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Arm backend: Add a simple weight sharing test #13021

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Arm backend: Add a simple weight sharing test #13021

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49 changes: 49 additions & 0 deletions backends/arm/test/models/test_nn_modules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,12 +20,37 @@
import torch
from executorch.backends.arm.test.common import parametrize
from executorch.backends.arm.test.tester.test_pipeline import (
EthosU55PipelineBI,
TosaPipelineBI,
TosaPipelineMI,
)

example_input = torch.rand(1, 6, 16, 16)


class SimpleWeightReuseModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(1, 16, kernel_size=3, padding=1)
self.relu = torch.nn.ReLU()
self.conv2 = torch.nn.Conv2d(16, 1, kernel_size=3, padding=1)
self.layer_scale = torch.nn.Parameter(torch.ones(1, 1, 1) * 1e-2)

def _apply_inner(self, input_tensor: torch.Tensor) -> torch.Tensor:
out = self.conv1(input_tensor)
out = self.relu(out)
out = self.conv2(out)
out = self.layer_scale * out
return out

def forward(
self, a: torch.Tensor, b: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
x = self._apply_inner(a)
y = self._apply_inner(x + b)
return (x, y)


module_tests = [
(torch.nn.Embedding(10, 10), (torch.LongTensor([[1, 2, 4, 5], [4, 3, 2, 9]]),)),
(torch.nn.LeakyReLU(), (example_input,)),
Expand All @@ -46,6 +71,8 @@
),
(torch.rand((10, 32, 64)), torch.rand((20, 32, 64))),
),
# Temporary!
(SimpleWeightReuseModel(), (torch.rand(1, 1, 20, 48), torch.rand(1, 1, 20, 48))),
]

input_t = tuple[torch.Tensor]
Expand Down Expand Up @@ -100,3 +127,25 @@ def test_nn_Modules_BI(test_data):
not in str(e)
):
raise e


@parametrize(
"test_data",
{"SimpleWeightReuseModel": test_parameters["SimpleWeightReuseModel"]},
xfails={
"SimpleWeightReuseModel": "RuntimeError: Non-passthrough operation could not run on NPU.",
},
)
def test_nn_Modules_U55BI(test_data):
module, inputs = test_data
pipeline = EthosU55PipelineBI[input_t](
module, inputs, "", use_to_edge_transform_and_lower=True
)
pipeline.pop_stage("check.aten")
pipeline.pop_stage("check_count.exir")
pipeline.pop_stage("check.quant_nodes")
pipeline.pop_stage("check_not.quant_nodes")
try:
pipeline.run()
except RuntimeError as e:
raise e
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