Support sine operator on XNNPACK (#14711)

Summary: Wire up the unary sine operator in xnnpack for fp32 and fp16.

Differential Revision: D83623086

(cherry picked from commit 6efddba)

Author: GregoryComer

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)