refine random related ops

paddle/operators/CMakeLists.txt

@@ -58,7 +58,7 @@ 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(gaussian_random_op SRCS gaussian_random_op.cc gaussian_random_op.cu DEPS math_function)
 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)
 
@@ -67,4 +67,4 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
 op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
     DEPS framework_proto tensor op_registry operator net_op)
 op_library(uniform_random_op
-        SRCS uniform_random_op.cc uniform_random_op.cu)
+        SRCS uniform_random_op.cc uniform_random_op.cu DEPS math_function)

paddle/operators/gaussian_random_op.cc

@@ -12,36 +12,11 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include <random>
-#include "paddle/framework/op_registry.h"
+#include "paddle/operators/gaussian_random_op.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 {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -70,10 +45,6 @@ Use to initialize tensor with gaussian random generator.
     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);
   }
 };
 
@@ -83,4 +54,6 @@ Use to initialize tensor with gaussian random generator.
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp,
                              ops::GaussianRandomOpMaker);
-REGISTER_OP_CPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);
+REGISTER_OP_CPU_KERNEL(
+    gaussian_random,
+    ops::GaussianRandomKernel<paddle::platform::CPUPlace, float>);

paddle/operators/gaussian_random_op.cu

@@ -12,42 +12,9 @@
    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) {
-      std::random_device rd;
-      seed = rd();
-    }
-    curandGenerator_t g;
-    PADDLE_ENFORCE(platform::dynload::curandCreateGenerator(
-        &g, CURAND_RNG_PSEUDO_DEFAULT));
-    PADDLE_ENFORCE(
-        platform::dynload::curandSetPseudoRandomGeneratorSeed(g, seed));
-    platform::dynload::curandGenerateNormal(
-        g, data, framework::product(tensor->dims()), mean, std);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
+#include "paddle/operators/gaussian_random_op.h"
 
 namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);
+REGISTER_OP_GPU_KERNEL(
+    gaussian_random,
+    ops::GaussianRandomKernel<paddle::platform::GPUPlace, float>);

paddle/operators/gaussian_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/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+template <typename Place, typename T>
+class GaussianRandomKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* tensor = context.Output<framework::Tensor>("Out");
+    T* data = tensor->mutable_data<T>(context.GetPlace());
+    T mean = static_cast<T>(context.op_.GetAttr<float>("mean"));
+    T std = static_cast<T>(context.op_.GetAttr<float>("std"));
+    auto n = framework::product(tensor->dims());
+
+    auto* device_context =
+        const_cast<platform::DeviceContext*>(context.device_context_);
+    math::RandGaussian<Place, T>(n, mean, std, data, device_context);
+  }
+};
+}
+}

paddle/operators/math/math_function.cc

@@ -109,6 +109,28 @@ void matmul<platform::CPUPlace, double>(const framework::Tensor& matrix_a,
       matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
 }
 
+template <>
+void RandUniform<platform::CPUPlace, float>(const int n, const float min,
+                                            const float max, float* output,
+                                            platform::DeviceContext* context) {
+  auto* cpu_context = reinterpret_cast<platform::CPUDeviceContext*>(context);
+  std::uniform_real_distribution<float> distribution(min, max);
+  for (int i = 0; i < n; i++) {
+    output[i] = distribution(cpu_context->rand_engine());
+  }
+}
+
+template <>
+void RandGaussian<platform::CPUPlace, float>(const int n, const float mean,
+                                             const float std, float* output,
+                                             platform::DeviceContext* context) {
+  auto* cpu_context = reinterpret_cast<platform::CPUDeviceContext*>(context);
+  std::normal_distribution<float> distribution(mean, std);
+  for (int i = 0; i < n; i++) {
+    output[i] = distribution(cpu_context->rand_engine());
+  }
+}
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/math/math_function.cu

@@ -12,6 +12,10 @@ 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 <thrust/device_ptr.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/random.h>
+#include <thrust/transform.h>
 #include "paddle/operators/math/math_function.h"
 
 namespace paddle {
@@ -122,6 +126,38 @@ void matmul<platform::GPUPlace, double>(const framework::Tensor& matrix_a,
       matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
 }
 
