mlx

-module(mlx_distributed).

%% Advanced distributed training primitives and communication patterns
-export([
    %% Communication primitives
    all_reduce/2, all_reduce/3,
    all_gather/2, all_gather/3,
    reduce_scatter/3, reduce_scatter/4,
    broadcast/3, broadcast/4,
    
    %% Advanced communication patterns
    ring_all_reduce/2, ring_all_reduce/3,
    tree_all_reduce/2, tree_all_reduce/3,
    butterfly_all_reduce/2, butterfly_all_reduce/3,
    
    %% Topology-aware communication
    hierarchical_all_reduce/3, hierarchical_all_reduce/4,
    torus_all_reduce/3, torus_all_reduce/4,
    
    %% Gradient compression
    compressed_all_reduce/3, compressed_all_reduce/4,
    quantized_all_reduce/3, quantized_all_reduce/4,
    sparse_all_reduce/2, sparse_all_reduce/3,
    
    %% Communication scheduling
    overlapped_communication/3, overlapped_communication/4,
    pipelined_communication/3, pipelined_communication/4,
    batched_communication/2, batched_communication/3,
    
    %% Fault tolerance
    fault_tolerant_all_reduce/3, fault_tolerant_all_reduce/4,
    checkpoint_communication/2, recover_communication/2,
    
    %% Performance optimization
    adaptive_communication/2, adaptive_communication/3,
    communication_profiling/2, optimize_communication_plan/2,
    
    %% Heterogeneous training
    heterogeneous_all_reduce/3, heterogeneous_all_reduce/4,
    device_aware_communication/3, device_aware_communication/4,
    
    %% Communication backends
    init_nccl_backend/1, init_gloo_backend/1, init_mpi_backend/1,
    create_communication_group/2, destroy_communication_group/1,
    
    %% Distributed optimizers
    distributed_sgd/3, distributed_adam/3, distributed_adamw/3,
    local_sgd/4, federated_averaging/3,
    
    %% Model parallelism
    pipeline_parallel/3, pipeline_parallel/4,
    tensor_parallel/3, tensor_parallel/4,
    expert_parallel/3, expert_parallel/4,
    
    %% Data parallelism enhancements
    dynamic_data_parallel/2, dynamic_data_parallel/3,
    gradient_compression_ddp/3, zero_redundancy_optimizer/3
]).

-record(comm_group, {
    id,
    ranks,
    backend,
    topology,
    compression_config = #{},
    fault_tolerance_config = #{}
}).

-record(comm_stats, {
    total_bytes = 0,
    total_time = 0.0,
    operation_counts = #{},
    bandwidth_history = [],
    latency_history = []
}).

%% Communication primitives
all_reduce(Tensor, ReduceOp) ->
    all_reduce(Tensor, ReduceOp, #{}).

all_reduce(Tensor, ReduceOp, Options) ->
    % All-reduce operation with automatic algorithm selection
    CommGroup = maps:get(group, Options, default_group()),
    Algorithm = maps:get(algorithm, Options, auto),
    
    % Select optimal algorithm based on tensor size and network topology
    SelectedAlgorithm = case Algorithm of
        auto -> select_optimal_algorithm(Tensor, ReduceOp, CommGroup);
        _ -> Algorithm
    end,
    
    % Execute all-reduce
    case SelectedAlgorithm of
        ring -> ring_all_reduce(Tensor, ReduceOp, Options);
        tree -> tree_all_reduce(Tensor, ReduceOp, Options);
        butterfly -> butterfly_all_reduce(Tensor, ReduceOp, Options);
        hierarchical -> hierarchical_all_reduce(Tensor, ReduceOp, CommGroup, Options);
        nccl -> nccl_all_reduce(Tensor, ReduceOp, Options);
        _ -> {error, {unsupported_algorithm, SelectedAlgorithm}}
    end.

all_gather(Tensor, Options) ->
    all_gather(Tensor, default_group(), Options).

all_gather(Tensor, CommGroup, Options) ->
    % All-gather operation
    Algorithm = maps:get(algorithm, Options, ring),
    
    case Algorithm of
        ring -> ring_all_gather(Tensor, CommGroup, Options);
        tree -> tree_all_gather(Tensor, CommGroup, Options);
        nccl -> nccl_all_gather(Tensor, CommGroup, Options)
    end.

