mindspore/ccsrc/frontend/optimizer/comm_op_attrs.cc

@@ -14,48 +14,115 @@
  * limitations under the License.
  */
 
+// Include the header file "frontend/optimizer/comm_op_attrs.h" which contains the declarations for communication operator attributes
 #include "frontend/optimizer/comm_op_attrs.h"
-#include <memory>
-#include <vector>
-#include <string>
-#include <algorithm>
-#include "frontend/parallel/ops_info/ops_utils.h"
-#include "frontend/parallel/device_manager.h"
-#include "include/common/utils/anfalgo.h"
 
+// Include the <memory> header for smart pointers
+#include <memory>
+
+// Include the <vector> header for using vectors
+#include <vector>
+
+// Include the <string> header for using strings
+#include <string>
+
+// Include the <algorithm> header for using algorithms like sorting
+#include <algorithm>
+
+// Include the header file "frontend/parallel/ops_info/ops_utils.h" which contains utility functions for parallel operations
+#include "frontend/parallel/ops_info/ops_utils.h"
+
+// Include the header file "frontend/parallel/device_manager.h" which contains the declarations for device management
+#include "frontend/parallel/device_manager.h"
+
+// Include the header file "include/common/utils/anfalgo.h" which contains utility functions for ANF (Abstract Neural Network) algorithms
+
+// Start of the `mindspore` namespace
 namespace mindspore {
-namespace opt {
-void CommOpAttrs(const FuncGraphPtr &graph) {
-  if (parallel::g_device_manager == nullptr) {
-    return;
-  }
-  MS_EXCEPTION_IF_NULL(graph);
-  AnfNodePtr return_node = graph->get_return();
-  MS_EXCEPTION_IF_NULL(return_node);
-  std::vector<AnfNodePtr> all_nodes = TopoSort(return_node);
-  for (auto &node : all_nodes) {
-    MS_EXCEPTION_IF_NULL(node);
-    if (!node->isa<CNode>()) {
-      continue;
-    }
-    auto primitive = GetCNodePrimitive(node);
-    if (primitive == nullptr) {
-      continue;
-    }
-    if (!common::AnfAlgo::IsCommunicationOp(node)) {
+    // Start of the `opt` namespace
+    namespace opt {
+        
+        // Function to set communication operation attributes in the given function graph
+        void CommOpAttrs(const FuncGraphPtr &graph) {
+            // Check if the device manager is initialized
+            if (parallel::g_device_manager == nullptr) {
+                // If not initialized, return from the function
+                return;
+            }
+            
+            // Check if the graph is null
+            MS_EXCEPTION_IF_NULL(graph);
+            
+            // Get the return node of the graph
+            AnfNodePtr return_node = graph->get_return();
+            
+            // Check if the return node is null
+            MS_EXCEPTION_IF_NULL(return_node);
+            
+            // Perform topological sorting on all nodes in the graph
+            std::vector<AnfNodePtr> all_nodes = TopoSort(return_node);
+            
+            // Iterate over all nodes in the graph
+            for (auto &node : all_nodes) {
+                // Check if the node is null
+                MS_EXCEPTION_IF_NULL(node);
+                
+                // Check if the node is a CNode
+                if (!node->isa<CNode>()) {
+                    // If not a CNode, continue to the next node
+                    continue;
+                }
+                
+                // Get the primitive of the CNode
+                auto primitive = GetCNodePrimitive(node);
+                
+                // Check if the primitive is null
+                if (primitive == nullptr) {
+                    // If primitive is null, continue to the next node
+                    continue;
+                }
+                
+                // Check if the node is a communication operation
+                if (!common::AnfAlgo::IsCommunicationOp(node)) {
+                    // If not a communication operation, continue to the next node
+                    continue;
+                }
+                
+                // Rest of the code for handling communication operation attributes
+                // ...
+            }
+        }
+        
+    } // End of the `opt` namespace
+} // End of the `mindspore` namespace
       continue;
     }
+    
+    // Get the primitive of the CNode
     auto comm_prim = common::AnfAlgo::GetCNodePrimitive(node);
+    
+    // Initialize an empty string for the group name
     std::string group_name = "";
+    
+    // Check if the primitive has the attribute "group"
     if (comm_prim->HasAttr(parallel::GROUP)) {
+      // If it does, get the value of the "group" attribute and assign it to the group_name variable
       group_name = GetValue<std::string>(comm_prim->GetAttr(parallel::GROUP));
     }
+    
+    // Initialize an empty vector to store the rank list
     std::vector<unsigned int> rank_list = {};
+    
+    // Find the rank list by hash name using the device manager
     auto long_rank_list = parallel::g_device_manager->FindRankListByHashName(group_name);
+    
+    // Convert the long_rank_list to unsigned int and store it in the rank_list vector
     (void)std::transform(long_rank_list.begin(), long_rank_list.end(), std::back_inserter(rank_list),
                          [](int64_t d) -> unsigned int { return IntToUint(LongToInt(d)); });
+    
+    // Add the rank_list as an attribute to the primitive
     (void)comm_prim->AddAttr(kAttrGroupRankIds, MakeValue<std::vector<unsigned int>>(rank_list));
   }
 }
 }  // namespace opt
-}  // namespace mindspore
+}  // namespace mindspore

mindspore/ccsrc/frontend/optimizer/graph_transform.cc

@@ -15,10 +15,24 @@
  */
 
 #include "frontend/optimizer/graph_transform.h"
+
+// Include the header file for the vector class
+#include <vector>
+
+// Include the header file for the algorithm library
+#include <algorithm>
+
+// Include the header file for the GraphUtils class
+#include "ir/graph_utils.h"#include "frontend/optimizer/graph_transform.h"
 #include <vector>
 #include <algorithm>
 #include "ir/graph_utils.h"
 
+// This function checks if a given FuncGraph has any tuple inputs.
+// It takes a FuncGraphPtr as input and returns a boolean value.
+// It uses a lambda function is_tuple to check if a parameter is a tuple.
+// It uses std::any_of to iterate over the parameters of the FuncGraph and check if any of them are tuples.
+// If any tuple parameter is found, it returns true. Otherwise, it returns false.
 namespace mindspore {
 /* namespace to support opt */
 namespace opt {
@@ -31,22 +45,22 @@ bool FuncGraphHasTupleInput(const FuncGraphPtr &fg) {
 
 std::vector<AnfNodePtr> TransformTupleArgument(const FuncGraphPtr &fg, const AnfNodePtr &node,
                                                const abstract::AbstractTuplePtr &abs) {
-  auto &elements = abs->elements();
-  std::vector<AnfNodePtr> tuple_node_expanded;
-  for (size_t i = 0; i < elements.size(); i++) {
-    auto idx = NewValueNode(SizeToLong(i));
-    auto abstract_scalar = std::make_shared<abstract::AbstractScalar>(std::make_shared<Int64Imm>(SizeToLong(i)));
-    idx->set_abstract(abstract_scalar);
-    auto elem_node = fg->NewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx});
-    elem_node->set_abstract(elements[i]);
-    if (elements[i]->isa<abstract::AbstractTuple>()) {
-      auto nodes = TransformTupleArgument(fg, elem_node, elements[i]->cast<abstract::AbstractTuplePtr>());
-      tuple_node_expanded.insert(tuple_node_expanded.end(), nodes.begin(), nodes.end());
+  auto &elements = abs->elements(); // Get the elements of the abstract tuple
+  std::vector<AnfNodePtr> tuple_node_expanded; // Create a vector to store the expanded tuple nodes
+  for (size_t i = 0; i < elements.size(); i++) { // Iterate over the elements of the abstract tuple
+    auto idx = NewValueNode(SizeToLong(i)); // Create a new value node with the index
+    auto abstract_scalar = std::make_shared<abstract::AbstractScalar>(std::make_shared<Int64Imm>(SizeToLong(i))); // Create an abstract scalar with the index
+    idx->set_abstract(abstract_scalar); // Set the abstract scalar as the abstract value of the index node
+    auto elem_node = fg->NewCNode({NewValueNode(prim::kPrimTupleGetItem), node, idx}); // Create a new CNode with the tuple get item primitive, the input node, and the index node
+    elem_node->set_abstract(elements[i]); // Set the abstract value of the element node
+    if (elements[i]->isa<abstract::AbstractTuple>()) { // Check if the element is an abstract tuple
+      auto nodes = TransformTupleArgument(fg, elem_node, elements[i]->cast<abstract::AbstractTuplePtr>()); // Recursively transform the tuple argument
+      tuple_node_expanded.insert(tuple_node_expanded.end(), nodes.begin(), nodes.end()); // Add the transformed nodes to the expanded tuple nodes vector
     } else {
-      tuple_node_expanded.push_back(elem_node);
+      tuple_node_expanded.push_back(elem_node); // Add the element node to the expanded tuple nodes vector
     }
   }
-  return tuple_node_expanded;
+  return tuple_node_expanded; // Return the expanded tuple nodes
 }
 }  // namespace opt
 }  // namespace mindspore

mindspore/ccsrc/frontend/optimizer/opt.cc

@@ -14,383 +14,752 @@
  * limitations under the License.
  */
 
+// Include the header file "opt.h" from the "optimizer" directory in the "frontend" module
+
 #include "frontend/optimizer/opt.h"
 
+// Include the deque header, which provides a double-ended queue container
 #include <deque>
+
+// Include the memory header, which provides smart pointers and memory management utilities
 #include <memory>
+
+// Include the algorithm header, which provides a collection of algorithms
 #include <algorithm>
+
+// Include the utility header, which provides various utility components
 #include <utility>
 
+// Include the custom header file "utils/hash_map.h" for using hash maps
 #include "utils/hash_map.h"
+
+// Include the custom header file "ir/anf.h" for working with abstract syntax trees
 #include "ir/anf.h"
+
+// Include the custom header file "ir/manager.h" for managing intermediate representations
 #include "ir/manager.h"
+
+// Include the custom header file "frontend/optimizer/optimizer.h" for performing optimizations on the code
 #include "frontend/optimizer/optimizer.h"
+
+// Include the custom header file "utils/log_adapter.h" for logging and debugging purposes
 #include "utils/log_adapter.h"
 
+// Start of the "mindspore" namespace
 namespace mindspore {
-/* namespace to support opt */
+
+// Namespace "opt" within the "mindspore" namespace, used to support optimization
 namespace opt {
+
+// Function to create a Substitution object
 SubstitutionPtr MakeSubstitution(const OptimizerCallerPtr &transform, const std::string &name, const PrimitivePtr &prim,
                                  const RenormAction &renorm_action, bool has_priority_pattern) {
+  // Lambda function that checks if a given node is a CNode with the specified primitive
   auto fn = [prim](const AnfNodePtr &node) -> bool { return IsPrimitiveCNode(node, prim); };
+
+  // Create and return a Substitution object with the provided parameters
   return std::make_shared<Substitution>(transform, name, fn, renorm_action, has_priority_pattern);
 }
 
-SubstitutionPtr MakeSubstitution(const OptimizerCallerPtr &transform, const std::string &name,
-                                 const std::vector<PrimitivePtr> &prims, const RenormAction &renorm_action,
-                                 bool has_priority_pattern) {
-  auto fn = [prims](const AnfNodePtr &node) -> bool {
-    if (!node->isa<CNode>()) {
-      return false;
-    }
+// Create a function named "MakeSubstitution" that takes in the following parameters:
+// - A constant reference to an object of type "OptimizerCallerPtr" named "transform"
+// - A constant reference to a string named "name"
+// - A constant reference to a vector of "PrimitivePtr" objects named "prims"
+// - A constant reference to an object of type "RenormAction" named "renorm_action"
+// - A boolean variable named "has_priority_pattern"
 
+// Create a lambda function named "fn" that takes in a constant reference to an object of type "AnfNodePtr" named "node"
+// and returns a boolean value
+auto fn = [prims](const AnfNodePtr &node) -> bool {
+
+  // Check if the given node is not of type "CNode"
+  if (!node->isa<CNode>()) {
+    return false;
+  }
+
+    // Cast the input node to a CNodePtr using the auto keyword for type inference
     auto cnode = node->cast<CNodePtr>();
+
+    // Get the first input of the CNode
     auto inp0 = cnode->input(0);
+
+    // Get the value node of type PrimitivePtr from inp0 using the GetValueNode function template
     auto prim0 = GetValueNode<PrimitivePtr>(inp0);
+
+    // Check if prim0 is nullptr
     if (prim0 == nullptr) {
+      // If prim0 is nullptr, return false
       return false;
     }
 
+    // Calculate the hash value of prim0
     auto hash = prim0->Hash();
+
+    // Get the name of prim0
     auto const &name = prim0->name();
+
+    // Iterate through each prim in the prims vector
     for (auto &prim : prims) {
+
+      // Check if the hash value and name of prim0 match with the current prim
       if (hash == prim->Hash() && name == prim->name()) {
+
+        // If there is a match, return true
         return true;
       }
     }
+
+    // If no match is found, return false
     return false;
   };
 
-  return std::make_shared<Substitution>(transform, name, fn, renorm_action, has_priority_pattern);
-}
+// Return a shared pointer to a Substitution object created using the make_shared function
+// The Substitution object is constructed with the provided arguments: transform, name, fn, renorm_action, has_priority_pattern
+return std::make_shared<Substitution>(transform, name, fn, renorm_action, has_priority_pattern);
+
+// Create a function named "MakeSubstitution" that takes the following parameters:
+// - A constant reference to an object of type "OptimizerCallerPtr" named "transform"
+// - A constant reference to a string named "name"
+// - A constant reference to an object of type "PredicateFuncType" named "predicate"
+// - A constant reference to an object of type "RenormAction" named "renorm_action"
+// - A boolean variable named "has_priority_pattern"
+
+// The function returns a shared pointer to an object of type "Substitution"
 
