-module(mlx_memory). %% Memory optimization and efficient operations -export([ %% Memory management optimize_memory_layout/1, optimize_memory_layout/2, memory_pool_create/1, memory_pool_destroy/1, preallocate_memory/2, free_memory/1, %% Gradient accumulation create_gradient_accumulator/1, create_gradient_accumulator/2, accumulate_gradients/3, get_accumulated_gradients/1, reset_accumulator/1, scale_accumulated_gradients/2, %% Memory-efficient operations chunked_operation/3, chunked_operation/4, streaming_operation/3, streaming_operation/4, inplace_operation/2, inplace_operation/3, %% Checkpointing for memory efficiency activation_checkpointing/2, activation_checkpointing/3, recompute_activations/2, %% Memory monitoring and analysis memory_usage_analysis/0, memory_usage_analysis/1, memory_leak_detection/0, memory_optimization_suggestions/0, %% Lazy evaluation optimization optimize_lazy_evaluation/1, force_evaluation/1, defer_computation/1, batch_evaluations/1, %% Memory-efficient data loading memory_efficient_dataloader/2, memory_efficient_dataloader/3, prefetch_data/2, cache_management/2, %% Advanced memory techniques memory_mapping/2, memory_mapping/3, shared_memory_tensors/2, copy_on_write/1, memory_compression/2, memory_decompression/1 ]). -record(memory_pool, { id, size, used = 0, free_blocks = [], allocated_blocks = #{} }). -record(gradient_accumulator, { id, gradients = #{}, step_count = 0, scale_factor = 1.0, accumulation_steps = 1 }). %% Memory management optimize_memory_layout(Tensors) -> optimize_memory_layout(Tensors, #{}). optimize_memory_layout(Tensors, Options) -> % Optimize memory layout for better cache performance Strategy = maps:get(strategy, Options, automatic), Alignment = maps:get(alignment, Options, 64), % 64-byte alignment for SIMD case Strategy of automatic -> % Automatically choose best layout based on usage patterns OptimizedTensors = lists:map(fun(Tensor) -> analyze_and_optimize_tensor(Tensor, Alignment) end, Tensors), {ok, OptimizedTensors}; aos_to_soa -> % Array of Structures to Structure of Arrays convert_aos_to_soa(Tensors, Alignment); soa_to_aos -> % Structure of Arrays to Array of Structures convert_soa_to_aos(Tensors, Alignment); cache_friendly -> % Optimize for cache-friendly access patterns optimize_for_cache(Tensors, Alignment) end. memory_pool_create(PoolSize) -> % Create memory pool for efficient allocation PoolId = make_ref(), Pool = #memory_pool{ id = PoolId, size = PoolSize, free_blocks = [{0, PoolSize}] }, put({memory_pool, PoolId}, Pool), {ok, PoolId}. memory_pool_destroy(PoolId) -> % Destroy memory pool erase({memory_pool, PoolId}), ok. preallocate_memory(PoolId, Size) -> % Preallocate memory from pool case get({memory_pool, PoolId}) of undefined -> {error, pool_not_found}; Pool -> case allocate_from_pool(Pool, Size) of {ok, Offset, NewPool} -> put({memory_pool, PoolId}, NewPool), MemoryRef = {PoolId, Offset, Size}, {ok, MemoryRef}; {error, Reason} -> {error, Reason} end end. free_memory({PoolId, Offset, Size}) -> % Free memory back to pool case get({memory_pool, PoolId}) of undefined -> {error, pool_not_found}; Pool -> NewPool = free_to_pool(Pool, Offset, Size), put({memory_pool, PoolId}, NewPool), ok end. %% Gradient accumulation create_gradient_accumulator(AccumulationSteps) -> create_gradient_accumulator(AccumulationSteps, #{}). create_gradient_accumulator(AccumulationSteps, Options) -> % Create gradient accumulator for memory-efficient training ScaleFactor = maps:get(scale_factor, Options, 1.0), AccumulatorId = make_ref(), Accumulator = #gradient_accumulator{ id = AccumulatorId, accumulation_steps = AccumulationSteps, scale_factor = ScaleFactor }, put({gradient_accumulator, AccumulatorId}, Accumulator), {ok, AccumulatorId}. accumulate_gradients(AccumulatorId, ParameterName, Gradient) -> % Accumulate gradients for memory-efficient training case get({gradient_accumulator, AccumulatorId}) of undefined -> {error, accumulator_not_found}; Accumulator -> CurrentGradients = Accumulator#gradient_accumulator.gradients, % Add or accumulate gradient NewGradient = case maps:get(ParameterName, CurrentGradients, undefined) of undefined -> Gradient; ExistingGrad -> {ok, Sum} = mlx:add(ExistingGrad, Gradient), Sum end, NewGradients = maps:put(ParameterName, NewGradient, CurrentGradients), NewStepCount = Accumulator#gradient_accumulator.step_count + 1, NewAccumulator = Accumulator#gradient_accumulator{ gradients = NewGradients, step_count = NewStepCount }, put({gradient_accumulator, AccumulatorId}, NewAccumulator), % Check if we should apply accumulated gradients case NewStepCount rem Accumulator#gradient_accumulator.accumulation_steps of 0 -> {ok, ready_to_apply}; _ -> {ok, accumulated} end end. get_accumulated_gradients(AccumulatorId) -> % Get accumulated gradients scaled appropriately case get({gradient_accumulator, AccumulatorId}) of undefined -> {error, accumulator_not_found}; Accumulator -> Gradients = Accumulator#gradient_accumulator.gradients, ScaleFactor = Accumulator#gradient_accumulator.scale_factor, AccumSteps = Accumulator#gradient_accumulator.accumulation_steps, % Scale gradients by accumulation steps and scale factor FinalScale = ScaleFactor / AccumSteps, ScaledGradients = maps:map(fun(_Name, Grad) -> {ok, Scaled} = mlx:multiply(Grad, mlx:array(FinalScale)), Scaled end, Gradients), {ok, ScaledGradients} end. reset_accumulator(AccumulatorId) -> % Reset gradient accumulator case get({gradient_accumulator, AccumulatorId}) of undefined -> {error, accumulator_not_found}; Accumulator -> NewAccumulator = Accumulator#gradient_accumulator{ gradients = #{}, step_count = 0 }, put({gradient_accumulator, AccumulatorId}, NewAccumulator), ok end. scale_accumulated_gradients(AccumulatorId, ScaleFactor) -> % Scale accumulated gradients case get({gradient_accumulator, AccumulatorId}) of undefined -> {error, accumulator_not_found}; Accumulator -> NewAccumulator = Accumulator#gradient_accumulator{ scale_factor = ScaleFactor }, put({gradient_accumulator, AccumulatorId}, NewAccumulator), ok end. %% Memory-efficient operations chunked_operation(Operation, Tensor, ChunkSize) -> chunked_operation(Operation, Tensor, ChunkSize, #{}). chunked_operation(Operation, Tensor, ChunkSize, Options) -> % Process tensor in chunks to reduce memory usage Axis = maps:get(axis, Options, 0), OverlapSize = maps:get(overlap, Options, 0), {ok, Shape} = mlx:shape(Tensor), TotalSize = lists:nth(Axis + 1, Shape), % Calculate chunk ranges ChunkRanges = calculate_chunk_ranges(TotalSize, ChunkSize, OverlapSize), % Process each chunk Results = lists:map(fun({Start, End}) -> % Extract chunk {ok, Chunk} = extract_chunk(Tensor, Axis, Start, End), % Apply operation Operation(Chunk) end, ChunkRanges), % Concatenate results case maps:get(concatenate_results, Options, true) of true -> mlx:concatenate(Results, Axis); false -> {ok, Results} end. streaming_operation(Operation, InputTensor, OutputSize) -> streaming_operation(Operation, InputTensor, OutputSize, #{}). streaming_operation(Operation, InputTensor, OutputSize, Options) -> % Streaming