text: // Copyright (c) Microsoft Corporation. All rights reserved.
  // Licensed under the MIT License.
  
  #pragma once
  #include <memory>
  #include "core/common/optional.h"
  #include "core/common/path_string.h"
  #include "core/session/inference_session.h"
  #include "orttraining/core/framework/pipeline.h"
  #include "orttraining/core/graph/loss_func/loss_func_common.h"
  #include "orttraining/core/graph/loss_function_registry.h"
  #include "orttraining/core/graph/optimizer_graph_output_key.h"
  #include "orttraining/core/graph/optimizer_config.h"
  #include "orttraining/core/graph/gradient_config.h"
  #include "orttraining/core/optimizer/graph_transformer_config.h"
  
  namespace onnxruntime {
  namespace training {
  
  constexpr char SHARED_OPTIMIZER_STATES_KEY[] = "shared_optimizer_state";
  
  class TrainingSession : public InferenceSession {
   public:
    typedef std::unordered_map<std::string /*OpType*/,
                               std::vector<std::pair<size_t /*InputIndex*/, float /*value*/>>>
        ImmutableWeights;
  
    typedef std::unordered_map<std::string /* Model weight name*/,
                               NameMLValMap /* 'Moment_1': OrtValue, 'Moment_2': OrtValue etc...*/>
        OptimizerState;
  
    /**
     * Partition information of each paritioned weight
     */
    struct PartitionInfo {
      // value of the original shape of the weight
      std::vector<int64_t> original_dim;
      // indicates whether weight was megatron partitioned or not.
      // -1: not partitioned; 0: column partitioned; 1: row partitioned
      int megatron_row_partition = -1;
      // name of the partition used to look up partitioned weight and optimizer state values
      std::string partition_name;
      // whether the weight itself was paritioned or not(eg:just the optimizer state for fp32 Zero-1)
      bool weight_partitioned = false;
    };
  
    TrainingSession(const SessionOptions& session_options, const Environment& env)
        : InferenceSession(session_options, env) {}
    virtual ~TrainingSession(){};
  
    /**
     * The training configuration options.
     */
    struct TrainingConfiguration {
      // The path at which to save the intermediate model with the added loss function.
      optional<PathString> model_with_loss_function_path{};
      // The path at which to save the model after applying the graph transformations.
      optional<PathString> model_after_graph_transforms_path{};
      // The path at which to save the model with gradient graph added.
      optional<PathString> model_with_gradient_graph_path{};
      // The path at which to save the intermediate model with the whole training graph.
      optional<PathString> model_with_training_graph_path{};
  
      // The names of the weights to train.
      // If empty, a default set is used.
      std::unordered_set<std::string> weight_names_to_train{};
      // The names of the weights to not train.
      // These are removed from the set of names of weights to train.
      std::unordered_set<std::string> weight_names_to_not_train{};
  
      // The immutable weights specification.
      ImmutableWeights immutable_weights;
  
      // Gradient graph configuration
      GradientGraphConfiguration gradient_graph_config{};
  
      // The number of gradient accumulation steps.
      int gradient_accumulation_steps{1};
  
      struct DistributedConfiguration {
        // The rank of the node.
        int world_rank{0};
        // The local rank.
        int local_rank{0};
        // The total number of ranks.
        int world_size{1};
        // The number of local ranks on a node.
        int local_size{1};
        // The number of ranks for data parallel group.
        int data_parallel_size{1};
        // The number of ranks for horizontal model parallel group.
        int horizontal_parallel_size{1};
        // The number of pipeline stages.
        int pipeline_parallel_size{1};
        // The number of micro-batches run by pipeline parallel after calling one session.Run(...).
        int num_pipeline_micro_batches{1};
        // We assume one process only run a portion of the graph when pipeline parallel is enabled.
        // This field is the graph partition's ID this process run.
        int pipeline_stage_id{0};
        // This field contains ONNX model's names for input and output tensors to be sliced.
        std::vector<std::string> sliced_tensor_names;
        // Shapes of inputs and outputs for micro-batch.
        std::unordered_map<std::string, std::vector<int>> sliced_schema;
        // The axies to slice tensors along to create tensors in micro-batch.
        // If we have a tensor named "x", slicing x along axis sliced_axes["x"] generates
        // "x" in micro-batch.
        std::unordered_map<std::string, int> sliced_axes;
      };
      // The distributed training configuration.
      DistributedConfiguration distributed_config{};
  
      struct MixedPrecisionConfiguration {
        // Whether to use mixed precision initializers.
        bool use_mixed_precision_initializers{};
        MixedPrecisionDataType mixed_precision_type{MixedPrecisionDataType::FP16};
  
        bool layernorm_stash_as_fp32{true};
  