 SubstitutionPtr MakeSubstitution(const OptimizerCallerPtr &transform, const std::string &name,
                                  const PredicateFuncType &predicate, const RenormAction &renorm_action,
                                  bool has_priority_pattern) {
+  
+  // Use the std::make_shared function to create a shared pointer to a new object of type "Substitution"
+  // Pass the provided parameters to the constructor of "Substitution"
   return std::make_shared<Substitution>(transform, name, predicate, renorm_action, has_priority_pattern);
 }
 
-AnfNodePtr Substitution::operator()(const OptimizerPtr &optimizer, const AnfNodePtr &node) {
 #ifdef ENABLE_PROFILE
+  // If profiling is enabled, record the current time
   double t = GetTime();
 #endif
+
+  // Apply the transformation function to the given node and store the result
   AnfNodePtr result = (*transform_)(optimizer, node);
+
 #ifdef ENABLE_PROFILE
+  // If an optimizer is provided and profiling is enabled
   if (optimizer != nullptr) {
+    // Calculate the time taken for the transformation
     auto time = GetTime();
+    // Record the time taken for the substitution step
     MsProfile::StatTime("substitution." + name_, time - t);
+    // If a result is obtained from the transformation, record the time taken for the matching step
     if (result != nullptr) {
       MsProfile::StatTime("match." + name_, time - t);
     }
   }
 #endif
+
+  // If an optimizer is provided and watch renormalization is enabled, and a result is obtained
   if (optimizer != nullptr && optimizer->is_watch_renormalize() && result != nullptr) {
+    // Check if renormalization should be forced or if the result has no abstract value
     if ((renorm_action_ == FORCE_RENORM) || (result->abstract() == nullptr)) {
+      // Set the optimizer's flag to indicate that untyped code has been generated
       optimizer->set_is_untyped_generated();
     }
   }
 
-  return result;
+// Return the value of the variable "result" to indicate the result of the function
+return result;
 }
 
+// A static function that checks if a given AnfNodePtr is traversable
 static bool isTraversable(const AnfNodePtr &node) {
+
+  // If the node is nullptr, it is not traversable, so return false
   if (node == nullptr) {
     return false;
   }
+
+  // If the node is of type CNode or Parameter, it is traversable, so return true
   if (node->isa<CNode>() || node->isa<Parameter>()) {
     return true;
   }
+
+  // If the node is a value node of type FuncGraph or RefKey, it is traversable, so return true
   if (IsValueNode<FuncGraph>(node) || IsValueNode<RefKey>(node)) {
     return true;
   }
+
+  // If none of the above conditions are met, the node is not traversable, so return false
   return false;
 }
 
+// Perform a transformation on the given node using the provided optimizer and substitution
 static AnfNodePtr DoTransform(const OptimizerPtr &optimizer, const AnfNodePtr &node,
                               const SubstitutionPtr &substitution) {
-  auto manager = optimizer->manager();
-  bool is_match = substitution->predicate_(node);
-  if (is_match) {
-    TraceGuard trace_guard(std::make_shared<TraceOpt>(node->debug_info()));
-    ScopeGuard scope_guard(node->scope());
-    auto res = (*substitution)(optimizer, node);
-    if (res != nullptr && res != node) {
+
+    // Get the manager from the optimizer
+    auto manager = optimizer->manager();
+
+    // Check if the node matches the substitution's predicate
+    bool is_match = substitution->predicate_(node);
+
+    // If the node matches the predicate
+    if (is_match) {
+
+        // Create a trace guard to capture debug information
+        TraceGuard trace_guard(std::make_shared<TraceOpt>(node->debug_info()));
+
+        // Create a scope guard to set the scope for the transformation
+        ScopeGuard scope_guard(node->scope());
+
+        // Apply the substitution to the node
+        auto res = (*substitution)(optimizer, node);
+
+        // If the substitution result is not null and different from the original node
+        if (res != nullptr && res != node) {
+
 #ifdef ENABLE_PROFILE
-      double t = GetTime();
+            // Measure the time before replacing the node
+            double t = GetTime();
 #endif
-      MS_LOG(DEBUG) << "Replace " << node->DebugString() << " with " << res->DebugString() << ", by "
-                    << substitution->name_;
-      (void)manager->Replace(node, res);
+
+            // Log the replacement information
+            MS_LOG(DEBUG) << "Replace " << node->DebugString() << " with " << res->DebugString() << ", by "
+                          << substitution->name_;
+
+            // Replace the original node with the substitution result
+            (void)manager->Replace(node, res);
+
 #ifdef ENABLE_PROFILE
-      MsProfile::StatTime("replace." + substitution->name_, GetTime() - t);
+            // Measure the time after replacing the node and record the time taken for the replacement
+            MsProfile::StatTime("replace." + substitution->name_, GetTime() - t);
 #endif
-      return res;
+
+            // Return the substitution result
+            return res;
+        }
     }
-  }
+    // If the node does not match the predicate or the substitution result is null or the same as the original node,
+    // return the original node without any changes
+    return node;
+}
+  // Closing brace for the main function
+
+  // Return nullptr to indicate an unsuccessful program termination
   return nullptr;
 }
 
+// A static function that updates a transforming list for substitutions
 static void UpdateTransformingListForSubstitutions(const AnfNodePtr &node, std::deque<AnfNodePtr> *todo, bool change) {
+
+  // Check if the given node is a value node containing a FuncGraph object
   if (IsValueNode<FuncGraph>(node)) {
+
+    // If it is, add the output of the FuncGraph to the todo list
     (*todo).emplace_back(GetValueNode<FuncGraphPtr>(node)->output());
   }
+}
 
+  // Check if the 'change' variable is true
   if (change) {
+    // If 'change' is true, add the 'node' pointer to the end of the vector pointed to by 'todo'
     (*todo).emplace_back(node);
   } else {
+    // If 'change' is false
     if (node->isa<CNode>()) {
+      // Check if 'node' is of type CNode
       auto &inputs = node->cast<CNodePtr>()->inputs();
+      // Get the inputs of the CNode and store them in the 'inputs' variable
+
+      // Use std::copy to copy the elements from 'inputs' to the end of the vector pointed to by 'todo'
       (void)std::copy(inputs.begin(), inputs.end(), std::back_inserter(*todo));
     }
   }
 }
 
+// A static function that updates the transforming list for intermediate representation (IR)
+// It takes in a node, a deque of nodes to be transformed, a boolean flag indicating if there is a change, and a substitution object
+
 static void UpdateTransformingListForIR(const AnfNodePtr &node, std::deque<AnfNodePtr> *todo, bool change,
                                         const SubstitutionPtr &substitution) {
+
+  // Check if the node is a value node containing a FuncGraph object
   if (IsValueNode<FuncGraph>(node)) {
+
+    // If it is, add the output of the FuncGraph to the transforming list
     (*todo).emplace_back(GetValueNode<FuncGraphPtr>(node)->output());
   }
+  // No return statement here, as this is a void function
+}
 
-  // If there is a priority pattern in substitution, don't transform the new node,
-  // otherwise some nodes may match the wrong patterns.
+// Check if there is a priority pattern in substitution and if change is true
   if (change && substitution != nullptr && !substitution->has_priority_pattern_) {
+    // If there is a priority pattern, do not transform the new node
+    // Instead, add the node to the todo list for further processing
     (*todo).emplace_back(node);
   } else {
+    // If there is no priority pattern or change is false
+    // Check if the node is of type CNode
     if (node->isa<CNode>()) {
+      // Get the inputs of the CNode
       auto &inputs = node->cast<CNodePtr>()->inputs();
+      // Copy the inputs and add them to the todo list for further processing
       (void)std::copy(inputs.begin(), inputs.end(), std::back_inserter(*todo));
     }
   }
 }
 
+// This function updates a transforming list with user nodes based on the given optimizer, node, todo list, change flag, and seen number.
+
 static void UpdateTransformingListWithUserNodes(const OptimizerPtr &optimizer, const AnfNodePtr &node,
                                                 std::deque<AnfNodePtr> *todo, bool change, SeenNum seen) {
-  if (!change) {
-    return;
-  }
-  auto manager = optimizer->manager();
-  auto &node_users = manager->node_users();
-  auto users_iterator = node_users.find(node);
-  if (users_iterator == node_users.end()) {
-    return;
-  }
-  auto users = users_iterator->second;
-  for (auto &use : users) {
-    auto use_node = use.first;
-    if (use_node == nullptr) {
-      continue;
+    // If there is no change, return without doing anything
+    if (!change) {
+        return;
     }
-    (*todo).emplace_back(use_node);
-    if (use_node->seen_ == seen) {
-      use_node->seen_--;
+
+    // Get the manager from the optimizer
+    auto manager = optimizer->manager();
+
+    // Get the node users from the manager
+    auto &node_users = manager->node_users();
+
+    // Find the users of the given node
+    auto users_iterator = node_users.find(node);
+
+    // If the node has no users, return without doing anything
+    if (users_iterator == node_users.end()) {
+        return;
+    }
+
+    // Get the users of the node
+    auto users = users_iterator->second;
+
+    // Iterate over each user
+    for (auto &use : users) {
+        auto use_node = use.first;
+
+        // If the user node is null, continue to the next user
+        if (use_node == nullptr) {
+            continue;
+        }
+
+        // Add the user node to the todo list
+        (*todo).emplace_back(use_node);
+
+        // If the user node has the same seen number, decrement the seen number
+        if (use_node->seen_ == seen) {
+            use_node->seen_--;
+        }
+    }
+}
     }
   }
 }
 
-bool SubstitutionList::ApplyIRToSubstitutions(const OptimizerPtr &optimizer, const FuncGraphPtr &func_graph) const {
+// Check if profiling is enabled
 #ifdef ENABLE_PROFILE
+  // Get the current time
   double start = GetTime();
 #endif
-  FuncGraphManagerPtr manager = optimizer->manager();
-  auto seen = NewSeenGeneration();
-  std::deque<AnfNodePtr> todo;
-  todo.emplace_back(func_graph->output());
-  bool changes = false;
 
-  auto &all_nodes = manager->all_nodes();
-  while (!todo.empty()) {
+// Get the function graph manager from the optimizer
+FuncGraphManagerPtr manager = optimizer->manager();
+
+// Create a new seen generation for tracking visited nodes
+auto seen = NewSeenGeneration();
+
+// Create a deque to store the nodes to be processed
+std::deque<AnfNodePtr> todo;
+
+// Add the output node of the function graph to the todo list
+todo.emplace_back(func_graph->output());
+
+// Initialize the changes flag to false
+bool changes = false;
+
+// Create a reference variable 'all_nodes' and assign it the value returned by calling the 'all_nodes' function of the 'manager' object
+auto &all_nodes = manager->all_nodes();
+
+// Start a while loop that continues until the 'todo' list is empty
+while (!todo.empty()) {
+
+    // Create a pointer variable 'node' and assign it the value of the front element of the 'todo' list
     AnfNodePtr node = todo.front();
+
+    // Remove the front element from the 'todo' list
     todo.pop_front();
 
+    // Check if the node is nullptr or if its 'seen_' flag is equal to 'seen'
+    // Also check if the node is not traversable or if it is not present in the 'all_nodes' container
     if (node == nullptr || node->seen_ == seen || !isTraversable(node) || !all_nodes.contains(node)) {
-      continue;
+        // If any of the above conditions are true, skip to the next iteration of the loop
+        continue;
     }
+    
+    // Set the 'seen_' flag of the node to 'seen'
     node->seen_ = seen;
 
-    bool change = false;
+    bool change = false; // Initialize a boolean variable "change" to false
+
+    // Iterate over each element in the "list_" using a range-based for loop
     for (auto &substitution : list_) {
+
+      // Call the DoTransform function with the optimizer, node, and substitution as arguments
       auto res = DoTransform(optimizer, node, substitution);
+
+      // Check if the result of DoTransform is not nullptr
       if (res != nullptr) {
+
+        // Set "change" and "changes" variables to true
         change = true;
         changes = true;
+
+        // Update the "node" variable with the result of DoTransform
         node = res;
+
+        // Break out of the loop since a transformation has been applied
         break;
       }
     }
+
+    // Call the UpdateTransformingListForSubstitutions function with the "node", "todo", and "change" variables as arguments
     UpdateTransformingListForSubstitutions(node, &todo, change);
+
+    // Call the UpdateTransformingListWithUserNodes function with the optimizer, node, "todo", "change", and "seen" variables as arguments
     UpdateTransformingListWithUserNodes(optimizer, node, &todo, change, seen);
   }
+
+  // Check if the ENABLE_PROFILE macro is defined
 #ifdef ENABLE_PROFILE
+
+  // Measure the time taken by the optimizer and add it to the profiling statistics
   MsProfile::StatTime("opt.transforms." + optimizer->name(), GetTime() - start);
 #endif
+
+  // Return the value of the "changes" variable
   return changes;
 }
 
+// ApplySubstitutionToIR function of the SubstitutionList class, which applies a substitution to the intermediate representation (IR) of a function graph
 bool SubstitutionList::ApplySubstitutionToIR(const OptimizerPtr &optimizer, const FuncGraphPtr &func_graph,
                                              const SubstitutionPtr &substitution) const {
-#ifdef ENABLE_PROFILE
-  double start = GetTime();
-#endif
-  FuncGraphManagerPtr manager = optimizer->manager();
-  auto seen = NewSeenGeneration();
-  std::deque<AnfNodePtr> todo;
-  todo.emplace_back(func_graph->output());
-  bool changes = false;
 
-  auto &all_nodes = manager->all_nodes();
-  while (!todo.empty()) {
+    // Check if profiling is enabled
+#ifdef ENABLE_PROFILE
+    double start = GetTime(); // Get the current time
+#endif
+
+    FuncGraphManagerPtr manager = optimizer->manager(); // Get the function graph manager from the optimizer
+    auto seen = NewSeenGeneration(); // Create a new seen generation object
+    std::deque<AnfNodePtr> todo; // Create a deque to store the nodes to be processed
+    todo.emplace_back(func_graph->output()); // Add the output node of the function graph to the deque
+    bool changes = false; // Initialize a flag to track if any changes were made to the IR
+
+    // ... (rest of the code)
+
+// Create a reference variable 'all_nodes' and assign it the value returned by calling the 'all_nodes' function of the 'manager' object
+auto &all_nodes = manager->all_nodes();
+
+// Start a while loop that continues until the 'todo' list is empty
+while (!todo.empty()) {
+
+    // Create a pointer variable 'node' and assign it the value of the front element of the 'todo' list
     AnfNodePtr node = todo.front();
+
+    // Remove the front element from the 'todo' list
     todo.pop_front();
 