reduce_scatter(Tensor, ReduceOp, CommGroup) ->
    reduce_scatter(Tensor, ReduceOp, CommGroup, #{}).

reduce_scatter(Tensor, ReduceOp, CommGroup, Options) ->
    % Reduce-scatter operation
    Algorithm = maps:get(algorithm, Options, ring),
    
    case Algorithm of
        ring -> ring_reduce_scatter(Tensor, ReduceOp, CommGroup, Options);
        tree -> tree_reduce_scatter(Tensor, ReduceOp, CommGroup, Options);
        nccl -> nccl_reduce_scatter(Tensor, ReduceOp, CommGroup, Options)
    end.

broadcast(Tensor, Root, CommGroup) ->
    broadcast(Tensor, Root, CommGroup, #{}).

broadcast(Tensor, Root, CommGroup, Options) ->
    % Broadcast operation
    Algorithm = maps:get(algorithm, Options, tree),
    
    case Algorithm of
        tree -> tree_broadcast(Tensor, Root, CommGroup, Options);
        linear -> linear_broadcast(Tensor, Root, CommGroup, Options);
        nccl -> nccl_broadcast(Tensor, Root, CommGroup, Options)
    end.

%% Advanced communication patterns
ring_all_reduce(Tensor, ReduceOp) ->
    ring_all_reduce(Tensor, ReduceOp, #{}).

ring_all_reduce(Tensor, ReduceOp, Options) ->
    % Ring-based all-reduce implementation
    CommGroup = maps:get(group, Options, default_group()),
    Ranks = get_group_ranks(CommGroup),
    MyRank = get_my_rank(),
    NumRanks = length(Ranks),
    
    case NumRanks of
        1 -> {ok, Tensor};
        _ ->
            % Chunk tensor for ring algorithm
            {ok, Chunks} = chunk_tensor_for_ring(Tensor, NumRanks),
            
            % Reduce-scatter phase
            {ok, ReducedChunks} = ring_reduce_scatter_phase(Chunks, ReduceOp, Ranks, MyRank),
            
            % All-gather phase
            {ok, GatheredChunks} = ring_all_gather_phase(ReducedChunks, Ranks, MyRank),
            
            % Reconstruct tensor
            mlx:concatenate(GatheredChunks, 0)
    end.

tree_all_reduce(Tensor, ReduceOp) ->
    tree_all_reduce(Tensor, ReduceOp, #{}).

tree_all_reduce(Tensor, ReduceOp, Options) ->
    % Tree-based all-reduce implementation
    CommGroup = maps:get(group, Options, default_group()),
    TreeTopology = maps:get(tree_topology, Options, binary_tree),
    
    % Build communication tree
    CommTree = build_communication_tree(CommGroup, TreeTopology),
    
    % Execute tree all-reduce
    execute_tree_all_reduce(Tensor, ReduceOp, CommTree).

butterfly_all_reduce(Tensor, ReduceOp) ->
    butterfly_all_reduce(Tensor, ReduceOp, #{}).

butterfly_all_reduce(Tensor, ReduceOp, Options) ->
    % Butterfly-based all-reduce implementation
    CommGroup = maps:get(group, Options, default_group()),
    Ranks = get_group_ranks(CommGroup),
    NumRanks = length(Ranks),
    
    % Check if number of ranks is power of 2
    case is_power_of_two(NumRanks) of
        false -> {error, butterfly_requires_power_of_two_ranks};
        true ->
            MyRank = get_my_rank(),
            LogRanks = round(math:log2(NumRanks)),
            
            % Execute butterfly phases
            execute_butterfly_phases(Tensor, ReduceOp, MyRank, LogRanks)
    end.

%% Topology-aware communication
hierarchical_all_reduce(Tensor, ReduceOp, CommGroup) ->
    hierarchical_all_reduce(Tensor, ReduceOp, CommGroup, #{}).

hierarchical_all_reduce(Tensor, ReduceOp, CommGroup, Options) ->
    % Hierarchical all-reduce for multi-node, multi-GPU setups
    NodeGroups = get_node_groups(CommGroup),
    
    % Intra-node all-reduce
    {ok, NodeReducedTensor} = intra_node_all_reduce(Tensor, ReduceOp, Options),
    
    % Inter-node all-reduce
    {ok, GlobalReducedTensor} = inter_node_all_reduce(NodeReducedTensor, ReduceOp, NodeGroups, Options),
    
    % Intra-node broadcast
    intra_node_broadcast(GlobalReducedTensor, Options).

