[ONNX] Enable remaining failed tests in opset13

test/onnx/test_pytorch_onnx_onnxruntime.py

@@ -436,7 +436,6 @@ def get_test_images(self):
 
         return [image], [image2]
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     @disableScriptTest()  # Faster RCNN model is not scriptable
     def test_faster_rcnn(self):
@@ -487,7 +486,6 @@ def test_paste_mask_in_image(self):
 
         assert torch.all(out2.eq(out_trace2))
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     @disableScriptTest()
     def test_mask_rcnn(self):
@@ -532,7 +530,6 @@ def test_heatmaps_to_keypoints(self):
         assert torch.all(out2[0].eq(out_trace2[0]))
         assert torch.all(out2[1].eq(out_trace2[1]))
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     @disableScriptTest()
     def test_keypoint_rcnn(self):
@@ -554,7 +551,6 @@ def test_keypoint_rcnn(self):
                       dynamic_axes={"images_tensors": [0, 1, 2]},
                       rtol=5e-3, atol=1e-5)
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     @disableScriptTest()
     def test_shufflenet_v2_dynamic_axes(self):
@@ -1241,8 +1237,8 @@ def forward(self, x):
         x = torch.randn(2, 3, 4)
         self.run_test(FloatingPoint(), x)
 
-    @unittest.skip("If operator rank mismatch between outputs of two branches.")
-    @skipIfUnsupportedMinOpsetVersion(9)
+    # Operator rank mismatch between outputs of two branches for opsets below 11.
+    @skipIfUnsupportedMinOpsetVersion(11)
     @skipIfONNXShapeInference(False)
     def test_floating_point_infer_dtype(self):
         class FloatingPoint(torch.jit.ScriptModule):
@@ -1795,7 +1791,6 @@ def forward(self, hidden_states):
                       dynamic_axes={'x': {0: 'seq_length', 1: 'batch_size'}}, test_with_inputs=[y])
 
     @skipIfUnsupportedMinOpsetVersion(11)
-    @skipIfUnsupportedOpsetVersion([13])
     def test_copy_(self):
         class CopyModel(torch.nn.Module):
             def forward(self, x, data):
@@ -3602,7 +3597,6 @@ def forward(self, x, y, z, ind):
         ind = torch.tensor(-2, dtype=torch.long)
         self.run_test(GetItemModel(), (x, y, z, ind))
 
-    @skipIfUnsupportedOpsetVersion([13])
     @disableScriptTest()  # torch.nonzero(x, as_tuple=True) is not scriptable.
     @skipIfUnsupportedMinOpsetVersion(9)
     def test_nonzero(self):
@@ -3834,7 +3828,6 @@ def forward(self, x):
         inputs = torch.zeros(1, 2, 3, dtype=torch.long)
         self.run_test(model, inputs)
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     def test_loop_with_list(self):
         class ListLoopModel(torch.jit.ScriptModule):
@@ -4382,7 +4375,6 @@ def forward(self, input):
         x = torch.randint(10, (2, 3))
         self.run_test(FModModel(), x)
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(9)
     def test_glu(self):
         class GluModel(torch.nn.Module):
@@ -5476,7 +5468,6 @@ def forward(self, cond, input, other):
         z = torch.ones(2, 3, 1)
         self.run_test(Model(), (x, y, z))
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(9)
     def test_where_condition(self):
         class Model1(torch.nn.Module):
@@ -6070,7 +6061,6 @@ def forward(self, x, y):
             "ScriptModel - Initializers' sequence is not as same as named_parameters(). Expected: (" \
             + ', '.join(named_params_list) + "). Actual:(" + ', '.join(actual_list) + ")."
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     def test_nms(self):
         boxes = torch.rand(5, 4)
@@ -6100,15 +6090,13 @@ def forward(self, boxes, size):
                       dynamic_axes={"size": [0, 1]},
                       test_with_inputs=[(boxes, size), (boxes, size_2)])
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     def test_roi_align(self):
         x = torch.rand(1, 1, 10, 10, dtype=torch.float32)
         single_roi = torch.tensor([[0, 0, 0, 4, 4]], dtype=torch.float32)
         model = ops.RoIAlign((5, 5), 1, 2)
         self.run_test(model, (x, single_roi))
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     def test_roi_align_aligned(self):
         x = torch.rand(1, 1, 10, 10, dtype=torch.float32)
@@ -6183,7 +6171,6 @@ def get_features(self, images):
         features = OrderedDict(features)
         return features
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     def test_rpn(self):
 
@@ -6212,7 +6199,6 @@ def forward(self, images, features):
                       test_with_inputs=[(images, features), (images2, test_features)],
                       dict_check=False)
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     def test_multi_scale_roi_align(self):
 
@@ -6239,7 +6225,6 @@ def forward(self, input, boxes):
 
         self.run_test(TransformModule(), (i, [boxes],), test_with_inputs=[(i, [boxes],), (i1, [boxes1],)])
 
-    @skipIfUnsupportedOpsetVersion([13])
     @skipIfUnsupportedMinOpsetVersion(11)
     def test_roi_heads(self):
         class RoiHeadsModule(torch.nn.Module):

torch/onnx/symbolic_helper.py

@@ -567,8 +567,10 @@ def __interpolate_helper(g, input, size, scale_factor, mode, align_corners, reco
 def _unbind_helper(g, self, dim, _outputs):
     if _export_onnx_opset_version <= 9:
         from torch.onnx.symbolic_opset9 import unbind
-    else:
+    elif _export_onnx_opset_version <= 12:
         from torch.onnx.symbolic_opset11 import unbind  # type: ignore[no-redef]
+    else:
+        from torch.onnx.symbolic_opset13 import unbind  # type: ignore[no-redef]
     return unbind(g, self, dim, _outputs)
 
 

torch/onnx/symbolic_opset13.py

@@ -5,7 +5,7 @@
 import torch
 import torch.onnx.symbolic_helper as sym_help
 from torch.onnx.symbolic_helper import parse_args, _unimplemented
-from torch.onnx.symbolic_opset9 import overload_by_arg_count, _maybe_cast_reduce_op_input
+from torch.onnx.symbolic_opset9 import overload_by_arg_count, _maybe_cast_reduce_op_input, nonzero
 
 
 # EDITING THIS FILE? READ THIS FIRST!
@@ -106,9 +106,20 @@ def unbind(g, self, dim=0, _outputs=None):
     return squeezed_outputs
 
 
-def glu(g, input, dim):
-    first, second = g.op('Split', input, dim, outputs=2)
-    return g.op('Mul', first, g.op('Sigmoid', second))
+# Emitted from `torch.nonzero(x, as_tuple=True)`
+def nonzero_numpy(g, input, _outputs=None):
+    return unbind(g, nonzero(g, input), 1, _outputs=_outputs)
+
+
+@parse_args('v', 'v', 'v', 'i')
+def where(g, condition, self=None, other=None, _outputs=None):
+    # Assumes that torch.where's first argument takes only Bool and Byte tensors.
+    if condition.type().scalarType() != 'Bool':
+        condition = g.op("Cast", condition, to_i=sym_help.cast_pytorch_to_onnx['Bool'])
+    if self is None:
+        condition = nonzero(g, condition)
+        return sym_help._unbind_helper(g, condition, g.op("Constant", value_t=torch.tensor(1)), _outputs)
+    return g.op("Where", condition, self, other)
 
 
 def _reduce_op_symbolic(onnx_op_name):