MobileNetv3 FP32 + QS8 (#142)

Summary:
Pull Request resolved: #142

Tester for MobileNetv3

Reviewed By: digantdesai, guangy10, kirklandsign

Differential Revision: D48667678

fbshipit-source-id: f5b8f94320ca6ddeb063a98e51dcb8bc05c3c4d6

Author: mcr229

backends/xnnpack/test/models/mobilenet_v3.py

@@ -0,0 +1,82 @@
+# Copyright (c) Meta Platforms, Inc. and 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 unittest
+
+import torch
+import torchvision.models as models
+from executorch.backends.xnnpack.partition.xnnpack_partitioner import (
+    XnnpackQuantizedPartitioner2,
+)
+from executorch.backends.xnnpack.test.tester import Partition, Tester
+from executorch.backends.xnnpack.test.tester.tester import Export
+from executorch.backends.xnnpack.utils.configs import get_xnnpack_capture_config
+
+
+class TestMobileNetV3(unittest.TestCase):
+    export_stage = Export(get_xnnpack_capture_config(enable_aot=True))
+
+    mv3 = models.mobilenetv3.mobilenet_v3_small(pretrained=True)
+    mv3 = mv3.eval()
+    model_inputs = (torch.ones(1, 3, 224, 244),)
+
+    all_operators = {
+        "executorch_exir_dialects_edge__ops_aten__native_batch_norm_legit_no_training_default",
+        "executorch_exir_dialects_edge__ops_aten_clamp_default",
+        "executorch_exir_dialects_edge__ops_aten_permute_copy_default",
+        "executorch_exir_dialects_edge__ops_aten_addmm_default",
+        "executorch_exir_dialects_edge__ops_aten__to_copy_default",
+        "executorch_exir_dialects_edge__ops_aten_convolution_default",
+        "executorch_exir_dialects_edge__ops_aten_relu_default",
+        "executorch_exir_dialects_edge__ops_aten_add_Tensor",
+        "executorch_exir_dialects_edge__ops_aten_mul_Tensor",
+        "executorch_exir_dialects_edge__ops_aten_div_Tensor",
+        "executorch_exir_dialects_edge__ops_aten_mean_dim",
+    }
+
+    def test_fp32(self):
+        (
+            Tester(self.mv3, self.model_inputs)
+            .export(self.export_stage)
+            .to_edge()
+            .check(list(self.all_operators))
+            .partition()
+            .check(["torch.ops.executorch_call_delegate"])
+            .check_not(list(self.all_operators))
+            .to_executorch()
+            .serialize()
+            .run_method()
+            .compare_outputs()
+        )
+
+    def test_qs8_pt2e(self):
+        ops_after_quantization = self.all_operators - {
+            "executorch_exir_dialects_edge__ops_aten__native_batch_norm_legit_no_training_default",
+        }
+        ops_after_lowering = self.all_operators - {
+            # TODO: unified partitioner since hardswish/hardsigmoid decomposed operators are not quantized
+            # They will not be partitioned by quantized partitioner
+            "executorch_exir_dialects_edge__ops_aten_add_Tensor",
+            "executorch_exir_dialects_edge__ops_aten_mul_Tensor",
+            "executorch_exir_dialects_edge__ops_aten_div_Tensor",
+            "executorch_exir_dialects_edge__ops_aten_clamp_default",
+            "executorch_exir_dialects_edge__ops_aten__to_copy_default",
+        }
+
+        (
+            Tester(self.mv3, self.model_inputs)
+            .quantize2()
+            .export(self.export_stage)
+            .to_edge()
+            .check(list(ops_after_quantization))
+            .partition(Partition(partitioner=XnnpackQuantizedPartitioner2))
+            .check(["torch.ops.executorch_call_delegate"])
+            .check_not(list(ops_after_lowering))
+            .to_executorch()
+            .serialize()
+            .run_method()
+            .compare_outputs()
+        )