fix randomchoicewithmask's random property

mindspore/ccsrc/backend/kernel_compiler/gpu/random/random_choice_with_mask_gpu_kernel.h

@@ -18,6 +18,8 @@
 #define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_RANDOM_RANDOM_CHOICE_WITH_MASK_GPU_KERNEL_H_
 
 #include <vector>
+#include <chrono>
+#include <random>
 #include "backend/kernel_compiler/gpu/gpu_kernel.h"
 #include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
 #include "backend/kernel_compiler/gpu/cuda_impl/random_choice_with_mask_impl.cuh"
@@ -27,7 +29,8 @@ namespace kernel {
 template <typename T, typename S>
 class RandomChoiceWithMaskGpuKernel : public GpuKernel {
  public:
-  RandomChoiceWithMaskGpuKernel() : input_shape_size_(0), seedc_(0), input_size_(1), count_(0), ceil_power2_(0) {}
+  RandomChoiceWithMaskGpuKernel()
+      : input_shape_size_(0), seed_(0), seed2_(0), input_size_(1), count_(0), ceil_power2_(0) {}
   ~RandomChoiceWithMaskGpuKernel() override = default;
 
   const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@@ -39,6 +42,14 @@ class RandomChoiceWithMaskGpuKernel : public GpuKernel {
     T *input = GetDeviceAddress<T>(inputs, 0);
     S *output_index = GetDeviceAddress<S>(outputs, 0);
     T *output_mask = GetDeviceAddress<T>(outputs, 1);
+    int seedc = 0;
+    if (seed2_ != 0) {
+      seedc = seed2_;
+    } else if (seed_ != 0) {
+      seedc = seed_;
+    } else {
+      seedc = generator_();
+    }
     if (count_ > kSmallK || input_shape_size_ > 1) {
       S *index_buff = GetDeviceAddress<S>(workspaces, 0);
       S *mask_buff = GetDeviceAddress<S>(workspaces, 1);
@@ -48,17 +59,18 @@ class RandomChoiceWithMaskGpuKernel : public GpuKernel {
       void *States = GetDeviceAddress<void *>(workspaces, 5);
       curandState *devStates = reinterpret_cast<curandState *>(States);
       CalRandomChoiceWithMask(input_size_, input_shape_size_, input_shape_5D_[0], input_shape_5D_[1],
-                              input_shape_5D_[2], input_shape_5D_[3], input_shape_5D_[4], seedc_, count_, input,
+                              input_shape_5D_[2], input_shape_5D_[3], input_shape_5D_[4], seedc, count_, input,
                               output_index, output_mask, index_buff, mask_buff, rank_buff, Tnum_buff, tmp_buff,
                               devStates, reinterpret_cast<cudaStream_t>(stream_ptr));
     } else {
-      CalRandomChoiceWithMaskSmall<float, S, T>(input_size_, seedc_, count_, input, output_index, output_mask,
+      CalRandomChoiceWithMaskSmall<float, S, T>(input_size_, seedc, count_, input, output_index, output_mask,
                                                 reinterpret_cast<cudaStream_t>(stream_ptr));
     }
     return true;
   }
 
   bool Init(const CNodePtr &kernel_node) override {
+    uint32_t time_interval = std::chrono::system_clock::now().time_since_epoch().count();
     size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
     if (input_num != 1) {
       MS_LOG(ERROR) << "Input number is " << input_num << ", but RandomChoiceWithMask needs 1 input.";
@@ -84,15 +96,10 @@ class RandomChoiceWithMaskGpuKernel : public GpuKernel {
     while (input_shape_5D_.size() != MAX_DIMENSION) {
       input_shape_5D_.insert(input_shape_5D_.begin(), 1);
     }
-    // init seedc_
-    int seed = static_cast<int>(GetAttr<int64_t>(kernel_node, "seed"));
-    int seed2 = static_cast<int>(GetAttr<int64_t>(kernel_node, "seed2"));
-    if (seed2 != 0)
-      seedc_ = seed2;
-    else if (seed != 0)
-      seedc_ = seed;
-    else
-      seedc_ = time(NULL);
+    // init seedc
+    seed_ = static_cast<int>(GetAttr<int64_t>(kernel_node, "seed"));
+    seed2_ = static_cast<int>(GetAttr<int64_t>(kernel_node, "seed2"));
+    generator_.seed(time_interval);
     // init memory
     for (size_t i = 0; i < input_shape.size(); i++) {
       input_size_ *= input_shape[i];
@@ -125,10 +132,12 @@ class RandomChoiceWithMaskGpuKernel : public GpuKernel {
  private:
   const int kSmallK = 2048;
   int input_shape_size_;
-  int seedc_;
+  int seed_;
+  int seed2_;
   int input_size_;
   int count_;
   int ceil_power2_;
+  std::mt19937 generator_;
   std::vector<int> input_shape_5D_;
   std::vector<size_t> input_size_list_;
   std::vector<size_t> output_size_list_;