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train_gsm8k_gact.py
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train_gsm8k_gact.py
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# Modified from https://github.com/tatsu-lab/stanford_alpaca/blob/main/train.py
import copy
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional, Sequence
import gact
import torch
import transformers
from torch.utils.data import Dataset
from transformers import Trainer
from gact.controller import Controller
from peft import PeftModel, LoraConfig, TaskType, get_peft_model
from datasets import load_dataset
IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "<s>"
DEFAULT_UNK_TOKEN = "<unk>"
ANSWER_PROMPT = "The final answer is: "
QUESTION_PROMPT = "\nAnswer the above question. First think step by step and then answer the final number.\n"
import os
os.environ["WANDB_PROJECT"]="gsm8k"
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(
default="LoftQ/Mistral-7B-v0.1-4bit-64rank",
metadata={"help": "Path to the model."},
)
adapter_name_or_path: Optional[str] = field(
default=None,
metadata={"help": "Path to the LoRA adapter. Used in evaluation or resuming from the checkpoint."},
)
lora_init: bool = field(
default=False,
metadata={"help": "True: Use zero and gaussian initialization; False: Load adapters from LoftQ in HF hub."},
)
full_precision: bool = field(
default=False,
metadata={"help": "False: Use bitsandbytes Linear4bit, real quantization"
"True: Use quantization equivalent fp16/fp32 weights."
"Note: Set True for data parallel training"
},
)
rank: int = field(
default=64,
metadata={"help": "Rank of LoRA adapters. LoftQ does not require this config. Used for fp16 LoRA or QLoRA."},
)
bits: int = field(
default=4,
metadata={"help": "Bit of the backbone. LoftQ does not require this config. Used for QLoRA."},
)
lora_alpha: int = field(
default=16,
metadata={"help": "LoftQ does not require this config. Used for QLoRA."},
)
token: Optional[str] = field(
default=None,
metadata={"help": "HF token to access to private models, e.g., meta-llama"},
)
gact: bool = field(
default=True,
metadata={"help": "True: Use GACT; False: Do not use GACT"},
)
gact_level: str = field(
default="L1",
metadata={"help": "GACT level."},
)
@dataclass
class DataArguments:
data_name: str = field(
default="gsm8k",
metadata={"help": "Dataset name."}
)
@dataclass
class TrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
)
expt_name: str = field(
default="default",
metadata={"help": "Experiment name"},
)
class GACTTrainer(Trainer):
def __init__(self, controller, *args, **kwargs):
super().__init__(*args, **kwargs)
self.begin = False
self.step = 0
self.controller = controller
def compute_loss(self, model, inputs, return_outputs=False):
# if the optimizer has state, we need to clear it
if self.step % 1 == 0 and self.step != 0: # use gradient accumulation(TODO: with bug)
self.controller.iterate(self.gact_backward)
loss = super().compute_loss(model, inputs, return_outputs)
# generate the small batch for iterate
self.small_batch = {}
small_length = len(inputs["input_ids"]) // 4
for k, v in inputs.items():
self.small_batch[k] = v[:small_length]
self.model = model
self.step += 1
return loss
def gact_backward(self):
optimizer_tmp = self.create_optimizer()
loss = super().compute_loss(self.model, self.small_batch, return_outputs=False)
optimizer_tmp.zero_grad()
loss.backward()
def smart_tokenizer_and_embedding_resize(
special_tokens_dict: Dict,
tokenizer: transformers.PreTrainedTokenizer,
model: transformers.PreTrainedModel,
):
"""Resize tokenizer and embedding.
Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
"""
num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
model.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = model.get_input_embeddings().weight.data
output_embeddings = model.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict:
"""Tokenize a list of strings."""
tokenized_list = [
tokenizer(
text,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
)
for text in strings
]
input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list]
input_ids_lens = labels_lens = [
tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list
]
return dict(
input_ids=input_ids,
labels=labels,
input_ids_lens=input_ids_lens,
labels_lens=labels_lens,
)
def preprocess(sources: Sequence[str], targets: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict:
"""Preprocess the data by tokenizing."""
# sources are questions, and targets are answers
examples = [s + t for s, t in zip(sources, targets)]
examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)]
input_ids = examples_tokenized["input_ids"]
labels = copy.deepcopy(input_ids)
for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]):
label[:source_len] = IGNORE_INDEX
return dict(input_ids=input_ids, labels=labels)
class SupervisedDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer):
super(SupervisedDataset, self).__init__()
logging.warning("Formatting inputs...")
sources = [f"{example['question']}{QUESTION_PROMPT}" for example in raw_data]
targets = [f"{example['answer']}{tokenizer.eos_token}".replace("####", ANSWER_PROMPT) for example in raw_data]
logging.warning("Tokenizing inputs... This may take some time...")
data_dict = preprocess(sources, targets, tokenizer)
self.input_ids = data_dict["input_ids"]
self.labels = data_dict["labels"]
def __len__(self):
return len(self.input_ids)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
return dict(input_ids=self.input_ids[i], labels=self.labels[i])
@dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
)
labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
return dict(
input_ids=input_ids,
labels=labels,
attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
)
def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) -> Dict:
"""Make dataset and collator for supervised fine-tuning."""
logging.warning("Downloading Data")
dataset = load_dataset(data_args.data_name, "main")
train_set = dataset['train']
train_dataset = SupervisedDataset(raw_data=train_set, tokenizer=tokenizer)
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
return dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator)
def train():
parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if model_args.full_precision:
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
low_cpu_mem_usage=True,
torch_dtype=torch.bfloat16,
token=model_args.token,
)
else:
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
low_cpu_mem_usage=True,
torch_dtype=torch.float32,
token=model_args.token,
quantization_config=transformers.BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_type='nf4',
),
)
##########################
# Peft Model #
##########################
if model_args.lora_init:
task_type = TaskType.CAUSAL_LM
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "down_proj", "gate_proj"]
lora_config = LoraConfig(
task_type=task_type,
inference_mode=False,
r=model_args.rank,
lora_alpha=model_args.lora_alpha,
lora_dropout=0.1,
target_modules=target_modules,
init_lora_weights=True,
)
model = get_peft_model(model, lora_config)
elif model_args.adapter_name_or_path is not None:
model = PeftModel.from_pretrained(model,
model_args.adapter_name_or_path,
is_trainable=True,
token=model_args.token,
)
else:
model = PeftModel.from_pretrained(model,
model_args.model_name_or_path,
subfolder='loftq_init',
is_trainable=True,
token=model_args.token,
)
# enable gact
gact.set_optimization_level(model_args.gact_level)
controller = Controller(model)
controller.install_hook()
print(f"GACT is enabled, with level {model_args.gact_level}")
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
token=model_args.token,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=False,
)
special_tokens_dict = dict()
if tokenizer.pad_token is None:
special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN
if tokenizer.eos_token is None:
special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN
if tokenizer.bos_token is None:
special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN
if tokenizer.unk_token is None:
special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN
smart_tokenizer_and_embedding_resize(
special_tokens_dict=special_tokens_dict,
tokenizer=tokenizer,
model=model,
)
data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)
training_args.output_dir = os.path.join(
training_args.output_dir,
training_args.expt_name,
model_args.model_name_or_path.split('/')[-1],
f"ep_{int(training_args.num_train_epochs)}",
f"lr_{training_args.learning_rate}",
f"seed_{training_args.seed}",
)
trainer = GACTTrainer(model=model, controller=controller, tokenizer=tokenizer, args=training_args, **data_module)
trainer.train()
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)
if __name__ == "__main__":
train()