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Co-authored-by: sergiopaniego <sergiopaniegoblanco@gmail.com> Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
160 lines
5.4 KiB
Python
160 lines
5.4 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# /// script
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# dependencies = [
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# "trl",
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# "trackio",
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# "kernels",
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# ]
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# ///
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"""
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Usage:
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python examples/scripts/nash_md.py \
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--model_name_or_path trl-lib/pythia-1b-deduped-tldr-sft \
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--reward_model_path trl-lib/pythia-1b-deduped-tldr-rm \
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--dataset_name trl-lib/tldr \
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--learning_rate 5.0e-7 \
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--output_dir pythia-1b-tldr-nash-md \
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--per_device_train_batch_size 4 \
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--gradient_accumulation_steps 32 \
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--num_train_epochs 3 \
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--max_new_tokens 64 \
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--warmup_ratio 0.1 \
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--missing_eos_penalty 1.0 \
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--push_to_hub
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accelerate launch --config_file examples/accelerate_configs/deepspeed_zero2.yaml \
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examples/scripts/nash_md.py \
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--model_name_or_path trl-lib/pythia-1b-deduped-tldr-sft \
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--reward_model_path trl-lib/pythia-1b-deduped-tldr-rm \
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--dataset_name trl-lib/tldr \
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--learning_rate 5.0e-7 \
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--output_dir pythia-1b-tldr-nash-md \
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--per_device_train_batch_size 4 \
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--gradient_accumulation_steps 32 \
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--num_train_epochs 3 \
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--max_new_tokens 64 \
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--warmup_ratio 0.1 \
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--missing_eos_penalty 1.0 \
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--push_to_hub
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"""
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import os
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import torch
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from datasets import load_dataset
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from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, GenerationConfig
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from trl import (
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HfPairwiseJudge,
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LogCompletionsCallback,
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ModelConfig,
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NashMDConfig,
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NashMDTrainer,
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OpenAIPairwiseJudge,
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PairRMJudge,
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ScriptArguments,
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TrlParser,
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get_kbit_device_map,
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get_quantization_config,
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)
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from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
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# Enable logging in a Hugging Face Space
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os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio")
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JUDGES = {"pair_rm": PairRMJudge, "openai": OpenAIPairwiseJudge, "hf": HfPairwiseJudge}
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if __name__ == "__main__":
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parser = TrlParser((ScriptArguments, NashMDConfig, ModelConfig))
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script_args, training_args, model_args = parser.parse_args_and_config()
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training_args.gradient_checkpointing_kwargs = {"use_reentrant": True}
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dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
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model_kwargs = dict(
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revision=model_args.model_revision,
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attn_implementation=model_args.attn_implementation,
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dtype=dtype,
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use_cache=False if training_args.gradient_checkpointing else True,
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)
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quantization_config = get_quantization_config(model_args)
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if quantization_config is not None:
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# Passing None would not be treated the same as omitting the argument, so we include it only when valid.
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model_kwargs["device_map"] = get_kbit_device_map()
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model_kwargs["quantization_config"] = quantization_config
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model = AutoModelForCausalLM.from_pretrained(
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model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, **model_kwargs
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)
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ref_model = AutoModelForCausalLM.from_pretrained(
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model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, **model_kwargs
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)
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if training_args.reward_model_path is not None:
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reward_model = AutoModelForSequenceClassification.from_pretrained(
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training_args.reward_model_path,
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num_labels=1,
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trust_remote_code=model_args.trust_remote_code,
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**model_kwargs,
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)
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else:
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reward_model = None
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if training_args.judge is not None:
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judge_cls = JUDGES[training_args.judge]
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judge = judge_cls()
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else:
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judge = None
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path, padding_side="left", trust_remote_code=model_args.trust_remote_code
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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if tokenizer.chat_template is None:
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tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
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dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
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trainer = NashMDTrainer(
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model=model,
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ref_model=ref_model,
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reward_funcs=reward_model,
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judge=judge,
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args=training_args,
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train_dataset=dataset[script_args.dataset_train_split],
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eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
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processing_class=tokenizer,
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)
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if training_args.eval_strategy != "no":
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generation_config = GenerationConfig(
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max_new_tokens=training_args.max_new_tokens, do_sample=True, temperature=training_args.temperature
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)
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completions_callback = LogCompletionsCallback(trainer, generation_config, num_prompts=8)
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trainer.add_callback(completions_callback)
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trainer.train()
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# Save and push to hub
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trainer.save_model(training_args.output_dir)
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if training_args.push_to_hub:
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trainer.push_to_hub(dataset_name=script_args.dataset_name)
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