turn on dim order in cadence test (pytorch#7756)

Summary:


This diff turns on dim order in cadence test. Also we get around `to_copy` operator in verifier to keep the verifier check enable.

Reviewed By: digantdesai, mcremon-meta

Differential Revision: D68246404

backends/cadence/aot/compiler.py

@@ -33,6 +33,7 @@
     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
@@ -186,14 +187,17 @@ 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

@@ -11,6 +11,7 @@
 
 # pyre-unsafe
 
+import copy
 import math
 from operator import neg
 from typing import cast, Dict, Iterable, Sequence, Set, Tuple
@@ -1799,6 +1800,46 @@ 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 != exir_ops.edge.dim_order_ops._to_dim_order_copy.default:
+            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
+
+        assert args[0] == range(
+            # pyre-ignore[16]: `None` has no attribute `to_tensor`.
+            args[0].to_tensor().dim()
+        ), "Only sequential dims supported"
+
+        # remove dim_order from kwargs
+        nkwargs.pop("dim_order", None)
+
+        # bring back memory format
+        nkwargs["memory_format"] = torch.contiguous_format
+
+        return super().call_operator(
+            exir_ops.edge.aten._to_copy.default,
+            args,
+            nkwargs,
+            meta,
+        )
+
+
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
 class ReplaceFullLikeWithFullPass(ExportPass):
     """
@@ -2108,4 +2149,5 @@ class CadenceReplaceOpsInGraph:
         ReplaceSingleElementTensorArgumentsFromFullOpWithScalarPass,
         ReplaceAtenAvgPoolWithJarvisAvgPoolPass,
         ReplaceAtenLinalgVectorNormWithCadenceLinalgVectorNormPass,
+        ReplaceToDimOrderCopyWithToCopyPass,
     ]

extension/flat_tensor/serialize/flat_tensor_schema.py

@@ -14,6 +14,7 @@
 # Note: check executorch/extension/data_format/flat_tensor.fbs for explanations of these fields.
 
 
+
 @dataclass
 class TensorMetadata:
     fully_qualified_name: str