mlx

-module(mlx_profiler).

%% Comprehensive performance monitoring and optimization for MLX
-export([
    %% Profiling control
    start_profiling/0, start_profiling/1,
    stop_profiling/0, get_profile_stats/0,
    reset_stats/0,
    
    %% Memory profiling
    memory_snapshot/0, memory_diff/2,
    track_memory_usage/1, memory_timeline/0,
    detect_memory_leaks/0,
    
    %% Compute profiling
    profile_computation/1, profile_computation/2,
    benchmark_operation/2, benchmark_operation/3,
    compare_implementations/2,
    
    %% GPU profiling
    gpu_utilization/0, gpu_memory_usage/0,
    gpu_kernel_stats/0, gpu_timeline/0,
    
    %% Performance optimization
    optimize_computation_graph/1, optimize_computation_graph/2,
    suggest_optimizations/1,
    auto_tune_parameters/2,
    
    %% Bottleneck analysis
    find_bottlenecks/1, analyze_performance/1,
    generate_performance_report/1,
    
    %% Real-time monitoring
    start_monitoring/0, start_monitoring/1,
    stop_monitoring/0, get_monitoring_data/0,
    
    %% Debugging utilities
    trace_execution/1, trace_execution/2,
    debug_tensor_flow/1,
    validate_numerics/1,
    
    %% Performance metrics
    throughput_analysis/2, latency_analysis/2,
    efficiency_metrics/1, scalability_analysis/2
]).

-record(profile_stats, {
    start_time,
    operations = #{},
    memory_usage = [],
    gpu_stats = #{},
    timeline = []
}).

-record(operation_stats, {
    count = 0,
    total_time = 0.0,
    min_time = infinity,
    max_time = 0.0,
    memory_allocated = 0,
    memory_peak = 0
}).

%% Profiling control
start_profiling() ->
    start_profiling(#{}).

start_profiling(Options) ->
    EnableMemory = maps:get(memory, Options, true),
    EnableGPU = maps:get(gpu, Options, true),
    EnableTimeline = maps:get(timeline, Options, true),
    SamplingInterval = maps:get(sampling_interval, Options, 100), % ms
    
    Stats = #profile_stats{
        start_time = erlang:system_time(microsecond)
    },
    
    put(profiling_enabled, true),
    put(profile_stats, Stats),
    put(profiling_options, Options),
    
    % Start background monitoring
    case EnableMemory of
        true -> start_memory_monitor(SamplingInterval);
        false -> ok
    end,
    
    case EnableGPU of
        true -> start_gpu_monitor(SamplingInterval);
        false -> ok
    end,
    
    ok.

stop_profiling() ->
    put(profiling_enabled, false),
    stop_memory_monitor(),
    stop_gpu_monitor(),
    ok.

get_profile_stats() ->
    case get(profile_stats) of
        undefined -> {error, profiling_not_started};
        Stats -> {ok, Stats}
    end.

reset_stats() ->
    case get(profiling_enabled) of
        true ->
            Stats = #profile_stats{
                start_time = erlang:system_time(microsecond)
            },
            put(profile_stats, Stats),
            ok;
        _ ->
            {error, profiling_not_enabled}
    end.

%% Memory profiling
memory_snapshot() ->
    % Take snapshot of current memory usage
    #{
        timestamp => erlang:system_time(microsecond),
        mlx_memory => mlx_nif:memory_info(),
        system_memory => erlang:memory(),
        gpu_memory => gpu_memory_usage()
    }.

memory_diff(Snapshot1, Snapshot2) ->
    % Calculate memory difference between snapshots
    MLXDiff = maps:get(mlx_memory, Snapshot2) - maps:get(mlx_memory, Snapshot1),
    SystemDiff = calculate_memory_diff(maps:get(system_memory, Snapshot2),
                                      maps:get(system_memory, Snapshot1)),
    GPUDiff = maps:get(gpu_memory, Snapshot2) - maps:get(gpu_memory, Snapshot1),
    
