[torchlib] Improve pixel_shuffle (#2537)

Simplify the graph when input rank is 4, in which case we don't need to
do any shape manipulation.

Fix pytorch/pytorch#162061

---------

Signed-off-by: Justin Chu <justinchuby@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: justinchuby

onnxscript/function_libs/torch_lib/ops/core.py

@@ -6691,34 +6691,41 @@ def aten_pinverse(self: TensorType, rcond: float = 1e-15) -> TensorType:
     raise NotImplementedError()
 
 
-@torch_op("aten::pixel_shuffle")
+@torch_op("aten::pixel_shuffle", trace_only=True)
 def aten_pixel_shuffle(self: TReal, upscale_factor: int) -> TReal:
     """pixel_shuffle(Tensor self, int upscale_factor) -> Tensor"""
-    self_shape = op.Shape(self)
-    batch_dims = self_shape[:-3]
-    chw_in_dims = self_shape[-3:]
+    if len(self.shape) == 4:
+        return op.DepthToSpace(self, blocksize=upscale_factor, mode="CRD")
+
     # Reshaping input by collapsing all leading dimensions to match ONNX op requirement (4D)
+    batch_dims = op.Shape(self, end=-3)
+    chw_in_dims = op.Shape(self, start=-3)
+
     reshaped_self = op.Reshape(
         self, op.Concat(op.Constant(value_ints=[-1]), chw_in_dims, axis=0)
     )
     depth_to_space = op.DepthToSpace(reshaped_self, blocksize=upscale_factor, mode="CRD")
-    output_shape = op.Concat(batch_dims, op.Shape(depth_to_space)[1:], axis=0)
+    final_dims = op.Shape(depth_to_space, start=1)
+    output_shape = op.Concat(batch_dims, final_dims, axis=0)
     return op.Reshape(depth_to_space, output_shape, allowzero=True)
 
 
-@torch_op("aten::pixel_unshuffle")
+@torch_op("aten::pixel_unshuffle", trace_only=True)
 def aten_pixel_unshuffle(self: TReal, downscale_factor: int) -> TReal:
     """pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor"""
+    if len(self.shape) == 4:
+        return op.SpaceToDepth(self, blocksize=downscale_factor)
 
-    self_shape = op.Shape(self)
-    batch_dims = self_shape[:-3]
-    chw_in_dims = self_shape[-3:]
     # Reshaping input by collapsing all leading dimensions to match ONNX op requirement (4D)
+    batch_dims = op.Shape(self, end=-3)
+    chw_in_dims = op.Shape(self, start=-3)
+
     reshaped_self = op.Reshape(
         self, op.Concat(op.Constant(value_ints=[-1]), chw_in_dims, axis=0)
     )
     space_to_depth = op.SpaceToDepth(reshaped_self, blocksize=downscale_factor)
-    output_shape = op.Concat(batch_dims, op.Shape(space_to_depth)[1:], axis=0)
+    final_dims = op.Shape(space_to_depth, start=1)
+    output_shape = op.Concat(batch_dims, final_dims, axis=0)
     return op.Reshape(space_to_depth, output_shape, allowzero=True)
 
 

tests/function_libs/torch_lib/ops_test_data.py

@@ -1084,26 +1084,16 @@ def _where_input_wrangler(
     TorchLibOpInfo(
         "nn.functional.pixel_shuffle",
         core_ops.aten_pixel_shuffle,
-    )
-    .xfail(
+    ).xfail(
         dtypes=(torch.int32, torch.int64),
         reason="fixme: ONNX Runtime does not support int32/64 inputs",
-    )
-    .xfail(
-        matcher=lambda sample: sample.input.numel() == 0,
-        reason="fixme: ORT does not support empty tensor as input",
     ),
     TorchLibOpInfo(
         "nn.functional.pixel_unshuffle",
         core_ops.aten_pixel_unshuffle,
-    )
-    .xfail(
+    ).xfail(
         dtypes=(torch.int32, torch.int64),
         reason="fixme: ONNX Runtime does not support int32/64 inputs",
-    )
-    .xfail(
-        matcher=lambda sample: sample.input.numel() == 0,
-        reason="fixme: ORT does not support empty tensor as input",
     ),
     TorchLibOpInfo(
         "ops.aten.reflection_pad1d",