!1056 [Auto parallel] Memory calculation in the inference phase

Merge pull request !1056 from Xiaoda/memory-estimation-in-inference-phase

mindspore/ccsrc/parallel/auto_parallel/costmodel.h

@@ -53,7 +53,7 @@ struct Cost {
     communication_redis_backward_ = 0.0;
     communication_forward_ = 0.0;
   }
-  // 'memory_with_reuse_' calculates the peak memory usage in a training phase
+  // 'memory_with_reuse_' calculates the peak memory usage in a training (or inference) phase
   double memory_with_reuse_;
   // 'computation_cost_'  models the training time of an iteration in a training phase. Currently, this is calculated
   // by ONLY forward phase

mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.cc

@@ -300,5 +300,20 @@ Status Edge::CalculateMemoryCost() {
 
   return SUCCESS;
 }
+
+Status Edge::CalculateMemoryCostForInference() {
+  // Currently, memory cost is NOT calculated for redistribution
+  if ((is_output_critical_ != 0) && (is_output_critical_ != 1)) {
+    MS_LOG(ERROR) << "Failure: unexpected output critical flag value: " << is_output_critical_;
+    return FAILED;
+  }
+  for (auto &cost_kv : cost_map_) {
+    auto &cost_v = cost_kv.second;
+    if (!cost_v.empty()) {
+      cost_v[0]->memory_with_reuse_ = 0;
+    }
+  }
+  return SUCCESS;
+}
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/parallel/auto_parallel/edge_costmodel.h

@@ -131,9 +131,13 @@ class Edge {
   void set_selected_cost(const CostPtr &cost) { selected_cost_ = cost; }
   const CostPtr &selected_cost() const { return selected_cost_; }
   void set_parameter_involve(int para_invol) { is_output_parameter_involve_ = para_invol; }
-  // When the input of a operator contains WEIGHT or a output from other operators involving WEIGHT, then these input
-  // should stay in memory until it is used in the backward phase, which is kept in memory at the end of forward phase.
+  // In the training phase, when the input of a operator contains WEIGHT or a output from other operators involving
+  // WEIGHT, then these input should stay in memory until it is used in the backward phase, which is kept in memory
+  // at the end of forward phase.
   Status CalculateMemoryCost();
+  // In the inference phase,
+  Status CalculateMemoryCostForInference();
+  void mark_output_critical() { is_output_critical_ = 1; }
 
  private:
   std::string edge_name_;
@@ -156,7 +160,11 @@ class Edge {
   // If it is true, then we should guarantee that the strategy for output tensor consistent with the input tensor.
   bool is_identity_edge;
   CostPtr selected_cost_;
+  // In the training phase, 'is_output_parameter_involve_' is used to mark whether the output of the previous operator
+  // is parameter-involved
   int is_output_parameter_involve_ = -1;  // -1: unset; 0: not parameter_involved; 1: parameter_involved
+  // In the inference phase, this is used to mark whether the output of the previous operator is critical.
+  int is_output_critical_ = 0;
 };
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.cc

@@ -369,7 +369,7 @@ CostPtr CostGraph::SelectCostWithMinInferenceTime(const CostPtrList &cost_list,
                << ", communication_with_partial_para_: " << ret->communication_with_partial_para_
                << ", communication_cost_: " << ret->communication_cost_
                << ", communication_without_parameter_: " << ret->communication_without_parameter_ << ".";
-  MS_LOG(INFO) << "Cost 0: totoal_cost: " << minimum;
+  MS_LOG(INFO) << "Cost 0: total_cost: " << minimum;
   for (size_t i = 1; i < after_mem_filter.size(); ++i) {
     MS_EXCEPTION_IF_NULL(after_mem_filter[i]);
     MS_LOG(INFO) << "Cost " << i << ": memory_cost: " << after_mem_filter[i]->memory_with_reuse_
@@ -422,7 +422,7 @@ CostPtr CostGraph::SelectCostWithMinTrainingTime(const CostPtrList &cost_list, d
                << ", communication_with_partial_para_: " << ret->communication_with_partial_para_
                << ", communication_cost_: " << ret->communication_cost_
                << ", communication_without_parameter_: " << ret->communication_without_parameter_ << ".";
-  MS_LOG(INFO) << "Cost 0: totoal_cost: " << minimum;
+  MS_LOG(INFO) << "Cost 0: total_cost: " << minimum;
   for (size_t i = 1; i < after_mem_filter.size(); ++i) {
     MS_EXCEPTION_IF_NULL(after_mem_filter[i]);
     MS_LOG(INFO) << "Cost " << i << ": memory_cost: " << after_mem_filter[i]->memory_with_reuse_
@@ -1351,6 +1351,14 @@ std::vector<std::shared_ptr<Edge>> CostGraph::EliminationStar(const OperatorInfo
   return succ_edges;
 }
 
