Support sine operator on XNNPACK

backends/xnnpack/operators/__init__.py

@@ -41,6 +41,7 @@
  op_relu,
  op_rsqrt,
  op_sigmoid,
+ op_sin,
  op_skip_ops,
  op_slice_copy,
  op_softmax,

backends/xnnpack/operators/op_sin.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,
+ XNNSin,
+ XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class SinVisitor(NodeVisitor):
+ target = "aten.sin.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=XNNSin(
+ 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

@@ -45,6 +45,7 @@
  ReciprocalSquareRootConfig,
  ReLUConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,
@@ -105,6 +106,7 @@
  TanhConfig,
  ToDimOrderCopyConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -636,3 +636,10 @@ class BMMConfig(GenericNodePartitionerConfig):
 
  def supported_precision_types(self) -> List[ConfigPrecisionType]:
  return [ConfigPrecisionType.FP32]
+
+
+class SinConfig(GenericNodePartitionerConfig):
+ target_name = "sin.default"
+
+ def supported_precision_types(self) -> List[ConfigPrecisionType]:
+ return [ConfigPrecisionType.FP32]

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1690,6 +1690,7 @@ _DEFINE_UNARY_NODE_NO_PARAMS(Log, xnn_unary_log)
 _DEFINE_UNARY_NODE_NO_PARAMS(Negate, xnn_unary_negate)
 _DEFINE_UNARY_NODE_NO_PARAMS(Square, xnn_unary_square)
 _DEFINE_UNARY_NODE_NO_PARAMS(Abs, xnn_unary_abs)
+_DEFINE_UNARY_NODE_NO_PARAMS(Sin, xnn_unary_sine)
 
 // Unary Ops with min/max params
 _DEFINE_UNARY_NODE_WITH_MINMAX(Clamp, xnn_unary_clamp)
@@ -1737,6 +1738,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
  _DEFINE(Floor)
  _DEFINE(PReLU)
  _DEFINE(Sigmoid)
+ _DEFINE(Sin)
 
  // Others
  _DEFINE(FullyConnected)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -156,6 +156,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -152,6 +152,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -347,6 +347,11 @@ class XNNPReLU(XNNNode2x1):
  pass
 
 
+@dataclass
+class XNNSin(XNNNode1x1):
+ pass
+
+
 @dataclass
 class XNNScaledDotProductAttention:
  query_id: int
@@ -402,6 +407,8 @@ class XNNScaledDotProductAttention:
  XNNLog,
  XNNGelu,
  XNNTanh,
+ XNNExp,
+ XNNSin,
 ]
 
 

backends/xnnpack/test/ops/test_sin.py

@@ -0,0 +1,87 @@
+# 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 TestSin(unittest.TestCase):
+ def setUp(self):
+ torch._dynamo.reset()
+
+ class Sin(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ z = torch.sin(x)
+ return z
+
+ def _test_sin(self, inputs, legacy_mode: bool = False):
+ tester = (
+ Tester(self.Sin(), inputs)
+ .export()
+ .check_count({"torch.ops.aten.sin.default": 1})
+ )
+
+ if legacy_mode:
+ tester = tester.to_edge().partition()
+ else:
+ tester = tester.to_edge_transform_and_lower()
+
+ (
+ tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+ .check_not(["executorch_exir_dialects_edge__ops_aten_sin_default"])
+ .to_executorch()
+ .serialize()
+ .run_method_and_compare_outputs()
+ )
+
+ def test_fp16_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp16_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=True)
+
+ def test_fp32_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp32_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=True)

backends/xnnpack/operators/__init__.py

@@ -41,6 +41,7 @@
  op_relu,
  op_rsqrt,
  op_sigmoid,
+ op_sin,
  op_skip_ops,
  op_slice_copy,
  op_softmax,

backends/xnnpack/operators/op_sin.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,
+ XNNSin,
+ XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class SinVisitor(NodeVisitor):
+ target = "aten.sin.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=XNNSin(
+ 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

