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# Copyright 2020-2025 The HuggingFace 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.
import config
import torch
from custom_trainer import LayerSkipSFTTrainer
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import DataCollatorForCompletionOnlyLM, SFTConfig
def formatting_prompts_func(example):
text = f"### Instruction: {example['utterance']}\n ### Response: {example['semantic_parse']}"
# Inject eos_token as a string before tokenization, because they are not always added
# See: https://github.com/huggingface/transformers/issues/22794 and
# https://github.com/huggingface/trl/issues/1623
if tokenizer.eos_token: # usually something like "</s>" for GPT2 or "<|endoftext|>"
text += f"{tokenizer.eos_token}"
return text
if __name__ == "__main__":
# load the dataset
print("[INFO] loading the dataset...")
train_dataset = load_dataset(config.dataset_name, split="train")
print(f"output_root_dir: {config.output_root_dir}")
print(f"hub_model_id: {config.hub_model_id}")
# load the model and tokenizer
print("[INFO] loading the model and tokenizer...")
model = AutoModelForCausalLM.from_pretrained(config.model_name, device_map="auto", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, add_eos_token=True)
# adding pad and eos tokens if not provided in the tokenizer
if tokenizer.pad_token is None:
# Add '[PAD]' token if it doesn't exist
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
model.resize_token_embeddings(len(tokenizer))
model.config.pad_token_id = tokenizer.pad_token_id
if tokenizer.eos_token is None or tokenizer.eos_token == tokenizer.bos_token:
# Add '[EOS]' token if it doesn't exist
tokenizer.add_special_tokens({"eos_token": "[EOS]"})
model.resize_token_embeddings(len(tokenizer))
model.config.eos_token_id = tokenizer.eos_token_id
response_template = " ### Response:"
collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)
args = SFTConfig(
do_train=True,
bf16=True,
max_seq_length=None,
per_device_train_batch_size=config.per_device_train_batch_size,
gradient_accumulation_steps=config.gradient_accumulation_steps,
learning_rate=config.learning_rate,
packing=False,
num_train_epochs=1.0,
report_to="none",
push_to_hub=True,
hub_model_id=config.hub_model_id,
output_dir=config.output_dir,
logging_steps=500,
save_steps=1000,
save_total_limit=2,
)
trainer = LayerSkipSFTTrainer(
model,
train_dataset=train_dataset,
args=args,
formatting_func=formatting_prompts_func,
data_collator=collator,
)
trainer.train()
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