+size_t CostGraph::GetNumEdges() const {
+  size_t sum = 0;
+  for (const auto &kv : edges_) {
+    auto &edges = kv.second;
+    sum += edges.size();
+  }
+  return sum;
+}
 Status CostGraph::InitSelectedStrategy() {
   for (auto &op : ops_) {
     MS_EXCEPTION_IF_NULL(op);
@@ -1416,6 +1424,122 @@ Status CostGraph::ComputeOpsAndEdgesParameterInvolved() {
   return SUCCESS;
 }
 
+void CostGraph::DFSForTopoOrder(const OperatorInfoPtr &current_op, std::map<OperatorInfoPtr, bool> *visited,
+                                std::vector<OperatorInfoPtr> *topo_order) {
+  MS_EXCEPTION_IF_NULL(current_op);
+  MS_EXCEPTION_IF_NULL(visited);
+  MS_EXCEPTION_IF_NULL(topo_order);
+
+  visited->at(current_op) = true;
+  for (const auto &s_edge : current_op->succ_edges()) {
+    if (!visited->at(s_edge->next_operator())) {
+      DFSForTopoOrder(s_edge->next_operator(), visited, topo_order);
+    }
+  }
+  topo_order->push_back(current_op);
+}
+
+// Compute a topological order of the costgraph
+void CostGraph::TopologyOrder(std::vector<OperatorInfoPtr> *topo_order) {
+  std::map<OperatorInfoPtr, bool> visited;
+  for (auto &op : ops_) {
+    visited[op] = false;
+  }
+
+  for (auto &op : ops_) {
+    if (!visited[op]) {
+      DFSForTopoOrder(op, &visited, topo_order);
+    }
+  }
+}
+void CostGraph::MarkCriticalOpsAndEdges(const std::map<OperatorInfoPtr, int> &candidate_ops) {
+  for (auto &op : ops_) {
+    auto search = candidate_ops.find(op);
+    if (search != candidate_ops.end()) {
+      // Mark the critical operators
+      op->mark_output_critical();
+      // Mark the successive edges
+      for (auto &s_edge : op->succ_edges()) {
+        s_edge->mark_output_critical();
+      }
+    } else {
+      op->mark_output_not_critical();
+    }
+  }
+}
+
+Status CostGraph::DetermineCriticalOps(const std::vector<OperatorInfoPtr> &topo_order) {
+  if (topo_order.size() == 0) {
+    MS_LOG(ERROR) << "0 operator in costgraph.";
+    return FAILED;
+  }
+  auto &first_op = topo_order[0];
+  if (first_op->prev_edges().size() > 0) {
+    MS_LOG(ERROR) << "The first operator in the first of topological order of "
+                     "costgraph should have 0 incoming edge, but has "
+                  << first_op->prev_edges() << "edges.";
+    return FAILED;
+  }
+  // The 'curr_memory_state' records <OperatorInfo, remaining_output_cnt>, where remaining_output_cnt is the number
+  // of the output of OperatorInfo that currently has not been used
+  std::map<OperatorInfoPtr, int> curr_memory_state;
+  (void)curr_memory_state.emplace(std::make_pair(first_op, SizeToInt(first_op->succ_edges().size())));
+  std::map<OperatorInfoPtr, int> max_memory_state = curr_memory_state;
+  // The 'curr_memory_size' records the current total memory size, which is the sum of outputs of operators that has
+  // not been used
+  double curr_memory_size = first_op->GetOutputsTotalSize();
+  double max_memory_size = curr_memory_size;
+
+  for (size_t finished = 1; finished < topo_order.size(); ++finished) {
+    // Produce
+    (void)curr_memory_state.emplace(
+      std::make_pair(topo_order[finished], SizeToInt(topo_order[finished]->succ_edges().size())));
+    curr_memory_size += topo_order[finished]->GetOutputsTotalSize();
+    // Consume
+    for (const auto &prev_edge : topo_order[finished]->prev_edges()) {
+      const auto &prev_op = prev_edge->prev_operator();
+      curr_memory_state[prev_op]--;
+    }
+    for (const auto &prev_edge : topo_order[finished]->prev_edges()) {
+      const auto &prev_op = prev_edge->prev_operator();
+      if (curr_memory_state[prev_op] < 0) {
+        MS_LOG(ERROR) << "Failure: " << prev_op->name() << "'s current output count: " << curr_memory_state[prev_op];
+        return FAILED;
+      } else if (curr_memory_state[prev_op] == 0) {
+        curr_memory_state.erase(prev_op);
+        curr_memory_size -= prev_op->GetOutputsTotalSize();
+      }
+    }
+
+    if (curr_memory_size < 0) {
+      MS_LOG(ERROR) << "Memory size calculation failed: " << curr_memory_size;
+    }
+    // Modify the max
+    if (curr_memory_size > max_memory_size) {
+      max_memory_size = curr_memory_size;
+      max_memory_state = curr_memory_state;
+    }
+  }
+  // Mark those critical operators
+  MarkCriticalOpsAndEdges(max_memory_state);
+  return SUCCESS;
+}
+
+Status CostGraph::ComputeOpsAndEdgesOutputCritical() {
+  // Two steps to do:
+  // 1. Compute a topological order of the costgraph
+  // 2. Determine and mark the operators (and necessary edges) that are critical
+  std::vector<OperatorInfoPtr> topo_order;
+  TopologyOrder(&topo_order);
+  std::reverse(std::begin(topo_order), std::end(topo_order));
+
+  if (DetermineCriticalOps(topo_order) != SUCCESS) {
+    MS_LOG(ERROR) << "Determining critical operators failed.";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
 Status CostGraph::CalculateOpsMemoryCost() {
   for (auto &op : ops_) {
     MS_EXCEPTION_IF_NULL(op);
@@ -1427,6 +1551,17 @@ Status CostGraph::CalculateOpsMemoryCost() {
   return SUCCESS;
 }
 