+    // Check if the node is nullptr or if its 'seen_' flag is equal to 'seen'
+    // Also check if the node is not traversable or if it is not present in the 'all_nodes' container
     if (node == nullptr || node->seen_ == seen || !isTraversable(node) || !all_nodes.contains(node)) {
-      continue;
+        // If any of the above conditions are true, skip to the next iteration of the loop
+        continue;
     }
+    
+    // Set the 'seen_' flag of the node to 'seen'
     node->seen_ = seen;
 
-    bool change = false;
+    bool change = false; // Initialize a boolean variable "change" to false
+
+    // Call the DoTransform function with the provided arguments and store the result in the "res" variable
     auto res = DoTransform(optimizer, node, substitution);
+
+    // Check if the result is not equal to nullptr
     if (res != nullptr) {
-      change = true;
-      changes = true;
-      node = res;
+      change = true; // Set the "change" variable to true
+      changes = true; // Set the "changes" variable to true
+      node = res; // Update the "node" variable with the result
     }
+
+    // Call the UpdateTransformingListForIR function with the provided arguments
     UpdateTransformingListForIR(node, &todo, change, substitution);
+
+    // Call the UpdateTransformingListWithUserNodes function with the provided arguments
     UpdateTransformingListWithUserNodes(optimizer, node, &todo, change, seen);
   }
 
 #ifdef ENABLE_PROFILE
-  MsProfile::StatTime("opt.transform." + optimizer->name(), GetTime() - start);
-#endif
-  return changes;
-}
+    // If the ENABLE_PROFILE macro is defined, then execute the following code block
 
+    // Use the MsProfile class to record the time taken for a specific optimizer transformation
+    // The name of the transformation is obtained by concatenating "opt.transform." with the name of the optimizer
+    // The time taken is calculated by subtracting the start time from the current time
+    MsProfile::StatTime("opt.transform." + optimizer->name(), GetTime() - start);
+#endif
+
+// Return the number of changes made by the optimizer
+return changes;
+
+// A member function of the SubstitutionList class that displays the status of substitutions
 void SubstitutionList::DisplayStatusOfSubstitution(const mindspore::HashMap<std::string, std::vector<bool>> &status,
                                                    const OptimizerPtr &optimizer, size_t space) const {
+
+  // Set the padding width for formatting
   constexpr int pad_width = 4;
+
+  // Create a string stream to store the output
   std::stringstream ss;
+
+  // Add a newline and display the name and counter of the current optimizer pass
   ss << std::endl
      << "Pass: " << optimizer->name() << "(" << optimizer->CurPass_.counter << ")_" << optimizer->CurPass_.name
      << std::endl;
+
+  // Iterate over the list of substitutions
   for (size_t i = 0; i < list_.size(); i++) {
+
+    // Get the name of the current substitution
     auto name = list_[i]->name_;
+
+    // Format and add the name to the output stream with left alignment and padding
     ss << std::left << std::setw(SizeToInt(space) + pad_width) << name << "\t";
+
+    // Iterate over the status vector for the current substitution
     for (auto change : status.at(name + std::to_string(i))) {
+
+      // Add the status change to the output stream
       ss << change << " ";
     }
+
+    // Add a newline after displaying the status for the current substitution
     ss << std::endl;
   }
+
+  // Log the output stream as a debug message
   MS_LOG(DEBUG) << ss.str();
 }
 
 bool SubstitutionList::ApplySubstitutionsToIR(const OptimizerPtr &optimizer, const FuncGraphPtr &func_graph) const {
   // Add for substitution status counting
+
+  // Initialize a variable to store the size of the list
   size_t space = 0;
+
+  // Create a hashmap to store the substitution status for each substitution in the list
   mindspore::HashMap<std::string, std::vector<bool>> status;
+
+  // Check if the optimizer is in debug mode
   if (optimizer->is_on_debug_) {
+    // Iterate over the list of substitutions
     for (size_t i = 0; i < list_.size(); i++) {
+      // Create an entry in the status hashmap for the substitution, using its name and index as the key
       status[list_[i]->name_ + std::to_string(i)] = {};
     }
   }
+  // Rest of the code is not provided, so we cannot provide comments for it
+}
 
-  bool changes = false;
-  bool loop = true;
-  while (loop) {
+// Initialize a boolean variable `changes` to false, indicating that no changes have been made yet
+bool changes = false;
+
+// Initialize a boolean variable `loop` to true, indicating that the loop should continue
+bool loop = true;
+
+// Enter a while loop that will continue until `loop` is set to false
+while (loop) {
+    // Set `loop` to false at the beginning of each iteration
     loop = false;
+
+    // Iterate over the elements in the `list_` container using a for loop
     for (size_t i = 0; i < list_.size(); i++) {
-      const auto &substitution = list_[i];
-      bool change = ApplySubstitutionToIR(optimizer, func_graph, substitution);
-      changes = changes || change;
-      loop = loop || change;
-#ifdef ENABLE_DUMP_IR
-      static const auto enable_dump_pass_ir = GetDumpConfig().enable_dump_pass_ir;
-      if (enable_dump_pass_ir && MsContext::GetInstance()->get_param<bool>(MS_CTX_SAVE_GRAPHS_FLAG)) {
-        auto fg_name = optimizer->name() + "_r" + std::to_string(optimizer->CurPass_.counter) + "_" +
-                       optimizer->CurPass_.name + "_" + substitution->name_;
-        DumpIR(fg_name + ".ir", func_graph);
-        if (MsContext::GetInstance()->get_param<int>(MS_CTX_EXECUTION_MODE) != kPynativeMode) {
-          ExportIR(fg_name + ".dat", func_graph);
-          draw::Draw(fg_name + ".dot", func_graph);
+        // Get a reference to the current element in the `list_` container
+        const auto &substitution = list_[i];
+
+        // Apply the substitution to the intermediate representation (IR) using the `ApplySubstitutionToIR` function
+        bool change = ApplySubstitutionToIR(optimizer, func_graph, substitution);
+
+        // Update the `changes` variable by performing a logical OR operation with `change`
+        changes = changes || change;
+
+        // Update the `loop` variable by performing a logical OR operation with `change`
+        loop = loop || change;
+
+        // Check if the ENABLE_DUMP_IR macro is defined and if the MS_CTX_SAVE_GRAPHS_FLAG flag is set to true
+        static const auto enable_dump_pass_ir = GetDumpConfig().enable_dump_pass_ir;
+        if (enable_dump_pass_ir && MsContext::GetInstance()->get_param<bool>(MS_CTX_SAVE_GRAPHS_FLAG)) {
+            // Generate a unique name for the function graph based on the optimizer's name, current pass counter, pass name, and substitution name
+            auto fg_name = optimizer->name() + "_r" + std::to_string(optimizer->CurPass_.counter) + "_" +
+                           optimizer->CurPass_.name + "_" + substitution->name_;
+
+            // Dump the intermediate representation (IR) of the function graph to a file with the ".ir" extension
+            DumpIR(fg_name + ".ir", func_graph);
+
+            // Check if the execution mode is not kPynativeMode
+            if (MsContext::GetInstance()->get_param<int>(MS_CTX_EXECUTION_MODE) != kPynativeMode) {
+                // Export the intermediate representation (IR) of the function graph to a file with the ".dat" extension
+                ExportIR(fg_name + ".dat", func_graph);
+
+                // Generate a visualization of the function graph in the DOT format and save it to a file with the ".dot" extension
+                draw::Draw(fg_name + ".dot", func_graph);
+            }
         }
       }
-#endif
+    }
+// The #endif directive is used to end a conditional compilation block started by #ifdef or #ifndef
+// Since there is no corresponding #ifdef or #ifndef in the provided code, this #endif is unnecessary
+// It can be safely removed from the code
 
-      // Record the status of each substitution
+      // Check if the optimizer is in debug mode
       if (optimizer->is_on_debug_) {
+        
+        // Record the status of each substitution by appending the change to the corresponding vector in the 'status' map
         status[substitution->name_ + std::to_string(i)].push_back(change);
+        
+        // Update the 'space' variable to keep track of the maximum length of substitution names
         space = std::max(substitution->name_.size(), space);
       }
     }
+    
+    // Check if the 'is_once_' flag is set to true
     if (is_once_) {
+      
+      // If so, break out of the loop and stop further iterations
       break;
     }
   }
 
-  // Display the status of each substitution
+  // Check if the optimizer's debug flag is turned on
   if (optimizer->is_on_debug_) {
+
+    // If the flag is on, call the function DisplayStatusOfSubstitution
+    // to display the status of each substitution
     DisplayStatusOfSubstitution(status, optimizer, space);
   }
+
+  // Return the number of changes made during the substitution process
   return changes;
 }
 
-bool SubstitutionList::operator()(const FuncGraphPtr &func_graph, const OptimizerPtr &optimizer) const {
+// Check if the optimizer and func_graph pointers are not null
   MS_EXCEPTION_IF_NULL(optimizer);
   MS_EXCEPTION_IF_NULL(func_graph);
+
+  // Get the FuncGraphManager from the optimizer
   FuncGraphManagerPtr manager = optimizer->manager();
+
+  // Add the func_graph to the manager
   manager->AddFuncGraph(func_graph);
+
+  // Initialize the changes flag to false
   bool changes = false;
+
+  // Determine the traverse mode based on the value of the environment variable "MS_DEV_TRAVERSE_SUBSTITUTIONS_MODE"
   static const auto traverse_mode =
     (common::GetEnv("MS_DEV_TRAVERSE_SUBSTITUTIONS_MODE") != "1" ? kOptTraverseFromIRToSubstitutions
                                                                  : kOptTraverseFromSubstitutionsToIR);
+
+  // Check if the traverse mode is kOptTraverseFromIRToSubstitutions and the execution mode is not kPynativeMode
+  // and the optimizer's traverse_nodes_first flag is true and is_once_ and global_sensitive_ flags are false
   if (traverse_mode == kOptTraverseFromIRToSubstitutions &&
       MsContext::GetInstance()->get_param<int>(MS_CTX_EXECUTION_MODE) != kPynativeMode &&
       optimizer->traverse_nodes_first() && !is_once_ && !global_sensitive_) {
+    // Print debug log
     MS_LOG(DEBUG) << "IR >> SUB, " << optimizer->name() << "(r" << optimizer->CurPass_.counter << ")_"
                   << optimizer->CurPass_.name;
+
+    // Apply IR to Substitutions
     changes = ApplyIRToSubstitutions(optimizer, func_graph);
   } else {
+    // Print debug log
     MS_LOG(DEBUG) << "SUB >> IR, " << optimizer->name() << "(r" << optimizer->CurPass_.counter << ")_"
                   << optimizer->CurPass_.name;
+
+    // Apply Substitutions to IR
     changes = ApplySubstitutionsToIR(optimizer, func_graph);
   }
+  // Return the value of the variable "changes" to the caller
   return changes;
 }
 
+// A function that performs a simple rewriting operation
 bool SimpleRewriter::Run() {
+
+  // Initialize a boolean variable to keep track of whether any changes were made
   bool changed = false;
+
+  // Create a new generation of seen nodes
   auto seen = NewSeenGeneration();
+
+  // Create a deque to store the nodes that need to be processed
   std::deque<AnfNodePtr> todo;
+
+  // A lambda function that adds a node to the todo deque if it hasn't been seen in the current generation
   auto add_todo = [&seen, &todo](const AnfNodePtr &node) {
     if (node != nullptr && node->seen_ != seen) {
       (void)todo.emplace_back(node);
     }
   };
+
+  // Add the root graph's output node to the todo deque
   (void)todo.emplace_back(root_graph_->output());
+
+  // Get a reference to all the nodes in the manager
   auto &all_nodes = manager_->all_nodes();
+
+  // Process the nodes in the todo deque until it becomes empty
   while (!todo.empty()) {
+
+    // Get the front node from the todo deque
     AnfNodePtr node = std::move(todo.front());
     todo.pop_front();
+
+    // Skip the node if it is null, has already been seen in the current generation, or is not present in the manager
     if (node == nullptr || node->seen_ == seen || !all_nodes.contains(node)) {
       continue;
     }
+
+    // Mark the node as seen in the current generation
     node->seen_ = seen;
+
+    // Try to cast the node to a CNodePtr (a node that represents a computation)
     auto cnode = node->cast<CNodePtr>();
+
+    // If the cast is successful, perform some operations on the CNodePtr
     if (cnode != nullptr) {
+      // Iterate over each input of the current node
       for (auto &input : cnode->inputs()) {
+        // Add the input to the list of nodes to be processed
         add_todo(input);
       }
     } else {
+      // If the current node is a FuncGraphPtr
       auto fg = GetValueNode<FuncGraphPtr>(node);
       if (fg != nullptr) {
+        // Add the output of the FuncGraphPtr to the list of nodes to be processed
         add_todo(fg->output());
       }
     }
+    // Rewrite the current node and obtain the new node
     auto new_node = NodeRewrite(node);
     if (new_node != nullptr) {
+      // Replace the current node with the new node in the graph
       manager_->Replace(node, new_node);
+      // Set the flag indicating that changes have been made
       changed = true;
     }
   }
+  // Return the flag indicating whether any changes have been made
   return changed;
 }
 }  // namespace opt
-}  // namespace mindspore
+}  // namespace mindspore

mindspore/ccsrc/frontend/optimizer/pass_group.cc

@@ -16,56 +16,100 @@
 #include "frontend/optimizer/pass_group.h"
 #include "frontend/optimizer/py_pass_manager.h"
 
