File size: 2,166 Bytes
1fa3c6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | # 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()
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