mirror of
https://github.com/wassname/alignment-handbook.git
synced 2026-07-05 18:45:28 +08:00
Lewis Tunstall
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
from typing import Dict, Union
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import torch
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from transformers import AutoTokenizer, BitsAndBytesConfig, PreTrainedTokenizer
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from accelerate import Accelerator
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from peft import LoraConfig, PeftConfig
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from .configs import DataArguments, ModelArguments
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from .data import DEFAULT_CHAT_TEMPLATE
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def get_current_device() -> int:
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"""Get the current device. For GPU we return the local process index to enable multiple GPU training."""
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return Accelerator().local_process_index if torch.cuda.is_available() else "cpu"
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def get_kbit_device_map() -> Dict[str, int] | None:
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"""Useful for running inference with quantized models by setting `device_map=get_peft_device_map()`"""
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return {"": get_current_device()} if torch.cuda.is_available() else None
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def get_quantization_config(model_args) -> BitsAndBytesConfig | None:
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if model_args.load_in_4bit:
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16, # For consistency with model weights, we use the same value as `torch_dtype` which is float16 for PEFT models
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bnb_4bit_quant_type=model_args.bnb_4bit_quant_type,
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bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant,
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)
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elif model_args.load_in_8bit:
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quantization_config = BitsAndBytesConfig(
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load_in_8bit=True,
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)
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else:
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quantization_config = None
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return quantization_config
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def get_tokenizer(model_args: ModelArguments, data_args: DataArguments) -> PreTrainedTokenizer:
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"""Get the tokenizer for the model."""
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path,
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revision=model_args.model_revision,
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)
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if tokenizer.pad_token_id is None:
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tokenizer.pad_token_id = tokenizer.eos_token_id
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if data_args.truncation_side is not None:
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tokenizer.truncation_side = data_args.truncation_side
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# Set reasonable default for models without max length
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if tokenizer.model_max_length > 100_000:
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tokenizer.model_max_length = 2048
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if data_args.chat_template is not None:
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tokenizer.chat_template = data_args.chat_template
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elif tokenizer.chat_template is None:
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tokenizer.chat_template = DEFAULT_CHAT_TEMPLATE
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return tokenizer
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def get_peft_config(model_args: ModelArguments) -> Union[PeftConfig, None]:
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if model_args.use_peft is False:
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return None
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peft_config = LoraConfig(
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r=model_args.lora_r,
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lora_alpha=model_args.lora_alpha,
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lora_dropout=model_args.lora_dropout,
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bias="none",
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task_type="CAUSAL_LM",
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target_modules=model_args.lora_target_modules,
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modules_to_save=model_args.lora_modules_to_save,
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)
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return peft_config
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