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"""
Fine-Tune SantaCoder on code/text dataset
"""
# copied from https://github.com/loubnabnl/santacoder-finetuning
# removed all parts related to FIM
# set --subset to default to None instead of "data" to avoid issues with my own datasets.
# added --resume_from_checkpoint to resume training from a checkpoint (untested)
import argparse
import os
import random
import sys
import numpy as np
import torch
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
Trainer,
TrainingArguments,
logging,
set_seed,
)
# import fim
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--resume_from_checkpoint", type=str, default=None) #can pass a checkpoint dir to resume training
parser.add_argument("--model_path", type=str, default="bigcode/santacoder")
parser.add_argument("--dataset_name", type=str, default="bigcode/the-stack-dedup")
parser.add_argument("--subset", type=str, default=None) #None a bodge but not the solution
parser.add_argument("--split", type=str, default="train")
parser.add_argument("--size_valid_set", type=int, default=4000)
parser.add_argument("--streaming", action="store_true")
parser.add_argument("--shuffle_buffer", type=int, default=5000)
parser.add_argument("--data_column", type=str, default="content")
parser.add_argument("--seq_length", type=int, default=1024)
parser.add_argument("--max_steps", type=int, default=10000)
parser.add_argument("--batch_size", type=int, default=2)
parser.add_argument("--gradient_accumulation_steps", type=int, default=8)
parser.add_argument("--eos_token_id", type=int, default=49152)
parser.add_argument("--learning_rate", type=float, default=5e-5)
parser.add_argument("--lr_scheduler_type", type=str, default="cosine")
parser.add_argument("--num_warmup_steps", type=int, default=100)
parser.add_argument("--weight_decay", type=float, default=0.05)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument("--no_fp16", action="store_false")
parser.add_argument("--bf16", action="store_true")
parser.add_argument("--no_gradient_checkpointing", action="store_false")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--num_workers", type=int, default=None)
parser.add_argument("--output_dir", type=str, default="./checkpoints")
parser.add_argument("--log_freq", default=1, type=int)
parser.add_argument("--eval_freq", default=1000, type=int)
parser.add_argument("--save_freq", default=1000, type=int)
# parser.add_argument("--fim_rate", type=float, default=0)
# parser.add_argument("--fim_spm_rate", type=float, default=0)
return parser.parse_args()
def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):
"""
Estimate the average number of characters per token in the dataset.
"""
total_characters, total_tokens = 0, 0
for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):
total_characters += len(example[data_column])
total_tokens += len(tokenizer(example[data_column]).tokens())
return total_characters / total_tokens
class ConstantLengthDataset(IterableDataset):
"""
Iterable dataset that returns constant length chunks of tokens from stream of text files.
Args:
tokenizer (Tokenizer): The processor used for proccessing the data.
dataset (dataset.Dataset): Dataset with text files.
infinite (bool): If True the iterator is reset after dataset reaches end else stops.
seq_length (int): Length of token sequences to return.
num_of_sequences (int): Number of token sequences to keep in buffer.
chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
# fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.
# fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.
seed (int): Seed for random number generator.
