# this code is modified from lora_alpaca https://github.com/tloen/alpaca-lora under Apache-2.0 license
import os
from typing import List
import fire
import torch
import transformers
from datasets import load_dataset
from transformers import BertTokenizerFast
"""
Unused imports:
import torch.nn as nn
import bitsandbytes as bnb
"""
from peft import (
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
prepare_model_for_int8_training,
set_peft_model_state_dict,
)
from transformers import LlamaForCausalLM, LlamaTokenizer
from utils.prompter import Prompter
def train(
# model/data params
base_model: str = "", # the only required argument
data_path: str = "",
output_dir: str = "",
# training hyperparams
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 hyperparams
lora_r: int = 8,
lora_alpha: int = 16,
lora_dropout: float = 0.05,
lora_target_modules: List[str] = [
"q_proj",
"v_proj",
],
# llm hyperparams
train_on_inputs: bool = True, # 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
wandb_project: str = "gama",
wandb_run_name: str = "",
wandb_watch: str = "false", # options: false | gradients | all
wandb_log_model: str = "false", # options: false | true
resume_from_checkpoint: str = None, # either training checkpoint or final adapter
prompt_template_name: str = "alpaca_short", # The prompt template to use, will default to alpaca.
save_steps: int = 100,
trainable_params = 'all'
):
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"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"add_eos_token: {add_eos_token}\n"
f"group_by_length: {group_by_length}\n"
f"wandb_project: {wandb_project}\n"
f"wandb_run_name: {wandb_run_name}\n"
f"wandb_watch: {wandb_watch}\n"
f"wandb_log_model: {wandb_log_model}\n"
f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
f"prompt template: {prompt_template_name}\n"
)
assert (
base_model
), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
# trick to load checkpoints correctly from HF
if '/fs/nexus-projects/brain_project/acl_sk_24/GAMA/src/Llama-2-7b-chat-hf-qformer' not in base_model:
# start from a different model with original vicuna
# temporally first load the original vicuna, then load the actual checkpoint
start_model = base_model # need to point to a specific bin file that contains state dict.
# TODO: change to your vicuna_tltr path
base_model = '/fs/nexus-projects/brain_project/acl_sk_24/GAMA/src/Llama-2-7b-chat-hf-qformer'
print('Will load from {:s} later, for implementation purpose, first load from {:s}'.format(start_model, base_model))
else:
start_model = None
gradient_accumulation_steps = batch_size // micro_batch_size
prompter = Prompter(prompt_template_name)
device_map = "auto"
world_size = int(torch.cuda.device_count())
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
)
# Only overwrite environ if wandb param passed
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
# base_model = '/fs/nexus-projects/brain_project/acl_sk_24/GAMA/src/Llama-2-7b-chat-hf-qformer'
model = LlamaForCausalLM.from_pretrained(
base_model,
load_in_8bit=False,
# torch_dtype=torch.float16,
device_map=device_map,
)
tokenizer = LlamaTokenizer.from_pretrained(base_model)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left" # Allow batched inference
bert_tokenizer = BertTokenizerFast.from_pretrained("google-bert/bert-base-uncased")
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 flatten_c(example):
if 'tokenized_full_prompt' in example:
example.update(example['tokenized_full_prompt']) # Merge 'c' into the root
del example['tokenized_full_prompt'] # Remove 'c' from the example
return example
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
tokenizer_input_bert = []
# print(tokenized_full_prompt)
return tokenized_full_prompt
# return {'tokenized_full_prompt': tokenized_full_prompt, 'tokenizer_input_bert':tokenizer_input_bert}
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)
# print(model)
# for audio params, lora always trainable, llama always frozen
for name, param in model.named_parameters():
if trainable_params == 'all':
if "audio" in name:
param.requires_grad = True
if trainable_params == 'proj':
if "audio_proj" in name:
param.requires_grad = True
if trainable_params == 'qformer':
if "audio_aggregator_layer_1" in name or "audio_aggregator_layer_2" in name or "audio_proj_qformer" in name or "audio_proj_audioenc" in name or "audio_proj_norm_qformer" in name or "audio_proj_norm_audioenc" in name:
param.requires_grad = True
if trainable_params == 'qformer_all':
if "audio_aggregator_layer_1" in name or "audio_aggregator_layer_2" in name or "audio_proj_qformer" in name or "audio_proj_audioenc" in name or "audio_proj_norm_qformer" in name or "audio_proj_norm_audioenc" in name or 'audio_encoder' in name or 'Qformer' in name or 'query_tokens' in name or 'qformer_proj_norm' in name:
param.requires_grad = True
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:
# 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):
state_dict = torch.load(checkpoint_name, map_location='cpu')
msg = model.load_state_dict(state_dict, strict=False)
else:
print(f"Checkpoint {checkpoint_name} not found")
# # now load from real checkpoint
if start_model != None and (resume_from_checkpoint == None or resume_from_checkpoint == False):
state_dict = torch.load(start_model, map_location='cpu')
msg = model.load_state_dict(state_dict, strict=False)
# print('load checkpoint', msg)
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
# train_data = train_data.map(flatten_c)
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
from transformers import TrainerCallback
class PrecisionLoggingCallback(TrainerCallback):
def on_log(self, args, state, control, logs=None, **kwargs):
# Modify this method to log the loss with more decimal points
if logs is not None and 'loss' in logs:
# Assuming 'logs' is a dictionary that contains the loss
high_precision_loss = format(logs['loss'], '.10f') # Adjust the '.4f' for more or fewer decimals
# print(f"High Precision Loss: {high_precision_loss}")
trainer = transformers.Trainer(
model=model,
train_dataset=train_data,
eval_dataset=val_data,
callbacks=[PrecisionLoggingCallback],
args=transformers.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,
bf16=True,
logging_steps=10,
optim="adamw_torch",
evaluation_strategy="no",
save_strategy="steps",
eval_steps=None,
save_steps=save_steps,
dataloader_num_workers=8,
output_dir=output_dir,
save_total_limit=50,
load_best_model_at_end=False,
ddp_find_unused_parameters=True,
group_by_length=group_by_length,
report_to="wandb" if use_wandb else None,
run_name=wandb_run_name if use_wandb else None,
remove_unused_columns=False ),
data_collator=transformers.DataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
),
)
model.config.use_cache = False
trainer.train(resume_from_checkpoint=resume_from_checkpoint)
model.save_pretrained(output_dir)
if __name__ == "__main__":
fire.Fire(train)