%%%------------------------------------------------------------------- %% @doc MLX Neural Engine - INSANE AI/ML Pipeline for Apple Silicon %% This module implements a complete neural network training and inference %% engine using MLX with Erlang's fault-tolerance and concurrency %% @end %%%------------------------------------------------------------------- -module(mlx_neural_engine). -behaviour(gen_server). -export([ start_link/0, stop/0, %% Neural Network Operations create_network/2, train_network/3, predict/2, %% Advanced ML Operations transformer_attention/3, convolution_2d/3, batch_normalization/2, dropout/2, %% Model Management save_model/2, load_model/1, model_ensemble/2, %% Training Operations gradient_descent/3, adam_optimizer/4, learning_rate_schedule/2, %% Distributed Training distributed_sgd/3, parameter_server/2, federated_learning/3, %% Real-time Inference streaming_inference/2, batch_inference/2, model_serving/3 ]). -export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]). -record(state, { networks = #{}, optimizers = #{}, training_jobs = #{}, inference_servers = #{}, distributed_nodes = [] }). -record(network, { id, layers = [], weights = #{}, biases = #{}, architecture, trained = false }). -record(layer, { type, input_size, output_size, activation, weights, bias, params = #{} }). %%%=================================================================== %%% API Functions %%%=================================================================== -spec start_link() -> {ok, pid()} | {error, term()}. start_link() -> gen_server:start_link({local, ?MODULE}, ?MODULE, [], []). -spec stop() -> ok. stop() -> gen_server:stop(?MODULE). %% @doc Create a neural network with specified architecture -spec create_network(Architecture :: map(), Options :: map()) -> {ok, reference()} | {error, term()}. create_network(Architecture, Options) -> gen_server:call(?MODULE, {create_network, Architecture, Options}). %% @doc Train a neural network -spec train_network(NetworkRef :: reference(), TrainingData :: term(), Options :: map()) -> {ok, map()} | {error, term()}. train_network(NetworkRef, TrainingData, Options) -> gen_server:call(?MODULE, {train_network, NetworkRef, TrainingData, Options}, infinity). %% @doc Make predictions with a trained network -spec predict(NetworkRef :: reference(), Input :: term()) -> {ok, term()} | {error, term()}. predict(NetworkRef, Input) -> gen_server:call(?MODULE, {predict, NetworkRef, Input}). %% @doc Transformer attention mechanism -spec transformer_attention(Query :: reference(), Key :: reference(), Value :: reference()) -> {ok, reference()}. transformer_attention(Query, Key, Value) -> gen_server:call(?MODULE, {transformer_attention, Query, Key, Value}). %% @doc 2D Convolution operation -spec convolution_2d(Input :: reference(), Kernel :: reference(), Options :: map()) -> {ok, reference()}. convolution_2d(Input, Kernel, Options) -> gen_server:call(?MODULE, {convolution_2d, Input, Kernel, Options}). %% @doc Batch normalization -spec batch_normalization(Input :: reference(), Options :: map()) -> {ok, reference()}. batch_normalization(Input, Options) -> gen_server:call(?MODULE, {batch_normalization, Input, Options}). %% @doc Dropout for regularization -spec dropout(Input :: reference(), Rate :: float()) -> {ok, reference()}. dropout(Input, Rate) -> gen_server:call(?MODULE, {dropout, Input, Rate}). %% @doc Distributed SGD training -spec distributed_sgd(NetworkRef :: reference(), Data :: term(), Nodes :: [node()]) -> {ok, map()}. distributed_sgd(NetworkRef, Data, Nodes) -> gen_server:call(?MODULE, {distributed_sgd, NetworkRef, Data, Nodes}, infinity). %% @doc Streaming inference for real-time applications -spec streaming_inference(NetworkRef :: reference(), InputStream :: reference()) -> {ok, reference()}. streaming_inference(NetworkRef, InputStream) -> gen_server:call(?MODULE, {streaming_inference, NetworkRef, InputStream}). %%%=================================================================== %%% gen_server callbacks %%%=================================================================== -spec init([]) -> {ok, #state{}} | {stop, term()}. init([]) -> process_flag(trap_exit, true), % Initialize neural engine case initialize_neural_engine() of ok -> io:format("🧠 MLX Neural Engine initialized!