409 lines
16 KiB
Python
409 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2023 HuggingFace Inc..
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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 logging
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import os
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import shutil
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import subprocess
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import sys
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import tempfile
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import unittest
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from typing import List
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from accelerate.utils import write_basic_config
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from diffusers import DiffusionPipeline, UNet2DConditionModel
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger()
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# These utils relate to ensuring the right error message is received when running scripts
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class SubprocessCallException(Exception):
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pass
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def run_command(command: List[str], return_stdout=False):
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"""
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Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
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if an error occurred while running `command`
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"""
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try:
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output = subprocess.check_output(command, stderr=subprocess.STDOUT)
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if return_stdout:
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if hasattr(output, "decode"):
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output = output.decode("utf-8")
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return output
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except subprocess.CalledProcessError as e:
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raise SubprocessCallException(
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f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
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) from e
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stream_handler = logging.StreamHandler(sys.stdout)
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logger.addHandler(stream_handler)
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class ExamplesTestsAccelerate(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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cls._tmpdir = tempfile.mkdtemp()
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cls.configPath = os.path.join(cls._tmpdir, "default_config.yml")
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write_basic_config(save_location=cls.configPath)
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cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]
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@classmethod
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def tearDownClass(cls):
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super().tearDownClass()
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shutil.rmtree(cls._tmpdir)
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def test_train_unconditional(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/unconditional_image_generation/train_unconditional.py
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--dataset_name hf-internal-testing/dummy_image_class_data
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--model_config_name_or_path diffusers/ddpm_dummy
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--resolution 64
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--output_dir {tmpdir}
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--train_batch_size 2
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--num_epochs 1
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--gradient_accumulation_steps 1
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--ddpm_num_inference_steps 2
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--learning_rate 1e-3
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--lr_warmup_steps 5
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""".split()
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run_command(self._launch_args + test_args, return_stdout=True)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
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def test_textual_inversion(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/textual_inversion/textual_inversion.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
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--train_data_dir docs/source/en/imgs
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--learnable_property object
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--placeholder_token <cat-toy>
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--initializer_token a
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "learned_embeds.bin")))
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def test_dreambooth(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/dreambooth/train_dreambooth.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
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--instance_data_dir docs/source/en/imgs
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--instance_prompt photo
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
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def test_dreambooth_checkpointing(self):
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instance_prompt = "photo"
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pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
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with tempfile.TemporaryDirectory() as tmpdir:
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# Run training script with checkpointing
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# max_train_steps == 5, checkpointing_steps == 2
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# Should create checkpoints at steps 2, 4
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initial_run_args = f"""
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examples/dreambooth/train_dreambooth.py
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--pretrained_model_name_or_path {pretrained_model_name_or_path}
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--instance_data_dir docs/source/en/imgs
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--instance_prompt {instance_prompt}
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 5
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--checkpointing_steps=2
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--seed=0
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""".split()
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run_command(self._launch_args + initial_run_args)
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# check can run the original fully trained output pipeline
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pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
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pipe(instance_prompt, num_inference_steps=2)
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# check checkpoint directories exist
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
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# check can run an intermediate checkpoint
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unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
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pipe(instance_prompt, num_inference_steps=2)
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# Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
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shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
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# Run training script for 7 total steps resuming from checkpoint 4
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resume_run_args = f"""
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examples/dreambooth/train_dreambooth.py
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--pretrained_model_name_or_path {pretrained_model_name_or_path}
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--instance_data_dir docs/source/en/imgs
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--instance_prompt {instance_prompt}
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--resolution 64
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 7
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--checkpointing_steps=2
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--resume_from_checkpoint=checkpoint-4
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--seed=0
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""".split()
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run_command(self._launch_args + resume_run_args)
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# check can run new fully trained pipeline
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pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
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pipe(instance_prompt, num_inference_steps=2)
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# check old checkpoints do not exist
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self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
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# check new checkpoints exist
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
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def test_text_to_image(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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test_args = f"""
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examples/text_to_image/train_text_to_image.py
