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123 lines
5.4 KiB
Python
123 lines
5.4 KiB
Python
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is inspired by the HuggingFace's transformers library.
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# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py
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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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from typing import TYPE_CHECKING, List, Optional
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from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset
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from ...extras.constants import IGNORE_INDEX
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from ...extras.misc import get_logits_processor
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ..trainer_utils import create_modelcard_and_push
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from .metric import ComputeAccuracy, ComputeSimilarity, eval_logit_processor
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from .trainer import CustomSeq2SeqTrainer
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments, TrainerCallback
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from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
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def run_sft(
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model_args: "ModelArguments",
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data_args: "DataArguments",
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training_args: "Seq2SeqTrainingArguments",
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finetuning_args: "FinetuningArguments",
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generating_args: "GeneratingArguments",
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callbacks: Optional[List["TrainerCallback"]] = None,
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):
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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dataset_module = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
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if getattr(model, "is_quantized", False) and not training_args.do_train:
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setattr(model, "_hf_peft_config_loaded", True) # hack here: make model compatible with prediction
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data_collator = SFTDataCollatorWith4DAttentionMask(
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tokenizer=tokenizer,
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pad_to_multiple_of=8 if training_args.do_train else None, # for shift short attention
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label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
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block_diag_attn=model_args.block_diag_attn,
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attn_implementation=getattr(model.config, "_attn_implementation", None),
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compute_dtype=model_args.compute_dtype,
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)
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# Override the decoding parameters of Seq2SeqTrainer
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training_args.generation_max_length = training_args.generation_max_length or data_args.cutoff_len
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training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
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# Metric utils
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metric_module = {}
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if training_args.predict_with_generate:
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metric_module["compute_metrics"] = ComputeSimilarity(tokenizer=tokenizer)
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elif finetuning_args.compute_accuracy:
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metric_module["compute_metrics"] = ComputeAccuracy()
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metric_module["preprocess_logits_for_metrics"] = eval_logit_processor
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# Initialize our Trainer
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trainer = CustomSeq2SeqTrainer(
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model=model,
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args=training_args,
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finetuning_args=finetuning_args,
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data_collator=data_collator,
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callbacks=callbacks,
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**dataset_module,
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**tokenizer_module,
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**metric_module,
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)
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# Keyword arguments for `model.generate`
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gen_kwargs = generating_args.to_dict()
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gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
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gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
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gen_kwargs["logits_processor"] = get_logits_processor()
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# Training
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if training_args.do_train:
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train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
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trainer.save_model()
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trainer.log_metrics("train", train_result.metrics)
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trainer.save_metrics("train", train_result.metrics)
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trainer.save_state()
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if trainer.is_world_process_zero() and finetuning_args.plot_loss:
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plot_loss(training_args.output_dir, keys=["loss", "eval_loss", "eval_accuracy"])
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if training_args.predict_with_generate:
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tokenizer.padding_side = "left" # use left-padding in generation
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# Evaluation
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if training_args.do_eval:
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metrics = trainer.evaluate(metric_key_prefix="eval", **gen_kwargs)
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if training_args.predict_with_generate: # eval_loss will be wrong if predict_with_generate is enabled
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metrics.pop("eval_loss", None)
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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# Predict
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if training_args.do_predict:
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predict_results = trainer.predict(dataset_module["eval_dataset"], metric_key_prefix="predict", **gen_kwargs)
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if training_args.predict_with_generate: # predict_loss will be wrong if predict_with_generate is enabled
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predict_results.metrics.pop("predict_loss", None)
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trainer.log_metrics("predict", predict_results.metrics)
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trainer.save_metrics("predict", predict_results.metrics)
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trainer.save_predictions(dataset_module["eval_dataset"], predict_results)
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# Create model card
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create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)
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