        ONNX_NAMESPACE::TensorProto_DataType TensorProtoDataType() const {
          switch (mixed_precision_type) {
            case MixedPrecisionDataType::FP16:
              return ONNX_NAMESPACE::TensorProto_DataType_FLOAT16;
            case MixedPrecisionDataType::BF16:
              return ONNX_NAMESPACE::TensorProto_DataType_BFLOAT16;
            default:
              return ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED;
          }
        }
      };
      // The mixed precision configuration.
      // If not provided, mixed precision is disabled.
      optional<MixedPrecisionConfiguration> mixed_precision_config{};
  
      struct LossFunctionConfiguration {
        // The loss function configuration options.
        LossFunctionInfo loss_function_info{};
      };
      // The loss function configuration.
      // If not provided, no loss function is added and an external one is expected.
      // Exactly one of loss_function_config or loss_name should be given.
      optional<LossFunctionConfiguration> loss_function_config{};
      // The name of the external loss function's output.
      // Exactly one of loss_function_config or loss_name should be given.
      optional<std::string> loss_name{};
  
      struct GistConfiguration {
        // The operator type to which GIST is applied. Valid Values - 1 (Softmax), 2 (Transpose), 3 (Reshape),
        // 4 (Add), 5 (Dropout), 6 (LayerNormalization), 7 (MatMul), 8 (Relu), 9 (All the above)
        int op_type{};
        // The compression type used for GIST. Valid values - GistBinarize, GistPack1, GistPack8, GistPack16, GistPackMsfp15
        std::string compr_type{};
      };
      // The GIST configuration.
      // If not provided, GIST is disabled.
      optional<GistConfiguration> gist_config{};
  
      struct TensorboardConfiguration {
        // The summary name.
        std::string summary_name{};
        // The names of the scalar nodes.
        std::vector<std::string> scalar_node_names{};
        // The names of the histogram nodes.
        std::vector<std::string> histogram_node_names{};
        // The names of the norm nodes.
        std::vector<std::string> norm_node_names{};
        // Whether to dump the convergence metrics.
        bool dump_convergence_metrics{};
      };
      // The TensorBoard configuration.
      // If not provided, TensorBoard output is disabled.
      optional<TensorboardConfiguration> tensorboard_config{};
  
      struct OptimizerConfiguration {
        // The optimizer name.
        std::string name{};
        // The learning rate input name.
        std::string learning_rate_input_name{};
        // The per-weight attribute map generator.
        // It should accept a weight name and return the appropriate attribute map.
        std::function<std::unordered_map<std::string, float>(const std::string&)> weight_attributes_generator{};
        std::function<std::unordered_map<std::string, int64_t>(const std::string&)> weight_int_attributes_generator{};
  
        // Whether to use mixed precision moments.
        bool use_mixed_precision_moments{};
        // Whether to use mixed precision type for the all reduce.
        bool do_all_reduce_in_mixed_precision_type{};
        // Whether to use NCCL.
        bool use_nccl{};
        // Whether to partition the optimizer state.
        ZeROConfig deepspeed_zero{};
        // Selects the reduction algorithm for Adasum.
        AdasumReductionType adasum_reduction_type{AdasumReductionType::None};
        // Whether to enable gradient clipping.
        bool enable_grad_norm_clip{true};
      };
      // The optimizer configuration.
      // If not provided, no optimizer is added.
      optional<OptimizerConfiguration> optimizer_config{};
  
      // optional initial states for optimizer
      // These states are partitioned wherever the weights are partitioned for eg in Zero, Megatron
      // This is loaded into the optimizer initializers when the optimizer graph is created
      optional<OptimizerState> init_optimizer_states{};
  