    #{
        mlx_memory_diff => MLXDiff,
        system_memory_diff => SystemDiff,
        gpu_memory_diff => GPUDiff,
        time_diff => maps:get(timestamp, Snapshot2) - maps:get(timestamp, Snapshot1)
    }.

track_memory_usage(Fun) ->
    % Track memory usage during function execution
    Before = memory_snapshot(),
    Result = Fun(),
    After = memory_snapshot(),
    
    Diff = memory_diff(Before, After),
    {Result, Diff}.

memory_timeline() ->
    % Get memory usage timeline
    case get(memory_timeline) of
        undefined -> [];
        Timeline -> Timeline
    end.

detect_memory_leaks() ->
    % Analyze memory timeline for potential leaks
    Timeline = memory_timeline(),
    case length(Timeline) < 10 of
        true -> {error, insufficient_data};
        false ->
            % Analyze growth trend
            MemoryValues = [maps:get(mlx_memory, Snapshot) || Snapshot <- Timeline],
            Trend = calculate_trend(MemoryValues),
            
            case Trend > 0.1 of % 10% growth threshold
                true -> {warning, potential_memory_leak, Trend};
                false -> {ok, no_leak_detected}
            end
    end.

%% Compute profiling
profile_computation(Fun) ->
    profile_computation(Fun, #{}).

profile_computation(Fun, Options) ->
    % Profile computation with detailed metrics
    EnableTrace = maps:get(trace, Options, false),
    EnableMemory = maps:get(memory, Options, true),
    
    % Setup profiling
    case EnableTrace of
        true -> mlx_nif:enable_tracing();
        false -> ok
    end,
    
    % Memory snapshot before
    MemoryBefore = case EnableMemory of
        true -> memory_snapshot();
        false -> undefined
    end,
    
    % Time execution
    StartTime = erlang:system_time(microsecond),
    Result = Fun(),
    EndTime = erlang:system_time(microsecond),
    
    % Memory snapshot after
    MemoryAfter = case EnableMemory of
        true -> memory_snapshot();
        false -> undefined
    end,
    
    % Collect profiling data
    ProfileData = #{
        execution_time => EndTime - StartTime,
        result => Result,
        memory_usage => case {MemoryBefore, MemoryAfter} of
            {undefined, undefined} -> undefined;
            _ -> memory_diff(MemoryBefore, MemoryAfter)
        end,
        trace_data => case EnableTrace of
            true -> mlx_nif:get_trace_data();
            false -> undefined
        end
    },
    
    % Cleanup
    case EnableTrace of
        true -> mlx_nif:disable_tracing();
        false -> ok
    end,
    
    {ok, ProfileData}.

benchmark_operation(Operation, NumRuns) ->
    benchmark_operation(Operation, NumRuns, #{}).

benchmark_operation(Operation, NumRuns, Options) ->
    % Benchmark operation multiple times
    WarmupRuns = maps:get(warmup, Options, erlang:max(1, NumRuns div 10)),
    
    % Warmup
    lists:foreach(fun(_) -> Operation() end, lists:seq(1, WarmupRuns)),
    
    % Actual benchmark
    Times = lists:map(fun(_) ->
        StartTime = erlang:system_time(microsecond),
        _Result = Operation(),
        EndTime = erlang:system_time(microsecond),
        EndTime - StartTime
    end, lists:seq(1, NumRuns)),
    
    % Calculate statistics
    MinTime = lists:min(Times),
    MaxTime = lists:max(Times),
    MeanTime = lists:sum(Times) / length(Times),
    MedianTime = lists:nth((length(Times) + 1) div 2, lists:sort(Times)),
    StdDev = math:sqrt(lists:sum([(T - MeanTime) * (T - MeanTime) || T <- Times]) / length(Times)),
    
    #{
        num_runs => NumRuns,
        min_time => MinTime,
        max_time => MaxTime,
        mean_time => MeanTime,
        median_time => MedianTime,
        std_dev => StdDev,
        throughput => 1000000 / MeanTime % ops per second
    }.