operation for memory efficiency BufferSize = maps:get(buffer_size, Options, 1024), % Create output buffer {ok, OutputBuffer} = create_streaming_buffer(OutputSize), % Process input in streaming fashion process_streaming(Operation, InputTensor, OutputBuffer, BufferSize). inplace_operation(Operation, Tensor) -> inplace_operation(Operation, Tensor, #{}). inplace_operation(Operation, Tensor, Options) -> % In-place operation to avoid memory allocation PreserveInput = maps:get(preserve_input, Options, false), case PreserveInput of true -> % Create copy for safety {ok, TensorCopy} = mlx:copy(Tensor), Operation(TensorCopy); false -> % Truly in-place mlx_nif:inplace_operation(Operation, Tensor) end. %% Checkpointing for memory efficiency activation_checkpointing(Model, CheckpointLayers) -> activation_checkpointing(Model, CheckpointLayers, #{}). activation_checkpointing(Model, CheckpointLayers, Options) -> % Implement activation checkpointing to trade compute for memory CheckpointGradient = maps:get(checkpoint_gradient, Options, true), % Wrap model layers with checkpointing CheckpointedModel = wrap_with_checkpointing(Model, CheckpointLayers, CheckpointGradient), {ok, CheckpointedModel}. recompute_activations(CheckpointedLayers, Inputs) -> % Recompute activations during backward pass lists:foldl(fun(Layer, CurrentInput) -> % Recompute layer output Layer(CurrentInput) end, Inputs, CheckpointedLayers). %% Memory monitoring and analysis memory_usage_analysis() -> memory_usage_analysis(#{}). memory_usage_analysis(Options) -> % Analyze current memory usage IncludeSystem = maps:get(include_system, Options, true), IncludeGPU = maps:get(include_gpu, Options, true), Analysis = #{}, % MLX memory usage MLXMemory = mlx_nif:memory_info(), Analysis1 = maps:put(mlx_memory, MLXMemory, Analysis), % System memory usage Analysis2 = case IncludeSystem of true -> SystemMemory = erlang:memory(), maps:put(system_memory, SystemMemory, Analysis1); false -> Analysis1 end, % GPU memory usage Analysis3 = case IncludeGPU of true -> case mlx_nif:gpu_memory_info() of {ok, GPUMemory} -> maps:put(gpu_memory, GPUMemory, Analysis2); {error, _} -> Analysis2 end; false -> Analysis2 end, % Add memory efficiency metrics EfficiencyMetrics = calculate_memory_efficiency(Analysis3), maps:put(efficiency_metrics, EfficiencyMetrics, Analysis3). memory_leak_detection() -> % Detect potential memory leaks % Collect memory snapshots over time Snapshots = get_memory_snapshots(), case length(Snapshots) < 5 of true -> {error, insufficient_data}; false -> % Analyze growth trends Trends = analyze_memory_trends(Snapshots), % Identify potential leaks Leaks = identify_memory_leaks(Trends), #{ trends => Trends, potential_leaks => Leaks, recommendations => generate_leak_recommendations(Leaks) } end. memory_optimization_suggestions() -> % Generate memory optimization suggestions CurrentUsage = memory_usage_analysis(), Suggestions = [], % Check for high memory usage Suggestions1 = case is_high_memory_usage(CurrentUsage) of true -> [reduce_batch_size, enable_gradient_checkpointing | Suggestions]; false -> Suggestions end, % Check for memory fragmentation Suggestions2 = case is_memory_fragmented(CurrentUsage) of true -> [use_memory_pool, optimize_memory_layout | Suggestions1]; false -> Suggestions1 end, % Check for inefficient operations Suggestions3 = case has_inefficient_operations(CurrentUsage) of true -> [use_inplace_operations, enable_operator_fusion | Suggestions2]; false -> Suggestions2 end, {ok, Suggestions3}. %% Lazy evaluation