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# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 logging
import os
import sys
import tempfile
sys.path.append("..")
from test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class Unconditional(ExamplesTestsAccelerate):
def test_train_unconditional(self):
with tempfile.TemporaryDirectory() as tmpdir:
test_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name hf-internal-testing/dummy_image_class_data
--model_config_name_or_path diffusers/ddpm_dummy
--resolution 64
--output_dir {tmpdir}
--train_batch_size 2
--num_epochs 1
--gradient_accumulation_steps 1
--ddpm_num_inference_steps 2
--learning_rate 1e-3
--lr_warmup_steps 5
""".split()
run_command(self._launch_args + test_args, return_stdout=True)
# save_pretrained smoke test
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.safetensors")))
self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
def test_unconditional_checkpointing_checkpoints_total_limit(self):
with tempfile.TemporaryDirectory() as tmpdir:
initial_run_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name hf-internal-testing/dummy_image_class_data
--model_config_name_or_path diffusers/ddpm_dummy
--resolution 64
--output_dir {tmpdir}
--train_batch_size 1
--num_epochs 1
--gradient_accumulation_steps 1
--ddpm_num_inference_steps 2
--learning_rate 1e-3
--lr_warmup_steps 5
--checkpointing_steps=2
--checkpoints_total_limit=2
""".split()
run_command(self._launch_args + initial_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
# checkpoint-2 should have been deleted
{"checkpoint-4", "checkpoint-6"},
)
def test_unconditional_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
initial_run_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name hf-internal-testing/dummy_image_class_data
--model_config_name_or_path diffusers/ddpm_dummy
--resolution 64
--output_dir {tmpdir}
--train_batch_size 1
--num_epochs 1
--gradient_accumulation_steps 1
--ddpm_num_inference_steps 1
--learning_rate 1e-3
--lr_warmup_steps 5
--checkpointing_steps=2
""".split()
run_command(self._launch_args + initial_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-2", "checkpoint-4", "checkpoint-6"},
)
resume_run_args = f"""
examples/unconditional_image_generation/train_unconditional.py
--dataset_name hf-internal-testing/dummy_image_class_data
--model_config_name_or_path diffusers/ddpm_dummy
--resolution 64
--output_dir {tmpdir}
--train_batch_size 1
--num_epochs 2
--gradient_accumulation_steps 1
--ddpm_num_inference_steps 1
--learning_rate 1e-3
--lr_warmup_steps 5
--resume_from_checkpoint=checkpoint-6
--checkpointing_steps=2
--checkpoints_total_limit=2
""".split()
run_command(self._launch_args + resume_run_args)
# check checkpoint directories exist
self.assertEqual(
{x for x in os.listdir(tmpdir) if "checkpoint" in x},
{"checkpoint-10", "checkpoint-12"},
)
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