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+ DeepZig V3: A High-Performance LLM Architecture
+
+## Overview
+
+This document outlines the initial architecture proposal for implementing DeepSeek V3 in the Zig programming language. The focus is on leveraging Zig's unique features to create a high-performance, memory-efficient, and robust implementation of the DeepSeek V3 architecture.
+
+1. **Superior Performance**: Leverage Zig's compile-time metaprogramming, SIMD vectorization, and low-level control to achieve optimal performance across platforms
+2. **Memory Efficiency**: Utilize Zig's explicit allocator system and arena allocation patterns for precise resource management
+3. **Concurrent Processing**: Implement efficient parallel execution using Zig's advanced async/await framework and evented I/O
+4. **Type Safety & Reliability**: Employ Zig's strong type system, comptime checks, and explicit error handling to prevent runtime errors
+5. **Cross-Platform Support**: Create a portable implementation with seamless support across architectures (x86_64, ARM64, etc.)
+
+## Why DeepSeek V3 in Zig?
+
+The migration of DeepSeek V3 to Zig represents a significant advancement in language model implementation. By leveraging Zig's unique features, particularly compile-time metaprogramming and fine-grained memory control, we aim to create a highly optimized implementation that outperforms the original Python/PyTorch version significantly while maintaining flexibility and ease of use.
+
+Key advantages of the Zig implementation include:
+
+1. **Superior Performance**
+ - Compile-time specialization eliminates runtime overhead
+ - Direct hardware access for maximum efficiency
+ - Zero-cost abstractions for clean yet fast code
+ - SIMD vectorization through native vector types
+ - Cache-aware memory layout optimization
+
+2. **Memory Efficiency**
+ - Explicit allocation strategies tailored to LLM workloads
+ - Reduced memory fragmentation through custom allocators
+ - Lower overall memory footprint through data structure optimization
+ - Precise control over tensor memory layouts
+ - Arena allocation for temporary computations
+
+3. **Reliability**
+ - Comprehensive error handling with explicit error sets
+ - No runtime exceptions, all errors are explicitly handled
+ - Deterministic resource cleanup through defer and errdefer
+ - Compile-time correctness guarantees
+ - Clear separation of error paths from happy paths
+
+4. **Portability**
+ - Integrated cross-compilation for all supported platforms
+ - No external dependencies for core functionality
+ - C ABI compatibility for integration with existing libraries
+ - Consistent behavior across environments
+ - WebAssembly target support for browser deployment
+
+5. **Scalability**
+ - Explicit threading model for compute-intensive operations
+ - Efficient parallel execution of independent tensor operations
+ - Multi-token prediction support
+ - Quantization-aware data structures
+ - Optimized KV-cache for efficient sequence generation
+
+The resulting system will be particularly well-suited for deployment on resource-constrained devices and will provide superior performance on all platforms. This architectural approach sets the foundation for future innovations in large language model deployment.
+
+
+## Table of Contents
+- [Overview](#overview)
+- [Why DeepSeek V3 in Zig?](#why-deepseek-v3-in-zig)
+- [Table of Contents](#table-of-contents)
+- [System Architecture](#system-architecture)
+ - [High-Level Component Overview](#high-level-component-overview)
+- [Detailed Component Design](#detailed-component-design)
+ - [1. Core Systems](#1-core-systems)
+ - [1.1 Memory Management System](#11-memory-management-system)
+ - [1.2 Tensor Implementation](#12-tensor-implementation)
