mlx

-module(mlx_quantization).

%% Advanced quantization for efficient inference and training
-export([
    %% Quantization schemes
    quantize_int8/2, quantize_int8/3,
    quantize_int4/2, quantize_int4/3,
    quantize_dynamic/1, quantize_dynamic/2,
    
    %% Dequantization
    dequantize/3, dequantize/4,
    
    %% Mixed precision
    enable_mixed_precision/0, disable_mixed_precision/0,
    autocast/1, autocast/2,
    
    %% Quantization-aware training
    fake_quantize/3, fake_quantize/4,
    straight_through_estimator/3,
    
    %% Calibration
    calibrate_int8/2, calibrate_int8/3,
    collect_stats/2, compute_quantization_params/2,
    
    %% Weight quantization
    quantize_weights/2, quantize_weights/3,
    quantize_activations/2, quantize_activations/3,
    
    %% Advanced quantization methods
    quantize_qdq/3, quantize_qdq/4,
    quantize_smooth/3, quantize_smooth/4,
    quantize_outlier_aware/3,
    
    %% Bit manipulation
    pack_int4/1, unpack_int4/1,
    pack_bits/2, unpack_bits/2,
    
    %% Quantized operations
    quantized_linear/4, quantized_linear/5,
    quantized_conv2d/4, quantized_conv2d/5,
    quantized_matmul/4, quantized_matmul/5,
    
    %% Sparsity-aware quantization
    sparse_quantize/3, sparse_quantize/4,
    structured_prune_quantize/4,
    
    %% Model compression
    compress_model/2, decompress_model/1,
    estimate_compression_ratio/2
]).

%% Quantization schemes
quantize_int8(Array, Scale) ->
    quantize_int8(Array, Scale, 0).

quantize_int8(Array, Scale, ZeroPoint) ->
    % Quantize to 8-bit integers
    {ok, Scaled} = mlx:divide(Array, Scale),
    {ok, Shifted} = mlx:add(Scaled, mlx:array(ZeroPoint)),
    {ok, Rounded} = mlx:round(Shifted),
    {ok, Clamped} = mlx:clip(Rounded, mlx:array(-128), mlx:array(127)),
    mlx:cast(Clamped, int8).

quantize_int4(Array, Scale) ->
    quantize_int4(Array, Scale, 0).

quantize_int4(Array, Scale, ZeroPoint) ->
    % Quantize to 4-bit integers
    {ok, Scaled} = mlx:divide(Array, Scale),
    {ok, Shifted} = mlx:add(Scaled, mlx:array(ZeroPoint)),
    {ok, Rounded} = mlx:round(Shifted),
    {ok, Clamped} = mlx:clip(Rounded, mlx:array(-8), mlx:array(7)),
    mlx:cast(Clamped, int8).

quantize_dynamic(Array) ->
    quantize_dynamic(Array, int8).

quantize_dynamic(Array, TargetType) ->
    % Dynamic quantization with automatic scale computation
    {ok, MinVal} = mlx:min(Array),
    {ok, MaxVal} = mlx:max(Array),
    
    {QMin, QMax} = case TargetType of
        int8 -> {-128, 127};
        int4 -> {-8, 7};
        uint8 -> {0, 255}
    end,
    
    % Compute scale and zero point
    {ok, Range} = mlx:subtract(MaxVal, MinVal),
    {ok, QRange} = mlx:array(QMax - QMin),
    {ok, Scale} = mlx:divide(Range, QRange),
    
    {ok, ZeroPointFloat} = mlx:subtract(mlx:array(QMin), 
                                       mlx:divide(MinVal, Scale)),
    {ok, ZeroPoint} = mlx:round(ZeroPointFloat),
    {ok, ZeroPointClamped} = mlx:clip(ZeroPoint, 
                                     mlx:array(QMin), mlx:array(QMax)),
    
    % Quantize
    case TargetType of
        int8 -> quantize_int8(Array, Scale, ZeroPointClamped);
        int4 -> quantize_int4(Array, Scale, ZeroPointClamped);
        uint8 -> quantize_uint8(Array, Scale, ZeroPointClamped)
    end.

%% Dequantization
dequantize(QuantizedArray, Scale, ZeroPoint) ->
    dequantize(QuantizedArray, Scale, ZeroPoint, float32).

dequantize(QuantizedArray, Scale, ZeroPoint, TargetType) ->
    % Dequantize back to floating point
    {ok, FloatArray} = mlx:cast(QuantizedArray, float32),
    {ok, Shifted} = mlx:subtract(FloatArray, ZeroPoint),
    {ok, Scaled} = mlx:multiply(Shifted, Scale),
    
    case TargetType of
        float32 -> {ok, Scaled};
        float16 -> mlx:cast(Scaled, float16);
        bfloat16 -> mlx:cast(Scaled, bfloat16)
    end.