compare_implementations(Implementations, NumRuns) ->
    % Compare multiple implementations
    Results = maps:map(fun(Name, Implementation) ->
        io:format("Benchmarking ~s...~n", [Name]),
        benchmark_operation(Implementation, NumRuns)
    end, Implementations),
    
    % Find fastest implementation
    BestName = maps:fold(fun(Name, Stats, AccName) ->
        case AccName of
            undefined -> Name;
            _ ->
                AccStats = maps:get(AccName, Results),
                case maps:get(mean_time, Stats) < maps:get(mean_time, AccStats) of
                    true -> Name;
                    false -> AccName
                end
        end
    end, undefined, Results),
    
    #{
        results => Results,
        fastest => BestName,
        speedup_ratios => calculate_speedup_ratios(Results, BestName)
    }.

%% GPU profiling
gpu_utilization() ->
    % Get current GPU utilization
    mlx_nif:gpu_utilization().

gpu_memory_usage() ->
    % Get current GPU memory usage
    mlx_nif:gpu_memory_usage().

gpu_kernel_stats() ->
    % Get GPU kernel execution statistics
    mlx_nif:gpu_kernel_stats().

gpu_timeline() ->
    % Get GPU execution timeline
    case get(gpu_timeline) of
        undefined -> [];
        Timeline -> Timeline
    end.

%% Performance optimization
optimize_computation_graph(ComputationGraph) ->
    optimize_computation_graph(ComputationGraph, #{}).

optimize_computation_graph(ComputationGraph, Options) ->
    % Optimize computation graph for performance
    Optimizations = maps:get(optimizations, Options, [
        constant_folding,
        dead_code_elimination,
        operator_fusion,
        memory_layout_optimization
    ]),
    
    OptimizedGraph = lists:foldl(fun(Optimization, Graph) ->
        apply_optimization(Optimization, Graph)
    end, ComputationGraph, Optimizations),
    
    {ok, OptimizedGraph}.

suggest_optimizations(ProfileData) ->
    % Analyze profile data and suggest optimizations
    Suggestions = [],
    
    % Check for memory inefficiencies
    MemorySuggestions = case maps:get(memory_usage, ProfileData, undefined) of
        undefined -> [];
        MemoryData ->
            analyze_memory_patterns(MemoryData)
    end,
    
    % Check for compute inefficiencies
    ComputeSuggestions = case maps:get(trace_data, ProfileData, undefined) of
        undefined -> [];
        TraceData ->
            analyze_compute_patterns(TraceData)
    end,
    
    MemorySuggestions ++ ComputeSuggestions ++ Suggestions.

auto_tune_parameters(Model, ValidationData) ->
    % Automatically tune model parameters for optimal performance
    Parameters = [
        {batch_size, [16, 32, 64, 128, 256]},
        {learning_rate, [0.1, 0.01, 0.001, 0.0001]},
        {optimizer, [adam, sgd, rmsprop]}
    ],
    
    BestConfig = grid_search_optimization(Model, ValidationData, Parameters),
    {ok, BestConfig}.

%% Bottleneck analysis
find_bottlenecks(ProfileData) ->
    % Identify performance bottlenecks
    TraceData = maps:get(trace_data, ProfileData, []),
    MemoryData = maps:get(memory_usage, ProfileData, #{}),
    
    % Analyze execution time bottlenecks
    TimeBottlenecks = analyze_time_bottlenecks(TraceData),
    
    % Analyze memory bottlenecks
    MemoryBottlenecks = analyze_memory_bottlenecks(MemoryData),
    
    #{
        time_bottlenecks => TimeBottlenecks,
        memory_bottlenecks => MemoryBottlenecks,
        recommendations => generate_recommendations(TimeBottlenecks, MemoryBottlenecks)
    }.

analyze_performance(Model) ->
    % Comprehensive performance analysis
    {ok, ProfileData} = profile_computation(fun() ->
        % Run model inference
        {ok, TestInput} = mlx:random([1, 224, 224, 3]),
        Model(TestInput)
    end, #{trace => true, memory => true}),
    