+Status CostGraph::CalculateOpsMemoryCostForInference() {
+  for (auto &op : ops_) {
+    MS_EXCEPTION_IF_NULL(op);
+    if (op->CalculateMemoryCostForInference() != SUCCESS) {
+      MS_LOG(ERROR) << "Calculate Operator: " << op->name() << " cost for memory usage failed.";
+      return FAILED;
+    }
+  }
+  return SUCCESS;
+}
+
 Status CostGraph::CalculateEdgesMemoryCost() {
   for (auto &edge_pair : edges_) {
     const auto &edges = edge_pair.second;
@@ -1440,6 +1575,19 @@ Status CostGraph::CalculateEdgesMemoryCost() {
   return SUCCESS;
 }
 
+Status CostGraph::CalculateEdgesMemoryCostForInference() {
+  for (auto &edge_pair : edges_) {
+    const auto &edges = edge_pair.second;
+    for (auto &one_edge : edges) {
+      if (one_edge->CalculateMemoryCostForInference() != SUCCESS) {
+        MS_LOG(ERROR) << "Calculate Edge: " << one_edge->edge_name() << " cost for memory usage failed.";
+        return FAILED;
+      }
+    }
+  }
+  return SUCCESS;
+}
+
 OperatorInfoPtr CostGraph::FindTmpIdentityByParameterName(std::string &p_name) const {
   for (auto one_op : ops_) {
     if (one_op->name().find(IDENTITY_INFO) != std::string::npos) {
@@ -1480,5 +1628,49 @@ Status CostGraph::CorrectOpsMemoryCost() {
   }
   return SUCCESS;
 }
+
+Status CostGraph::CalculateMemoryCost() {
+  if (RUN_PHASE == TRAINING_PHASE) {
+    // training phase
+    if (ComputeOpsAndEdgesParameterInvolved() == SUCCESS) {
+      // Calculate operators' memory usage
+      if (CalculateOpsMemoryCost() != SUCCESS) {
+        MS_LOG(ERROR) << "Calculating operators' cost for memory cost failed.";
+        return FAILED;
+      }
+      // Calculate edges' memory usage
+      if (CalculateEdgesMemoryCost() != SUCCESS) {
+        MS_LOG(ERROR) << "Calculating edges' cost for memory cost failed.";
+        return FAILED;
+      }
+      // Correct memory usage caused by TmpIdentity
+      if (CorrectOpsMemoryCost() != SUCCESS) {
+        MS_LOG(ERROR) << "Correcting operators' cost for memory cost failed.";
+        return FAILED;
+      }
+    } else {
+      MS_LOG(ERROR) << "Computing operators' parameter_involved failed.";
+      return FAILED;
+    }
+  } else {
+    // inference phase
+    if (ComputeOpsAndEdgesOutputCritical() == SUCCESS) {
+      // Calculate operators' memory usage
+      if (CalculateOpsMemoryCostForInference() != SUCCESS) {
+        MS_LOG(ERROR) << "Calculating operators' memory cost for inference failed.";
+        return FAILED;
+      }
+      // Calculate edges's memory usage
+      if (CalculateEdgesMemoryCostForInference() != SUCCESS) {
+        MS_LOG(ERROR) << "Calculating operators' memory cost for inference failed.";
+        return FAILED;
+      }
+    } else {
+      MS_LOG(ERROR) << "Computing operators' critical flag failed.";