@@ -45,6 +45,7 @@
  ReciprocalSquareRootConfig,
  ReLUConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,
@@ -105,6 +106,7 @@
  TanhConfig,
  ToDimOrderCopyConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -636,3 +636,10 @@ class BMMConfig(GenericNodePartitionerConfig):
 
  def supported_precision_types(self) -> List[ConfigPrecisionType]:
  return [ConfigPrecisionType.FP32]
+
+
+class SinConfig(GenericNodePartitionerConfig):
+ target_name = "sin.default"
+
+ def supported_precision_types(self) -> List[ConfigPrecisionType]:
+ return [ConfigPrecisionType.FP32]

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1690,6 +1690,7 @@ _DEFINE_UNARY_NODE_NO_PARAMS(Log, xnn_unary_log)
 _DEFINE_UNARY_NODE_NO_PARAMS(Negate, xnn_unary_negate)
 _DEFINE_UNARY_NODE_NO_PARAMS(Square, xnn_unary_square)
 _DEFINE_UNARY_NODE_NO_PARAMS(Abs, xnn_unary_abs)
+_DEFINE_UNARY_NODE_NO_PARAMS(Sin, xnn_unary_sine)
 
 // Unary Ops with min/max params
 _DEFINE_UNARY_NODE_WITH_MINMAX(Clamp, xnn_unary_clamp)
@@ -1737,6 +1738,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
  _DEFINE(Floor)
  _DEFINE(PReLU)
  _DEFINE(Sigmoid)
+ _DEFINE(Sin)
 
  // Others
  _DEFINE(FullyConnected)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -156,6 +156,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -152,6 +152,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -347,6 +347,11 @@ class XNNPReLU(XNNNode2x1):
  pass
 
 
+@dataclass
+class XNNSin(XNNNode1x1):
+ pass
+
+
 @dataclass
 class XNNScaledDotProductAttention:
  query_id: int
@@ -402,6 +407,8 @@ class XNNScaledDotProductAttention:
  XNNLog,
  XNNGelu,
  XNNTanh,
+ XNNExp,
+ XNNSin,
 ]
 
 

backends/xnnpack/test/ops/test_sin.py

@@ -0,0 +1,87 @@
+# 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 TestSin(unittest.TestCase):
+ def setUp(self):
+ torch._dynamo.reset()
+
+ class Sin(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ z = torch.sin(x)
+ return z
+
+ def _test_sin(self, inputs, legacy_mode: bool = False):
+ tester = (
+ Tester(self.Sin(), inputs)
+ .export()
+ .check_count({"torch.ops.aten.sin.default": 1})
+ )
+
+ if legacy_mode:
+ tester = tester.to_edge().partition()
+ else:
+ tester = tester.to_edge_transform_and_lower()
+
+ (
+ tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+ .check_not(["executorch_exir_dialects_edge__ops_aten_sin_default"])
+ .to_executorch()
+ .serialize()
+ .run_method_and_compare_outputs()
+ )
+
+ def test_fp16_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp16_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=True)
+
+ def test_fp32_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp32_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=True)

backends/xnnpack/operators/__init__.py

@@ -41,6 +41,7 @@
  op_relu,
  op_rsqrt,
  op_sigmoid,
+ op_sin,
  op_skip_ops,
  op_slice_copy,
  op_softmax,

backends/xnnpack/operators/op_sin.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,
+ XNNSin,
+ XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class SinVisitor(NodeVisitor):
+ target = "aten.sin.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=XNNSin(
+ 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

@@ -45,6 +45,7 @@
  ReciprocalSquareRootConfig,
  ReLUConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,
@@ -105,6 +106,7 @@
  TanhConfig,
  ToDimOrderCopyConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -636,3 +636,10 @@ class BMMConfig(GenericNodePartitionerConfig):
 
  def supported_precision_types(self) -> List[ConfigPrecisionType]:
  return [ConfigPrecisionType.FP32]
+
+
+class SinConfig(GenericNodePartitionerConfig):
+ target_name = "sin.default"
+
+ def supported_precision_types(self) -> List[ConfigPrecisionType]:
+ return [ConfigPrecisionType.FP32]

