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545 lines
24 KiB
545 lines
24 KiB
import os
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import sys
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import torch
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import hashlib
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from itertools import chain
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from typing import List, Literal, Optional, Tuple
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import transformers
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from transformers import (
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AutoConfig,
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AutoModelForCausalLM,
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AutoTokenizer,
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HfArgumentParser,
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Seq2SeqTrainingArguments,
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BitsAndBytesConfig
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)
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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from transformers.modeling_utils import PreTrainedModel
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from transformers.tokenization_utils import PreTrainedTokenizer
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import datasets
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from datasets import Dataset, concatenate_datasets, load_dataset
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from peft import (
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PeftModel,
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TaskType,
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LoraConfig,
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get_peft_model
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)
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from peft.utils import CONFIG_NAME
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from trl import AutoModelForCausalLMWithValueHead
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from .config import (
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ModelArguments,
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DataTrainingArguments,
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FinetuningArguments
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)
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from .template import Template
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from .other import (
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get_logger,
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load_trainable_params,
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load_valuehead_params,
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print_trainable_params,
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prepare_model_for_training,
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IGNORE_INDEX
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)
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check_min_version("4.29.1")
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require_version("datasets>=2.12.0", "To fix: pip install datasets>=2.12.0")
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require_version("accelerate>=0.19.0", "To fix: pip install accelerate>=0.19.0")
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require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")
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require_version("trl>=0.4.1", "To fix: pip install trl>=0.4.1")
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logger = get_logger(__name__)
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def _init_adapter(
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model: PreTrainedModel,
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model_args: ModelArguments,
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finetuning_args: FinetuningArguments,
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is_trainable: bool,
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is_mergeable: bool
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) -> PreTrainedModel:
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r"""
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Initializes the adapters.
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Support full-parameter, freeze and LoRA training.
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Note that the trainable parameters must be cast to float32.
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"""
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if finetuning_args.finetuning_type == "none" and is_trainable:
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raise ValueError("You cannot use finetuning_type=none while training.")
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if finetuning_args.finetuning_type == "full":
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logger.info("Fine-tuning method: Full")
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model = model.float()
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if finetuning_args.finetuning_type == "freeze":
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logger.info("Fine-tuning method: Freeze")
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for name, param in model.named_parameters():
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if not any(trainable_layer in name for trainable_layer in finetuning_args.trainable_layers):
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param.requires_grad_(False)
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else:
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param.data = param.data.to(torch.float32)
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if model_args.checkpoint_dir is not None:
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if finetuning_args.finetuning_type != "lora":
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assert is_mergeable and len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
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load_trainable_params(model, model_args.checkpoint_dir[0]) # load model checkpoints for non-peft methods
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else:
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assert is_mergeable or len(model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
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if finetuning_args.finetuning_type == "lora":
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logger.info("Fine-tuning method: LoRA")
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lastest_checkpoint = None
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assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
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"The given checkpoint is not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."
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if model_args.checkpoint_dir is not None:
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if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
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checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
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else:
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checkpoints_to_merge = model_args.checkpoint_dir
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for checkpoint in checkpoints_to_merge:
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model = PeftModel.from_pretrained(model, checkpoint)
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model = model.merge_and_unload()
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if len(checkpoints_to_merge) > 0:
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logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
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if lastest_checkpoint is not None: # resume lora training or quantized inference
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model = PeftModel.from_pretrained(model, lastest_checkpoint, is_trainable=is_trainable)
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if is_trainable and lastest_checkpoint is None: # create new lora weights while training
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lora_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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inference_mode=False,
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r=finetuning_args.lora_rank,
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lora_alpha=finetuning_args.lora_alpha,
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lora_dropout=finetuning_args.lora_dropout,
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target_modules=finetuning_args.lora_target
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)
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model = get_peft_model(model, lora_config)
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if model_args.checkpoint_dir is not None:
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logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))
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return model
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def load_pretrained(
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model_args: ModelArguments,
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finetuning_args: FinetuningArguments,
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is_trainable: Optional[bool] = False,
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stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = "sft"
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) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
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r"""
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Loads pretrained model and tokenizer.