+template <>
+void RandUniform<platform::GPUPlace, float>(const int n, const float min,
+                                            const float max, float* output,
+                                            platform::DeviceContext* context) {
+  auto* cuda_context = reinterpret_cast<platform::CUDADeviceContext*>(context);
+  thrust::uniform_real_distribution<float> distribution(min, max);
+  thrust::minstd_rand engine = cuda_context->rand_enigne();
+  engine->discard(n);
+
+  thrust::counting_iterator<unsigned int> index_sequence_begin(0);
+
+  thrust::transform(thrust::cuda::par.on(cuda_context->stream()),
+                    index_sequence_begin, index_sequence_begin + n,
+                    thrust::device_ptr<float>(output), distribution(engine));
+}
+
+template <>
+void RandGaussian<platform::GPUPlace, float>(const int n, const float mean,
+                                             const float std, float* output,
+                                             platform::DeviceContext* context) {
+  auto* cuda_context = reinterpret_cast<platform::CUDADeviceContext*>(context);
+  thrust::normal_distribution<float> distribution(mean, std);
+  thrust::minstd_rand engine = cuda_context->rand_enigne();
+  engine->discard(n);
+
+  thrust::counting_iterator<unsigned int> index_sequence_begin(0);
+
+  thrust::transform(thrust::cuda::par.on(cuda_context->stream()),
+                    index_sequence_begin, index_sequence_begin + n,
+                    thrust::device_ptr<float>(output), distribution(engine));
+}
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/math/math_function.h

@@ -77,6 +77,14 @@ void matmul(const framework::Tensor& matrix_a, bool trans_a,
             framework::Tensor* matrix_out, T beta,
             platform::DeviceContext* context);
 
+template <typename Place, typename T>
+void RandUniform(const int n, const T min, const T max, T* output,
+                 platform::DeviceContext* context);
+
+template <typename Place, typename T>
+void RandGaussian(const int n, const T mean, const T std, T* output,
+                  platform::DeviceContext* context);
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/operators/mul_op.cc

@@ -13,7 +13,6 @@
    limitations under the License. */
 
 #include "paddle/operators/mul_op.h"
-#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {

paddle/operators/uniform_random_op.cc

@@ -12,39 +12,11 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include <random>
-#include <type_traits>
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/uniform_random_op.h"
 
 namespace paddle {
 namespace operators {
 
-// It seems that Eigen::Tensor::random in GPU will SEGFAULT.
-// Use std::random and thrust::random(thrust is a std library in CUDA) to
-// implement uniform random.
-template <typename T>
-class CPUUniformRandomKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* tensor = context.Output<framework::Tensor>("Out");
-    T* data = tensor->mutable_data<T>(context.GetPlace());
-    unsigned int seed =
-        static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
-    std::minstd_rand engine;
-    if (seed == 0) {
-      seed = std::random_device()();
-    }
-    engine.seed(seed);
-    std::uniform_real_distribution<T> dist(
-        static_cast<T>(context.op_.GetAttr<float>("min")),
-        static_cast<T>(context.op_.GetAttr<float>("max")));
-    for (ssize_t i = 0; i < framework::product(tensor->dims()); ++i) {
-      data[i] = dist(engine);
-    }
-  }
-};
-
 class UniformRandomOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -72,10 +44,6 @@ Used to initialize tensor with uniform random generator.
     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
@@ -83,5 +51,6 @@ Used to initialize tensor with uniform random generator.
 
 REGISTER_OP_WITHOUT_GRADIENT(uniform_random, paddle::operators::UniformRandomOp,
                              paddle::operators::UniformRandomOpMaker);
-REGISTER_OP_CPU_KERNEL(uniform_random,
-                       paddle::operators::CPUUniformRandomKernel<float>);
+REGISTER_OP_CPU_KERNEL(
+    uniform_random,
+    paddle::operators::UniformRandomKernel<paddle::platform::CPUPlace, float>);

paddle/operators/uniform_random_op.cu

@@ -12,60 +12,11 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include <thrust/device_ptr.h>
-#include <thrust/iterator/counting_iterator.h>
-#include <thrust/random.h>
-#include <thrust/transform.h>
-#include "paddle/framework/op_registry.h"
-#include "paddle/framework/operator.h"
+#include "paddle/operators/uniform_random_op.h"
 