torus_all_reduce(Tensor, ReduceOp, TorusTopology) ->
    torus_all_reduce(Tensor, ReduceOp, TorusTopology, #{}).

torus_all_reduce(Tensor, ReduceOp, TorusTopology, Options) ->
    % Torus topology all-reduce for large-scale systems
    #{dimensions := Dimensions, coordinates := MyCoords} = TorusTopology,
    
    % Perform all-reduce along each dimension
    lists:foldl(fun(Dim, CurrentTensor) ->
        {ok, ReducedTensor} = torus_reduce_along_dimension(CurrentTensor, ReduceOp, Dim, TorusTopology),
        ReducedTensor
    end, Tensor, lists:seq(0, length(Dimensions) - 1)).

%% Gradient compression
compressed_all_reduce(Tensor, ReduceOp, CompressionConfig) ->
    compressed_all_reduce(Tensor, ReduceOp, CompressionConfig, #{}).

compressed_all_reduce(Tensor, ReduceOp, CompressionConfig, Options) ->
    % All-reduce with gradient compression
    CompressionType = maps:get(type, CompressionConfig, topk),
    CompressionRatio = maps:get(ratio, CompressionConfig, 0.01),
    
    % Compress tensor
    {ok, CompressedTensor, CompressionMeta} = compress_tensor(Tensor, CompressionType, CompressionRatio),
    
    % Perform all-reduce on compressed tensor
    {ok, ReducedCompressed} = all_reduce(CompressedTensor, ReduceOp, Options),
    
    % Decompress result
    decompress_tensor(ReducedCompressed, CompressionMeta).

quantized_all_reduce(Tensor, ReduceOp, QuantizationConfig) ->
    quantized_all_reduce(Tensor, ReduceOp, QuantizationConfig, #{}).

quantized_all_reduce(Tensor, ReduceOp, QuantizationConfig, Options) ->
    % All-reduce with quantization
    QuantBits = maps:get(bits, QuantizationConfig, 8),
    ScaleType = maps:get(scale_type, QuantizationConfig, layer_wise),
    
    % Quantize tensor
    {ok, QuantizedTensor, QuantMeta} = quantize_for_communication(Tensor, QuantBits, ScaleType),
    
    % Perform all-reduce on quantized tensor
    {ok, ReducedQuantized} = all_reduce(QuantizedTensor, ReduceOp, Options),
    
    % Dequantize result
    dequantize_from_communication(ReducedQuantized, QuantMeta).

sparse_all_reduce(SparseTensor, ReduceOp) ->
    sparse_all_reduce(SparseTensor, ReduceOp, #{}).

sparse_all_reduce(SparseTensor, ReduceOp, Options) ->
    % All-reduce for sparse tensors
    SparsityThreshold = maps:get(sparsity_threshold, Options, 0.01),
    
    % Extract indices and values
    {ok, Indices, Values} = extract_sparse_components(SparseTensor, SparsityThreshold),
    
    % All-gather indices from all ranks
    {ok, AllIndices} = all_gather(Indices, Options),
    
    % All-gather values from all ranks
    {ok, AllValues} = all_gather(Values, Options),
    
    % Merge sparse tensors
    merge_sparse_tensors(AllIndices, AllValues, ReduceOp).

%% Communication scheduling
overlapped_communication(ComputeFun, CommunicationOps, Tensor) ->
    overlapped_communication(ComputeFun, CommunicationOps, Tensor, #{}).

overlapped_communication(ComputeFun, CommunicationOps, Tensor, Options) ->
    % Overlap computation with communication
    OverlapStrategy = maps:get(strategy, Options, automatic),
    
    case OverlapStrategy of
        automatic ->
            % Automatically determine optimal overlap
            auto_overlap_communication(ComputeFun, CommunicationOps, Tensor);
        manual ->
            % Manual overlap control
            manual_overlap_communication(ComputeFun, CommunicationOps, Tensor, Options)
    end.

pipelined_communication(TensorList, CommunicationOp, PipelineStages) ->
    pipelined_communication(TensorList, CommunicationOp, PipelineStages, #{}).

pipelined_communication(TensorList, CommunicationOp, PipelineStages, Options) ->
    % Pipelined communication for better bandwidth utilization
    ChunkSize = length(TensorList) div PipelineStages,
    
    % Create pipeline stages
    PipelineChunks = create_pipeline_chunks(TensorList, ChunkSize),
    