+      return FAILED;
+    }
+  }
+  return SUCCESS;
+}
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/parallel/auto_parallel/graph_costmodel.h

@@ -179,16 +179,24 @@ class CostGraph {
   void CreateStarEliminationSubCostList(const StrategyPtr &, const CostPtrList &, const CostPtrList &,
                                         const StrategyPtr &, const CostPtrList &, std::vector<StrategyPtr>,
                                         CostPtrList &, CostPtrList &, CostPtrList *);
+  // Calculate memory cost for training phase or inference phase.
+  Status CalculateMemoryCost();
   // When the input of a operator is neither a WEIGHT, nor a output of a subsequent operator involving WEIGHT, then
-  // the memory cost can be resused.
+  // the memory cost can be resused. This is used to calculate memory in the training phase.
   Status CalculateOpsMemoryCost();
   // When the input of the edge is neither a WEIGHT, nor a output of a subsequent operator involving WEIGHT, then
-  // the memory cost can be resused.
+  // the memory cost can be reused. This is used to calculate memory in the training phase.
   Status CalculateEdgesMemoryCost();
+  // Calculate memory cost of operators in the inference phase.
+  Status CalculateOpsMemoryCostForInference();
+  // Calculate memory cost of edges in the inference phase.
+  Status CalculateEdgesMemoryCostForInference();
   Status ComputeOpsAndEdgesParameterInvolved();
+  // Compute for each operator whether the output is critical.
+  Status ComputeOpsAndEdgesOutputCritical();
 
   std::vector<OperatorInfoPtr> GetOperators() const { return ops_; }
-  size_t GetNumPairs() const { return edges_.size(); }
+  size_t GetNumEdges() const;
   Status InitSelectedStrategy();
   OperatorInfoPtr FindTmpIdentityByParameterName(std::string &) const;
   // When TmpIdentity is used by mulitple operators, the corresponding parameter's memory cost should be calculated only
@@ -208,6 +216,10 @@ class CostGraph {
   const std::map<std::string, std::string> get_tuple_getitem_list() const { return tuple_getitem_list_; }
 
  private:
+  void TopologyOrder(std::vector<OperatorInfoPtr> *);
+  void DFSForTopoOrder(const OperatorInfoPtr &, std::map<OperatorInfoPtr, bool> *, std::vector<OperatorInfoPtr> *);
+  Status DetermineCriticalOps(const std::vector<OperatorInfoPtr> &);
+  void MarkCriticalOpsAndEdges(const std::map<OperatorInfoPtr, int> &);
   // Needed by rec_parser
   std::vector<std::vector<std::string>> inputs_tensor_name_list_;
   std::map<std::string, std::string> tuple_getitem_list_;

mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.cc

@@ -37,6 +37,8 @@ void OperatorCost::SetInputAndOutputTypeLength(const std::vector<size_t> &input_
   outputs_type_lengths_ = output_lengths;
 }
 