 namespace paddle {
 namespace operators {
 
-template <typename T>
-struct UniformGenerator {
-  T min_, max_;
-  unsigned int seed_;
-
-  __host__ __device__ UniformGenerator(T min, T max, int seed)
-      : min_(min), max_(max), seed_(seed) {}
-
-  __host__ __device__ T operator()(const unsigned int n) const {
-    thrust::minstd_rand rng;
-    rng.seed(seed_);
-    thrust::uniform_real_distribution<T> dist(min_, max_);
-    rng.discard(n);
-    return dist(rng);
-  }
-};
-
-// It seems that Eigen::Tensor::random in GPU will SEGFAULT.
-// Use std::random and thrust::random(thrust is a std library in CUDA) to
-// implement uniform random.
-template <typename T>
-class GPUUniformRandomKernel : public framework::OpKernel {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* tensor = context.Output<framework::Tensor>("Out");
-    T* data = tensor->mutable_data<T>(context.GetPlace());
-    unsigned int seed =
-        static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
-    if (seed == 0) {
-      std::random_device rd;
-      seed = rd();
-    }
-    T min = static_cast<T>(context.op_.GetAttr<float>("min"));
-    T max = static_cast<T>(context.op_.GetAttr<float>("max"));
-    thrust::counting_iterator<unsigned int> index_sequence_begin(0);
-    ssize_t N = framework::product(tensor->dims());
-    thrust::transform(index_sequence_begin, index_sequence_begin + N,
-                      thrust::device_ptr<T>(data),
-                      UniformGenerator<T>(min, max, seed));
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
 REGISTER_OP_GPU_KERNEL(uniform_random,
-                       paddle::operators::GPUUniformRandomKernel<float>);
+                       paddle::operators::GPUUniformRandomKernel<
+                           paddle::platform::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/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+template <typename Place, typename T>
+class UniformRandomKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* tensor = context.Output<framework::Tensor>("Out");
+    T* data = tensor->mutable_data<T>(context.GetPlace());
+    T min = static_cast<T>(context.op_.GetAttr<float>("min"));
+    T max = static_cast<T>(context.op_.GetAttr<float>("max"));
+    auto n = framework::product(tensor->dims());
+
+    auto* device_context =
+        const_cast<platform::DeviceContext*>(context.device_context_);
+    math::RandUniform<Place, T>(n, min, max, data, device_context);
+  }
+};
+}
+}

paddle/platform/device_context.cc

@@ -25,8 +25,17 @@ CPUDeviceContext::CPUDeviceContext() {
   eigen_device_.reset(new Eigen::DefaultDevice());
 }
 
-CPUDeviceContext::CPUDeviceContext(CPUPlace place) {
+CPUDeviceContext::CPUDeviceContext(CPUPlace place, int rand_seed) {
   eigen_device_.reset(new Eigen::DefaultDevice());
+  rand_seed_ = rand_seed;
+}
+
+std::minstd_rand& CPUDeviceContext::rand_engine() {
+  if (!rand_engine_) {
+    rand_engine_.reset(new std::minstd_rand());
+    rand_engine_->seed(rand_seed_);
+  }
+  return *(rand_engine_.get());
 }
 
 Eigen::DefaultDevice* CPUDeviceContext::eigen_device() const {
@@ -95,7 +104,8 @@ Eigen::GpuDevice* DeviceContext::get_eigen_device<Eigen::GpuDevice>() const {
   return reinterpret_cast<const CUDADeviceContext*>(this)->eigen_device();
 }
 
-CUDADeviceContext::CUDADeviceContext(GPUPlace place) : place_(place) {
+CUDADeviceContext::CUDADeviceContext(GPUPlace place, uint64_t seed)
+    : place_(place), seed_(seed) {
   SetDeviceId(place_.device);
   PADDLE_ENFORCE(cudaStreamCreate(&stream_));
   eigen_stream_.reset(new EigenCudaStreamDevice());
@@ -114,9 +124,6 @@ CUDADeviceContext::~CUDADeviceContext() {
     PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_));
   }
 
-  if (curand_generator_) {
-    PADDLE_ENFORCE(dynload::curandDestroyGenerator(curand_generator_));
-  }
   eigen_stream_.reset();
   eigen_device_.reset();
   PADDLE_ENFORCE(cudaStreamDestroy(stream_));
@@ -150,21 +157,16 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() {
   return cudnn_handle_;
 }
 
-cudaStream_t CUDADeviceContext::stream() { return stream_; }
-
-curandGenerator_t CUDADeviceContext::curand_generator() {
-  if (!curand_generator_) {
-    SetDeviceId(place_.device);
-    PADDLE_ENFORCE(dynload::curandCreateGenerator(&curand_generator_,
-                                                  CURAND_RNG_PSEUDO_DEFAULT));
-    PADDLE_ENFORCE(
-        dynload::curandSetPseudoRandomGeneratorSeed(curand_generator_, seed_));
-
-    PADDLE_ENFORCE(dynload::curandSetStream(curand_generator_, stream_));
+thrust::minstd_rand& CPUDeviceContext::rand_engine() {
+  if (!rand_engine_) {
+    rand_engine_.reset(new thrust::minstd_rand());
+    rand_engine_->seed(rand_seed_);
   }
-  return curand_generator_;
+  return *(rand_engine_.get());
 }
 