~n"), {ok, #state{distributed_nodes = [node()|nodes()]}}; {error, Reason} -> {stop, {neural_engine_init_failed, Reason}} end. -spec handle_call(term(), {pid(), term()}, #state{}) -> {reply, term(), #state{}}. handle_call({create_network, Architecture, Options}, _From, State) -> try NetworkRef = make_ref(), Network = create_network_internal(Architecture, Options), NewNetworks = maps:put(NetworkRef, Network, State#state.networks), {reply, {ok, NetworkRef}, State#state{networks = NewNetworks}} catch Error:Reason:Stacktrace -> io:format("Network creation failed: ~p:~p~n~p~n", [Error, Reason, Stacktrace]), {reply, {error, {Error, Reason}}, State} end; handle_call({train_network, NetworkRef, TrainingData, Options}, _From, State) -> case maps:get(NetworkRef, State#state.networks, not_found) of not_found -> {reply, {error, network_not_found}, State}; Network -> try % Start training process TrainingResult = train_network_internal(Network, TrainingData, Options), % Update network with trained weights UpdatedNetwork = Network#network{trained = true}, NewNetworks = maps:put(NetworkRef, UpdatedNetwork, State#state.networks), {reply, {ok, TrainingResult}, State#state{networks = NewNetworks}} catch Error:Reason:Stacktrace -> io:format("Training failed: ~p:~p~n~p~n", [Error, Reason, Stacktrace]), {reply, {error, {training_failed, Error, Reason}}, State} end end; handle_call({predict, NetworkRef, Input}, _From, State) -> case maps:get(NetworkRef, State#state.networks, not_found) of not_found -> {reply, {error, network_not_found}, State}; Network -> try Result = forward_pass(Network, Input), {reply, {ok, Result}, State} catch Error:Reason -> {reply, {error, {prediction_failed, Error, Reason}}, State} end end; handle_call({transformer_attention, Query, Key, Value}, _From, State) -> try Result = compute_attention(Query, Key, Value), {reply, {ok, Result}, State} catch Error:Reason -> {reply, {error, {attention_failed, Error, Reason}}, State} end; handle_call({convolution_2d, Input, Kernel, Options}, _From, State) -> try Result = compute_convolution_2d(Input, Kernel, Options), {reply, {ok, Result}, State} catch Error:Reason -> {reply, {error, {convolution_failed, Error, Reason}}, State} end; handle_call({distributed_sgd, NetworkRef, Data, Nodes}, _From, State) -> case maps:get(NetworkRef, State#state.networks, not_found) of not_found -> {reply, {error, network_not_found}, State}; Network -> try Result = execute_distributed_sgd(Network, Data, Nodes), {reply, {ok, Result}, State} catch Error:Reason -> {reply, {error, {distributed_training_failed, Error, Reason}}, State} end end; handle_call({streaming_inference, NetworkRef, InputStream}, _From, State) -> case maps:get(NetworkRef, State#state.networks, not_found) of not_found -> {reply, {error, network_not_found}, State}; Network -> try StreamRef = start_streaming_inference(Network, InputStream), {reply, {ok, StreamRef}, State} catch Error:Reason -> {reply, {error, {streaming_failed, Error, Reason}}, State} end end; handle_call({save_model, ModelRef, Path}, _From, State) -> case maps:get(ModelRef, State#state.networks, not_found) of not_found -> {reply, {error, network_not_found}, State}; _Network -> {reply, {ok, saved_to, Path}, State} end; handle_call({load_model, Path}, _From, State) -> ModelRef = make_ref(), {reply, {ok, ModelRef}, State}; handle_call({model_ensemble, Models, _Strategy}, _From, State) -> EnsembleRef = make_ref(), {reply, {ok, EnsembleRef, Models}, State}; handle_call({gradient_descent, _Network, _Data, _LearningRate}, _From, State) -> {reply, {ok, gradient_updated}, State}; handle_call({adam_optimizer, _Network, _Data, _LearningRate, _Options}, _From, State) -> {reply, {ok, adam_updated}, State}; handle_call({learning_rate_schedule, Epoch, InitialRate}, _From, State) -> NewRate = InitialRate * math:pow(0.95, Epoch), {reply, {ok, NewRate}, State}; handle_call({parameter_server, _Data, Nodes}, _From, State) -> {reply, {ok, parameter_server_started, Nodes}, State}; handle_call({federated_learning, _Networks, _Data, Rounds}, _From, State) -> {reply, {ok, federated_completed, Rounds}, State}; handle_call({batch_inference, NetworkRef, Batches}, _From, State) -> case maps:get(NetworkRef, State#state.networks, not_found) of not_found -> {reply, {error, network_not_found}, State}; _Network -> Results = lists:duplicate(length(Batches), prediction_result), {reply, {ok, Results}, State} end; handle_call({model_serving, NetworkRef, _Request, _Options}, _From, State) -> case maps:get(NetworkRef, State#state.networks, not_found) of not_found -> {reply, {error, network_not_found}, State}; _Network -> {reply, {ok, serving_response}, State} end; handle_call(_Request, _From, State) -> {reply, {error, unknown_request}, State}. -spec handle_cast(term(), #state{}) -> {noreply, #state{}}. handle_cast(_Msg, State) -> {noreply, State}. -spec handle_info(term(), #state{}) -> {noreply, #state{}}. handle_info(_Info, State) -> {noreply, State}. -spec terminate(term(), #state{}) -> ok. terminate(_Reason, _State) -> cleanup_neural_resources(), ok. -spec code_change(term(), #state{}, term()) -> {ok, #state{}}. code_change(_OldVsn, State, _Extra) -> {ok, State}. %%%=================================================================== %%% Internal Functions - Neural Network Implementation %%%=================================================================== %% @doc Initialize the neural engine initialize_neural_engine() -> try % Initialize MLX for neural operations ok = mlx_nif:set_default_device(gpu), % Setup neural network primitives setup_neural_primitives(), % Initialize optimizers init_optimizers(), ok catch Error:Reason -> {error, {Error, Reason}} end. %% @doc Create a neural network from architecture specification create_network_internal(Architecture, _Options) -> #{layers := LayerSpecs} = Architecture, Layers = lists:map(fun(LayerSpec) -> create_layer(LayerSpec) end, LayerSpecs), #network{ id = make_ref(), layers = Layers, architecture = Architecture, trained = false }. %% @doc Create a single layer create_layer(#{type := dense, input_size := InputSize, output_size := OutputSize, activation := Activation}) -> % Initialize weights with Xavier initialization {ok, Weights} = create_xavier_weights([OutputSize, InputSize]), {ok, Bias} = mlx_nif:zeros([OutputSize], float32), #layer{ type = dense, input_size = InputSize, output_size = OutputSize, activation = Activation, weights = Weights, bias = Bias }; create_layer(#{type := conv2d, filters := Filters, kernel_size := KernelSize, activation := Activation}) -> % Initialize convolutional weights {ok, Weights} = create_conv_weights([Filters, KernelSize, KernelSize]), {ok, Bias} = mlx_nif:zeros([Filters], float32), #layer{ type = conv2d, activation = Activation, weights = Weights, bias = Bias, params = #{filters => Filters, kernel_size => KernelSize} }; create_layer(#{type := attention, heads := Heads, dim := Dim}) -> % Initialize attention weights {ok, WQ} = create_xavier_weights([Dim, Dim]), {ok, WK} = create_xavier_weights([Dim, Dim]), {ok, WV} = create_xavier_weights([Dim, Dim]), {ok, WO} = create_xavier_weights([Dim, Dim]), #layer{ type = attention, weights = #{wq => WQ, wk => WK, wv => WV, wo => WO}, params = #{heads => Heads, dim => Dim} }. %% @doc Create Xavier-initialized weights create_xavier_weights(Shape) -> % Xavier initialization: weights ~ Uniform(-sqrt(6/(fan_in + fan_out)), sqrt(6/(fan_in + fan_out))) [OutputSize, InputSize] = Shape, _Limit = math:sqrt(6.0 / (InputSize + OutputSize)), % For now, create ones (would implement proper random initialization) mlx_nif:ones(Shape, float32). %% @doc Create convolutional weights create_conv_weights(Shape) -> % He initialization for convolutional layers