[MLU] add gaussian_random mlu kernel (#39338)

paddle/fluid/operators/gaussian_random_op_mlu.cc

+/* Copyright (c) 2022 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. */
+
+#include <random>
+
+#include "paddle/fluid/framework/generator.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T>
+class MLUGaussianRandomKernel : 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");
+    tensor->mutable_data<T>(context.GetPlace());
+
+    Tensor cpu_tensor(tensor->type());
+    cpu_tensor.Resize(tensor->dims());
+    T* cpu_data = cpu_tensor.mutable_data<T>(platform::CPUPlace());
+    std::normal_distribution<T> dist(mean, std);
+
+    int64_t size = tensor->numel();
+
+    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
+    auto engine = framework::GetCPURandomEngine(seed);
+    for (int64_t i = 0; i < size; ++i) {
+      cpu_data[i] = dist(*engine);
+    }
+    auto& dev_ctx =
+        context.template device_context<paddle::platform::MLUDeviceContext>();
+    framework::TensorCopy(cpu_tensor, context.GetPlace(), dev_ctx, tensor);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_MLU_KERNEL(gaussian_random, ops::MLUGaussianRandomKernel<float>);

python/paddle/fluid/tests/unittests/mlu/test_gaussian_random_op_mlu.py

+#   Copyright (c) 2022 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
+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
+import sys
+sys.path.append('..')
+from op_test import OpTest
+import paddle
+
+
+class TestGaussianRandomOp(OpTest):
+    def setUp(self):
+        self.op_type = "gaussian_random"
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.set_attrs()
+        self.inputs = {}
+        self.attrs = {
+            "shape": [123, 92],
+            "mean": self.mean,
+            "std": self.std,
+            "seed": 10,
+        }
+        paddle.seed(10)
+
+        self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}
+
+    def set_attrs(self):
+        self.mean = 1.0
+        self.std = 2.
+
+    def test_check_output(self):
+        self.check_output_with_place_customized(self.verify_output, self.place)
+        # self.check_output_customized(self.verify_output, self.place)
+
+    def verify_output(self, outs):
+        self.assertEqual(outs[0].shape, (123, 92))
+        hist, _ = np.histogram(outs[0], range=(-3, 5))
+        hist = hist.astype("float32")
+        hist /= float(outs[0].size)
+        data = np.random.normal(size=(123, 92), loc=1, scale=2)
+        hist2, _ = np.histogram(data, range=(-3, 5))
+        hist2 = hist2.astype("float32")
+        hist2 /= float(outs[0].size)
+        self.assertTrue(
+            np.allclose(
+                hist, hist2, rtol=0, atol=0.01),
+            "hist: " + str(hist) + " hist2: " + str(hist2))
+
+
+class TestMeanStdAreInt(TestGaussianRandomOp):
+    def set_attrs(self):
+        self.mean = 1
+        self.std = 2
+
+
+if __name__ == "__main__":
+    paddle.enable_static()
+    unittest.main()