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1690,6 +1690,7 @@ _DEFINE_UNARY_NODE_NO_PARAMS(Log, xnn_unary_log)
 _DEFINE_UNARY_NODE_NO_PARAMS(Negate, xnn_unary_negate)
 _DEFINE_UNARY_NODE_NO_PARAMS(Square, xnn_unary_square)
 _DEFINE_UNARY_NODE_NO_PARAMS(Abs, xnn_unary_abs)
+_DEFINE_UNARY_NODE_NO_PARAMS(Sin, xnn_unary_sine)
 
 // Unary Ops with min/max params
 _DEFINE_UNARY_NODE_WITH_MINMAX(Clamp, xnn_unary_clamp)
@@ -1737,6 +1738,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
  _DEFINE(Floor)
  _DEFINE(PReLU)
  _DEFINE(Sigmoid)
+ _DEFINE(Sin)
 
  // Others
  _DEFINE(FullyConnected)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -156,6 +156,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -152,6 +152,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -347,6 +347,11 @@ class XNNPReLU(XNNNode2x1):
  pass
 
 
+@dataclass
+class XNNSin(XNNNode1x1):
+ pass
+
+
 @dataclass
 class XNNScaledDotProductAttention:
  query_id: int
@@ -402,6 +407,8 @@ class XNNScaledDotProductAttention:
  XNNLog,
  XNNGelu,
  XNNTanh,
+ XNNExp,
+ XNNSin,
 ]
 
 

backends/xnnpack/test/ops/test_sin.py

@@ -0,0 +1,87 @@
+# 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 TestSin(unittest.TestCase):
+ def setUp(self):
+ torch._dynamo.reset()
+
+ class Sin(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ z = torch.sin(x)
+ return z
+
+ def _test_sin(self, inputs, legacy_mode: bool = False):
+ tester = (
+ Tester(self.Sin(), inputs)
+ .export()
+ .check_count({"torch.ops.aten.sin.default": 1})
+ )
+
+ if legacy_mode:
+ tester = tester.to_edge().partition()
+ else:
+ tester = tester.to_edge_transform_and_lower()
+
+ (
+ tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+ .check_not(["executorch_exir_dialects_edge__ops_aten_sin_default"])
+ .to_executorch()
+ .serialize()
+ .run_method_and_compare_outputs()
+ )
+
+ def test_fp16_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp16_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=True)
+
+ def test_fp32_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp32_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=True)

backends/xnnpack/operators/__init__.py

@@ -41,6 +41,7 @@
  op_relu,
  op_rsqrt,
  op_sigmoid,
+ op_sin,
  op_skip_ops,
  op_slice_copy,
  op_softmax,

backends/xnnpack/operators/op_sin.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,
+ XNNSin,
+ XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class SinVisitor(NodeVisitor):
+ target = "aten.sin.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=XNNSin(
+ 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

@@ -45,6 +45,7 @@
  ReciprocalSquareRootConfig,
  ReLUConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,
@@ -105,6 +106,7 @@
  TanhConfig,
  ToDimOrderCopyConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -636,3 +636,10 @@ class BMMConfig(GenericNodePartitionerConfig):
 
  def supported_precision_types(self) -> List[ConfigPrecisionType]:
  return [ConfigPrecisionType.FP32]
+
+
+class SinConfig(GenericNodePartitionerConfig):
+ target_name = "sin.default"
+
+ def supported_precision_types(self) -> List[ConfigPrecisionType]:
+ return [ConfigPrecisionType.FP32]

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1690,6 +1690,7 @@ _DEFINE_UNARY_NODE_NO_PARAMS(Log, xnn_unary_log)
 _DEFINE_UNARY_NODE_NO_PARAMS(Negate, xnn_unary_negate)
 _DEFINE_UNARY_NODE_NO_PARAMS(Square, xnn_unary_square)
 _DEFINE_UNARY_NODE_NO_PARAMS(Abs, xnn_unary_abs)
+_DEFINE_UNARY_NODE_NO_PARAMS(Sin, xnn_unary_sine)
 
 // Unary Ops with min/max params
 _DEFINE_UNARY_NODE_WITH_MINMAX(Clamp, xnn_unary_clamp)
@@ -1737,6 +1738,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
  _DEFINE(Floor)
  _DEFINE(PReLU)
  _DEFINE(Sigmoid)
+ _DEFINE(Sin)
 