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--pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
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--dataset_name hf-internal-testing/dummy_image_text_data
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--resolution 64
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--center_crop
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--random_flip
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 2
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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""".split()
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run_command(self._launch_args + test_args)
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# save_pretrained smoke test
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
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self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
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def test_text_to_image_checkpointing(self):
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pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
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prompt = "a prompt"
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with tempfile.TemporaryDirectory() as tmpdir:
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# Run training script with checkpointing
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# max_train_steps == 5, checkpointing_steps == 2
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# Should create checkpoints at steps 2, 4
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initial_run_args = f"""
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examples/text_to_image/train_text_to_image.py
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--pretrained_model_name_or_path {pretrained_model_name_or_path}
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--dataset_name hf-internal-testing/dummy_image_text_data
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--resolution 64
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--center_crop
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--random_flip
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 5
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--checkpointing_steps=2
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--seed=0
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""".split()
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run_command(self._launch_args + initial_run_args)
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pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
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pipe(prompt, num_inference_steps=2)
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# check checkpoint directories exist
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
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# check can run an intermediate checkpoint
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unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
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pipe(prompt, num_inference_steps=2)
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# Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
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shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
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# Run training script for 7 total steps resuming from checkpoint 4
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resume_run_args = f"""
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examples/text_to_image/train_text_to_image.py
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--pretrained_model_name_or_path {pretrained_model_name_or_path}
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--dataset_name hf-internal-testing/dummy_image_text_data
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--resolution 64
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--center_crop
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--random_flip
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 7
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--checkpointing_steps=2
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--resume_from_checkpoint=checkpoint-4
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--seed=0
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""".split()
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run_command(self._launch_args + resume_run_args)
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# check can run new fully trained pipeline
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pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
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pipe(prompt, num_inference_steps=2)
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# check old checkpoints do not exist
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self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
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# check new checkpoints exist
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
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def test_text_to_image_checkpointing_use_ema(self):
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pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
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prompt = "a prompt"
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with tempfile.TemporaryDirectory() as tmpdir:
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# Run training script with checkpointing
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# max_train_steps == 5, checkpointing_steps == 2
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# Should create checkpoints at steps 2, 4
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initial_run_args = f"""
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examples/text_to_image/train_text_to_image.py
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--pretrained_model_name_or_path {pretrained_model_name_or_path}
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--dataset_name hf-internal-testing/dummy_image_text_data
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--resolution 64
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--center_crop
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--random_flip
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 5
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--checkpointing_steps=2
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--use_ema
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--seed=0
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""".split()
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run_command(self._launch_args + initial_run_args)
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pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
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pipe(prompt, num_inference_steps=2)
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# check checkpoint directories exist
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
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# check can run an intermediate checkpoint
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unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
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pipe(prompt, num_inference_steps=2)
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# Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
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shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
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# Run training script for 7 total steps resuming from checkpoint 4
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resume_run_args = f"""
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examples/text_to_image/train_text_to_image.py
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--pretrained_model_name_or_path {pretrained_model_name_or_path}
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--dataset_name hf-internal-testing/dummy_image_text_data
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--resolution 64
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--center_crop
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--random_flip
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--train_batch_size 1
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--gradient_accumulation_steps 1
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--max_train_steps 7
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--learning_rate 5.0e-04
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--scale_lr
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--lr_scheduler constant
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--lr_warmup_steps 0
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--output_dir {tmpdir}
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--checkpointing_steps=2
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--resume_from_checkpoint=checkpoint-4
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--use_ema
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--seed=0
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""".split()
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run_command(self._launch_args + resume_run_args)
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# check can run new fully trained pipeline
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pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
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pipe(prompt, num_inference_steps=2)
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# check old checkpoints do not exist
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self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
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# check new checkpoints exist
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
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self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
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