+ - [1.3 Error Handling Framework](#13-error-handling-framework)
+ - [1.4 Concurrency Model](#14-concurrency-model)
+ - [2. Model Architecture](#2-model-architecture)
+ - [2.1 Transformer Core](#21-transformer-core)
+ - [2.2 Attention Mechanism](#22-attention-mechanism)
+ - [2.3 Mixture of Experts (MoE)](#23-mixture-of-experts-moe)
+ - [3. Computation Backend](#3-computation-backend)
+ - [3.1 Backend Interface](#31-backend-interface)
+ - [3.2 Cross-Platform Compilation](#32-cross-platform-compilation)
+ - [3.2.1 Cross-Compilation Support](#321-cross-compilation-support)
+ - [3.2.2 C ABI Compatibility](#322-c-abi-compatibility)
+ - [3.3 Platform-Specific Implementations](#33-platform-specific-implementations)
+ - [3.4 SIMD Vectorization](#34-simd-vectorization)
+ - [3.5 Runtime CPU Feature Detection](#35-runtime-cpu-feature-detection)
+ - [3.6 Backend Configuration](#36-backend-configuration)
+ - [3.7 GPU Integration](#37-gpu-integration)
+ - [3.7.1 CUDA Backend](#371-cuda-backend)
+ - [3.7.2 Vulkan Backend](#372-vulkan-backend)
+ - [3.8 Quantization Framework](#38-quantization-framework)
+ - [3.9 Memory Management](#39-memory-management)
+ - [3.10 Metal Integration for Apple Silicon](#310-metal-integration-for-apple-silicon)
+ - [4. Inference Pipeline](#4-inference-pipeline)
+ - [4.1 Model Loading](#41-model-loading)
+ - [4.2 Generation Strategies](#42-generation-strategies)
+ - [5. Optimization Layer](#5-optimization-layer)
+ - [5.1 Compile-Time Optimizations](#51-compile-time-optimizations)
+ - [5.2 Quantization Framework](#52-quantization-framework)
+- [Platform-Specific Optimizations](#platform-specific-optimizations)
+ - [Apple Silicon (M-Series)](#apple-silicon-m-series)
+ - [x86\_64 Architecture](#x86_64-architecture)
+ - [NVIDIA GPUs](#nvidia-gpus)
+- [Development Roadmap](#development-roadmap)
+ - [Phase 1: Core Infrastructure](#phase-1-core-infrastructure)
+ - [Phase 2: Model Architecture](#phase-2-model-architecture)
+ - [Phase 3: Backend Integration](#phase-3-backend-integration)
+ - [Phase 4: Inference Pipeline](#phase-4-inference-pipeline)
+ - [Phase 5: Optimization](#phase-5-optimization)
+ - [Phase 6: Testing and Benchmarking](#phase-6-testing-and-benchmarking)
+
+## System Architecture
+
+### High-Level Component Overview
+
+The DeepSeek V3 Zig implementation consists of the following major components:
+
+```
+DeepSeek V3 Zig
+│
+├── Core
+│ ├── Memory Management System
+│ │ ├── Custom Allocator Framework
+│ │ ├── Arena Allocation Strategy
+│ │ └── Memory Pool Implementation
+│ ├── Tensor Implementation
+│ │ ├── SIMD-Optimized Operations
+│ │ ├── Compile-Time Specialization
+│ │ └── Zero-Cost Abstractions
+│ └── Error Handling Framework
+│ ├── Comprehensive Error Types
+│ └── Performance-Optimized Error Paths
+│
+├── Model Architecture
+│ ├── Transformer Layers
+│ │ ├── Comptime-Generated Layer Variants
+│ │ └── Optimized Forward Pass
+│ ├── Attention Mechanisms
+│ │ ├── Vectorized Multi-Head Attention
+│ │ └── Efficient KV-Cache Management
+│ ├── MoE (Mixture of Experts)
+│ │ ├── Parallel Expert Execution
+│ │ └── Optimized Router Implementation
+│ └── Embedding Systems
+│ ├── Memory-Efficient Token Embeddings
+│ └── Positional Encoding Optimizations
+│
+├── Computation Backend
+│ ├── CPU Implementation
+│ │ ├── SIMD Vectorization
+│ │ └── Multi-Threaded Execution
+│ ├── GPU Integration (Optional)
+│ │ ├── CUDA Support (NVIDIA)
+│ │ ├── Metal Support (Apple)
+│ │ └── ROCm Support (AMD)
+│ └── Backend Interface Layer
+│ ├── Zero-Cost Abstraction
+│ └── Compile-Time Dispatch
+│
+├── Inference Pipeline
+│ ├── Model Loading & Weight Management
+│ ├── Tokenization System
+│ ├── Advanced Generation Strategies
+│ │ ├── Speculative Decoding
+│ │ └── Beam Search
