import os
import pickle
import random
import sys
from typing import List, Optional
import fire
import torch
import transformers
from datasets import load_dataset
import wandb
from torch import nn
from torch.utils.data import Sampler
from transformers.modeling_utils import unwrap_model
from local_config import WANDB_ENTITY
from utils.datacollator import MyDataCollatorForSeq2Seq
from model.lavis.models.blip2_models.modeling_llama_imgemb import LlamaForCausalLM
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 AutoTokenizer, PreTrainedModel
from utils.prompter import Prompter
import logging
logger = logging.getLogger(__name__)
#how are input and instruction put together:
'''
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
or
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input}
### Response:
'''
class BalancedSampler(Sampler):
def __init__(self, true_indices, false_indices):
self.true_indices = true_indices
self.false_indices = false_indices
self.num_samples = 2 * min(len(self.true_indices), len(self.false_indices))
def __iter__(self):
# Randomly sample from true_indices
sampled_true_indices = random.sample(self.true_indices, len(self.false_indices))
# Merge and shuffle the two lists of indices
indices = sampled_true_indices + self.false_indices
random.shuffle(indices)
return iter(indices)
def __len__(self):
return self.num_samples
class InstructTrainer(transformers.Trainer):
def __init__(self, *args, rep_idxs=None, inst_idxs=None, **kwargs):
super().__init__(*args, **kwargs)
self.rep_idxs = rep_idxs
self.inst_idxs = inst_idxs
def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
return BalancedSampler(self.rep_idxs, self.inst_idxs)
WEIGHTS_NAME = "pytorch_model.bin"
WEIGHTS_NAME_FINAL = "adapter_model.bin"
TRAINING_ARGS_NAME = "training_args.bin"
class ImgTrainer(transformers.Trainer): #also save img projector
def _save(self, output_dir: Optional[str] = None, state_dict=None):
# If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving model checkpoint to {output_dir}")
# Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
if not isinstance(self.model, PreTrainedModel):
if state_dict is None:
state_dict = self.model.state_dict()
base_state_dict = self.model.base_model.state_dict()
if 'model.model.img_proj_layer.weight' in base_state_dict:
state_dict['base_model.model.model.img_proj_layer.weight'] = base_state_dict['model.model.img_proj_layer.weight']
state_dict['base_model.model.model.img_proj_layer.bias'] = base_state_dict['model.model.img_proj_layer.bias']
if isinstance(unwrap_model(self.model), PreTrainedModel):
unwrap_model(self.model).save_pretrained(
output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
)
else:
logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
else:
self.model.save_pretrained(
output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
)
if self.tokenizer is not None:
self.tokenizer.save_pretrained(output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
def save_pretrained(model, save_directory, **kwargs):
r"""
This function saves the adapter model and the adapter configuration files to a directory, so that it can be
reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]
method.
Args:
save_directory (`str`):
Directory where the adapter model and configuration files will be saved (will be created if it does not
exist).
kwargs (additional keyword arguments, *optional*):
Additional keyword arguments passed along to the `push_to_hub` method.
"""
if os.path.isfile(save_directory):
raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
# save only the trainable weights
output_state_dict = get_peft_model_state_dict(model, kwargs.get("state_dict", None))
base_state_dict = model.base_model.state_dict()
if 'model.model.img_proj_layer.weight' in base_state_dict:
output_state_dict['base_model.model.model.img_proj_layer.weight'] = base_state_dict['model.model.img_proj_layer.weight']
output_state_dict['base_model.model.model.img_proj_layer.bias'] = base_state_dict['model.model.img_proj_layer.bias']
torch.save(output_state_dict, os.path.join(save_directory, WEIGHTS_NAME_FINAL))
inference_mode = model.peft_config.inference_mode
model.peft_config.inference_mode = True
model.peft_config.save_pretrained(save_directory)
model.peft_config.inference_mode = inference_mode
def train(
# model/data params
base_model: str = "", # the only required argument
lora_weights: str = None,
data_path: str = "yahma/alpaca-cleaned",
output_dir: str = "./lora-cxr",
# training hyperparams
batch_size: int = 128,
micro_batch_size: int = 2,
num_epochs: int = 10,
learning_rate: float = 3e-4,
cutoff_len: int = 1024, #256 -> need much more with examples in prompt (1024), 512 for without examples but long IG labels
val_set_size: int = 5,
# lora hyperparams
lora_r: int = 8,
lora_alpha: int = 16,
lora_dropout: float = 0.05,
lora_target_modules: List[str] = [ #default is for llama models
"q_proj",
"v_proj",
],
# 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
wandb_project: str = "lora_training",
wandb_run_name: str = "lora_mimic_cxr",
wandb_entity: str = WANDB_ENTITY,
wandb_watch: str = "", # options: false | gradients | all
wandb_log_model: str = "", # options: false | true
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.
use_embs=False,
use_instruct_data=False
):
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"lora_weights: {lora_weights}\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_entity: {wandb_entity}\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'"
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
# Check if parameter passed or if set within environ
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
if base_model == 'vicuna_v13':
model = LlamaForCausalLM.from_pretrained("lmsys/vicuna-13b-v1.3", torch_dtype=torch.float16, device_map='auto', load_in_8bit=False)
tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-13b-v1.3", use_fast=False, truncation_side="right", padding_side="right")
else: #7b
model = LlamaForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3", torch_dtype=torch.float16, device_map='auto', load_in_8bit=False)
tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.3", use_fast=False, truncation_side="right", padding_side="right")
tokenizer.pad_token = tokenizer.unk_token
if use_embs:
model.base_model.img_proj_layer = nn.Linear(768, model.base_model.config.hidden_size).to(model.base_model.device)
# add special token to tokenizer
tokenizer.add_special_tokens({"additional_special_tokens": ["<IMG>"]})
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) #this sets requires_grad for all params to False
# unfreeze the img_proj_layer
model.model.base_model.img_proj_layer.weight.requires_grad = True
model.model.base_model.img_proj_layer.bias.requires_grad = True
print("Loading data from ", data_path)
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):
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")
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 use_instruct_data:
report_indices = [i for i, item in enumerate(train_data) if item['is_report']][:5]
instruct_indices = [i for i, item in enumerate(train_data) if not item['is_report']][:5]
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
wandb.init(
project=wandb_project,
entity=wandb_entity,
name=wandb_run_name
)
if use_instruct_data:
trainer = InstructTrainer(
model=model,
train_dataset=train_data,
eval_dataset=val_data,
rep_idxs = report_indices,
inst_idxs = instruct_indices,
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,
fp16=True,
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=None,
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,
max_steps=-1,
dataloader_num_workers=8,
remove_unused_columns=False if use_embs else True,
),
data_collator=MyDataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
) if use_embs else
transformers.DataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
),
)
else:
trainer = ImgTrainer(
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=100,
num_train_epochs=num_epochs,
learning_rate=learning_rate,
fp16=True,
logging_steps=10,
optim="adamw_torch",
evaluation_strategy="steps" if val_set_size > 0 else "no",
save_strategy="steps",
eval_steps=400 if val_set_size > 0 else None,
save_steps=400,
output_dir=output_dir,
save_total_limit=None,
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,
max_steps=-1,
dataloader_num_workers=8,
remove_unused_columns=False if use_embs else True
),
data_collator=MyDataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
) if use_embs else
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=resume_from_checkpoint)
save_pretrained(model, output_dir)
print(
"\n If there's a warning about missing keys above, please disregard :)"
)
if __name__ == "__main__":
fire.Fire(train)