    % Execute pipelined communication
    execute_pipelined_communication(PipelineChunks, CommunicationOp, Options).

batched_communication(CommunicationOps, MaxBatchSize) ->
    batched_communication(CommunicationOps, MaxBatchSize, #{}).

batched_communication(CommunicationOps, MaxBatchSize, Options) ->
    % Batch multiple communication operations
    BatchingStrategy = maps:get(strategy, Options, size_based),
    
    % Group operations into batches
    Batches = create_communication_batches(CommunicationOps, MaxBatchSize, BatchingStrategy),
    
    % Execute batched operations
    lists:map(fun(Batch) ->
        execute_communication_batch(Batch)
    end, Batches).

%% Fault tolerance
fault_tolerant_all_reduce(Tensor, ReduceOp, FaultConfig) ->
    fault_tolerant_all_reduce(Tensor, ReduceOp, FaultConfig, #{}).

fault_tolerant_all_reduce(Tensor, ReduceOp, FaultConfig, Options) ->
    % Fault-tolerant all-reduce implementation
    ReplicationFactor = maps:get(replication_factor, FaultConfig, 2),
    MaxRetries = maps:get(max_retries, FaultConfig, 3),
    
    % Execute with fault tolerance
    execute_with_fault_tolerance(fun() ->
        all_reduce(Tensor, ReduceOp, Options)
    end, ReplicationFactor, MaxRetries).

checkpoint_communication(CommState, CheckpointPath) ->
    % Checkpoint communication state for fault recovery
    CheckpointData = #{
        communication_state => CommState,
        timestamp => erlang:system_time(microsecond),
        version => "1.0"
    },
    
    mlx_io:save_model_data(CheckpointData, CheckpointPath).

recover_communication(CheckpointPath, RecoveryOptions) ->
    % Recover communication state from checkpoint
    case mlx_io:load_model_data(CheckpointPath) of
        {ok, CheckpointData} ->
            CommState = maps:get(communication_state, CheckpointData),
            {ok, CommState};
        {error, Reason} ->
            {error, {recovery_failed, Reason}}
    end.

%% Performance optimization
adaptive_communication(Tensor, CommunicationHistory) ->
    adaptive_communication(Tensor, CommunicationHistory, #{}).

adaptive_communication(Tensor, CommunicationHistory, Options) ->
    % Adaptively choose communication algorithm based on history
    {ok, TensorSize} = mlx:size(Tensor),
    NetworkBandwidth = estimate_network_bandwidth(CommunicationHistory),
    Latency = estimate_network_latency(CommunicationHistory),
    
    % Choose optimal algorithm
    Algorithm = choose_adaptive_algorithm(TensorSize, NetworkBandwidth, Latency),
    
    % Execute with chosen algorithm
    all_reduce(Tensor, sum, maps:put(algorithm, Algorithm, Options)).

communication_profiling(CommunicationOp, ProfilingOptions) ->
    % Profile communication operation
    EnableDetailedProfiling = maps:get(detailed, ProfilingOptions, false),
    
    % Start profiling
    StartTime = erlang:system_time(microsecond),
    
    % Execute operation
    Result = CommunicationOp(),
    
    % End profiling
    EndTime = erlang:system_time(microsecond),
    
    % Collect profiling data
    ProfilingData = #{
        execution_time => EndTime - StartTime,
        result => Result,
        detailed_metrics => case EnableDetailedProfiling of
            true -> collect_detailed_metrics();
            false -> #{}
        end
    },
    
    {ok, ProfilingData}.

optimize_communication_plan(CommunicationOps, NetworkTopology) ->
    % Optimize communication plan based on network topology
    % Analyze dependencies between operations
    Dependencies = analyze_communication_dependencies(CommunicationOps),
    
    % Create optimal schedule
    OptimalSchedule = create_optimal_schedule(Dependencies, NetworkTopology),
    
    {ok, OptimalSchedule}.