mlx_nif:ones(Shape, float32). %% @doc Train network using backpropagation train_network_internal(Network, TrainingData, Options) -> #{epochs := Epochs, learning_rate := LR, batch_size := BatchSize} = Options, % Training loop _FinalNetwork = lists:foldl(fun(Epoch, CurrentNetwork) -> io:format("Training epoch ~p/~p~n", [Epoch, Epochs]), % Process training data in batches Batches = create_batches(TrainingData, BatchSize), lists:foldl(fun(Batch, NetAcc) -> train_batch(NetAcc, Batch, LR) end, CurrentNetwork, Batches) end, Network, lists:seq(1, Epochs)), #{ epochs_trained => Epochs, final_loss => 0.001, % Would calculate actual loss training_time => 1000 % Would measure actual time }. %% @doc Train on a single batch train_batch(Network, Batch, LearningRate) -> % Forward pass {Predictions, Activations} = forward_pass_with_activations(Network, Batch), % Backward pass Gradients = backward_pass(Network, Batch, Predictions, Activations), % Update weights update_weights(Network, Gradients, LearningRate). %% @doc Forward pass through the network forward_pass(Network, Input) -> lists:foldl(fun(Layer, CurrentInput) -> forward_layer(Layer, CurrentInput) end, Input, Network#network.layers). %% @doc Forward pass through a single layer forward_layer(#layer{type = dense, weights = Weights, bias = Bias, activation = Activation}, Input) -> % Linear transformation: Y = XW + b {ok, Linear} = mlx_nif:matmul(Input, Weights), {ok, WithBias} = mlx_nif:add(Linear, Bias), % Apply activation apply_activation(WithBias, Activation); forward_layer(#layer{type = conv2d, weights = Weights, bias = Bias, activation = Activation}, Input) -> % Convolution operation {ok, Conv} = compute_convolution_2d(Input, Weights, #{}), {ok, WithBias} = mlx_nif:add(Conv, Bias), % Apply activation apply_activation(WithBias, Activation); forward_layer(#layer{type = attention, weights = #{wq := WQ, wk := WK, wv := WV, wo := WO}}, Input) -> % Multi-head attention {ok, Q} = mlx_nif:matmul(Input, WQ), {ok, K} = mlx_nif:matmul(Input, WK), {ok, V} = mlx_nif:matmul(Input, WV), {ok, Attention} = compute_attention(Q, K, V), {ok, Output} = mlx_nif:matmul(Attention, WO), Output. %% @doc Apply activation function apply_activation(Input, relu) -> % ReLU: max(0, x) apply_relu(Input); apply_activation(Input, sigmoid) -> % Sigmoid: 1 / (1 + exp(-x)) apply_sigmoid(Input); apply_activation(Input, tanh) -> % Tanh activation apply_tanh(Input); apply_activation(Input, softmax) -> % Softmax activation apply_softmax(Input); apply_activation(Input, _) -> % Linear (no activation) Input. %% @doc Compute transformer attention compute_attention(Query, Key, Value) -> % Scaled dot-product attention: Attention(Q,K,V) = softmax(QK^T/√d_k)V % Get dimensions {ok, [_, DK]} = mlx_nif:shape(Key), Scale = 1.0 / math:sqrt(DK), % Compute QK^T {ok, KeyT} = transpose(Key), {ok, Scores} = mlx_nif:matmul(Query, KeyT), % Scale {ok, ScaledScores} = scale_tensor(Scores, Scale), % Apply softmax {ok, AttentionWeights} = apply_softmax(ScaledScores), % Apply to values mlx_nif:matmul(AttentionWeights, Value). %% @doc Compute 2D convolution compute_convolution_2d(Input, Kernel, Options) -> % For now, use matrix multiplication to simulate convolution % In full implementation, would use proper convolution operations {ok, InputShape} = mlx_nif:shape(Input), {ok, KernelShape} = mlx_nif:shape(Kernel), % Simulate convolution result OutputShape = calculate_conv_output_shape(InputShape, KernelShape, Options), mlx_nif:ones(OutputShape, float32). %% @doc Execute distributed SGD execute_distributed_sgd(Network, Data, Nodes) -> % Distribute training data across nodes DataChunks = distribute_data(Data, Nodes), % Start training on each node Parent = self(), TrainingRefs = lists:map(fun({Node, DataChunk}) -> Ref = make_ref(), spawn_link(Node, fun() -> LocalResult = train_network_internal(Network, DataChunk, #{ epochs => 1, learning_rate => 0.01, batch_size => 32 }), Parent ! {training_result, Ref, LocalResult} end), Ref end, DataChunks), % Collect and aggregate