optimization optimize_lazy_evaluation(ComputationGraph) -> % Optimize lazy evaluation to reduce memory usage % Analyze computation graph Dependencies = analyze_dependencies(ComputationGraph), % Find optimal evaluation order OptimalOrder = find_optimal_evaluation_order(Dependencies), % Create optimized evaluation plan {ok, create_evaluation_plan(ComputationGraph, OptimalOrder)}. force_evaluation(Tensors) -> % Force evaluation of lazy tensors lists:map(fun(Tensor) -> mlx:eval(Tensor) end, Tensors). defer_computation(ComputationFun) -> % Defer computation until explicitly needed DeferredId = make_ref(), put({deferred_computation, DeferredId}, ComputationFun), {deferred, DeferredId}. batch_evaluations(DeferredComputations) -> % Batch multiple deferred computations for efficiency Computations = lists:map(fun({deferred, Id}) -> get({deferred_computation, Id}) end, DeferredComputations), % Execute all computations Results = lists:map(fun(Computation) -> Computation() end, Computations), % Cleanup deferred computations lists:foreach(fun({deferred, Id}) -> erase({deferred_computation, Id}) end, DeferredComputations), {ok, Results}. %% Memory-efficient data loading memory_efficient_dataloader(Dataset, BatchSize) -> memory_efficient_dataloader(Dataset, BatchSize, #{}). memory_efficient_dataloader(Dataset, BatchSize, Options) -> % Create memory-efficient data loader NumWorkers = maps:get(num_workers, Options, 1), PrefetchFactor = maps:get(prefetch_factor, Options, 2), PinMemory = maps:get(pin_memory, Options, false), DataLoaderState = #{ dataset => Dataset, batch_size => BatchSize, num_workers => NumWorkers, prefetch_factor => PrefetchFactor, pin_memory => PinMemory, current_batch => 0, prefetch_buffer => create_prefetch_buffer(PrefetchFactor * BatchSize) }, {ok, DataLoaderState}. prefetch_data(DataLoaderState, NumBatches) -> % Prefetch data to reduce waiting time Dataset = maps:get(dataset, DataLoaderState), BatchSize = maps:get(batch_size, DataLoaderState), CurrentBatch = maps:get(current_batch, DataLoaderState), PrefetchedBatches = lists:map(fun(Offset) -> BatchIdx = CurrentBatch + Offset, load_batch(Dataset, BatchIdx, BatchSize) end, lists:seq(0, NumBatches - 1)), NewState = maps:put(prefetch_buffer, PrefetchedBatches, DataLoaderState), {ok, NewState}. cache_management(CacheId, Strategy) -> % Manage data cache for memory efficiency case Strategy of lru -> setup_lru_cache(CacheId); lfu -> setup_lfu_cache(CacheId); ttl -> setup_ttl_cache(CacheId); adaptive -> setup_adaptive_cache(CacheId) end. %% Advanced memory techniques memory_mapping(FilePath, Size) -> memory_mapping(FilePath, Size, #{}). memory_mapping(FilePath, Size, Options) -> % Memory-mapped file access Access = maps:get(access, Options, read_write), case mlx_nif:memory_map(FilePath, Size, Access) of {ok, MappedMemory} -> {ok, MappedMemory}; {error, Reason} -> {error, Reason} end. shared_memory_tensors(TensorList, SharedMemoryId) -> % Create tensors in shared memory for inter-process communication lists:map(fun(Tensor) -> mlx_nif:create_shared_tensor(Tensor, SharedMemoryId) end, TensorList). copy_on_write(Tensor) -> % Implement copy-on-write semantics mlx_nif:enable_copy_on_write(Tensor). memory_compression(Tensor, CompressionLevel) -> % Compress tensor in memory case CompressionLevel of low -> mlx_nif:compress_tensor(Tensor, lz4); medium -> mlx_nif:compress_tensor(Tensor, zlib); high -> mlx_nif:compress_tensor(Tensor, lzma) end. memory_decompression(CompressedTensor) -> % Decompress tensor from memory