"""
def __init__(
self,
tokenizer,
dataset,
infinite=False,
seq_length=1024,
num_of_sequences=1024,
chars_per_token=3.6,
content_field="content",
# fim_rate=0.5,
# fim_spm_rate=0.5,
seed=0,
):
self.tokenizer = tokenizer
self.concat_token_id = (
tokenizer.eos_token_id if tokenizer.eos_token_id else args.eos_token_id
)
self.dataset = dataset
self.seq_length = seq_length
self.infinite = infinite
self.current_size = 0
self.max_buffer_size = seq_length * chars_per_token * num_of_sequences
self.content_field = content_field
# self.fim_rate = fim_rate
# self.fim_spm_rate = fim_spm_rate
self.seed = seed
# (
# self.suffix_tok_id,
# self.prefix_tok_id,
# self.middle_tok_id,
# self.pad_tok_id,
# ) = fim.get_fim_token_ids(self.tokenizer)
# if not self.suffix_tok_id and self.fim_rate > 0:
# print("FIM is not supported by tokenizer, disabling FIM")
# self.fim_rate = 0
def __iter__(self):
iterator = iter(self.dataset)
more_examples = True
while more_examples:
buffer, buffer_len = [], 0
while True:
if buffer_len >= self.max_buffer_size:
break
try:
buffer.append(next(iterator)[self.content_field])
buffer_len += len(buffer[-1])
except StopIteration:
if self.infinite:
iterator = iter(self.dataset)
else:
more_examples = False
break
tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
all_token_ids = []
np_rng = np.random.RandomState(seed=self.seed)
for tokenized_input in tokenized_inputs:
# optionally do FIM permutations
# if self.fim_rate > 0:
# tokenized_input, np_rng = fim.permute(
# tokenized_input,
# np_rng,
# self.suffix_tok_id,
# self.prefix_tok_id,
# self.middle_tok_id,
# self.pad_tok_id,
# fim_rate=self.fim_rate,
# fim_spm_rate=self.fim_spm_rate,
# truncate_or_pad=False,
# )
all_token_ids.extend(tokenized_input + [self.concat_token_id])
examples = []
for i in range(0, len(all_token_ids), self.seq_length):
input_ids = all_token_ids[i : i + self.seq_length]
if len(input_ids) == self.seq_length:
examples.append(input_ids)
random.shuffle(examples)
for example in examples:
self.current_size += 1
yield {
"input_ids": torch.LongTensor(example),
"labels": torch.LongTensor(example),
}
def create_datasets(tokenizer, args):
dataset = load_dataset(
args.dataset_name,
data_dir=args.subset,
split=args.split,
use_auth_token=True,
num_proc=args.num_workers if not args.streaming else None,
streaming=args.streaming,
)
if args.streaming:
print("Loading the dataset in streaming mode")
valid_data = dataset.take(args.size_valid_set)
train_data = dataset.skip(args.size_valid_set)
train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
else:
dataset = dataset.train_test_split(test_size=0.005, seed=args.seed)
train_data = dataset["train"]
valid_data = dataset["test"]
print(
f"Size of the train set: {len(train_data)}. Size of the validation set: {len(valid_data)}"
)
chars_per_token = chars_token_ratio(train_data, tokenizer, args.data_column)
print(f"The character to token ratio of the dataset is: {chars_per_token:.2f}")
train_dataset = ConstantLengthDataset(
tokenizer,
train_data,
infinite=True,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
content_field=args.data_column,
# fim_rate=args.fim_rate,
# fim_spm_rate=args.fim_spm_rate,
seed=args.seed,
)
valid_dataset = ConstantLengthDataset(
tokenizer,
valid_data,
infinite=False,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
content_field=args.data_column,
# fim_rate=args.fim_rate,
# fim_spm_rate=args.fim_spm_rate,
seed=args.seed,
)
return train_dataset, valid_dataset
def run_training(args, train_data, val_data):
print("Loading the model")
# disable caching mechanism when using gradient checkpointing
model = AutoModelForCausalLM.from_pretrained(
args.model_path,
trust_remote_code=True,
use_cache=not args.no_gradient_checkpointing,
)
train_data.start_iteration = 0
print(f"Starting main loop")
training_args = TrainingArguments(
output_dir=args.output_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=args.max_steps,
eval_steps=args.eval_freq,
save_steps=args.save_freq,
logging_steps=args.log_freq,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
learning_rate=args.learning_rate,
lr_scheduler_type=args.lr_scheduler_type,
warmup_steps=args.num_warmup_steps,
gradient_accumulation_steps=args.gradient_accumulation_steps,
gradient_checkpointing=args.no_gradient_checkpointing,
fp16=args.no_fp16,
bf16=args.bf16,
weight_decay=args.weight_decay,
run_name=f"santacoder-{args.subset}",
# report_to="wandb", #I am not using that, so I just comment it out to avoid errors?
)
trainer = Trainer(
model=model, args=training_args, train_dataset=train_data, eval_dataset=val_data
)
print("Training...")
trainer.train(args.resume_from_checkpoint) #can resume here
print("Saving last checkpoint of the model")
model.save_pretrained(os.path.join(args.output_dir, "final_checkpoint/"))
def main(args):
tokenizer = AutoTokenizer.from_pretrained(args.model_path, use_auth_token=True)
train_dataset, eval_dataset = create_datasets(tokenizer, args)
run_training(args, train_dataset, eval_dataset)
if __name__ == "__main__":
print(sys.argv) #to abort early
args = get_args()
print(args) #see if the file actually red?
set_seed(args.seed)
os.makedirs(args.output_dir, exist_ok=True)
logging.set_verbosity_info() #lower verbosity
main(args)
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