!8271 modify_normal_seed

From: @Somnus2020
Reviewed-by: @kingxian
Signed-off-by: @kingxian

mindspore/ccsrc/pybind_api/random_normal/philox_generator.h

@@ -32,6 +32,15 @@ class PhiloxGenerator {
     counter_[3] = static_cast<uint32_t>(seed_ >> 32);
   }
 
+  explicit PhiloxGenerator(uint64_t seed_, uint64_t seed2_) {
+    key_var_[0] = static_cast<uint32_t>(seed_);
+    key_var_[1] = static_cast<uint32_t>(seed_ >> 32);
+    counter_[0] = 0;
+    counter_[1] = 0;
+    counter_[2] = static_cast<uint32_t>(seed2_);
+    counter_[3] = static_cast<uint32_t>(seed2_ >> 32);
+  }
+
   ~PhiloxGenerator() = default;
 
   void Jump();

mindspore/ccsrc/pybind_api/random_normal/random_cpu_kernel.cc

@@ -20,7 +20,7 @@
 #include "runtime/device/cpu/cpu_device_address.h"
 
 namespace mindspore {
-bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> out_shape, int64_t seed,
+bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> out_shape, int64_t seed, int64_t seed2,
                       const py::object &output_tensor) {
   if (out_shape.size() == 0) {
     std::cout << "output data shape is error" << std::endl;
@@ -41,7 +41,8 @@ bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> out_shape,
   }
   int64_t batchSize = total_count / thread_num;
   std::vector<std::thread> threads(thread_num);
-  mindspore::PhiloxGenerator generator = mindspore::PhiloxGenerator(seed);
+  seed = (seed == 0 && seed2 == 0) ? clock() : seed;
+  mindspore::PhiloxGenerator generator = mindspore::PhiloxGenerator(seed, seed2);
   if (thread_num != 1) {
     for (uint32_t i = 0; i < thread_num - 1; i++) {
       float *offset_ptr = start_ptr + batchSize * i;

mindspore/ccsrc/pybind_api/random_normal/random_cpu_kernel.h

@@ -85,7 +85,7 @@ bool FillRandoms(PhiloxGenerator generator, float *output, int64_t vet_size, int
   }
   return true;
 }
-bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> out_shape, int64_t seed,
+bool InitRandomNormal(float mean, float stddev, std::vector<int64_t> out_shape, int64_t seed, int64_t seed2,
                       const py::object &output_tensor);
 }  // namespace mindspore
 

mindspore/common/initializer.py

@@ -45,16 +45,13 @@ class Initializer:
     @property
     def seed(self):
         if self._seed is None:
-            seed_ = get_seed() if get_seed() is not None else 1
-            _, seed = _get_graph_seed(seed_, "init")
+            seed, seed2 = _get_graph_seed(get_seed(), "init")
         else:
-            seed = self._seed
-        return seed
+            seed, seed2 = self._seed + 1, 0
+        return seed, seed2
 
     @seed.setter
     def seed(self, value):
-        if not isinstance(value, int):
-            raise TypeError("'value' must be int type.")
         self._seed = value
 
     def _initialize(self, *kwargs):
@@ -367,9 +364,9 @@ class Normal(Initializer):
         self.sigma = sigma
 
     def _initialize(self, arr):
-        seed = self.seed
+        seed, seed2 = self.seed
         output_tensor = Tensor(np.zeros(arr.shape, dtype=np.float32))
-        random_normal(0, self.sigma, arr.shape, seed, output_tensor)
+        random_normal(0, self.sigma, arr.shape, seed, seed2, output_tensor)
         output_data = output_tensor.asnumpy()
         output_data *= self.sigma
         _assignment(arr, output_data)

mindspore/common/seed.py

@@ -18,6 +18,7 @@ import mindspore.dataset as de
 from mindspore._checkparam import Validator
 
 # constants
+DEFAULT_GRAPH_SEED = 87654321
 _MAXINT32 = 2**31 - 1
 keyConstant = [3528531795, 2654435769, 3449720151, 3144134277]
 
@@ -210,7 +211,9 @@ def _get_graph_seed(op_seed, kernel_name):
         >>> _get_graph_seed(seed, 'normal')
     """
     global_seed = get_seed()
-    if global_seed is None:
+    if global_seed == 0:
+        global_seed = DEFAULT_GRAPH_SEED
+    elif global_seed is None:
         global_seed = 0
     if op_seed is None:
         op_seed = 0

mindspore/common/tensor.py

@@ -465,7 +465,7 @@ class MetaTensor(MetaTensor_):
             def __exit__(self, ptype, value, trace):
                 if self.need_set_seed:
                     np.random.seed(self._np_seed)
-                    self.init.seed = self.seed
+                    self.init.seed, _ = self.seed
 
         with seed_context(self.init):
             self.init(arr)

mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py

@@ -39,7 +39,7 @@ class WithBNNLossCell(Cell):
 
     Examples:
         >>> net = Net()
-        >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+        >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
         >>> net_with_criterion_object = WithBNNLossCell(net, loss_fn)
         >>> net_with_criterion = net_with_criterion_object()
         >>>

mindspore/nn/wrap/cell_wrapper.py

@@ -46,7 +46,7 @@ class WithLossCell(Cell):
 
     Examples:
         >>> net = Net()
-        >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+        >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
         >>> net_with_criterion = nn.WithLossCell(net, loss_fn)
         >>>
         >>> batch_size = 2