-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).