      // struct to describe a specific edge. An edge is not the same as a node_arg. Edge represents a connection between two operators.
      // For example, an operator A's output tensor T is connecting to another operator B's input, then this constructs
      // an edge from A to B. If A's output tensor T has multiple consumers, i.e. it's fed into multiple operators' inputs,
      // there would be multiple edges, each from A, to a consumer operator.
      // CutEdge information is used in pipeline online partition tool to identify which edge to cut to make the
      // corresponding partition.
      struct CutEdge {
        std::string node_arg_name;
        optional<std::vector<std::string>> consumer_nodes;
  
        // If the edge is unique, i.e. only have one consumer node, or all the edges
        // with the same node_arg_name needs to be cut, specify the node_arg_name
        // suffices.
        CutEdge(std::string edge) : node_arg_name(edge){};
        // If the edges with same node_arg_name belongs to different cut, i.e. some of its
        // consumer node belongs to one partition, and some belongs to another, specify
        // the consumer node names which you want to perform the cut on.
        CutEdge(std::string edge, std::vector<std::string> nodes) : node_arg_name(edge), consumer_nodes(nodes){};
      };
      // CutInfo is a group of CutEdges that describes a specific cut that composed of splitting those edges.
      typedef std::vector<CutEdge> CutInfo;
  
      struct PipelineConfiguration {
        // If model partition happens outside ORT, this flag should be false.
        // Otherwise, use true to trigger ORT's pipeline partition.
        bool do_partition;
        // Tensors to fetch as specified by the user.
        // Each pipeline stage should pick up some strings from this field.
        std::vector<std::string> fetch_names;
        // cut_list contains the list of CutInfo to make the graph partitions.
        // cut_list[i] contains the CutInfo to make the partition between stage i and stage i+1
        std::vector<CutInfo> cut_list;
        // Alternative for partition. We map each operator's string identifier to
        // a stage identifier. We identify operators using the name of any of
        // their outputs. All operators in the graph must be in the domain of this
        // map.
        std::map<std::string, int> op_id_to_stage;
  
        // The base path at which to save the intermediate partitioned input model (forward pass only).
        optional<PathString> partitioned_model_path{};
      };
  
      // If pipeline is enabled, this field's has_value() returns true.
      // Otherwise, it returns false.
      optional<PipelineConfiguration> pipeline_config{};
  
      TrainingGraphTransformerConfiguration graph_transformer_config{};
    };
  
    /**
     * The training configuration output.
     */
    struct TrainingConfigurationResult {
      struct MixedPrecisionConfigurationResult {
        // The name of the loss scaling factor input.
        std::string loss_scale_input_name;
      };
      // The mixed precision configuration output.
      // This is only set if mixed precision is enabled.
      optional<MixedPrecisionConfigurationResult> mixed_precision_config_result;
  
      struct OptimizerConfigurationResult {
        // The mapping of optimizer output key to graph output name.
        OptimizerOutputKeyMap<std::string> output_key_to_graph_output_name;
      };
      // The optimizer configuration output.
      // This is only set if an optimizer is added.
      optional<OptimizerConfigurationResult> opt_config_result;
  
      // The names of pipeline events in model's input list.
      // If an event is not used, its name should be empty.
      struct PipelineConfigurationResult {
        // Index of obtained pipeline stage. The first stage is indexed by 0.
        int pipeline_stage_id;
        // The names of pipeline events in model's input list.
        // This field also includes the first output name of each event operator.
        pipeline::PipelineTensorNames pipeline_tensor_names;
        // Tensors to feed at this pipeline stage.
        std::vector<std::string> feed_names;
        // Tensors to fetch at this pipeline stage.
        // It's a subset of PipelineConfiguration.fetch_names.
        std::vector<std::string> fetch_names;
      };
  
      // The pipeline configuration output.
      // This is only set if an pipeline is enabled.
      optional<PipelineConfigurationResult> pipeline_config_result;
  
      // Mapped initialized names after weight partitioning for example MegatronTransformer
      std::unordered_map<std::string, std::string> weight_name_map_after_graph_transform{};
  
      std::unordered_map<std::string, PartitionInfo> weight_partition_info;
    };
  
