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WANDB_PROJECT=Qwen-Qwen2.5-7B-Instruct-qlora-32k \ | ||
deepspeed run-instruction-packing-qlora.py \ | ||
--deepspeed ds_config_zero3.json \ | ||
--model_name_or_path Qwen/Qwen2.5-7B-Instruct \ | ||
--per_device_train_batch_size 2 \ | ||
--gradient_accumulation_steps 6 \ | ||
--output_dir fpf-0.5-instructions-16k \ | ||
--bf16 \ | ||
--do_train \ | ||
--do_eval false \ | ||
--num_train_epochs 5 \ | ||
--train_file 'malaysian-qwen2.5-32k' \ | ||
--logging_steps 1 \ | ||
--learning_rate 2e-5 \ | ||
--block_size 32768 \ | ||
--save_steps 50 \ | ||
--save_total_limit 3 \ | ||
--gradient_checkpointing true \ | ||
--neftune_noise_alpha 5.0 \ | ||
--torch_dtype bfloat16 |
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#!/usr/bin/env python | ||
# coding=utf-8 | ||
# Copyright 2020 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. | ||
""" | ||
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset. | ||
Here is the full list of checkpoints on the hub that can be fine-tuned by this script: | ||
https://huggingface.co/models?filter=text-generation | ||
""" | ||
# You can also adapt this script on your own causal language modeling | ||
# task. Pointers for this are left as comments. | ||
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import logging | ||
import math | ||
import os | ||
import sys | ||
import warnings | ||
from dataclasses import dataclass, field | ||
from itertools import chain | ||
from typing import Optional | ||
|
||
import datasets | ||
import evaluate | ||
import torch | ||
from datasets import load_dataset | ||
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import transformers | ||
import random | ||
from transformers import ( | ||
CONFIG_MAPPING, | ||
MODEL_FOR_CAUSAL_LM_MAPPING, | ||
AutoConfig, | ||
AutoModelForCausalLM, | ||
AutoTokenizer, | ||
HfArgumentParser, | ||
Trainer, | ||
TrainingArguments, | ||
default_data_collator, | ||
DataCollatorWithPadding, | ||
DataCollatorForLanguageModeling, | ||
is_torch_tpu_available, | ||
set_seed, | ||
) | ||
from transformers.testing_utils import CaptureLogger | ||
from transformers.trainer_utils import get_last_checkpoint | ||
from transformers.utils import check_min_version, send_example_telemetry | ||
from transformers.utils.versions import require_version | ||
import streaming | ||
import json | ||
import numpy as np | ||
from streaming import LocalDataset | ||
from streaming.base.format.mds.encodings import Encoding, _encodings | ||
from peft import LoraConfig, get_peft_model | ||
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require_version( | ||
"datasets>=1.8.0", | ||
"To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") | ||
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logger = logging.getLogger(__name__) | ||
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MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys()) | ||
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) | ||
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@dataclass | ||
class ModelArguments: | ||
""" | ||
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. | ||
""" | ||
|
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model_name_or_path: Optional[str] = field( | ||
default=None, | ||
metadata={ | ||
"help": ( | ||
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." | ||
) | ||
}, | ||
) | ||
model_type: Optional[str] = field( | ||
default=None, | ||
metadata={ | ||
"help": "If training from scratch, pass a model type from the list: " + | ||
", ".join(MODEL_TYPES)}, | ||
) | ||
config_overrides: Optional[str] = field( | ||
default=None, metadata={ | ||
"help": ( | ||
"Override some existing default config settings when a model is trained from scratch. Example: " | ||
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index")}, ) | ||
config_name: Optional[str] = field( | ||
default=None, metadata={ | ||
"help": "Pretrained config name or path if not the same as model_name"}) | ||
tokenizer_name: Optional[str] = field( | ||
default=None, metadata={ | ||
"help": "Pretrained tokenizer name or path if not the same as model_name"}) | ||
cache_dir: Optional[str] = field( | ||
default=None, metadata={ | ||
"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) | ||
use_fast_tokenizer: bool = field( | ||
default=True, metadata={ | ||
"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) | ||
model_revision: str = field( | ||
default="main", metadata={ | ||
"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) | ||
token: str = field( | ||
default=None, | ||
metadata={ | ||
"help": ( | ||
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " | ||
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)." | ||
) | ||
}, | ||
) | ||
use_auth_token: bool = field( | ||
default=None, | ||
metadata={ | ||
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`." | ||
}, | ||
) | ||
trust_remote_code: bool = field( | ||
default=False, metadata={ | ||
"help": ( | ||
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option" | ||
"should only be set to `True` for repositories you trust and in which you have read the code, as it will" | ||
"execute code present on the Hub on your local machine.")}, ) | ||
