If a torch.* returns non-Tensor, make this unimplemented rather than assert. (#89918)

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/89918
Approved by: https://github.com/albanD

test/ao/sparsity/test_kernels.py

@@ -246,6 +246,7 @@ class SparseQuantizedModel(nn.Module):
 
 class TestQuantizedSparseLayers(TestCase):
     @override_qengines
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_sparse_qlinear(self):
         # Note: At the moment, for sparse kernels
         # fbgemm supports only static quantized sparse linear
@@ -278,6 +279,7 @@ class TestQuantizedSparseLayers(TestCase):
         )
 
     @override_qengines
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_sparse_qlinear_serdes(self):
         # Note: At the moment, for sparse kernels
         # fbgemm supports only static quantized sparse linear

test/jit/test_list_dict.py

@@ -2552,6 +2552,7 @@ class TestScriptList(JitTestCase):
         with self.assertRaises(TypeError):
             script_data.append("str")
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_clear(self):
         """
         Test clear.

test/test_cpp_extensions_aot.py

@@ -8,7 +8,7 @@ from typing import Union
 import unittest
 
 import torch.testing._internal.common_utils as common
-from torch.testing._internal.common_utils import IS_WINDOWS
+from torch.testing._internal.common_utils import IS_WINDOWS, skipIfTorchDynamo
 from torch.testing._internal.common_cuda import TEST_CUDA
 import torch
 import torch.backends.cudnn
@@ -295,6 +295,7 @@ class TestRNGExtension(common.TestCase):
     def setUp(self):
         super(TestRNGExtension, self).setUp()
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_rng(self):
         fourty_two = torch.full((10,), 42, dtype=torch.int64)
 

test/test_fake_tensor.py

@@ -1,6 +1,6 @@
 # Owner(s): ["module: meta tensors"]
 
-from torch.testing._internal.common_utils import TestCase, run_tests, skipIfCrossRef, skipIfRocm
+from torch.testing._internal.common_utils import TestCase, run_tests, skipIfCrossRef, skipIfRocm, skipIfTorchDynamo
 import torch
 import torch._dynamo
 import itertools
@@ -557,6 +557,7 @@ class FakeTensorConverterTest(TestCase):
         y_conv = converter(mode, y)
         self.assertEqual(torch._C._storage_id(x_conv), torch._C._storage_id(y_conv))
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_separate_tensor_storages_non_view(self):
         x = torch.rand(2, 2, 2)
         y = torch.rand(4, 2)
@@ -577,6 +578,7 @@ class FakeTensorConverterTest(TestCase):
         self.assertEqual(len(converter.meta_converter.storage_memo), 0)
 
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_dead_weak_ref(self):
         x = torch.rand(2, 2, 2)
         y = x[0]
@@ -589,6 +591,7 @@ class FakeTensorConverterTest(TestCase):
         y_conv = converter(mode, y)
         self.assertEqual(x_conv_storage, torch._C._storage_id(y_conv))
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_dead_key(self):
         x = torch.rand(2, 2, 2)
         mode = FakeTensorMode()
@@ -617,6 +620,7 @@ class FakeTensorConverterTest(TestCase):
             y = torch.empty(2, 2, device="cpu")
         self.assertRaises(Exception, lambda: x, y)
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_no_ref_cycle(self):
         x = torch.rand([4])
         mode = FakeTensorMode()

test/test_meta.py

@@ -12,6 +12,7 @@ from torch._dispatch.python import enable_python_dispatcher
 from torch.testing._internal.common_utils import (
     TestCase,
     skipIfCrossRef,
+    skipIfTorchDynamo,
     suppress_warnings,
     TEST_WITH_ASAN,
     run_tests,
@@ -251,6 +252,7 @@ class TestMetaConverter(TestCase):
         m = MetaConverter()(y)
         self.assertMetadataMatches(m, y)
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_weakref(self):
         x = torch.randn(4, 4, 4)
         m = MetaConverter()
@@ -274,6 +276,7 @@ class TestMetaConverter(TestCase):
         m.check_for_expired_weak_storages()
         self.assertEqual(len(m.storage_memo), 0)
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_tensor_outlives_converter(self):
         m = MetaConverter()
         ref = weakref.ref(m)

test/test_sparse.py

@@ -235,6 +235,7 @@ class TestSparse(TestSparseBase):
     @coalescedonoff
     @dtypes(torch.double, torch.cdouble, torch.bfloat16)
     @precisionOverride({torch.bfloat16: 1e-2})
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     def test_coalesce(self, device, dtype, coalesced):
 
         def _test_coalesce(t):

test/test_tensor_creation_ops.py

@@ -2390,6 +2390,7 @@ class TestTensorCreation(TestCase):
         self.assertEqual(d.shape[0], 800)
 
     # TODO: this test should be updated
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     @onlyCPU
     def test_arange_inference(self, device):
         saved_dtype = torch.get_default_dtype()

test/test_torch.py

@@ -222,6 +222,7 @@ class TestTorchDeviceType(TestCase):
         s[2:7] = 1
         self.assertEqual(s, storage_type(l))
 
+    @skipIfTorchDynamo("https://github.com/pytorch/torchdynamo/issues/1991")
     @onlyNativeDeviceTypes
     @dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
     def test_tensor_storage_type(self, device, dtype):

torch/_dynamo/variables/builder.py

@@ -919,7 +919,7 @@ def wrap_fx_proxy_cls(
         proxy.node.meta["example_value"] = example_value
         return DynamicShapeVariable(proxy, example_value, **options)
     else:
-        raise AssertionError(
+        unimplemented(
             "torch.* op returned non-Tensor "
             + f"{typestr(example_value)} {proxy.node.op} {proxy.node.target}"
         )