  // Others
  _DEFINE(FullyConnected)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -156,6 +156,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -152,6 +152,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -347,6 +347,11 @@ class XNNPReLU(XNNNode2x1):
  pass
 
 
+@dataclass
+class XNNSin(XNNNode1x1):
+ pass
+
+
 @dataclass
 class XNNScaledDotProductAttention:
  query_id: int
@@ -402,6 +407,8 @@ class XNNScaledDotProductAttention:
  XNNLog,
  XNNGelu,
  XNNTanh,
+ XNNExp,
+ XNNSin,
 ]
 
 

backends/xnnpack/test/ops/test_sin.py

@@ -0,0 +1,87 @@
+# 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 TestSin(unittest.TestCase):
+ def setUp(self):
+ torch._dynamo.reset()
+
+ class Sin(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ z = torch.sin(x)
+ return z
+
+ def _test_sin(self, inputs, legacy_mode: bool = False):
+ tester = (
+ Tester(self.Sin(), inputs)
+ .export()
+ .check_count({"torch.ops.aten.sin.default": 1})
+ )
+
+ if legacy_mode:
+ tester = tester.to_edge().partition()
+ else:
+ tester = tester.to_edge_transform_and_lower()
+
+ (
+ tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+ .check_not(["executorch_exir_dialects_edge__ops_aten_sin_default"])
+ .to_executorch()
+ .serialize()
+ .run_method_and_compare_outputs()
+ )
+
+ def test_fp16_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp16_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=True)
+
+ def test_fp32_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp32_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=True)

backends/xnnpack/operators/__init__.py

@@ -41,6 +41,7 @@
  op_relu,
  op_rsqrt,
  op_sigmoid,
+ op_sin,
  op_skip_ops,
  op_slice_copy,
  op_softmax,

backends/xnnpack/operators/op_sin.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,
+ XNNSin,
+ XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class SinVisitor(NodeVisitor):
+ target = "aten.sin.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=XNNSin(
+ 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

@@ -45,6 +45,7 @@
  ReciprocalSquareRootConfig,
  ReLUConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,
@@ -105,6 +106,7 @@
  TanhConfig,
  ToDimOrderCopyConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -636,3 +636,10 @@ class BMMConfig(GenericNodePartitionerConfig):
 
  def supported_precision_types(self) -> List[ConfigPrecisionType]:
  return [ConfigPrecisionType.FP32]
+
+
+class SinConfig(GenericNodePartitionerConfig):
+ target_name = "sin.default"
+
+ def supported_precision_types(self) -> List[ConfigPrecisionType]:
+ return [ConfigPrecisionType.FP32]

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1690,6 +1690,7 @@ _DEFINE_UNARY_NODE_NO_PARAMS(Log, xnn_unary_log)
 _DEFINE_UNARY_NODE_NO_PARAMS(Negate, xnn_unary_negate)
 _DEFINE_UNARY_NODE_NO_PARAMS(Square, xnn_unary_square)
 _DEFINE_UNARY_NODE_NO_PARAMS(Abs, xnn_unary_abs)
+_DEFINE_UNARY_NODE_NO_PARAMS(Sin, xnn_unary_sine)
 
 // Unary Ops with min/max params
 _DEFINE_UNARY_NODE_WITH_MINMAX(Clamp, xnn_unary_clamp)
@@ -1737,6 +1738,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
  _DEFINE(Floor)
  _DEFINE(PReLU)
  _DEFINE(Sigmoid)
+ _DEFINE(Sin)
 
  // Others
  _DEFINE(FullyConnected)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -156,6 +156,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -152,6 +152,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -347,6 +347,11 @@ class XNNPReLU(XNNNode2x1):
  pass
 
 
+@dataclass
+class XNNSin(XNNNode1x1):
+ pass
+
+
 @dataclass
 class XNNScaledDotProductAttention:
  query_id: int
@@ -402,6 +407,8 @@ class XNNScaledDotProductAttention:
  XNNLog,
  XNNGelu,
  XNNTanh,
+ XNNExp,
+ XNNSin,
 ]
 
