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train_llava.py
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train_llava.py
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import os
import pathlib
from typing import Dict
import torch
import transformers
from llava import conversation as conversation_lib
from llava.model.llava_gpt2 import LlavaGpt2ForCausalLM
from llava.model.llava_gpt_neox import LlavaGptNeoxForCausalLM
from llava.model.llava_llama import LlavaLlamaForCausalLM
from llava.train.dataset import LazySupervisedDataset, DataCollatorForSupervisedDataset
from llava.train.arguments_dataclass import ModelArguments, DataArguments, TrainingArguments
from llava.train.llava_trainer import LLaVATrainer
def rank0_print(*args):
if local_rank == 0:
print(*args)
# Borrowed from peft.utils.get_peft_model_state_dict
def get_peft_state(named_params, bias):
if bias == "none":
to_return = {k: t for k, t in named_params if "lora_" in k}
elif bias == "all":
to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
elif bias == "lora_only":
to_return = {}
maybe_lora_bias = {}
lora_bias_names = set()
for k, t in named_params:
if "lora_" in k:
to_return[k] = t
bias_name = k.split("lora_")[0] + "bias"
lora_bias_names.add(bias_name)
elif "bias" in k:
maybe_lora_bias[k] = t
for k, t in maybe_lora_bias:
if bias_name in lora_bias_names:
to_return[bias_name] = t
else:
raise NotImplementedError
to_return = {k: v.detach().cpu().clone() for k, v in to_return.items()}
return to_return
def get_peft_state_non_lora(named_params, require_grad_only=True):
to_return = {k: t for k, t in named_params if "lora_" not in k}
if require_grad_only:
to_return = {k: t for k, t in to_return.items() if t.requires_grad}
to_return = {k: v.detach().cpu().clone() for k, v in to_return.items()}
return to_return
def get_mm_adapter_state(named_params, keys_to_match):
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: v.detach().cpu().clone() for k, v in to_return.items()}
return to_return
def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
output_dir: str):
"""Collects the state dict and dump to disk."""
if getattr(trainer.args, "tune_mm_mlp_adapter", False):
# Only save Adapter
keys_to_match = ['mm_projector']
if getattr(trainer.args, "use_im_start_end", False):
keys_to_match.extend(['embed_tokens', 'embed_in'])
weight_to_save = get_mm_adapter_state(trainer.model.named_parameters(), keys_to_match)
trainer.model.config.save_pretrained(output_dir)
current_folder = output_dir.split('/')[-1]
parent_folder = os.path.dirname(output_dir)
if trainer.args.local_rank == 0 or trainer.args.local_rank == -1:
if current_folder.startswith('checkpoint-'):
mm_projector_folder = os.path.join(parent_folder, "mm_projector")
os.makedirs(mm_projector_folder, exist_ok=True)
torch.save(weight_to_save, os.path.join(mm_projector_folder, f'{current_folder}.bin'))
else:
torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
return
state_dict = trainer.model.state_dict()
if trainer.args.should_save:
cpu_state_dict = {
key: value.cpu()
for key, value in state_dict.items()
}
del state_dict
trainer._save(output_dir, state_dict=cpu_state_dict) # noqa
def find_all_linear_names(model):
cls = torch.nn.Linear
lora_module_names = set()
multimodal_keywords = ['mm_projector', 'vision_tower', 'vision_resampler']
for name, module in model.named_modules():
if any(mm_keyword in name for mm_keyword in multimodal_keywords):
continue
if isinstance(module, cls):
names = name.split('.')
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
if 'lm_head' in lora_module_names: # needed for 16-bit
lora_module_names.remove('lm_head')
return list(lora_module_names)
def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
data_args) -> Dict:
"""Make dataset and collator for supervised fine-tuning."""