%% Heterogeneous training
heterogeneous_all_reduce(Tensor, ReduceOp, DeviceConfig) ->
    heterogeneous_all_reduce(Tensor, ReduceOp, DeviceConfig, #{}).

heterogeneous_all_reduce(Tensor, ReduceOp, DeviceConfig, Options) ->
    % All-reduce for heterogeneous devices (CPU/GPU/TPU mix)
    DeviceGroups = group_by_device_type(DeviceConfig),
    
    % Perform device-specific optimizations
    lists:foldl(fun({DeviceType, Devices}, CurrentTensor) ->
        DeviceOptions = maps:put(device_type, DeviceType, Options),
        {ok, ReducedTensor} = device_specific_all_reduce(CurrentTensor, ReduceOp, Devices, DeviceOptions),
        ReducedTensor
    end, Tensor, DeviceGroups).

device_aware_communication(Tensor, CommunicationOp, DeviceTopology) ->
    device_aware_communication(Tensor, CommunicationOp, DeviceTopology, #{}).

device_aware_communication(Tensor, CommunicationOp, DeviceTopology, Options) ->
    % Communication aware of device capabilities and interconnects
    OptimalPath = find_optimal_communication_path(DeviceTopology),
    
    % Execute communication along optimal path
    execute_along_path(Tensor, CommunicationOp, OptimalPath, Options).

%% Communication backends
init_nccl_backend(NCCLConfig) ->
    % Initialize NCCL backend
    CommId = mlx_nif:nccl_get_unique_id(),
    mlx_nif:nccl_init_rank(maps:get(rank, NCCLConfig), 
                          maps:get(world_size, NCCLConfig), 
                          CommId).

init_gloo_backend(GlooConfig) ->
    % Initialize Gloo backend
    mlx_nif:gloo_init(GlooConfig).

init_mpi_backend(MPIConfig) ->
    % Initialize MPI backend
    mlx_nif:mpi_init(MPIConfig).

create_communication_group(Ranks, Backend) ->
    % Create communication group
    GroupId = make_ref(),
    Group = #comm_group{
        id = GroupId,
        ranks = Ranks,
        backend = Backend
    },
    put({comm_group, GroupId}, Group),
    {ok, GroupId}.

destroy_communication_group(GroupId) ->
    % Destroy communication group
    erase({comm_group, GroupId}),
    ok.

%% Distributed optimizers
distributed_sgd(Gradients, LearningRate, Momentum) ->
    % Distributed SGD optimizer
    % All-reduce gradients
    {ok, ReducedGradients} = all_reduce(Gradients, sum),
    
    % Scale by world size
    WorldSize = get_world_size(),
    {ok, ScaledGradients} = mlx:divide(ReducedGradients, mlx:array(WorldSize)),
    
    % Apply momentum
    apply_momentum_update(ScaledGradients, LearningRate, Momentum).

distributed_adam(Gradients, LearningRate, AdamConfig) ->
    % Distributed Adam optimizer
    Beta1 = maps:get(beta1, AdamConfig, 0.9),
    Beta2 = maps:get(beta2, AdamConfig, 0.999),
    Epsilon = maps:get(epsilon, AdamConfig, 0.00000001),
    
    % All-reduce gradients
    {ok, ReducedGradients} = all_reduce(Gradients, sum),
    
    % Scale by world size
    WorldSize = get_world_size(),
    {ok, ScaledGradients} = mlx:divide(ReducedGradients, mlx:array(WorldSize)),
    
    % Apply Adam update
    apply_adam_update(ScaledGradients, LearningRate, Beta1, Beta2, Epsilon).

distributed_adamw(Gradients, LearningRate, AdamWConfig) ->
    % Distributed AdamW optimizer
    WeightDecay = maps:get(weight_decay, AdamWConfig, 0.01),
    
    % First apply weight decay, then distributed Adam
    {ok, DecayedGradients} = apply_weight_decay(Gradients, WeightDecay),
    distributed_adam(DecayedGradients, LearningRate, AdamWConfig).

local_sgd(Gradients, LearningRate, Momentum, LocalSteps) ->
    % Local SGD with periodic synchronization
    CurrentStep = get_local_sgd_step(),
    
    case CurrentStep rem LocalSteps of
        0 ->
            % Synchronization step
            distributed_sgd(Gradients, LearningRate, Momentum);
        _ ->
            % Local update
            apply_momentum_update(Gradients, LearningRate, Momentum)
    end.

federated_averaging(LocalModels, AggregationWeights, FedConfig) ->
    % Federated averaging algorithm
    % Weighted average of local models
    WeightedModels = lists:zipwith(fun(Model, Weight) ->
        maps:map(fun(_Name, Param) ->
            {ok, Weighted} = mlx:multiply(Param, mlx:array(Weight)),
            Weighted
        end, Model)
    end, LocalModels, AggregationWeights),
    
    % Sum weighted models
    aggregate_models(WeightedModels).

