mirror of
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94 lines
3.2 KiB
Python
94 lines
3.2 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 gc
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import unittest
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import pytest
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import torch
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from parameterized import parameterized
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from transformers.utils import is_peft_available
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from trl.import_utils import is_diffusers_available
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from .testing_utils import require_diffusers
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if is_diffusers_available() and is_peft_available():
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from trl import AlignPropConfig, AlignPropTrainer, DefaultDDPOStableDiffusionPipeline
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def scorer_function(images, prompts, metadata):
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return torch.randn(1) * 3.0, {}
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def prompt_function():
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return ("cabbages", {})
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@pytest.mark.low_priority
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@require_diffusers
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class AlignPropTrainerTester(unittest.TestCase):
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"""
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Test the AlignPropTrainer class.
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"""
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def setUp(self):
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training_args = AlignPropConfig(
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num_epochs=2,
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train_gradient_accumulation_steps=1,
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train_batch_size=2,
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truncated_backprop_rand=False,
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mixed_precision=None,
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save_freq=1000000,
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)
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pretrained_model = "hf-internal-testing/tiny-stable-diffusion-torch"
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pretrained_revision = "main"
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pipeline_with_lora = DefaultDDPOStableDiffusionPipeline(
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pretrained_model, pretrained_model_revision=pretrained_revision, use_lora=True
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)
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pipeline_without_lora = DefaultDDPOStableDiffusionPipeline(
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pretrained_model, pretrained_model_revision=pretrained_revision, use_lora=False
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)
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self.trainer_with_lora = AlignPropTrainer(training_args, scorer_function, prompt_function, pipeline_with_lora)
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self.trainer_without_lora = AlignPropTrainer(
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training_args, scorer_function, prompt_function, pipeline_without_lora
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)
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def tearDown(self) -> None:
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gc.collect()
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@parameterized.expand([True, False])
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def test_generate_samples(self, use_lora):
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trainer = self.trainer_with_lora if use_lora else self.trainer_without_lora
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output_pairs = trainer._generate_samples(2, with_grad=True)
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self.assertEqual(len(output_pairs.keys()), 3)
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self.assertEqual(len(output_pairs["images"]), 2)
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@parameterized.expand([True, False])
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def test_calculate_loss(self, use_lora):
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trainer = self.trainer_with_lora if use_lora else self.trainer_without_lora
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sample = trainer._generate_samples(2)
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images = sample["images"]
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prompts = sample["prompts"]
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self.assertTupleEqual(images.shape, (2, 3, 128, 128))
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self.assertEqual(len(prompts), 2)
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rewards = trainer.compute_rewards(sample)
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loss = trainer.calculate_loss(rewards)
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self.assertTrue(torch.isfinite(loss.cpu()))
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