auto parallel context add notes and func mv

mindspore/ccsrc/frontend/parallel/auto_parallel/graph_costmodel.cc

@@ -1542,17 +1542,8 @@ size_t CostGraph::GetNumEdges() const {
   }
   return sum;
 }
-Status CostGraph::InitSelectedStrategy() {
+
-  for (auto &op : ops_) {
+Status CostGraph::InitReshapeStrategy() {
-    MS_EXCEPTION_IF_NULL(op);
-    if (op->name().find(RESHAPEINFO) != std::string::npos) {
-      continue;
-    }
-    auto result = op->InitSelectedStrategy(op->selected_strategy());
-    if (result != SUCCESS) {
-      return result;
-    }
-  }
   // reshape init should be apply after the init of it's previous node and next node.
   for (size_t i = 0; i < ops_.size(); ++i) {
     if (ops_[i]->name().find(RESHAPEINFO) != std::string::npos) {
@@ -1606,6 +1597,21 @@ Status CostGraph::InitSelectedStrategy() {
   return SUCCESS;
 }
 
+Status CostGraph::InitSelectedStrategy() {
+  for (auto &op : ops_) {
+    MS_EXCEPTION_IF_NULL(op);
+    if (op->name().find(RESHAPEINFO) != std::string::npos) {
+      continue;
+    }
+    auto result = op->InitSelectedStrategy(op->selected_strategy());
+    if (result != SUCCESS) {
+      return result;
+    }
+  }
+  auto result = InitReshapeStrategy();
+  return result;
+}
+
 Status CostGraph::ComputeOpsAndEdgesParameterInvolved() {
   for (auto &op : ops_) {
     MS_EXCEPTION_IF_NULL(op);

mindspore/ccsrc/frontend/parallel/auto_parallel/graph_costmodel.h

@@ -186,6 +186,7 @@ class CostGraph {
 
   std::vector<OperatorInfoPtr> GetOperators() const { return ops_; }
   size_t GetNumEdges() const;
+  Status InitReshapeStrategy();
   Status InitSelectedStrategy();
   OperatorInfoPtr FindTmpIdentityByParameterName(std::string &) const;
   // When TmpIdentity is used by mulitple operators, the corresponding parameter's memory cost should be calculated only

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -2275,7 +2275,6 @@ std::vector<std::pair<std::string, int>> NodeParameterName(const CNodePtr &node)
 }
 
 void CheckpointStrategy(const std::vector<AnfNodePtr> &all_nodes) {
-  MS_LOG(DEBUG) << "Save strategy to checkpoint begin";
   StrategyMap stra_map;
   TensorInfoMap tensor_info_map;
   ManualShapeMap manual_shape_map;
@@ -2298,10 +2297,8 @@ void CheckpointStrategy(const std::vector<AnfNodePtr> &all_nodes) {
         continue;
       }
       std::vector<TensorInfo> input_tensor_info = operator_info->inputs_tensor_info();
-      StrategyPtr strategyPtr = operator_info->strategy();
-      MS_EXCEPTION_IF_NULL(node->scope());
       std::string stratey_key_name = prim->name() + "_" + param_name;
-      stra_map[stratey_key_name] = strategyPtr;
+      stra_map[stratey_key_name] = operator_info->strategy();
       for (auto param_name_pair : param_names) {
         if (param_name_pair.second - 1 >= UintToInt(input_tensor_info.size())) {
           continue;

mindspore/context.py

@@ -395,9 +395,10 @@ def set_auto_parallel_context(**kwargs):
                        should be set with True. Default: False.
         enable_parallel_optimizer (bool): This is a developing feature, which shards the weight update computation for
                        data parallel training in the benefit of time and memory saving. For now,
-                       `Lamb` and `AdamWeightDecay` are supported in data parallel mode.
+                       `Lamb` and `AdamWeightDecay` are supported in data parallel mode. No Default, if it is not set,
+                       the fusion is closed.
         all_reduce_fusion_config (list): Set allreduce fusion strategy by parameters indices. Only support ReduceOp.SUM
-                       and HCCL_WORLD_GROUP/NCCL_WORLD_GROUP.
+                       and HCCL_WORLD_GROUP/NCCL_WORLD_GROUP. No Default, if it is not set, the fusion is closed.
 
     Raises:
         ValueError: If input key is not attribute in auto parallel context.
@@ -408,9 +409,13 @@ def set_auto_parallel_context(**kwargs):
         >>> context.set_auto_parallel_context(gradients_mean=True)
         >>> context.set_auto_parallel_context(gradient_fp32_sync=False)
         >>> context.set_auto_parallel_context(parallel_mode="auto_parallel")
+        >>> context.set_auto_parallel_context(auto_parallel_search_mode="dynamic_programming")
         >>> context.set_auto_parallel_context(parameter_broadcast=False)
         >>> context.set_auto_parallel_context(strategy_ckpt_load_file="./strategy_stage1.ckpt")
         >>> context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_stage1.ckpt")
+        >>> context.set_auto_parallel_context(full_batch=True)
+        >>> context.set_auto_parallel_context(enable_parallel_optimizer=False)
+        >>> context.set_auto_parallel_context(all_reduce_fusion_config=[8, 160])
     """
     _set_auto_parallel_context(**kwargs)
 
@@ -439,10 +444,12 @@ def reset_auto_parallel_context():
     - global_rank: 0.
     - gradients_mean: False.
     - gradient_fp32_sync: True.
-    - parallel_mode: "stand_alone".
+    - parallel_mode: 'stand_alone'.
+    - auto_parallel_search_mode: 'dynamic_programming'.
     - parameter_broadcast: False.
-    - strategy_ckpt_load_file: "".
+    - strategy_ckpt_load_file: ''.
-    - strategy_ckpt_save_file: "".
+    - strategy_ckpt_save_file: ''.
+    - full_batch: False.
     - enable_parallel_optimizer: False.
     """
     _reset_auto_parallel_context()

mindspore/parallel/_auto_parallel_context.py

@@ -245,6 +245,10 @@ class _AutoParallelContext:
             strategy_ckpt_save_file (bool): Path to save parallel strategy checkpoint.
         """
         self.check_context_handle()
+        import os
+        dir_path = os.path.dirname(strategy_ckpt_save_file)
+        if dir_path and not os.path.exists(dir_path):
+            os.makedirs(dir_path)
         self._context_handle.set_strategy_ckpt_save_file(strategy_ckpt_save_file)
 
     def get_strategy_ckpt_save_file(self):