%% Model parallelism
pipeline_parallel(Model, InputData, PipelineConfig) ->
    pipeline_parallel(Model, InputData, PipelineConfig, #{}).

pipeline_parallel(Model, InputData, PipelineConfig, Options) ->
    % Pipeline parallelism implementation
    Stages = maps:get(stages, PipelineConfig),
    MicroBatchSize = maps:get(micro_batch_size, PipelineConfig),
    
    % Split model into pipeline stages
    PipelineStages = split_model_into_stages(Model, Stages),
    
    % Execute pipeline
    execute_pipeline(PipelineStages, InputData, MicroBatchSize, Options).

tensor_parallel(Model, InputData, TensorParallelConfig) ->
    tensor_parallel(Model, InputData, TensorParallelConfig, #{}).

tensor_parallel(Model, InputData, TensorParallelConfig, Options) ->
    % Tensor parallelism implementation
    ParallelDimension = maps:get(parallel_dim, TensorParallelConfig),
    TensorParallelSize = maps:get(tp_size, TensorParallelConfig),
    
    % Partition tensors
    PartitionedModel = partition_model_tensors(Model, ParallelDimension, TensorParallelSize),
    
    % Execute with tensor parallelism
    execute_tensor_parallel(PartitionedModel, InputData, Options).

expert_parallel(Model, InputData, ExpertConfig) ->
    expert_parallel(Model, InputData, ExpertConfig, #{}).

expert_parallel(Model, InputData, ExpertConfig, Options) ->
    % Expert parallelism (Mixture of Experts)
    NumExperts = maps:get(num_experts, ExpertConfig),
    ExpertCapacity = maps:get(expert_capacity, ExpertConfig),
    
    % Route inputs to experts
    {ok, RoutingDecisions} = route_to_experts(InputData, NumExperts),
    
    % Execute expert computation
    execute_expert_parallel(Model, InputData, RoutingDecisions, ExpertCapacity, Options).

%% Data parallelism enhancements
dynamic_data_parallel(Model, DataLoader) ->
    dynamic_data_parallel(Model, DataLoader, #{}).

dynamic_data_parallel(Model, DataLoader, Options) ->
    % Dynamic data parallelism with load balancing
    LoadBalancingStrategy = maps:get(load_balancing, Options, round_robin),
    
    % Monitor worker performance
    WorkerStats = monitor_worker_performance(),
    
    % Dynamically adjust data distribution
    AdjustedDataLoader = adjust_data_distribution(DataLoader, WorkerStats, LoadBalancingStrategy),
    
    {ok, AdjustedDataLoader}.

gradient_compression_ddp(Model, CompressionConfig, DDPConfig) ->
    % Data parallel with gradient compression
    CompressionRatio = maps:get(compression_ratio, CompressionConfig, 0.01),
    CompressionType = maps:get(compression_type, CompressionConfig, topk),
    
    % Wrap model with compression
    CompressedModel = wrap_model_with_compression(Model, CompressionType, CompressionRatio),
    
    {ok, CompressedModel}.

zero_redundancy_optimizer(Optimizer, OptimizerConfig, ZeROConfig) ->
    % ZeRO (Zero Redundancy Optimizer) implementation
    Stage = maps:get(stage, ZeROConfig, 1),
    
    case Stage of
        1 -> zero_stage1(Optimizer, OptimizerConfig);
        2 -> zero_stage2(Optimizer, OptimizerConfig);
        3 -> zero_stage3(Optimizer, OptimizerConfig)
    end.

%% Helper functions
default_group() ->
    case get(default_comm_group) of
        undefined ->
            {ok, GroupId} = create_communication_group(get_all_ranks(), nccl),
            put(default_comm_group, GroupId),
            GroupId;
        GroupId -> GroupId
    end.

get_group_ranks(GroupId) ->
    case get({comm_group, GroupId}) of
        undefined -> [0]; % Fallback for single rank
        Group -> Group#comm_group.ranks
    end.

get_my_rank() ->
    case get(my_rank) of
        undefined -> 0;
        Rank -> Rank
    end.

get_world_size() ->
    length(get_all_ranks()).

get_all_ranks() ->
    case get(all_ranks) of
        undefined -> [0];
        Ranks -> Ranks
    end.

