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train_raw.py
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train_raw.py
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#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Supervised fine-tuning script for decoder language models.
"""
import logging
import random
import sys
import datetime
import pandas as pd
import datasets
import torch
import transformers
from transformers import set_seed, DataCollatorForLanguageModeling,AutoModelForCausalLM
from datasets import load_dataset, Dataset, concatenate_datasets
# import sys
# sys.path.append('./alignment-handbook/src')
from accelerate import Accelerator
from src.alignment import (
DataArguments,
H4ArgumentParser,
ModelArguments,
SFTConfig,
# apply_chat_template,
# get_datasets,
get_checkpoint,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
# get_tokenizer,
)
from trl import SFTTrainer,setup_chat_format
from src.data import get_datasets, apply_chat_template
from src.model.tokenizer import get_tokenizer, get_tokenizer_phi2, get_tokenizer_qwen15, get_tokenizer_code_llama
# import deepspeed
# from deepspeed.ops.op_builder import builder
logger = logging.getLogger(__name__)
def main():
parser = H4ArgumentParser((ModelArguments, DataArguments, SFTConfig))
model_args, data_args, training_args = parser.parse()
# Set seed for reproducibility
set_seed(training_args.seed)
# accelerator = Accelerator()
###############
# Setup logging
###############
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process a small summary
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Model parameters {model_args}")
logger.info(f"Data parameters {data_args}")
logger.info(f"Training/evaluation parameters {training_args}")
# Check for last checkpoint
last_checkpoint = get_checkpoint(training_args)
if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")
###############
# Load datasets
###############
# raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
mainframe_ds = Dataset.from_pandas(pd.read_feather("./data/mainframe_df_v1.1_chunks_4096.feather"))
textbook_ds = Dataset.from_pandas(pd.read_feather("./data/textbook_quality_programming.feather"))
longcontext = Dataset.from_pandas(pd.read_feather(".data/long_context_data/long_data.feather"))
raw_datasets = longcontext.train_test_split(test_size=0.1)
logger.info(
f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
)
column_names = list(raw_datasets["train"].features)
################
# Load tokenizer
################
tokenizer = get_tokenizer_code_llama(model_args, data_args)
train_dataset = raw_datasets["train"]
eval_dataset = raw_datasets["test"]
with training_args.main_process_first(desc="Log a few random samples from the processed training set"):
for index in random.sample(range(len(raw_datasets["train"])), 3):
logger.info(f"Sample {index} of the processed training set:\n\n{raw_datasets['train'][index]['text']}")
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False,
return_tensors='pt'
)
#######################
# Load pretrained model
#######################
logger.info("*** Load pretrained model ***")
torch_dtype = (
model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
)
# torch_dtype = 'float16'
quantization_config = get_quantization_config(model_args)
model_kwargs = dict(
revision=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
use_flash_attention_2=model_args.use_flash_attention_2,
torch_dtype=torch_dtype,
use_cache=False if training_args.gradient_checkpointing else True,
# device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
logger.info("*** Model loaded! ***")
########################
# Initialize the Trainer
########################
trainer = SFTTrainer(
model=model_args.model_name_or_path,
model_init_kwargs=model_kwargs,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
dataset_text_field="text",
max_seq_length=training_args.max_seq_length,
tokenizer=tokenizer,
packing=True,
peft_config=get_peft_config(model_args),
neftune_noise_alpha=5,
data_collator=data_collator,
)
###############
# Training loop
###############
logger.info("*** Train ***")
# train_result = trainer.train()
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
metrics["train_samples"] = len(train_dataset)
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
##########
# Evaluate
##########
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] = len(eval_dataset)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
##################################
# Save model and create model card
##################################
logger.info("*** Save model ***")
trainer.save_model(training_args.output_dir)
logger.info(f"Model saved to {training_args.output_dir}")
kwargs = {
"finetuned_from": model_args.model_name_or_path,
"dataset": list(data_args.dataset_mixer.keys()),
"dataset_tags": list(data_args.dataset_mixer.keys()),
"tags": ["alignment-handbook"],
}
# if trainer.accelerator.is_main_process:
trainer.create_model_card(**kwargs)
# Restore k,v cache for fast inference
trainer.model.config.use_cache = True
trainer.model.config.save_pretrained(training_args.output_dir)
if training_args.push_to_hub is True:
logger.info("Pushing to hub...")
trainer.push_to_hub(**kwargs)
logger.info("*** Training complete ***")
if __name__ == "__main__":
main()