    /**
     * Configures the session for training.
     * Note: This is known to NOT be thread-safe.
     * @param config The training configuration.
     * @param[out] config_result The configuration output.
     * @return The status of the configuration.
     */
    virtual common::Status ConfigureForTraining(
        const TrainingConfiguration& config, TrainingConfigurationResult& config_result);
  
    /**
     * Overrides the graph outputs with the specified output names.
     * @param outputs The new output names.
     * @return The status of the operation.
     */
    common::Status OverrideGraphOutputs(const std::vector<std::string>& outputs);
  
    /** Save a model, 3 options:
    1. save with updated weights
    2. save with updated weights and loss function
    3. save with updated weights, loss function and gradients
    */
    enum class SaveOption {
      NO_RELOAD,
      WITH_UPDATED_WEIGHTS,
      WITH_UPDATED_WEIGHTS_AND_LOSS_FUNC,
      WITH_UPDATED_WEIGHTS_AND_LOSS_FUNC_AND_GRADIENTS
    };
  
    /** Save the new model.
    @param model_uri the path for the new model.
    @param opt see SaveOption.
    */
    common::Status Save(const PathString& model_uri, SaveOption opt);
  
    /** Save the model using an external file for initializers larger than the threshold (in bytes).
    This function is useful to avoid hitting the size limit for protobufs when using large models, in
    particular after auto-diff.
    @param model_uri the path for the new model.
    @param external_file_uri the name for the external initializers file. This is a plain string because
    it needs to be saved into the onnx protobuf, where wchar is not supported.
    @param initializer_size_threshold initializers larger or equal to this threshold (in bytes) are saved
    in the external file. Initializer smaller than this threshold are included in the onnx file.
    */
    common::Status SaveWithExternalInitializers(const PathString& model_uri,
                                                const std::string& external_file_name,
                                                size_t initializer_size_threshold);
  
    /** Update the session initializers with passed-in state tensors
     * @param state_tensors A map of state tensors to set, usually loaded from a checkpoint.
     * @param strict Whether entries in state_tensors which are unknown or not present in the model are treated as an error or ignored.
     */
    common::Status SetStateTensors(const NameMLValMap& state_tensors, bool strict = false);
  
    /**
     * Gets the state tensors.
     * @param[out] The state tensors.
     * @return The status of the operation.
     */
    common::Status GetStateTensors(NameMLValMap& state_tensors);
  
    common::Status GetOptimizerState(std::unordered_map<std::string, NameMLValMap>& opt_state_tensors);
  
    common::Status GetModelState(std::unordered_map<std::string, NameMLValMap>& model_state_tensors, bool include_mixed_precision_weights = false);
  
    common::Status GetPartitionInfoMap(std::unordered_map<std::string, std::unordered_map<std::string, std::vector<int>>>& part_info_map);
  
    /** Gets the DataTransferManager instance. */
    const DataTransferManager& GetDataTransferManager() const;
  
    /** Gets the model location. */
    const PathString& GetModelLocation() const { return model_location_; }
  
    /**
     * Checks to be see if given graph output is produced by an fp32-only node.
     * @param The name of the output.
     * @return Whether output is from fp32-only node or not.
     */
    bool IsGraphOutputFp32Node(const std::string& output_name) const;
  
    /**
     * Gets the list of Dropout ratio inputs that will be used as feeds in eval mode,
     * since each ratio input has its own name.
     * @return The list of feed names.
     */
    std::unordered_set<std::string> GetDropoutEvalFeeds() const { return dropout_eval_feeds_; }
  
    /** Override Run function in InferenceSession to inject some training-specific logics **/
    using InferenceSession::Run;  // For overload resolution.
    common::Status Run(const RunOptions& run_options, IOBinding& io_binding) override;
  
