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import datetime
import gc
import time
import os
import os.path as osp
import re
import itertools
import functools
import random
import math
import shutil
from typing import Optional, Union
import torch
import numpy as np
from safetensors import safe_open
import logging
from accelerate.logging import get_logger
from accelerate import Accelerator, DistributedType
from accelerate.utils import set_seed
from peft import get_peft_model, LoraConfig, TaskType
from dataset import create_dataset, create_loader
from tasks.shared_utils import get_media_types
from utils.basic_utils import (MetricLogger, SmoothedValue, setup_seed)
from utils.config_utils import setup_main
from transformers.utils import TensorType
from tasks.shared_utils import create_optimizer, create_scheduler
import copy
from transformers import (
DataCollatorWithPadding,
get_scheduler,
AutoModel,
AutoModelForCausalLM
)
from models.pllava import PllavaConfig, PllavaForConditionalGeneration, PllavaProcessor
# logger = logging.getLogger(__name__)
IMAGE_TOKEN='<image>'
logger = get_logger(__name__)
def maybe_zero_3(param, ignore_status=False, name=None):
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
if hasattr(param, "ds_id"):
if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
if not ignore_status:
print(name, 'no ignore status')
with zero.GatheredParameters([param]):
param = param.data.detach().cpu().clone()
else:
param = param.detach().cpu().clone()
return param
def get_state_maybe_zero_3(named_params, keys_to_match=["lora_","multi_modal_projector"]):
to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()}
return to_return
def setup_dataloaders(config, mode="pt", collate_fn=None):
# train datasets, create a list of data loaders
logger.info(f"Creating dataset for {mode}")
train_datasets = create_dataset(f"{mode}_train", config)
media_types = get_media_types(train_datasets)
samplers = [None] * len(media_types)
train_loaders = create_loader(
train_datasets,
samplers,
batch_size=[config.inputs.batch_size[k] for k in media_types],
num_workers=[config.num_workers] * len(media_types),
is_trains=[True] * len(media_types),
collate_fns=[collate_fn] * len(media_types),
) # [0]
return train_loaders, media_types
def setup_model(
config, find_unused_parameters=False
):
if config.model.torch_dtype in ('bfloat16', 'float16', 'float32'):
torch_dtype = eval(f'torch.{config.model.torch_dtype}')
else:
torch_dtype = config.model.torch_dtype
logger.info("Creating model")
processor = PllavaProcessor.from_pretrained(config.model.repo_id,
padding_side='right',
center_pad=config.preprocess.center_pad,
)
model_config = PllavaConfig.from_pretrained(config.model.repo_id,
torch_dtype=torch_dtype,
num_frames=config.model.num_frames,
pooling_method=config.model.pooling_method,
image_token_index=config.preprocess.image_token_index,
frame_shape=config.model.frame_shape,
pooling_shape=config.model.pooling_shape,
use_pooling=config.model.use_pooling,
gradient_checkpointing=config.gradient_checkpointing,
)
print("====>gradient_checkpointing",model_config.gradient_checkpointing)
model = PllavaForConditionalGeneration.from_pretrained(config.model.repo_id, config=model_config, torch_dtype=torch_dtype)
if config.model.load_from_origin:
with torch.no_grad():
lm_model = AutoModelForCausalLM.from_pretrained(config.model.origin_llm, torch_dtype=torch_dtype, device_map="cpu",)
with torch.no_grad():
clip = AutoModel.from_pretrained(config.model.origin_vision, torch_dtype=torch_dtype, device_map="cpu",)
msg = model.vision_tower.load_state_dict(clip.state_dict(), strict=False)
# print(msg)
msg = model.language_model.load_state_dict(lm_model.state_dict(), strict=False)
print(msg)
if config.model.freeze_lm:
logger.info("freezing parameters in model.language_model")
for p in model.language_model.parameters():
p.requires_grad = False
if config.model.freeze_projector:
logger.info("freezing parameters in model.multi_modal_projector")
for p in model.multi_modal_projector.parameters():
p.requires_grad = False
if config.model.freeze_vision_tower:
logger.info("freezing parameters in model.vision_tower")
for p in model.vision_tower.parameters():
p.requires_grad = False
if config.model.use_lora:
logger.info("getting LoRA Language Model")
kwargs = {}
if config.model.lora_target_modules is not None and len(config.model.lora_target_modules) > 0:
kwargs.update({"target_modules": config.model.lora_target_modules})
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM, inference_mode=False,
r=config.model.lora_r, lora_alpha=config.model.lora_alpha, lora_dropout=config.model.lora_dropout,
**kwargs
)
model.language_model = get_peft_model(model.language_model, peft_config)
model.language_model.print_trainable_parameters()
if config.model.pretrained_path is not None and not config.deepspeed:
logger.info("======> loading pretrained weights from " + str(config.model.pretrained_path))
state_dict = {}
save_fnames = os.listdir(config.model.pretrained_path)
if "model.safetensors" in save_fnames:
print("Loading weight from", config.model.pretrained_path, "model.safetensors")
with safe_open(f"{config.model.pretrained_path}/model.safetensors", framework="pt", device="cpu") as f:
for k in f.keys():
state_dict[k] = f.get_tensor(k)
else:
print("Loading weight from", config.model.pretrained_path)
for fn in save_fnames:
if fn.startswith('model-0000'):
with safe_open(f"{config.model.pretrained_path}/{fn}", framework="pt", device="cpu") as f:
for k in f.keys():
state_dict[k] = f.get_tensor(k)
if 'model' in state_dict.keys():
msg = model.load_state_dict(state_dict['model'], strict=False)
else:
msg = model.load_state_dict(state_dict, strict=False)
logger.info(msg)
logger.info("=====> Finish loading")
return model, processor
def setup_optimizer_and_scheduler(config, model):
optimizer = create_optimizer(config.optimizer, model) # do you want to filter bias and bn?
if config.scheduler.is_videochat2_custom:
scheduler = create_scheduler(config.scheduler, optimizer)
else:
scheduler=None
return optimizer, scheduler
class RandomMappingIterator():
# a random iter through the multiple mapping style dataloaders
def __init__(self, train_loaders, media_types, resume_step=0):
self.train_loaders = train_loaders
self.media_types = media_types
self.total_num_samples = sum(len(train_loader) for train_loader in self.train_loaders)
self.weights = [len(loader) / self.total_num_samples for loader in train_loaders]
self.resume_step = resume_step
if resume_step != 0:
self.total_num_samples= self.total_num_samples-resume_step
# remove corresponding iters from each loader
def __iter__(self):
train_loaders = self.train_loaders
iters = [iter(train_loader) for train_loader in train_loaders]
media_types = copy.deepcopy(self.media_types)
weights = copy.deepcopy(self.weights)
while len(iters) > 0:
index = np.random.choice(list(range(len(iters))), p=weights, replace=True)
try:
batch = next(iters[index])
except StopIteration as e:
iters.pop(index)
media_types.pop(index)
weights.pop(index)
total = sum(weights)
weights = [w/total for w in weights]
continue
media_type = media_types[index]
yield media_type, batch
def __len__(self):
return self.total_num_samples
def split_and_record_separators(input_string, separators) -> list:
texts = [input_string]
for sep in separators:
new_texts = []
for text in texts:
if sep not in text:
new_texts.append(text)
else:
split_strings = text.split(sep)
joint_strings = [t for pair in zip(split_strings[:-1], itertools.repeat(sep)) for t in pair ] + split_strings[-1:]
