Add uniform random operator

It can be run both CPU/GPU. configure attributes are:

* min: the min value of uniform random
* max: the max value of uniform random
* dims: the dimension of output tensor
* seed: the random seed of uniform random. 0 means generate a seed each
        time.

paddle/framework/CMakeLists.txt

@@ -43,4 +43,5 @@ cc_library(paddle_pybind SHARED
     add_op
     mean_op
     cross_entropy_op
-	recurrent_op)
+    recurrent_op
+    uniform_random_op)

paddle/framework/pybind.cc

@@ -41,6 +41,7 @@ USE_OP(sigmoid);
 USE_OP(softmax);
 USE_OP(rowwise_add);
 USE_OP_WITHOUT_KERNEL(recurrent_op);
+USE_OP(uniform_random);
 namespace paddle {
 namespace framework {
 template <typename ClassType>

paddle/operators/CMakeLists.txt

@@ -66,3 +66,5 @@ op_library(fc_op
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
     DEPS op_desc tensor op_registry operator net_op)
 cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)
+op_library(uniform_random_op
+        SRCS uniform_random_op.cc uniform_random_op.cu)

paddle/operators/uniform_random_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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 "paddle/operators/uniform_random_op.h"
+
+namespace paddle {
+namespace operators {
+class RandomOp : public OperatorWithKernel {
+ protected:
+  void InferShape(const InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE(GetAttr<float>("min") < GetAttr<float>("max"),
+                   "uniform_random's min must less then max");
+    auto tensor = ctx.Output<Tensor>(0);
+    auto dims = GetAttr<std::vector<int>>("dims");
+    tensor->Resize(framework::make_ddim(dims));
+  }
+};
+
+class RandomOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  RandomOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddOutput("Out", "The output tensor of uniform random op");
+    AddComment(R"DOC(Uniform random operator.
+
+Used to initialize tensor with uniform random generator.
+)DOC");
+    AddAttr<std::vector<int>>("dims", "the dimension of random tensor");
+    AddAttr<float>("min", "Minimum value of uniform random").SetDefault(-1.0f);
+    AddAttr<float>("max", "Maximun value of uniform random").SetDefault(1.0f);
+    AddAttr<int>("seed",
+                 "Random seed of uniform random. "
+                 "0 means generate a seed by system")
+        .SetDefault(0);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP(uniform_random, ops::RandomOp, ops::RandomOpMaker);
+REGISTER_OP_CPU_KERNEL(uniform_random,
+                       ops::UniformRandomKernel<ops::CPUPlace, float>);

paddle/operators/uniform_random_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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 "paddle/operators/uniform_random_op.h"
+
+REGISTER_OP_GPU_KERNEL(uniform_random,
+                       ops::UniformRandomKernel<ops::GPUPlace, float>);

paddle/operators/uniform_random_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#pragma once
+#include "paddle/operators/type_alias.h"
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class UniformRandomKernel : public OpKernel {
+ public:
+  void Compute(const ExecutionContext &context) const override {
+    auto tensor = context.Output<Tensor>(0);
+    tensor->mutable_data<T>(context.GetPlace());
+
+    auto eigenTensor = EigenVector<T>::Flatten(*tensor);
+    auto dev = context.GetEigenDevice<Place>();
+    auto min = context.op_.GetAttr<float>("min");
+    auto max = context.op_.GetAttr<float>("max");
+    auto seed = static_cast<uint64_t>(context.op_.GetAttr<int>("seed"));
+    auto diff = max - min;
+    Eigen::internal::UniformRandomGenerator<T> gen(seed);
+    eigenTensor.device(dev) = eigenTensor.random(gen) * diff + min;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/framework/tests/CMakeLists.txt

@@ -14,4 +14,5 @@ add_python_test(test_framework
     test_softmax_op.py
     test_rowwise_add_op.py
     test_network.py
-    gradient_checker.py)
+    gradient_checker.py
+    test_uniform_random_op.py)

python/paddle/v2/framework/tests/test_uniform_random_op.py

+import unittest
+from paddle.v2.framework.op import Operator
+import paddle.v2.framework.core as core
+import numpy
+
+
+class UniformRandomTest(unittest.TestCase):
+    def test_uniform_random_cpu(self):
+        self.uniform_random_test(place=core.CPUPlace())
+
+    def test_uniform_random_gpu(self):
+        if core.is_compile_gpu():
+            self.uniform_random_test(place=core.GPUPlace(0))
+
+    def uniform_random_test(self, place):
+        scope = core.Scope()
+        scope.new_var("X").get_tensor()
+
+        op = Operator(
+            "uniform_random",
+            Out="X",
+            dims=[1000, 784],
+            min=-5.0,
+            max=10.0,
+            seed=10)
+
+        op.infer_shape(scope)
+        ctx = core.DeviceContext.create(place)
+        op.run(scope, ctx)
+        tensor = numpy.array(scope.find_var("X").get_tensor())
+        self.assertAlmostEqual(tensor.mean(), 2.5, delta=0.1)
+
+
+if __name__ == '__main__':
+    unittest.main()