testbed/huggingface__trl/examples/scripts/gkd.py

# 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.
"""
# Full training:
python examples/scripts/gkd.py \
    --model_name_or_path Qwen/Qwen2-0.5B-Instruct \
    --teacher_model_name_or_path Qwen/Qwen2-1.5B-Instruct \
    --dataset_name trl-lib/chatbot_arena_completions \
    --learning_rate 2e-5 \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 8 \
    --output_dir gkd-model \
    --logging_steps 10 \
    --num_train_epochs 1 \
    --push_to_hub \
    --gradient_checkpointing

# LoRA:
python examples/scripts/gkd.py \
    --model_name_or_path Qwen/Qwen2-0.5B-Instruct \
    --teacher_model_name_or_path Qwen/Qwen2-1.5B-Instruct \
    --dataset_name trl-lib/chatbot_arena_completions \
    --learning_rate 2e-4 \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 8 \
    --output_dir gkd-model \
    --logging_steps 10 \
    --num_train_epochs 1 \
    --push_to_hub \
    --gradient_checkpointing \
    --use_peft \
    --lora_r 64 \
    --lora_alpha 16
"""

from accelerate import PartialState
from datasets import load_dataset
from transformers import AutoTokenizer, GenerationConfig

from trl import (
    GKDConfig,
    GKDTrainer,
    LogCompletionsCallback,
    ModelConfig,
    ScriptArguments,
    TrlParser,
    get_kbit_device_map,
    get_peft_config,
    get_quantization_config,
)


if __name__ == "__main__":
    parser = TrlParser((ScriptArguments, GKDConfig, ModelConfig))
    script_args, training_args, model_config = parser.parse_args_and_config()

    ################
    # Model & Tokenizer
    ################
    quantization_config = get_quantization_config(model_config)
    model_kwargs = dict(
        revision=model_config.model_revision,
        trust_remote_code=model_config.trust_remote_code,
        attn_implementation=model_config.attn_implementation,
        torch_dtype=model_config.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,
    )
    training_args.model_init_kwargs = model_kwargs

    teacher_model_kwargs = dict(
        revision=model_config.model_revision,
        trust_remote_code=model_config.trust_remote_code,
        attn_implementation=model_config.attn_implementation,
        torch_dtype=model_config.torch_dtype,
        use_cache=True,
        device_map=get_kbit_device_map() if quantization_config is not None else None,
        quantization_config=quantization_config,
    )
    training_args.teacher_model_init_kwargs = teacher_model_kwargs

    tokenizer = AutoTokenizer.from_pretrained(
        model_config.model_name_or_path,
        revision=model_config.model_revision,
        trust_remote_code=model_config.trust_remote_code,
        padding_side="left",
    )
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    ################
    # Dataset
    ################
    dataset = load_dataset(script_args.dataset_name)

    with PartialState().local_main_process_first():
        dataset = dataset.map(
            lambda x: {
                "prompt": tokenizer.apply_chat_template(x["prompt"], tokenize=False, add_generation_prompt=True)
            },
            num_proc=training_args.dataset_num_proc,
        )

    ################
    # Training
    ################
    trainer = GKDTrainer(
        model=model_config.model_name_or_path,
        teacher_model=training_args.teacher_model_name_or_path,
        args=training_args,
        train_dataset=dataset[script_args.dataset_train_split],
        eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
        processing_class=tokenizer,
        peft_config=get_peft_config(model_config),
    )

    if training_args.eval_strategy != "no":
        generation_config = GenerationConfig(
            max_new_tokens=training_args.max_new_tokens, do_sample=True, temperature=training_args.temperature
        )
        completions_callback = LogCompletionsCallback(trainer, generation_config, num_prompts=8)
        trainer.add_callback(completions_callback)

    trainer.train()

    # Save and push to hub
    trainer.save_model(training_args.output_dir)
    if training_args.push_to_hub:
        trainer.push_to_hub(dataset_name=script_args.dataset_name)