select_optimal_algorithm(Tensor, ReduceOp, CommGroup) ->
    % Select optimal algorithm based on tensor size and topology
    {ok, TensorSize} = mlx:size(Tensor),
    NumRanks = length(get_group_ranks(CommGroup)),
    
    case {TensorSize, NumRanks} of
        {Size, _} when Size < 1024 -> tree;
        {_, Ranks} when Ranks < 8 -> ring;
        {Size, Ranks} when Size > 1024*1024, Ranks >= 8 -> hierarchical;
        _ -> ring
    end.

chunk_tensor_for_ring(Tensor, NumChunks) ->
    % Chunk tensor for ring algorithm
    mlx:split(Tensor, NumChunks, 0).

ring_reduce_scatter_phase(Chunks, ReduceOp, Ranks, MyRank) ->
    % Ring reduce-scatter phase implementation
    {ok, Chunks}. % Simplified

ring_all_gather_phase(Chunks, Ranks, MyRank) ->
    % Ring all-gather phase implementation
    {ok, Chunks}. % Simplified

build_communication_tree(CommGroup, TreeTopology) ->
    % Build communication tree
    #{root => 0, children => []}. % Simplified

execute_tree_all_reduce(Tensor, ReduceOp, CommTree) ->
    % Execute tree all-reduce
    {ok, Tensor}. % Simplified

is_power_of_two(N) ->
    (N > 0) andalso ((N band (N - 1)) =:= 0).

execute_butterfly_phases(Tensor, ReduceOp, MyRank, LogRanks) ->
    % Execute butterfly all-reduce phases
    {ok, Tensor}. % Simplified

get_node_groups(CommGroup) ->
    % Get node groups for hierarchical communication
    []. % Simplified

intra_node_all_reduce(Tensor, ReduceOp, Options) ->
    % Intra-node all-reduce
    {ok, Tensor}. % Simplified

inter_node_all_reduce(Tensor, ReduceOp, NodeGroups, Options) ->
    % Inter-node all-reduce
    {ok, Tensor}. % Simplified

intra_node_broadcast(Tensor, Options) ->
    % Intra-node broadcast
    {ok, Tensor}. % Simplified

torus_reduce_along_dimension(Tensor, ReduceOp, Dim, TorusTopology) ->
    % Torus reduce along dimension
    {ok, Tensor}. % Simplified

compress_tensor(Tensor, CompressionType, CompressionRatio) ->
    % Compress tensor for communication
    {ok, Tensor, #{}}. % Simplified

decompress_tensor(CompressedTensor, CompressionMeta) ->
    % Decompress tensor
    {ok, CompressedTensor}. % Simplified

quantize_for_communication(Tensor, QuantBits, ScaleType) ->
    % Quantize tensor for communication
    {ok, Tensor, #{}}. % Simplified

dequantize_from_communication(QuantizedTensor, QuantMeta) ->
    % Dequantize tensor
    {ok, QuantizedTensor}. % Simplified

extract_sparse_components(SparseTensor, SparsityThreshold) ->
    % Extract sparse tensor components
    {ok, [], []}. % Simplified

merge_sparse_tensors(AllIndices, AllValues, ReduceOp) ->
    % Merge sparse tensors
    {ok, []}. % Simplified

auto_overlap_communication(ComputeFun, CommunicationOps, Tensor) ->
    % Automatic overlap optimization
    {ok, ComputeFun(Tensor)}. % Simplified

manual_overlap_communication(ComputeFun, CommunicationOps, Tensor, Options) ->
    % Manual overlap control
    {ok, ComputeFun(Tensor)}. % Simplified

create_pipeline_chunks(TensorList, ChunkSize) ->
    % Create pipeline chunks
    []. % Simplified

execute_pipelined_communication(PipelineChunks, CommunicationOp, Options) ->
    % Execute pipelined communication
    {ok, []}. % Simplified

create_communication_batches(CommunicationOps, MaxBatchSize, BatchingStrategy) ->
    % Create communication batches
    []. % Simplified

execute_communication_batch(Batch) ->
    % Execute communication batch
    ok. % Simplified

execute_with_fault_tolerance(Operation, ReplicationFactor, MaxRetries) ->
    % Execute with fault tolerance
    Operation(). % Simplified

estimate_network_bandwidth(CommunicationHistory) ->
    % Estimate network bandwidth
    1000000. % Simplified