 

backends/xnnpack/test/ops/test_sin.py

@@ -0,0 +1,87 @@
+# 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 TestSin(unittest.TestCase):
+ def setUp(self):
+ torch._dynamo.reset()
+
+ class Sin(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ z = torch.sin(x)
+ return z
+
+ def _test_sin(self, inputs, legacy_mode: bool = False):
+ tester = (
+ Tester(self.Sin(), inputs)
+ .export()
+ .check_count({"torch.ops.aten.sin.default": 1})
+ )
+
+ if legacy_mode:
+ tester = tester.to_edge().partition()
+ else:
+ tester = tester.to_edge_transform_and_lower()
+
+ (
+ tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+ .check_not(["executorch_exir_dialects_edge__ops_aten_sin_default"])
+ .to_executorch()
+ .serialize()
+ .run_method_and_compare_outputs()
+ )
+
+ def test_fp16_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp16_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=True)
+
+ def test_fp32_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp32_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=True)

backends/xnnpack/operators/__init__.py

@@ -41,6 +41,7 @@
  op_relu,
  op_rsqrt,
  op_sigmoid,
+ op_sin,
  op_skip_ops,
  op_slice_copy,
  op_softmax,

backends/xnnpack/operators/op_sin.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,
+ XNNSin,
+ XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class SinVisitor(NodeVisitor):
+ target = "aten.sin.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=XNNSin(
+ 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

@@ -45,6 +45,7 @@
  ReciprocalSquareRootConfig,
  ReLUConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,
@@ -105,6 +106,7 @@
  TanhConfig,
  ToDimOrderCopyConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -636,3 +636,10 @@ class BMMConfig(GenericNodePartitionerConfig):
 
  def supported_precision_types(self) -> List[ConfigPrecisionType]:
  return [ConfigPrecisionType.FP32]
+
+
+class SinConfig(GenericNodePartitionerConfig):
+ target_name = "sin.default"
+
+ def supported_precision_types(self) -> List[ConfigPrecisionType]:
+ return [ConfigPrecisionType.FP32]

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1690,6 +1690,7 @@ _DEFINE_UNARY_NODE_NO_PARAMS(Log, xnn_unary_log)
 _DEFINE_UNARY_NODE_NO_PARAMS(Negate, xnn_unary_negate)
 _DEFINE_UNARY_NODE_NO_PARAMS(Square, xnn_unary_square)
 _DEFINE_UNARY_NODE_NO_PARAMS(Abs, xnn_unary_abs)
+_DEFINE_UNARY_NODE_NO_PARAMS(Sin, xnn_unary_sine)
 
 // Unary Ops with min/max params
 _DEFINE_UNARY_NODE_WITH_MINMAX(Clamp, xnn_unary_clamp)
@@ -1737,6 +1738,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
  _DEFINE(Floor)
  _DEFINE(PReLU)
  _DEFINE(Sigmoid)
+ _DEFINE(Sin)
 
  // Others
  _DEFINE(FullyConnected)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -156,6 +156,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -152,6 +152,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -347,6 +347,11 @@ class XNNPReLU(XNNNode2x1):
  pass
 
 
+@dataclass
+class XNNSin(XNNNode1x1):
+ pass
+
+
 @dataclass
 class XNNScaledDotProductAttention:
  query_id: int
@@ -402,6 +407,8 @@ class XNNScaledDotProductAttention:
  XNNLog,
  XNNGelu,
  XNNTanh,
+ XNNExp,
+ XNNSin,
 ]
 
 

backends/xnnpack/test/ops/test_sin.py

@@ -0,0 +1,87 @@
+# 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 TestSin(unittest.TestCase):
+ def setUp(self):
+ torch._dynamo.reset()
+
+ class Sin(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ z = torch.sin(x)
+ return z
+
+ def _test_sin(self, inputs, legacy_mode: bool = False):
+ tester = (
+ Tester(self.Sin(), inputs)
+ .export()
+ .check_count({"torch.ops.aten.sin.default": 1})
+ )
+
+ if legacy_mode:
+ tester = tester.to_edge().partition()
+ else:
+ tester = tester.to_edge_transform_and_lower()
+
+ (
+ tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+ .check_not(["executorch_exir_dialects_edge__ops_aten_sin_default"])
+ .to_executorch()
+ .serialize()
+ .run_method_and_compare_outputs()
+ )
+
+ def test_fp16_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp16_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=True)
+
+ def test_fp32_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp32_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=True)

backends/xnnpack/operators/__init__.py

@@ -41,6 +41,7 @@
  op_relu,
  op_rsqrt,
  op_sigmoid,
+ op_sin,
  op_skip_ops,
  op_slice_copy,
  op_softmax,

backends/xnnpack/operators/op_sin.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,
+ XNNSin,
+ XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class SinVisitor(NodeVisitor):
+ target = "aten.sin.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=XNNSin(
+ 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

