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remove exir.capture from example delegate test #3101

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remove exir.capture from example delegate test #3101

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1 change: 0 additions & 1 deletion backends/example/TARGETS
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,6 @@ python_unittest(
"//caffe2:torch",
"//executorch/exir:delegate",
"//executorch/exir:lib",
"//executorch/exir/backend:backend_api",
"//executorch/exir/backend/canonical_partitioners:canonical_partitioner_lib",
"//pytorch/vision:torchvision",
],
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34 changes: 17 additions & 17 deletions backends/example/test_example_delegate.py
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Expand Up @@ -11,16 +11,16 @@
from executorch import exir
from executorch.backends.example.example_partitioner import ExamplePartitioner
from executorch.backends.example.example_quantizer import ExampleQuantizer
from executorch.exir.backend.backend_api import to_backend
from executorch.exir import to_edge

from executorch.exir.backend.canonical_partitioners.duplicate_dequant_node_pass import (
DuplicateDequantNodePass,
)
from executorch.exir.delegate import executorch_call_delegate

from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
from torch.export import export

# @manual=//pytorch/vision:torchvision
from torchvision.models.quantization import mobilenet_v2


Expand All @@ -40,7 +40,6 @@ def get_example_inputs():

model = Conv2dModule()
example_inputs = Conv2dModule.get_example_inputs()
CAPTURE_CONFIG = exir.CaptureConfig(enable_aot=True)
EDGE_COMPILE_CONFIG = exir.EdgeCompileConfig(
_check_ir_validity=False,
)
Expand All @@ -59,24 +58,23 @@ def get_example_inputs():
m = convert_pt2e(m)

quantized_gm = m
exported_program = exir.capture(
quantized_gm, copy.deepcopy(example_inputs), CAPTURE_CONFIG
).to_edge(EDGE_COMPILE_CONFIG)
exported_program = to_edge(
export(quantized_gm, copy.deepcopy(example_inputs)),
compile_config=EDGE_COMPILE_CONFIG,
)

lowered_export_program = to_backend(
exported_program.exported_program,
lowered_export_program = exported_program.to_backend(
ExamplePartitioner(),
)

print("After lowering to qnn backend: ")
lowered_export_program.graph.print_tabular()
lowered_export_program.exported_program().graph.print_tabular()

def test_delegate_mobilenet_v2(self):
model = mobilenet_v2(num_classes=3)
model.eval()
example_inputs = (torch.rand(1, 3, 320, 240),)

CAPTURE_CONFIG = exir.CaptureConfig(enable_aot=True)
EDGE_COMPILE_CONFIG = exir.EdgeCompileConfig(
_check_ir_validity=False,
)
Expand All @@ -91,20 +89,22 @@ def test_delegate_mobilenet_v2(self):
m = convert_pt2e(m)

quantized_gm = m
exported_program = exir.capture(
quantized_gm, copy.deepcopy(example_inputs), CAPTURE_CONFIG
).to_edge(EDGE_COMPILE_CONFIG)
exported_program = to_edge(
export(quantized_gm, copy.deepcopy(example_inputs)),
compile_config=EDGE_COMPILE_CONFIG,
)

lowered_export_program = to_backend(
exported_program.transform(DuplicateDequantNodePass()).exported_program,
lowered_export_program = exported_program.transform(
[DuplicateDequantNodePass()]
).to_backend(
ExamplePartitioner(),
)

lowered_export_program.graph.print_tabular()
lowered_export_program.exported_program().graph.print_tabular()

call_deleage_node = [
node
for node in lowered_export_program.graph.nodes
for node in lowered_export_program.exported_program().graph.nodes
if node.target == executorch_call_delegate
]
self.assertEqual(len(call_deleage_node), 1)