   protected:
    /** Configures the loss function.
    The loss function can either be provided externally or built from the provided loss function information.
    Exactly one of external_loss_name or loss_function_info should be given.
    Optionally, a loss scaling factor can be applied to the loss function output.
    @param external_loss_name The name of the externally provided loss function output. Specifies that an external loss
           function should be used.
    @param loss_func_info The loss function information. Specifies that the loss function should be built.
    @param loss_scale_input_name[in,out] If provided, indicates that loss scaling should be applied and will be set to
           the name of the loss scale input.
    @param actual_loss_name[out] The actual name of the loss function output from which to start the backward graph.
    @returns Status indicating success or providing an error message.
    @remarks When using a custom/standard op as loss function, 2 ops must have been registered:
               1. an op for loss function, schema:
                   Inputs:
                       OUT
                       LABEL
                   Outputs:
                       LOSS
               2. an op to calculate gradients, schema:
                   Inputs:
                       GRADIENT_OF_OUTPUT
                       OUT
                       LABEL
                   Outputs:
                       GRADIENT_OF_OUT
                       GRADIENT_OF_LABEL
             And also in gradient_builder.cc, the gradient builder must have been registered.
    */
    common::Status ConfigureLossFunction(
        const optional<std::string>& external_loss_name,
        const optional<LossFunctionInfo>& loss_func_info,
        std::string* loss_scale_input_name,
        std::string& actual_loss_name);
  
    common::Status AddGistEncoding(const int op_type, const std::string compr_type);
  
    /** Add tensorboard summary nodes to the graph.
    @param summary_name name for the merged summary node.
    @param scalar_nodes tensor names to add scalar summary nodes for.
    @param histogram_nodes tensor names to add histogram summary nodes for.
    @param norm_nodes tensor names to add norm summary nodes for.
    @param dump_convergence_metrics add convergence metrics such as gradient norm to the summary or not.
    */
    common::Status AddTensorboard(const std::string& summary_name,
                                  const std::vector<std::string>& scalar_nodes,
                                  const std::vector<std::string>& histogram_nodes,
                                  const std::vector<std::string>& norm_nodes,
                                  const bool dump_convergence_metrics);
  
    virtual common::Status PartitionGraphForPipeline(
        const int32_t pipeline_stage_id,
        const optional<TrainingConfiguration::PipelineConfiguration>& pipeline_config,
        const optional<TrainingConfiguration::DistributedConfiguration>& distributed_config,
        const std::unordered_set<std::string>& weight_names_to_train,
        std::unordered_set<std::string>& filtered_config_weight_names_to_train);
  
    // Insert operators for running pipeline and return event tensor names.
    // For an intermediate pipeline stage, its original computation is
    //
    //  Recv --> Forward --> Send -->
    //  Recv --> Backward --> Send
    //
    // After this function, the resulted computation is
    //
    //  WaitEvent --> Recv --> RecordEvent --> WaitEvent --> Forward --> RecordEvent --> WaitEvent --> Send --> RecordEvent -->
    //  WaitEvent --> Recv --> RecordEvent --> WaitEvent --> Backward --> RecordEvent --> WaitEvent --> Send --> RecordEvent
    //
    // As you can see, some event operators are inserted. For each event operator, its dependent
    // event tensor name is written to an input references, for example, "forward_waited_event_name".
    //
    // This function assumes that
    //  1. Only one Recv and only one Send present in forward pass.
    //  2. Only one Recv and only one Send present in backward pass.
    //  3. Backward operators' descriptions are all "Backward pass". This assumption is used to
    //     identify backward nodes.
    //  4. No event operator is inserted by other graph transform.
    virtual common::Status SetEventSynchronization(
        const int32_t pipeline_stage_id,
        const optional<TrainingConfiguration::PipelineConfiguration>& pipeline_config,
        const optional<TrainingConfiguration::DistributedConfiguration>& distributed_config,
        const std::unordered_set<std::string>& weight_names_to_train,
        optional<TrainingConfigurationResult::PipelineConfigurationResult>& pipeline_config_result);
  
    common::Status ApplyTransformationsToMainGraph(const std::unordered_set<std::string>& weights_to_train,
                                                   const TrainingGraphTransformerConfiguration& config);
  
    common::Status ApplyModelParallelTransformationsToMainGraph(std::unordered_set<std::string>& weights_to_train,
                                                                TrainingConfigurationResult& config_result_out);
  
    /** configure initial transformers for training */
    void AddPreTrainingTransformers(const IExecutionProvider& execution_provider,  // for constant folding
                                    GraphTransformerManager& transformer_manager,
                                    const std::unordered_set<std::string>& weights_to_train,
                                    const TrainingGraphTransformerConfiguration& config,
                                    TransformerLevel graph_optimization_level = TransformerLevel::MaxLevel);
  
