass(config: "PretrainedConfig", model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer"):
|
|
if "AutoConfig" in getattr(config, "auto_map", {}):
|
|
config.__class__.register_for_auto_class()
|
|
if "AutoModelForCausalLM" in getattr(config, "auto_map", {}):
|
|
model.__class__.register_for_auto_class()
|
|
if "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}):
|
|
tokenizer.__class__.register_for_auto_class()
|