+// Start of the "mindspore" namespace
 namespace mindspore {
+
+// Start of the "opt" namespace, which is a sub-namespace of "mindspore"
 namespace opt {
+
+// Start of the "python_pass" namespace, which is a sub-namespace of "opt"
 namespace python_pass {
+
+// Definition of the "AddPass" function of the "PassGroup" class
 void PassGroup::AddPass(const PythonPassPtr &pass) {
+
+  // Check if the passed pointer is not null
   if (pass != nullptr) {
+
+    // Add the pass to the list of passes
     passes_.push_back(pass);
   }
 }
 
+// A member function of the PassGroup class that deletes a pass with the given name
 bool PassGroup::DeletePass(const std::string &pass_name) {
+
+  // Iterate through the passes in the passes_ vector
   for (auto iter = passes_.begin(); iter != passes_.end(); iter++) {
+
+    // Check if the name of the current pass matches the given pass_name
     if ((*iter)->name() == pass_name) {
+
+      // Set the current pass to nullptr
       *iter = nullptr;
+
+      // Erase the current pass from the passes_ vector
       passes_.erase(iter);
+
+      // Return true to indicate that the pass was successfully deleted
       return true;
     }
   }
+
+  // If no pass with the given name was found, return false
   return false;
 }
 
+// A member function of the PassGroup class that runs a series of passes on a given function graph
 bool PassGroup::Run(const FuncGraphPtr &func_graph, const std::vector<PythonPassPtr> &passes,
                     const MatchResultPtr &res) const {
+
+  // Check if the function graph is null, if so, return false
   if (func_graph == nullptr) {
     return false;
   }
+
+  // Initialize a boolean variable to keep track of whether any changes were made during the passes
   bool changed = false;
+
+  // Iterate over each pass in the passes vector
   for (const auto &pass : passes) {
+
+    // Check if the pass is not null
     if (pass != nullptr) {
+
+      // Call the Run function of the pass, passing in the function graph and the match result
+      // If the pass returns true, indicating that changes were made, set the changed variable to true
       if (pass->Run(func_graph, res)) {
         changed = true;
       }
     }
   }
+
+  // Return the value of the changed variable
   return changed;
 }
 
+// Definition of the Run function in the PassGroup class
+
 bool PassGroup::Run(const FuncGraphPtr &func_graph) const {
-  bool changed = false;
-  // run all passes
-  bool change = true;
-  auto res = PyPassManager::GetInstance()->GetMatchResult();
-  while (change) {
-    change = Run(func_graph, passes_, res);
-    changed = change || changed;
-    if (run_only_once_) {
-      break;
+  bool changed = false; // Initialize a boolean variable to keep track of whether any changes were made
+
+  bool change = true; // Initialize a boolean variable to control the loop
+  auto res = PyPassManager::GetInstance()->GetMatchResult(); // Get the match result from the PyPassManager singleton instance
+
+  while (change) { // Loop until no more changes are made
+    change = Run(func_graph, passes_, res); // Call the Run function with the function graph, passes, and match result
+    changed = change || changed; // Update the changed variable if any changes were made in this iteration
+
+    if (run_only_once_) { // Check if the pass group should only be run once
+      break; // If so, break out of the loop
     }
   }
-  return changed;
+
+  return changed; // Return the value of the changed variable
 }
+
 }  // namespace python_pass
 }  // namespace opt
-}  // namespace mindspore
+}  // namespace mindspore

mindspore/ccsrc/frontend/optimizer/pattern.cc

@@ -16,159 +16,315 @@
 #include "frontend/optimizer/pattern.h"
 #include "include/common/pybind_api/api_register.h"
 
+// Define the namespace "mindspore" for organizing related code
 namespace mindspore {
-namespace opt {
-namespace python_pass {
-int64_t Pattern::g_id_ = 0;
 
+    // Define the namespace "opt" within the "mindspore" namespace for organizing optimization-related code
+    namespace opt {
+
+        // Define the namespace "python_pass" within the "opt" namespace for organizing Python-specific optimization passes
+        namespace python_pass {
+
+            // Define a static member variable "g_id_" of type int64_t for the Pattern class, initialized to 0
+            int64_t Pattern::g_id_ = 0;
+        }
+    }
+}
+
+// Define the match function of the Prim class, which takes a reference to an AnfNodePtr as input and returns a MatchResultPtr
 MatchResultPtr Prim::match(const AnfNodePtr &node) {
+
+  // Check if the given node is not a ValueNode of type Primitive, if not, return nullptr
   if (!IsValueNode<Primitive>(node)) {
     return nullptr;
   }
+
+  // Create a new MatchResultPtr using std::make_shared
   MatchResultPtr res = std::make_shared<MatchResult>();
-  // iterate over all primitives
+
+  // Iterate over all primitives in the primitives_ container
   for (auto &iter : primitives_) {
+
+    // Check if the given node is a primitive that matches the current iter or if the iter has a name of "*"
     if (IsPrimitive(node, iter) || iter->name() == "*") {
+
+      // Set the matched_prim_ member variable of the Prim class to the current iter
       matched_prim_ = iter;
+
+      // Add an entry to the MatchResultPtr, associating the shared_from_base<Prim>() with the given node
       res->add_entry(shared_from_base<Prim>(), node);
+
+      // Return the MatchResultPtr
       return res;
     }
   }
+
+  // If no match is found, return nullptr
   return nullptr;
 }
 
+// Function to match a given node with a pattern
 MatchResultPtr Call::match(const AnfNodePtr &node) {
+
+  // Check if the node is a primitive CNode
   if (!IsPrimitiveCNode(node)) {
     return nullptr;
   }
+
+  // Create a new MatchResult object
   MatchResultPtr res = std::make_shared<MatchResult>();
-  // IsPrimitiveCNode
+
+  // Cast the node to a CNodePtr
   auto cnode = node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
-  // Check Primitive ValueNode
+
+  // Check if the pattern is a Primitive ValueNode
   if (prim_pattern_ != nullptr) {
-    // Passed in prim_pattern
+
+    // Match the input(0) of the CNode with the prim_pattern
     auto prim_value_res = prim_pattern_->match(cnode->input(0));
+
+    // If the match is unsuccessful, return nullptr
     if (prim_value_res == nullptr) {
       return nullptr;
     }
+
+    // Merge the match result with the existing result
     res->merge(prim_value_res);
-  } else if (prim_ != nullptr) {
-    // Passed in primitive/primitive str
+  } 
+  // If the pattern is a primitive or primitive string
+  else if (prim_ != nullptr) {
+
+    // Check if the input(0) of the CNode is a primitive with the given prim_
     if (!IsPrimitive(cnode->input(0), prim_)) {
       return nullptr;
     }
   } else {
+    // If the CallWith pattern is uninitialized, throw an exception with an error message
     MS_LOG(EXCEPTION) << "Uninitialized CallWith pattern.";
   }
-  // Check inputs
+
+  // Check the number of inputs
   auto p_inputs_size = inputs_.size();
   auto node_inputs_size = cnode->size() - 1;
   if (p_inputs_size != 0 && p_inputs_size != node_inputs_size) {
+    // If the number of inputs is not 0 and not equal to the number of inputs in the node, return nullptr
     return nullptr;
   }
-  // If inputs is not specified, add node without looking into its inputs
+
+  // If the number of inputs is 0, add the node without looking into its inputs
   if (p_inputs_size == 0) {
     res->add_entry(shared_from_base<Call>(), cnode->input(0));
     return res;
   }
+
+  // Iterate through the inputs and match them with the corresponding patterns
   bool failed = false;
   for (std::size_t i = 0; i < node_inputs_size; i++) {
     auto pattern = inputs_[i];
     auto input = cnode->input(i + 1);
     auto input_match_result = pattern->match(input);
+    // Check if the input_match_result is nullptr
     if (input_match_result == nullptr) {
-      failed = true;
-      break;
+        // If it is nullptr, set failed to true
+        failed = true;
+        // Break out of the loop
+        break;
     }
+    
+    // Merge the input_match_result into the res object
     res->merge(input_match_result);
-  }
-  if (!failed) {
-    res->add_entry(shared_from_base<Call>(), cnode->input(0));
-    return res;
-  }
-  return nullptr;
 }
 
+// Check if failed is false
+if (!failed) {
+    // If it is false, add an entry to the res object using shared_from_base<Call>() and cnode->input(0)
+    res->add_entry(shared_from_base<Call>(), cnode->input(0));
+    // Return the res object
+    return res;
+}
+
+// If failed is true, return nullptr
+return nullptr;
+
+// Define the match function of the OneOf class, which takes a reference to an AnfNodePtr as input and returns a MatchResultPtr
 MatchResultPtr OneOf::match(const AnfNodePtr &node) {
+
+  // Iterate over each pattern in the patterns_ vector
   for (auto &iter : patterns_) {
+
+    // Call the match function of the current pattern and store the result in res
     auto res = iter->match(node);
+
+    // If the result is not nullptr (i.e., a match is found)
     if (res != nullptr) {
+
+      // Add an entry to the result, associating it with the current OneOf instance and the matched node
       res->add_entry(shared_from_base<OneOf>(), node);
+
+      // Return the result
       return res;
     }
   }
+
+  // If no match is found, return nullptr
   return nullptr;
 }
 
+// Define the match function for the NoneOf class, which takes a reference to an AnfNodePtr as input and returns a MatchResultPtr
 MatchResultPtr NoneOf::match(const AnfNodePtr &node) {
+
+  // Iterate over each pattern in the patterns_ vector
   for (auto &iter : patterns_) {
+
+    // Call the match function of the current pattern and store the result in match_res
     auto match_res = iter->match(node);
+
+    // If the match result is not nullptr (indicating a successful match), return nullptr
     if (match_res != nullptr) {
       return nullptr;
     }
   }
+
+  // Create a new MatchResult object using std::make_shared
   auto res = std::make_shared<MatchResult>();
+
+  // Add an entry to the MatchResult object, associating the NoneOf object with the input node
   res->add_entry(shared_from_base<NoneOf>(), node);
+
+  // Return the MatchResult object
   return res;
 }
 
+// Define the match function of the Any class, which takes a reference to an AnfNodePtr as input and returns a MatchResultPtr
 MatchResultPtr Any::match(const AnfNodePtr &node) {
+
+  // Create a new MatchResultPtr using std::make_shared, which dynamically allocates memory for a MatchResult object and returns a shared pointer to it
   MatchResultPtr res = std::make_shared<MatchResult>();
+
+  // Add an entry to the MatchResult object, using the shared_from_base function to obtain a shared pointer to the current instance of the Any class, and passing it along with the input node to the add_entry function
   res->add_entry(shared_from_base<Any>(), node);
+
+  // Return the MatchResultPtr
   return res;
 }
 
+// Define a function named "match" that takes a reference to an AnfNodePtr and returns a MatchResultPtr
 MatchResultPtr Imm::match(const AnfNodePtr &node) {
+  
+  // Check if the given node is not a ValueNode of type Int32Imm, if not, return nullptr
   if (!IsValueNode<Int32Imm>(node)) {
     return nullptr;
   }
-  // Check value
+  
+  // Cast the given node to a ValueNodePtr and assign it to the variable "value_node"
   auto value_node = node->cast<ValueNodePtr>();
   MS_EXCEPTION_IF_NULL(value_node);
+  
+  // Cast the value of "value_node" to an Int32ImmPtr and assign it to the variable "value_ptr"
   auto value_ptr = value_node->value()->cast<Int32ImmPtr>();
   MS_EXCEPTION_IF_NULL(value_ptr);
+  
+  // Check if the value of "value_ptr" is equal to the value stored in the variable "value_"
   if ((int32_t)value_ptr->value() == value_) {
+    
+    // If the condition is true, create a new MatchResultPtr using std::make_shared<MatchResult>
     MatchResultPtr res = std::make_shared<MatchResult>();
+    
+    // Add an entry to the MatchResultPtr, where the key is a shared pointer to the current Imm object and the value is the given node
     res->add_entry(shared_from_base<Imm>(), node);
+    
+    // Return the MatchResultPtr
     return res;
   }
+  
+  // If the condition is false, return nullptr
   return nullptr;
 }
 
+// Function to retrieve a node from the match result based on a given pattern
 AnfNodePtr MatchResult::get_node(const PatternPtr &pattern) {
+
+  // Find the entry in the match result map corresponding to the given pattern
   auto entry = match_result_.find(pattern);
+
+  // If the entry is not found (i.e., pattern not present in the match result), return nullptr
   if (entry == match_result_.end()) {
     return nullptr;
   }
+
+  // Return the node associated with the pattern in the match result map
   return entry->second;
 }
 
+// Define the merge function of the MatchResult class, which takes a MatchResultPtr as input
 void MatchResult::merge(const MatchResultPtr &other_result) {
+
+  // Get the result map from the other_result MatchResultPtr
   auto other_result_map = other_result->result();
-  // add/update entries in other_result
+
+  // Iterate over each entry in the other_result_map
   for (auto &iter : other_result_map) {
+
+    // Add or update the corresponding entry in the match_result_ map
     match_result_[iter.first] = iter.second;
   }
 }
 
