[mm] rust-server: native multimodal processing for Qwen VL (integrate sglang-mm, e2e) (#32365)

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>

rust/sglang-mm/tests/test_resize_parity.py

@@ -34,15 +34,34 @@ def py_scaled_dims(
     return scale(width), scale(height)
 
 
-def pil_resize(arr: np.ndarray, tw: int, th: int) -> np.ndarray:
-    return np.array(
-        Image.fromarray(arr).resize((tw, th), resample=Image.Resampling.LANCZOS),
-        dtype=np.uint8,
+def pil_resize(arr: np.ndarray, tw: int, th: int, filter=Image.Resampling.LANCZOS):
+    return np.array(Image.fromarray(arr).resize((tw, th), resample=filter), np.uint8)
+
+
+def tv_resize(arr: np.ndarray, tw: int, th: int) -> np.ndarray:
+    """torchvision's uint8 antialias bicubic — ATen's fixed-point kernel."""
+    import torch
+    from torchvision.transforms.v2 import functional as F
+
+    tensor = torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0)
+    out = F.resize(
+        tensor, [th, tw], interpolation=F.InterpolationMode.BICUBIC, antialias=True
     )
+    return out[0].permute(1, 2, 0).numpy()
 
 
-def rs_resize(arr: np.ndarray, tw: int, th: int) -> np.ndarray:
-    return _rs_common.resize_rgb(arr, tw, th).reshape(th, tw, 3)
+# Every resampler the Rust resize claims, and its reference: `aten_u8` for a
+# default server, `pil_bicubic` for --disable-fast-image-processor, `pil_lanczos`
+# for inkling.
+REFERENCES = {
+    "pil_lanczos": lambda a, tw, th: pil_resize(a, tw, th, Image.Resampling.LANCZOS),
+    "pil_bicubic": lambda a, tw, th: pil_resize(a, tw, th, Image.Resampling.BICUBIC),
+    "aten_u8": tv_resize,
+}
+
+
+def rs_resize(arr, tw: int, th: int, resample: str = "pil_lanczos") -> np.ndarray:
+    return _rs_common.resize_rgb(arr, tw, th, resample).reshape(th, tw, 3)
 
 
 CASES = [
@@ -58,13 +77,36 @@ CASES = [
 ]
 
 
+@pytest.mark.parametrize("resample", sorted(REFERENCES))
 @pytest.mark.parametrize(
     "h,w,th,tw", CASES, ids=[f"{h}x{w}->{th}x{tw}" for h, w, th, tw in CASES]
 )
-def test_resize_bit_exact(h, w, th, tw):
+def test_resize_bit_exact(h, w, th, tw, resample):
     rng = np.random.default_rng(h * 10000 + w)
     arr = rng.integers(0, 256, (h, w, 3), dtype=np.uint8)
-    np.testing.assert_array_equal(rs_resize(arr, tw, th), pil_resize(arr, tw, th))
+    np.testing.assert_array_equal(
+        rs_resize(arr, tw, th, resample), REFERENCES[resample](arr, tw, th)
+    )
+
+
+@pytest.mark.parametrize("resample", sorted(REFERENCES))
+def test_resize_bit_exact_random_sweep(resample):
+    """`aten_u8`'s weight precision varies with the scale factor, so the fixed
+    cases above are not enough coverage on their own."""
+    rng = np.random.default_rng(7)
+    for h, w, th, tw in rng.integers(1, 200, (40, 4)):
+        arr = rng.integers(0, 256, (h, w, 3), dtype=np.uint8)
+        np.testing.assert_array_equal(
+            rs_resize(arr, tw, th, resample),
+            REFERENCES[resample](arr, tw, th),
+            err_msg=f"{h}x{w}->{th}x{tw} under {resample}",
+        )
+
+
+def test_unknown_resample_rejected():
+    arr = np.zeros((4, 4, 3), dtype=np.uint8)
+    with pytest.raises(ValueError, match="unknown resample"):
+        _rs_common.resize_rgb(arr, 2, 2, "nearest")
 
 
 def test_scaled_dims_sweep():