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Support both training and inference.
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"""
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if (not is_trainable) and model_args.checkpoint_dir is None:
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logger.warning("Checkpoint is not found at evaluation, load the original model.")
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finetuning_args = FinetuningArguments(finetuning_type="none")
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assert stage in ["pt", "sft"] or finetuning_args.finetuning_type == "lora", \
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"RM and PPO training can only be performed with the LoRA method."
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config_kwargs = {
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"trust_remote_code": True,
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"cache_dir": model_args.cache_dir,
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"revision": model_args.model_revision,
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"use_auth_token": True if model_args.use_auth_token else None,
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}
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path,
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use_fast=model_args.use_fast_tokenizer,
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padding_side="left",
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**config_kwargs
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)
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tokenizer.pad_token_id = 0 if tokenizer.pad_token_id is None else tokenizer.pad_token_id # set as the <unk> token
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
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is_mergeable = True
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# Quantization configurations (using bitsandbytes library).
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if model_args.quantization_bit is not None:
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if model_args.quantization_bit == 8:
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require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
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config_kwargs["load_in_8bit"] = True
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config_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_8bit=True,
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llm_int8_threshold=6.0
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)
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elif model_args.quantization_bit == 4:
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require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
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require_version("transformers>=4.30.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git")
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require_version("peft>=0.4.0.dev0", "To fix: pip install git+https://github.com/huggingface/peft.git")
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require_version("accelerate>=0.20.0.dev0", "To fix: pip install git+https://github.com/huggingface/accelerate.git")
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config_kwargs["load_in_4bit"] = True
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config_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=model_args.compute_dtype,
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bnb_4bit_use_double_quant=model_args.double_quantization,
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bnb_4bit_quant_type=model_args.quantization_type
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)
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else:
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raise NotImplementedError
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is_mergeable = False
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config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK") or 0)}
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logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
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if not is_trainable:
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config_kwargs["device_map"] = "auto"
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# Load and prepare pretrained models (without valuehead).
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model = AutoModelForCausalLM.from_pretrained(
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model_args.model_name_or_path,
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config=config,
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torch_dtype=torch.bfloat16 if model_args.compute_dtype == torch.bfloat16 else torch.float16,
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low_cpu_mem_usage=True,
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**config_kwargs
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)
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model = prepare_model_for_training(model) if is_trainable else model
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model = _init_adapter(model, model_args, finetuning_args, is_trainable, is_mergeable)
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if stage == "rm" or stage == "ppo": # add value head
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model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
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if stage == "ppo": # load reward model
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assert is_trainable, "PPO stage cannot be performed at evaluation."
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assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
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logger.info("Load reward model from {}".format(model_args.reward_model))
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model.pretrained_model.load_adapter(model_args.reward_model, "reward", is_trainable=False)
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load_valuehead_params(model, model_args.reward_model)
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# Set the parameter _is_int8_training_enabled for the AutoModelForCausalLMWithValueHead model
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# To meet the compliance requirements of the transformers library
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if model_args.quantization_bit is not None:
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model._is_int8_training_enabled = True
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if not is_trainable:
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model.requires_grad_(False) # fix all model params
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model = model.half() if model_args.quantization_bit is None else model # cast from fp32 to fp16
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print_trainable_params(model)
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return model, tokenizer
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def prepare_args(
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stage: Literal["pt", "sft", "rm", "ppo"]
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) -> Tuple[ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments]:
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
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model_args, data_args, training_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args, finetuning_args = parser.parse_args_into_dataclasses()
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# Setup logging
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if training_args.should_log:
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# The default of training_args.log_level is passive, so we set log level at info here to have that default.