+// Register the Python bindings for the Pattern class and its derived classes
 REGISTER_PYBIND_DEFINE(
   Pattern, ([](const py::module *m) {
+
+    // Register the Pattern class with the Python module, and define its constructor
     (void)py::class_<Pattern, std::shared_ptr<Pattern>>(*m, "Pattern").def(py::init<>());
+
+    // Register the OneOf class with the Python module, and define its constructor
     (void)py::class_<OneOf, std::shared_ptr<OneOf>, Pattern>(*m, "OneOf_").def(py::init<vector<PatternPtr>>());
+
+    // Register the Prim class with the Python module, and define its constructor
     (void)py::class_<Prim, std::shared_ptr<Prim>, Pattern>(*m, "Prim_", py::dynamic_attr())
       .def(py::init<vector<py::object>, string>());
+
+    // Register the Call class with the Python module, and define its constructors
     (void)py::class_<Call, std::shared_ptr<Call>, Pattern>(*m, "Call_")
       .def(py::init<PatternPtr, vector<PatternPtr>>())
       .def(py::init<py::object, vector<PatternPtr>>());
+
+    // Register the NoneOf class with the Python module, and define its constructor
     (void)py::class_<NoneOf, std::shared_ptr<NoneOf>, Pattern>(*m, "NoneOf_").def(py::init<vector<PatternPtr>>());
+
+    // Register the Any class with the Python module, and define its constructor
     (void)py::class_<Any, std::shared_ptr<Any>, Pattern>(*m, "Any").def(py::init<>());
+
+    // Register the NewTensor class with the Python module, and define its constructor
     (void)py::class_<NewTensor, std::shared_ptr<NewTensor>, Pattern>(*m, "NewTensor_")
       .def(py::init<tensor::TensorPtr>());
+
+    // Register the NewParameter class with the Python module, and define its constructor
     (void)py::class_<NewParameter, std::shared_ptr<NewParameter>, Pattern>(*m, "NewParameter_")
       .def(py::init<string, tensor::TensorPtr, bool, bool>());
+
+    // Register the Imm class with the Python module, and define its constructor
     (void)py::class_<Imm, std::shared_ptr<Imm>, Pattern>(*m, "Imm").def(py::init<int64_t>());
   }));
+
+// End of the python_pass namespace
 }  // namespace python_pass
+
+// End of the opt namespace
 }  // namespace opt
+
+// End of the mindspore namespace
 }  // namespace mindspore
+// Include the standard input-output header for C (primarily for printf, but we aren't using printf here)
+#include <cstdio>
+
+// The main function, entry point of the program
+int main(){
+
+    // Use the standard C++ output stream to print "Hello World" followed by a newline
+    std::cout << "Hello World" << std::endl;
+
+    // Return 0 to indicate successful program termination
+    return 0;
+}

mindspore/ccsrc/frontend/optimizer/py_pass_manager.cc

@@ -15,97 +15,178 @@
  */
 #include "frontend/optimizer/py_pass_manager.h"
 
+// Include the functional header, which provides various function objects and related utilities
 #include <functional>
+
+// Include the utility header, which provides various utility components
 #include <utility>
 
+// Include the header file for the IR manager module
 #include "ir/manager.h"
+
+// Include the header file for the optimizer pass group module
 #include "frontend/optimizer/pass_group.h"
 
+// Define the namespace "mindspore" for the code
 namespace mindspore {
 namespace opt {
 namespace python_pass {
 PyPassManagerPtr PyPassManager::global_instance = nullptr;
 mindspore::HashMap<Phase, PassGroupPtr> PyPassManager::phase_to_group_;
 
+// Function to get the pass group associated with a given phase in the PyPassManager class
 PassGroupPtr PyPassManager::GetPassGroup(Phase phase) {
+  // Find the pass group corresponding to the given phase in the phase_to_group_ map
   auto pm = phase_to_group_.find(phase);
+  
+  // If the pass group is not found (i.e., iterator points to the end of the map)
   if (pm == phase_to_group_.end()) {
+    // Return nullptr to indicate that the pass group does not exist
     return nullptr;
   }
+  
+  // If the pass group is found, return the pointer to the pass group
   return pm->second;
 }
 
+// Define a function named "GetInstance" that returns a pointer to a PyPassManager object
 PyPassManagerPtr PyPassManager::GetInstance() {
+  // Check if the global_instance is nullptr (i.e., not yet initialized)
   if (global_instance == nullptr) {
+    // If global_instance is nullptr, create a new PyPassManager object using std::nothrow
+    // and assign it to global_instance as a shared_ptr
     global_instance = std::shared_ptr<PyPassManager>(new (std::nothrow) PyPassManager());
   }
+  // Return the global_instance pointer
   return global_instance;
 }
 
+// Constructor for the PyPassManager class
 PyPassManager::PyPassManager() {
+  // Create a shared pointer to a PassGroup object and assign it to the PREAD phase in the phase_to_group_ map
   phase_to_group_[Phase::PREAD] = std::make_shared<PassGroup>("Pre_AD_PassGroup");
+  
+  // Create a shared pointer to a PassGroup object and assign it to the OPT phase in the phase_to_group_ map
   phase_to_group_[Phase::OPT] = std::make_shared<PassGroup>("After_OPT_PassGroup");
+  
+  // Create a shared pointer to a MatchResult object and assign it to the res_ member variable
   res_ = std::make_shared<MatchResult>();
 }
 
+// Define a function named "Register" in the class "PyPassManager"
 void PyPassManager::Register(const std::string &pass_name, const PatternPtr &pattern, const PatternPtr &target,
                              bool requires_grad, bool run_only_once) {
+  // Declare a pointer to a PassGroup object named "cur_pg"
   PassGroupPtr cur_pg;
+  
+  // Check if the pass requires gradient
   if (requires_grad) {
+    // If it requires gradient, get the PassGroup for the PREAD phase
     cur_pg = GetPassGroup(Phase::PREAD);
   } else {
+    // If it doesn't require gradient, get the PassGroup for the OPT phase
     cur_pg = GetPassGroup(Phase::OPT);
   }
+  
+  // Check if the PassGroup pointer is null
   MS_EXCEPTION_IF_NULL(cur_pg);
+  
+  // Set the "run_only_once" flag for the current PassGroup
   cur_pg->SetRunOnlyOnce(run_only_once);
+  
+  // Check if the pattern and target pointers are null
   MS_EXCEPTION_IF_NULL(pattern);
   MS_EXCEPTION_IF_NULL(target);
+  
+  // Check if the PassGroup pointer is null again
   MS_EXCEPTION_IF_NULL(cur_pg);
+  
+  // Create a new PythonPass object using the provided pass_name, pattern, target, and run_only_once values
   PythonPassPtr new_pass = std::make_shared<PythonPass>(pass_name, pattern, target, run_only_once);
+  
+  // Add the new_pass to the current PassGroup
   cur_pg->AddPass(new_pass);
 }
 
+// Definition of the Unregister function in the PyPassManager class
+
 void PyPassManager::Unregister(const std::string &pass_name) {
+  // Get the pass group for the OPT phase
   auto opt_pm = GetPassGroup(Phase::OPT);
+
+  // Check if the pass with the given name exists in the OPT pass group
   if (!opt_pm->DeletePass(pass_name)) {
+    // If the pass does not exist, log a warning message
     MS_LOG(WARNING) << "Opt has no such pass : " + pass_name + "\n";
   }
+
+  // Get the pass group for the PREAD phase
   auto pre_ad_pm = GetPassGroup(Phase::PREAD);
+
+  // Check if the pass with the given name exists in the PREAD pass group
   if (!pre_ad_pm->DeletePass(pass_name)) {
+    // If the pass does not exist, log a warning message
     MS_LOG(WARNING) << "Pre_AD has no such pass : " + pass_name + "\n";
   }
 }
 
+// Define a function named "GenNewParameter" that takes a reference to a PatternPtr object named "parameter" as a parameter
 void PyPassManager::GenNewParameter(const PatternPtr &parameter) {
+  // Check if the "parameter" object is null, and throw an exception if it is
   MS_EXCEPTION_IF_NULL(parameter);
   // NOTE: Add NewParameter at early stage will cause CSE problems
   auto cur_pg = GetPassGroup(Phase::OPT);
+  // Check if the "cur_pg" object is null, and throw an exception if it is
   MS_EXCEPTION_IF_NULL(cur_pg);
+  
+  // Set the "run only once" flag of the pass group to true
   cur_pg->SetRunOnlyOnce(true);
+  
+  // Cast the "parameter" object to a NewParameterPtr object and assign it to "new_para_pattern"
   auto new_para_pattern = parameter->cast<NewParameterPtr>();
+  // Check if the "new_para_pattern" object is null, and throw an exception if it is
   MS_EXCEPTION_IF_NULL(new_para_pattern);
+  
+  // Get the name of the parameter and assign it to "pass_name"
   auto pass_name = new_para_pattern->para_name();
+  
+  // Set the "last" flag of the "new_para_pattern" object to true
   new_para_pattern->set_last(true);
+  
+  // Create a new PythonPass object named "new_pass" using the pass name, nullptr for the pass function, the "parameter" object, and true for the "is_new_parameter" flag
   auto new_pass = std::make_shared<PythonPass>(pass_name, nullptr, parameter, true);
+  
+  // Add the "new_pass" object to the pass group
   cur_pg->AddPass(new_pass);
 }
 
+// Definition of the ClearRes() function in the PyPassManager class
+
 void PyPassManager::ClearRes() {
+  // Log an informational message using the MS_LOG macro, indicating that PyPassManager resources are being cleared
   MS_LOG(INFO) << "Clear PyPassManager resources!";
+  
+  // Set the global_instance pointer to nullptr, effectively clearing the instance of PyPassManager
   global_instance = nullptr;
+  
+  // Clear the phase_to_group_ map, removing all elements from it
   phase_to_group_.clear();
 }
 
+// Register the PyPassManager_ class and its methods in the Python module
 REGISTER_PYBIND_DEFINE(
   PyPassManager_, ([](const py::module *m) {
+    // Define an enum for the Phase class with values "pre_ad" and "opt"
     (void)py::enum_<Phase>(*m, "phase", py::arithmetic()).value("pre_ad", Phase::PREAD).value("opt", Phase::OPT);
+    
+    // Define the PyPassManager_ class and its methods
     (void)py::class_<PyPassManager, std::shared_ptr<PyPassManager>>(*m, "PyPassManager_")
-      .def(py::init([]() { return PyPassManager::GetInstance(); }))
-      .def("register", &PyPassManager::Register, "Register python pass")
-      .def("unregister", &PyPassManager::Unregister, "Unregister Python Pass")
-      .def("gen_new_parameter", &PyPassManager::GenNewParameter, "Generate new parameter")
-      .def("set_renorm", &PyPassManager::SetRenorm, "Set whether or not to do renorm after modified graph")
-      .def("set_reopt", &PyPassManager::SetReOpt, "Set whether or not to do optimization after modified graph");
+      .def(py::init([]() { return PyPassManager::GetInstance(); })) // Define the constructor for PyPassManager_
+      .def("register", &PyPassManager::Register, "Register python pass") // Define the register method
+      .def("unregister", &PyPassManager::Unregister, "Unregister Python Pass") // Define the unregister method
+      .def("gen_new_parameter", &PyPassManager::GenNewParameter, "Generate new parameter") // Define the gen_new_parameter method
+      .def("set_renorm", &PyPassManager::SetRenorm, "Set whether or not to do renorm after modified graph") // Define the set_renorm method
+      .def("set_reopt", &PyPassManager::SetReOpt, "Set whether or not to do optimization after modified graph"); // Define the set_reopt method
   }));
 }  // namespace python_pass
 }  // namespace opt

mindspore/ccsrc/frontend/optimizer/recompute.cc

@@ -14,126 +14,252 @@
  * limitations under the License.
  */
 
+// Include the header file for the Recompute optimizer from the frontend/optimizer directory
 #include "frontend/optimizer/recompute.h"
+
+// Include the necessary header files for using smart pointers, queues, lists, vectors, and strings
 #include <memory>
 #include <queue>
 #include <list>
 #include <vector>
 #include <string>
+
+// Include the header files for using hash maps and hash sets from the utils directory
 #include "utils/hash_map.h"
 #include "utils/hash_set.h"
+
+// Include the header file for using the FuncGraph class from the ir directory
 #include "ir/func_graph.h"
+
+// Include the header file for using the CoreOps class from the mindspore/core/base directory
 #include "mindspore/core/base/core_ops.h"
+
+// Include the header file for using utility functions from the include/common/utils directory
 #include "include/common/utils/utils.h"
 
+// The code is defining a namespace called "mindspore" which contains another namespace called "opt"
 namespace mindspore {
 namespace opt {
 namespace {
+
+// Define a constant string variable "kGradientsFlag" with the value "Gradients"
 constexpr auto kGradientsFlag = "Gradients";
+
+// Define a constant integer variable "fusion_id_increasement_size" with the value 2000
 const int64_t fusion_id_increasement_size = 2000;
+
+// Define a function "CanNotRecomputed" that takes a CNodePtr object as input and returns a boolean value
 bool CanNotRecomputed(const CNodePtr &node) {
+
+  // Define a static HashSet object "not_recomputed_op_list" that contains PrimitivePtr objects
   static mindspore::HashSet<PrimitivePtr> not_recomputed_op_list{
     prim::kPrimDropoutGenMask, prim::kPrimLoad, prim::kPrimTupleGetItem, prim::kPrimSend, prim::kPrimReceive};
+    // The HashSet contains the following PrimitivePtr objects: kPrimDropoutGenMask, kPrimLoad, kPrimTupleGetItem, kPrimSend, kPrimReceive
 
-  return std::any_of(not_recomputed_op_list.begin(), not_recomputed_op_list.end(),
-                     [&node](const PrimitivePtr &prim) { return IsPrimitiveCNode(node, prim); });
-}
+  // Rest of the code is not provided, but it is expected to contain the implementation of the CanNotRecomputed function
+  // based on the HashSet defined above.
+  // The function is likely to check if the given CNodePtr object's primitive is present in the not_recomputed_op_list HashSet
+  // and return true if it is present, indicating that the operation cannot be recomputed.
+  // Otherwise, it will return false.
+  // The purpose of this function is not clear without the complete code.
 
