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apple mps: training support for SDXL (ControlNet, LoRA, Dreambooth, T2I) (#7447)

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* apple mps: training support for SDXL LoRA

* sdxl: support training lora, dreambooth, t2i, pix2pix, and controlnet on apple mps

---------

Co-authored-by: bghira <bghira@users.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
This commit is contained in:
Bagheera
2024-03-28 02:56:18 -06:00
committed by GitHub
parent 6df103deba
commit d78acdedc1
5 changed files with 94 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
examples/controlnet/train_controlnet_sdxl.py
View File

@@ -125,7 +125,11 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step,
)
image_logs = []
inference_ctx = contextlib.nullcontext() if is_final_validation else torch.autocast("cuda")
inference_ctx = (
contextlib.nullcontext()
if (is_final_validation or torch.backends.mps.is_available())
else torch.autocast("cuda")
)
for validation_prompt, validation_image in zip(validation_prompts, validation_images):
validation_image = Image.open(validation_image).convert("RGB")
@@ -792,6 +796,12 @@ def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(

39
examples/dreambooth/train_dreambooth_lora_sdxl.py
View File

@@ -14,7 +14,6 @@
# See the License for the specific language governing permissions and
import argparse
import contextlib
import gc
import itertools
import json
@@ -208,11 +207,18 @@ def log_validation(
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
# Currently the context determination is a bit hand-wavy. We can improve it in the future if there's a better
# way to condition it. Reference: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051
inference_ctx = (
contextlib.nullcontext() if "playground" in args.pretrained_model_name_or_path else torch.cuda.amp.autocast()
)
enable_autocast = True
if torch.backends.mps.is_available() or (
accelerator.mixed_precision == "fp16" or accelerator.mixed_precision == "bf16"
):
enable_autocast = False
if "playground" in args.pretrained_model_name_or_path:
enable_autocast = False
with inference_ctx:
with torch.autocast(
accelerator.device.type,
enabled=enable_autocast,
):
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
for tracker in accelerator.trackers:
@@ -230,7 +236,8 @@ def log_validation(
)
del pipeline
torch.cuda.empty_cache()
if torch.cuda.is_available():
torch.cuda.empty_cache()
return images
@@ -967,6 +974,12 @@ def main(args):
if args.do_edm_style_training and args.snr_gamma is not None:
raise ValueError("Min-SNR formulation is not supported when conducting EDM-style training.")
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
@@ -1009,7 +1022,8 @@ def main(args):
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available()
torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32
if args.prior_generation_precision == "fp32":
torch_dtype = torch.float32
elif args.prior_generation_precision == "fp16":
@@ -1134,6 +1148,12 @@ def main(args):
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
# Move unet, vae and text_encoder to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
@@ -1278,7 +1298,7 @@ def main(args):
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
if args.allow_tf32 and torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
@@ -1455,7 +1475,8 @@ def main(args):
if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
del tokenizers, text_encoders
gc.collect()
torch.cuda.empty_cache()
if torch.cuda.is_available():
torch.cuda.empty_cache()
# If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
# pack the statically computed variables appropriately here. This is so that we don't

25
examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py
View File

@@ -71,12 +71,7 @@ TORCH_DTYPE_MAPPING = {"fp32": torch.float32, "fp16": torch.float16, "bf16": tor
def log_validation(
pipeline,
args,
accelerator,
generator,
global_step,
is_final_validation=False,
pipeline, args, accelerator, generator, global_step, is_final_validation=False, enable_autocast=True
):
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
@@ -96,7 +91,7 @@ def log_validation(
else Image.open(image_url_or_path).convert("RGB")
)(args.val_image_url_or_path)
with torch.autocast(str(accelerator.device).replace(":0", ""), enabled=accelerator.mixed_precision == "fp16"):
with torch.autocast(accelerator.device.type, enabled=enable_autocast):
edited_images = []
# Run inference
for val_img_idx in range(args.num_validation_images):
@@ -497,6 +492,13 @@ def main():
),
)
logging_dir = os.path.join(args.output_dir, args.logging_dir)
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
@@ -981,6 +983,13 @@ def main():
if accelerator.is_main_process:
accelerator.init_trackers("instruct-pix2pix-xl", config=vars(args))
# Some configurations require autocast to be disabled.
enable_autocast = True
if torch.backends.mps.is_available() or (
accelerator.mixed_precision == "fp16" or accelerator.mixed_precision == "bf16"
):
enable_autocast = False
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -1193,6 +1202,7 @@ def main():
generator,
global_step,
is_final_validation=False,
enable_autocast=enable_autocast,
)
if args.use_ema:
@@ -1242,6 +1252,7 @@ def main():
generator,
global_step,
is_final_validation=True,
enable_autocast=enable_autocast,
)
accelerator.end_training()

18
examples/text_to_image/train_text_to_image_lora_sdxl.py
View File

@@ -501,6 +501,12 @@ def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
@@ -973,6 +979,13 @@ def main(args):
if accelerator.is_main_process:
accelerator.init_trackers("text2image-fine-tune", config=vars(args))
# Some configurations require autocast to be disabled.
enable_autocast = True
if torch.backends.mps.is_available() or (
accelerator.mixed_precision == "fp16" or accelerator.mixed_precision == "bf16"
):
enable_autocast = False
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -1199,7 +1212,10 @@ def main(args):
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
pipeline_args = {"prompt": args.validation_prompt}
with torch.cuda.amp.autocast():
with torch.autocast(
accelerator.device.type,
enabled=enable_autocast,
):
images = [
pipeline(**pipeline_args, generator=generator).images[0]
for _ in range(args.num_validation_images)

20
examples/text_to_image/train_text_to_image_sdxl.py
View File

@@ -590,6 +590,12 @@ def main(args):
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
@@ -980,6 +986,13 @@ def main(args):
model = model._orig_mod if is_compiled_module(model) else model
return model
# Some configurations require autocast to be disabled.
enable_autocast = True
if torch.backends.mps.is_available() or (
accelerator.mixed_precision == "fp16" or accelerator.mixed_precision == "bf16"
):
enable_autocast = False
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
@@ -1213,7 +1226,10 @@ def main(args):
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
pipeline_args = {"prompt": args.validation_prompt}
with torch.cuda.amp.autocast():
with torch.autocast(
accelerator.device.type,
enabled=enable_autocast,
):
images = [
pipeline(**pipeline_args, generator=generator, num_inference_steps=25).images[0]
for _ in range(args.num_validation_images)
@@ -1268,7 +1284,7 @@ def main(args):
if args.validation_prompt and args.num_validation_images > 0:
pipeline = pipeline.to(accelerator.device)
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
with torch.cuda.amp.autocast():
with torch.autocast(accelerator.device.type, enabled=enable_autocast):
images = [
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
for _ in range(args.num_validation_images)