new_texts.extend(joint_strings)
texts = new_texts
return texts
def preprocess(
batch,
args,
processor,
collate_fn,
dtype=torch.bfloat16,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
):
tokenizer = processor.tokenizer
# tokenization for training
max_length = args.max_txt_l
input_list, images = [], []
for sample in batch:
image, tex, instruction, index = sample # (nframe, 3, h, w), (0-255)
num_img = image.shape[0]
tex = tex.replace(args.dataset_video_placeholder, IMAGE_TOKEN).replace(args.dataset_image_placeholder, IMAGE_TOKEN)
seps = [role for role in args.roles]
segs = split_and_record_separators(tex, seps)
input_ids, labels, attention_mask = [], [], []
for i, seg in enumerate(segs):
seg_ignore = False if seg == seps[1] else \
(True if i == 0 or seg in seps else seg_ignore) # not ignoring assistant, changing in sepecific situations
current_ignore = True if seg in seps else seg_ignore # serve for only this one iteration
seg_input_ids = tokenizer.encode(seg, add_special_tokens=True if i==0 else False) # only add bos token
seg_labels = [args.ignore_index] * len(seg_input_ids) if current_ignore else seg_input_ids
seg_attention_mask = [1] * len(seg_input_ids) # do attend
input_ids.extend(seg_input_ids)
labels.extend(seg_labels)
attention_mask.extend(seg_attention_mask)
pad_length = max_length - len(input_ids)
labels = labels[:max_length]
attention_mask = attention_mask[:max_length]
input_ids=input_ids[:max_length]
labels = labels + [args.ignore_index] * pad_length # padding doesn't take care of labels. do the padding here
input_ids = input_ids + [tokenizer.pad_token_id] * pad_length
attention_mask = attention_mask + [0]*pad_length
sample_input = {
'input_ids': input_ids,
'labels': labels,
'attention_mask': attention_mask,
}
input_list.append(sample_input)
images.append(image if image.ndim==4 else image.unsqueeze(0)) # made 4 dim for image, remain 4 dim for video
inputs = collate_fn(input_list)
# interpolate frames if the total frame is smaller than needed
for i, video in enumerate(images):
if video.shape[0] < args.num_frames:
multiplier = int(args.num_frames/video.shape[0]) + 1
video = video.repeat_interleave(multiplier, dim=0)[:args.num_frames]
images[i] = video
assert video.shape[0] == args.num_frames
if args.clip_transform:
multimodal_features = processor(images=images)
inputs.update(**multimodal_features)
else:
inputs["pixel_values"] = torch.concat(images) # already processed to features in dataset get item
return inputs
def main(config):
accelerator_log_kwargs=dict(
log_with=config.report_to,
project_dir=config.output_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=config.gradient_accumulation_steps,
**accelerator_log_kwargs
)
logger.info(f"train_file: {config.train_file}")
model, processor = setup_model(
config,
find_unused_parameters=True,
)
if accelerator.is_main_process:
logger.setLevel(logging.INFO)
else:
logger.setLevel(logging.WARNING)
collate_fn = DataCollatorWithPadding(tokenizer=processor.tokenizer, padding='max_length', max_length=config.max_txt_l, return_tensors='pt',)
collate_fn = functools.partial(preprocess, args=config.preprocess, processor=processor, collate_fn=collate_fn)
train_loaders, train_media_types = setup_dataloaders(config, mode=config.mode, collate_fn=collate_fn)
num_steps_per_epoch = math.ceil(sum(len(d) for d in train_loaders) / config.gradient_accumulation_steps)
# load optimizer and custom scheduler
config.scheduler.num_training_steps = num_steps_per_epoch * config.scheduler.epochs
config.scheduler.num_warmup_steps = math.ceil(config.scheduler.num_training_steps * config.scheduler.warmup_ratio)
optimizer, lr_scheduler = setup_optimizer_and_scheduler(config, model)
# if not set customized scheduler, default hf scheduler
overrode_max_train_steps = False
