[pt2] add meta for _adaptive_avg_pool3d_backward (pytorch#105816)

Pull Request resolved: pytorch#105816
Approved by: https://github.com/ezyang

test/functorch/test_aotdispatch.py

@@ -2817,7 +2817,6 @@ def forward(self, x):
     xfail('masked_select', ''),  # aten.masked_select.default - couldn't find symbolic meta function/decompos...
     xfail('median', ''),  # could not find kernel
     xfail('mode', ''),  # Cannot call sizes() on tensor with symbolic sizes/strides
-    xfail('nn.functional.adaptive_avg_pool3d', ''),  # aten._adaptive_avg_pool3d_backward.default - couldn't ...
     xfail('nn.functional.adaptive_max_pool2d', ''),  # aten.adaptive_max_pool2d.default - couldn't find symbo...
     xfail('nn.functional.adaptive_max_pool3d', ''),  # argument 'output_size' (position 2...
     skip('nn.functional.batch_norm', ''),  # '0 is not tracked with proxy for <torch.fx.experimental.proxy_te..
@@ -2829,7 +2828,6 @@ def forward(self, x):
     xfail('nn.functional.fractional_max_pool3d', ''),  # rand() received an invalid combination of arguments - g...
     xfail('nn.functional.grid_sample', ''),  # RuntimeError: aten.grid_sampler_3d.default - couldn't find sym ...
     xfail('nn.functional.group_norm', ''),  # Cannot call sizes() on tensor with symbolic sizes/strides
-    xfail('nn.functional.interpolate', 'area'),  # Cannot call sizes() on tensor with symbolic sizes/strides
     xfail('nn.functional.interpolate', 'linear'),  # Cannot call sizes() on tensor with symbolic sizes/strides
     xfail('nn.functional.interpolate', 'trilinear'),  # Cannot call sizes() on tensor with symbolic sizes/st...
     xfail('nn.functional.nll_loss', ''),  # Cannot call sizes() on tensor with symbolic sizes/strides
@@ -2966,8 +2964,6 @@ def test_aot_autograd_symbolic_exhaustive(self, device, dtype, op):
     torch.nn.Transformer,  # DataDependentOutputException: aten.equal compares a mask input to a mask producing a bool
     torch.nn.TransformerEncoder,  # DataDependentOutputException: aten.equal compares a mask input to a mask producing a bool
     torch.nn.GaussianNLLLoss,  # NotImplementedError: local_scalar_dense/item NYI for torch.bool
-    torch.nn.AdaptiveAvgPool3d,  # could not find kernel for aten._adaptive_avg_pool3d_backward.default at dispatch key
-                                 # DispatchKey.Meta
     torch.nn.AdaptiveMaxPool2d,  # Cannot call sizes() on tensor with symbolic sizes/strides
     torch.nn.AdaptiveMaxPool3d,  # Cannot call sizes() on tensor with symbolic sizes/strides
     torch.nn.GroupNorm,  # in native_group_norm_backward cpg, _rem = divmod(C, group)

torch/_meta_registrations.py

@@ -2241,6 +2241,13 @@ def meta__adaptive_avg_pool2d_backward(grad_out, self):
     return self.new_empty(self.shape).to(memory_format=memory_format)
 
 
+@register_meta(aten._adaptive_avg_pool3d_backward)
+@out_wrapper()
+def meta__adaptive_avg_pool3d_backward(grad_output, self):
+    _adaptive_pool_empty_output_check(grad_output, "adaptive_avg_pool3d_backward")
+    return torch.empty_like(self, memory_format=torch.legacy_contiguous_format)
+
+
 def _adaptive_pool_empty_output_check(grad_output: Tensor, arg_name: str):
     ndim = grad_output.ndim
     for i in range(1, ndim):