Add SamsungTester and build basic test pipeline

Add SamsungTester which provide basic function of lowering module.
Add test for some common ops. Currently, export, partition, lowering to enn backend,
to executorch are included, on-device test and evaluation is skipped.

Outputs of command : python -m unittest discover backends/samsung/test/ops/ -p "test_*.py"
```
INFO:root:Find 1 subgraphs to partition and lowering.
INFO:root:Visiting: aten_add_tensor, aten.add.Tensor
WARNING: Logging before InitGoogleLogging() is written to STDERR
.INFO:root:Find 1 subgraphs to partition and lowering.
INFO:root:Visiting: aten_mul_tensor, aten.mul.Tensor
.
----------------------------------------------------------------------
Ran 17 tests in 15.939s

OK
```

Co-authored-by: chong-chen <[email protected]>

Author: chong-chen01

backends/samsung/test/ops/test_add.py

@@ -0,0 +1,62 @@
+# Copyright (c) Samsung Electronics Co. LTD
+# All rights reserved
+#
+# Licensed under the BSD License (the "License"); you may not use this file
+# except in compliance with the License. See the license file in the root
+# directory of this source tree for more details.
+
+
+import unittest
+
+import torch
+
+from executorch.backends.samsung.serialization.compile_options import (
+    gen_samsung_backend_compile_spec,
+)
+from executorch.backends.samsung.test.tester import SamsungTester
+
+
+class Add(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        return x + y
+
+
+class AddConstant(torch.nn.Module):
+    def __init__(self, constant) -> None:
+        super().__init__()
+        self.constant = constant
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.constant
+
+
+class TestAdd(unittest.TestCase):
+    def _test(self, module: torch.nn.Module, inputs):
+        tester = SamsungTester(
+            module,
+            inputs,
+            [gen_samsung_backend_compile_spec("E9955")],
+        )
+        (
+            tester.export()
+            .check_count({"torch.ops.aten.add.Tensor": 1})
+            .to_edge_transform_and_lower()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_add_Tensor"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+
+    def test_fp32_simple_add(self):
+        inputs = (torch.randn(1, 3, 8, 8), torch.randn(1, 3, 8, 8))
+        self._test(Add(), inputs)
+
+    def test_fp32_const_add(self):
+        inputs = (torch.randn(1, 3, 8, 8),)
+        self._test(AddConstant(torch.randn(1, 3, 8, 8)), inputs)
+
+    def test_fp32_add_broadcast(self):
+        inputs = (torch.randn(1, 1, 8, 8), torch.randn(1, 3, 8, 8))
+        self._test(Add(), inputs)

backends/samsung/test/ops/test_avg_pool2d.py

@@ -0,0 +1,66 @@
+# Copyright (c) Samsung Electronics Co. LTD
+# All rights reserved
+#
+# Licensed under the BSD License (the "License"); you may not use this file
+# except in compliance with the License. See the license file in the root
+# directory of this source tree for more details.
+
+
+import unittest
+
+import torch
+
+from executorch.backends.samsung.serialization.compile_options import (
+    gen_samsung_backend_compile_spec,
+)
+from executorch.backends.samsung.test.tester import SamsungTester
+
+
+class AvgPool2d(torch.nn.Module):
+    def __init__(
+        self,
+        kernel_size=2,
+        stride=1,
+        padding=0,
+    ) -> None:
+        super().__init__()
+        self.avg_pool = torch.nn.AvgPool2d(
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            count_include_pad=False,
+            ceil_mode=False,
+        ).to(torch.float)
+
+    def get_example_inputs(self) -> tuple[torch.Tensor]:
+        input_1 = torch.randn(1, 16, 24, 24)
+        return (input_1,)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.avg_pool(x)
+
+
+class TestAvgPool2d(unittest.TestCase):
+    def _test(self, module: torch.nn.Module):
+        tester = SamsungTester(
+            module,
+            module.get_example_inputs(),
+            [gen_samsung_backend_compile_spec("E9955")],
+        )
+        (
+            tester.export()
+            .check_count({"torch.ops.aten.avg_pool2d.default": 1})
+            .to_edge_transform_and_lower()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_avg_pool2d_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+
+    def test_fp32_avg_pool2d(self):
+        self._test(AvgPool2d())
+
+    def test_fp32_avg_pool2d_with_stride(self):
+        self._test(AvgPool2d(stride=2))
+
+    def test_fp32_avg_pool2d_with_kernel_size(self):
+        self._test(AvgPool2d(kernel_size=4))