%% Mixed precision
enable_mixed_precision() ->
    put(mixed_precision_enabled, true),
    put(loss_scale, 65536.0),
    ok.

disable_mixed_precision() ->
    put(mixed_precision_enabled, false),
    erase(loss_scale),
    ok.

autocast(Fun) ->
    autocast(Fun, #{}).

autocast(Fun, Options) ->
    % Automatic mixed precision casting
    case get(mixed_precision_enabled) of
        true ->
            % Save current dtypes
            OldDtype = get(default_dtype),
            put(default_dtype, float16),
            
            try
                Result = Fun(),
                % Scale loss if this is a loss computation
                case maps:get(scale_loss, Options, false) of
                    true ->
                        LossScale = get(loss_scale),
                        {ok, ScaledResult} = mlx:multiply(Result, mlx:array(LossScale)),
                        {ok, ScaledResult};
                    false ->
                        {ok, Result}
                end
            after
                put(default_dtype, OldDtype)
            end;
        _ ->
            {ok, Fun()}
    end.

%% Quantization-aware training
fake_quantize(Array, Scale, ZeroPoint) ->
    fake_quantize(Array, Scale, ZeroPoint, int8).

fake_quantize(Array, Scale, ZeroPoint, QuantType) ->
    % Fake quantization for training
    {QMin, QMax} = case QuantType of
        int8 -> {-128, 127};
        int4 -> {-8, 7};
        uint8 -> {0, 255}
    end,
    
    % Forward: quantize then dequantize
    {ok, Scaled} = mlx:divide(Array, Scale),
    {ok, Shifted} = mlx:add(Scaled, ZeroPoint),
    {ok, Quantized} = mlx:clip(mlx:round(Shifted), 
                              mlx:array(QMin), mlx:array(QMax)),
    {ok, Dequantized} = mlx:subtract(Quantized, ZeroPoint),
    mlx:multiply(Dequantized, Scale).

straight_through_estimator(Array, Scale, ZeroPoint) ->
    % Straight-through estimator for gradient flow
    case fake_quantize(Array, Scale, ZeroPoint) of
        {ok, FakeQuantized} ->
            % In backward pass, gradients flow through unchanged
            mlx_nif:straight_through(Array, FakeQuantized);
        Error -> Error
    end.

%% Calibration
calibrate_int8(Model, CalibrationData) ->
    calibrate_int8(Model, CalibrationData, #{}).

calibrate_int8(Model, CalibrationData, Options) ->
    % Calibrate quantization parameters using representative data
    NumBatches = maps:get(num_batches, Options, 100),
    Percentile = maps:get(percentile, Options, 99.99),
    
    % Collect activation statistics
    Stats = lists:foldl(fun(Batch, Acc) ->
        % Forward pass to collect stats
        mlx_autograd:no_grad(fun() ->
            _Output = Model(Batch),
            % Collect intermediate activations
            collect_activation_stats(Acc)
        end)
    end, #{}, lists:sublist(CalibrationData, NumBatches)),
    
    % Compute quantization parameters from stats
    QuantParams = maps:map(fun(_Layer, LayerStats) ->
        compute_quantization_params(LayerStats, Percentile)
    end, Stats),
    
    {ok, QuantParams}.

collect_stats(Arrays, ExistingStats) ->
    % Collect min/max statistics for quantization
    lists:foldl(fun(Array, Acc) ->
        {ok, Min} = mlx:min(Array),
        {ok, Max} = mlx:max(Array),
        
        LayerId = get_layer_id(Array),
        CurrentStats = maps:get(LayerId, Acc, #{mins => [], maxs => []}),
        
        NewStats = #{
            mins => [Min | maps:get(mins, CurrentStats, [])],
            maxs => [Max | maps:get(maxs, CurrentStats, [])]
        },
        
        maps:put(LayerId, NewStats, Acc)
    end, ExistingStats, Arrays).

compute_quantization_params(Stats, Percentile) ->
    % Compute scale and zero point from collected statistics
    #{mins := Mins, maxs := Maxs} = Stats,
    
    % Use percentile to handle outliers
    {ok, MinTensor} = mlx:stack(Mins, 0),
    {ok, MaxTensor} = mlx:stack(Maxs, 0),
    
    {ok, MinPercentile} = mlx_nif:percentile(MinTensor, (100 - Percentile) / 2),
    {ok, MaxPercentile} = mlx_nif:percentile(MaxTensor, (100 + Percentile) / 2),
    
    % Compute symmetric scale
    {ok, AbsMax} = mlx:maximum(mlx:abs(MinPercentile), mlx:abs(MaxPercentile)),
    {ok, Scale} = mlx:divide(AbsMax, mlx:array(127)),
    
    #{scale => Scale, zero_point => 0}.