    Bottlenecks = find_bottlenecks(ProfileData),
    Suggestions = suggest_optimizations(ProfileData),
    
    #{
        profile_data => ProfileData,
        bottlenecks => Bottlenecks,
        optimization_suggestions => Suggestions
    }.

generate_performance_report(AnalysisResults) ->
    % Generate comprehensive performance report
    Report = [
        "=== MLX Performance Analysis Report ===\n",
        format_execution_summary(AnalysisResults),
        format_bottleneck_analysis(AnalysisResults),
        format_optimization_suggestions(AnalysisResults),
        format_recommendations(AnalysisResults)
    ],
    
    ReportString = lists:flatten(Report),
    {ok, ReportString}.

%% Real-time monitoring
start_monitoring() ->
    start_monitoring(#{}).

start_monitoring(Options) ->
    % Start real-time performance monitoring
    Interval = maps:get(interval, Options, 1000), % ms
    Metrics = maps:get(metrics, Options, [memory, gpu, throughput]),
    
    MonitorPid = spawn_link(fun() -> monitoring_loop(Interval, Metrics) end),
    put(monitor_pid, MonitorPid),
    ok.

stop_monitoring() ->
    case get(monitor_pid) of
        undefined -> ok;
        Pid ->
            Pid ! stop,
            erase(monitor_pid),
            ok
    end.

get_monitoring_data() ->
    case get(monitoring_data) of
        undefined -> [];
        Data -> Data
    end.

%% Debugging utilities
trace_execution(Fun) ->
    trace_execution(Fun, #{}).

trace_execution(Fun, Options) ->
    % Trace execution with detailed information
    LogLevel = maps:get(log_level, Options, debug),
    IncludeStackTrace = maps:get(stack_trace, Options, false),
    
    mlx_nif:set_trace_level(LogLevel),
    mlx_nif:enable_tracing(),
    
    try
        Result = Fun(),
        TraceData = mlx_nif:get_trace_data(),
        
        case IncludeStackTrace of
            true ->
                StackTrace = erlang:get_stacktrace(),
                {Result, TraceData, StackTrace};
            false ->
                {Result, TraceData}
        end
    after
        mlx_nif:disable_tracing()
    end.

debug_tensor_flow(Tensors) ->
    % Debug tensor data flow
    lists:map(fun(Tensor) ->
        {ok, Shape} = mlx:shape(Tensor),
        {ok, Dtype} = mlx:dtype(Tensor),
        
        % Check for common issues
        Issues = check_tensor_issues(Tensor),
        
        #{
            shape => Shape,
            dtype => Dtype,
            issues => Issues,
            stats => compute_tensor_stats(Tensor)
        }
    end, Tensors).

validate_numerics(Tensor) ->
    % Validate tensor for numerical issues
    {ok, HasNaN} = mlx:any(mlx:isnan(Tensor)),
    {ok, HasInf} = mlx:any(mlx:isinf(Tensor)),
    {ok, Min} = mlx:min(Tensor),
    {ok, Max} = mlx:max(Tensor),
    
    Issues = [],
    Issues1 = case HasNaN of
        true -> [nan_detected | Issues];
        false -> Issues
    end,
    Issues2 = case HasInf of
        true -> [inf_detected | Issues1];
        false -> Issues1
    end,
    
    #{
        has_nan => HasNaN,
        has_inf => HasInf,
        min_value => Min,
        max_value => Max,
        issues => Issues2
    }.