train_dataset = LazySupervisedDataset(
tokenizer=tokenizer,
data_path=data_args.data_path,
data_args=data_args
)
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
return dict(train_dataset=train_dataset,
eval_dataset=None,
data_collator=data_collator)
def train():
global local_rank
parser = transformers.HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
local_rank = training_args.local_rank
compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
bnb_model_from_pretrained_args = {}
if training_args.bits in [4, 8]:
from transformers import BitsAndBytesConfig
bnb_model_from_pretrained_args.update(dict(
device_map="auto",
load_in_4bit=training_args.bits == 4,
load_in_8bit=training_args.bits == 8,
quantization_config=BitsAndBytesConfig(
load_in_4bit=training_args.bits == 4,
load_in_8bit=training_args.bits == 8,
llm_int8_skip_modules=["mm_projector"],
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=training_args.double_quant,
bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
)
))
if model_args.base_model == "gpt2":
model = LlavaGpt2ForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
**bnb_model_from_pretrained_args
)
elif model_args.base_model == "gpt_neox":
model = LlavaGptNeoxForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
**bnb_model_from_pretrained_args
)
elif model_args.base_model == "llama":
model = LlavaLlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
**bnb_model_from_pretrained_args
)
else:
print(f"{model_args.base_model} is not found")
exit(-1)
model.config.use_cache = False
if model_args.freeze_backbone:
model.model.requires_grad_(False)
if training_args.bits in [4, 8]:
from peft import prepare_model_for_kbit_training
model.config.torch_dtype=(torch.float32 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
if training_args.gradient_checkpointing:
# Adapterの重みを調整するときに使うみたい
if hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()
if training_args.lora_enable:
from peft import LoraConfig, get_peft_model
print("target_modules")
if model_args.base_model == "gpt2":
target_modules = ["c_attn"]
elif model_args.base_model == "gpt_neox":
target_modules = ["query_key_value"]
elif model_args.base_model == "llama":
target_modules = find_all_linear_names(model)
else:
print(f"{model_args.base_model} is not found")
exit(-1)
lora_config = LoraConfig(
r=training_args.lora_r,
lora_alpha=training_args.lora_alpha,
target_modules=target_modules,
lora_dropout=training_args.lora_dropout,
bias=training_args.lora_bias,
task_type="CAUSAL_LM",
)
if training_args.bits == 16:
if training_args.bf16:
model.to(torch.bfloat16)
if training_args.fp16:
model.to(torch.float16)
rank0_print("Adding LoRA adapters...")
model = get_peft_model(model, lora_config)
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=True,
)
tokenizer.pad_token = tokenizer.unk_token
conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version]
model.get_model().initialize_vision_modules(
model_args=model_args,
)
vision_tower = model.get_vision_tower()
vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
data_args.image_processor = vision_tower.image_processor
model.config.image_aspect_ratio = data_args.image_aspect_ratio
model.config.tokenizer_padding_side = tokenizer.padding_side
model.config.tokenizer_model_max_length = tokenizer.model_max_length
model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter
if model_args.tune_mm_mlp_adapter:
model.requires_grad_(False)
for p in model.get_model().mm_projector.parameters():
p.requires_grad = True
if training_args.bits in [4, 8]:
model.get_model().mm_projector.to(dtype=compute_dtype, device=training_args.device)
if training_args.bits in [4, 8]:
from peft.tuners.lora import LoraLayer
for name, module in model.named_modules():
if isinstance(module, LoraLayer):
if training_args.bf16:
module = module.to(torch.bfloat16)
if 'norm' in name:
module = module.to(torch.float32)
if 'lm_head' in name or 'embed_tokens' in name:
if hasattr(module, 'weight'):
if training_args.bf16 and module.weight.dtype == torch.float32:
module = module.to(torch.bfloat16)
if data_args.image_size is None:
data_args.image_size = data_args.image_processor.size["height"]
data_module = make_supervised_data_module(tokenizer=tokenizer,
data_args=data_args)
trainer = LLaVATrainer(model=model,
tokenizer=tokenizer,
args=training_args,
**data_module)
if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
trainer.train(resume_from_checkpoint=True)
else:
trainer.train()
trainer.save_state()
#torch.save(
# model.state_dict(),
# f'{training_args.output_dir}/pytorch_model.bin'
#)
model.config.use_cache = True
if training_args.lora_enable:
state_dict = get_peft_state(
model.named_parameters(), training_args.lora_bias
)
non_lora_state_dict = get_peft_state_non_lora(
model.named_parameters()
)
if training_args.local_rank == 0 or training_args.local_rank == -1:
model.config.save_pretrained(training_args.output_dir)
model.save_pretrained(training_args.output_dir, state_dict=state_dict)
torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin'))
else:
safe_save_model_for_hf_trainer(trainer=trainer,
output_dir=training_args.output_dir)
if __name__ == '__main__':
train()