+│ └── Streaming Output Processing
+│
+└── Optimization Layer
+ ├── Compile-Time Specialization
+ │ ├── Architecture-Specific Code Gen
+ │ └── Tensor Operation Optimization
+ ├── Runtime Performance Tuning
+ │ ├── Cache-Aware Memory Layout
+ │ └── Workload Balancing
+ └── Quantization Framework
+ ├── Mixed-Precision Support
+ └── Hardware-Accelerated Execution
+```
+
+## Detailed Component Design
+
+### 1. Core Systems
+
+#### 1.1 Memory Management System
+
+Memory management in Zig represents a significant advancement over Python's garbage collection. Zig provides explicit allocator interfaces that give fine-grained control over memory allocation and deallocation strategies:
+
+```zig
+const std = @import("std");
+
+// Define a custom tensor allocator that combines multiple strategies
+pub const TensorAllocator = struct {
+ // Use arena for temporary tensor operations during inference
+ arena: std.heap.ArenaAllocator,
+ // Use a fixed buffer for small activations
+ fixed_buffer: [1024 * 1024]u8 = undefined,
+ fixed_allocator: std.heap.FixedBufferAllocator,
+ // General purpose allocator for long-lived objects
+ gpa: std.heap.GeneralPurposeAllocator(.{}),
+
+ pub fn init(backing_allocator: std.mem.Allocator) !*TensorAllocator {
+ var self = try backing_allocator.create(TensorAllocator);
+ self.* = .{
+ .arena = std.heap.ArenaAllocator.init(backing_allocator),
+ .fixed_allocator = std.heap.FixedBufferAllocator.init(&self.fixed_buffer),
+ .gpa = std.heap.GeneralPurposeAllocator(.{}){},
+ };
+ return self;
+ }
+
+ pub fn deinit(self: *TensorAllocator) void {
+ self.arena.deinit();
+ _ = self.gpa.deinit();
+ // backing allocator will free self
+ }
+
+ // Create a stack fallback allocator for small tensors that can be stack-allocated
+ pub fn smallTensorAllocator(self: *TensorAllocator, comptime size: usize) std.heap.StackFallbackAllocator(size) {
+ return std.heap.stackFallbackAllocator(size, self.arena.allocator());
+ }
+
+ // Get a leak-detecting allocator for debugging builds
+ pub fn debugAllocator(self: *TensorAllocator) std.mem.Allocator {
+ if (builtin.mode == .Debug) {
+ return self.gpa.allocator(); // GPA tracks leaks in debug mode
+ } else {
+ return self.persistentAllocator();
+ }
+ }
+
+ // Specialized allocator for model weights that need to be memory-mapped
+ pub fn weightAllocator(self: *TensorAllocator, path: []const u8) !std.mem.Allocator {
+ // In real implementation, this would return a memory-mapped allocator
+ // For now, just use the persistent allocator
+ return self.persistentAllocator();
+ }
+
+ // Get the right allocator for specific tensor use cases
+ pub fn temporaryAllocator(self: *TensorAllocator) std.mem.Allocator {
+ return self.arena.allocator();
+ }
+
+ pub fn smallActivationAllocator(self: *TensorAllocator) std.mem.Allocator {
+ return self.fixed_allocator.allocator();
+ }
+
+ pub fn persistentAllocator(self: *TensorAllocator) std.mem.Allocator {
+ return self.gpa.allocator();
+ }
+};
+
+// Inference function example with specialized memory allocation
+pub fn performInference(model: *Model, input: Tensor) !Tensor {
+ var allocator = try TensorAllocator.init(std.heap.page_allocator);
+ defer allocator.deinit();
+
+ // Use different allocators for different tensor operations
+ var activations = try computeActivations(model, input, allocator.temporaryAllocator());
+ var weights = try loadModelWeights(model, allocator.persistentAllocator());
+
+ // Results are automatically freed when the arena is deinitialized
+ return try generateOutput(activations, weights, allocator.temporaryAllocator());
+}
+```
+
+**Key Features:**
+- **Tiered Allocation Strategy**: Different allocators for different memory usage patterns
+- **Arena Allocation**: Bulk allocation and freeing for intermediate tensors, dramatically reducing memory management overhead
