fix nccl broadcast

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/lstm_cpu_kernel.cc

@@ -86,6 +86,9 @@ void LstmCPUKernel::CheckParam(const CNodePtr &kernel_node) {
     num_directions_ = 2;
   }
   const int gate_size = 4 * hidden_size_;
+  if (num_layers_ <= 0) {
+    MS_LOG(EXCEPTION) << "layers must be greater than zero!";
+  }
   for (int i = 0; i < num_layers_; ++i) {
     weight_size_ += gate_size * (i == 0 ? input_size_ : hidden_size_ * num_directions_);
     weight_h_size_ += gate_size * hidden_size_;
@@ -95,9 +98,6 @@ void LstmCPUKernel::CheckParam(const CNodePtr &kernel_node) {
   if (num_directions_ * num_layers_ != SizeToInt(src_h_shape[0])) {
     MS_LOG(EXCEPTION) << "error iteration shape!";
   }
-  if (num_layers_ <= 0) {
-    MS_LOG(EXCEPTION) << "layers must be greater than zero!";
-  }
   if (src_shape.size() != 3 || src_h_shape.size() != 3 || src_c_shape.size() != 3) {
     MS_LOG(EXCEPTION) << "lstm only support 3-D input!";
   }

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/lstm_grad_cpu_kernel.cc

@@ -104,6 +104,9 @@ void LSTMGradCPUKernel::CheckParam(const CNodePtr &kernel_node) {
     num_directions_ = 2;
   }
   const int gate_size = 4 * hidden_size_;
+  if (num_layers_ <= 0) {
+    MS_LOG(EXCEPTION) << "layers must be greater than zero!";
+  }
   for (int i = 0; i < num_layers_; ++i) {
     weight_size_ += gate_size * (i == 0 ? input_size_ : hidden_size_ * num_directions_);
     weight_h_size_ += gate_size * hidden_size_;
@@ -113,9 +116,6 @@ void LSTMGradCPUKernel::CheckParam(const CNodePtr &kernel_node) {
   if (num_directions_ * num_layers_ != SizeToInt(src_h_shape[0])) {
     MS_LOG(EXCEPTION) << "error iteration shape!";
   }
-  if (num_layers_ <= 0) {
-    MS_LOG(EXCEPTION) << "layers must be greater than zero!";
-  }
   if (src_shape.size() != 3 || src_h_shape.size() != 3 || src_c_shape.size() != 3) {
     MS_LOG(EXCEPTION) << "lstm only support 3-D input!";
   }

mindspore/ccsrc/backend/kernel_compiler/gpu/nccl/nccl_gpu_kernel.h

@@ -109,9 +109,13 @@ class NcclGpuKernel : public GpuKernel {
         auto broadcast_funcptr =
           reinterpret_cast<Broadcast>(dlsym(const_cast<void *>(collective_handle_), "Broadcast"));
         MS_EXCEPTION_IF_NULL(broadcast_funcptr);
-        CHECK_NCCL_RET_WITH_EXCEPT((*broadcast_funcptr)(input_addr, output_addr, output_size_ / sizeof(T),
-                                                        nccl_data_type_, root_, stream, group_name_),
-                                   "ncclBroadcast failed");
+        for (int i = 0; i < SizeToInt(input_size_list_.size()); ++i) {
+          input_addr = GetDeviceAddress<T>(inputs, i);
+          output_addr = GetDeviceAddress<T>(outputs, i);
+          CHECK_NCCL_RET_WITH_EXCEPT((*broadcast_funcptr)(input_addr, output_addr, output_size_ / sizeof(T),
+                                                          nccl_data_type_, root_, stream, group_name_),
+                                     "ncclBroadcast failed");
+        }
         break;
       }
       default: {

mindspore/nn/probability/distribution/categorical.py

@@ -28,7 +28,7 @@ class Categorical(Distribution):
         probs (Tensor, list, numpy.ndarray, Parameter, float): event probabilities.
         logits (Tensor, list, numpy.ndarray, Parameter, float): event log-odds.
         seed (int): seed to use in sampling. Default: 0.
-        dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
+        dtype (mstype.int32): type of the distribution. Default: mstype.int32.
         name (str): name of the distribution. Default: Categorical.
 
     Note:
@@ -49,7 +49,7 @@ class Categorical(Distribution):
         >>>
         >>>         # Similar calls can be made to logits
         >>>         ans = self.ca.probs
-        >>>         # value should be Tensor
+        >>>         # value should be Tensor(mstype.float32, bool, mstype.int32)
         >>>         ans = self.ca.log_prob(value)
         >>>
         >>>         # Usage of enumerate_support
@@ -210,6 +210,8 @@ class Categorical(Distribution):
     def enumerate_support(self, expand=True):
         r"""
        Enumerate categories.
+       Args:
+            expand (Bool): whether to expand.
        """
         num_events = self._num_events
         values = nn.Range(0., num_events, 1)()

mindspore/ops/operations/random_ops.py

@@ -439,7 +439,7 @@ class Multinomial(PrimitiveWithInfer):
     Args:
         seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
           Must be non-negative. Default: 0.
-        replacement(bool) - whether to draw with replacement or not.
+        replacement(bool): Whether to draw with replacement or not.
 
     Inputs:
         - **input** (Tensor[float32]) - the input tensor containing the cumsum of probabilities, must be 1 or 2 dims.