llama-7b-adapter / alpaca_lora.py

Create alpaca_lora.py

9bcad37 about 1 year ago

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	import os
	import sys
	from typing import List

	import fire
	import torch
	import transformers
	from datasets import load_dataset
	from typing import List
	import json

	def jload(data_path:str)-> List:
	with open(data_path,'r') as f:
	data = json.load(f)
	return data

	"""
	Unused imports:
	import torch.nn as nn
	import bitsandbytes as bnb
	"""

	from peft import (
	LoraConfig,
	get_peft_model,
	PeftModel,
	get_peft_model_state_dict,
	prepare_model_for_int8_training,
	set_peft_model_state_dict,
	)
	from transformers import LlamaForCausalLM, LlamaTokenizer,EarlyStoppingCallback




	import json
	import os.path as osp
	from typing import Union

	# os.environ["WANDB_DISABLED"] = "true"
	class Prompter(object):
	__slots__ = ("template", "_verbose")

	def __init__(self, template_name: str = "", verbose: bool = False):
	self._verbose = verbose
	if not template_name:
	# Enforce the default here, so the constructor can be called with '' and will not break.
	template_name = "alpaca"
	file_name = osp.join("data/templates", f"{template_name}.json")
	if not osp.exists(file_name):
	raise ValueError(f"{file_name} 文件不存在")
	with open(file_name) as fp:
	self.template = json.load(fp)
	if self._verbose:
	print(
	f"Using prompt template {file_name}: {self.template['description']}"
	)

	def generate_prompt(
	self,
	instruction: str,
	input: Union[None, str] = None,
	label: Union[None, str] = None,
	) -> str:
	# returns the full prompt from instruction and optional input
	# if a label (=response, =output) is provided, it's also appended.
	if input:
	res = self.template["prompt_input"].format(
	instruction=instruction, input=input
	)
	else:
	res = self.template["prompt_no_input"].format(
	instruction=instruction
	)
	if label:
	res = f"{res}{label}"
	if self._verbose:
	print(res)
	return res

	def get_response(self, output: str) -> str:
	return output.split(self.template["response_split"])[1].strip()


	def train(
	# model/data params
	base_model: str = "", # the only required argument
	data_path: str = "data/alapa",
	output_dir: str = "./lora-alpaca",
	# training hyperparams
	batch_size: int = 12,
	micro_batch_size: int = 4,
	num_epochs: int = 3,
	learning_rate: float = 3e-4,
	cutoff_len: int = 512,
	val_set_size: int = 200,
	# lora hyperparams
	lora_r: int = 64,
	lora_alpha: int = 128,
	lora_dropout: float = 0.05,
	lora_target_modules: List[str] = [
	"q_proj",
	"v_proj",
	],
	cache_dir=None,
	peft_path='',
	report_to='none',
	# llm hyperparams
	train_on_inputs: bool = False, # if False, masks out inputs in loss
	add_eos_token: bool = False,
	group_by_length: bool = False, # faster, but produces an odd training loss curve
	# wandb params

	resume_from_checkpoint: str = None, # either training checkpoint or final adapter
	prompt_template_name: str = "alpaca", # The prompt template to use, will default to alpaca.
	):
	if int(os.environ.get("LOCAL_RANK", 0)) == 0:
	print(
	f"Training Alpaca-LoRA model with params:\n"
	f"base_model: {base_model}\n"
	f"data_path: {data_path}\n"
	f"output_dir: {output_dir}\n"
	f"batch_size: {batch_size}\n"
	f"micro_batch_size: {micro_batch_size}\n"
	f"num_epochs: {num_epochs}\n"
	f"learning_rate: {learning_rate}\n"
	f"cutoff_len: {cutoff_len}\n"
	f"val_set_size: {val_set_size}\n"
	f"lora_r: {lora_r}\n"
	f"cache_dir: {cache_dir}\n"
	f"lora_alpha: {lora_alpha}\n"
	f"lora_dropout: {lora_dropout}\n"
	f"lora_target_modules: {lora_target_modules}\n"
	f"train_on_inputs: {train_on_inputs}\n"
	f"group_by_length: {group_by_length}\n"
	f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
	f"prompt template: {prompt_template_name}\n"
	f"peft_path: {peft_path}\n"
	)
	assert (
	base_model
	), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
	gradient_accumulation_steps = batch_size // micro_batch_size

	prompter = Prompter(prompt_template_name)

	device_map = "auto"
	world_size = int(os.environ.get("WORLD_SIZE", 1))
	ddp = world_size != 1
	if ddp:
	device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
	gradient_accumulation_steps = gradient_accumulation_steps // world_size



	model = LlamaForCausalLM.from_pretrained(
	base_model,
	load_in_8bit=False,
	torch_dtype=torch.float16,
	device_map=device_map,
	cache_dir=cache_dir,
	)

	tokenizer = LlamaTokenizer.from_pretrained(base_model)
	if tokenizer.pad_token_id is None:
	tokenizer.pad_token_id = (
	len(tokenizer) + 1 # unk. we want this to be different from the eos token
	)
	tokenizer.padding_side = "left" # Allow batched inference
	if model.get_input_embeddings().weight.size(0) != len(tokenizer):

