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| import os
from typing import List
import fire
import torch
from datasets import load_dataset
from peft import (
LoraConfig,
get_peft_model,
prepare_model_for_int8_training,
set_peft_model_state_dict, PeftModel,
)
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, DataCollatorForSeq2Seq, TrainingArguments
from utils.prompter import Prompter
def train(
base_model: str = "",
data_path: str = "./data/train.json",
output_dir: str = "./models",
batch_size: int = 128,
micro_batch_size: int = 4,
num_epochs: int = 3,
learning_rate: float = 3e-4,
cutoff_len: int = 256,
val_set_size: int = 2000,
lora_r: int = 8,
lora_alpha: int = 16,
lora_dropout: float = 0.05,
lora_target_modules: List[str] = [
"q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"
],
train_on_inputs: bool = True,
add_eos_token: bool = False,
group_by_length: bool = False,
wandb_project: str = "",
wandb_run_name: str = "",
wandb_watch: str = "",
wandb_log_model: str = "",
resume_from_checkpoint: str = None,
prompt_template_name: str = "alpaca",
):
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
use_wandb = len(wandb_project) > 0 or (
"WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0
)
if len(wandb_project) > 0:
os.environ["WANDB_PROJECT"] = wandb_project
if len(wandb_watch) > 0:
os.environ["WANDB_WATCH"] = wandb_watch
if len(wandb_log_model) > 0:
os.environ["WANDB_LOG_MODEL"] = wandb_log_model
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.float16,
device_map=device_map,
)
tokenizer = AutoTokenizer.from_pretrained(base_model)
tokenizer.pad_token_id = (
0
)
tokenizer.padding_side = "left"
def tokenize(prompt, add_eos_token=True):
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:
]
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",
)
peft_model = get_peft_model(model, config)
if data_path.endswith(".json") or data_path.endswith(".jsonl"):
data = load_dataset("json", data_files=data_path)
else:
data = load_dataset(data_path)
if resume_from_checkpoint:
checkpoint_name = os.path.join(
resume_from_checkpoint, "pytorch_model.bin"
)
if not os.path.exists(checkpoint_name):
checkpoint_name = os.path.join(
resume_from_checkpoint, "adapter_model.bin"
)
resume_from_checkpoint = (
False
)
if os.path.exists(checkpoint_name):
print(f"Restarting from {checkpoint_name}")
adapters_weights = torch.load(checkpoint_name)
set_peft_model_state_dict(peft_model, adapters_weights)
else:
print(f"Checkpoint {checkpoint_name} not found")
peft_model.print_trainable_parameters()
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:
peft_model.is_parallelizable = True
peft_model.model_parallel = True
trainer = Trainer(
model=peft_model,
train_dataset=train_data,
eval_dataset=val_data,
args=TrainingArguments(
per_device_train_batch_size=micro_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
warmup_steps=100,
num_train_epochs=num_epochs,
learning_rate=learning_rate,
logging_steps=10,
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,
output_dir=output_dir,
save_total_limit=3,
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="wandb" if use_wandb else None,
run_name=wandb_run_name if use_wandb else None,
),
data_collator=DataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
),
)
peft_model.config.use_cache = False
trainer.train(resume_from_checkpoint=resume_from_checkpoint)
trainer.model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
model_to_merge = PeftModel.from_pretrained(
AutoModelForCausalLM.from_pretrained(base_model), output_dir)
merged_model = model_to_merge.merge_and_unload()
merged_model.save_pretrained(output_dir)
if __name__ == "__main__":
fire.Fire(train)
|
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