Add convolution unit test for Arm backend (#2427)

Summary:
* Added both depthwise and regular conv
* Removed corresponding conv legacy unit tests
* Fix zero point usage solves issues with random input
* Removed add/add2 legacy unit tests as they are already implemented
* Bump serialization lib submodule to avoid warning spam
* Fixed rounding issue tosa_test_utils

Pull Request resolved: #2427

Reviewed By: mcr229

Differential Revision: D55228457

Pulled By: digantdesai

fbshipit-source-id: 2e7c9acfba582916c9d5c8e047f5aa57a37807f3

Author: freddan80

backends/arm/operators/op_conv2d.py

@@ -11,7 +11,10 @@
     register_node_visitor,
 )
 from executorch.backends.arm.tosa_mapping import TosaArg
-from executorch.backends.arm.tosa_quant_utils import build_rescale_conv_output
+from executorch.backends.arm.tosa_quant_utils import (
+    build_rescale_conv_output,
+    get_quant_node_args,
+)
 from executorch.backends.arm.tosa_utils import build_reshape, getNodeArgs
 
 from serializer.tosa_serializer import TosaOp
@@ -76,11 +79,15 @@ def define_node(
             dilation_attr[1],
         )
 
+        input_zp = (
+            get_quant_node_args(node.all_input_nodes[0])[1] if is_quant_node else 0
+        )
+
         attr.ConvAttribute(
             pad=pad_attr,
             stride=stride_attr,
             dilation=dilation_attr,
-            input_zp=0,
+            input_zp=input_zp,
             weight_zp=0,
             local_bound=False,
         )
@@ -125,37 +132,31 @@ def define_node(
             build_reshape(
                 tosa_graph, weight.name, weight_post_shape, weight_reshaped.name
             )
-
-            tosa_graph.addOperator(
-                TosaOp.Op().DEPTHWISE_CONV2D,
-                [
-                    input.name,
-                    weight_reshaped.name,
-                    bias.name,
-                ],
-                [conv2d_output_name],
-                attr,
-            )
+            tosa_op = TosaOp.Op().DEPTHWISE_CONV2D
+            weight_name = weight_reshaped.name
         else:
             """Regular convolution case"""
-            tosa_graph.addOperator(
-                TosaOp.Op().CONV2D,
-                [
-                    input.name,
-                    weight.name,
-                    bias.name,
-                ],
-                [conv2d_output_name],
-                attr,
-            )
+            tosa_op = TosaOp.Op().CONV2D
+            weight_name = weight.name
+
+        tosa_graph.addOperator(
+            tosa_op,
+            [
+                input.name,
+                weight_name,
+                bias.name,
+            ],
+            [conv2d_output_name],
+            attr,
+        )
 
         # For quantized convolution, rescale the output value back to the same
         # integer value domain of the next op. Otherwise return float32 output.
         if is_quant_node:
             # Get scale_factor from input, weight, and output.
             _, input_scale, _, _, _, _ = getNodeArgs(node.args[0])
             _, weight_scale, _, _, _, _ = getNodeArgs(node.args[1])
-            _, output_scale, _, _, _, _ = getNodeArgs(list(node.users)[0])
+            _, output_scale, output_zp, _, _, _ = getNodeArgs(list(node.users)[0])
             build_rescale_conv_output(
                 tosa_graph,
                 conv2d_res,
@@ -164,4 +165,5 @@ def define_node(
                 input_scale,
                 weight_scale,
                 output_scale,
+                output_zp,
             )

backends/arm/test/arm_tosa_reference.py

@@ -39,15 +39,7 @@
 )
 
 SUPPORTED_BI_TEST_LIST = [
-    "simple_add",
     "simple_add_broadcast",
-    "simple_conv2d_3x3_1x3x256x256_stride1",
-    "simple_conv2d_1x1_1x2x128x128_stride1",
-    "simple_conv2d_2x2_1x1x14x14_stride2",
-    "simple_conv2d_5x5_3x2x128x128_stride1",
-    "simple_conv2d_2x2_3x1x40x40_non_bias",
-    "block_two_conv2d",
-    "block_two_conv2d_non_bias",
     "block_bottleneck_residual",
 ]
 

backends/arm/test/common.py

@@ -0,0 +1,15 @@
+# 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 shutil
+
+# TODO: fixme! These globs are a temporary workaround. Reasoning:
+# Running the jobs in _unittest.yml will not work since that environment doesn't
+# have the vela tool, nor the tosa_reference_model tool. Hence, we need a way to
+# run what we can in that env temporarily. Long term, vela and tosa_reference_model
+# should be installed in the CI env.
+TOSA_REF_MODEL_INSTALLED = shutil.which("tosa_reference_model")
+VELA_INSTALLED = shutil.which("vela")

backends/arm/test/models/test_mobilenet_v2_arm.py

@@ -6,22 +6,16 @@
 # LICENSE file in the root directory of this source tree.
 
