mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
# Copyright 2025 the LlamaFactory team.
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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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import os
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from dataclasses import dataclass, field
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from typing import Any
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import pytest
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from transformers import DataCollatorWithPadding
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from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
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from llamafactory.hparams import get_train_args
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from llamafactory.model import load_model, load_tokenizer
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from llamafactory.train.sft.trainer import CustomSeq2SeqTrainer
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DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
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TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
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TRAIN_ARGS = {
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"model_name_or_path": TINY_LLAMA3,
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"stage": "sft",
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"do_train": True,
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"finetuning_type": "lora",
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"dataset": "llamafactory/tiny-supervised-dataset",
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"dataset_dir": "ONLINE",
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"template": "llama3",
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"cutoff_len": 1024,
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"overwrite_output_dir": True,
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"per_device_train_batch_size": 1,
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"max_steps": 1,
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"report_to": "none",
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}
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@dataclass
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class DataCollatorWithVerbose(DataCollatorWithPadding):
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verbose_list: list[dict[str, Any]] = field(default_factory=list)
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def __call__(self, features: list[dict[str, Any]]) -> dict[str, Any]:
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features = [
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{k: v for k, v in feature.items() if k in ["input_ids", "attention_mask", "labels"]}
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for feature in features
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]
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self.verbose_list.extend(features)
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batch = super().__call__(features)
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return {k: v[:, :1] for k, v in batch.items()} # truncate input length
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@pytest.mark.parametrize("disable_shuffling", [False, True])
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def test_shuffle(disable_shuffling: bool):
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model_args, data_args, training_args, finetuning_args, _ = get_train_args(
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{
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"output_dir": os.path.join("output", f"shuffle{str(disable_shuffling).lower()}"),
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"disable_shuffling": disable_shuffling,
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**TRAIN_ARGS,
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}
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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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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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dataset_module = get_dataset(template, 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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data_collator = DataCollatorWithVerbose(tokenizer=tokenizer)
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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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**dataset_module,
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**tokenizer_module,
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)
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trainer.train()
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if disable_shuffling:
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assert data_collator.verbose_list[0]["input_ids"] == dataset_module["train_dataset"][0]["input_ids"]
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else:
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assert data_collator.verbose_list[0]["input_ids"] != dataset_module["train_dataset"][0]["input_ids"]
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