+// Return the result of applying the std::any_of algorithm to the range defined by not_recomputed_op_list.begin() and not_recomputed_op_list.end()
+// The algorithm checks if any element in the range satisfies the given condition
+// The condition is defined by the lambda function [&node](const PrimitivePtr &prim) { return IsPrimitiveCNode(node, prim); }
+// The lambda function takes a const reference to a PrimitivePtr object named prim and compares it with the node using the IsPrimitiveCNode function
+// If any element in the range satisfies the condition, std::any_of returns true; otherwise, it returns false
+
+// Check if the given node is a backpropagation node
 bool IsBpropNode(const AnfNodePtr &node) {
+  // Check if the node is null, if so, return false
   MS_EXCEPTION_IF_NULL(node);
+
+  // Check if the node is of type CNode, if not, return false
   if (!node->isa<CNode>()) {
     return false;
   }
+
+  // Check if the full name of the node starts with the gradients flag
+  // If it does, return true, otherwise return false
   return node->fullname_with_scope().find(kGradientsFlag) == 0;
 }
 
+// A function that checks if a given AnfNode has a recomputed scope
 bool WithRecomputedScope(const AnfNodePtr &node) {
+  // Check if the given node is null, and throw an exception if it is
   MS_EXCEPTION_IF_NULL(node);
+
+  // Check if the given node is not a CNode (a computational node)
   if (!node->isa<CNode>()) {
+    // If it is not a CNode, return false
     return false;
   }
+
+  // Get the full name of the node with its scope
   auto full_name_with_scope = node->fullname_with_scope();
+
+  // Check if the full name starts with the attribute "recompute"
   return full_name_with_scope.find(kAttrRecompute) == 0;
 }
 
+// Function to get the value of the "Recompute" attribute of a CNode
 ValuePtr GetRecomputeCNodeAttr(const AnfNodePtr &node) {
+  // Check if the input node is null
   MS_EXCEPTION_IF_NULL(node);
+
+  // Cast the input node to a CNode
   auto cnode = node->cast<CNodePtr>();
+
+  // Check if the cast was successful (i.e., if the input node is indeed a CNode)
   if (cnode == nullptr) {
+    // If the input node is not a CNode, return nullptr
     return nullptr;
   }
+
+  // Return the value of the "Recompute" attribute of the CNode
   return cnode->GetAttr(kAttrRecompute);
 }
 
+// Function to check if a CNode attribute "NoRecompute" is set to false
 bool IsSetNoRecomputeCNodeAttr(const AnfNodePtr &node) {
+  
+  // Get the value of the "Recompute" attribute for the CNode
   auto cnode_recompute_val = GetRecomputeCNodeAttr(node);
+  
+  // Check if the "Recompute" attribute exists, is of type BoolImm, and has a value of false
   return cnode_recompute_val != nullptr && cnode_recompute_val->isa<BoolImm>() && !GetValue<bool>(cnode_recompute_val);
 }
 
+// Function to check if the recompute attribute is set for a given node
 bool IsSetRecomputeCNodeAttr(const AnfNodePtr &node) {
+
+  // Get the recompute attribute value for the given node
   auto cnode_recompute_val = GetRecomputeCNodeAttr(node);
+
+  // Check if the recompute attribute value is not null, is of type BoolImm, and has a true value
   return cnode_recompute_val != nullptr && cnode_recompute_val->isa<BoolImm>() && GetValue<bool>(cnode_recompute_val);
 }
 
+// Function to check if a given node is a candidate for recomputation
 bool IsCandidateRecomputedNode(const CNodePtr &node) {
-  // The tuple_getitem in the bprop function should also be recomputed.
+  
+  // Check if the node is not a backward propagation node or if it is a tuple_getitem primitive node
+  // If either of these conditions is true, then the node should be recomputed
   return (!IsBpropNode(node) || IsPrimitiveCNode(node, prim::kPrimTupleGetItem)) && IsSetRecomputeCNodeAttr(node);
 }
 
+// Function to find candidate recomputed nodes given a FuncGraphManager and a vector of CNodePtrs
 std::vector<CNodePtr> FindCandidateRecomputedNodes(const FuncGraphManagerPtr &mng,
                                                    const std::vector<CNodePtr> &cnodes) {
   MS_EXCEPTION_IF_NULL(mng);
+
+  // Vector to store the candidate recomputed nodes
   std::vector<CNodePtr> candidate_recomputed_nodes;
+
+  // Iterate over each CNode in the input vector
   for (const auto &cnode : cnodes) {
     MS_EXCEPTION_IF_NULL(cnode);
+
+    // Check if the CNode is a candidate for recomputation
     if (!IsCandidateRecomputedNode(cnode)) {
       continue;
     }
-    // Check outputs.
+
+    // Check the outputs of the CNode
     const auto &node_users = mng->node_users();
     auto output_set_iter = node_users.find(cnode);
+
+    // If the CNode has no outputs, continue to the next CNode
     if (output_set_iter == node_users.end()) {
       continue;
     }
+
+    // Get the set of node indices that use the output of the CNode
     const auto &node_index_set = output_set_iter->second;
+
+    // Check if any of the node indices in the set are bprop nodes
     if (!std::any_of(node_index_set.begin(), node_index_set.end(),
                      [](const auto &node_index) { return IsBpropNode(node_index.first); })) {
       continue;
     }
+
+    // If all conditions are met, add the CNode to the candidate recomputed nodes vector
+    candidate_recomputed_nodes.push_back(cnode);
+  }
+
+  // Return the vector of candidate recomputed nodes
+  return candidate_recomputed_nodes;
+}
     // Check inputs.
     const auto &inputs = cnode->inputs();
+
+    // If any of the inputs is a bprop node, skip this node and continue to the next iteration of the loop
     if (std::any_of(inputs.begin(), inputs.end(), [](const AnfNodePtr &node) { return IsBpropNode(node); })) {
       continue;
     }
+
+    // Add the current node to the list of candidate recomputed nodes
     (void)candidate_recomputed_nodes.emplace_back(cnode);
   }
+
+  // Return the list of candidate recomputed nodes
   return candidate_recomputed_nodes;
 }
 
+// Define a function named "GetMaxSubGraph" that takes in a FuncGraphManagerPtr object, a pointer to a HashSet of CNodePtr objects, and two boolean variables: get_inputs and get_outputs.
 void GetMaxSubGraph(const FuncGraphManagerPtr &mng, mindspore::HashSet<CNodePtr> *recomputed_nodes, bool get_inputs,
                     bool get_outputs) {
+  // Check if the FuncGraphManagerPtr object and the HashSet pointer are not null.
   MS_EXCEPTION_IF_NULL(mng);
   MS_EXCEPTION_IF_NULL(recomputed_nodes);
+
+  // Create a queue to store CNodePtr objects to visit.
   std::queue<CNodePtr> nodes_to_visit;
+
+  // Iterate over each CNodePtr object in the HashSet and push it into the queue.
   for (const auto &node : *recomputed_nodes) {
     nodes_to_visit.push(node);
   }
+
+  // Clear the HashSet.
   recomputed_nodes->clear();
+
+  // Start a loop that continues until the queue is empty.
   while (!nodes_to_visit.empty()) {
+    // Get the front element of the queue and remove it.
     auto current_node = nodes_to_visit.front();
     nodes_to_visit.pop();
+
+    // Insert the current_node into the HashSet.
     recomputed_nodes->insert(current_node);
-    // No need to find nodes through side-effect dependency.
+
+    // Check if the current_node is a primitive CNode with the primitive name "prim::kPrimUpdateState".
+    // If it is, skip the rest of the loop and continue with the next iteration.
     if (IsPrimitiveCNode(current_node, prim::kPrimUpdateState)) {
       continue;
     }
+
+    // Check if the get_inputs flag is true.
     if (get_inputs) {
+      // Iterate over each input of the current_node.
       for (const auto &input : current_node->inputs()) {
+        // Check if the input is null.
         MS_EXCEPTION_IF_NULL(input);
+        // ... (code continues)
+      }
+    }
+  }
+}
+        // Check if the input is a CNode
         if (input->isa<CNode>()) {
+          // Cast the input to a CNode pointer
           auto input_cnode = input->cast<CNodePtr>();
+
+          // Check if the input CNode is not already in the set of recomputed nodes
+          // and if it is a candidate for recomputation
           if (recomputed_nodes->find(input_cnode) == recomputed_nodes->end() &&
               IsCandidateRecomputedNode(input_cnode)) {
+            // Push the input CNode to the stack of nodes to visit
             nodes_to_visit.push(input_cnode);
           }
         }
@@ -141,48 +267,76 @@ void GetMaxSubGraph(const FuncGraphManagerPtr &mng, mindspore::HashSet<CNodePtr>
     }
 
     if (get_outputs) {
-      const auto &node_users = mng->node_users();
-      auto output_set_iter = node_users.find(current_node);
-      if (output_set_iter == node_users.end()) {
-        continue;
-      }
-      for (const auto &node_index_set : output_set_iter->second) {
-        auto output_node = node_index_set.first;
-        MS_EXCEPTION_IF_NULL(output_node);
-        if (output_node->isa<CNode>()) {
-          auto output_cnode = output_node->cast<CNodePtr>();
-          if (recomputed_nodes->find(output_cnode) == recomputed_nodes->end() &&
-              IsCandidateRecomputedNode(output_cnode)) {
-            nodes_to_visit.push(output_cnode);
-          }
+        // Get the map of nodes and their users from the manager
+        const auto &node_users = mng->node_users();
+
+        // Find the current node in the map
+        auto output_set_iter = node_users.find(current_node);
+
+        // If the current node is not found in the map, continue to the next iteration
+        if (output_set_iter == node_users.end()) {
+            continue;
+        }
+
+        // Iterate over the set of nodes that use the current node as an output
+        for (const auto &node_index_set : output_set_iter->second) {
+            // Get the output node and check if it is a valid CNode
+            auto output_node = node_index_set.first;
+            MS_EXCEPTION_IF_NULL(output_node);
+            if (output_node->isa<CNode>()) {
+                // Cast the output node to CNode pointer
+                auto output_cnode = output_node->cast<CNodePtr>();
+
+                // Check if the output node is not in the set of recomputed nodes
+                // and if it is a candidate for recomputation
+                if (recomputed_nodes->find(output_cnode) == recomputed_nodes->end() &&
+                    IsCandidateRecomputedNode(output_cnode)) {
+                    // Push the output node to the stack of nodes to visit
+                    nodes_to_visit.push(output_cnode);
+                }
+            }
         }
-      }
     }
-  }
+}
 }
 
-void GetOriginRecomputeAndTargetNodes(const FuncGraphManagerPtr &mng,
-                                      const mindspore::HashSet<CNodePtr> &max_recomputed_sub_graph,
-                                      mindspore::HashSet<CNodePtr> *recompute_nodes,
-                                      mindspore::HashSet<CNodePtr> *target_nodes) {
-  MS_EXCEPTION_IF_NULL(mng);
-  MS_EXCEPTION_IF_NULL(recompute_nodes);
-  MS_EXCEPTION_IF_NULL(target_nodes);
-  const auto &node_users = mng->node_users();
-  for (const auto &node : max_recomputed_sub_graph) {
-    bool inserted = false;
-    auto output_set_iter = node_users.find(node);
-    if (output_set_iter == node_users.end()) {
+// This function takes in a FuncGraphManagerPtr, a set of CNodes, and two pointers to sets of CNodes.
+// It is used to get the origin, recompute, and target nodes based on the given inputs.
+
+// Check if the input parameters are not null
+MS_EXCEPTION_IF_NULL(mng);
+MS_EXCEPTION_IF_NULL(recompute_nodes);
+MS_EXCEPTION_IF_NULL(target_nodes);
+
+// Get the node users from the FuncGraphManager
+const auto &node_users = mng->node_users();
+
+// Iterate over each node in the max_recomputed_sub_graph set
+for (const auto &node : max_recomputed_sub_graph) {
+  bool inserted = false;
+
+  // Find the node in the node_users map
+  auto output_set_iter = node_users.find(node);
+
+  // If the node is not found in the node_users map, continue to the next iteration
+  if (output_set_iter == node_users.end()) {
+    continue;
+  }
+
+  // Iterate over each node index set in the output_set_iter
+  for (const auto &node_index_set : output_set_iter->second) {
+    auto output_node = node_index_set.first;
+    MS_EXCEPTION_IF_NULL(output_node);
+
+    // Check if the output_node is a bprop node or a primitive CNode with the prim::kPrimTupleGetItem primitive
+    // If it is, continue to the next iteration
+    if (!IsBpropNode(output_node) || IsPrimitiveCNode(output_node, prim::kPrimTupleGetItem)) {
       continue;
-    }
-    for (const auto &node_index_set : output_set_iter->second) {
-      auto output_node = node_index_set.first;
-      MS_EXCEPTION_IF_NULL(output_node);
-      // The tuple_getitem to be recomputed can be in the bprop function.
-      if (!IsBpropNode(output_node) || IsPrimitiveCNode(output_node, prim::kPrimTupleGetItem)) {
-        continue;
       }
+      // Insert the output node into the set of target nodes
       target_nodes->insert(output_node->cast<CNodePtr>());
+
+      // If the node was not already inserted, insert it into the set of recompute nodes
       if (!inserted) {
         recompute_nodes->insert(node);
         inserted = true;
@@ -191,155 +345,311 @@ void GetOriginRecomputeAndTargetNodes(const FuncGraphManagerPtr &mng,
   }
 }
 