@@ -45,6 +45,7 @@
  ReciprocalSquareRootConfig,
  ReLUConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,
@@ -105,6 +106,7 @@
  TanhConfig,
  ToDimOrderCopyConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -636,3 +636,10 @@ class BMMConfig(GenericNodePartitionerConfig):
 
  def supported_precision_types(self) -> List[ConfigPrecisionType]:
  return [ConfigPrecisionType.FP32]
+
+
+class SinConfig(GenericNodePartitionerConfig):
+ target_name = "sin.default"
+
+ def supported_precision_types(self) -> List[ConfigPrecisionType]:
+ return [ConfigPrecisionType.FP32]

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1690,6 +1690,7 @@ _DEFINE_UNARY_NODE_NO_PARAMS(Log, xnn_unary_log)
 _DEFINE_UNARY_NODE_NO_PARAMS(Negate, xnn_unary_negate)
 _DEFINE_UNARY_NODE_NO_PARAMS(Square, xnn_unary_square)
 _DEFINE_UNARY_NODE_NO_PARAMS(Abs, xnn_unary_abs)
+_DEFINE_UNARY_NODE_NO_PARAMS(Sin, xnn_unary_sine)
 
 // Unary Ops with min/max params
 _DEFINE_UNARY_NODE_WITH_MINMAX(Clamp, xnn_unary_clamp)
@@ -1737,6 +1738,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
  _DEFINE(Floor)
  _DEFINE(PReLU)
  _DEFINE(Sigmoid)
+ _DEFINE(Sin)
 
  // Others
  _DEFINE(FullyConnected)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -156,6 +156,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -152,6 +152,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -347,6 +347,11 @@ class XNNPReLU(XNNNode2x1):
  pass
 
 
+@dataclass
+class XNNSin(XNNNode1x1):
+ pass
+
+
 @dataclass
 class XNNScaledDotProductAttention:
  query_id: int
@@ -402,6 +407,8 @@ class XNNScaledDotProductAttention:
  XNNLog,
  XNNGelu,
  XNNTanh,
+ XNNExp,
+ XNNSin,
 ]
 
 

backends/xnnpack/test/ops/test_sin.py

@@ -0,0 +1,87 @@
+# 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 TestSin(unittest.TestCase):
+ def setUp(self):
+ torch._dynamo.reset()
+
+ class Sin(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ z = torch.sin(x)
+ return z
+
+ def _test_sin(self, inputs, legacy_mode: bool = False):
+ tester = (
+ Tester(self.Sin(), inputs)
+ .export()
+ .check_count({"torch.ops.aten.sin.default": 1})
+ )
+
+ if legacy_mode:
+ tester = tester.to_edge().partition()
+ else:
+ tester = tester.to_edge_transform_and_lower()
+
+ (
+ tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+ .check_not(["executorch_exir_dialects_edge__ops_aten_sin_default"])
+ .to_executorch()
+ .serialize()
+ .run_method_and_compare_outputs()
+ )
+
+ def test_fp16_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp16_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=True)
+
+ def test_fp32_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp32_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=True)

backends/xnnpack/operators/__init__.py

@@ -41,6 +41,7 @@
  op_relu,
  op_rsqrt,
  op_sigmoid,
+ op_sin,
  op_skip_ops,
  op_slice_copy,
  op_softmax,

backends/xnnpack/operators/op_sin.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,
+ XNNSin,
+ XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+
+@register_node_visitor
+class SinVisitor(NodeVisitor):
+ target = "aten.sin.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=XNNSin(
+ 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