%% Weight quantization
quantize_weights(Weights, Method) ->
    quantize_weights(Weights, Method, #{}).

quantize_weights(Weights, Method, Options) ->
    % Quantize model weights
    case Method of
        int8_symmetric ->
            maps:map(fun(_Name, Weight) ->
                {ok, Scale} = compute_symmetric_scale(Weight, 8),
                quantize_int8(Weight, Scale, 0)
            end, Weights);
        
        int4_symmetric ->
            maps:map(fun(_Name, Weight) ->
                {ok, Scale} = compute_symmetric_scale(Weight, 4),
                quantize_int4(Weight, Scale, 0)
            end, Weights);
        
        gptq ->
            gptq_quantize(Weights, Options);
        
        awq ->
            awq_quantize(Weights, Options)
    end.

quantize_activations(Activations, Method) ->
    quantize_activations(Activations, Method, #{}).

quantize_activations(Activations, Method, Options) ->
    % Quantize activations using calibration data
    case Method of
        int8_dynamic ->
            lists:map(fun(Act) ->
                quantize_dynamic(Act, int8)
            end, Activations);
        
        int8_static ->
            CalibParams = maps:get(calib_params, Options, #{}),
            lists:map(fun(Act) ->
                LayerId = get_layer_id(Act),
                #{scale := Scale, zero_point := ZeroPoint} = maps:get(LayerId, CalibParams),
                quantize_int8(Act, Scale, ZeroPoint)
            end, Activations)
    end.

%% Advanced quantization methods
quantize_qdq(Array, Scale, ZeroPoint) ->
    quantize_qdq(Array, Scale, ZeroPoint, int8).

quantize_qdq(Array, Scale, ZeroPoint, QuantType) ->
    % Quantize-Dequantize (QDQ) operation
    case fake_quantize(Array, Scale, ZeroPoint, QuantType) of
        {ok, Result} -> {ok, Result};
        Error -> Error
    end.

quantize_smooth(Array, Alpha, Scale) ->
    quantize_smooth(Array, Alpha, Scale, int8).

quantize_smooth(Array, Alpha, Scale, QuantType) ->
    % SmoothQuant: smooth activation outliers to weights
    {ok, SmoothedArray} = mlx:divide(Array, mlx:power(Alpha, mlx:array(0.5))),
    {ok, AdjustedScale} = mlx:multiply(Scale, mlx:power(Alpha, mlx:array(0.5))),
    quantize_int8(SmoothedArray, AdjustedScale, 0).

quantize_outlier_aware(Array, OutlierThreshold, Scale) ->
    % Outlier-aware quantization
    {ok, AbsArray} = mlx:abs(Array),
    {ok, OutlierMask} = mlx:greater(AbsArray, OutlierThreshold),
    
    % Separate outliers and normal values
    {ok, NormalValues} = mlx:where(OutlierMask, mlx:array(0.0), Array),
    {ok, OutlierValues} = mlx:where(OutlierMask, Array, mlx:array(0.0)),
    
    % Quantize normal values
    {ok, QuantizedNormal} = quantize_int8(NormalValues, Scale, 0),
    
    % Keep outliers in FP16
    {ok, OutliersFP16} = mlx:cast(OutlierValues, float16),
    
    {ok, {QuantizedNormal, OutliersFP16, OutlierMask}}.

%% Bit manipulation
pack_int4(Array) ->
    % Pack two 4-bit values into one 8-bit value
    mlx_nif:pack_int4(Array).

unpack_int4(PackedArray) ->
    % Unpack 8-bit values into two 4-bit values
    mlx_nif:unpack_int4(PackedArray).

pack_bits(Array, NumBits) ->
    % Pack values into specified bit width
    mlx_nif:pack_bits(Array, NumBits).

unpack_bits(PackedArray, NumBits) ->
    % Unpack values from specified bit width
    mlx_nif:unpack_bits(PackedArray, NumBits).

