Revert D68246404

Differential Revision: D68780760

Pull Request resolved: #8002

Author: Gasoonjia

backends/cadence/aot/compiler.py

@@ -33,7 +33,6 @@
     ExecutorchProgramManager,
     to_edge,
 )
-from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import PassResult
 from executorch.exir.passes import ToOutVarPass
 from executorch.exir.passes.sym_shape_eval_pass import HintBasedSymShapeEvalPass
@@ -187,17 +186,14 @@ def export_to_edge(
     edge_prog_manager = to_edge(
         expo_program,
         compile_config=EdgeCompileConfig(
+            _skip_dim_order=True,
             # Allow specific non-core aten ops in the IR.
             _core_aten_ops_exception_list=[
                 torch.ops.aten._native_batch_norm_legit_functional.default,
                 torch.ops.aten.linear.default,
                 torch.ops.aten.linalg_vector_norm.default,
                 torch.ops.aten.unfold.default,
                 torch.ops.aten.angle.default,
-                # cadence replaced to_dim_order_copy with _to_copy for performance
-                # skip _to_copy op to get around of dim order check
-                # We should remove this op once cadence can support dim order
-                exir_ops.edge.aten._to_copy.default,
             ],
         ),
         constant_methods=constant_methods,

backends/cadence/aot/replace_ops.py

@@ -11,7 +11,6 @@
 
 # pyre-unsafe
 
-import copy
 import math
 from operator import neg
 from typing import cast, Dict, Iterable, Sequence, Set, Tuple
@@ -36,12 +35,7 @@
 from executorch.backends.cadence.aot.utils import get_edge_overload_packet
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.dialects.edge._ops import EdgeOpOverload, EdgeOpOverloadPacket
-from executorch.exir.dim_order_utils import get_memory_format
 from executorch.exir.pass_base import ExportPass, NodeMetadata, PassResult, ProxyValue
-from executorch.exir.passes.dim_order_ops_registry import (
-    DimOrderOpsMap,
-    MemoryFormatOpsMap,
-)
 from torch._subclasses import FakeTensor
 from torch.fx.node import Argument
 
@@ -1805,72 +1799,6 @@ def call_operator(
         )
 
 
-@register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceToDimOrderCopyWithToCopyPass(ExportPass):
-    """
-    dim_order_ops::to_dim_order_copy is not supported, so this is an opt_level=0 pass.
-    If the dim order is sequential, we don't need the extra work with strides and
-    can just use to_copy.
-    """
-
-    def call_operator(
-        self,
-        op,
-        args: Tuple[Argument, ...],
-        kwargs: Dict[str, Argument],
-        meta: NodeMetadata,
-    ) -> ProxyValue:
-        if op not in DimOrderOpsMap:
-            return super().call_operator(op, args, kwargs, meta)
-
-        # new kwargs with dim_order, and no memory_format for the new op
-        nkwargs = dict(copy.deepcopy(kwargs))  # orig kwargs are immutable
-
-        ndim = None
-
-        # can always get the shape, assuming rank is specialized
-
-        # pyre-ignore[16]: `None` has no attribute `to_tensor`
-        if isinstance(args[0], ProxyValue) and args[0].is_tensor():
-            # pyre-ignore[16]: `None` has no attribute `to_tensor`
-            ndim = args[0].to_tensor().dim()
-        elif isinstance(args[0], torch.Tensor):
-            # pyre-ignore[16]: `None` has no attribute `dim`
-            ndim = args[0].dim()
-        elif isinstance(args[0], torch.fx.immutable_collections.immutable_list):
-            # pyre-ignore[6]: Incompatible parameter type
-            ndim = len(args[0])
-        else:
-            assert 0, f"Expecting a Tensor or a ProxyValue but got {type(args[0])}"
-
-        # get the "to" memory format for the EdgeOp
-        contiguous_dim_order = list(range(ndim))
-        dim_order = nkwargs.pop("dim_order", None)
-
-        # Cadence only supports contiguous memory format
-        assert (
-            dim_order is None
-            # pyre-ignore[6]: Incompatible parameter type
-            or len(dim_order) == 0
-            or dim_order == contiguous_dim_order
-        ), "Expected dim order in congituous or prevserve memory format, but got {}".format(
-            dim_order
-        )
-
-        # bring back memory format
-        # pyre-ignore[6]: Incompatible parameter type
-        nkwargs["memory_format"] = get_memory_format(dim_order)
-
-        memory_format_op = MemoryFormatOpsMap[op]
-
-        return super().call_operator(
-            memory_format_op,
-            args,
-            nkwargs,
-            meta,
-        )
-
-
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
 class ReplaceFullLikeWithFullPass(ExportPass):
     """
@@ -2180,5 +2108,4 @@ class CadenceReplaceOpsInGraph:
         ReplaceSingleElementTensorArgumentsFromFullOpWithScalarPass,
         ReplaceAtenAvgPoolWithJarvisAvgPoolPass,
         ReplaceAtenLinalgVectorNormWithCadenceLinalgVectorNormPass,
-        ReplaceToDimOrderCopyWithToCopyPass,
     ]