tests/test_rich_progress_callback.py

# Copyright 2020-2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch
import torch.nn as nn
from datasets import Dataset
from transformers import Trainer, TrainingArguments

from trl.trainer.callbacks import RichProgressCallback

from .testing_utils import TrlTestCase, require_rich


class DummyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.a = nn.Parameter(torch.tensor(1.0))

    def forward(self, x):
        return self.a * x


@require_rich
class TestRichProgressCallback(TrlTestCase):
    def setup_method(self):
        self.dummy_model = DummyModel()
        self.dummy_train_dataset = Dataset.from_list([{"x": 1.0, "y": 2.0}] * 5)
        self.dummy_val_dataset = Dataset.from_list([{"x": 1.0, "y": 2.0}] * 101)

    def test_rich_progress_callback_logging(self):
        training_args = TrainingArguments(
            output_dir=self.tmp_dir,
            per_device_eval_batch_size=2,
            per_device_train_batch_size=2,
            num_train_epochs=4,
            eval_strategy="steps",
            eval_steps=1,
            logging_strategy="steps",
            logging_steps=1,
            save_strategy="no",
            report_to="none",
            disable_tqdm=True,
        )
        callbacks = [RichProgressCallback()]
        trainer = Trainer(
            model=self.dummy_model,
            train_dataset=self.dummy_train_dataset,
            eval_dataset=self.dummy_val_dataset,
            args=training_args,
            callbacks=callbacks,
        )

        trainer.train()