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from trl import AutoModelForCausalLMWithValueHead
from typing import TYPE_CHECKING, Optional, Tuple
from transformers import AutoTokenizer
from transformers.integrations import is_deepspeed_zero3_enabled
from .adapter import init_adapter
from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
from .utils import load_valuehead_params, register_autoclass
from ..extras.logging import get_logger
from ..extras.misc import count_parameters, get_current_device, try_download_model_from_ms
if TYPE_CHECKING:
from transformers import PreTrainedModel, PreTrainedTokenizer
from ..hparams import FinetuningArguments, ModelArguments
# 🔍🔍🔍
from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
logger = get_logger(__name__)
def load_model_and_tokenizer(
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments",
is_trainable: Optional[bool] = False,
add_valuehead: Optional[bool] = False,
) -> Tuple["PreTrainedModel", "PreTrainedTokenizer"]:
r"""
Loads pretrained model and tokenizer.
Support both training and inference.
"""
try_download_model_from_ms(model_args)
# config_kwargs = {
# "trust_remote_code": True,
# "cache_dir": model_args.cache_dir,
# "revision": model_args.model_revision,
# "token": model_args.hf_hub_token,
# "attn_implementation": "flash_attention_2", # 🔍
# }
config_kwargs = {
"trust_remote_code": True,
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"token": model_args.hf_hub_token,
"attn_implementation": "eager", # 🔍
}
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
use_fast=model_args.use_fast_tokenizer,
split_special_tokens=model_args.split_special_tokens,
padding_side="right",
**config_kwargs,
)
patch_tokenizer(tokenizer)
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
config.use_cache=False
print(config)
patch_config(config, tokenizer, model_args, config_kwargs, is_trainable)
model = None
if is_trainable and model_args.use_unsloth:
from unsloth import FastLanguageModel # type: ignore
unsloth_kwargs = {
"model_name": model_args.model_name_or_path,
"max_seq_length": model_args.model_max_length,
"dtype": model_args.compute_dtype,
"load_in_4bit": model_args.quantization_bit == 4,
"token": model_args.hf_hub_token,
"device_map": {"": get_current_device()},
"rope_scaling": getattr(config, "rope_scaling", None),
}
try:
model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
except NotImplementedError:
logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
model_args.use_unsloth = False
if model_args.adapter_name_or_path:
model_args.adapter_name_or_path = None
logger.warning("Unsloth does not support loading adapters.")
if model is None:
if not model_args.autogptq:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
config=config,
torch_dtype=model_args.compute_dtype,
device_map="auto",
low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()),
**config_kwargs,
)
else:
model = AutoGPTQForCausalLM.from_quantized(
model_args.model_name_or_path,
trust_remote_code=False,
# model_basename=None if autogptq is True else Path(autogptq).stem,
use_safetensors=True
if model_args.autogptq is True
else model_args.autogptq.endswith(".safetensors"),
# **model_kwargs,
)
patch_model(model, tokenizer, model_args, is_trainable)
register_autoclass(config, model, tokenizer)
model = init_adapter(model, model_args, finetuning_args, is_trainable)
if add_valuehead:
model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
patch_valuehead_model(model)
if model_args.adapter_name_or_path is not None:
vhead_path = model_args.adapter_name_or_path[-1]
else:
vhead_path = model_args.model_name_or_path
vhead_params = load_valuehead_params(vhead_path, model_args)
if vhead_params is not None:
model.load_state_dict(vhead_params, strict=False)
logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
if not is_trainable:
model.requires_grad_(False)
model = model.to(model_args.compute_dtype) if not getattr(model, "quantization_method", None) else model
model.eval()
else:
model.train()
trainable_params, all_param = count_parameters(model)
logger.info(
"trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
trainable_params, all_param, 100 * trainable_params / all_param
)
)
if not is_trainable:
logger.info("This IS expected that the trainable params is 0 if you are using model for inference only.")
if model_args.print_param_status:
for name, param in model.named_parameters():
print(
"name: {}, dtype: {}, device: {}, trainable: {}".format(
name, param.dtype, param.device, param.requires_grad
)
)
for name, module in model.named_modules():
if hasattr(module, "sparseThreshold"):
module.sparseThreshold.requires_grad = True
return model, tokenizer
def load_tokenizer(
model_args: "ModelArguments",
) -> Tuple["PreTrainedTokenizer"]:
r"""
Loads pretrained model and tokenizer.
Support both training and inference.
"""
try_download_model_from_ms(model_args)
config_kwargs = {
"trust_remote_code": True,
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"token": model_args.hf_hub_token,
"attn_implementation": "flash_attention_2", # 🔍
}
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
use_fast=model_args.use_fast_tokenizer,
split_special_tokens=model_args.split_special_tokens,
padding_side="right",
**config_kwargs,
)
return tokenizer
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