cherry pick 27853 Add gaussian_random XPU kernels, test=kunlun (#28059)

* Add gaussian_random XPU kernels

* commit kunlun, test=kunlun

* new version, test=kunlun

* change boost_get to BOOST_GET_CONST, test=kunlun

* use Generator to generate random number and optimize format, test=kunlun

* add TODO, test=kunlun

paddle/fluid/operators/gaussian_random_op_xpu.cc

+/* Copyright (c) 2020 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. */
+
+#ifdef PADDLE_WITH_XPU
+
+#include <random>
+#include "paddle/fluid/framework/generator.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class XPUGaussianRandomKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    float mean = context.Attr<float>("mean");
+    float std = context.Attr<float>("std");
+    auto* tensor = context.Output<framework::Tensor>("Out");
+
+    std::normal_distribution<T> dist(mean, std);
+    int64_t size = tensor->numel();
+    T* data = tensor->mutable_data<T>(context.GetPlace());
+    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
+    // TODO(pangyoki): implement GetXPURandomEngine to set different seeds on
+    // corresponding XPU device.
+    auto engine = framework::GetCPURandomEngine(seed);
+
+    std::unique_ptr<T[]> data_cpu(new T[size]);
+    for (int64_t i = 0; i < size; ++i) {
+      data_cpu[i] = dist(*engine);
+    }
+    memory::Copy(BOOST_GET_CONST(platform::XPUPlace, context.GetPlace()), data,
+                 platform::CPUPlace(), reinterpret_cast<void*>(data_cpu.get()),
+                 size * sizeof(T));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(gaussian_random, ops::XPUGaussianRandomKernel<float>);
+#endif

python/paddle/fluid/tests/unittests/xpu/test_gaussian_random_op_xpu.py

+#   Copyright (c) 2020 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 sys
+sys.path.append("..")
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+from paddle.fluid.executor import Executor
+from op_test import OpTest
+from test_gaussian_random_op import TestGaussianRandomOp
+
+paddle.enable_static()
+
+
+class TestXPUGaussianRandomOp(TestGaussianRandomOp):
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            outs = self.calc_output(place)
+            outs = [np.array(out) for out in outs]
+            outs.sort(key=len)
+            self.verify_output(outs)
+
+
+if __name__ == "__main__":
+    unittest.main()