Add meta function for channel_shuffle operation (#123033)

This commit introduces a meta function for the `channel_shuffle` operation, enabling PyTorch to perform shape inference and optimizations related to this operation without actual computation. The meta function assumes input shape (*, C, H, W) and validates that the number of channels (C) is divisible by the specified number of groups.

Fixes #122771

Pull Request resolved: #123033
Approved by: https://github.com/ezyang, https://github.com/mikaylagawarecki

Author: Episkey0109

torch/_meta_registrations.py

@@ -6161,6 +6161,22 @@ def meta_polygamma(n: int, self: Tensor) -> Tensor:
     return torch.empty_like(self, dtype=result_dtype)
 
 
+@register_meta(aten.channel_shuffle.default)
+def meta_channel_shuffle(input, groups):
+    # Assume the input shape is (*, C, H, W), where * represents any number of leading dimensions
+    *leading_dims, C, H, W = input.size()
+    # The output shape is the same as the input
+    return torch.empty(
+        *leading_dims,
+        C,
+        H,
+        W,
+        dtype=input.dtype,
+        layout=input.layout,
+        device=input.device,
+    )
+
+
 def _create_unary_float_meta_func(func):
     @register_meta(func)
     @out_wrapper()

torch/testing/_internal/common_methods_invocations.py

@@ -8828,6 +8828,20 @@ def sample_inputs_pixel_unshuffle(op_info, device, dtype, requires_grad, **kwarg
         ]
     )
 
+def sample_inputs_channel_shuffle(op_info, device, dtype, requires_grad, **kwargs):
+    make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
+
+    shapes_groups = [
+        ((1, 4, 10, 10), 2),
+        ((2, 6, 8, 8), 3),
+        ((2, 8, 5, 5), 4),
+    ]
+
+    yield from (
+        SampleInput(make_arg(shape), args=(groups,))
+        for shape, groups in shapes_groups
+    )
+
 def sample_inputs_binary_cross_entropy(op_info, device, dtype, requires_grad, logits=False, **kwargs):
     make = partial(make_tensor, device=device, dtype=dtype)
     # Lower bounds must be greater than 'eps' defined in gradcheck.py::gradgradcheck() -> eps
@@ -19610,6 +19624,25 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
             ),
         ),
     ),
+    OpInfo(
+        "nn.functional.channel_shuffle",
+        sample_inputs_func=sample_inputs_channel_shuffle,
+        dtypes=all_types_and(torch.bool, torch.float16, torch.bfloat16),
+        backward_dtypes=integral_types_and(torch.bool),
+        supports_out=False,
+        supports_autograd=False,
+        allow_cow_input_materialize_forward=[0],
+        skips=(
+            # Skip due to NotImplementedError for MPS device.
+            DecorateInfo(unittest.expectedFailure, 'TestConsistency'),
+            # vmap: calling random operator not supported
+            DecorateInfo(unittest.skip("Test expects tensor input"), "TestVmapOperatorsOpInfo", "test_vmap_exhaustive"),
+            DecorateInfo(unittest.skip("Test expects tensor input"), "TestVmapOperatorsOpInfo", "test_op_has_batch_rule"),
+            DecorateInfo(unittest.expectedFailure, 'TestInductorOpInfo', 'test_comprehensive'),
+            DecorateInfo(unittest.expectedFailure, 'TestDTensorOps', 'test_dtensor_op_db'),
+            DecorateInfo(unittest.expectedFailure, "TestMeta", "test_dispatch_symbolic_meta_outplace_all_strides"),
+        ),
+    ),
     OpInfo(
         "nn.functional.kl_div",
         sample_inputs_func=sample_inputs_kl_div,