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67 lines
2.2 KiB
Python
67 lines
2.2 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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import torch
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import torch.nn as nn
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from datasets import Dataset
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from transformers import Trainer, TrainingArguments
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from trl.trainer.callbacks import RichProgressCallback
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class DummyModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.a = nn.Parameter(torch.tensor(1.0))
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def forward(self, x):
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return self.a * x
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class TestRichProgressCallback(unittest.TestCase):
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def setUp(self):
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self.dummy_model = DummyModel()
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self.dummy_train_dataset = Dataset.from_list([{"x": 1.0, "y": 2.0}] * 5)
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self.dummy_val_dataset = Dataset.from_list([{"x": 1.0, "y": 2.0}] * 101)
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def test_rich_progress_callback_logging(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = TrainingArguments(
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output_dir=tmp_dir,
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per_device_eval_batch_size=2,
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per_device_train_batch_size=2,
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num_train_epochs=4,
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eval_strategy="steps",
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eval_steps=1,
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logging_strategy="steps",
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logging_steps=1,
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save_strategy="no",
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report_to="none",
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disable_tqdm=True,
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)
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callbacks = [RichProgressCallback()]
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trainer = Trainer(
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model=self.dummy_model,
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train_dataset=self.dummy_train_dataset,
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eval_dataset=self.dummy_val_dataset,
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args=training_args,
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callbacks=callbacks,
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)
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trainer.train()
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trainer.train()
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