diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index dfbd56270..1a688a427 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -3688,23 +3688,27 @@ def python_math_floor(self: TFloat) -> TInt:
 
 
 @torch_op("aten::floor_divide", trace_only=True)
-def aten_floor_divide(self: TFloat, other: TFloat) -> TFloat:
+def aten_floor_divide(self: TTensor, other: TTensor) -> TTensor:
     """floor_divide(Tensor self, Tensor other) -> Tensor"""
 
-    return op.Floor(op.Div(self, other))
+    if self.dtype.is_floating_point():
+        return op.Floor(op.Div(self, other))
 
+    assert self.dtype.is_integer()
 
-@torch_op("aten::floor_divide", trace_only=True)
-def aten_floor_divide_int(self: TInt, other: TInt) -> TInt:
-    """floor_divide(Tensor self, Tensor other) -> Tensor"""
+    if not self.dtype.is_signed():
+        return op.Div(self, other)
 
-    # TODO(justinchuby): This can be simplified if we can constrain the
-    # inputs to be positive integers. Consider how we can embed constraints in the model.
-    dtype = self.dtype
-    self = op.Cast(self, to=FLOAT.dtype)
-    other = op.Cast(other, to=FLOAT.dtype)
-    result = op.Floor(op.Div(self, other))
-    return op.Cast(result, to=dtype)
+    # Convert truncation to flooring
+    # Reference: https://github.com/pytorch/pytorch/blob/ffc645c870f0abd368606ba1e2b3b58cacb03046/torch/_refs/__init__.py#L1401C1-L1409C70
+    # offset = (torch.signbit(a) != torch.signbit(b)).logical_and(torch.fmod(a, b) != 0)
+    # return prims.div(a, b) - _maybe_convert_to_dtype(offset, a.dtype)
+    offset = op.And(
+        op.Not(op.Equal(op.Sign(self), op.Sign(other))),
+        op.Cast(op.Mod(self, other), to=BOOL.dtype),
+    )
+    offset = op.Cast(offset, to=self.dtype)
+    return op.Sub(op.Div(self, other), offset)
 
 
 @torch_op("_operator::floordiv", trace_only=True)
diff --git a/tests/function_libs/torch_lib/extra_opinfo.py b/tests/function_libs/torch_lib/extra_opinfo.py
index 4f4a3872e..b03cb5880 100644
--- a/tests/function_libs/torch_lib/extra_opinfo.py
+++ b/tests/function_libs/torch_lib/extra_opinfo.py
@@ -2270,18 +2270,9 @@ def __init__(self):
     opinfo_core.BinaryUfuncInfo(
         "ops.aten.floor_divide",
         aten_name="floor_divide",
-        dtypes=common_dtype.floating_types_and_half(),
+        dtypes=common_dtype.all_types_and_half(),
         rhs_make_tensor_kwargs=dict(exclude_zero=True),
     ),
-    opinfo_core.BinaryUfuncInfo(
-        "ops.aten.floor_divide.int",
-        aten_name="floor_divide",
-        op=torch.ops.aten.floor_divide,
-        dtypes=common_dtype.integral_types(),
-        # Create only positive inputs
-        lhs_make_tensor_kwargs=dict(low=0),
-        rhs_make_tensor_kwargs=dict(exclude_zero=True, low=0),
-    ),
     opinfo_core.OpInfo(
         "ops.aten.hamming_window",
         aten_name="hamming_window",
diff --git a/tests/function_libs/torch_lib/ops_test_data.py b/tests/function_libs/torch_lib/ops_test_data.py
index 98d10d9e5..92495d201 100644
--- a/tests/function_libs/torch_lib/ops_test_data.py
+++ b/tests/function_libs/torch_lib/ops_test_data.py
@@ -794,7 +794,6 @@ def _where_input_wrangler(
     TorchLibOpInfo("flatten", core_ops.aten_flatten),
     TorchLibOpInfo("floor", core_ops.aten_floor),
     TorchLibOpInfo("ops.aten.floor_divide", core_ops.aten_floor_divide),
-    TorchLibOpInfo("ops.aten.floor_divide.int", core_ops.aten_floor_divide_int),
     TorchLibOpInfo("fmod", core_ops.aten_fmod),
     TorchLibOpInfo("frac", core_ops.aten_frac),
     TorchLibOpInfo("full", core_ops.aten_full),