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transformers.utils.logging.set_verbosity_info()
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log_level = training_args.get_process_log_level()
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
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if stage != "sft" and training_args.predict_with_generate:
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raise ValueError("`predict_with_generate` cannot be set as True at PT, RM and PPO stages.")
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if training_args.do_train and training_args.predict_with_generate:
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raise ValueError("`predict_with_generate` cannot be set as True while training.")
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if training_args.do_predict and (not training_args.predict_with_generate):
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raise ValueError("Please enable `predict_with_generate` to save model predictions.")
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if model_args.quantization_bit is not None and finetuning_args.finetuning_type == "full":
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raise ValueError("Quantization is incompatible with the full-parameter tuning.")
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if model_args.quantization_bit is not None and (not training_args.do_train):
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logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
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if training_args.do_train and (not training_args.fp16):
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logger.warning("We recommend enable fp16 mixed precision training.")
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if data_args.prompt_template == "alpaca":
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logger.warning("Please specify `prompt_template` if you are using other pre-trained models.")
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if training_args.local_rank != -1 and training_args.ddp_find_unused_parameters is None:
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logger.warning("`ddp_find_unused_parameters` needs to be set as False in DDP training.")
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training_args.ddp_find_unused_parameters = False
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training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
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if model_args.quantization_bit is not None:
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if training_args.fp16:
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model_args.compute_dtype = torch.float16
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elif training_args.bf16:
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model_args.compute_dtype = torch.bfloat16
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else:
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model_args.compute_dtype = torch.float32
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# Log on each process the small summary:
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logger.info(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}\n"
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+ f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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)
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logger.info(f"Training/evaluation parameters {training_args}")
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# Set seed before initializing model.
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transformers.set_seed(training_args.seed)
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return model_args, data_args, training_args, finetuning_args
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def prepare_infer_args() -> Tuple[ModelArguments, DataTrainingArguments, FinetuningArguments]:
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, FinetuningArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
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model_args, data_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, finetuning_args = parser.parse_args_into_dataclasses()
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if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
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raise ValueError("Quantization is only compatible with the LoRA method.")
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if data_args.prompt_template == "alpaca":
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logger.warning("Please specify `prompt_template` if you are using other pre-trained models.")
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return model_args, data_args, finetuning_args
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def prepare_data(
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model_args: ModelArguments,
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data_args: DataTrainingArguments
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) -> Dataset:
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def checksum(file_path, hash):
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with open(file_path, "rb") as datafile:
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binary_data = datafile.read()
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sha1 = hashlib.sha1(binary_data).hexdigest()
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if sha1 != hash:
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logger.warning("Checksum failed for {}. It may vary depending on the platform.".format(file_path))
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max_samples = data_args.max_samples
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all_datasets: List[Dataset] = [] # support multiple datasets
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for dataset_attr in data_args.dataset_list:
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logger.info("Loading dataset {}...".format(dataset_attr))
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if dataset_attr.load_from == "hf_hub":
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raw_datasets = load_dataset(dataset_attr.dataset_name, cache_dir=model_args.cache_dir)
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elif dataset_attr.load_from == "script":
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raw_datasets = load_dataset(
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os.path.join(data_args.dataset_dir, dataset_attr.dataset_name),
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cache_dir=model_args.cache_dir
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)
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elif dataset_attr.load_from == "file":
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data_file = os.path.join(data_args.dataset_dir, dataset_attr.file_name)
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extension = dataset_attr.file_name.split(".")[-1]
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if dataset_attr.file_sha1 is not None:
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checksum(data_file, dataset_attr.file_sha1)
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else:
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logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json.")