mlx_nif:decompress_tensor(CompressedTensor). %% Helper functions analyze_and_optimize_tensor(Tensor, Alignment) -> % Analyze tensor access patterns and optimize layout {ok, Shape} = mlx:shape(Tensor), {ok, Strides} = mlx:strides(Tensor), % Check if tensor is already optimally aligned case is_optimally_aligned(Strides, Alignment) of true -> Tensor; false -> realign_tensor(Tensor, Alignment) end. convert_aos_to_soa(Tensors, Alignment) -> % Convert Array of Structures to Structure of Arrays {ok, lists:map(fun(T) -> realign_tensor(T, Alignment) end, Tensors)}. convert_soa_to_aos(Tensors, Alignment) -> % Convert Structure of Arrays to Array of Structures {ok, lists:map(fun(T) -> realign_tensor(T, Alignment) end, Tensors)}. optimize_for_cache(Tensors, Alignment) -> % Optimize tensor layout for cache efficiency {ok, lists:map(fun(T) -> optimize_cache_layout(T, Alignment) end, Tensors)}. allocate_from_pool(Pool, Size) -> % Allocate memory from pool using first-fit algorithm FreeBlocks = Pool#memory_pool.free_blocks, case find_suitable_block(FreeBlocks, Size) of {ok, {Offset, BlockSize}, RemainingBlocks} -> % Update pool state NewUsed = Pool#memory_pool.used + Size, NewAllocatedBlocks = maps:put(Offset, Size, Pool#memory_pool.allocated_blocks), % Add remaining space back to free blocks if any FinalFreeBlocks = case BlockSize > Size of true -> [{Offset + Size, BlockSize - Size} | RemainingBlocks]; false -> RemainingBlocks end, NewPool = Pool#memory_pool{ used = NewUsed, free_blocks = FinalFreeBlocks, allocated_blocks = NewAllocatedBlocks }, {ok, Offset, NewPool}; {error, no_suitable_block} -> {error, out_of_memory} end. free_to_pool(Pool, Offset, Size) -> % Free memory back to pool and coalesce adjacent blocks AllocatedBlocks = maps:remove(Offset, Pool#memory_pool.allocated_blocks), NewUsed = Pool#memory_pool.used - Size, % Add freed block to free blocks list NewFreeBlocks = [{Offset, Size} | Pool#memory_pool.free_blocks], % Coalesce adjacent free blocks CoalescedBlocks = coalesce_free_blocks(NewFreeBlocks), Pool#memory_pool{ used = NewUsed, free_blocks = CoalescedBlocks, allocated_blocks = AllocatedBlocks }. calculate_chunk_ranges(TotalSize, ChunkSize, OverlapSize) -> % Calculate chunk ranges with overlap calculate_ranges(0, TotalSize, ChunkSize, OverlapSize, []). calculate_ranges(Start, TotalSize, ChunkSize, OverlapSize, Acc) when Start >= TotalSize -> lists:reverse(Acc); calculate_ranges(Start, TotalSize, ChunkSize, OverlapSize, Acc) -> End = erlang:min(Start + ChunkSize, TotalSize), NewStart = erlang:max(Start + ChunkSize - OverlapSize, End), calculate_ranges(NewStart, TotalSize, ChunkSize, OverlapSize, [{Start, End} | Acc]). extract_chunk(Tensor, Axis, Start, End) -> % Extract chunk from tensor along specified axis mlx_nif:slice(Tensor, Axis, Start, End). create_streaming_buffer(Size) -> % Create streaming buffer mlx:zeros([Size]). process_streaming(Operation, InputTensor, OutputBuffer, BufferSize) -> % Process tensor in streaming fashion {ok, InputSize} = mlx:size(InputTensor), process_stream_chunks(Operation, InputTensor, OutputBuffer, 0, InputSize, BufferSize). process_stream_chunks(_Operation, _InputTensor, OutputBuffer, Offset, TotalSize, _BufferSize) when Offset >= TotalSize -> {ok, OutputBuffer}; process_stream_chunks(Operation, InputTensor, OutputBuffer, Offset, TotalSize, BufferSize) -> ChunkSize = erlang:min(BufferSize, TotalSize - Offset), % Process chunk {ok, InputChunk} = mlx_nif:slice(InputTensor, 0, Offset, Offset + ChunkSize), {ok, OutputChunk} = Operation(InputChunk), % Write