torch_dtype: Optional[str] = field( | ||
default=None, | ||
metadata={ | ||
"help": ( | ||
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the " | ||
"dtype will be automatically derived from the model's weights."), | ||
"choices": [ | ||
"auto", | ||
"bfloat16", | ||
"float16", | ||
"float32"], | ||
}, | ||
) | ||
low_cpu_mem_usage: bool = field( | ||
default=False, | ||
metadata={ | ||
"help": ( | ||
"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded." | ||
"set True will benefit LLM loading time and RAM consumption." | ||
) | ||
}, | ||
) | ||
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def __post_init__(self): | ||
if self.config_overrides is not None and ( | ||
self.config_name is not None or self.model_name_or_path is not None): | ||
raise ValueError( | ||
"--config_overrides can't be used in combination with --config_name or --model_name_or_path" | ||
) | ||
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@dataclass | ||
class DataTrainingArguments: | ||
""" | ||
Arguments pertaining to what data we are going to input our model for training and eval. | ||
""" | ||
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dataset_name: Optional[str] = field( | ||
default=None, metadata={ | ||
"help": "The name of the dataset to use (via the datasets library)."}) | ||
dataset_config_name: Optional[str] = field( | ||
default=None, metadata={ | ||
"help": "The configuration name of the dataset to use (via the datasets library)."}) | ||
train_file: Optional[str] = field( | ||
default=None, metadata={ | ||
"help": "The input training data file (a text file)."}) | ||
validation_file: Optional[str] = field( | ||
default=None, metadata={ | ||
"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) | ||
max_train_samples: Optional[int] = field( | ||
default=None, metadata={ | ||
"help": ( | ||
"For debugging purposes or quicker training, truncate the number of training examples to this " | ||
"value if set.")}, ) | ||
max_eval_samples: Optional[int] = field( | ||
default=None, metadata={ | ||
"help": ( | ||
"For debugging purposes or quicker training, truncate the number of evaluation examples to this " | ||
"value if set.")}, ) | ||
streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"}) | ||
block_size: Optional[int] = field( | ||
default=None, | ||
metadata={ | ||
"help": ( | ||
"Optional input sequence length after tokenization. " | ||
"The training dataset will be truncated in block of this size for training. " | ||
"Default to the model max input length for single sentence inputs (take into account special tokens)." | ||
) | ||
}, | ||
) | ||
overwrite_cache: bool = field( | ||
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} | ||
) | ||
validation_split_percentage: Optional[int] = field( | ||
default=5, | ||
metadata={ | ||
"help": "The percentage of the train set used as validation set in case there's no validation split" | ||
}, | ||
) | ||
preprocessing_num_workers: Optional[int] = field( | ||
default=None, | ||
metadata={"help": "The number of processes to use for the preprocessing."}, | ||
) | ||
keep_linebreaks: bool = field( | ||
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."} | ||
) | ||
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|
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def main(): | ||
# See all possible arguments in src/transformers/training_args.py | ||
# or by passing the --help flag to this script. | ||
# We now keep distinct sets of args, for a cleaner separation of concerns. | ||
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) | ||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): | ||
# If we pass only one argument to the script and it's the path to a json file, | ||
# let's parse it to get our arguments. | ||
model_args, data_args, training_args = parser.parse_json_file( | ||
json_file=os.path.abspath(sys.argv[1])) | ||
else: | ||
model_args, data_args, training_args = parser.parse_args_into_dataclasses() | ||
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if model_args.use_auth_token is not None: | ||
warnings.warn( | ||
"The `use_auth_token` argument is deprecated and will be removed in v4.34.", | ||
FutureWarning) | ||
if model_args.token is not None: | ||
raise ValueError( | ||
"`token` and `use_auth_token` are both specified. Please set only the argument `token`.") | ||
model_args.token = model_args.use_auth_token | ||
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The | ||
# information sent is the one passed as arguments along with your Python/PyTorch versions. | ||
send_example_telemetry("run_clm", model_args, data_args) | ||
|
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# Setup logging | ||
logging.basicConfig( | ||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", | ||
datefmt="%m/%d/%Y %H:%M:%S", | ||
handlers=[logging.StreamHandler(sys.stdout)], | ||
) | ||
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if training_args.should_log: | ||
# The default of training_args.log_level is passive, so we set log level | ||
# at info here to have that default. | ||
transformers.utils.logging.set_verbosity_info() | ||
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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() | ||
