mirror of
https://github.com/huggingface/trl.git
synced 2025-10-20 10:03:51 +08:00
92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
# 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()
|