+void OperatorCost::set_output_critical(int critical) { is_outputs_critical_ = critical; }
+
 double OperatorCost::GetMemoryCost(const std::vector<TensorInfo> &inputs,
                                    const std::vector<TensorInfo> &outputs) const {
   double result = 0.0;
@@ -63,6 +65,20 @@ double OperatorCost::GetMemoryCost(const std::vector<TensorInfo> &inputs,
   return result;
 }
 
+double OperatorCost::GetMemoryCostForInference(const std::vector<TensorInfo> &,
+                                               const std::vector<TensorInfo> &outputs) const {
+  double result = 0.0;
+  if (is_outputs_critical_ == -1) {
+    MS_LOG(EXCEPTION) << "The critical flag is not set.";
+  }
+  if (is_outputs_critical_ == 1) {
+    for (size_t i = 0; i < outputs.size(); ++i) {
+      result += ListProduct(outputs[i].slice_shape()) * static_cast<double>(outputs_type_lengths_[i]);
+    }
+  }
+  return result;
+}
+
 // return the per device communication cost in the forward phase.
 double MatMulCost::GetForwardCommCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
                                       int32_t) const {

mindspore/ccsrc/parallel/auto_parallel/operator_costmodel.h

@@ -70,6 +70,7 @@ class OperatorCost {
   void set_is_parameter(const std::vector<bool> &is_parameter);
   void set_is_parameter_involve(const std::vector<bool> &);
   void set_output_parameter_involve(int);
+  void set_output_critical(int);
   void SetInputAndOutputTypeLength(const std::vector<size_t> &input_lengths, const std::vector<size_t> &output_lengths);
   std::vector<size_t> inputs_type_lengths() const { return inputs_type_lengths_; }
   std::vector<size_t> outputs_type_lengths() const { return outputs_type_lengths_; }
@@ -92,6 +93,8 @@ class OperatorCost {
   // Typically, the PEAK memory cost contributed by an operator is its output (if the output is parameter-invovled),
   // plus necessary inputs.
   virtual double GetMemoryCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs) const;
+  // per device memory cost in a inference phase
+  double GetMemoryCostForInference(const std::vector<TensorInfo> &, const std::vector<TensorInfo> &) const;
 
  protected:
   // For each input in 'inputs_', a bool variable is true if the corresponding one is a parameter or a output of
@@ -106,6 +109,9 @@ class OperatorCost {
   // for each input and output, the followings record the number of bytes of each element
   std::vector<size_t> inputs_type_lengths_;
   std::vector<size_t> outputs_type_lengths_;
+  // Whether the output is critical, which means that this output is included in calculating peak memory cost
+  // in the inference phase.
+  int is_outputs_critical_ = -1;
 };
 
 using OperatorCostPtr = std::shared_ptr<OperatorCost>;

mindspore/ccsrc/parallel/ops_info/operator_info.cc

@@ -1119,6 +1119,21 @@ Status OperatorInfo::CalculateMemoryCost() {
   return SUCCESS;
 }
 
+Status OperatorInfo::CalculateMemoryCostForInference() {
+  // First, set the 'is_outputs_critical_' flag into OperatorCost.
+  if (is_output_critical_ == -1) {
+    MS_LOG(EXCEPTION) << "The critical flag is not set.";
+    return FAILED;
+  }
+  operator_cost()->set_output_critical(is_output_critical_);
+  // Set the memory cost in the 'strategy_cost_'
+  for (auto &swc : strategy_cost_) {
+    auto mem_cost = operator_cost()->GetMemoryCostForInference(swc->inputs_ptr, swc->outputs_ptr);
+    swc->cost_list[0]->memory_with_reuse_ = mem_cost;
+  }
+  return SUCCESS;
+}
+
 Status OperatorInfo::CorrectMemoryCost(size_t input_index) {
   for (auto &swc : strategy_cost_) {
     double parameter_mem_cost = ListProduct(swc->inputs_ptr[input_index].slice_shape()) *
@@ -1230,6 +1245,25 @@ Status OperatorInfo::SetInputAndOutputTypeLength(const std::vector<size_t> &inpu
   return SUCCESS;
 }
 