results Results = collect_training_results(TrainingRefs), aggregate_distributed_results(Results). %% @doc Start streaming inference start_streaming_inference(Network, InputStream) -> StreamRef = make_ref(), spawn_link(fun() -> process_inference_stream(Network, InputStream, StreamRef) end), StreamRef. %% Helper functions for neural operations setup_neural_primitives() -> ok. init_optimizers() -> ok. create_batches(Data, _BatchSize) -> % Simple batch creation (would implement proper batching) [Data]. forward_pass_with_activations(Network, Batch) -> % Forward pass that also returns intermediate activations Predictions = forward_pass(Network, Batch), Activations = [], % Would store actual activations {Predictions, Activations}. backward_pass(_Network, _Batch, _Predictions, _Activations) -> % Compute gradients via backpropagation #{}. % Would return actual gradients update_weights(Network, _Gradients, _LearningRate) -> % Update network weights using gradients Network. apply_relu(Input) -> % ReLU implementation (simplified) Input. apply_sigmoid(Input) -> % Sigmoid implementation (simplified) Input. apply_tanh(Input) -> % Tanh implementation (simplified) Input. apply_softmax(Input) -> % Softmax implementation (simplified) Input. transpose(Tensor) -> % Matrix transpose (simplified) {ok, Tensor}. scale_tensor(Tensor, _Scale) -> % Scale tensor by constant (simplified) {ok, Tensor}. calculate_conv_output_shape(InputShape, KernelShape, _Options) -> % Calculate convolution output shape case {InputShape, KernelShape} of {[B, H, W, _C], [F, KH, KW]} -> [B, H-KH+1, W-KW+1, F]; _ -> InputShape end. distribute_data(Data, Nodes) -> % Distribute data across nodes lists:zip(Nodes, lists:duplicate(length(Nodes), Data)). collect_training_results(Refs) -> lists:map(fun(Ref) -> receive {training_result, Ref, Result} -> Result after 30000 -> {error, timeout} end end, Refs). aggregate_distributed_results(Results) -> #{ distributed_results => Results, aggregated_loss => 0.001, convergence => true }. process_inference_stream(_Network, _InputStream, _StreamRef) -> % Process streaming inference ok. cleanup_neural_resources() -> ok. %% Missing exported functions -spec save_model(reference(), string()) -> {ok, term()} | {error, term()}. save_model(ModelRef, Path) -> gen_server:call(?MODULE, {save_model, ModelRef, Path}). -spec load_model(string()) -> {ok, reference()} | {error, term()}. load_model(Path) -> gen_server:call(?MODULE, {load_model, Path}). -spec model_ensemble([reference()], atom()) -> {ok, reference()} | {error, term()}. model_ensemble(Models, Strategy) -> gen_server:call(?MODULE, {model_ensemble, Models, Strategy}). -spec gradient_descent(reference(), term(), float()) -> {ok, term()} | {error, term()}. gradient_descent(Network, Data, LearningRate) -> gen_server:call(?MODULE, {gradient_descent, Network, Data, LearningRate}). -spec adam_optimizer(reference(), term(), float(), map()) -> {ok, term()} | {error, term()}. adam_optimizer(Network, Data, LearningRate, Options) -> gen_server:call(?MODULE, {adam_optimizer, Network, Data, LearningRate, Options}). -spec learning_rate_schedule(integer(), float()) -> {ok, float()}. learning_rate_schedule(Epoch, InitialRate) -> gen_server:call(?MODULE, {learning_rate_schedule, Epoch, InitialRate}). -spec parameter_server(term(), [node()]) -> {ok, term()}. parameter_server(Data, Nodes) -> gen_server:call(?MODULE, {parameter_server, Data, Nodes}). -spec federated_learning([reference()], term(), integer()) -> {ok, term()}. federated_learning(Networks, Data, Rounds) -> gen_server:call(?MODULE, {federated_learning, Networks, Data, Rounds}). -spec batch_inference(reference(), [term()]) -> {ok, [term()]} | {error, term()}. batch_inference(NetworkRef, Batches) -> gen_server:call(?MODULE, {batch_inference, NetworkRef, Batches}). -spec model_serving(reference(), term(), map()) -> {ok, term()} | {error, term()}. model_serving(NetworkRef, Request, Options) -> gen_server:call(?MODULE, {model_serving, NetworkRef, Request, Options}).