diff --git a/examples/controlnet/train_controlnet_sdxl.py b/examples/controlnet/train_controlnet_sdxl.py index 47ed405af30ac..204c61b59b749 100644 --- a/examples/controlnet/train_controlnet_sdxl.py +++ b/examples/controlnet/train_controlnet_sdxl.py @@ -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 +796,10 @@ def main(args): logging_dir = Path(args.output_dir, args.logging_dir) + if torch.backends.mps.is_available(): + # due to pytorch#99272, MPS does not yet support bfloat16. + args.mixed_precision = "fp16" + accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( diff --git a/examples/dreambooth/train_dreambooth_lora_sdxl.py b/examples/dreambooth/train_dreambooth_lora_sdxl.py index 0cbecd0e2c9a7..28f5ef525efb8 100644 --- a/examples/dreambooth/train_dreambooth_lora_sdxl.py +++ b/examples/dreambooth/train_dreambooth_lora_sdxl.py @@ -14,7 +14,6 @@ # See the License for the specific language governing permissions and import argparse -import contextlib import gc import itertools import json @@ -206,11 +205,9 @@ def log_validation( # 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 enable_autocast = True - if ( - not torch.backends.mps.is_available() - or (accelerator.mixed_precision == "fp16" - or accelerator.mixed_precision == "bf16") - ): + 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 diff --git a/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py b/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py index 36517e8ff6451..202aedc418599 100644 --- a/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py +++ b/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py @@ -71,12 +71,7 @@ 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(str(accelerator.device).replace(":0", ""), enabled=enable_autocast): edited_images = [] # Run inference for val_img_idx in range(args.num_validation_images): @@ -497,6 +492,11 @@ def main(): ), ) logging_dir = os.path.join(args.output_dir, args.logging_dir) + + if torch.backends.mps.is_available(): + # due to pytorch#99272, MPS does not yet support bfloat16. + args.mixed_precision = "fp16" + accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, @@ -981,6 +981,13 @@ def collate_fn(examples): 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 +1200,7 @@ def collate_fn(examples): generator, global_step, is_final_validation=False, + enable_autocast=enable_autocast, ) if args.use_ema: @@ -1242,6 +1250,7 @@ def collate_fn(examples): generator, global_step, is_final_validation=True, + enable_autocast=enable_autocast, ) accelerator.end_training() diff --git a/examples/text_to_image/train_text_to_image_lora_sdxl.py b/examples/text_to_image/train_text_to_image_lora_sdxl.py index f1d8e1b093e26..c88f039dea6bb 100644 --- a/examples/text_to_image/train_text_to_image_lora_sdxl.py +++ b/examples/text_to_image/train_text_to_image_lora_sdxl.py @@ -501,6 +501,10 @@ def main(args): logging_dir = Path(args.output_dir, args.logging_dir) + if torch.backends.mps.is_available(): + # due to pytorch#99272, MPS does not yet support bfloat16. + args.mixed_precision = "fp16" + accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) accelerator = Accelerator( @@ -973,6 +977,13 @@ def collate_fn(examples): 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 +1210,10 @@ def compute_time_ids(original_size, crops_coords_top_left): 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( + str(accelerator.device).replace(":0", ""), + enabled=enable_autocast, + ): images = [ pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images) diff --git a/examples/text_to_image/train_text_to_image_sdxl.py b/examples/text_to_image/train_text_to_image_sdxl.py index cb1feb806c8ee..1ef29cc64d737 100644 --- a/examples/text_to_image/train_text_to_image_sdxl.py +++ b/examples/text_to_image/train_text_to_image_sdxl.py @@ -590,6 +590,10 @@ def main(args): accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) + if torch.backends.mps.is_available(): + # due to pytorch#99272, MPS does not yet support bfloat16. + args.mixed_precision = "fp16" + accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, @@ -980,6 +984,13 @@ def unwrap_model(model): 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 +1224,10 @@ def compute_time_ids(original_size, crops_coords_top_left): 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( + str(accelerator.device).replace(":0", ""), + enabled=enable_autocast, + ): images = [ pipeline(**pipeline_args, generator=generator, num_inference_steps=25).images[0] for _ in range(args.num_validation_images) @@ -1268,7 +1282,7 @@ def compute_time_ids(original_size, crops_coords_top_left): 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(str(accelerator.device).replace(":0", ""), enabled=enable_autocast): images = [ pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0] for _ in range(args.num_validation_images)