    /** override the parent method in inference session for training specific transformers */
    common::Status AddPredefinedTransformers(GraphTransformerManager& transformer_manager,
                                             TransformerLevel graph_optimization_level,
                                             bool saving_runtime_optimizations) const override;
  
    /** Perform auto-diff to add backward graph into the model.
    @param weights_to_train a set of weights to be training.
    @param loss_function_output_name the name of the loss function's output.
    */
    common::Status BuildGradientGraph(const std::unordered_set<std::string>& weights_to_train,
                                      const std::string& loss_function_output_name,
                                      const GradientGraphConfiguration& gradient_graph_config,
                                      const logging::Logger& logger);
  
    common::Status BuildAccumulationNode(const std::unordered_set<std::string>& weights_to_train);
  
    /** Add optimizer into the model. Each trainable weight will have an optimizer
    @param opt_graph_config The configuration that applies to all optimizers.
    @param opt_configs specify the optimizers used by each weight in weights_to_train, 1-1 mapping to weights_to_train.
    @param opt_graph_outputs The outputs of optimizer graph
    */
    common::Status BuildOptimizer(
        const OptimizerGraphConfig& opt_graph_config,
        const std::unordered_map<std::string, OptimizerNodeConfig>& opt_configs,
        OptimizerOutputKeyMap<std::string>& opt_graph_outputs);
  
    common::Status BuildLoss(
        const optional<std::string>& external_loss_name,
        std::string& loss_name,
        const optional<TrainingConfiguration::LossFunctionConfiguration>& loss_function_config,
        optional<std::string>& loss_scale_input_name);
  
    virtual common::Status BuildLossAndLossScaling(
        const int32_t pipeline_stage_id,
        const optional<std::string>& external_loss_name,
        const optional<TrainingConfiguration::MixedPrecisionConfiguration>& mixed_precision_config,
        const optional<TrainingConfiguration::DistributedConfiguration>& distributed_config,
        const optional<TrainingConfiguration::LossFunctionConfiguration>& loss_function_config,
        std::string& loss_name,
        optional<std::string>& loss_scale_input_name,
        optional<TrainingConfigurationResult::MixedPrecisionConfigurationResult>& mixed_precision_config_result);
  
    /** Enable mixed precision training
    @param weights_to_train a set of weights to be training.
    @param mixed_precision_config The mixed precision configuration.
    @param fp32_weight_name_to_mixed_precision_node_arg the map between weights and mixed precision weights.
    */
    common::Status EnableMixedPrecision(const std::unordered_set<std::string>& weights_to_train,
                                        const TrainingConfiguration::MixedPrecisionConfiguration& mixed_precision_config,
                                        std::unordered_map<std::string, NodeArg*>& fp32_weight_name_to_mixed_precision_node_arg);
  
    /** Discover all trainable initializers by reverse DFS starting from a given tensor (for example, the loss value)
    @param immutable_weights do not include initializers matching an (op_type, input_index, value) entry from this table
    @param backprop_source_name reverse DFS back propagation source name (i.e. loss name or pipeline send output name)
    */
    std::unordered_set<std::string> GetTrainableModelInitializers(const ImmutableWeights& immutable_weights,
                                                                  const std::string& backprop_source_name) const;
  
    std::unordered_set<std::string> GetStateTensorNames() const;
  
    common::Status SetEvalFeedNames();
  
    NameMLValMap GetWeights() const;
  
    void FilterUnusedWeights(const std::unordered_set<std::string>& weight_names_to_train,
                             std::unordered_set<std::string>& filtered_weight_names_to_train);
  
    static bool IsImmutableWeight(const ImmutableWeights& immutable_weights,
                                  const Node* node,
                                  const TensorProto* weight_tensor,
                                  const logging::Logger* logger = nullptr);
  
    static bool IsUntrainable(const Node* node,
                              const std::string& initializer_name,
                              const logging::Logger* logger = nullptr);
  
    bool is_configured_{false};
  