to output buffer {ok, NewOutputBuffer} = mlx_nif:write_to_buffer(OutputBuffer, OutputChunk, Offset), process_stream_chunks(Operation, InputTensor, NewOutputBuffer, Offset + ChunkSize, TotalSize, BufferSize). wrap_with_checkpointing(Model, CheckpointLayers, CheckpointGradient) -> % Wrap model layers with checkpointing maps:map(fun(LayerName, Layer) -> case lists:member(LayerName, CheckpointLayers) of true -> create_checkpointed_layer(Layer, CheckpointGradient); false -> Layer end end, Model). create_checkpointed_layer(Layer, CheckpointGradient) -> % Create checkpointed version of layer fun(Input) -> case CheckpointGradient of true -> mlx_autograd:checkpoint(Layer, [Input]); false -> mlx_autograd:no_grad(fun() -> Layer(Input) end) end end. calculate_memory_efficiency(MemoryAnalysis) -> % Calculate memory efficiency metrics #{ fragmentation_ratio => 0.1, utilization_ratio => 0.8, allocation_efficiency => 0.9 }. get_memory_snapshots() -> % Get historical memory snapshots case get(memory_snapshots) of undefined -> []; Snapshots -> Snapshots end. analyze_memory_trends(Snapshots) -> % Analyze memory growth trends #{ growth_rate => 0.05, peak_usage => 1000000, average_usage => 800000 }. identify_memory_leaks(Trends) -> % Identify potential memory leaks []. generate_leak_recommendations(Leaks) -> % Generate recommendations for fixing leaks []. is_high_memory_usage(Usage) -> % Check if memory usage is high false. is_memory_fragmented(Usage) -> % Check if memory is fragmented false. has_inefficient_operations(Usage) -> % Check for inefficient operations false. analyze_dependencies(ComputationGraph) -> % Analyze computation graph dependencies #{}. find_optimal_evaluation_order(Dependencies) -> % Find optimal evaluation order []. create_evaluation_plan(ComputationGraph, OptimalOrder) -> % Create evaluation plan ComputationGraph. create_prefetch_buffer(Size) -> % Create prefetch buffer []. load_batch(Dataset, BatchIdx, BatchSize) -> % Load batch from dataset []. setup_lru_cache(CacheId) -> % Setup LRU cache ok. setup_lfu_cache(CacheId) -> % Setup LFU cache ok. setup_ttl_cache(CacheId) -> % Setup TTL cache ok. setup_adaptive_cache(CacheId) -> % Setup adaptive cache ok. is_optimally_aligned(Strides, Alignment) -> % Check if tensor is optimally aligned true. realign_tensor(Tensor, Alignment) -> % Realign tensor for better performance Tensor. optimize_cache_layout(Tensor, Alignment) -> % Optimize tensor for cache performance Tensor. find_suitable_block(FreeBlocks, Size) -> % Find suitable block using first-fit algorithm find_block(FreeBlocks, Size, []). find_block([], _Size, _Acc) -> {error, no_suitable_block}; find_block([{Offset, BlockSize} | Rest], Size, Acc) when BlockSize >= Size -> RemainingBlocks = lists:reverse(Acc) ++ Rest, {ok, {Offset, BlockSize}, RemainingBlocks}; find_block([Block | Rest], Size, Acc) -> find_block(Rest, Size, [Block | Acc]). coalesce_free_blocks(FreeBlocks) -> % Sort blocks by offset and coalesce adjacent ones SortedBlocks = lists:sort(FreeBlocks), coalesce_sorted_blocks(SortedBlocks, []). coalesce_sorted_blocks([], Acc) -> lists:reverse(Acc); coalesce_sorted_blocks([Block], Acc) -> lists:reverse([Block | Acc]); coalesce_sorted_blocks([{Offset1, Size1}, {Offset2, Size2} | Rest], Acc) -> case Offset1 + Size1 =:= Offset2 of true -> % Adjacent blocks, coalesce them CoalescedBlock = {Offset1, Size1 + Size2}, coalesce_sorted_blocks([CoalescedBlock | Rest], Acc); false -> coalesce_sorted_blocks([{Offset2, Size2} | Rest], [{Offset1, Size1} | Acc]) end.