Arm backend: Make NSS unit tests public (#14891)

NSS has now been made public so unit tests
have been moved to public executorch repository.

Signed-off-by: Michiel Olieslagers <michiel.olieslagers@arm.com>

Author: Michiel-Olieslagers

backends/arm/requirements-arm-models-test.txt

@@ -3,4 +3,4 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-diffusers[torch] == 0.33.1
+diffusers[torch] == 0.33.1

backends/arm/scripts/install_models_for_test.sh

@@ -6,3 +6,16 @@
 
 set -e
 pip install -r backends/arm/requirements-arm-models-test.txt
+
+# Install model gym repository
+git clone https://github.com/arm/neural-graphics-model-gym.git
+cd neural-graphics-model-gym
+# Remove model-converter installation from model-gym repository (to prevent overwriting executorch version)
+if [[ "$(uname)" == "Darwin" ]]; then
+    sed -i '' 's/^model-converter  = "ng_model_gym.bin.model_converter_launcher:main"/#&/' pyproject.toml
+else
+    sed -i 's/^model-converter  = "ng_model_gym.bin.model_converter_launcher:main"/#&/' pyproject.toml
+fi
+pip install . --no-deps
+cd ..
+rm -rf neural-graphics-model-gym

backends/arm/test/models/test_nss.py

@@ -0,0 +1,138 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Tuple
+
+import pytest
+import torch
+
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.test_pipeline import (
+    EthosU55PipelineINT,
+    EthosU85PipelineINT,
+    TosaPipelineFP,
+    TosaPipelineINT,
+    VgfPipeline,
+)
+
+from huggingface_hub import hf_hub_download
+
+from ng_model_gym.usecases.nss.model.model_blocks import AutoEncoderV1
+
+input_t = Tuple[torch.Tensor]  # Input x
+
+
+class NSS(torch.nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.auto_encoder = AutoEncoderV1()
+
+
+def nss() -> AutoEncoderV1:
+    """Get an instance of NSS with weights loaded."""
+
+    weights = hf_hub_download(
+        repo_id="Arm/neural-super-sampling", filename="nss_v0.1.0_fp32.pt"
+    )
+
+    nss_model = NSS()
+    nss_model.load_state_dict(
+        torch.load(weights, map_location=torch.device("cpu"), weights_only=True),
+        strict=False,
+    )
+    return nss_model.auto_encoder
+
+
+def example_inputs():
+    return (torch.randn((1, 12, 544, 960)),)
+
+
+def test_nss_tosa_FP():
+    pipeline = TosaPipelineFP[input_t](
+        nss().eval(),
+        example_inputs(),
+        aten_op=[],
+        exir_op=[],
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.add_stage_after("export", pipeline.tester.dump_operator_distribution)
+    pipeline.run()
+
+
+def test_nss_tosa_INT():
+    pipeline = TosaPipelineINT[input_t](
+        nss().eval(),
+        example_inputs(),
+        aten_op=[],
+        exir_op=[],
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.run()
+
+
+@pytest.mark.skip(reason="No support for aten_upsample_nearest2d_vec on U55")
+@common.XfailIfNoCorstone300
+def test_nss_u55_INT():
+    pipeline = EthosU55PipelineINT[input_t](
+        nss().eval(),
+        example_inputs(),
+        aten_ops=[],
+        exir_ops=[],
+        run_on_fvp=True,
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.run()
+
+
+@pytest.mark.skip(
+    reason="Fails at input memory allocation for input shape: [1, 12, 544, 960]"
+)
+@common.XfailIfNoCorstone320
+def test_nss_u85_INT():
+    pipeline = EthosU85PipelineINT[input_t](
+        nss().eval(),
+        example_inputs(),
+        aten_ops=[],
+        exir_ops=[],
+        run_on_fvp=True,
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.run()
+
+
+@pytest.mark.xfail(
+    reason="[MLETORCH-1430]: Double types are not supported in buffers in MSL"
+)
+@common.SkipIfNoModelConverter
+def test_nss_vgf_FP():
+    pipeline = VgfPipeline[input_t](
+        nss().eval(),
+        example_inputs(),
+        aten_op=[],
+        exir_op=[],
+        tosa_version="TOSA-1.0+FP",
+        use_to_edge_transform_and_lower=True,
+        run_on_vulkan_runtime=True,
+    )
+    pipeline.run()
+
+
+@common.SkipIfNoModelConverter
+def test_nss_vgf_INT():
+    pipeline = VgfPipeline[input_t](
+        nss().eval(),
+        example_inputs(),
+        aten_op=[],
+        exir_op=[],
+        tosa_version="TOSA-1.0+INT",
+        symmetric_io_quantization=True,
+        use_to_edge_transform_and_lower=True,
+        run_on_vulkan_runtime=True,
+    )
+    pipeline.run()
+
+
+ModelUnderTest = nss().eval()
+ModelInputs = example_inputs()