mindspore/ccsrc/common/graph_kernel/substitute_dropout.cc

@@ -37,29 +37,38 @@ inline const PrimitivePtr kPrimGkDropout = std::make_shared<Primitive>("GkDropou
 namespace graphkernel {
 using opt::CheckCNodeInputSize;
 using opt::kDropoutInputTensorNum;
-
+// 静态成员变量，用于保存随机数生成器的种子，初始化为当前时间戳
 int64_t DropoutExpander::seed_ = time(nullptr);
 
+// 预处理函数，用于处理Dropout节点之前的操作
 AnfNodePtr DropoutExpander::PreProcess(const FuncGraphPtr &func_graph, const AnfNodePtr &node) {
+  // 检查节点和节点类型是否有效
   MS_EXCEPTION_IF_NULL(node);
   CNodePtr cnode = node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
+  
+  // 检查Dropout节点输入的数量是否正确
   CheckCNodeInputSize(cnode, kDropoutInputTensorNum);
+  
+  // 获取输入节点的设备上的形状
   auto shape = AnfAlgo::GetInputDeviceShape(cnode, 0);
   ShapeVector shape_i64;
   (void)std::transform(shape.begin(), shape.end(), std::back_inserter(shape_i64), SizeToLong);
-  // Get seed from original dropout's attrs, rather than set seed by time.
-  // Only seed0 and seed1 are all equal to 0, then set seed = time.
+  
+  // 从Dropout节点的属性中获取种子值
   auto node_prim = GetCNodePrimitive(node);
   MS_EXCEPTION_IF_NULL(node_prim);
   int64_t seed = GetValue<int64_t>(node_prim->GetAttr("Seed0"));
+  
+  // 如果Seed0和Seed1都等于0，则使用全局种子值seed_
   if (seed == 0) {
     seed = GetValue<int64_t>(node_prim->GetAttr("Seed1"));
     if (seed == 0) {
       seed = seed_++;
     }
   }
-  // Create a uniform_real kernel to generate random value.
+  
+  // 创建一个uniform_real节点，用于生成随机值
   auto tensor = std::make_shared<tensor::Tensor>(kNumberTypeInt64, ShapeVector(1, SizeToLong(shape.size())),
                                                  static_cast<void *>(&shape[0]), kNumberTypeInt64);
   AnfNodePtrList uniform_real_input = {NewValueNode(prim::kPrimCudnnUniformReal), NewValueNode(tensor)};
@@ -69,7 +78,8 @@ AnfNodePtr DropoutExpander::PreProcess(const FuncGraphPtr &func_graph, const Anf
   SetNodeAttrSafely("seed", MakeValue(seed), uniform_real_node);
   common::AnfAlgo::SetNodeAttr("seed2", MakeValue(static_cast<int64_t>(0)), uniform_real_node);
   uniform_real_node->set_abstract(std::make_shared<abstract::AbstractTensor>(kFloat32, shape_i64));
-  // Set kernel_info for uniform_real node
+  
+  // 为uniform_real节点设置kernel_info
   auto uniform_real_kernel_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
   uniform_real_kernel_info_builder->SetInputsFormat({kOpFormat_DEFAULT});
   uniform_real_kernel_info_builder->SetInputsDeviceType({kNumberTypeInt32});
@@ -79,23 +89,29 @@ AnfNodePtr DropoutExpander::PreProcess(const FuncGraphPtr &func_graph, const Anf
   uniform_real_kernel_info_builder->SetProcessor(kernel::Processor::CUDA);
   AnfAlgo::SetSelectKernelBuildInfo(uniform_real_kernel_info_builder->Build(), uniform_real_node.get());
 
-  // Create a GKDropout node with uniform_real as its second input.
+  // 创建一个GKDropout节点，其第二个输入为uniform_real节点
   AnfNodePtrList gkdropout_inputs = {NewValueNode(prim::kPrimGkDropout), cnode->input(1), uniform_real_node};
   auto new_dropout_node = func_graph->NewCNode(gkdropout_inputs);
   SetNodeAttrSafely("keep_prob", MakeValue(common::AnfAlgo::GetNodeAttr<float>(cnode, "keep_prob")), new_dropout_node);
-  // the output info is unchanged.
+  
+  // 输出信息与原始节点相同
   new_dropout_node->set_abstract(node->abstract());
   auto old_kernel_info = AnfAlgo::GetSelectKernelBuildInfo(node);
   auto dropout_kernel_info_builder = std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>(old_kernel_info);
   dropout_kernel_info_builder->SetInputsFormat({old_kernel_info->GetInputFormat(0), kOpFormat_DEFAULT});
   dropout_kernel_info_builder->SetInputsDeviceType({old_kernel_info->GetInputDeviceType(0), kNumberTypeFloat32});
   AnfAlgo::SetSelectKernelBuildInfo(dropout_kernel_info_builder->Build(), new_dropout_node.get());
+  
   return new_dropout_node;
 }
 
+// 运行函数，用于处理Dropout节点
 AnfNodePtr DropoutExpander::Run(const AnfNodePtr &node) {
+  // 预处理Dropout节点
   auto gkdropout_node = PreProcess(node->func_graph(), node);
+  // 调用父类的Run方法处理GKDropout节点
   return PyExpander::Run(gkdropout_node);
 }
+
 }  // namespace graphkernel
 }  // namespace mindspore