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Arm backend: Correct type annotations in aot_arm_compiler #14627

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Merged
merged 3 commits into from
Oct 1, 2025
Merged

Arm backend: Correct type annotations in aot_arm_compiler #14627

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5fab6a5
Arm backend: Correct type annotations in aot_arm_compiler
Sep 16, 2025
b01a640
Merge branch 'main' into marlin-aot-arm-compiler
martinlsm Sep 29, 2025
979ae13
Merge branch 'main' into marlin-aot-arm-compiler
martinlsm Oct 1, 2025
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26 changes: 20 additions & 6 deletions examples/arm/aot_arm_compiler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,8 @@

from executorch.extension.export_util.utils import save_pte_program
from tabulate import tabulate
from torch.export import ExportedProgram
from torch.fx import GraphModule
from torch.utils.data import DataLoader

# Quantize model if required using the standard export quantizaion flow.
Expand Down Expand Up @@ -145,13 +147,13 @@ def get_model_and_inputs_from_name(


def quantize(
model: torch.nn.Module,
model: GraphModule,
model_name: str,
compile_specs: EthosUCompileSpec | VgfCompileSpec | TosaCompileSpec,
example_inputs: Tuple[torch.Tensor],
evaluator_name: str | None,
evaluator_config: Dict[str, Any] | None,
) -> torch.nn.Module:
) -> GraphModule:
"""This is the official recommended flow for quantization in pytorch 2.0
export"""
logging.info("Quantizing Model...")
Expand Down Expand Up @@ -601,7 +603,12 @@ def save_bpte_program(exec_prog, original_model: torch.nn.Module, output_name: s
save_bundled_program(exec_prog, method_test_suites, output_name)


def quantize_model(args, model: torch.nn.Module, example_inputs, compile_spec):
def quantize_model(
args,
model: GraphModule,
example_inputs: Tuple[torch.Tensor],
compile_spec,
) -> Tuple[GraphModule, ExportedProgram]:
model_int8 = quantize(
model,
args.model_name,
Expand All @@ -619,7 +626,10 @@ def quantize_model(args, model: torch.nn.Module, example_inputs, compile_spec):


def to_edge_TOSA_delegate(
exported_program, args, model: torch.nn.Module, example_inputs
exported_program: ExportedProgram,
args,
model: GraphModule,
example_inputs: Tuple[torch.Tensor],
):
# As we can target multiple output encodings, one must
# be specified.
Expand All @@ -638,7 +648,6 @@ def to_edge_TOSA_delegate(
model_int8, exported_program = quantize_model(
args, model, example_inputs, compile_spec
)
model = model_int8

if isinstance(compile_spec, EthosUCompileSpec):
partitioner = EthosUPartitioner(compile_spec)
Expand All @@ -660,7 +669,12 @@ def to_edge_TOSA_delegate(
return model_int8, edge


def to_edge_no_delegate(exported_program, args, model: torch.nn.Module, example_inputs):
def to_edge_no_delegate(
exported_program: ExportedProgram,
args,
model: GraphModule,
example_inputs: Tuple[torch.Tensor],
):
model_int8 = None
if args.quantize:
# As we can target multiple output encodings, one must
Expand Down
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