estimate_network_latency(CommunicationHistory) ->
    % Estimate network latency
    0.001. % Simplified

choose_adaptive_algorithm(TensorSize, NetworkBandwidth, Latency) ->
    % Choose adaptive algorithm
    ring. % Simplified

collect_detailed_metrics() ->
    % Collect detailed profiling metrics
    #{}. % Simplified

analyze_communication_dependencies(CommunicationOps) ->
    % Analyze dependencies
    #{}. % Simplified

create_optimal_schedule(Dependencies, NetworkTopology) ->
    % Create optimal schedule
    []. % Simplified

group_by_device_type(DeviceConfig) ->
    % Group devices by type
    []. % Simplified

device_specific_all_reduce(Tensor, ReduceOp, Devices, DeviceOptions) ->
    % Device-specific all-reduce
    {ok, Tensor}. % Simplified

find_optimal_communication_path(DeviceTopology) ->
    % Find optimal communication path
    []. % Simplified

execute_along_path(Tensor, CommunicationOp, OptimalPath, Options) ->
    % Execute along path
    {ok, Tensor}. % Simplified

apply_momentum_update(Gradients, LearningRate, Momentum) ->
    % Apply momentum update
    {ok, Gradients}. % Simplified

apply_adam_update(Gradients, LearningRate, Beta1, Beta2, Epsilon) ->
    % Apply Adam update
    {ok, Gradients}. % Simplified

apply_weight_decay(Gradients, WeightDecay) ->
    % Apply weight decay
    {ok, Gradients}. % Simplified

get_local_sgd_step() ->
    case get(local_sgd_step) of
        undefined -> 0;
        Step -> Step
    end.

aggregate_models(WeightedModels) ->
    % Aggregate weighted models
    {}. % Simplified

split_model_into_stages(Model, Stages) ->
    % Split model into pipeline stages
    []. % Simplified

execute_pipeline(PipelineStages, InputData, MicroBatchSize, Options) ->
    % Execute pipeline
    {ok, []}. % Simplified

partition_model_tensors(Model, ParallelDimension, TensorParallelSize) ->
    % Partition model tensors
    Model. % Simplified

execute_tensor_parallel(PartitionedModel, InputData, Options) ->
    % Execute tensor parallel
    {ok, []}. % Simplified

route_to_experts(InputData, NumExperts) ->
    % Route inputs to experts
    {ok, []}. % Simplified

execute_expert_parallel(Model, InputData, RoutingDecisions, ExpertCapacity, Options) ->
    % Execute expert parallel
    {ok, []}. % Simplified

monitor_worker_performance() ->
    % Monitor worker performance
    #{}. % Simplified

adjust_data_distribution(DataLoader, WorkerStats, LoadBalancingStrategy) ->
    % Adjust data distribution
    DataLoader. % Simplified

wrap_model_with_compression(Model, CompressionType, CompressionRatio) ->
    % Wrap model with compression
    Model. % Simplified

zero_stage1(Optimizer, OptimizerConfig) ->
    % ZeRO Stage 1
    {ok, Optimizer}. % Simplified

zero_stage2(Optimizer, OptimizerConfig) ->
    % ZeRO Stage 2
    {ok, Optimizer}. % Simplified

zero_stage3(Optimizer, OptimizerConfig) ->
    % ZeRO Stage 3
    {ok, Optimizer}. % Simplified

% Additional NCCL/backend implementations would be stubs calling actual NIF functions
nccl_all_reduce(Tensor, ReduceOp, Options) -> {ok, Tensor}.
nccl_all_gather(Tensor, CommGroup, Options) -> {ok, [Tensor]}.
nccl_reduce_scatter(Tensor, ReduceOp, CommGroup, Options) -> {ok, Tensor}.
nccl_broadcast(Tensor, Root, CommGroup, Options) -> {ok, Tensor}.

ring_all_gather(Tensor, CommGroup, Options) -> {ok, [Tensor]}.
tree_all_gather(Tensor, CommGroup, Options) -> {ok, [Tensor]}.
ring_reduce_scatter(Tensor, ReduceOp, CommGroup, Options) -> {ok, Tensor}.
tree_reduce_scatter(Tensor, ReduceOp, CommGroup, Options) -> {ok, Tensor}.
tree_broadcast(Tensor, Root, CommGroup, Options) -> {ok, Tensor}.
linear_broadcast(Tensor, Root, CommGroup, Options) -> {ok, Tensor}.