Arm backend: Add 16A8W support and test for add operation

backends/arm/operators/op_add.py

@@ -47,10 +47,16 @@ def define_node(
 
         validate_num_inputs(self.target, inputs, 2)
         validate_same_dtype(self.target, [*inputs, output], ts)
+        valid_dtypes = []
+        if self.tosa_spec.support_integer():
+            valid_dtypes.extend([ts.DType.INT8, ts.DType.INT16, ts.DType.INT32])
+        if self.tosa_spec.support_float():
+            valid_dtypes.extend([ts.DType.INT32])
+
         validate_valid_dtype(
             self.target,
             [*inputs, output],
-            [ts.DType.INT8, ts.DType.INT32],
+            valid_dtypes,
             output.tosa_spec,
         )
         scale_back = 1.0
@@ -59,15 +65,15 @@ def define_node(
                 tosa_graph, inputs, node, self.tosa_spec
             )
         else:
-            # input[0].dtype == ts.DType.INT32
+            # input[0].dtype == ts.DType.INT16 or ts.DType.INT32
             # Non quantized input, natively support by TOSA.ADD
             rescaled_inputs = inputs
 
         if output.dtype == ts.DType.INT8:
             broadcasted_shape = tutils.tosa_shape(output.shape, output.dim_order)
             add_output = tosa_graph.addIntermediate(broadcasted_shape, ts.DType.INT32)
         else:
-            # output.dtype == ts.DType.INT32
+            # output.dtype == ts.DType.INT16 or ts.DType.INT32
             add_output = output
 
         input1, input2 = rescaled_inputs
@@ -117,7 +123,7 @@ def define_node(
         validate_num_inputs(self.target, inputs, 2)
         validate_same_dtype(self.target, [*inputs, output], ts)
 
-        if inputs[0].dtype in [ts.DType.INT8, ts.DType.INT32]:
+        if inputs[0].dtype in [ts.DType.INT8, ts.DType.INT16, ts.DType.INT32]:
             # Call the inherited define_node for handling integers
             super().define_node(node, tosa_graph, inputs, output)
         else:

backends/arm/test/ops/test_add.py

@@ -10,6 +10,10 @@
 import pytest
 import torch
 from executorch.backends.arm.quantizer import arm_quantizer
+from executorch.backends.arm.quantizer.arm_quantizer import (
+    get_symmetric_a16w8_quantization_config,
+    TOSAQuantizer,
+)
 from executorch.backends.arm.test import common, conftest
 from executorch.backends.arm.test.tester.test_pipeline import (
     EthosU55PipelineINT,
@@ -235,3 +239,105 @@ def test_add_tensor_vgf_INT(test_data: input_t1):
         pipeline.run()
     except FileNotFoundError as e:
         pytest.skip(f"VKML executor_runner not found - not built - skip {e}")
+
+
+def get_symmetric_a16w8_add_quantizer(per_channel_quantization=False):
+    tosa_version = conftest.get_option("tosa_version")
+    tosa_profiles = {
+        "1.0": TosaSpecification.create_from_string("TOSA-1.0+INT+int16"),
+    }
+
+    quantizer = TOSAQuantizer(tosa_profiles[tosa_version])
+    quantizer.set_global(
+        get_symmetric_a16w8_quantization_config(is_per_channel=per_channel_quantization)
+    )
+
+    return Quantize(
+        quantizer,
+        get_symmetric_a16w8_quantization_config(
+            is_per_channel=per_channel_quantization
+        ),
+    )
+
+
+@common.parametrize("test_data", Add.test_data)
+@pytest.mark.xfail(
+    reason="missing int16 add ops support; fails at TOSA reference model with Unsupported operation type or rank. See: https://github.com/pytorch/executorch/issues/13730"
+)
+def test_add_tensor_16a8w_tosa_INT(test_data: input_t1):
+    """Test add operation with 16A8W quantization (16-bit activations, 8-bit weights)"""
+    per_channel_quantization = False
+
+    pipeline = TosaPipelineINT[input_t1](
+        Add(),
+        test_data(),
+        aten_op,
+        exir_op=[],
+        per_channel_quantization=per_channel_quantization,
+        use_to_edge_transform_and_lower=True,
+        tosa_extensions=["int16"],
+    )
+
+    pipeline.change_args(
+        "quantize",
+        get_symmetric_a16w8_add_quantizer(
+            per_channel_quantization=per_channel_quantization
+        ),
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Add.test_data)
+@common.XfailIfNoCorstone300
+@pytest.mark.xfail(
+    reason="Vela compilation fails with 'Invalid arguments' for int16 add operations. See: https://github.com/pytorch/executorch/issues/13730"
+)
+def test_add_tensor_16a8w_u55_INT16(test_data: input_t1):
+    """Test add operation with 16A8W quantization on U55 (16-bit activations, 8-bit weights)"""
+    per_channel_quantization = False
+
+    pipeline = EthosU55PipelineINT[input_t1](
+        Add(),
+        test_data(),
+        aten_op,
+        exir_op,
+        per_channel_quantization=per_channel_quantization,
+        use_to_edge_transform_and_lower=True,
+        run_on_fvp=True,
+    )
+
+    pipeline.change_args(
+        "quantize",
+        get_symmetric_a16w8_add_quantizer(
+            per_channel_quantization=per_channel_quantization
+        ),
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Add.test_data)
+@common.XfailIfNoCorstone320
+@pytest.mark.xfail(
+    reason="Vela compilation fails with 'Invalid arguments' for int16 add operations. See: https://github.com/pytorch/executorch/issues/13730"
+)
+def test_add_tensor_16a8w_u85_INT16(test_data: input_t1):
+    """Test add operation with 16A8W quantization on U85 (16-bit activations, 8-bit weights)"""
+    per_channel_quantization = False
+
+    pipeline = EthosU85PipelineINT[input_t1](
+        Add(),
+        test_data(),
+        aten_op,
+        exir_op,
+        per_channel_quantization=per_channel_quantization,
+        use_to_edge_transform_and_lower=True,
+        run_on_fvp=True,
+    )
+
+    pipeline.change_args(
+        "quantize",
+        get_symmetric_a16w8_add_quantizer(
+            per_channel_quantization=per_channel_quantization
+        ),
+    )
+    pipeline.run()

backends/arm/test/targets.bzl

@@ -13,6 +13,7 @@ def define_arm_tests():
 
     # Operators
     test_files += [
+        "ops/test_add.py",
         "ops/test_avg_pool2d.py",
         "ops/test_linear.py", 
         "ops/test_slice.py",