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finetune.py
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import os
import torch
import transformers
from transformers import AutoTokenizer
from llmtune.llms.autollm import AutoLLMForCausalLM
from llmtune.engine.lora.config import FinetuneConfig
from llmtune.data import TrainSAD
from llmtune.engine.lora.peft import quant_peft
from llmtune.utils import to_half_precision
# model config
model_name = ''
# model_name = './llama-7b-quantized' # can generate local dir via quantize.py
tokenizer_name = 'huggyllama/llama-13b'
DEV = 'cuda'
# load model
transformers.logging.set_verbosity_info()
llm = AutoLLMForCausalLM.from_pretrained(model_name)
llm.eval()
llm = llm.to(DEV)
llm = to_half_precision(llm)
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
tokenizer.pad_token_id = 0
# finetune training config
mbatch_size=1
batch_size=2
epochs=3
lr=2e-4
cutoff_len=256
lora_r=8
lora_alpha=16
lora_dropout=0.05
val_set_size=0.2
warmup_steps=50
save_steps=50
save_total_limit=3
logging_steps=10
data_type = 'alpaca'
dataset = None # will load alpaca from HF
adapter_path = './llama-7b-quantized-lora'
# set up finetuning config
tune_config = FinetuneConfig(
dataset=dataset,
ds_type=data_type,
lora_out_dir=adapter_path,
mbatch_size=mbatch_size,
batch_size=batch_size,
epochs=epochs,
lr=lr,
cutoff_len=cutoff_len,
lora_r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
val_set_size=val_set_size,
warmup_steps=warmup_steps,
save_steps=save_steps,
save_total_limit=save_total_limit,
logging_steps=logging_steps,
)
# set up lora config
lora_config = quant_peft.LoraConfig(
r=tune_config.lora_r,
lora_alpha=tune_config.lora_alpha,
target_modules=["q_proj", "v_proj"],
lora_dropout=tune_config.lora_dropout,
bias="none",
task_type="CAUSAL_LM",
)
# create a new lora from config
model = quant_peft.get_peft_model(llm, lora_config)
# load stanford alpaca data
data = TrainSAD(
tune_config.dataset,
tune_config.val_set_size,
tokenizer,
tune_config.cutoff_len
)
data.prepare_data() # this tokenizes the dataset
# training args
training_arguments = transformers.TrainingArguments(
per_device_train_batch_size=tune_config.mbatch_size,
gradient_accumulation_steps=tune_config.gradient_accumulation_steps,
warmup_steps=tune_config.warmup_steps,
num_train_epochs=tune_config.epochs,
learning_rate=tune_config.lr,
fp16=True,
logging_steps=tune_config.logging_steps,
evaluation_strategy="no",
save_strategy="steps",
eval_steps=None,
save_steps=tune_config.save_steps,
output_dir=tune_config.lora_out_dir,
save_total_limit=tune_config.save_total_limit,
load_best_model_at_end=False,
ddp_find_unused_parameters=False if tune_config.ddp else None,
)
# start trainer
trainer = transformers.Trainer(
model=model,
train_dataset=data.train_data,
eval_dataset=data.val_data,
args=training_arguments,
data_collator=transformers.DataCollatorForLanguageModeling(
tokenizer, mlm=False
),
)
print(training_arguments.parallel_mode)
model.config.use_cache = False
# use half precision
model = to_half_precision(model)
# start training
checkpoint_dir = tune_config.lora_out_dir
if os.path.exists(checkpoint_dir) and os.listdir(checkpoint_dir):
trainer.train(resume_from_checkpoint=True)
else:
trainer.train()
# Save Model
model.save_pretrained(tune_config.lora_out_dir)