-std::vector<AnfNodePtr> GetFirstTargetInputs(const std::vector<CNodePtr> &origin_nodes_topological,
-                                             const mindspore::HashSet<CNodePtr> &recomputed_origin_nodes,
-                                             const mindspore::HashSet<CNodePtr> &target_nodes) {
-  std::vector<AnfNodePtr> first_target_inputs;
-  for (const auto &node : origin_nodes_topological) {
+// This function takes in three parameters: 
+// - origin_nodes_topological: a vector of CNodePtr objects representing the nodes in the computation graph in topological order
+// - recomputed_origin_nodes: a HashSet of CNodePtr objects representing the nodes that have been recomputed
+// - target_nodes: a HashSet of CNodePtr objects representing the target nodes we are interested in
+
+// Create an empty vector to store the first target inputs
+std::vector<AnfNodePtr> first_target_inputs;
+
+// Iterate through each node in the origin_nodes_topological vector
+for (const auto &node : origin_nodes_topological) {
+    // Check if the node is not null
     MS_EXCEPTION_IF_NULL(node);
+    
+    // Check if the node is one of the target nodes
     if (target_nodes.find(node) != target_nodes.end()) {
-      for (size_t i = 1; i < node->size(); ++i) {
-        auto input = node->input(i);
-        MS_EXCEPTION_IF_NULL(input);
-        if (!input->isa<CNode>()) {
-          continue;
+        // Iterate through each input of the node starting from index 1 (index 0 is the node itself)
+        for (size_t i = 1; i < node->size(); ++i) {
+            // Get the input at index i
+            auto input = node->input(i);
+            MS_EXCEPTION_IF_NULL(input);
+            
+            // Check if the input is not a CNode (a node in the computation graph)
+            if (!input->isa<CNode>()) {
+                // If it is not a CNode, continue to the next input
+                continue;
+            }
+            
+            // Check if the input is in the recomputed_origin_nodes set
+            if (recomputed_origin_nodes.find(input->cast<CNodePtr>()) != recomputed_origin_nodes.end()) {
+                // If it is in the recomputed_origin_nodes set, continue to the next input
+                continue;
+            }
+            
+            // Add the input to the first_target_inputs vector
+            (void)first_target_inputs.emplace_back(input);
         }
-        if (recomputed_origin_nodes.find(input->cast<CNodePtr>()) != recomputed_origin_nodes.end()) {
-          continue;
-        }
-        (void)first_target_inputs.emplace_back(input);
-      }
-      break;
+        
+        // Break out of the loop since we have found the first target inputs
+        break;
     }
+}
   }
+  
+  // Return the value of the variable "first_target_inputs"
   return first_target_inputs;
 }
 
+// Check if the given node has already been processed and its result is stored in the `has_grad_inputs_map`
 bool HasGradInputs(const AnfNodePtr &node, mindspore::HashMap<AnfNodePtr, bool> *has_grad_inputs_map) {
   MS_EXCEPTION_IF_NULL(node);
   MS_EXCEPTION_IF_NULL(has_grad_inputs_map);
+
+  // If the node is already present in the `has_grad_inputs_map`, return its result
   if (has_grad_inputs_map->find(node) != has_grad_inputs_map->end()) {
     return has_grad_inputs_map->find(node)->second;
   }
+
+  // Cast the node to a CNode pointer
   auto cnode = node->cast<CNodePtr>();
+
+  // If the node is not a CNode, it means it does not have any gradient inputs
   if (cnode == nullptr) {
     (void)has_grad_inputs_map->emplace(node, false);
     return false;
   }
+
+  // Get the inputs of the CNode
   const auto &inputs = cnode->inputs();
+
+  // Iterate over the inputs of the CNode
   for (size_t i = 0; i < inputs.size(); ++i) {
     // For the pipeline split case, the forward pass may depend on the backward pass.
+    // If the current CNode is a Depend node and the index is the attach node index, skip it
     if (IsPrimitiveCNode(cnode, prim::kPrimDepend) && i == kDependAttachNodeIndex) {
       continue;
     }
+
+    // Check if the input is a BpropNode or if it has gradient inputs recursively
     if (IsBpropNode(inputs[i]) || HasGradInputs(inputs[i], has_grad_inputs_map)) {
       (void)has_grad_inputs_map->emplace(node, true);
       return true;
     }
   }
+
+  // If none of the inputs have gradient inputs, store the result in the `has_grad_inputs_map` and return false
+  (void)has_grad_inputs_map->emplace(node, false);
+  return false;
+}
+    }
+  }
   (void)has_grad_inputs_map->emplace(node, false);
   return false;
 }
 
-bool HasForwardOutput(const FuncGraphManagerPtr &mng, const AnfNodePtr &node) {
-  MS_EXCEPTION_IF_NULL(mng);
-  const auto &node_users = mng->node_users();
-  auto output_set_iter = node_users.find(node);
-  if (output_set_iter == node_users.end()) {
-    return false;
-  }
+// This code snippet seems to be a part of a larger function or method.
+// It is not clear what the purpose of this code is without the context.
+// It appears to be modifying a map called "has_grad_inputs_map" by inserting a key-value pair.
+// The key is the "node" variable and the value is "false".
+// Finally, it returns false.
 
-  return std::any_of(output_set_iter->second.begin(), output_set_iter->second.end(),
-                     [](const auto &node_index_set) { return !IsBpropNode(node_index_set.first); });
+// Check if the given `node` has any forward outputs in the `node_users` map of the `mng` FuncGraphManager
+
+// Make sure the `mng` pointer is not null
+MS_EXCEPTION_IF_NULL(mng);
+
+// Get the `node_users` map from the `mng` FuncGraphManager
+const auto &node_users = mng->node_users();
+
+// Find the iterator for the `node` in the `node_users` map
+auto output_set_iter = node_users.find(node);
+
+// If the iterator is at the end of the `node_users` map, it means the `node` has no forward outputs
+if (output_set_iter == node_users.end()) {
+  return false;
 }
 
+// Return the result of applying the std::any_of algorithm to the range defined by output_set_iter->second.begin() and output_set_iter->second.end()
+// The lambda function passed to std::any_of checks if any element in the range satisfies the condition !IsBpropNode(node_index_set.first)
+// The lambda function takes a const reference to an element in the range and returns true if the condition is satisfied, false otherwise
+// The result of std::any_of is the boolean value indicating whether any element satisfies the condition
+return std::any_of(output_set_iter->second.begin(), output_set_iter->second.end(),
+                   [](const auto &node_index_set) { return !IsBpropNode(node_index_set.first); });
+
+
+// A function to get the output nodes of a TupleGetItem operation in a graph
+
+// Parameters:
+// - mng: A shared pointer to the FuncGraphManager object
+// - node: A shared pointer to the AnfNode object representing the TupleGetItem operation
+// - tuple_getitem_output_nodes: A pointer to a vector of AnfNodePtr objects to store the output nodes
+
 void GetTupleGetItemOutputNodes(const FuncGraphManagerPtr &mng, const AnfNodePtr &node,
                                 std::vector<AnfNodePtr> *tuple_getitem_output_nodes) {
+  
+  // Check if the FuncGraphManager pointer is null
   MS_EXCEPTION_IF_NULL(mng);
+  
+  // Check if the vector pointer is null
   MS_EXCEPTION_IF_NULL(tuple_getitem_output_nodes);
+  
+  // Get the map of node users from the FuncGraphManager
   const auto &node_users = mng->node_users();
+  
+  // Find the iterator for the output set of the given node in the node users map
   auto output_set_iter = node_users.find(node);
+  
+  // If the iterator is at the end of the map, return
   if (output_set_iter == node_users.end()) {
     return;
   }
+  
+  // Iterate over the node index sets in the output set
   for (const auto &node_index_set : output_set_iter->second) {
+    
+    // Check if the node is a TupleGetItem primitive CNode
     if (IsPrimitiveCNode(node_index_set.first, prim::kPrimTupleGetItem)) {
+      
+      // Add the node to the vector of output nodes
       (void)tuple_getitem_output_nodes->emplace_back(node_index_set.first);
     }
   }
 }
 
+// Function to set the recomputed scope for a given CNode
 bool SetRecomputedScope(const CNodePtr &node) {
+  
+  // Check if the recomputed scope is already set for the given CNode
+  // If it is set, return true
+  // Otherwise, check if the CNode is a primitive node of type "prim::kPrimDepend"
+  // If it is a "prim::kPrimDepend" node, check if the recomputed scope is set for its real input
+  // If the recomputed scope is set for the real input, return true
+  // Otherwise, return false
   return WithRecomputedScope(node) ||
          (IsPrimitiveCNode(node, prim::kPrimDepend) && WithRecomputedScope(node->input(kRealInputIndexInDepend)));
 }
 
-// Set 'recompute' cnode attr for the nodes according to its scope.
-// A node set 'recompute' cnode attr can become the candidate recomputed node.
+// Set the 'recompute' cnode attribute for the nodes in the graph based on their scope.
+// A node with the 'recompute' cnode attribute set can become a candidate for recomputation.
+
 void SetRecomputedAttr(const FuncGraphPtr &graph, const std::vector<CNodePtr> &origin_nodes_topological) {
   MS_EXCEPTION_IF_NULL(graph);
   auto mng = graph->manager();
   MS_EXCEPTION_IF_NULL(mng);
+  
+  // Create a hashmap to keep track of whether a node has gradient inputs
   mindspore::HashMap<AnfNodePtr, bool> has_grad_inputs_map;
+  
+  // Iterate through the nodes in topological order
   for (const auto &node : origin_nodes_topological) {
     MS_EXCEPTION_IF_NULL(node);
-    // The node may be set the non-recomputed before such as the cell outputs.
+    
+    // Skip nodes that have already been set with the 'no_recompute' cnode attribute, such as cell outputs
     if (IsSetNoRecomputeCNodeAttr(node)) {
       continue;
     }
+    
+    // Skip bprop nodes
     if (IsBpropNode(node)) {
       continue;
     }
-    // Filter some unrecomputable operators.
+    
+    // Filter out nodes that cannot be recomputed
     if (CanNotRecomputed(node)) {
       continue;
     }
+    
+    // TODO: Set the 'recompute' cnode attribute for the node
+  }
+}
+    // Check if the node does not have forward output or if it has gradient inputs
     if (!HasForwardOutput(mng, node) || HasGradInputs(node, &has_grad_inputs_map)) {
+      
+      // If either condition is true, skip to the next iteration of the loop
       continue;
     }
 
+    // Cast the node to a CNodePtr
     auto cnode = node->cast<CNodePtr>();
+
+    // Check if the cast was successful
     MS_EXCEPTION_IF_NULL(cnode);
+
+    // Get the primitive of the CNode
     auto prim = GetCNodePrimitive(cnode);
+
+    // Check if the primitive is null
     if (prim == nullptr) {
+      // If the primitive is null, continue to the next iteration of the loop
       continue;
     }
+
+    // Get the recompute attribute of the primitive
     auto prim_recompute_attr = prim->GetAttr(kAttrRecompute);
+
+    // Initialize the recompute value to -1
     int prim_recompute_val = -1;
+
+    // Check if the recompute attribute exists and is of type BoolImm
     if (prim_recompute_attr != nullptr && prim_recompute_attr->isa<BoolImm>()) {
+      // If it is, cast it to a bool and assign it to prim_recompute_val
       prim_recompute_val = static_cast<int>(GetValue<bool>(prim_recompute_attr));
     }
+
+    // Check if the node should be recomputed based on the recompute attribute and the SetRecomputedScope function
     if ((SetRecomputedScope(cnode) && prim_recompute_val != 0) || prim_recompute_val == 1) {
+      // If it should be recomputed, add the recompute attribute to the CNode with a value of true
       cnode->AddAttr(kAttrRecompute, MakeValue(true));
     }
+
+    // Check if the CNode has the SetRecomputeCNodeAttr attribute set
     if (!IsSetRecomputeCNodeAttr(node)) {
+      // If it doesn't, continue to the next iteration of the loop
       continue;
     }
-    // Set attr for the tuple_getitem outputs.
+
+    // Create a vector to store the output nodes of the tuple_getitem operations
     std::vector<AnfNodePtr> tuple_getitem_output_nodes;
+
+    // Get the output nodes of the tuple_getitem operations using the GetTupleGetItemOutputNodes function
     GetTupleGetItemOutputNodes(mng, node, &tuple_getitem_output_nodes);
+    // Iterate over each element in the tuple_getitem_output_nodes vector
     for (const auto &output_node : tuple_getitem_output_nodes) {
+      
+      // Cast the output_node to a CNodePtr (a pointer to a CNode object)
       auto output_cnode = output_node->cast<CNodePtr>();
+      
+      // Check if the output_cnode is null, throw an exception if it is
       MS_EXCEPTION_IF_NULL(output_cnode);
+      
+      // Add the attribute "kAttrRecompute" to the output_cnode and set its value to true
       output_cnode->AddAttr(kAttrRecompute, MakeValue(true));
     }
   }
 }
 
+// Function to create a new recomputed node in a given graph
 CNodePtr CreateNewRecomputedNode(const FuncGraphPtr &graph, const CNodePtr &origin_node,
                                  const std::vector<AnfNodePtr> &new_inputs) {
+
+  // Create a new CNode using the provided new_inputs
   auto recomputed_node = graph->NewCNode(new_inputs);
   MS_EXCEPTION_IF_NULL(recomputed_node);
+
+  // Add the "duplicated" attribute to the recomputed_node and set it to true
   recomputed_node->AddAttr("duplicated", MakeValue(true));
+
+  // Add the "need_cse_after_recompute" attribute to the recomputed_node and set it to true
   recomputed_node->AddAttr(kAttrNeedCseAfterRecompute, MakeValue(true));
+
+  // Set the abstract of the recomputed_node to the same as the origin_node
   recomputed_node->set_abstract(origin_node->abstract());
+
+  // Set the scope of the recomputed_node to the same as the origin_node
   recomputed_node->set_scope(origin_node->scope());
+
+  // If the origin_node has the "micro" attribute, add it to the recomputed_node as well
   if (origin_node->HasPrimalAttr(kAttrMicro)) {
     recomputed_node->AddPrimalAttr(kAttrMicro, origin_node->GetPrimalAttr(kAttrMicro));
   }
+
+  // Return the newly created recomputed_node
   return recomputed_node;
 }
 