%% Quantized operations
quantized_linear(Input, QuantWeight, Scale, ZeroPoint) ->
    quantized_linear(Input, QuantWeight, Scale, ZeroPoint, undefined).

quantized_linear(Input, QuantWeight, Scale, ZeroPoint, Bias) ->
    % Quantized linear layer
    {ok, DequantWeight} = dequantize(QuantWeight, Scale, ZeroPoint),
    mlx_nn:linear(Input, DequantWeight, Bias).

quantized_conv2d(Input, QuantWeight, Scale, ZeroPoint) ->
    quantized_conv2d(Input, QuantWeight, Scale, ZeroPoint, #{}).

quantized_conv2d(Input, QuantWeight, Scale, ZeroPoint, Options) ->
    % Quantized 2D convolution
    {ok, DequantWeight} = dequantize(QuantWeight, Scale, ZeroPoint),
    Stride = maps:get(stride, Options, 1),
    Padding = maps:get(padding, Options, 0),
    Bias = maps:get(bias, Options, undefined),
    mlx_nn:conv2d(Input, DequantWeight, Bias, Stride, Padding).

quantized_matmul(A, QuantB, Scale, ZeroPoint) ->
    quantized_matmul(A, QuantB, Scale, ZeroPoint, float32).

quantized_matmul(A, QuantB, Scale, ZeroPoint, OutputType) ->
    % Quantized matrix multiplication
    {ok, DequantB} = dequantize(QuantB, Scale, ZeroPoint),
    {ok, Result} = mlx:matmul(A, DequantB),
    
    case OutputType of
        float32 -> {ok, Result};
        _ -> mlx:cast(Result, OutputType)
    end.

%% Sparsity-aware quantization
sparse_quantize(Array, Mask, Scale) ->
    sparse_quantize(Array, Mask, Scale, int8).

sparse_quantize(Array, Mask, Scale, QuantType) ->
    % Quantize only non-sparse elements
    {ok, SparseArray} = mlx:multiply(Array, Mask),
    {ok, QuantizedSparse} = case QuantType of
        int8 -> quantize_int8(SparseArray, Scale, 0);
        int4 -> quantize_int4(SparseArray, Scale, 0)
    end,
    {ok, {QuantizedSparse, Mask}}.

structured_prune_quantize(Array, PruningRatio, QuantMethod, Options) ->
    % Structured pruning followed by quantization
    
    % 1. Structured pruning (e.g., channel-wise)
    {ok, PrunedArray} = structured_prune(Array, PruningRatio, Options),
    
    % 2. Quantization of remaining weights
    case QuantMethod of
        int8 ->
            {ok, Scale} = compute_symmetric_scale(PrunedArray, 8),
            quantize_int8(PrunedArray, Scale, 0);
        int4 ->
            {ok, Scale} = compute_symmetric_scale(PrunedArray, 4),
            quantize_int4(PrunedArray, Scale, 0)
    end.

%% Model compression
compress_model(Model, CompressionConfig) ->
    % Comprehensive model compression
    #{
        quantization := QuantConfig,
        pruning := PruneConfig,
        distillation := DistillConfig
    } = CompressionConfig,
    
    % 1. Quantization
    {ok, QuantizedModel} = apply_quantization(Model, QuantConfig),
    
    % 2. Pruning
    {ok, PrunedModel} = apply_pruning(QuantizedModel, PruneConfig),
    
    % 3. Knowledge distillation (if teacher model provided)
    case maps:get(teacher_model, DistillConfig, undefined) of
        undefined -> {ok, PrunedModel};
        TeacherModel -> apply_distillation(PrunedModel, TeacherModel, DistillConfig)
    end.

decompress_model(CompressedModel) ->
    % Decompress model for inference
    #{
        quantized_weights := QuantWeights,
        quantization_params := QuantParams,
        pruning_masks := PruneMasks
    } = CompressedModel,
    
    % Dequantize weights
    DequantizedWeights = maps:map(fun(LayerName, QuantWeight) ->
        #{scale := Scale, zero_point := ZeroPoint} = maps:get(LayerName, QuantParams),
        {ok, Dequantized} = dequantize(QuantWeight, Scale, ZeroPoint),
        Dequantized
    end, QuantWeights),
    
    % Apply pruning masks
    FinalWeights = maps:map(fun(LayerName, Weight) ->
        case maps:get(LayerName, PruneMasks, undefined) of
            undefined -> Weight;
            Mask -> 
                {ok, Pruned} = mlx:multiply(Weight, Mask),
                Pruned
        end
    end, DequantizedWeights),
    
    {ok, FinalWeights}.

estimate_compression_ratio(OriginalModel, CompressedModel) ->
    % Estimate compression ratio
    OriginalSize = estimate_model_size(OriginalModel),
    CompressedSize = estimate_model_size(CompressedModel),
    CompressionRatio = OriginalSize / CompressedSize,
    
    #{
        original_size => OriginalSize,
        compressed_size => CompressedSize,
        compression_ratio => CompressionRatio,
        space_savings => (1.0 - CompressedSize / OriginalSize) * 100
    }.

