Revert "Milestone2.1: Partition to_dim_order_copy op in XNN delegate"

backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py

@@ -395,11 +395,6 @@ def call(self, graph_module: torch.fx.GraphModule):  # noqa: C901
                 # The node requires nchw inputs
                 for input_node in node.all_input_nodes:
                     self.input_to_nchw(graph_module, input_node, node)
-            elif node.target == exir_ops.edge.aten._to_copy.default:
-                if node.kwargs["memory_format"] == torch.channels_last:
-                    self.mark_as_nhwc_node(node)
-                else:
-                    self.mark_as_nchw_node(node)
             else:
                 # The node can have inputs in any format (but all must be the
                 # same format)

backends/xnnpack/partition/config/__init__.py

@@ -50,7 +50,6 @@
     SquareRootConfig,
     SubConfig,
     TanhConfig,
-    ToDimOrderCopyConfig,
     UpsampleBilinear2dConfig,
 )
 from executorch.backends.xnnpack.partition.config.node_configs import (
@@ -103,8 +102,6 @@
     ReciprocalSquareRootConfig,
     ReLUConfig,
     TanhConfig,
-    ToDimOrderCopyConfig,
-    # SDPAConfig, TODO: D60553559: preserving SDPA for fairseq fails
     SigmoidConfig,
     SliceCopyConfig,
     SoftmaxConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -397,35 +397,6 @@ def supported_precision_types(self) -> List[ConfigPrecisionType]:
         return [ConfigPrecisionType.FP32]
 
 
-class ToDimOrderCopyConfig(GenericNodePartitionerConfig):
-    target_name = "_to_dim_order_copy.default"
-
-    def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool:
-        """
-        Only support dim order conversion partitioning, not DType conversions
-        """
-        if not self.check_common_constraints(node, ep):
-            return False
-
-        # Get input node and compare dtypes
-        input_node = get_input_node(node, 0)
-        input_dtype = input_node.meta["val"].dtype
-        output_dtype = node.meta["val"].dtype
-
-        # Return False if doing dtype conversion
-        if input_dtype != output_dtype:
-            why(
-                node,
-                reason=f"dtype conversion from {input_dtype} to {output_dtype} is not supported",
-            )
-            return False
-
-        return True
-
-    def supported_precision_types(self) -> List[ConfigPrecisionType]:
-        return [ConfigPrecisionType.FP32]
-
-
 class MeanDimConfig(GenericNodePartitionerConfig):
     target_name = "mean.dim"
 

backends/xnnpack/test/passes/test_channels_last_tagged_reshape.py

@@ -173,23 +173,6 @@ def test_fp32_channels_last_tagged_reshape_pass(self):
                 .run_method_and_compare_outputs()
             )
 
-    class LinearConvDimSwap(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.conv1 = torch.nn.Conv2d(3, 3, 3)
-            self.linear1 = torch.nn.Linear(4, 3)
-
-        def forward(self, x):
-            y = self.linear1(x)
-            y = y.to(memory_format=torch.channels_last)
-            y = y.to(memory_format=torch.contiguous_format)
-            return self.conv1(y)
-
-    LinearConvDimSwapModule = LinearConvDimSwap()
-
-    def test_conv_linear_dim_order_swap_partitioner(self):
-        self.run_tester(self.LinearConvDimSwapModule, (torch.randn(1, 3, 6, 4),))
-
     def test_qs8_channels_last_tagged_reshape_pass(self):
         for module, num_reshape in self.modules.items():
             (