Arm backend: Add tanh operator

Differential Revision: D64427390

Pull Request resolved: pytorch#6226

backends/arm/arm_partitioner.py

@@ -65,6 +65,7 @@ def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
             exir_ops.edge.aten.slice_copy.Tensor,
             exir_ops.edge.aten.sub.Tensor,
             exir_ops.edge.aten.sum.dim_IntList,
+            exir_ops.edge.aten.tanh.default,
             exir_ops.edge.aten.view_copy.default,
             exir_ops.edge.aten.clone.default,
             exir_ops.edge.aten.mean.dim,

backends/arm/operators/__init__.py

@@ -34,6 +34,7 @@
     op_squeeze,
     op_sub,
     op_sum,
+    op_tanh,
     op_transpose,
     op_unsqueeze,
     op_view,

backends/arm/operators/op_tanh.py

@@ -0,0 +1,86 @@
+# Copyright 2024 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+from typing import List
+
+import numpy as np
+
+import serializer.tosa_serializer as ts
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.tosa_mapping import TosaArg
+
+from executorch.backends.arm.tosa_quant_utils import (
+    dequantize_value,
+    get_quant_node_args,
+    QuantArgs,
+    quantize_value,
+)
+from serializer.tosa_serializer import TosaOp
+from torch.fx import Node
+
+
+@register_node_visitor
+class TanhVisitor(NodeVisitor):
+    target = "aten.tanh.default"
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+        is_quant_node: bool,
+    ) -> None:
+
+        assert len(node.all_input_nodes) == 1
+
+        if is_quant_node:
+            # Assume quantized input is 8 bit.
+            assert len(node.users) == 1
+
+            # Create attribute for 8 bit table lookup.
+            input_node = node.all_input_nodes[0]
+            in_quantargs = get_quant_node_args(input_node)
+            output_node = list(node.users)[0]
+            out_quantargs = get_quant_node_args(output_node)
+
+            table = tanh_table_8bit(in_quantargs, out_quantargs)
+            table_attr = ts.TosaSerializerAttribute()
+            table_attr.TableAttribute(table)
+
+            tosa_graph.addOperator(
+                TosaOp.Op().TABLE, [inputs[0].name], [output.name], table_attr
+            )
+        else:
+            tosa_graph.addOperator(TosaOp.Op().TANH, [inputs[0].name], [output.name])
+
+
+def tanh_table_8bit(in_quantargs: QuantArgs, out_quantargs: QuantArgs):
+    """
+    Returns a table mapping 256 entries to tanh([qmin,qmax])
+    Reference: https://www.mlplatform.org/tosa/tosa_spec.html#_tanh
+    """
+
+    def tanh(x):
+        # Convert quantized input to floating point tanh input space.
+        v = dequantize_value(x, in_quantargs)
+        # Compute tanh.
+        v = np.exp(-2.0 * v)
+        v = (1.0 - v) / (1.0 + v)
+
+        # Convert tanh output back to quantized space.
+        return quantize_value(v, out_quantargs)
+
+    return [
+        tanh(x)
+        for x in np.linspace(in_quantargs.qmin, in_quantargs.qmax, 256, dtype=np.int8)
+    ]

backends/arm/quantizer/quantization_annotation/one_to_one_annotator.py

@@ -41,6 +41,7 @@ def _annotate_one_to_one(
         torch.ops.aten.reciprocal.default,
         torch.ops.aten.rsqrt.default,
         torch.ops.aten.sigmoid.default,
+        torch.ops.aten.tanh.default,
     )
     for node in gm.graph.nodes:
         if node.op != "call_function" or node.target not in one_to_one_ops:

backends/arm/test/ops/test_tanh.py

@@ -0,0 +1,134 @@
+# 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 unittest
+
+from typing import Tuple
+
+import torch
+
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.arm_tester import ArmTester
+from executorch.exir.backend.compile_spec_schema import CompileSpec
+from parameterized import parameterized
+
+
+test_data_suite = [
+    # (test_name, test_data)
+    ("zeros", torch.zeros(10, 10, 10, 10)),
+    ("ones", torch.ones(10, 10, 10)),
+    ("rand", torch.rand(10, 10) - 0.5),
+    ("randn_pos", torch.randn(10) + 10),
+    ("randn_neg", torch.randn(10) - 10),
+    ("ramp", torch.arange(-16, 16, 0.2)),
+]
+
+
+class TestTanh(unittest.TestCase):
+    class Tanh(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.tanh = torch.nn.Tanh()
+
+        def forward(self, x):
+            return self.tanh(x)
+
+    def _test_tanh_tosa_MI_pipeline(
+        self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
+    ):
+        (
+            ArmTester(
+                module,
+                example_inputs=test_data,
+                compile_spec=common.get_tosa_compile_spec(),
+            )
+            .export()
+            .check(["torch.ops.aten.tanh.default"])
+            .check_not(["torch.ops.quantized_decomposed"])
+            .to_edge()
+            .partition()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_tanh_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+            .run_method_and_compare_outputs(inputs=test_data)
+        )
+
+    def _test_tanh_tosa_BI_pipeline(self, module: torch.nn.Module, test_data: Tuple):
+        (
+            ArmTester(
+                module,
+                example_inputs=test_data,
+                compile_spec=common.get_tosa_compile_spec(),
+            )
+            .quantize()
+            .export()
+            .check(["torch.ops.aten.tanh.default"])
+            .check(["torch.ops.quantized_decomposed"])
+            .to_edge()
+            .partition()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_tanh_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+            .run_method_and_compare_outputs(inputs=test_data)
+        )
+
+    def _test_tanh_tosa_ethos_BI_pipeline(
+        self,
+        compile_spec: list[CompileSpec],
+        module: torch.nn.Module,
+        test_data: Tuple[torch.tensor],
+    ):
+        (
+            ArmTester(
+                module,
+                example_inputs=test_data,
+                compile_spec=compile_spec,
+            )
+            .quantize()
+            .export()
+            .check_count({"torch.ops.aten.tanh.default": 1})
+            .check(["torch.ops.quantized_decomposed"])
+            .to_edge()
+            .partition()
+            .check_not(["executorch_exir_dialects_edge__ops_aten_tanh_default"])
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+        )
+
+    def _test_tanh_tosa_u55_BI_pipeline(
+        self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
+    ):
+        self._test_tanh_tosa_ethos_BI_pipeline(
+            common.get_u55_compile_spec(), module, test_data
+        )
+
+    def _test_tanh_tosa_u85_BI_pipeline(
+        self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
+    ):
+        self._test_tanh_tosa_ethos_BI_pipeline(
+            common.get_u85_compile_spec(), module, test_data
+        )
+
+    @parameterized.expand(test_data_suite)
+    def test_tanh_tosa_MI(
+        self,
+        test_name: str,
+        test_data: torch.Tensor,
+    ):
+        self._test_tanh_tosa_MI_pipeline(self.Tanh(), (test_data,))
+
+    @parameterized.expand(test_data_suite)
+    def test_tanh_tosa_BI(self, test_name: str, test_data: torch.Tensor):
+        self._test_tanh_tosa_BI_pipeline(self.Tanh(), (test_data,))
+
+    @parameterized.expand(test_data_suite)
+    def test_tanh_tosa_u55_BI(self, test_name: str, test_data: torch.Tensor):
+        self._test_tanh_tosa_u55_BI_pipeline(self.Tanh(), (test_data,))
+
+    @parameterized.expand(test_data_suite)
+    def test_tanh_tosa_u85_BI(self, test_name: str, test_data: torch.Tensor):
+        self._test_tanh_tosa_u85_BI_pipeline(self.Tanh(), (test_data,))