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raw_datasets = load_dataset(
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extension if extension in ["csv", "json"] else "text",
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data_files=data_file,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None
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)
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else:
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raise NotImplementedError
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dataset = raw_datasets[data_args.split]
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if max_samples is not None:
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max_samples_temp = min(len(dataset), max_samples)
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dataset = dataset.select(range(max_samples_temp))
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dummy_data = [None] * len(dataset)
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for column_name, target_name in [
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("prompt_column", "prompt"),
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("query_column", "query"),
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("response_column", "response"),
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("history_column", "history")
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]: # every dataset will have 4 columns same as each other
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if getattr(dataset_attr, column_name) != target_name:
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if getattr(dataset_attr, column_name):
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dataset = dataset.rename_column(getattr(dataset_attr, column_name), target_name)
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else: # None or empty string
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dataset = dataset.add_column(target_name, dummy_data)
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all_datasets.append(dataset)
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if len(data_args.dataset_list) == 1:
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all_datasets = all_datasets[0]
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else:
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all_datasets = concatenate_datasets(all_datasets)
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return all_datasets
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def preprocess_data(
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dataset: Dataset,
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tokenizer: PreTrainedTokenizer,
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data_args: DataTrainingArguments,
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training_args: Seq2SeqTrainingArguments,
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stage: Literal["pt", "sft", "rm", "ppo"]
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) -> Dataset:
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column_names = list(dataset.column_names)
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prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
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prompt_template = Template(data_args.prompt_template)
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# support question with a single answer or multiple answers
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def format_example(examples):
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for i in range(len(examples["prompt"])):
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if examples["prompt"][i] and examples["response"][i]:
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query, answer = examples["prompt"][i], examples["response"][i]
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if examples["query"][i]:
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query += "\n" + examples["query"][i]
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prompt = prompt_template.get_prompt(query, examples["history"][i], prefix)
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yield prompt, answer
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def preprocess_pretrain_dataset(examples):
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# build grouped texts with format `<s> X1 X2 X3 ...` (without </s>)
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text_ids = tokenizer(examples["prompt"])["input_ids"]
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concatenated_ids = list(chain(*text_ids))
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total_length = len(concatenated_ids)
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# we drop the small remainder, and if the total_length < block_size, we exclude this batch
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total_length = (total_length // data_args.max_source_length) * data_args.max_source_length
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# split by chunks of max_source_length
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result = [concatenated_ids[i: i+data_args.max_source_length] for i in range(0, total_length, data_args.max_source_length)]
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return {