@@ -45,6 +45,7 @@
  ReciprocalSquareRootConfig,
  ReLUConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,
@@ -105,6 +106,7 @@
  TanhConfig,
  ToDimOrderCopyConfig,
  SigmoidConfig,
+ SinConfig,
  SliceCopyConfig,
  SoftmaxConfig,
  SquareRootConfig,

backends/xnnpack/partition/config/generic_node_configs.py

@@ -636,3 +636,10 @@ class BMMConfig(GenericNodePartitionerConfig):
 
  def supported_precision_types(self) -> List[ConfigPrecisionType]:
  return [ConfigPrecisionType.FP32]
+
+
+class SinConfig(GenericNodePartitionerConfig):
+ target_name = "sin.default"
+
+ def supported_precision_types(self) -> List[ConfigPrecisionType]:
+ return [ConfigPrecisionType.FP32]

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -1690,6 +1690,7 @@ _DEFINE_UNARY_NODE_NO_PARAMS(Log, xnn_unary_log)
 _DEFINE_UNARY_NODE_NO_PARAMS(Negate, xnn_unary_negate)
 _DEFINE_UNARY_NODE_NO_PARAMS(Square, xnn_unary_square)
 _DEFINE_UNARY_NODE_NO_PARAMS(Abs, xnn_unary_abs)
+_DEFINE_UNARY_NODE_NO_PARAMS(Sin, xnn_unary_sine)
 
 // Unary Ops with min/max params
 _DEFINE_UNARY_NODE_WITH_MINMAX(Clamp, xnn_unary_clamp)
@@ -1737,6 +1738,7 @@ DefineNodeFunc getDefineNodeFunc(fb_xnnpack::XNodeUnion nodeType) {
  _DEFINE(Floor)
  _DEFINE(PReLU)
  _DEFINE(Sigmoid)
+ _DEFINE(Sin)
 
  // Others
  _DEFINE(FullyConnected)

backends/xnnpack/serialization/runtime_schema.fbs

@@ -156,6 +156,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/schema.fbs

@@ -152,6 +152,7 @@ union XNodeUnion {
  XNNGelu: _XNNNode1x1,
  XNNTanh: _XNNNode1x1,
  XNNExp: _XNNNode1x1,
+ XNNSin: _XNNNode1x1,
 }
 
 union XValueUnion {

backends/xnnpack/serialization/xnnpack_graph_schema.py

@@ -347,6 +347,11 @@ class XNNPReLU(XNNNode2x1):
  pass
 
 
+@dataclass
+class XNNSin(XNNNode1x1):
+ pass
+
+
 @dataclass
 class XNNScaledDotProductAttention:
  query_id: int
@@ -402,6 +407,8 @@ class XNNScaledDotProductAttention:
  XNNLog,
  XNNGelu,
  XNNTanh,
+ XNNExp,
+ XNNSin,
 ]
 
 

backends/xnnpack/test/ops/test_sin.py

@@ -0,0 +1,87 @@
+# 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 TestSin(unittest.TestCase):
+ def setUp(self):
+ torch._dynamo.reset()
+
+ class Sin(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ z = torch.sin(x)
+ return z
+
+ def _test_sin(self, inputs, legacy_mode: bool = False):
+ tester = (
+ Tester(self.Sin(), inputs)
+ .export()
+ .check_count({"torch.ops.aten.sin.default": 1})
+ )
+
+ if legacy_mode:
+ tester = tester.to_edge().partition()
+ else:
+ tester = tester.to_edge_transform_and_lower()
+
+ (
+ tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+ .check_not(["executorch_exir_dialects_edge__ops_aten_sin_default"])
+ .to_executorch()
+ .serialize()
+ .run_method_and_compare_outputs()
+ )
+
+ def test_fp16_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp16_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ).to(torch.float16),
+ )
+ self._test_sin(inputs, legacy_mode=True)
+
+ def test_fp32_sin(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=False)
+
+ def test_fp32_sin_legacy_mode(self):
+ inputs = (
+ torch.Tensor(
+ [
+ [0.0, 0.1, 0.5, 0.785398],
+ [-0.5, -0.785398, 1.5708, -1.5708],
+ ],
+ ),
+ )
+ self._test_sin(inputs, legacy_mode=True)