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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
import os
from dataclasses import dataclass
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
import datasets
import torch
import torch.distributed
import transformers
from accelerate.logging import get_logger
from transformers import AutoTokenizer
from trl import SFTTrainer
import modelopt.torch.opt as mto
from modelopt.torch.distill.plugins.huggingface import KDTrainer, LMLogitsLoss
logger = get_logger(__name__, log_level="INFO")
@dataclass
class ModelArguments:
teacher_name_or_path: str | None = None
student_name_or_path: str | None = None
@dataclass
class TrainingArguments(transformers.TrainingArguments):
do_train: bool = True
do_eval: bool = True
save_strategy: str = "no"
max_length: int = 1024
optim: str = "adamw_torch"
learning_rate: float = 1e-5
lr_scheduler_type: str = "cosine"
dataloader_drop_last: bool = True
dataset_num_proc: int = 8
bf16: bool = True
#tf32: bool = True
def _format_smoltalk_chat_template(sample, tokenizer):
# smol-smoltalk-Interaction-SFT dataset has "query" and "answer" fields
# Convert them to messages format and use tokenizer's apply_chat_template
messages = [
{"role": "user", "content": sample["query"]},
{"role": "assistant", "content": sample["answer"]},
]
return tokenizer.apply_chat_template(messages, tokenize=False)
class KDSFTTrainer(KDTrainer, SFTTrainer):
pass
def train():
parser = transformers.HfArgumentParser((ModelArguments, TrainingArguments))
model_args, training_args = parser.parse_args_into_dataclasses()
# Enable automatic save/load of modelopt state huggingface checkpointing
# modelopt state will be saved automatically to "modelopt_state.pth"
mto.enable_huggingface_checkpointing()
# Set total batch size across all ranks to equal 64
total_batch_size = 64
num_accum_steps = total_batch_size / (
training_args.per_device_train_batch_size * torch.distributed.get_world_size()
)
if not num_accum_steps.is_integer():
raise ValueError(
f"`per_device_train_batch_size` * `world_size` must be a factor of {total_batch_size}"
)
training_args.gradient_accumulation_steps = int(num_accum_steps)
logger.info(
f"Using {int(num_accum_steps)} grad accumulation steps for effective batchsize of {total_batch_size}."
)
# Dataset
logger.info("Loading dataset...")
dset = datasets.load_dataset("ReactiveAI/smol-smoltalk-Interaction-SFT", split="train")
dset_splits = dset.train_test_split(train_size=12800, test_size=1280, seed=420)
dset_train, dset_eval = dset_splits["train"], dset_splits["test"]
logger.info("Dataset loaded.")
# Tokenizer
logger.info("Loading tokenizer...")
model_path = model_args.teacher_name_or_path or model_args.student_name_or_path
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
logger.info("Tokenizer loaded.")
# Model(s)
logger.info("Loading student model...")
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.student_name_or_path, dtype=torch.bfloat16 if training_args.bf16 else None
)
logger.info("Student loaded.")
logger.info("Loading teacher model...")
teacher_model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.teacher_name_or_path, dtype=torch.bfloat16 if training_args.bf16 else None
)
# Distillation configuration
kd_config = {
"teacher_model": teacher_model,
"criterion": LMLogitsLoss(),
}
# Fix problematic settings that logger.info excessive warnings
model.generation_config.temperature = None
model.generation_config.top_p = None
# Trainer
trainer = KDSFTTrainer(
model,
training_args,
distill_config=kd_config,
train_dataset=dset_train,
eval_dataset=dset_eval,
formatting_func=lambda sample: _format_smoltalk_chat_template(sample, tokenizer),
processing_class=tokenizer,
)
# Do training
if training_args.do_train:
logger.info("Beginning training...")
trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
logger.info("Training done.")
# Do evaluation
if training_args.do_eval:
logger.info("Evaluating...")
eval_results = trainer.evaluate()
logger.info(eval_results)
logger.info("Evaluation complete.")
# Save checkpoint
logger.info("Saving checkpoint...")
trainer.save_state()
trainer.save_model(trainer.args.output_dir)
logger.info("Checkpoint saved.")
if __name__ == "__main__":
train()
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