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"input_ids": result,
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"labels": result.copy()
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}
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def preprocess_supervised_dataset(examples):
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# build inputs with format `X <s> Y </s>` and labels with format `<ignore> ... <ignore> <s> Y </s>`
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model_inputs = {"input_ids": [], "labels": []}
|
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for prompt, answer in format_example(examples):
|
|
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
|
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target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
|
|
|
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if len(source_ids) > data_args.max_source_length - 1: # bos token
|
|
source_ids = source_ids[:data_args.max_source_length - 1]
|
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if len(target_ids) > data_args.max_target_length - 1: # eos token
|
|
target_ids = target_ids[:data_args.max_target_length - 1]
|
|
|
|
input_ids = source_ids + [tokenizer.bos_token_id] + target_ids + [tokenizer.eos_token_id]
|
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labels = [IGNORE_INDEX] * len(source_ids) + [tokenizer.bos_token_id] + target_ids + [tokenizer.eos_token_id]
|
|
|
|
model_inputs["input_ids"].append(input_ids)
|
|
model_inputs["labels"].append(labels)
|
|
return model_inputs
|
|
|
|
def preprocess_unsupervised_dataset(examples):
|
|
# build inputs with format `X <s>` and labels with format `Y <s>`
|
|
model_inputs = {"input_ids": [], "labels": []}
|
|
for prompt, answer in format_example(examples):
|
|
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
|
|
target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
|
|
|
|
if len(source_ids) > data_args.max_source_length - 1: # bos token
|
|
source_ids = source_ids[:data_args.max_source_length - 1]
|
|
if len(target_ids) > data_args.max_target_length - 1: # bos token
|
|
target_ids = target_ids[:data_args.max_target_length - 1]
|
|
|
|
input_ids = source_ids + [tokenizer.bos_token_id]
|
|
labels = target_ids + [tokenizer.bos_token_id]
|
|
|
|
model_inputs["input_ids"].append(input_ids)
|
|
model_inputs["labels"].append(labels)
|
|
return model_inputs
|
|
|
|
def preprocess_pairwise_dataset(examples):
|
|
# build input pairs with format `X <s> Y1 </s>` and `X <s> Y2 </s>`
|
|
model_inputs = {"accept_ids": [], "reject_ids": []}
|
|
for prompt, answer in format_example(examples):
|
|
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
|
|
accept_ids = tokenizer.encode(text=answer[0], add_special_tokens=False)
|
|
reject_ids = tokenizer.encode(text=answer[1], add_special_tokens=False)
|
|
|
|
if len(source_ids) > data_args.max_source_length - 1: # bos token
|
|
source_ids = source_ids[:data_args.max_source_length - 1]
|
|
if len(accept_ids) > data_args.max_target_length - 1: # eos token
|
|
accept_ids = accept_ids[:data_args.max_target_length - 1]
|
|
if len(reject_ids) > data_args.max_target_length - 1: # eos token
|
|
reject_ids = reject_ids[:data_args.max_target_length - 1]
|
|
|
|
accept_ids = source_ids + [tokenizer.bos_token_id] + accept_ids + [tokenizer.eos_token_id]
|
|
reject_ids = source_ids + [tokenizer.bos_token_id] + reject_ids + [tokenizer.eos_token_id]
|
|
|
|
model_inputs["accept_ids"].append(accept_ids)
|
|
model_inputs["reject_ids"].append(reject_ids)
|
|
return model_inputs
|
|
|
|
def print_supervised_dataset_example(example):
|
|
print("input_ids:\n{}".format(example["input_ids"]))
|
|
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"])))
|
|
print("label_ids:\n{}".format(example["labels"]))
|
|
print("labels:\n{}".format(
|
|
tokenizer.decode([d if d != IGNORE_INDEX else tokenizer.pad_token_id for d in example["labels"]]))
|
|
)
|
|
|
|
def print_pairwise_dataset_example(example):
|
|
print("accept_ids:\n{}".format(example["accept_ids"]))
|
|
print("accepts:\n{}".format(tokenizer.decode(example["accept_ids"])))
|
|
print("reject_ids:\n{}".format(example["reject_ids"]))
|
|
print("rejects:\n{}".format(tokenizer.decode(example["reject_ids"])))
|
|
|
|
def print_unsupervised_dataset_example(example):
|
|
print("input_ids:\n{}".format(example["input_ids"]))
|
|
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"])))
|
|
|
|
if stage == "pt":
|
|
preprocess_function = preprocess_pretrain_dataset
|
|
elif stage == "sft":
|
|
preprocess_function = preprocess_unsupervised_dataset \
|
|
if training_args.predict_with_generate else preprocess_supervised_dataset
|
|
elif stage == "rm":
|
|
preprocess_function = preprocess_pairwise_dataset
|
|
elif stage == "ppo":
|
|
preprocess_function = preprocess_unsupervised_dataset
|
|
|
|
with training_args.main_process_first(desc="dataset map pre-processing"):
|
|
dataset = dataset.map(
|
|
preprocess_function,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
desc="Running tokenizer on dataset"
|
|
)
|
|
|
|
if stage == "pt":
|
|
print_unsupervised_dataset_example(dataset[0])
|
|
elif stage == "sft":
|
|
print_supervised_dataset_example(dataset[0])
|
|
elif stage == "rm":
|
|
print_pairwise_dataset_example(dataset[0])
|
|
elif stage == "ppo":
|
|
print_unsupervised_dataset_example(dataset[0])
|
|
|
|
return dataset
|
|
|