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118 lines
4.5 KiB
Python
118 lines
4.5 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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import tempfile
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import unittest
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from functools import partial
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import torch
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from datasets import Dataset
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from parameterized import parameterized
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from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, TrainingArguments
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from trl import IterativeSFTTrainer
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class IterativeTrainerTester(unittest.TestCase):
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def setUp(self):
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self.model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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self.model = AutoModelForCausalLM.from_pretrained(self.model_id)
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
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self.tokenizer.pad_token = self.tokenizer.eos_token
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# get t5 as seq2seq example:
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model_id = "trl-internal-testing/tiny-T5ForConditionalGeneration"
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self.t5_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
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self.t5_tokenizer = AutoTokenizer.from_pretrained(model_id)
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def _init_tensor_dummy_dataset(self):
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dummy_dataset_dict = {
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"input_ids": [
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torch.tensor([5303, 3621, 3666, 1438, 318]),
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torch.tensor([3666, 1438, 318, 3666, 1438, 318]),
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torch.tensor([5303, 3621, 3666, 1438, 318]),
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],
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"attention_mask": [
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torch.tensor([1, 1, 1, 1, 1]),
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torch.tensor([1, 1, 1, 1, 1, 1]),
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torch.tensor([1, 1, 1, 1, 1]),
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],
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"labels": [
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torch.tensor([5303, 3621, 3666, 1438, 318]),
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torch.tensor([3666, 1438, 318, 3666, 1438, 318]),
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torch.tensor([5303, 3621, 3666, 1438, 318]),
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],
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}
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dummy_dataset = Dataset.from_dict(dummy_dataset_dict)
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dummy_dataset.set_format("torch")
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return dummy_dataset
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def _init_textual_dummy_dataset(self):
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dummy_dataset_dict = {
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"texts": ["Testing the IterativeSFTTrainer.", "This is a test of the IterativeSFTTrainer"],
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"texts_labels": ["Testing the IterativeSFTTrainer.", "This is a test of the IterativeSFTTrainer"],
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}
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dummy_dataset = Dataset.from_dict(dummy_dataset_dict)
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dummy_dataset.set_format("torch")
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return dummy_dataset
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@parameterized.expand(
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[
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["qwen", "tensor"],
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["qwen", "text"],
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["t5", "tensor"],
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["t5", "text"],
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]
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)
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def test_iterative_step_from_tensor(self, model_name, input_name):
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with tempfile.TemporaryDirectory() as tmp_dir:
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# initialize dataset
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if input_name == "tensor":
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dummy_dataset = self._init_tensor_dummy_dataset()
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inputs = {
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"input_ids": dummy_dataset["input_ids"],
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"attention_mask": dummy_dataset["attention_mask"],
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"labels": dummy_dataset["labels"],
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}
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else:
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dummy_dataset = self._init_textual_dummy_dataset()
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inputs = {
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"texts": dummy_dataset["texts"],
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"texts_labels": dummy_dataset["texts_labels"],
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}
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if model_name == "qwen":
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model = self.model
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tokenizer = self.tokenizer
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else:
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model = self.t5_model
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tokenizer = self.t5_tokenizer
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training_args = TrainingArguments(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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max_steps=2,
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learning_rate=1e-3,
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report_to="none",
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)
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iterative_trainer = IterativeSFTTrainer(model=model, args=training_args, processing_class=tokenizer)
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iterative_trainer.optimizer.zero_grad = partial(iterative_trainer.optimizer.zero_grad, set_to_none=False)
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iterative_trainer.step(**inputs)
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for param in iterative_trainer.model.parameters():
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self.assertIsNotNone(param.grad)
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