NXP backend: added support for aten.view_copy using new MLIR Neutron flow

### Summary

Added support for `aten.view_copy` using new Neutron flow.

### Test plan

tests can be manually run using `pytest -c /dev/null
backends/nxp/tests/`

cc @robert-kalmar @JakeStevens @digantdesai @rascani @MartinPavella

Author: novak-vaclav

backends/nxp/backend/ir/converter/node_converters/ops_converters/view_copy_converter.py

@@ -4,21 +4,14 @@
 # LICENSE file in the root directory of this source tree.
 
 import numpy as np
-from executorch.backends.nxp.backend.data_format import NXP_NODE_FORMAT
 
 from executorch.backends.nxp.backend.edge_helper import (
-    get_non_qdq_users,
     input_tensor,
     output_tensor,
     tensor_rank,
 )
 from executorch.backends.nxp.backend.ir.converter import quantization_utils
 from executorch.backends.nxp.backend.ir.converter.conversion.common import OpsList
-from executorch.backends.nxp.backend.ir.converter.conversion.translator import (
-    apply_permutation_to,
-    create_channels_first_to_channels_last_permutation,
-    create_channels_last_to_channels_first_permutation,
-)
 from executorch.backends.nxp.backend.ir.converter.node_converter import (
     CustomDelegationOptions,
     NodeConverter,
@@ -29,13 +22,7 @@
 from executorch.backends.nxp.backend.ir.tflite_generator.builtin_options import (
     reshape_options,
 )
-from executorch.backends.nxp.backend.neutron_operator_support import (
-    transposition_is_supported_on_neutron,
-)
-from executorch.backends.nxp.backend.neutron_target_spec import NeutronTargetSpec
-from executorch.exir.dialects._ops import ops as exir_ops
 from torch.fx import Node
-from torch.fx.passes.infra.partitioner import Partition
 from torch.nn import Parameter
 
 
@@ -58,92 +45,6 @@ def _is_supported_in_IR(
 
         return True
 
-    @classmethod
-    def supports_partitioning_result(
-        cls,
-        node: Node,
-        partition_list: list[Partition],
-        custom_delegation_options: CustomDelegationOptions,
-        neutron_target_spec: NeutronTargetSpec,
-        parameters_mapping: dict[str, Parameter],
-    ):
-        view_copy_partitions = [
-            partition for partition in partition_list if node in partition.nodes
-        ]
-        assert len(view_copy_partitions) == 1
-
-        input_format = node.args[0].meta[NXP_NODE_FORMAT]
-        output_format = node.meta[NXP_NODE_FORMAT]
-        input_shape = list(node.args[0].meta["val"].shape)
-        output_shape = list(node.meta["val"].shape)
-        to_nchw_perm = create_channels_last_to_channels_first_permutation(
-            len(input_shape), True
-        )
-        to_nhwc_perm = create_channels_first_to_channels_last_permutation(
-            len(output_shape), True
-        )
-        channels_last_input_shape = apply_permutation_to(
-            input_shape,
-            create_channels_first_to_channels_last_permutation(len(input_shape), True),
-        )
-
-        if input_format.is_channels_first() and (not output_format.is_channels_first()):
-            # The `view_copy` removes node format. Conversion will require the addition of a `Transpose` operator.
-            # Make sure the `Transpose` will be supported.
-
-            if not transposition_is_supported_on_neutron(
-                channels_last_input_shape, to_nchw_perm, neutron_target_spec
-            ):
-                # The `Transpose` would have to be removed by the `PermuteFullyConnectedWeightsAfterReshape` pass.
-                # Make sure it will be applied.
-                users = get_non_qdq_users(node)
-                if len(users) != 1 or (linear_node := users[0]).target not in [
-                    exir_ops.edge.aten.addmm.default,
-                    exir_ops.edge.aten.mm.default,
-                ]:
-                    return False
-
-                if linear_node not in view_copy_partitions[0].nodes:
-                    # The `mm` / `addmm` node will not be delegated within this partition.
-                    return False
-
-                # Make sure the specific requirements of the `PermuteFullyConnectedWeightsAfterReshape` are satisfied.
-                weights_index = (
-                    2 if linear_node.target == exir_ops.edge.aten.addmm.default else 1
-                )
-                if not (
-                    input_shape[0] == output_shape[0]  # Preserve batch.
-                    and len(output_shape) == 2
-                    and output_shape[1]
-                    == linear_node.args[weights_index].meta["val"].shape[0]
-                ):
-                    return False
-
-        elif (
-            not input_format.is_channels_first()
-        ) and output_format.is_channels_first():
-            # The `view_copy` introduces node format. Conversion will require the addition of a `Transpose` operator.
-            # Make sure the `Transpose` will be supported.
-            if not transposition_is_supported_on_neutron(
-                output_shape, to_nhwc_perm, neutron_target_spec
-            ):
-                return False
-
-        elif input_format.is_channels_first() and output_format.is_channels_first():
-            # The `view_copy` works with the channels first format, so both tensors will end up being transposed.
-            # Make sure these transpositions are supported.
-            if not (
-                transposition_is_supported_on_neutron(
-                    channels_last_input_shape, to_nchw_perm, neutron_target_spec
-                )
-                and transposition_is_supported_on_neutron(
-                    output_shape, to_nhwc_perm, neutron_target_spec
-                )
-            ):
-                return False
-
-        return True
-
     @staticmethod
     def _safe_compute_flat_size(shape: list[int | str]) -> int:
         """Compute the flat size of a tensor with given shape. Strings and negative dimensions are treated as '1'.