Add pooling and softmax unittests for Arm backend

backends/arm/test/ops/test_avg_pool.py

@@ -0,0 +1,165 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# Copyright 2024 Arm Limited and/or its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import unittest
+
+from typing import Tuple
+
+import torch
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.test_models import TosaProfile
+from executorch.backends.arm.test.tester.arm_tester import ArmBackendSelector, ArmTester
+from parameterized import parameterized
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+test_data_suite = [
+    # (test_name, test_data, [kernel_size, stride, padding])
+    ("zeros", torch.zeros(20, 16, 50, 32), [4, 2, 0]),
+    ("ones", torch.zeros(20, 16, 50, 32), [4, 2, 0]),
+    ("rand", torch.rand(20, 16, 50, 32), [4, 2, 0]),
+    ("randn", torch.randn(20, 16, 50, 32), [4, 2, 0]),
+]
+
+
+class TestAvgPool2d(unittest.TestCase):
+    class AvgPool2d(torch.nn.Module):
+        def __init__(
+            self,
+            kernel_size: int | Tuple[int, int],
+            stride: int | Tuple[int, int],
+            padding: int | Tuple[int, int],
+        ):
+            super().__init__()
+            self.avg_pool_2d = torch.nn.AvgPool2d(
+                kernel_size=kernel_size, stride=stride, padding=padding
+            )
+
+        def forward(self, x):
+            return self.avg_pool_2d(x)
+
+    def _test_avgpool2d_tosa_MI_pipeline(
+        self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
+    ):
+        tester = (
+            ArmTester(
+                module,
+                inputs=test_data,
+                profile=TosaProfile.MI,
+                backend=ArmBackendSelector.TOSA,
+                permute_memory_to_nhwc=True,
+            )
+            .export()
+            .check(["torch.ops.aten.avg_pool2d.default"])
+            .check_not(["torch.ops.quantized_decomposed"])
+            .to_edge()
+            .partition()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_avg_pool2d_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+        if common.TOSA_REF_MODEL_INSTALLED:
+            tester.run_method().compare_outputs()
+        else:
+            logger.warning(
+                "TOSA ref model tool not installed, skip numerical correctness tests"
+            )
+
+    def _test_avgpool2d_tosa_BI_pipeline(
+        self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
+    ):
+        tester = (
+            ArmTester(
+                module,
+                inputs=test_data,
+                profile=TosaProfile.BI,
+                backend=ArmBackendSelector.TOSA,
+                permute_memory_to_nhwc=True,
+            )
+            .quantize()
+            .export()
+            .check_count({"torch.ops.aten.avg_pool2d.default": 1})
+            .check(["torch.ops.quantized_decomposed"])
+            .to_edge()
+            .partition()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_avg_pool2d_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+        if common.TOSA_REF_MODEL_INSTALLED:
+            tester.run_method().compare_outputs(qtol=1)
+        else:
+            logger.warning(
+                "TOSA ref model tool not installed, skip numerical correctness tests"
+            )
+
+    def _test_avgpool2d_tosa_u55_BI_pipeline(
+        self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
+    ):
+        (
+            ArmTester(
+                module,
+                inputs=test_data,
+                profile=TosaProfile.BI,
+                backend=ArmBackendSelector.ETHOS_U55,
+                permute_memory_to_nhwc=True,
+            )
+            .quantize()
+            .export()
+            .check_count({"torch.ops.aten.avg_pool2d.default": 1})
+            .check(["torch.ops.quantized_decomposed"])
+            .to_edge()
+            .partition()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_avg_pool2d_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+
+    @parameterized.expand(test_data_suite)
+    def test_avgpool2d_tosa_MI(
+        self,
+        test_name: str,
+        test_data: torch.Tensor,
+        model_params: int | Tuple[int, int],
+    ):
+        self._test_avgpool2d_tosa_MI_pipeline(
+            self.AvgPool2d(*model_params), (test_data,)
+        )
+
+    # Expected to fail since ArmQuantizer cannot quantize a AvgPool2D layer
+    # TODO(MLETORCH-93)
+    @parameterized.expand(test_data_suite)
+    @unittest.expectedFailure
+    def test_avgpool2d_tosa_BI(
+        self,
+        test_name: str,
+        test_data: torch.Tensor,
+        model_params: int | Tuple[int, int],
+    ):
+        self._test_avgpool2d_tosa_BI_pipeline(
+            self.AvgPool2d(*model_params), (test_data,)
+        )
+
+    # Expected to fail since ArmQuantizer cannot quantize a AvgPool2D layer
+    # TODO(MLETORCH-93)
+    @parameterized.expand(test_data_suite)
+    @unittest.skipIf(
+        not common.VELA_INSTALLED,
+        "There is no point in running U55 tests if the Vela tool is not installed",
+    )
+    @unittest.expectedFailure
+    def test_avgpool2d_tosa_u55_BI(
+        self,
+        test_name: str,
+        test_data: torch.Tensor,
+        model_params: int | Tuple[int, int],
+    ):
+        self._test_avgpool2d_tosa_u55_BI_pipeline(
+            self.AvgPool2d(*model_params), (test_data,)
+        )

backends/arm/test/ops/test_linear.py

@@ -21,38 +21,42 @@
 
 torch.manual_seed(42)
 
-test_data_suite = [
+test_data_suite_rank1 = [
     # (test_name, test_data, out_features)
     (
         "model_linear_rank1_zeros",
-        torch.zeros(10, 10),
+        torch.zeros(10),
         10,
     ),
     (
         "model_linear_rank1_ones",
-        torch.ones(10, 10),
+        torch.ones(10),
         10,
     ),
     (
         "model_linear_rank1_negative_ones",
-        torch.ones(10, 10) * (-1),
+        torch.ones(10) * (-1),
         10,
     ),
     (
         "model_linear_rank1_rand",
-        torch.rand(10, 10),
+        torch.rand(10),
         10,
     ),
     (
         "model_linear_rank1_negative_large_rand",
-        torch.rand(10, 10) * (-100),
+        torch.rand(10) * (-100),
         10,
     ),
     (
         "model_linear_rank1_large_randn",
-        torch.randn(10, 10) * 100,
+        torch.randn(10) * 100,
         10,
     ),
+]
+
+test_data_suite_rank4 = [
+    # (test_name, test_data, out_features)
     (
         "model_linear_rank4_zeros",
         torch.zeros(5, 10, 25, 20),
@@ -175,7 +179,7 @@ def _test_linear_tosa_u55_BI_pipeline(
             .to_executorch()
         )
 
-    @parameterized.expand(test_data_suite)
+    @parameterized.expand(test_data_suite_rank1 + test_data_suite_rank4)
     def test_linear_tosa_MI(
         self,
         test_name: str,
@@ -192,7 +196,7 @@ def test_linear_tosa_MI(
             test_data,
         )
 
-    @parameterized.expand(test_data_suite)
+    @parameterized.expand(test_data_suite_rank1 + test_data_suite_rank4)
     def test_linear_tosa_BI(
         self,
         test_name: str,
@@ -205,8 +209,11 @@ def test_linear_tosa_BI(
             self.Linear(in_features=in_features, out_features=out_features), test_data
         )
 
-    @parameterized.expand(test_data_suite)
-    @unittest.skip("This does not work as of now")
+    @parameterized.expand(test_data_suite_rank1)
+    @unittest.skipIf(
+        not common.VELA_INSTALLED,
+        "There is no point in running U55 tests if the Vela tool is not installed",
+    )
     def test_linear_tosa_u55_BI(
         self,
         test_name: str,