Add 16A8W support and test for mul operation

Pull Request resolved: #13795

Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend.

This follows the pattern established for linear operations, extending int16 support to mul operations.

Changes:
- Add INT16 dtype validation support in op_mul.py
- Add test_mul_tensor_16a8w_tosa_INT test function
- Enable test_mul.py in test targets configuration

The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency.
ghstack-source-id: 308046859
@exported-using-ghexport

Differential Revision: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/)

Author: Ninja91

backends/arm/operators/op_mul.py

@@ -34,6 +34,7 @@ class MulVisitor_INT(NodeVisitor):
 
     tosa_specs = [
         TosaSpecification.create_from_string("TOSA-1.0+INT"),
+        TosaSpecification.create_from_string("TOSA-1.0+INT+int16"),
     ]
 
     def define_node(
@@ -51,11 +52,11 @@ def define_node(
         validate_valid_dtype(
             self.target,
             [*inputs, output],
-            [ts.DType.INT8, ts.DType.INT32],
+            [ts.DType.INT8, ts.DType.INT16, ts.DType.INT32],
             output.tosa_spec,
         )
 
-        if inputs[0].dtype == ts.DType.INT8:
+        if inputs[0].dtype == ts.DType.INT8 or inputs[0].dtype == ts.DType.INT16:
             input_A = inputs[0]
             input_B = inputs[1]
             input_qparams = get_input_qparams(node)
@@ -80,15 +81,15 @@ def define_node(
                 tosa_spec=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.MUL
             input_A_rescaled, input_B_rescaled = inputs[0], inputs[1]
 
-        if output.dtype == ts.DType.INT8:
+        if output.dtype == ts.DType.INT8 or output.dtype == ts.DType.INT16:
             output_shape = tutils.tosa_shape(output.shape, output.dim_order)
             mul_output = tosa_graph.addIntermediate(output_shape, ts.DType.INT32)
         else:
-            # output.dtype == ts.DType.INT32
+            # output.dtype == ts.DType.INT32 (non-quantized)
             mul_output = output
 
         # Do the INT32 Mul
@@ -110,6 +111,15 @@ def define_node(
             tqutils.insert_rescale_op_to_int8(
                 tosa_graph, mul_output, output_scale, node, self.tosa_spec
             )
+        elif output.dtype == ts.DType.INT16:
+            # Scale output back to 16 bit
+            output_scale = (
+                input_A_qargs.get_scale_per_tensor()  # type: ignore[possibly-undefined]
+                * input_B_qargs.get_scale_per_tensor()  # type: ignore[possibly-undefined]
+            )
+            tqutils.insert_rescale_op_to_int16(
+                tosa_graph, mul_output, output_scale, node, self.tosa_spec
+            )
 
 
 @register_node_visitor

backends/arm/test/ops/test_mul.py

@@ -8,16 +8,23 @@
 
 from typing import Tuple
 
+import pytest
 import torch
+from executorch.backends.arm.quantizer.arm_quantizer import (
+    get_symmetric_a16w8_quantization_config,
+    TOSAQuantizer,
+)
 
-from executorch.backends.arm.test import common
+from executorch.backends.arm.test import common, conftest
 from executorch.backends.arm.test.tester.test_pipeline import (
     EthosU55PipelineINT,
     EthosU85PipelineINT,
     TosaPipelineFP,
     TosaPipelineINT,
     VgfPipeline,
 )
+from executorch.backends.arm.tosa_specification import TosaSpecification
+from executorch.backends.xnnpack.test.tester import Quantize
 