+double OperatorInfo::GetOutputsTotalSize() {
+  if (is_calculated_outputs_size_) {
+    return outputs_total_size_;
+  }
+  if (outputs_type_lengths_.size() != outputs_shape_.size()) {
+    MS_LOG(EXCEPTION) << "Output_lengths: " << outputs_type_lengths_.size()
+                      << " do not have the same number of outputs shape: " << outputs_shape_.size();
+  }
+  double sum = 0.0;
+  for (size_t i = 0; i < outputs_type_lengths_.size(); ++i) {
+    auto size = std::accumulate(outputs_shape_[i].begin(), outputs_shape_[i].end(), static_cast<double>(1.0),
+                                std::multiplies<double>());
+    sum += size * static_cast<double>(outputs_type_lengths_[i]);
+  }
+  is_calculated_outputs_size_ = true;
+  outputs_total_size_ = sum;
+  return outputs_total_size_;
+}
+
 Status OperatorInfo::set_outputs_type(const std::vector<TypePtr> &outputs_type) {
   if (outputs_type.size() != outputs_shape_.size()) {
     MS_LOG(ERROR) << "Outputs type: " << outputs_type.size()

mindspore/ccsrc/parallel/ops_info/operator_info.h

@@ -72,6 +72,7 @@ class OperatorInfo {
   Status set_is_parameter(const std::vector<bool> &is_parameter);
   Status SetInputAndOutputTypeLength(const std::vector<size_t> &input_lengths,
                                      const std::vector<size_t> &output_lengths);
+  double GetOutputsTotalSize();
   // Set outputs dtype.
   // If only one output, outputs_type.size() is 1.
   // If output is tuple, outputs_type.size() is greater than 1.
@@ -96,9 +97,13 @@ class OperatorInfo {
   // is checked
   Status SetCostUnderStrategyBase(const StrategyPtr &strategy);
   std::vector<std::shared_ptr<StrategyWithCost>> GetStrategyCost() { return strategy_cost_; }
-  // When the input of a operator contains WEIGHT or a output from other operators involving WEIGHT, then these input
-  // should stay in memory until it is used in the backward phase, which is kept in memory at the end of forward phase.
+  // In the training phase, when the input of a operator contains WEIGHT or a output from other operators involving
+  // WEIGHT, then these input should stay in memory until it is used in the backward phase, which is kept in memory
+  // at the end of forward phase.
   Status CalculateMemoryCost();
+  // In the inference phase, the memory cost is incurred only when the operator is critical. The size is calculated
+  // by the output
+  Status CalculateMemoryCostForInference();
   int ComputeOpAndPrevEdgeParameterInvolved();
 
   ForwardOp forward_op() const { return forward_op_; }
@@ -147,6 +152,9 @@ class OperatorInfo {
   // multiple times. This method is to correct this, and makes the cost is calulated only once.
   Status CorrectMemoryCost(size_t input_index);
   int is_output_parameter_involve() const { return is_output_parameter_involve_; }
+  int is_output_critical() const { return is_output_critical_; }
+  void mark_output_critical() { is_output_critical_ = 1; }
+  void mark_output_not_critical() { is_output_critical_ = 0; }
   int used_devices() const { return used_devices_; }
   // needed by rec_parser
   void set_type(const std::string &type) { type_ = type; }
@@ -220,7 +228,16 @@ class OperatorInfo {
   // For each input in 'inputs_', a bool variable is true if the corresponding one is a parameter or a output of
   // pre-operator that has parameters as input.
   std::vector<bool> is_parameter_involve_;
-  int is_output_parameter_involve_ = -1;  // -1: unset; 0: not parameter_involved; 1: parameter_involved
+  // If any input is parameter-involved, the output is parameter-involved. This variable is used in calculating
+  // peak memory cost in the training phase.
+  // -1: unset; 0: not parameter_involved; 1: parameter_involved
+  int is_output_parameter_involve_ = -1;
+  // Whether this output is critical, which means that this output is included in calculating peak memory cost
+  // in the inference phase.
+  // -1 : unset; 0: not critical; 1: critical
+  int is_output_critical_ = -1;
+  double outputs_total_size_ = 0.0;
+  bool is_calculated_outputs_size_ = false;
   // for each input and output, the followings record the number of bytes of each element
   std::vector<size_t> inputs_type_lengths_;
   std::vector<size_t> outputs_type_lengths_;