if config.max_train_steps is None:
config.max_train_steps = config.scheduler.epochs * num_steps_per_epoch
overrode_max_train_steps = True
if lr_scheduler is None:
lr_scheduler = get_scheduler(
name=config.scheduler.sched,
optimizer=optimizer,
num_warmup_steps=config.scheduler.num_warmup_steps,
num_training_steps=config.max_train_steps
if overrode_max_train_steps
else config.max_train_steps * accelerator.num_processes,
)
model, optimizer, lr_scheduler, *train_loaders = accelerator.prepare(
model, optimizer, lr_scheduler, *train_loaders
)
if hasattr(config, 'seed'):
set_seed(config.seed)
experiment_config = { # include all the important hyperparam
'num_frames': config.num_frames,
'max_txt_l': config.max_txt_l,
'batch_size': config.batch_size,
}
model.train()
start_epoch = 0
num_batches = sum(len(loader) for loader in train_loaders)
global_step = start_epoch * num_batches # the steps before divided by accumulation
if osp.exists(config.output_dir):
subfolders = os.listdir(config.output_dir)
sample_saving = False
for subfolder in subfolders:
if subfolder.endswith("M"):
sample_saving = True
if sample_saving:
ckpt_paths = [subfolder for subfolder in subfolders if re.match(r'ckpt_resume_[\d.]+M$', subfolder) is not None]
ckpt_iters = [float(re.findall(r'[\d.]+', x)[0]) for x in ckpt_paths]
else:
ckpt_paths = [subfolder for subfolder in subfolders if re.match("ckpt_[^\d]+", subfolder) is not None]
ckpt_iters = [int(s.split(re.match("ckpt_[^\d]+", s).group())[-1]) for s in ckpt_paths]
resume_cur_epoch_step=0
if len(ckpt_iters) > 0:
resume_iter = max(ckpt_iters)
ckpt_path = osp.join(config.output_dir, ckpt_paths[ckpt_iters.index(resume_iter)])
accelerator.print(f"Resumed from checkpoint: {ckpt_path}")
accelerator.load_state(ckpt_path)
if sample_saving:
resume_iter = int(resume_iter*1e6/(config.batch_size*accelerator.state.num_processes))
if "epoch" in ckpt_path:
start_epoch = int(resume_iter) + 1
resume_cur_epoch_step = 0
global_step = start_epoch * num_batches
else:
# need to multiply `gradient_accumulation_steps` to reflect real steps
# num_finish_smaple = int(max_ckpt_num) * config.gradient_accumulation_steps
start_epoch = resume_iter // num_batches
global_step = resume_iter
resume_cur_epoch_step = resume_iter - start_epoch * num_batches
accelerator.print(f"Resume from epoch {start_epoch}, steps{resume_cur_epoch_step}")
# TensorBoard cannot log Enums, need the raw value
accelerator.init_trackers("train_pllava_nframe", experiment_config)
start_time = time.time()
logger.info(f"Start training {str(start_time)}, from start_epoch-{start_epoch}, step-{resume_cur_epoch_step}")
# skip the first `n` batches in the dataloader when resuming from a checkpoint
active_train_loaders = train_loaders
if resume_cur_epoch_step > 0:
active_train_loaders = []
total_dta_num = sum(len(train_loader) for train_loader in train_loaders)
for train_loader in train_loaders:
skip_batch_num = int((resume_cur_epoch_step/total_dta_num)*len(train_loader))
skipped_train_loader = accelerator.skip_first_batches(train_loader, num_batches=skip_batch_num)
active_train_loaders.append(skipped_train_loader)
media_types = get_media_types(active_train_loaders)
train_loader = RandomMappingIterator(active_train_loaders, media_types)
for epoch in range(start_epoch, config.scheduler.epochs):
if not config.evaluate:
gc.collect()
torch.cuda.empty_cache()
metric_logger = MetricLogger(delimiter=" ")
loss_names = ["loss"]
for name in loss_names:
for m in media_types:
metric_logger.add_meter(
f"{m}-{name}", SmoothedValue(window=config.metric_window_size, fmt="{value:.4f}")
)
header = f"Train Epoch: [{epoch}]"
log_freq = config.log_freq
iterator = metric_logger.log_every(train_loader, log_freq, header)
mini_batch_losses = []