%% Helper functions
quantize_uint8(Array, Scale, ZeroPoint) ->
    {ok, Scaled} = mlx:divide(Array, Scale),
    {ok, Shifted} = mlx:add(Scaled, ZeroPoint),
    {ok, Rounded} = mlx:round(Shifted),
    {ok, Clamped} = mlx:clip(Rounded, mlx:array(0), mlx:array(255)),
    mlx:cast(Clamped, uint8).

compute_symmetric_scale(Weight, NumBits) ->
    {ok, MaxVal} = mlx:max(mlx:abs(Weight)),
    QMax = (1 bsl (NumBits - 1)) - 1,
    mlx:divide(MaxVal, mlx:array(QMax)).

gptq_quantize(Weights, Options) ->
    % GPTQ quantization algorithm (simplified)
    % In practice, this would require Hessian computation and iterative quantization
    BlockSize = maps:get(block_size, Options, 128),
    maps:map(fun(_Name, Weight) ->
        {ok, Scale} = compute_symmetric_scale(Weight, 4),
        quantize_int4(Weight, Scale, 0)
    end, Weights).

awq_quantize(Weights, Options) ->
    % AWQ (Activation-aware Weight Quantization) - simplified
    ActivationStats = maps:get(activation_stats, Options, #{}),
    maps:map(fun(LayerName, Weight) ->
        SaliencyScore = maps:get(LayerName, ActivationStats, 1.0),
        AdjustedScale = compute_awq_scale(Weight, SaliencyScore),
        quantize_int4(Weight, AdjustedScale, 0)
    end, Weights).

compute_awq_scale(Weight, SaliencyScore) ->
    {ok, BaseScale} = compute_symmetric_scale(Weight, 4),
    {ok, AdjustedScale} = mlx:multiply(BaseScale, mlx:array(SaliencyScore)),
    {ok, AdjustedScale}.

collect_activation_stats(Stats) ->
    % Collect activation statistics during calibration
    % This would hook into the computation graph
    Stats.

get_layer_id(Array) ->
    % Generate layer ID for tracking
    erlang:phash2(Array).

structured_prune(Array, PruningRatio, Options) ->
    % Structured pruning implementation
    PruningType = maps:get(type, Options, channel),
    case PruningType of
        channel -> channel_prune(Array, PruningRatio);
        block -> block_prune(Array, PruningRatio, Options)
    end.

channel_prune(Array, PruningRatio) ->
    % Channel-wise pruning
    {ok, [OutChannels, InChannels | _]} = mlx:shape(Array),
    NumPruneChannels = round(OutChannels * PruningRatio),
    
    % Compute channel importance (L2 norm)
    {ok, ChannelNorms} = mlx:sqrt(mlx:sum(mlx:square(Array), [1, 2, 3])),
    {ok, _, PruneIndices} = mlx:topk(ChannelNorms, NumPruneChannels, false),
    
    % Create pruning mask
    {ok, Mask} = create_channel_mask(OutChannels, PruneIndices),
    mlx:multiply(Array, Mask).

block_prune(Array, PruningRatio, Options) ->
    BlockSize = maps:get(block_size, Options, [4, 4]),
    % Block-wise pruning implementation
    Array. % Simplified

create_channel_mask(NumChannels, PruneIndices) ->
    % Create channel pruning mask
    mlx_nif:create_channel_mask(NumChannels, PruneIndices).

apply_quantization(Model, QuantConfig) ->
    % Apply quantization to model
    {ok, Model}. % Simplified

apply_pruning(Model, PruneConfig) ->
    % Apply pruning to model
    {ok, Model}. % Simplified

apply_distillation(StudentModel, TeacherModel, DistillConfig) ->
    % Apply knowledge distillation
    {ok, StudentModel}. % Simplified

estimate_model_size(Model) ->
    % Estimate model size in bytes
    maps:fold(fun(_Name, Weight, Acc) ->
        {ok, NumElements} = mlx:size(Weight),
        {ok, Dtype} = mlx:dtype(Weight),
        BytesPerElement = case Dtype of
            float32 -> 4;
            float16 -> 2;
            int8 -> 1;
            int4 -> 0.5
        end,
        Acc + NumElements * BytesPerElement
    end, 0, Model).