 import logging
-import shutil
 import unittest
 
 import torch
 import torchvision.models as models
+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 executorch.backends.xnnpack.test.tester.tester import Quantize
 from torchvision.models.mobilenetv2 import MobileNet_V2_Weights
 
-# TODO: fixme! These globs are a temporary workaround. Reasoning:
-# Running the jobs in _unittest.yml will not work since that environment don't
-# have the vela tool, nor the tosa_reference_model tool. Hence, we need a way to
-# run what we can in that env temporarily. Long term, vela and tosa_reference_model
-# should be installed in the CI env.
-TOSA_REF_MODEL_INSTALLED = shutil.which("tosa_reference_model")
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
@@ -79,15 +73,17 @@ def test_mv2_tosa_BI(self):
             .partition()
             .to_executorch()
         )
-
-        if TOSA_REF_MODEL_INSTALLED:
+        if common.TOSA_REF_MODEL_INSTALLED:
             tester.run_method().compare_outputs()
         else:
             logger.warning(
                 "TOSA ref model tool not installed, skip numerical correctness tests"
             )
 
-    @unittest.skip("This test is not supported yet")
+    @unittest.skipIf(
+        not common.VELA_INSTALLED,
+        "There is no point in running U55 tests if the Vela tool is not installed",
+    )
     def test_mv2_u55_BI(self):
         (
             ArmTester(

backends/arm/test/ops/test_add.py

@@ -1,32 +1,26 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
-# Copyright 2023-2024 Arm Limited and/or its 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 shutil
 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
 
-# TODO: fixme! These globs are a temporary workaround. Reasoning:
-# Running the jobs in _unittest.yml will not work since that environment don't
-# have the vela tool, nor the tosa_reference_model tool. Hence, we need a way to
-# run what we can in that env temporarily. Long term, vela and tosa_reference_model
-# should be installed in the CI env.
-TOSA_REF_MODEL_INSTALLED = shutil.which("tosa_reference_model")
-VELA_INSTALLED = shutil.which("vela")
-
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
+torch.manual_seed(42)
+
 
 class TestSimpleAdd(unittest.TestCase):
     class Add(torch.nn.Module):
@@ -77,7 +71,7 @@ def _test_add_tosa_MI_pipeline(
             .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
             .to_executorch()
         )
-        if TOSA_REF_MODEL_INSTALLED:
+        if common.TOSA_REF_MODEL_INSTALLED:
             tester.run_method().compare_outputs()
         else:
             logger.warning(
@@ -104,7 +98,8 @@ def _test_add_tosa_BI_pipeline(
             .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
             .to_executorch()
         )
-        if TOSA_REF_MODEL_INSTALLED:
+
+        if common.TOSA_REF_MODEL_INSTALLED:
             tester.run_method().compare_outputs(qtol=1)
         else:
             logger.warning(
@@ -143,7 +138,7 @@ def test_add_tosa_BI(self, test_data: torch.Tensor):
 
     @parameterized.expand(Add.test_parameters)
     @unittest.skipIf(
-        not VELA_INSTALLED,
+        not common.VELA_INSTALLED,
         "There is no point in running U55 tests if the Vela tool is not installed",
     )
     def test_add_u55_BI(self, test_data: torch.Tensor):
@@ -162,7 +157,7 @@ def test_add2_tosa_BI(self, operand1: torch.Tensor, operand2: torch.Tensor):
 
     @parameterized.expand(Add2.test_parameters)
     @unittest.skipIf(
-        not VELA_INSTALLED,
+        not common.VELA_INSTALLED,
         "There is no point in running U55 tests if the Vela tool is not installed",
     )
     def test_add2_u55_BI(self, operand1: torch.Tensor, operand2: torch.Tensor):