+cudaStream_t CUDADeviceContext::stream() { return stream_; }
+
 #endif  // PADDLE_ONLY_CPU
 
 }  // namespace platform

paddle/platform/device_context.h

@@ -15,9 +15,10 @@ limitations under the License. */
 #include "paddle/platform/place.h"
 
 #ifndef PADDLE_ONLY_CPU
+#include <thrust/device_ptr.h>
+#include <thrust/random.h>
 #include "paddle/platform/dynload/cublas.h"
 #include "paddle/platform/dynload/cudnn.h"
-#include "paddle/platform/dynload/curand.h"
 #include "paddle/platform/gpu_info.h"
 #define EIGEN_USE_GPU
 #endif
@@ -40,14 +41,18 @@ class DeviceContext {
 class CPUDeviceContext : public DeviceContext {
  public:
   CPUDeviceContext();
-  explicit CPUDeviceContext(CPUPlace);
+  explicit CPUDeviceContext(CPUPlace place, int rand_seed = 0);
   virtual ~CPUDeviceContext() {}
 
   Eigen::DefaultDevice* eigen_device() const;
 
+  std::minstd_rand& rand_engine();
+
   Place GetPlace() const override;
 
  private:
+  int rand_seed_;
+  std::unique_ptr<std::minstd_rand> rand_engine_;
   std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
 };
 
@@ -56,7 +61,7 @@ class EigenCudaStreamDevice;
 
 class CUDADeviceContext : public DeviceContext {
  public:
-  explicit CUDADeviceContext(GPUPlace);
+  explicit CUDADeviceContext(GPUPlace place, uint64_t rand_seed = 0);
   virtual ~CUDADeviceContext();
 
   /*! \brief  Wait for all operations completion in the stream. */
@@ -75,8 +80,7 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Return cudnn  handle in the device context. */
   cudnnHandle_t     cudnn_handle();
 
-  /*! \brief  Return curand handle in the device context. */
-  curandGenerator_t curand_generator();
+  thrust::minstd_rand& CPUDeviceContext::rand_engine();
 
   /*! \brief  Return cuda stream in the device context. */
   cudaStream_t      stream();
@@ -85,18 +89,16 @@ class CUDADeviceContext : public DeviceContext {
  private:
   GPUPlace place_;
 
- private:
   std::unique_ptr<Eigen::GpuDevice> eigen_device_;
   std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
 
- private:
-  uint64_t seed_;
+  uint64_t rand_seed_;
+  std::unique_ptr<thrust::minstd_rand> rand_engine_;
 
   // clang-format off
   cudaStream_t       stream_{nullptr};
   cudnnHandle_t      cudnn_handle_{nullptr};
   cublasHandle_t     cublas_handle_{nullptr};
-  curandGenerator_t  curand_generator_{nullptr};
   // clang-format on
 };
 

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

@@ -22,7 +22,7 @@ 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_gaussian_random_op SRCS test_gaussian_random_op.py)
 py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
 py_test(test_recurrent_op SRCS test_recurrent_op.py)
 py_test(test_sgd_op SRCS test_sgd_op.py)

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

@@ -17,12 +17,7 @@ class GaussianRandomTest(unittest.TestCase):
         scope.new_var("Out").get_tensor()
 
         op = Operator(
-            "gaussian_random",
-            Out="Out",
-            dims=[1000, 784],
-            mean=.0,
-            std=1.,
-            seed=10)
+            "gaussian_random", Out="Out", dims=[1000, 784], mean=.0, std=1.)
 
         op.infer_shape(scope)
         context = core.DeviceContext.create(place)

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

@@ -17,12 +17,7 @@ class UniformRandomTest(unittest.TestCase):
         scope.new_var("X").get_tensor()
 
         op = Operator(
-            "uniform_random",
-            Out="X",
-            dims=[1000, 784],
-            min=-5.0,
-            max=10.0,
-            seed=10)
+            "uniform_random", Out="X", dims=[1000, 784], min=-5.0, max=10.0)
 
         op.infer_shape(scope)
         ctx = core.DeviceContext.create(place)