|
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# Log on each process the small summary: | ||
logger.warning( | ||
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + | ||
f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}") | ||
logger.info(f"Training/evaluation parameters {training_args}") | ||
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# Detecting last checkpoint. | ||
last_checkpoint = None | ||
if os.path.isdir( | ||
training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: | ||
last_checkpoint = get_last_checkpoint(training_args.output_dir) | ||
|
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# Set seed before initializing model. | ||
set_seed(training_args.seed) | ||
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at | ||
# https://huggingface.co/docs/datasets/loading_datasets.html. | ||
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# Load pretrained model and tokenizer | ||
# | ||
# Distributed training: | ||
# The .from_pretrained methods guarantee that only one local process can concurrently | ||
# download model & vocab. | ||
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config_kwargs = { | ||
"cache_dir": model_args.cache_dir, | ||
"revision": model_args.model_revision, | ||
"token": model_args.token, | ||
"trust_remote_code": model_args.trust_remote_code, | ||
} | ||
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if model_args.config_name: | ||
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) | ||
elif model_args.model_name_or_path: | ||
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) | ||
else: | ||
config = CONFIG_MAPPING[model_args.model_type]() | ||
logger.warning("You are instantiating a new config instance from scratch.") | ||
if model_args.config_overrides is not None: | ||
logger.info(f"Overriding config: {model_args.config_overrides}") | ||
config.update_from_string(model_args.config_overrides) | ||
logger.info(f"New config: {config}") | ||
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tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) | ||
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torch_dtype = ( | ||
model_args.torch_dtype | ||
if model_args.torch_dtype in ["auto", None] | ||
else getattr(torch, model_args.torch_dtype) | ||
) | ||
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model = AutoModelForCausalLM.from_pretrained( | ||
model_args.model_name_or_path, | ||
from_tf=bool(".ckpt" in model_args.model_name_or_path), | ||
config=config, | ||
cache_dir=model_args.cache_dir, | ||
revision=model_args.model_revision, | ||
token=model_args.token, | ||
trust_remote_code=model_args.trust_remote_code, | ||
torch_dtype=torch_dtype, | ||
low_cpu_mem_usage=model_args.low_cpu_mem_usage, | ||
use_flash_attention_2=True | ||
) | ||
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peft_config = LoraConfig( | ||
lora_alpha=64, | ||
lora_dropout=0.1, | ||
r=16, | ||
bias="none", | ||
task_type="CAUSAL_LM", | ||
target_modules=None, | ||
) | ||
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model = get_peft_model(model, peft_config) | ||
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class UInt32(Encoding): | ||
def encode(self, obj) -> bytes: | ||
return obj.tobytes() | ||
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def decode(self, data: bytes): | ||
return np.frombuffer(data, np.uint32) | ||
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_encodings['uint32'] = UInt32 | ||
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class DatasetFixed(torch.utils.data.Dataset): | ||
def __init__(self, local): | ||
self.dataset = LocalDataset(local=local) | ||
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def __getitem__(self, idx): | ||
data = self.dataset[idx] | ||
data['labels'] = data["input_ids"].copy() | ||
data.pop('token_type_ids', None) | ||
for k in data.keys(): | ||
data[k] = data[k].astype(np.int64) | ||
return data | ||
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def __len__(self): | ||
return len(self.dataset) | ||
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dataset = DatasetFixed(data_args.train_file) | ||
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trainer = Trainer( | ||
model=model, | ||
args=training_args, | ||
train_dataset=dataset, | ||
eval_dataset=None, | ||
tokenizer=tokenizer, | ||
data_collator=default_data_collator, | ||
compute_metrics=None, | ||
preprocess_logits_for_metrics=None, | ||
) | ||
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# Training | ||
if training_args.do_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 | ||
try: | ||
trainer.train(resume_from_checkpoint=checkpoint) | ||
trainer.save_model() | ||
trainer.save_state() | ||
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except Exception as e: | ||
e = str(e) | ||
print(e) | ||
if checkpoint and ('checkpoint' in e or 'central directory' in e): | ||
os.system(f'mv {checkpoint} {checkpoint}-temp') | ||
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def _mp_fn(index): | ||
# For xla_spawn (TPUs) | ||
main() | ||
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if __name__ == "__main__": | ||
main() |