[codemod] markDynamoStrictTest batch 14 (#117133)

[codemod] markDynamoStrictTest test_utils
[codemod] markDynamoStrictTest test_unary_ufuncs
[codemod] markDynamoStrictTest test_sparse_semi_structured
[codemod] markDynamoStrictTest test_sparse_csr
[codemod] markDynamoStrictTest test_sparse
[codemod] markDynamoStrictTest test_reductions
[codemod] markDynamoStrictTest test_proxy_tensor
[codemod] markDynamoStrictTest test_prims
[codemod] markDynamoStrictTest test_maskedtensor
[codemod] markDynamoStrictTest test_masked
[codemod] markDynamoStrictTest test_legacy_vmap
[codemod] markDynamoStrictTest test_binary_ufuncs

Pull Request resolved: https://github.com/pytorch/pytorch/pull/117133
Approved by: https://github.com/voznesenskym
ghstack dependencies: #117114, #117127, #117128, #117129

.ci/pytorch/test.sh

@@ -277,7 +277,6 @@ test_dynamo_shard() {
       test_ao_sparsity \
       test_autograd \
       test_jit \
-      test_proxy_tensor \
       test_quantization \
       test_public_bindings \
       test_dataloader \

test/test_binary_ufuncs.py

@@ -1331,6 +1331,7 @@ class TestBinaryUfuncs(TestCase):
                 self.assertEqual(res1, res2)
                 self.assertEqual(res1.dtype, expected_dtype)
 
+    @skipIfTorchDynamo("ConstantVariable(list) is banned")
     @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
     def test_pow(self, device, dtype):
         m1 = torch.empty(0, dtype=dtype, device=device)
@@ -1646,6 +1647,7 @@ class TestBinaryUfuncs(TestCase):
             cpu_out = t.cpu().pow(2)
             self.assertEqual(cpu_out, cuda_out)
 
+    @skipIfTorchDynamo()
     @onlyNativeDeviceTypes
     @dtypes(*all_types_and_complex_and(torch.half))
     def test_complex_scalar_pow_tensor(self, device, dtype):
@@ -3980,6 +3982,7 @@ class TestBinaryUfuncs(TestCase):
             doubles, sz, lambda input, out: torch.pow(42, input, out=out)
         )
 
+    @skipIfTorchDynamo("ConstantVariable(list) is banned")
     @dtypes(
         *list(
             product(
@@ -4087,6 +4090,7 @@ class TestBinaryUfuncs(TestCase):
             torch.float_power(i, exp, out=out)
             self.assertEqual(expected_scalar_base, out)
 
+    @skipIfTorchDynamo("ConstantVariable(list) is banned")
     def test_float_power_exceptions(self, device):
         def _promo_helper(x, y):
             for i in (x, y):

test/test_sparse_csr.py

@@ -1271,6 +1271,7 @@ class TestSparseCSR(TestCase):
                 new_shape = (2, 2)
                 a.resize_(new_shape)
 
+    @skipIfTorchDynamo()
     @dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
     def test_sparse_csr_from_dense(self, device, dtype):
         dense = torch.tensor([[4, 5, 0], [0, 0, 0], [1, 0, 0]], dtype=dtype, device=device)