prep for metal

pyproject.toml

@@ -36,8 +36,8 @@ url = "https://download.pytorch.org/whl/cu124"
 explicit = true
 
 [tool.uv.sources]
-torch = { index = "pytorch-cu124" }
+torch = [{ index = "pytorch-cu124", marker = "sys_platform == 'linux'" }]
-torchvision = { index = "pytorch-cu124" }
+torchvision = [{ index = "pytorch-cu124", marker = "sys_platform == 'linux'" }]
 
 [tool.ruff]
 line-length = 100

scripts/bench.py

@@ -0,0 +1,61 @@
+"""Quick CPU vs MPS benchmark for BiRefNet HR at 2048."""
+
+from __future__ import annotations
+
+import argparse
+import time
+
+import torch
+from PIL import Image
+
+from birefnet_service.model import BiRefNetService
+
+
+def bench(device: str, image: Image.Image, model: str, resolution: int, warmup: int, iters: int):
+    svc = BiRefNetService(device=device, default_model=model, default_resolution=resolution)
+    print(f"[{device}] loaded {model} @ {resolution}, runtime device={svc.device}")
+    for i in range(warmup):
+        t0 = time.perf_counter()
+        svc.remove_background(image, model=model, resolution=resolution)
+        if svc.device == "mps":
+            torch.mps.synchronize()
+        print(f"  warmup {i + 1}: {time.perf_counter() - t0:.2f}s")
+    times = []
+    for i in range(iters):
+        t0 = time.perf_counter()
+        svc.remove_background(image, model=model, resolution=resolution)
+        if svc.device == "mps":
+            torch.mps.synchronize()
+        dt = time.perf_counter() - t0
+        times.append(dt)
+        print(f"  run {i + 1}: {dt:.2f}s")
+    avg = sum(times) / len(times)
+    print(f"[{device}] avg={avg:.2f}s  min={min(times):.2f}s  best-of-{iters}")
+    return avg
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--input", default="test.jpg")
+    ap.add_argument("--model", default="HR")
+    ap.add_argument("--resolution", type=int, default=2048)
+    ap.add_argument("--devices", default="cpu,mps")
+    ap.add_argument("--warmup", type=int, default=1)
+    ap.add_argument("--iters", type=int, default=3)
+    args = ap.parse_args()
+
+    image = Image.open(args.input)
+    print(f"image: {args.input} {image.size} mode={image.mode}")
+    print(f"mps available: {torch.backends.mps.is_available()}")
+
+    results = {}
+    for d in args.devices.split(","):
+        d = d.strip()
+        results[d] = bench(d, image, args.model, args.resolution, args.warmup, args.iters)
+
+    if "cpu" in results and "mps" in results:
+        print(f"\nspeedup mps vs cpu: {results['cpu'] / results['mps']:.2f}x")
+
+
+if __name__ == "__main__":
+    main()

src/birefnet_service/model.py

@@ -130,11 +130,15 @@ class BiRefNetService:
         default_model: str = DEFAULT_MODEL,
         default_resolution: int = DEFAULT_RESOLUTION,
     ):
-        want_cuda = device != "cpu" and torch.cuda.is_available()
         if device and device not in ("auto", "cpu"):
             self.device = device
+        elif device != "cpu" and torch.cuda.is_available():
+            self.device = "cuda"
+        elif device != "cpu" and getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
+            self.device = "mps"
         else:
-            self.device = "cuda" if want_cuda else "cpu"
+            self.device = "cpu"
+        # fp16 is reliable on CUDA; on MPS it can introduce NaNs in BiRefNet — keep fp32.
         self.use_half = self.device.startswith("cuda")
         self.default_model = default_model
         self.default_resolution = default_resolution