Refactor device determination to function; add MPS fallback

finetune/make_captions.py

@@ -15,8 +15,9 @@ from torchvision.transforms.functional import InterpolationMode
 sys.path.append(os.path.dirname(__file__))
 from blip.blip import blip_decoder, is_url
 import library.train_util as train_util
+from library.device_utils import get_preferred_device
 
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+DEVICE = get_preferred_device()
 
 
 IMAGE_SIZE = 384

finetune/make_captions_by_git.py

@@ -10,9 +10,9 @@ from transformers import AutoProcessor, AutoModelForCausalLM
 from transformers.generation.utils import GenerationMixin
 
 import library.train_util as train_util
+from library.device_utils import get_preferred_device
 
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+DEVICE = get_preferred_device()
 
 PATTERN_REPLACE = [
     re.compile(r'(has|with|and) the (words?|letters?|name) (" ?[^"]*"|\w+)( ?(is )?(on|in) (the |her |their |him )?\w+)?'),

finetune/prepare_buckets_latents.py

@@ -14,7 +14,9 @@ from torchvision import transforms
 import library.model_util as model_util
 import library.train_util as train_util
 
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+from library.device_utils import get_preferred_device
+
+DEVICE = get_preferred_device()
 
 IMAGE_TRANSFORMS = transforms.Compose(
     [

gen_img_diffusers.py

@@ -66,7 +66,7 @@ import diffusers
 import numpy as np
 import torch
 
-from library.device_utils import clean_memory
+from library.device_utils import clean_memory, get_preferred_device
 from library.ipex_interop import init_ipex
 
 init_ipex()
@@ -2324,7 +2324,7 @@ def main(args):
         scheduler.config.clip_sample = True
 
     # deviceを決定する
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # "mps"を考量してない
+    device = get_preferred_device()
 
     # custom pipelineをコピったやつを生成する
     if args.vae_slices:

library/device_utils.py

@@ -1,9 +1,34 @@
+import functools
 import gc
 
 import torch
 
+try:
+    HAS_CUDA = torch.cuda.is_available()
+except Exception:
+    HAS_CUDA = False
+
+try:
+    HAS_MPS = torch.backends.mps.is_available()
+except Exception:
+    HAS_MPS = False
+
 
 def clean_memory():
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
     gc.collect()
+    if HAS_CUDA:
+        torch.cuda.empty_cache()
+    if HAS_MPS:
+        torch.mps.empty_cache()
+
+
+@functools.lru_cache(maxsize=None)
+def get_preferred_device() -> torch.device:
+    if HAS_CUDA:
+        device = torch.device("cuda")
+    elif HAS_MPS:
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
+    print(f"get_preferred_device() -> {device}")
+    return device

networks/lora_diffusers.py

@@ -11,6 +11,8 @@ from tqdm import tqdm
 from transformers import CLIPTextModel
 import torch
 
+from library.device_utils import get_preferred_device
+
 
 def make_unet_conversion_map() -> Dict[str, str]:
     unet_conversion_map_layer = []
@@ -476,7 +478,7 @@ if __name__ == "__main__":
     from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline
     import torch
 
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = get_preferred_device()
 
     parser = argparse.ArgumentParser()
     parser.add_argument("--model_id", type=str, default=None, help="model id for huggingface")

networks/lora_interrogator.py

@@ -9,11 +9,12 @@ import torch
 
 import library.model_util as model_util
 import lora
+from library.device_utils import get_preferred_device
 
 TOKENIZER_PATH = "openai/clip-vit-large-patch14"
 V2_STABLE_DIFFUSION_PATH = "stabilityai/stable-diffusion-2"     # ここからtokenizerだけ使う
 
-DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+DEVICE = get_preferred_device()
 
 
 def interrogate(args):

sdxl_gen_img.py

@@ -18,7 +18,7 @@ import diffusers
 import numpy as np
 import torch
 
-from library.device_utils import clean_memory
+from library.device_utils import clean_memory, get_preferred_device
 from library.ipex_interop import init_ipex
 
 init_ipex()
@@ -1495,7 +1495,7 @@ def main(args):
     #     scheduler.config.clip_sample = True
 
     # deviceを決定する
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # "mps"を考量してない
+    device = get_preferred_device()
 
     # custom pipelineをコピったやつを生成する
     if args.vae_slices:

sdxl_minimal_inference.py

@@ -10,6 +10,7 @@ from einops import repeat
 import numpy as np
 import torch
 
+from library.device_utils import get_preferred_device
 from library.ipex_interop import init_ipex
 
 init_ipex()
@@ -85,7 +86,7 @@ if __name__ == "__main__":
     guidance_scale = 7
     seed = None  # 1
 
-    DEVICE = "cuda"
+    DEVICE = get_preferred_device()
     DTYPE = torch.float16  # bfloat16 may work
 
     parser = argparse.ArgumentParser()

tools/latent_upscaler.py

@@ -15,6 +15,8 @@ from torch import nn
 from tqdm import tqdm
 from PIL import Image
 
+from library.device_utils import get_preferred_device
+
 
 class ResidualBlock(nn.Module):
     def __init__(self, in_channels, out_channels=None, kernel_size=3, stride=1, padding=1):
@@ -255,7 +257,7 @@ def create_upscaler(**kwargs):
 
 # another interface: upscale images with a model for given images from command line
 def upscale_images(args: argparse.Namespace):
-    DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    DEVICE = get_preferred_device()
     us_dtype = torch.float16  # TODO: support fp32/bf16
     os.makedirs(args.output_dir, exist_ok=True)