 input_t1 = Tuple[torch.Tensor, torch.Tensor]  # Input x
 aten_op = "torch.ops.aten.mul.Tensor"
@@ -284,3 +291,105 @@ def test_mul_tensor_vgf_INT_int32(test_data: torch.Tensor):
     )
     pipeline.pop_stage("check.quant_nodes")
     pipeline.run()
+
+
+def get_symmetric_a16w8_mul_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", test_data_suite)
+@pytest.mark.xfail(
+    reason="missing int16 mul ops support; fails at TOSA reference model with Unsupported operation type or rank. See: https://github.com/pytorch/executorch/issues/13947"
+)
+def test_mul_tensor_16a8w_tosa_INT(test_data: input_t1):
+    """Test mul operation with 16A8W quantization (16-bit activations, 8-bit weights)"""
+    per_channel_quantization = False
+
+    pipeline = TosaPipelineINT[input_t1](
+        Mul(),
+        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_mul_quantizer(
+            per_channel_quantization=per_channel_quantization
+        ),
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", test_data_suite)
+@common.XfailIfNoCorstone300
+@pytest.mark.xfail(
+    reason="Vela compilation fails with 'Invalid arguments' for int16 mul operations. See: https://github.com/pytorch/executorch/issues/13947"
+)
+def test_mul_tensor_16a8w_u55_INT16(test_data: input_t1):
+    """Test mul operation with 16A8W quantization on U55 (16-bit activations, 8-bit weights)"""
+    per_channel_quantization = False
+
+    pipeline = EthosU55PipelineINT[input_t1](
+        Mul(),
+        test_data(),
+        aten_op,
+        exir_ops=[],
+        per_channel_quantization=per_channel_quantization,
+        use_to_edge_transform_and_lower=True,
+        run_on_fvp=True,
+    )
+
+    pipeline.change_args(
+        "quantize",
+        get_symmetric_a16w8_mul_quantizer(
+            per_channel_quantization=per_channel_quantization
+        ),
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", test_data_suite)
+@common.XfailIfNoCorstone320
+@pytest.mark.xfail(
+    reason="Vela compilation fails with 'Invalid arguments' for int16 mul operations. See: https://github.com/pytorch/executorch/issues/13947"
+)
+def test_mul_tensor_16a8w_u85_INT16(test_data: input_t1):
+    """Test mul operation with 16A8W quantization on U85 (16-bit activations, 8-bit weights)"""
+    per_channel_quantization = False
+
+    pipeline = EthosU85PipelineINT[input_t1](
+        Mul(),
+        test_data(),
+        aten_op,
+        exir_ops=[],
+        per_channel_quantization=per_channel_quantization,
+        use_to_edge_transform_and_lower=True,
+        run_on_fvp=True,
+    )
+
+    pipeline.change_args(
+        "quantize",
+        get_symmetric_a16w8_mul_quantizer(
+            per_channel_quantization=per_channel_quantization
+        ),
+    )
+    pipeline.run()

backends/arm/test/targets.bzl

@@ -16,6 +16,7 @@ def define_arm_tests():
         "ops/test_add.py",
         "ops/test_avg_pool2d.py",
         "ops/test_linear.py", 
+        "ops/test_mul.py",
         "ops/test_slice.py",
         "ops/test_sigmoid.py",
         "ops/test_tanh.py",

backends/arm/tosa/quant_utils.py

@@ -140,6 +140,58 @@ def insert_rescale_op_to_int8(
         compute_rescale: boolean indicating whether we need to divide the output scale by the original scale.
         tosa_graph: the tosa_graph to manipulate.
 
