[NPU] Support npu kernel for sin op (#34844)

* add npu sin op

* [NPU] Support npu kernel for sin op

* modify support npu kernel for sin op

* modify support npu kernel for sin op

* modify nou sin op

* modify npu sin op

* add sin op npu

paddle/fluid/operators/activation_op_npu.cc

@@ -811,6 +811,27 @@ class ExpGradNPUKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename DeviceContext, typename T>
+class SinNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+
+    auto* out = ctx.Output<Tensor>("Out");
+
+    auto place = ctx.GetPlace();
+
+    out->mutable_data<T>(place);
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    const auto& runner = NpuOpRunner("Sin", {*x}, {*out}, {});
+    runner.Run(stream);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -975,3 +996,9 @@ REGISTER_OP_NPU_KERNEL(
 REGISTER_OP_NPU_KERNEL(
     exp_grad, ops::ExpGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
     ops::ExpGradNPUKernel<paddle::platform::NPUDeviceContext, double>);
+
+REGISTER_OP_NPU_KERNEL(
+    sin, ops::SinNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::SinNPUKernel<paddle::platform::NPUDeviceContext, double>,
+    ops::SinNPUKernel<paddle::platform::NPUDeviceContext,
+                      paddle::platform::float16>);

python/paddle/fluid/tests/unittests/npu/test_sin_op_npu.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+import unittest
+
+import numpy as np
+from scipy.special import expit, erf
+
+from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16, skip_check_grad_ci
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid import compiler, Program, program_guard
+
+paddle.enable_static()
+
+
+def test_class(op_type, typename):
+    class TestSin(OpTest):
+        def setUp(self):
+            self.op_type = "sin"
+            self.__class__.use_npu = True
+            self.place = paddle.NPUPlace(0)
+            self.__class__.no_need_check_grad = True
+            np.random.seed(1024)
+            x = np.random.uniform(-1, 1, [10, 12]).astype(typename)
+            out = np.sin(x)
+
+            self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+            self.outputs = {'Out': out}
+
+        def test_check_output(self):
+            self.check_output_with_place(self.place)
+
+        def test_check_grad(self):
+            pass
+
+        def test_out_name(self):
+            with fluid.program_guard(fluid.Program()):
+                np_x = np.array([0.1])
+                data = fluid.layers.data(name="X", shape=[1])
+                out = eval("paddle.%s(data, name='Y')" % self.op_type)
+                place = fluid.NPUPlace(0)
+                exe = fluid.Executor(place)
+                result, = exe.run(feed={"X": np_x}, fetch_list=[out])
+                expected = eval("np.%s(np_x)" % self.op_type)
+                self.assertEqual(result, expected)
+
+        def test_dygraph(self):
+            with fluid.dygraph.guard(paddle.NPUPlace(0)):
+                np_x = np.array([0.1])
+                x = fluid.dygraph.to_variable(np_x)
+                z = eval("paddle.%s(x).numpy()" % self.op_type)
+                z_expected = eval("np.%s(np_x)" % self.op_type)
+                self.assertEqual(z, z_expected)
+
+    cls_name = "{0}_{1}_1".format(op_type, typename)
+    TestSin.__name__ = cls_name
+    globals()[cls_name] = TestSin
+
+
+for _typename in {'float16', 'float32', 'float64'}:
+    test_class("sin", _typename)
+
+if __name__ == "__main__":
+    unittest.main()