Merge pull request #3060 from dzhwinter/random_op

Random op

paddle/framework/CMakeLists.txt

@@ -50,5 +50,6 @@ cc_library(paddle_pybind SHARED
     cross_entropy_op
     recurrent_op
     uniform_random_op
+    gaussian_random_op
     fill_zeros_like_op)
 endif(WITH_PYTHON)

paddle/framework/pybind.cc

@@ -40,7 +40,9 @@ USE_OP(softmax);
 USE_OP(rowwise_add);
 USE_OP(fill_zeros_like);
 USE_OP_WITHOUT_KERNEL(recurrent_op);
+USE_OP(gaussian_random);
 USE_OP(uniform_random);
+
 namespace paddle {
 namespace framework {
 

paddle/operators/CMakeLists.txt

@@ -53,6 +53,7 @@ op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
 
 op_library(sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu)
 op_library(softmax_op SRCS softmax_op.cc softmax_op.cu)
+op_library(gaussian_random_op SRCS gaussian_random_op.cc gaussian_random_op.cu)
 op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu)
 op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
 

paddle/operators/gaussian_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 <random>
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class GaussianRandomKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    float mean = context.op_.GetAttr<float>("mean");
+    float std = context.op_.GetAttr<float>("std");
+    auto* tensor = context.Output<framework::Tensor>(0);
+    T* data = tensor->mutable_data<T>(context.GetPlace());
+
+    // TODO(dzh): attribute does not support unsigned int.
+    // And we need a global random seed configuration.
+    int seed = context.op_.GetAttr<int>("seed");
+    if (seed == 0) {
+      seed = std::random_device()();
+    }
+    std::mt19937 g(seed);
+    std::normal_distribution<T> distribution(mean, std);
+    ssize_t size = framework::product(tensor->dims());
+    for (int i = 0; i < size; ++i) {
+      data[i] = distribution(g);
+    }
+  }
+};
+
+class GaussianRandomOp : public framework::OperatorWithKernel {
+ protected:
+  void InferShape(const framework::InferShapeContext& context) const override {
+    auto* tensor = context.Output<framework::Tensor>(0);
+    auto dims = GetAttr<std::vector<int>>("dims");
+    PADDLE_ENFORCE(dims.size() > 0UL,
+                   "dims can be one int or array. dims must be set.");
+    tensor->Resize(framework::make_ddim(dims));
+  }
+};
+
+class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  GaussianRandomOpMaker(framework::OpProto* proto,
+                        framework::OpAttrChecker* op_checker)
+      : framework::OpProtoAndCheckerMaker(proto, op_checker) {
+    AddOutput("Out", "output matrix of random op");
+    AddComment(R"DOC(
+GaussianRandom operator.
+Use to initialize tensor with gaussian random generator.
+)DOC");
+
+    AddAttr<std::vector<int>>("dims", "The dimension of random tensor.");
+    AddAttr<float>("mean", "mean value of random.").SetDefault(.0f);
+    AddAttr<float>("std", "minimum value of random value.").SetDefault(1.0f);
+    AddAttr<int>("seed",
+                 "Random seed of generator."
+                 "0 means use system wide seed")
+        .SetDefault(0);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker);
+REGISTER_OP_CPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);

paddle/operators/gaussian_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 <memory>
+#include <random>
+#include "paddle/platform/dynload/curand.h"
+#include "paddle/platform/gpu_info.h"
+
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class GaussianRandomKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    float mean = context.op_.GetAttr<float>("mean");
+    float std = context.op_.GetAttr<float>("std");
+    auto* tensor = context.Output<framework::Tensor>(0);
+    T* data = tensor->mutable_data<T>(context.GetPlace());
+
+    int seed = context.op_.GetAttr<int>("seed");
+    if (seed == 0) {
+      seed = std::random_device()();
+    }
+    curandGenerator_t g;
+    PADDLE_ENFORCE(platform::dynload::curandCreateGenerator(
+        &g, CURAND_RNG_PSEUDO_DEFAULT));
+    PADDLE_ENFORCE(
+        platform::dynload::curandSetPseudoRandomGeneratorSeed(g, seed));
+    curandGenerateNormal(g, data, framework::product(tensor->dims()), mean,
+                         std);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);
\ No newline at end of file

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

@@ -21,5 +21,8 @@ py_test(gradient_checker SRCS gradient_checker.py)
 py_test(test_rowwise_add_op SRCS test_rowwise_add_op.py)
 
 py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py)
+
 py_test(test_operator SRCS test_operator.py)
+
+py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)
 py_test(test_uniform_random_op SRCS test_uniform_random_op.py)

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

+import unittest
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
+import numpy
+
+
+class GaussianRandomTest(unittest.TestCase):
+    def test_cpu(self):
+        self.gaussian_random_test(place=core.CPUPlace())
+
+    def test_gpu(self):
+        if core.is_compile_gpu():
+            self.gaussian_random_test(place=core.GPUPlace(0))
+
+    def gaussian_random_test(self, place):
+        scope = core.Scope()
+        scope.new_var("Out").get_tensor()
+
+        op = Operator(
+            "gaussian_random",
+            Out="Out",
+            dims=[1000, 784],
+            mean=.0,
+            std=1.,
+            seed=10)
+
+        op.infer_shape(scope)
+        context = core.DeviceContext.create(place)
+        op.run(scope, context)
+        tensor = numpy.array(scope.find_var("Out").get_tensor())
+        self.assertAlmostEqual(numpy.mean(tensor), .0, delta=0.1)
+        self.assertAlmostEqual(numpy.std(tensor), 1., delta=0.1)
+
+
+if __name__ == '__main__':
+    unittest.main()