%% Performance metrics
throughput_analysis(Model, DataLoader) ->
    % Analyze model throughput
    NumBatches = 100,
    StartTime = erlang:system_time(microsecond),
    
    TotalSamples = lists:foldl(fun(Batch, Acc) ->
        _Output = Model(Batch),
        {ok, BatchSize} = mlx:shape(Batch),
        Acc + element(1, BatchSize)
    end, 0, lists:sublist(DataLoader, NumBatches)),
    
    EndTime = erlang:system_time(microsecond),
    TotalTime = (EndTime - StartTime) / 1000000, % seconds
    
    #{
        samples_per_second => TotalSamples / TotalTime,
        total_samples => TotalSamples,
        total_time => TotalTime,
        batches_processed => NumBatches
    }.

latency_analysis(Model, TestInputs) ->
    % Analyze model latency
    Latencies = lists:map(fun(Input) ->
        StartTime = erlang:system_time(microsecond),
        _Output = Model(Input),
        EndTime = erlang:system_time(microsecond),
        EndTime - StartTime
    end, TestInputs),
    
    MinLatency = lists:min(Latencies),
    MaxLatency = lists:max(Latencies),
    MeanLatency = lists:sum(Latencies) / length(Latencies),
    P50 = percentile(Latencies, 50),
    P95 = percentile(Latencies, 95),
    P99 = percentile(Latencies, 99),
    
    #{
        min_latency => MinLatency,
        max_latency => MaxLatency,
        mean_latency => MeanLatency,
        p50_latency => P50,
        p95_latency => P95,
        p99_latency => P99
    }.

efficiency_metrics(Model) ->
    % Compute efficiency metrics
    #{
        flops => estimate_flops(Model),
        params => count_parameters(Model),
        memory_footprint => estimate_memory_footprint(Model),
        efficiency_score => compute_efficiency_score(Model)
    }.

scalability_analysis(Model, BatchSizes) ->
    % Analyze model scalability across batch sizes
    Results = lists:map(fun(BatchSize) ->
        {ok, TestInput} = mlx:random([BatchSize, 224, 224, 3]),
        
        {ThroughputResult, _} = track_memory_usage(fun() ->
            benchmark_operation(fun() -> Model(TestInput) end, 10)
        end),
        
        #{
            batch_size => BatchSize,
            throughput => maps:get(throughput, ThroughputResult),
            mean_time => maps:get(mean_time, ThroughputResult)
        }
    end, BatchSizes),
    
    #{
        results => Results,
        scaling_efficiency => compute_scaling_efficiency(Results)
    }.

%% Helper functions
start_memory_monitor(Interval) ->
    MonitorPid = spawn_link(fun() -> memory_monitor_loop(Interval) end),
    put(memory_monitor_pid, MonitorPid).

stop_memory_monitor() ->
    case get(memory_monitor_pid) of
        undefined -> ok;
        Pid -> Pid ! stop, erase(memory_monitor_pid)
    end.

start_gpu_monitor(Interval) ->
    MonitorPid = spawn_link(fun() -> gpu_monitor_loop(Interval) end),
    put(gpu_monitor_pid, MonitorPid).

stop_gpu_monitor() ->
    case get(gpu_monitor_pid) of
        undefined -> ok;
        Pid -> Pid ! stop, erase(gpu_monitor_pid)
    end.

memory_monitor_loop(Interval) ->
    receive
        stop -> ok
    after Interval ->
        Snapshot = memory_snapshot(),
        Timeline = case get(memory_timeline) of
            undefined -> [Snapshot];
            Existing -> [Snapshot | lists:sublist(Existing, 999)] % Keep last 1000
        end,
        put(memory_timeline, Timeline),
        memory_monitor_loop(Interval)
    end.

gpu_monitor_loop(Interval) ->
    receive
        stop -> ok
    after Interval ->
        GPUStats = #{
            timestamp => erlang:system_time(microsecond),
            utilization => gpu_utilization(),
            memory => gpu_memory_usage()
        },
        Timeline = case get(gpu_timeline) of
            undefined -> [GPUStats];
            Existing -> [GPUStats | lists:sublist(Existing, 999)]
        end,
        put(gpu_timeline, Timeline),
        gpu_monitor_loop(Interval)
    end.

monitoring_loop(Interval, Metrics) ->
    receive
        stop -> ok
    after Interval ->
        Data = collect_monitoring_metrics(Metrics),
        MonitoringData = case get(monitoring_data) of
            undefined -> [Data];
            Existing -> [Data | lists:sublist(Existing, 999)]
        end,
        put(monitoring_data, MonitoringData),
        monitoring_loop(Interval, Metrics)
    end.