	print("Resize model embeddings to fit tokenizer")
	model.resize_token_embeddings(len(tokenizer))

	def tokenize(prompt, add_eos_token=True):
	# there's probably a way to do this with the tokenizer settings
	# but again, gotta move fast
	result = tokenizer(
	prompt,
	truncation=True,
	max_length=cutoff_len,
	padding=False,
	return_tensors=None,
	)
	if (
	result["input_ids"][-1] != tokenizer.eos_token_id
	and len(result["input_ids"]) < cutoff_len
	and add_eos_token
	):
	result["input_ids"].append(tokenizer.eos_token_id)
	result["attention_mask"].append(1)

	result["labels"] = result["input_ids"].copy()

	return result

	def generate_and_tokenize_prompt(data_point):
	full_prompt = prompter.generate_prompt(
	data_point["instruction"],
	data_point["input"],
	data_point["output"],
	)
	tokenized_full_prompt = tokenize(full_prompt)
	if not train_on_inputs:
	user_prompt = prompter.generate_prompt(
	data_point["instruction"], data_point["input"]
	)
	tokenized_user_prompt = tokenize(
	user_prompt, add_eos_token=add_eos_token
	)
	user_prompt_len = len(tokenized_user_prompt["input_ids"])

	if add_eos_token:
	user_prompt_len -= 1

	tokenized_full_prompt["labels"] = [
	-100
	] * user_prompt_len + tokenized_full_prompt["labels"][
	user_prompt_len:
	] # could be sped up, probably
	return tokenized_full_prompt


	# model = prepare_model_for_int8_training(model)

	config = LoraConfig(
	r=lora_r,
	lora_alpha=lora_alpha,
	target_modules=lora_target_modules,
	lora_dropout=lora_dropout,
	bias="none",
	task_type="CAUSAL_LM",
	)
	model = get_peft_model(model, config)


	if data_path.endswith(".json") or data_path.endswith(".jsonl"):
	data = load_dataset("json", data_files=data_path)
	# data = jload(data_path)
	else:
	data = load_dataset(data_path)

	if resume_from_checkpoint:
	# Check the available weights and load them
	checkpoint_name = os.path.join(
	resume_from_checkpoint, "pytorch_model.bin"
	) # Full checkpoint
	if not os.path.exists(checkpoint_name):
	checkpoint_name = os.path.join(
	resume_from_checkpoint, "adapter_model.bin"
	) # only LoRA model - LoRA config above has to fit
	resume_from_checkpoint = (
	False # So the trainer won't try loading its state
	)
	# The two files above have a different name depending on how they were saved, but are actually the same.
	if os.path.exists(checkpoint_name):
	print(f"Restarting from {checkpoint_name}")
	adapters_weights = torch.load(checkpoint_name)
	set_peft_model_state_dict(model, adapters_weights)
	else:
	print(f"Checkpoint {checkpoint_name} not found")

	if peft_path:
	adapters_weights = torch.load(f"{peft_path}/adapter_model.bin")
	set_peft_model_state_dict(model, adapters_weights)
	model.print_trainable_parameters() # Be more transparent about the % of trainable params.

	if val_set_size > 0:
	train_val = data["train"].train_test_split(
	test_size=val_set_size, shuffle=True, seed=42
	)
	train_data = (
	train_val["train"].shuffle().map(generate_and_tokenize_prompt)
	)
	val_data = (
	train_val["test"].shuffle().map(generate_and_tokenize_prompt)
	)
	else:
	train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
	val_data = None

	if not ddp and torch.cuda.device_count() > 1:
	# keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
	model.is_parallelizable = True
	model.model_parallel = True

	trainer = transformers.Trainer(
	model=model,
	train_dataset=train_data,
	eval_dataset=val_data,
	args=transformers.TrainingArguments(
	per_device_train_batch_size=micro_batch_size,
	gradient_accumulation_steps=gradient_accumulation_steps,
	warmup_steps=10,
	num_train_epochs=num_epochs,
	learning_rate=learning_rate,
	fp16=True,
	sharded_ddp=True,
	logging_steps=200,
	optim="adamw_torch",
	evaluation_strategy="steps" if val_set_size > 0 else "no",
	save_strategy="steps",
	eval_steps=200 if val_set_size > 0 else None,
	save_steps=200,
	logging_strategy='steps',
	output_dir=output_dir,
	save_total_limit=5,
	load_best_model_at_end=True if val_set_size > 0 else False,
	ddp_find_unused_parameters=False if ddp else None,
	group_by_length=group_by_length,
	report_to=report_to,


	),
	data_collator=transformers.DataCollatorForSeq2Seq(
	tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
	),

	)
	model.config.use_cache = False

	old_state_dict = model.state_dict
	model.state_dict = (
	lambda self, _, *__: get_peft_model_state_dict(
	self, old_state_dict()
	)
	).__get__(model, type(model))

	if torch.__version__ >= "2" and sys.platform != "win32":
	model = torch.compile(model)

	trainer.train(resume_from_checkpoint=None)

	model.save_pretrained(output_dir)
	trainer.save_model(output_dir)

	print(
	"\n If there's a warning about missing keys above, please disregard :)"
	)


	if __name__ == "__main__":
	fire.Fire(train)