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# Copyright 2020-2025 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 gc
import unittest
import torch
from transformers.utils import is_peft_available
from trl.import_utils import is_diffusers_available
from .testing_utils import require_diffusers
if is_diffusers_available() and is_peft_available():
from trl import DDPOConfig, DDPOTrainer, DefaultDDPOStableDiffusionPipeline
def scorer_function(images, prompts, metadata):
return torch.randn(1) * 3.0, {}
def prompt_function():
return ("cabbages", {})
@require_diffusers
class DDPOTrainerTester(unittest.TestCase):
"""
Test the DDPOTrainer class.
"""
def setUp(self):
self.training_args = DDPOConfig(
num_epochs=2,
train_gradient_accumulation_steps=1,
per_prompt_stat_tracking_buffer_size=32,
sample_num_batches_per_epoch=2,
sample_batch_size=2,
mixed_precision=None,
save_freq=1000000,
)
pretrained_model = "hf-internal-testing/tiny-stable-diffusion-torch"
pretrained_revision = "main"
pipeline = DefaultDDPOStableDiffusionPipeline(
pretrained_model, pretrained_model_revision=pretrained_revision, use_lora=False
)
self.trainer = DDPOTrainer(self.training_args, scorer_function, prompt_function, pipeline)
return super().setUp()
def tearDown(self) -> None:
gc.collect()
def test_loss(self):
advantage = torch.tensor([-1.0])
clip_range = 0.0001
ratio = torch.tensor([1.0])
loss = self.trainer.loss(advantage, clip_range, ratio)
self.assertEqual(loss.item(), 1.0)
def test_generate_samples(self):
samples, output_pairs = self.trainer._generate_samples(1, 2)
self.assertEqual(len(samples), 1)
self.assertEqual(len(output_pairs), 1)
self.assertEqual(len(output_pairs[0][0]), 2)
def test_calculate_loss(self):
samples, _ = self.trainer._generate_samples(1, 2)
sample = samples[0]
latents = sample["latents"][0, 0].unsqueeze(0)
next_latents = sample["next_latents"][0, 0].unsqueeze(0)
log_probs = sample["log_probs"][0, 0].unsqueeze(0)
timesteps = sample["timesteps"][0, 0].unsqueeze(0)
prompt_embeds = sample["prompt_embeds"]
advantage = torch.tensor([1.0], device=prompt_embeds.device)
self.assertTupleEqual(latents.shape, (1, 4, 64, 64))
self.assertTupleEqual(next_latents.shape, (1, 4, 64, 64))
self.assertTupleEqual(log_probs.shape, (1,))
self.assertTupleEqual(timesteps.shape, (1,))
self.assertTupleEqual(prompt_embeds.shape, (2, 77, 32))
loss, approx_kl, clipfrac = self.trainer.calculate_loss(
latents, timesteps, next_latents, log_probs, advantage, prompt_embeds
)
self.assertTrue(torch.isfinite(loss.cpu()))
@require_diffusers
class DDPOTrainerWithLoRATester(DDPOTrainerTester):
"""
Test the DDPOTrainer class.
"""
def setUp(self):
self.training_args = DDPOConfig(
num_epochs=2,
train_gradient_accumulation_steps=1,
per_prompt_stat_tracking_buffer_size=32,
sample_num_batches_per_epoch=2,
sample_batch_size=2,
mixed_precision=None,
save_freq=1000000,
)
pretrained_model = "hf-internal-testing/tiny-stable-diffusion-torch"
pretrained_revision = "main"
pipeline = DefaultDDPOStableDiffusionPipeline(
pretrained_model, pretrained_model_revision=pretrained_revision, use_lora=True
)
self.trainer = DDPOTrainer(self.training_args, scorer_function, prompt_function, pipeline)
return super().setUp()
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