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| # coding=utf-8 | |
| # coding=utf-8 | |
| # Copyright 2023 The HuggingFace Team. All rights reserved. | |
| # | |
| # 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. | |
| from typing import Dict | |
| import torch | |
| from transformers import AutoTokenizer, BitsAndBytesConfig, PreTrainedTokenizer | |
| from accelerate import Accelerator | |
| from huggingface_hub import list_repo_files | |
| from peft import LoraConfig, PeftConfig | |
| from .configs import DataArguments, ModelArguments | |
| from .data import DEFAULT_CHAT_TEMPLATE | |
| def get_current_device() -> int: | |
| """Get the current device. For GPU we return the local process index to enable multiple GPU training.""" | |
| return Accelerator().local_process_index if torch.cuda.is_available() else "cpu" | |
| def get_kbit_device_map() -> Dict[str, int] | None: | |
| """Useful for running inference with quantized models by setting `device_map=get_peft_device_map()`""" | |
| return {"": get_current_device()} if torch.cuda.is_available() else None | |
| def get_quantization_config(model_args) -> BitsAndBytesConfig | None: | |
| if model_args.load_in_4bit: | |
| quantization_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| 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 | |
| bnb_4bit_quant_type=model_args.bnb_4bit_quant_type, | |
| bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant, | |
| ) | |
| elif model_args.load_in_8bit: | |
| quantization_config = BitsAndBytesConfig( | |
| load_in_8bit=True, | |
| ) | |
| else: | |
| quantization_config = None | |
| return quantization_config | |
| def get_tokenizer(model_args: ModelArguments, data_args: DataArguments) -> PreTrainedTokenizer: | |
| """Get the tokenizer for the model.""" | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| model_args.model_name_or_path, | |
| revision=model_args.model_revision, | |
| ) | |
| if tokenizer.pad_token_id is None: | |
| tokenizer.pad_token_id = tokenizer.eos_token_id | |
| if data_args.truncation_side is not None: | |
| tokenizer.truncation_side = data_args.truncation_side | |
| # Set reasonable default for models without max length | |
| if tokenizer.model_max_length > 100_000: | |
| tokenizer.model_max_length = 2048 | |
| if data_args.chat_template is not None: | |
| tokenizer.chat_template = data_args.chat_template | |
| elif tokenizer.chat_template is None: | |
| tokenizer.chat_template = DEFAULT_CHAT_TEMPLATE | |
| return tokenizer | |
| def get_peft_config(model_args: ModelArguments) -> PeftConfig | None: | |
| if model_args.use_peft is False: | |
| return None | |
| peft_config = LoraConfig( | |
| r=model_args.lora_r, | |
| lora_alpha=model_args.lora_alpha, | |
| lora_dropout=model_args.lora_dropout, | |
| bias="none", | |
| task_type="CAUSAL_LM", | |
| target_modules=model_args.lora_target_modules, | |
| modules_to_save=model_args.lora_modules_to_save, | |
| ) | |
| return peft_config | |
| def is_adapter_model(model_name_or_path: str, revision: str = "main") -> bool: | |
| repo_files = list_repo_files(model_name_or_path, revision=revision) | |
| return "adapter_model.safetensors" in repo_files or "adapter_model.bin" in repo_files | |