mindspore/ccsrc/parallel/step_auto_parallel.cc

@@ -1055,6 +1055,9 @@ Status ParallelStrategySearch(const std::vector<AnfNodePtr> &all_nodes, const Fu
   // Step 1: Traverse the ANF graph, and create NODEs for costgraph:
   //      create the OperatorInfo object for each primitive, and enumerate the parallelization strategies
   //      for each OperatorInfo;
+  // Step 1.1: Deal with 'Reshape':
+  //      For 'Reshape', it takes its previous operator's layout as its input layout, and takes its next operator's
+  //      layout as its output layout.
   // Step 2: Traverse the ANF graph, and create EDGES for costgraph:
   //      create the Edge object for each pair of OperatorInfo, and enumerate the parallelization strategies
   //      for each edge, based on the strategies of two OperatorInfos;
@@ -1062,7 +1065,8 @@ Status ParallelStrategySearch(const std::vector<AnfNodePtr> &all_nodes, const Fu
   //      taking care for the case of a single Parameter being used by multiple operators. Create a TmpIdentity
   //      operator for this Parameter, and add an edge for the use of this Parameter by each
   //      subsequent operator;
-  // Step 3.1: Calculate memory usage
+  // Step 3.1: Calculate memory usage:
+  //      note the memory usage calculation is different in training phase and inference phase.
   // Step 4: Run the Dynamic Programming algorithm:
   //      in this process, cost is calculated based on not only the operators, but also the edges. Here, the edge
   //      cost is caused by the redistribution of a operator's output tensor layout to the next operator's input
@@ -1087,35 +1091,21 @@ Status ParallelStrategySearch(const std::vector<AnfNodePtr> &all_nodes, const Fu
       MS_LOG(EXCEPTION) << "Constructing nodes for cost graph failed.";
     }
   }
-  // reshape operator needs the next node's input_layout as its output_layout.
-  // and needs the previous node's output_layout as its input_layout.
+  // Step 1.1
   ReshapeCostCompute(all_nodes);
   // Step 2
   ConstructCostGraphEdges(all_nodes);
   MS_LOG(INFO) << "Constructing edges for cost graph succeeded. There are " << entire_costgraph->GetOperators().size()
-               << " operators, and " << entire_costgraph->GetNumPairs() << " edges.",
+               << " operators, and " << entire_costgraph->GetNumEdges() << " edges.";
 
-    // Step 3: Augment the costgraph.
-    AugmentCostGraph(all_nodes);
+  // Step 3: Augment the costgraph.
+  AugmentCostGraph(all_nodes);
   MS_LOG(INFO) << "After the augmenting procedure, there are " << entire_costgraph->GetOperators().size()
-               << " operators, and " << entire_costgraph->GetNumPairs() << " edges.";
+               << " operators, and " << entire_costgraph->GetNumEdges() << " edges.";
 
   // Step 3.1: Calculate the memory usage
-  if (entire_costgraph->ComputeOpsAndEdgesParameterInvolved() == SUCCESS) {
-    // Calculate operators' memory usage
-    if (entire_costgraph->CalculateOpsMemoryCost() != SUCCESS) {
-      MS_LOG(EXCEPTION) << "Calculating operators' cost for memory cost failed.";
-    }
-    // Calculate edges' memory usage
-    if (entire_costgraph->CalculateEdgesMemoryCost() != SUCCESS) {
-      MS_LOG(EXCEPTION) << "Calculating edges' cost for memory cost failed.";
-    }
-    // Correct memory usage caused by TmpIdentity
-    if (entire_costgraph->CorrectOpsMemoryCost() != SUCCESS) {
-      MS_LOG(EXCEPTION) << "Correcting operators' cost for memory cost failed.";
-    }
-  } else {
-    MS_LOG(EXCEPTION) << "Computing operators' parameter_involved failed.";
+  if (entire_costgraph->CalculateMemoryCost() != SUCCESS) {
+    MS_LOG(EXCEPTION) << "Calculating memory cost failed.";
   }
 
   // Step 4: run DP algorithm on the costgraph.

tests/ut/python/parallel/test_auto_parallel_inference.py

@@ -32,5 +32,6 @@ def test_inference_phase():
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, optimizer)
     train_network.set_train()
+    train_network.set_auto_parallel()
 
     output = train_network(predict, label)
\ No newline at end of file