backends/samsung/test/ops/test_conv2d.py

@@ -0,0 +1,98 @@
+# Copyright (c) Samsung Electronics Co. LTD
+# All rights reserved
+#
+# Licensed under the BSD License (the "License"); you may not use this file
+# except in compliance with the License. See the license file in the root
+# directory of this source tree for more details.
+
+
+import unittest
+
+import torch
+
+from executorch.backends.samsung.serialization.compile_options import (
+    gen_samsung_backend_compile_spec,
+)
+from executorch.backends.samsung.test.tester import SamsungTester
+
+
+class Conv2d(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels=3,
+        out_channels=16,
+        stride=(2, 2),
+        padding=(1, 1),
+        dilation=(1, 1),
+        groups=1,
+        bias=True,
+    ) -> None:
+        super().__init__()
+        self.conv = torch.nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        ).to(torch.float)
+
+        self.in_channels = in_channels
+
+    def get_example_inputs(self) -> tuple[torch.Tensor]:
+        input_1 = torch.randn(1, self.in_channels, 24, 24)
+        return (input_1,)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.conv(x)
+
+
+class TransposeConv2d(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.conv = torch.nn.ConvTranspose2d(
+            in_channels=32,
+            out_channels=8,
+            kernel_size=2,
+            stride=(2, 2),
+            padding=(0, 0),
+            dilation=(1, 1),
+            bias=True,
+        )
+
+    def get_example_inputs(self) -> tuple[torch.Tensor]:
+        input_1 = torch.randn(1, 32, 24, 24)
+        return (input_1,)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.conv(x)
+
+
+class TestConv2d(unittest.TestCase):
+    def _test(self, module: torch.nn.Module):
+        tester = SamsungTester(
+            module,
+            module.get_example_inputs(),
+            [gen_samsung_backend_compile_spec("E9955")],
+        )
+        (
+            tester.export()
+            .to_edge_transform_and_lower()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_convolution_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+
+    def test_fp32_conv2d_without_bias(self):
+        self._test(Conv2d(bias=False))
+
+    def test_fp32_conv2d_with_bias(self):
+        self._test(Conv2d(bias=True))
+
+    def test_fp32_depthwise_conv2d(self):
+        self._test(Conv2d(in_channels=8, out_channels=8, groups=8))
+
+    def test_fp32_transpose_conv2d(self):
+        self._test(TransposeConv2d())

backends/samsung/test/ops/test_max_pool2d.py

@@ -0,0 +1,71 @@
+# Copyright (c) Samsung Electronics Co. LTD
+# All rights reserved
+#
+# Licensed under the BSD License (the "License"); you may not use this file
+# except in compliance with the License. See the license file in the root
+# directory of this source tree for more details.
+
+
+import unittest
+
+import torch
+
+from executorch.backends.samsung.serialization.compile_options import (
+    gen_samsung_backend_compile_spec,
+)
+from executorch.backends.samsung.test.tester import SamsungTester
+
+
+class MaxPool2d(torch.nn.Module):
+    def __init__(
+        self,
+        kernel_size=2,
+        stride=1,
+        padding=0,
+        dilation=1,
+    ) -> None:
+        super().__init__()
+        self.max_pool = torch.nn.MaxPool2d(
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            return_indices=False,
+            ceil_mode=False,
+        ).to(torch.float)
+
+    def get_example_inputs(self) -> tuple[torch.Tensor]:
+        input_1 = torch.randn(1, 16, 24, 24)
+        return (input_1,)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.max_pool(x)
+
+
+class TestMaxPool2d(unittest.TestCase):
+    def _test(self, module: torch.nn.Module):
+        tester = SamsungTester(
+            module,
+            module.get_example_inputs(),
+            [gen_samsung_backend_compile_spec("E9955")],
+        )
+        (
+            tester.export()
+            .check_count({"torch.ops.aten.max_pool2d.default": 1})
+            .to_edge_transform_and_lower()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_max_pool2d_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+
+    def test_fp32_max_pool2d(self):
+        self._test(MaxPool2d())
+
+    def test_fp32_max_pool2d_with_padding(self):
+        self._test(MaxPool2d(padding=1))
+
+    def test_fp32_max_pool2d_with_kernel_size(self):
+        self._test(MaxPool2d(kernel_size=4))
+
+    def test_fp32_max_pool2d_with_dilation(self):
+        self._test(MaxPool2d(dilation=2))

backends/samsung/test/ops/test_mul.py

@@ -0,0 +1,62 @@
+# Copyright (c) Samsung Electronics Co. LTD
+# All rights reserved
+#
+# Licensed under the BSD License (the "License"); you may not use this file
+# except in compliance with the License. See the license file in the root
+# directory of this source tree for more details.
+
+
+import unittest
+
+import torch
+
+from executorch.backends.samsung.serialization.compile_options import (
+    gen_samsung_backend_compile_spec,
+)
+from executorch.backends.samsung.test.tester import SamsungTester
+
+
+class Mul(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        return x * y
+
+
+class MulConstant(torch.nn.Module):
+    def __init__(self, constant) -> None:
+        super().__init__()
+        self.constant = constant
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x * self.constant
+
+
+class TestMul(unittest.TestCase):
+    def _test(self, module: torch.nn.Module, inputs):
+        tester = SamsungTester(
+            module,
+            inputs,
+            [gen_samsung_backend_compile_spec("E9955")],
+        )
+        (
+            tester.export()
+            .check_count({"torch.ops.aten.mul.Tensor": 1})
+            .to_edge_transform_and_lower()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_mul_Tensor"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+
+    def test_fp32_simple_mul(self):
+        inputs = (torch.randn(1, 3, 8, 8), torch.randn(1, 3, 8, 8))
+        self._test(Mul(), inputs)
+
+    def test_fp32_const_mul(self):
+        inputs = (torch.randn(1, 3, 8, 8),)
+        self._test(MulConstant(torch.randn(1, 3, 8, 8)), inputs)
+
+    def test_fp32_mul_broadcast(self):
+        inputs = (torch.randn(1, 1, 8, 8), torch.randn(1, 3, 8, 8))
+        self._test(Mul(), inputs)

backends/samsung/test/tester/__init__.py

@@ -0,0 +1,11 @@
+# Copyright (c) Samsung Electronics Co. LTD
+# All rights reserved
+#
+# Licensed under the BSD License (the "License"); you may not use this file
+# except in compliance with the License. See the license file in the root
+# directory of this source tree for more details.
+
+from executorch.backends.samsung.test.tester.samsung_tester import SamsungTester
+
+
+__all__ = ["SamsungTester"]