[ONNX] add converters for sym_min, sym_max (#152196)

Conversion of Phi4-multimodel-instruct fails because of missing converters for torch.sym_max, and torch.sym_min.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152196
Approved by: https://github.com/justinchuby

test/onnx/exporter/test_small_models_e2e.py

@@ -542,6 +542,34 @@ class DynamoExporterTest(common_utils.TestCase):
         # make sre the naming is working
         self.assertEqual(onnx_program.model.graph.inputs[0].shape[0], "dx")
 
+    def test_export_sym_max(self):
+        class Model(torch.nn.Module):
+            def forward(self, x):
+                return torch.sym_max(*x.shape)
+
+        inputs = (torch.zeros((2, 3)),)
+        dynamic_shapes = ({0: torch.export.Dim.DYNAMIC, 1: torch.export.Dim.DYNAMIC},)
+        onnx_program = self.export(Model(), inputs, dynamic_shapes=dynamic_shapes)
+        onnx_testing.assert_onnx_program(onnx_program, args=inputs)
+        self.assertIn(
+            "Max",
+            [node.op_type for node in onnx_program.model.graph],
+        )
+
+    def test_export_sym_min(self):
+        class Model(torch.nn.Module):
+            def forward(self, x):
+                return torch.sym_min(*x.shape)
+
+        inputs = (torch.zeros((2, 3)),)
+        dynamic_shapes = ({0: torch.export.Dim.DYNAMIC, 1: torch.export.Dim.DYNAMIC},)
+        onnx_program = self.export(Model(), inputs, dynamic_shapes=dynamic_shapes)
+        onnx_testing.assert_onnx_program(onnx_program, args=inputs)
+        self.assertIn(
+            "Min",
+            [node.op_type for node in onnx_program.model.graph],
+        )
+
     def test_export_sym_not(self):
         class SymNotModel(torch.nn.Module):
             def forward(self, x):

torch/onnx/_internal/exporter/_torchlib/ops/symops.py

@@ -9,10 +9,22 @@ from __future__ import annotations
 from onnxscript.onnx_opset import opset18 as op
 
 import torch
-from torch.onnx._internal.exporter._torchlib._tensor_typing import BOOL
+from torch.onnx._internal.exporter._torchlib._tensor_typing import BOOL, IntType
 from torch.onnx._internal.exporter._torchlib._torchlib_registry import onnx_impl
 
 
+@onnx_impl(torch.sym_max, trace_only=True)
+def sym_max(x: IntType, y: IntType) -> IntType:
+    """sym_max(SymInt x, SymInt y) -> SymInt"""
+    return op.Max(x, y)
+
+
+@onnx_impl(torch.sym_min, trace_only=True)
+def sym_min(x: IntType, y: IntType) -> IntType:
+    """sym_min(SymInt x, SymInt y) -> SymInt"""
+    return op.Min(x, y)
+
+
 @onnx_impl(torch.sym_not, trace_only=True)
 def sym_not(self: BOOL) -> BOOL:
     """sym_not(SymBool self) -> SymBool"""