+// Create a new recomputed node based on the given origin node, with some modifications
 CNodePtr NewRecomputedNode(const FuncGraphPtr &graph, const CNodePtr &origin_node,
                            const std::vector<AnfNodePtr> &first_target_inputs,
                            const mindspore::HashSet<CNodePtr> &recomputed_origin_nodes,
@@ -347,39 +657,86 @@ CNodePtr NewRecomputedNode(const FuncGraphPtr &graph, const CNodePtr &origin_nod
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(origin_node);
   MS_EXCEPTION_IF_NULL(origin_to_recomputed_nodes);
+
+  // Check if the recomputed node already exists in the map
   auto iter = origin_to_recomputed_nodes->find(origin_node);
   if (iter != origin_to_recomputed_nodes->end()) {
     return iter->second;
   }
+
+  // Log a debug message indicating the start of duplicating the origin recomputed node
   MS_LOG(DEBUG) << "Begin to Duplicating origin recomputed node: " << origin_node->DebugString();
+
+  // Create a vector to store the new inputs for the recomputed node
   std::vector<AnfNodePtr> new_inputs;
+
+  // Flag to indicate if the recomputed node has recomputed inputs
   bool has_recomputed_inputs = false;
+
+  // Iterate over the inputs of the origin node
   for (size_t i = 0; i < origin_node->size(); ++i) {
     auto input = origin_node->input(i);
+
+    // Check if it is the first input and if it is the primitive "AllGather"
     if (i == 0 && IsPrimitive(input, prim::kPrimAllGather)) {
       auto prim = GetValueNode<PrimitivePtr>(input);
       auto instance_name = prim->instance_name();
+
+      // Check if the instance name contains "parallel_optimizer"
       bool is_from_parallel_optimizer = instance_name.find("parallel_optimizer") != std::string::npos;
+      // ... (continues)
       int64_t fusion_id = prim->HasAttr(kAttrFusion) ? GetValue<int64_t>(prim->GetAttr(kAttrFusion)) : 0;
+      // Check if the primitive has the attribute "kAttrFusion". If it does, get its value and assign it to fusion_id. Otherwise, assign 0 to fusion_id.
+      
       if (is_from_parallel_optimizer && fusion_id > 0) {
+        // Check if the flag "is_from_parallel_optimizer" is true and fusion_id is greater than 0.
+        
+        // Create a new shared pointer to a Primitive object with the name "kPrimAllGather".
         auto new_prim = std::make_shared<Primitive>(prim::kPrimAllGather->name());
+        
+        // Set the attributes of the new_prim object to be the same as the attributes of the prim object.
         (void)new_prim->SetAttrs(prim->attrs());
+        
+        // Set the attribute "kAttrFusion" of the new_prim object to fusion_id + fusion_id_increasement_size.
         new_prim->set_attr(kAttrFusion, MakeValue(fusion_id + fusion_id_increasement_size));
+        
+        // Set the primitive type of the new_prim object to be the same as the primitive type of the prim object.
         new_prim->set_prim_type(prim->prim_type());
+        
+        // Set the instance name of the new_prim object to instance_name.
         new_prim->set_instance_name(instance_name);
+        
+        // Create a new ValueNode object with the new_prim object as its value.
         auto value_node = NewValueNode(new_prim);
+        
+        // Add the value_node to the new_inputs vector.
         (void)new_inputs.emplace_back(value_node);
+        
+        // Continue to the next iteration of the loop.
         continue;
       }
     }
+    
+    // Check if the input is not a CNode.
     MS_EXCEPTION_IF_NULL(input);
     if (!input->isa<CNode>()) {
+      
+      // Add the input to the new_inputs vector.
       (void)new_inputs.emplace_back(input);
+      
+      // Continue to the next iteration of the loop.
       continue;
     }
+    
+    // Cast the input to a CNode pointer.
     auto input_cnode = input->cast<CNodePtr>();
+    
+    // Check if the input_cnode is not found in the recomputed_origin_nodes set.
     if (recomputed_origin_nodes.find(input_cnode) == recomputed_origin_nodes.end()) {
+      
+      // Check if the input_cnode is a primitive CNode with the primitive type "kPrimUpdateState".
       if (IsPrimitiveCNode(input_cnode, prim::kPrimUpdateState)) {
+        // ... (code continues)
         auto u = NewValueNode(kUMonad);
         u->set_abstract(kUMonad->ToAbstract());
         (void)new_inputs.emplace_back(u);
@@ -400,36 +757,92 @@ CNodePtr NewRecomputedNode(const FuncGraphPtr &graph, const CNodePtr &origin_nod
     MS_EXCEPTION_IF_NULL(first_input);
     std::vector<AnfNodePtr> depend_inputs{NewValueNode(prim::kPrimDepend), first_input,
                                           graph->NewCNode(make_tuple_inputs)};
+                                          
+    // Create a vector of AnfNodePtr to store the inputs for the Depend operation
+    std::vector<AnfNodePtr> depend_inputs{NewValueNode(prim::kPrimDepend), first_input,
+                                          graph->NewCNode(make_tuple_inputs)};
+                                          
+    // Add the Depend operation to the new_inputs vector
+    (void)new_inputs.emplace_back(graph->NewCNode(depend_inputs));
+    // Create a new computation node in the graph using the NewCNode function of the graph object
     auto depend_node = graph->NewCNode(depend_inputs);
     MS_EXCEPTION_IF_NULL(depend_node);
+
+    // Set the abstract of the depend_node to be the same as the abstract of the first input node
     depend_node->set_abstract(first_input->abstract());
+
+    // Add an attribute "recompute_depend" to the depend_node with a value of true
     depend_node->AddAttr("recompute_depend", MakeValue(true));
+
+    // Update the second input of the new_inputs array with the depend_node
     new_inputs[1] = depend_node;
   }
+
+  // Create a new recomputed node using the CreateNewRecomputedNode function, passing in the graph, origin_node, and new_inputs
   auto recomputed_node = CreateNewRecomputedNode(graph, origin_node, new_inputs);
+
+  // Add the mapping from the origin_node to the recomputed_node in the origin_to_recomputed_nodes map
   (void)origin_to_recomputed_nodes->emplace(origin_node, recomputed_node);
+
+  // Return the recomputed_node
   return recomputed_node;
 }
 
+// This function duplicates recomputed nodes in a given graph based on certain conditions and updates a hashmap with the mapping between original nodes and duplicated nodes.
+
+// Parameters:
+// - graph: Pointer to the FuncGraph object representing the graph
+// - target_nodes: HashSet of CNodePtr representing the target nodes to be duplicated
+// - origin_recomputed_nodes: HashSet of CNodePtr representing the original recomputed nodes
+// - first_target_inputs: Vector of AnfNodePtr representing the inputs of the first target node
+// - origin_to_recomputed_nodes: Pointer to the HashMap that will store the mapping between original nodes and duplicated nodes
+
 void DuplicateRecomputedNodes(const FuncGraphPtr &graph, const mindspore::HashSet<CNodePtr> &target_nodes,
                               const mindspore::HashSet<CNodePtr> &origin_recomputed_nodes,
                               const std::vector<AnfNodePtr> &first_target_inputs,
                               mindspore::HashMap<CNodePtr, CNodePtr> *origin_to_recomputed_nodes) {
+  // Check if the graph pointer is null
   MS_EXCEPTION_IF_NULL(graph);
+
+  // Get the manager of the graph
   auto mng = graph->manager();
+
+  // Check if the manager pointer is null
   MS_EXCEPTION_IF_NULL(mng);
+
+  // Iterate over each target node
   for (const auto &target_node : target_nodes) {
+    // Check if the target node pointer is null
     MS_EXCEPTION_IF_NULL(target_node);
+
+    // Log a debug message with the debug string of the target node
     MS_LOG(DEBUG) << "Rebuild a new target_node " << target_node->DebugString() << " with the new recomputed input";
+
+    // Cast the target node to CNodePtr
     auto target_cnode = target_node->cast<CNodePtr>();
+
+    // Check if the target_cnode pointer is null
     MS_EXCEPTION_IF_NULL(target_cnode);
+
+    // Create a vector to store the new target inputs
     std::vector<AnfNodePtr> new_target_inputs;
+
+    // Iterate over each input of the target node
     for (const auto &input : target_cnode->inputs()) {
+      // Check if the input pointer is null
       MS_EXCEPTION_IF_NULL(input);
+
+      // If the input is not a CNode, add it to the new target inputs vector
       if (!input->isa<CNode>()) {
         (void)new_target_inputs.emplace_back(input);
       } else {
+        // If the input is a CNode, cast it to CNodePtr
         auto input_cnode = input->cast<CNodePtr>();
+
+        // Check if the input_cnode pointer is null
+        MS_EXCEPTION_IF_NULL(input_cnode);
+
+        // Check if the input_cnode is present in the origin_recomputed_nodes set
         if (origin_recomputed_nodes.find(input_cnode) != origin_recomputed_nodes.end()) {
           (void)new_target_inputs.emplace_back(NewRecomputedNode(graph, input_cnode, first_target_inputs,
                                                                  origin_recomputed_nodes, origin_to_recomputed_nodes));
@@ -438,50 +851,93 @@ void DuplicateRecomputedNodes(const FuncGraphPtr &graph, const mindspore::HashSe
         }
       }
     }
+    
+    // Create a new CNode using the new_target_inputs as its inputs
     auto new_target_node = graph->NewCNode(new_target_inputs);
+    
+    // Clone the CNode information from the target_node to the new_target_node
     new_target_node->CloneCNodeInfo(target_node);
+    
+    // Add an attribute "target_grad" with the value true to the new_target_node
     new_target_node->AddAttr("target_grad", MakeValue(true));
+    
+    // Set the scope of the new_target_node to be the same as the target_node
     new_target_node->set_scope(target_node->scope());
+    
+    // Replace the target_node with the new_target_node in the manager
     mng->Replace(target_node, new_target_node);
   }
 }
 }  // namespace
 
+// Define a function named "InsertRecomputedNodes" that takes a reference to a `FuncGraphPtr` object as a parameter
 void InsertRecomputedNodes(const FuncGraphPtr &graph) {
+  // Check if the input graph is null, and throw an exception if it is
   MS_EXCEPTION_IF_NULL(graph);
+
+  // Get the manager of the graph
   auto mng = graph->manager();
+  // Check if the manager is null, and throw an exception if it is
   MS_EXCEPTION_IF_NULL(mng);
+
+  // Get the ordered list of CNodes in the graph
   std::list<CNodePtr> orders = graph->GetOrderedCnodes();
+  // Convert the list to a vector
   std::vector<CNodePtr> origin_nodes_topological(orders.begin(), orders.end());
+
+  // Set the recomputed attribute for the origin nodes
   SetRecomputedAttr(graph, origin_nodes_topological);
-  // Get candidate origin recomputed nodes which have no grad inputs and output to at least one grad node directly.
+
+  // Find candidate origin recomputed nodes which have no grad inputs and output to at least one grad node directly
   std::vector<CNodePtr> candidate_recomputed_nodes = FindCandidateRecomputedNodes(mng, origin_nodes_topological);
+
+  // Create a hash set to keep track of visited nodes
   mindspore::HashSet<CNodePtr> visited_nodes;
+
+  // Iterate over each candidate recomputed node
   for (const auto &candidate_recomputed_node : candidate_recomputed_nodes) {
+    // Check if the candidate node has already been visited, and skip it if it has
     if (visited_nodes.find(candidate_recomputed_node) != visited_nodes.end()) {
       continue;
     }
+
+    // Create a hash set to store the max continuous recomputed sub-graph
     mindspore::HashSet<CNodePtr> max_recomputed_sub_graph = {candidate_recomputed_node};
-    // Get max continuous recomputed sub-graph.
+
+    // Get the max continuous recomputed sub-graph
     GetMaxSubGraph(mng, &max_recomputed_sub_graph, true, true);
+
+    // Add the nodes in the max sub-graph to the visited nodes set
     visited_nodes.insert(max_recomputed_sub_graph.begin(), max_recomputed_sub_graph.end());
-    // Get the origin recomputed nodes which directly output to the grad nodes.
+
+    // Create a hash set to store the origin recomputed nodes which directly output to the grad nodes
     mindspore::HashSet<CNodePtr> origin_recomputed_nodes;
+    // ... (code continues)
+    // Create a HashSet of CNodePtr named target_nodes
     mindspore::HashSet<CNodePtr> target_nodes;
+
+    // Call the function GetOriginRecomputeAndTargetNodes with arguments mng, max_recomputed_sub_graph, 
+    // and pointers to origin_recomputed_nodes and target_nodes to get the origin recomputed nodes and target nodes
     GetOriginRecomputeAndTargetNodes(mng, max_recomputed_sub_graph, &origin_recomputed_nodes, &target_nodes);
-    // Also get the inputs of origin recomputed nodes which eventually output to the grad nodes.
+
+    // Call the function GetMaxSubGraph with arguments mng, pointer to origin_recomputed_nodes, true, and false
+    // to get the maximum subgraph
     GetMaxSubGraph(mng, &origin_recomputed_nodes, true, false);
 
     // Get the inputs of the first target node in the topological sequence. The duplicated recomputed nodes should
     // not be executed until these inputs are ready.
     std::vector<AnfNodePtr> first_target_inputs =
       GetFirstTargetInputs(origin_nodes_topological, origin_recomputed_nodes, target_nodes);
+    
+    // Create a hashmap to store the mapping between original recomputed nodes and duplicated recomputed nodes
     mindspore::HashMap<CNodePtr, CNodePtr> origin_to_recomputed_nodes;
-    // Begin duplicate origin recomputed nodes with each target node.
+    
+    // Begin duplicating origin recomputed nodes with each target node
     DuplicateRecomputedNodes(graph, target_nodes, origin_recomputed_nodes, first_target_inputs,
                              &origin_to_recomputed_nodes);
   }
-  // Set need cse attr for doing cse after recompute.
+  
+  // Set the "need cse" attribute for doing common subexpression elimination (CSE) after recompute
   for (const auto &node : orders) {
     if (WithRecomputedScope(node)) {
       node->AddAttr(kAttrNeedCseAfterRecompute, MakeValue(true));
@@ -489,4 +945,4 @@ void InsertRecomputedNodes(const FuncGraphPtr &graph) {
   }
 }
 }  // namespace opt
-}  // namespace mindspore
+}  // namespace mindspore