+    This functions is used in serialization to TOSA for target ops that are
+    handled by the DQ/D folding pass, which stores the quantization parameters
+    in the node meta dict.
+    """
+    _insert_rescale_op_to_dtype(
+        tosa_graph, last_tensor, scale, node, ts.DType.INT8, compute_rescale, tosa_spec
+    )
+
+
+def insert_rescale_op_to_int16(
+    tosa_graph: Any,
+    last_tensor: TosaArg,
+    scale: float,
+    node: Node,
+    compute_rescale=True,
+    tosa_spec=None,
+) -> None:
+    """Rescales the node back to int16, adding a suitable RESCALE op to 'tosa_graph'.
+    Parameters:
+        node: The original node that is being handled by the rescales.
+        last_tensor:the tosa tensor to rescale back.
+        scale: the scaling factor used to rescale to int32, from the function 'insert_rescale_op_to_int32'
+        compute_rescale: boolean indicating whether we need to divide the output scale by the original scale.
+        tosa_graph: the tosa_graph to manipulate.
+
+    This functions is used in serialization to TOSA for target ops that are
+    handled by the DQ/D folding pass, which stores the quantization parameters
+    in the node meta dict.
+    """
+    _insert_rescale_op_to_dtype(
+        tosa_graph, last_tensor, scale, node, ts.DType.INT16, compute_rescale, tosa_spec
+    )
+
+
+def _insert_rescale_op_to_dtype(
+    tosa_graph: Any,
+    last_tensor: TosaArg,
+    scale: float,
+    node: Node,
+    output_dtype: Any,
+    compute_rescale=True,
+    tosa_spec=None,
+) -> None:
+    """Common implementation for rescaling nodes back to a specific dtype.
+    Parameters:
+        node: The original node that is being handled by the rescales.
+        last_tensor:the tosa tensor to rescale back.
+        scale: the scaling factor used to rescale to int32, from the function 'insert_rescale_op_to_int32'
+        output_dtype: The target dtype (ts.DType.INT8 or ts.DType.INT16)
+        compute_rescale: boolean indicating whether we need to divide the output scale by the original scale.
+        tosa_graph: the tosa_graph to manipulate.
+
     This functions is used in serialization to TOSA for target ops that are
     handled by the DQ/D folding pass, which stores the quantization parameters
     in the node meta dict.
@@ -158,13 +210,14 @@ def insert_rescale_op_to_int8(
     else:
         output_rescale_scale = scale
 
-    # Rescale Back to INT8
-    build_rescale_from_int32(
+    # Rescale Back to the specified dtype
+    build_rescale_from_int32_to_dtype(
         tosa_graph,
         last_tensor,
         node.name,
         qargs_out.get_zp_per_tensor(),
         output_rescale_scale,
+        output_dtype,
         tosa_spec=tosa_spec,
     )
 
@@ -337,14 +390,55 @@ def build_rescale_from_int32(
     per_channel: bool = False,
     tosa_spec=None,
 ) -> None:
+    # For TOSA v1.0 multipliers, shifts, input_zp and output_zp are now inputs
+    # to the RESCALE op see: https://www.mlplatform.org/tosa/tosa_spec.html#_rescale
+    build_rescale_from_int32_to_dtype(
+        tosa_fb,
+        input_node,
+        output_name,
+        output_zp,
+        rescale_scale,
+        ts.DType.INT8,
+        is_scale32,
+        is_double_round,
+        per_channel,
+        tosa_spec,
+    )
+
+    return
+
+
+def build_rescale_from_int32_to_dtype(
+    tosa_fb: Any,
+    input_node: TosaArg,
+    output_name: str,
+    output_zp: int,
+    rescale_scale: float,
+    output_dtype: Any,
+    is_scale32: bool = True,
+    is_double_round: bool = False,
+    per_channel: bool = False,
+    tosa_spec=None,
+) -> None:
+    """Common implementation for rescaling from INT32 to a specific dtype (INT8 or INT16).
+
+    Parameters:
+        tosa_fb: The TOSA serializer
+        input_node: Input tensor (should be INT32)
+        output_name: Name for the output tensor
+        output_zp: Output zero point
+        rescale_scale: Rescaling factor
+        output_dtype: Target dtype (ts.DType.INT8 or ts.DType.INT16)
+        Other parameters: Standard rescale parameters
+    """
     # For TOSA v1.0 multipliers, shifts, input_zp and output_zp are now inputs
     # to the RESCALE op see: https://www.mlplatform.org/tosa/tosa_spec.html#_rescale
     build_rescale(
         tosa_fb,
         [rescale_scale],
         input_node,
         output_name=output_name,
-        output_type=ts.DType.INT8,
+        output_type=output_dtype,
         input_zp=[0],
         output_zp=[output_zp],
         rounding_mode=RoundingMode.SINGLE_ROUND,