Add tanh op to XNNPACK backend

Author: leafs1

backends/xnnpack/operators/__init__.py

@@ -50,5 +50,6 @@
     op_static_constant_pad,
     op_static_resize_bilinear_2d,
     op_sub,
+    op_tanh,
     op_to_copy,
 )

backends/xnnpack/operators/op_tanh.py

@@ -0,0 +1,52 @@
+# 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.
+
+from typing import Dict
+
+import torch
+from executorch.backends.xnnpack.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.xnnpack.serialization.xnnpack_graph_schema import (
+    XNNGraph,
+    XNNTanh,
+    XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class TanhVisitor(NodeVisitor):
+    target = "aten.tanh.default"
+
+    def __init__(self, *args) -> None:
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        xnn_graph: XNNGraph,
+        vals_to_ids: Dict[torch.fx.Node, int],
+        debug_handle: int,
+    ) -> None:
+        self.define_nodes_tensor_inputs_outputs(node, xnn_graph, vals_to_ids)
+
+        # input
+        input_id = vals_to_ids[get_input_node(node, 0)]
+
+        # output
+        output_id = vals_to_ids[node]
+
+        ser_node = XNode(
+            xnode_union=XNNTanh(
+                input_id=input_id,
+                output_id=output_id,
+                flags=0,
+            ),
+            debug_handle=debug_handle,
+        )
+        xnn_graph.xnodes.append(ser_node)

backends/xnnpack/partition/config/__init__.py

@@ -49,6 +49,7 @@
     SoftmaxConfig,
     SquareRootConfig,
     SubConfig,
+    TanhConfig,
     UpsampleBilinear2dConfig,
 )
 from executorch.backends.xnnpack.partition.config.node_configs import (
@@ -99,6 +100,7 @@
     PreluConfig,
     ReciprocalSquareRootConfig,
     ReLUConfig,
+    TanhConfig,
     # SDPAConfig, TODO: D60553559: preserving SDPA for fairseq fails
     SigmoidConfig,
     SliceCopyConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -371,6 +371,13 @@ def supported_precision_types(self) -> List[ConfigPrecisionType]:
         return [ConfigPrecisionType.FP32]
 
 
+class TanhConfig(GenericNodePartitionerConfig):
+    target_name = "tanh.default"
+
+    def supported_precision_types(self) -> List[ConfigPrecisionType]:
+        return [ConfigPrecisionType.FP32]
+
+
 class MeanDimConfig(GenericNodePartitionerConfig):
     target_name = "mean.dim"
 

backends/xnnpack/partition/configs.py

@@ -66,6 +66,7 @@
     exir_ops.edge.aten.rsqrt.default,
     exir_ops.edge.aten.log.default,
     exir_ops.edge.aten.gelu.default,
+    exir_ops.edge.aten.tanh.default,
 ]
 
 SUPPORTED_MODULES = [

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1162,6 +1162,36 @@ Error defineArgMaxPooling2dNode(
   return Error::Ok;
 }
 
+/*
+Define serialized tanh node into the subgraph, using the remapped ids
+to map the serialized ids, to the new ids generated when defining the
+tensor value
+*/
+Error defineTanhNode(
+    xnn_subgraph_t subgraph_ptr,
+    const std::unordered_map<uint32_t, uint32_t>& remapped_ids,
+    const NodePtr node,
+    const fb_xnnpack::XNNGraph* graph) noexcept {
+  MAYBE_UNUSED(graph);
+
+  auto graph_node = node->xnode_union_as_XNNTanh();
+
+  xnn_status status = xnn_define_tanh(
+      subgraph_ptr,
+      remapped_ids.at(graph_node->input_id()),
+      remapped_ids.at(graph_node->output_id()),
+      graph_node->flags());
+
+  ET_CHECK_OR_RETURN_ERROR(
+      status == xnn_status_success,
+      Internal,
+      "Failed to create tanh node %i with code: %s",
+      node->debug_handle(),
+      xnn_status_to_string(status));
+
+  return Error::Ok;
+}
+
 /*
 Defines serialized prelu node into the subgraph,
 using the remapped ids to map the serialized ids,
@@ -1697,6 +1727,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
     _DEFINE(Gelu)
     _DEFINE(Hardswish)
     _DEFINE(Log)
+    _DEFINE(Tanh)
     _DEFINE(Negate)
     _DEFINE(Square)
     _DEFINE(Clamp)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -154,6 +154,7 @@ union XNodeUnion {
   XNNReciprocalSquareRoot: _XNNNode1x1,
   XNNLog: _XNNNode1x1,
   XNNGelu: _XNNNode1x1,
+  XNNTanh: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -150,6 +150,7 @@ union XNodeUnion {
   XNNReciprocalSquareRoot: _XNNNode1x1,
   XNNLog: _XNNNode1x1,
   XNNGelu: _XNNNode1x1,
+  XNNTanh: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -319,6 +319,11 @@ class XNNLog(XNNNode1x1):
     pass
 
 
+@dataclass
+class XNNTanh(XNNNode1x1):
+    pass
+
+
 @dataclass
 class XNNMaximum(XNNNode2x1):
     pass
@@ -391,6 +396,7 @@ class XNNScaledDotProductAttention:
     XNNReciprocalSquareRoot,
     XNNLog,
     XNNGelu,
+    XNNTanh,
 ]
 
 

backends/xnnpack/test/ops/test_tanh.py

@@ -0,0 +1,43 @@
+# 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
+from executorch.backends.xnnpack.test.tester import Tester
+
+
+class TestTanh(unittest.TestCase):
+    def setUp(self):
+        torch._dynamo.reset()
+
+    class Tanh(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+
+        def forward(self, x):
+            return torch.tanh(x)
+
+    def run_tanh_test(self, inputs):
+        (
+            Tester(self.Tanh(), inputs)
+            .export()
+            .check_count({"torch.ops.aten.tanh.default": 1})
+            .to_edge_transform_and_lower()
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .check_not(["executorch_exir_dialects_edge__ops_aten_tanh_default"])
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )
+
+    def test_fp16_tanh(self):
+        inputs = (torch.randn(20).to(torch.float16),)
+        self.run_tanh_test(inputs)
+
+    def test_fp32_tanh(self):
+        inputs = (torch.randn(20),)
+        self.run_tanh_test(inputs)