for i, (media_type, inputs) in enumerate(iterator): # video/image, conversation, instruction, index
with accelerator.accumulate(model):
inputs['media_type'] = media_type
response = model(**inputs)
loss = response.loss
mini_batch_losses.append(loss.detach().item())
optimizer.zero_grad()
accelerator.backward(loss)
if config.optimizer.max_grad_norm > 0:
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), config.optimizer.max_grad_norm)
optimizer.step()
lr_scheduler.step()
# # logging
for name in loss_names:
value = loss
value = value if isinstance(value, float) else value.item()
metric_logger.update(**{f"{media_type}-{name}": value})
global_step += 1
resume_num_samples = global_step * config.batch_size * accelerator.state.num_processes/1e6
# save small global step checkpoint in case of breakdown
if global_step % config.ckpt_steps == 0:
accelerator.save_state(output_dir=osp.join(config.output_dir, f"ckpt_resume_{resume_num_samples:.4f}M"))
if accelerator.is_main_process:
for fn in os.listdir(config.output_dir):
if "resume" in fn and fn != f"ckpt_resume_{resume_num_samples:.4f}M":
shutil.rmtree(osp.join(config.output_dir, fn))
if global_step % config.save_steps == 0:
logger.info(f"global_step {global_step}")
with torch.no_grad():
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
if not config.deepspeed:
save_state_dict = {k:v for k,v in accelerator.get_state_dict(model).items() if "lora_" in k or "multi_modal_projector" in k}
else:
save_state_dict = accelerator.get_state_dict(model)
unwrapped_model.save_pretrained(osp.join(config.output_dir, f"pretrained_step{resume_num_samples:.4f}M"),
is_main_process=accelerator.is_main_process,
save_function=accelerator.save,
state_dict=save_state_dict)
processor.save_pretrained(osp.join(config.output_dir, f"pretrained_step{resume_num_samples:.4f}M"))
if global_step % log_freq == 0:
logs = metric_logger.get_global_avg_dict()
logs.update({
"step_loss_no_smoothing": accelerator.gather_for_metrics(loss).mean().item(),
"epoch": epoch,
"step": global_step,
"lr": lr_scheduler.get_last_lr()[0],
})
accelerator.log(logs, step=global_step,)
if accelerator.sync_gradients:
mini_batch_loss = torch.tensor(mini_batch_losses, device='cuda')
accelerator.log({"mini_batch_loss": accelerator.gather_for_metrics(mini_batch_loss).mean().item()},
step=global_step)
mini_batch_losses = []
if config.debug and global_step % 20 == 0:
logger.info("debug mode, break training loop")
break
if config.debug and global_step % (2 * log_freq + 3) == 0:
logger.info("debug mode, break training loop")
break
# gather the stats from all processes
metric_logger.synchronize_between_processes()
logger.info(f"Averaged stats: {metric_logger.global_avg()}")
logger.info(f"Epoch {epoch}")
with torch.no_grad():
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
if not config.deepspeed:
save_state_dict = {k:v for k,v in accelerator.get_state_dict(model).items() if "lora_" in k or "multi_modal_projector" in k}
else:
save_state_dict = accelerator.get_state_dict(model)
unwrapped_model.save_pretrained(osp.join(config.output_dir, f"pretrained_epoch{epoch:02d}"),
is_main_process=accelerator.is_main_process,
save_function=accelerator.save,
state_dict=save_state_dict)
processor.save_pretrained(osp.join(config.output_dir, f"pretrained_step{epoch:02d}"))
accelerator.save_state(output_dir=osp.join(config.output_dir, f"ckpt_epoch{epoch:02d}"))
if config.evaluate:
break
accelerator.end_training()
accelerator.wait_for_everyone()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info(f"Training time {total_time_str}")
logger.info(f"Checkpoints and Logs saved at {config.output_dir}")
if __name__ == "__main__":
cfg = setup_main()
print(cfg)
main(cfg)
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