Skip to content

NXP backend: added support for aten.view_copy using new MLIR Neutron flow #20626

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
novak-vaclav wants to merge 1 commit into
base: main
Choose a base branch
from
+191 −426
Open

NXP backend: added support for aten.view_copy using new MLIR Neutron flow #20626

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
99 changes: 0 additions & 99 deletions backends/nxp/backend/ir/converter/node_converters/ops_converters/view_copy_converter.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand All @@ -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


Expand All @@ -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'.
Expand Down
Loading
Loading