    std::unordered_set<std::string> weights_to_train_;
    OptimizerState init_optimizer_states_;
    // names of additional initializers to be included in checkpoints
    std::unordered_map<std::string, std::string> updated_weight_names_map_;
    std::unordered_set<std::string> opt_state_initializer_names_;
    std::unordered_map<std::string, std::string> weight_to_mixed_precision_map_;
    std::unordered_map<std::string, std::unordered_map<std::string, std::string>> weight_to_opt_mapping_;
    std::unordered_map<std::string, TrainingSession::PartitionInfo> weight_partition_info_;
  
    bool is_mixed_precision_enabled_;
    optional<std::string> external_loss_name_;
    std::unique_ptr<ILossFunction> loss_graph_builder_;
    optional<LossFunctionInfo> loss_function_info_;
  
    std::unordered_set<std::string> dropout_eval_feeds_;
    OptimizerGraphConfig opt_graph_config_;
    std::unordered_map<std::string, OptimizerNodeConfig> opt_configs_;
  
    GradientGraphConfiguration gradient_graph_config_;
    static const std::string training_mode_string_;
  };
  
  class PipelineTrainingSession final : public TrainingSession {
   public:
    PipelineTrainingSession(const SessionOptions& session_options, const Environment& env)
        : TrainingSession(session_options, env) {}
    common::Status ConfigureForTraining(const TrainingConfiguration& config, TrainingConfigurationResult& config_result_out) override;
    common::Status Run(const RunOptions& run_options, IOBinding& io_binding) override;
    ~PipelineTrainingSession();
  
   protected:
    common::Status PartitionGraphForPipeline(
        const int32_t pipeline_stage_id,
        const optional<TrainingConfiguration::PipelineConfiguration>& pipeline_config,
        const optional<TrainingConfiguration::DistributedConfiguration>& distributed_config,
        const std::unordered_set<std::string>& weight_names_to_train,
        std::unordered_set<std::string>& filtered_config_weight_names_to_train) override;
  
    common::Status SetEventSynchronization(
        const int32_t pipeline_stage_id,
        const optional<TrainingConfiguration::PipelineConfiguration>& pipeline_config,
        const optional<TrainingConfiguration::DistributedConfiguration>& distributed_config,
        const std::unordered_set<std::string>& weight_names_to_train,
        optional<TrainingConfigurationResult::PipelineConfigurationResult>& pipeline_config_result) override;
  
    common::Status BuildLossAndLossScaling(
        const int32_t pipeline_stage_id,
        const optional<std::string>& external_loss_name,
        const optional<TrainingConfiguration::MixedPrecisionConfiguration>& mixed_precision_config,
        const optional<TrainingConfiguration::DistributedConfiguration>& distributed_config,
        const optional<TrainingConfiguration::LossFunctionConfiguration>& loss_function_config,
        std::string& loss_name,
        optional<std::string>& loss_scale_input_name,
        optional<TrainingConfigurationResult::MixedPrecisionConfigurationResult>& mixed_precision_config_result) override;
  
    // Set some PipelineContext fields based on configuration result
    // returned by TrainingSession::ConfigureForTraining.
    common::Status SetPipelineContext(const TrainingConfigurationResult& config_result);
  
    common::Status SetExtraDataDependency();
  
    void CreatePipelineEvents(
        const bool traning_mode,
        const int batch_id,
        const int stage_id,
        IOBinding& io_binding);
  
    void CreateMicroBatchVariables(
        IOBinding& io_binding, IOBinding& sub_io_binding,
        const size_t slice_id, const size_t num_slices);
  
  #if defined(USE_CUDA) && defined(ORT_USE_NCCL) && defined(USE_NCCL_P2P)
    void LaunchNcclService(const int pipeline_stage_id);
  #endif
  
    common::Status RunWithPipeline(const RunOptions& run_options, IOBinding& io_binding);
  
    // Pipeline fields are valid only if params_.pipeline_parallel_size > 1.
    // Information for running pipeline.
    pipeline::PipelineContext pipeline_context_;
    // Pipeline schedule for deciding when to run batch, forward, or backward.
    pipeline::PipelineScheduler pipeline_schedule_;
    // Workers to run pipeline stage.
    pipeline::PipelineWorkerPool pipeline_worker_pool_;
  };
  }  // namespace training
  }  // namespace onnxruntime
  