diff --git a/onnxscript/function_libs/torch_lib/ops/nn.py b/onnxscript/function_libs/torch_lib/ops/nn.py
index bb767071e7..e9ee699aa3 100644
--- a/onnxscript/function_libs/torch_lib/ops/nn.py
+++ b/onnxscript/function_libs/torch_lib/ops/nn.py
@@ -2306,11 +2306,23 @@ def aten_upsample_bilinear2d(
         result = _aten_upsample_bilinear2d_output_size(self, output_size)
     else:
         assert scales_h is not None
-        assert scales_h == scales_w
+        assert scales_h == scales_w, f"scale_h({scales_h}) != scale_w({scales_w})"
         result = _aten_upsample_bilinear2d_scales(self, scales_h, scales_w)
     return result
 
 
+@torch_op("aten::upsample_bilinear2d.vec", trace_only=True)
+def aten_upsample_bilinear2d_vec(
+    self: TReal,
+    output_size: Optional[INT64] = None,
+    align_corners: bool = True,
+    scale_factors: Optional[Sequence[float]] = None,
+) -> TReal:
+    scales_h = scale_factors[0] if scale_factors is not None else None
+    scales_w = scale_factors[1] if scale_factors is not None else None
+    return aten_upsample_bilinear2d(self, output_size, scales_h, scales_w, align_corners)
+
+
 @torch_op("aten::upsample_bilinear2d", private=True)
 def _aten_upsample_bilinear2d_output_size(
     self: TReal,
diff --git a/onnxscript/tests/function_libs/torch_lib/ops_test_data.py b/onnxscript/tests/function_libs/torch_lib/ops_test_data.py
index 7aeba0d14b..cabc13268a 100644
--- a/onnxscript/tests/function_libs/torch_lib/ops_test_data.py
+++ b/onnxscript/tests/function_libs/torch_lib/ops_test_data.py
@@ -427,6 +427,18 @@ def _upsample_bilinear2d_input_wrangler(
     return args, kwargs
 
 
+def _upsample_bilinear2d_vec_input_wrangler(
+    args: list[Any], kwargs: dict[str, Any]
+) -> tuple[list[Any], dict[str, Any]]:
+    if "size" in kwargs:
+        args.append(np.array(kwargs["size"], dtype=np.int64))
+        del kwargs["size"]  # promote tensor type kwargs to args
+    if "scale_factor" in kwargs:
+        kwargs["scale_factors"] = [kwargs["scale_factor"]] * 2
+        del kwargs["scale_factor"]  # adapt the function signature
+    return args, kwargs
+
+
 def _upsample_input_wrangler(
     args: list[Any], kwargs: dict[str, Any]
 ) -> tuple[list[Any], dict[str, Any]]:
@@ -2122,6 +2134,12 @@ def _where_input_wrangler(
         input_wrangler=_upsample_bilinear2d_input_wrangler,
         trace_only=True,
     ),
+    TorchLibOpInfo(
+        "nn.functional.upsample_bilinear2d",
+        nn_ops.aten_upsample_bilinear2d_vec,
+        input_wrangler=_upsample_bilinear2d_vec_input_wrangler,
+        trace_only=True,
+    ),
     TorchLibOpInfo(
         "ops.aten.upsample_bicubic2d",
         nn_ops.aten_upsample_bicubic2d,