dynamic shape support for interpolate(antialias=True) backward (#141198)

Fixes https://github.com/pytorch/pytorch/issues/141187

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141198
Approved by: https://github.com/ezyang, https://github.com/Chillee
ghstack dependencies: #141161

test/functorch/test_aotdispatch.py

@@ -6501,9 +6501,6 @@ symbolic_aot_autograd_failures = {
         "nn.functional.nll_loss", ""
     ),  # Cannot call sizes() on tensor with symbolic sizes/strides
     xfail("trace", ""),  # Cannot call sizes() on tensor with symbolic sizes/strides
-    xfail(
-        "_upsample_bilinear2d_aa"
-    ),  # RuntimeError: isIntList() INTERNAL ASSERT FAILED  Expected IntList but got GenericList
     decorate(
         "linalg.householder_product",
         decorator=unittest.skipIf(IS_MACOS and IS_X86, "flaky"),

test/test_fake_tensor.py

@@ -1954,7 +1954,6 @@ class FakeTensorDispatchCache(TestCase):
                 extract_tensor_metadata(res4),
             )
 
-
     @unittest.skipIf(not RUN_CUDA, "requires cuda")
     def test_wrapper_tensor_subclass_different_device(self):
         class DifferentDeviceTensor(torch.Tensor):
@@ -2007,6 +2006,29 @@ class FakeTensorDispatchCache(TestCase):
         assert isinstance(fake_wrapped_a, DifferentDeviceTensor)
         self.assertFalse(fake_wrapped_a.inner_tensor.is_cpu)
 
+    def test__upsample_bilinear2d_aa_backward_dynamic_shapes(self):
+        def f(x):
+            return torch.nn.functional.interpolate(
+                x,
+                size=[256, 256],
+                mode='bilinear',
+                align_corners=False,
+                antialias=True,
+            )
+
+        shape_env = ShapeEnv()
+        fake_m = FakeTensorMode(shape_env=shape_env)
+        x = fake_m.from_tensor(
+            torch.randn(1, 3, 2005, 1920, requires_grad=True),
+            symbolic_context=StatelessSymbolicContext(
+                dynamic_sizes=[DimDynamic.STATIC, DimDynamic.STATIC, DimDynamic.DYNAMIC, DimDynamic.DYNAMIC],
+                constraint_sizes=[None, None, None, None]
+            ),
+        )
+        with fake_m, enable_python_dispatcher():
+            out = f(x)
+            out.sum().backward()
+            self.assertEqual(x.shape, x.grad.shape)
 
     def test_cache_tuple_outputs(self):
         """

torch/_meta_registrations.py

@@ -6535,6 +6535,34 @@ def meta_upsample_bimode2d_aa(
     )
 
 
+@register_meta([aten._upsample_bilinear2d_aa_backward.default])
+def meta_upsample_bimode2d_aa_backward(
+    grad_output,
+    output_size,
+    input_size,
+    align_corners,
+    scales_h=None,
+    scales_w=None,
+):
+    full_output_size = upsample_common_check(
+        input_size, output_size, num_spatial_dims=2
+    )
+    torch._check(
+        grad_output.ndim == 4,
+        lambda: f"Expected grad_output to be a tensor of dimension 4 but got: dimension {grad_output.ndim}",
+    )
+    for i in range(4):
+        torch._check(
+            grad_output.shape[i] == full_output_size[i],
+            lambda: f"""
+Expected grad_output to have the same shape as output; output.size({i}) = {full_output_size[i]}
+but got grad_output_size({i}) = {grad_output.size(i)}""",
+        )
+    return grad_output.new_empty(input_size).to(
+        memory_format=utils.suggest_memory_format(grad_output)
+    )
+
+
 # From aten/src/ATen/native/cuda/AmpKernels.cu
 @register_meta(aten._amp_foreach_non_finite_check_and_unscale_.default)
 def _amp_foreach_non_finite_check_and_unscale_(self, found_inf, inv_scale):

torch/testing/_internal/common_methods_invocations.py

@@ -15660,7 +15660,6 @@ op_db: List[OpInfo] = [
            skips=(
                DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'),
                DecorateInfo(unittest.expectedFailure, 'TestDTensorOps', 'test_dtensor_op_db'),
-               DecorateInfo(unittest.expectedFailure, 'TestEagerFusionOpInfo', 'test_aot_autograd_symbolic_exhaustive'),
                DecorateInfo(unittest.expectedFailure, 'TestInductorOpInfo', 'test_comprehensive'),
                DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'),
            )),