collect_monitoring_metrics(Metrics) ->
    lists:foldl(fun(Metric, Acc) ->
        Value = case Metric of
            memory -> memory_snapshot();
            gpu -> #{utilization => gpu_utilization(), memory => gpu_memory_usage()};
            throughput -> get_current_throughput()
        end,
        maps:put(Metric, Value, Acc)
    end, #{timestamp => erlang:system_time(microsecond)}, Metrics).

calculate_memory_diff(Memory1, Memory2) ->
    Keys = [total, processes, system, atom, binary, ets],
    maps:from_list([{Key, proplists:get_value(Key, Memory2, 0) - 
                          proplists:get_value(Key, Memory1, 0)} || Key <- Keys]).

calculate_trend(Values) ->
    N = length(Values),
    case N < 2 of
        true -> 0.0;
        false ->
            X = lists:seq(1, N),
            SumX = lists:sum(X),
            SumY = lists:sum(Values),
            SumXY = lists:sum([Xi * Yi || {Xi, Yi} <- lists:zip(X, Values)]),
            SumX2 = lists:sum([Xi * Xi || Xi <- X]),
            
            Slope = (N * SumXY - SumX * SumY) / (N * SumX2 - SumX * SumX),
            Slope / lists:nth(1, Values) % Relative trend
    end.

apply_optimization(constant_folding, Graph) ->
    % Apply constant folding optimization
    Graph; % Simplified

apply_optimization(dead_code_elimination, Graph) ->
    % Remove dead code
    Graph; % Simplified

apply_optimization(operator_fusion, Graph) ->
    % Fuse operators
    Graph; % Simplified

apply_optimization(memory_layout_optimization, Graph) ->
    % Optimize memory layout
    Graph. % Simplified

analyze_memory_patterns(MemoryData) ->
    % Analyze memory usage patterns
    []. % Simplified

analyze_compute_patterns(TraceData) ->
    % Analyze computation patterns
    []. % Simplified

grid_search_optimization(Model, ValidationData, Parameters) ->
    % Grid search parameter optimization
    #{}. % Simplified

analyze_time_bottlenecks(TraceData) ->
    % Analyze execution time bottlenecks
    []. % Simplified

analyze_memory_bottlenecks(MemoryData) ->
    % Analyze memory bottlenecks
    []. % Simplified

generate_recommendations(TimeBottlenecks, MemoryBottlenecks) ->
    % Generate optimization recommendations
    []. % Simplified

format_execution_summary(AnalysisResults) ->
    "Execution Summary:\n". % Simplified

format_bottleneck_analysis(AnalysisResults) ->
    "Bottleneck Analysis:\n". % Simplified

format_optimization_suggestions(AnalysisResults) ->
    "Optimization Suggestions:\n". % Simplified

format_recommendations(AnalysisResults) ->
    "Recommendations:\n". % Simplified

check_tensor_issues(Tensor) ->
    []. % Simplified

compute_tensor_stats(Tensor) ->
    #{}. % Simplified

percentile(Values, P) ->
    SortedValues = lists:sort(Values),
    Index = round(P / 100 * length(SortedValues)),
    lists:nth(erlang:max(1, Index), SortedValues).

estimate_flops(Model) ->
    0. % Simplified

count_parameters(Model) ->
    0. % Simplified

estimate_memory_footprint(Model) ->
    0. % Simplified

compute_efficiency_score(Model) ->
    0.0. % Simplified

compute_scaling_efficiency(Results) ->
    1.0. % Simplified

get_current_throughput() ->
    0.0. % Simplified

calculate_speedup_ratios(Results, BestName) ->
    BestTime = maps:get(mean_time, maps:get(BestName, Results)),
    maps:map(fun(_Name, Stats) ->
        maps:get(mean_time, Stats) / BestTime
    end, Results).