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Align aot_arm_compiler to latest export flow #6327

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Align aot_arm_compiler to latest export flow #6327

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70 changes: 39 additions & 31 deletions examples/arm/aot_arm_compiler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,12 @@
from executorch.backends.arm.util.arm_model_evaluator import GenericModelEvaluator

from executorch.devtools.backend_debug import get_delegation_info
from executorch.exir import EdgeCompileConfig, ExecutorchBackendConfig
from executorch.extension.export_util.utils import export_to_edge, save_pte_program
from executorch.exir import (
EdgeCompileConfig,
ExecutorchBackendConfig,
to_edge_transform_and_lower,
)
from executorch.extension.export_util.utils import save_pte_program
from tabulate import tabulate

# Quantize model if required using the standard export quantizaion flow.
Expand Down Expand Up @@ -170,7 +174,9 @@ def forward(self, x):
]


def get_compile_spec(target: str, intermediates: bool) -> ArmCompileSpecBuilder:
def get_compile_spec(
target: str, intermediates: Optional[str] = None
) -> ArmCompileSpecBuilder:
spec_builder = None
if target == "TOSA":
spec_builder = (
Expand All @@ -185,7 +191,7 @@ def get_compile_spec(target: str, intermediates: bool) -> ArmCompileSpecBuilder:
memory_mode="Shared_Sram",
extra_flags="--debug-force-regor --output-format=raw",
)
.set_permute_memory_format(args.model_name in MODEL_NAME_TO_MODEL.keys())
.set_permute_memory_format(True)
.set_quantize_io(True)
)
elif "ethos-u85" in target:
Expand All @@ -202,7 +208,7 @@ def get_compile_spec(target: str, intermediates: bool) -> ArmCompileSpecBuilder:
)

if intermediates is not None:
spec_builder.dump_intermediate_artifacts_to(args.intermediates)
spec_builder.dump_intermediate_artifacts_to(intermediates)

return spec_builder.build()

Expand Down Expand Up @@ -356,40 +362,42 @@ def get_args():
model, example_inputs = get_model_and_inputs_from_name(args.model_name)
model = model.eval()

# export_for_training under the assumption we quantize, the exported form also works
# in to_edge if we don't quantize
exported_program = torch.export.export_for_training(model, example_inputs)
model = exported_program.module()
model_fp32 = model

# pre-autograd export. eventually this will become torch.export
model = torch.export.export_for_training(model, example_inputs).module()

# Quantize if required
model_int8 = None
if args.quantize:
model = quantize(model, example_inputs)
model_int8 = model
# Wrap quantized model back into an exported_program
exported_program = torch.export.export_for_training(model, example_inputs)

if args.delegate:
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refactor this as

my_partitioner = this or []
edge = to_edge_transoform_and_lower(..., partitioner=my_partitioner)

# As we can target multiple output encodings from ArmBackend, one must
# be specified.
compile_spec = get_compile_spec(args.target, args.intermediates)
edge = to_edge_transform_and_lower(
exported_program,
partitioner=[ArmPartitioner(compile_spec)],
compile_config=EdgeCompileConfig(
_check_ir_validity=False,
_skip_dim_order=True,
),
)
else:
edge = to_edge_transform_and_lower(
exported_program,
compile_config=EdgeCompileConfig(
_check_ir_validity=False,
_skip_dim_order=True,
),
)

edge = export_to_edge(
model,
example_inputs,
edge_compile_config=EdgeCompileConfig(
_check_ir_validity=False,
),
)

# As we can target multiple output encodings from ArmBackend, one must
# be specified.
compile_spec = (
get_compile_spec(args.target, args.intermediates)
if args.delegate is True
else None
)

logging.debug(f"Exported graph:\n{edge.exported_program().graph}")
if args.delegate is True:
edge = edge.to_backend(ArmPartitioner(compile_spec))

dump_delegation_info(edge, args.intermediates)

logging.debug(f"Lowered graph:\n{edge.exported_program().graph}")
dump_delegation_info(edge, args.intermediates)

try:
exec_prog = edge.to_executorch(
Expand Down
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