+- **Fixed Buffer Allocation**: Zero-heap-allocation path for small, predictable tensor operations
+- **Memory Pool Implementation**: Custom pools for tensor data to minimize fragmentation
+- **Explicit Error Handling**: All allocation failures are explicitly handled with Zig's error system
+
+#### 1.2 Tensor Implementation
+
+Tensors are the fundamental data structure for DeepSeek. Our implementation leverages Zig's advanced compile-time features, SIMD capabilities, and memory layout optimizations for maximum performance:
+
+```zig
+pub fn Tensor(comptime DataType: type, comptime dimensions: usize) type {
+ return struct {
+ const Self = @This();
+
+ data: []DataType,
+ shape: [dimensions]usize,
+ strides: [dimensions]usize,
+ allocator: std.mem.Allocator,
+ is_contiguous: bool,
+
+ // Vector types for SIMD operations based on hardware capabilities
+ pub const VecType = switch (DataType) {
+ f32 => if (std.Target.x86.featureSetHas(builtin.cpu.features, .avx512f))
+ @Vector(16, f32) // AVX-512
+ else if (std.Target.x86.featureSetHas(builtin.cpu.features, .avx2))
+ @Vector(8, f32) // AVX2
+ else if (std.Target.x86.featureSetHas(builtin.cpu.features, .sse4_1))
+ @Vector(4, f32) // SSE4.1
+ else
+ @Vector(4, f32), // Fallback for non-x86 or basic x86
+ f16 => if (std.Target.aarch64.featureSetHas(builtin.cpu.features, .fp16))
+ @Vector(8, f16) // ARM with FP16 support
+ else
+ @Vector(4, f16), // Default for f16
+ i32 => @Vector(8, i32),
+ i8 => @Vector(16, i8),
+ i4 => @Vector(32, i4), // Support for 4-bit quantization
+ else => @compileError("Unsupported data type for SIMD"),
+ };
+
+ // Number of elements in the SIMD vector
+ pub const vec_width = @sizeOf(VecType) / @sizeOf(DataType);
+
+ pub fn init(allocator: std.mem.Allocator, shape: [dimensions]usize) !Self {
+ var strides: [dimensions]usize = undefined;
+ var total_size: usize = 1;
+
+ // Calculate C-contiguous (row-major) strides for optimal memory access
+ var i: usize = dimensions;
+ while (i > 0) {
+ i -= 1;
+ strides[i] = total_size;
+ total_size *= shape[i];
+ }
+
+ // Align memory for optimal SIMD access
+ const alignment = @alignOf(VecType);
+ const data = try allocator.alignedAlloc(DataType, alignment, total_size);
+
+ return Self{
+ .data = data,
+ .shape = shape,
+ .strides = strides,
+ .allocator = allocator,
+ .is_contiguous = true,
+ };
+ }
+
+ pub fn deinit(self: *Self) void {
+ self.allocator.free(self.data);
+ }
+
+ // Optimized SIMD matrix multiplication for 2D tensors
+ pub fn matmul(self: *Self, other: *Self, allocator: std.mem.Allocator) !Self {
+ std.debug.assert(dimensions == 2 and other.dimensions == 2);
+ std.debug.assert(self.shape[1] == other.shape[0]);
+
+ const M = self.shape[0];
+ const K = self.shape[1];
+ const N = other.shape[1];
+
+ var result = try Self.init(allocator, .{ M, N });
+
+ // Zero initialization
+ @memset(result.data, 0);
+
+ // Check if both tensors are contiguous for optimal performance
+ if (self.is_contiguous and other.is_contiguous) {
+ // Cache-aware blocked matrix multiplication with SIMD
+ const block_size = 64; // Tuned for L1 cache
+
+ // For each block
+ var i: usize = 0;
+ while (i < M) : (i += block_size) {
+ const i_end = @min(i + block_size, M);
+ var j: usize = 0;
+ while (j < N) : (j += block_size) {
+ const j_end = @min(j + block_size, N);
+ var k: usize = 0;
+ while (k < K) : (k += block_size) {
+ const k_end = @min(k + block_size, K);
+
+ // Process each block
+ var ii: usize = i;
+ while (ii < i_end) : (ii += 1) {
+ var jj: usize = j;
+ while (jj < j_end) : (jj += vec_width) {
+ // SIMD-optimized inner loop
+ if (jj + vec_width <= j_end) {
+ var sum: VecType = @splat(0);
+ var kk: usize = k;
+ while (kk < k_end) : (kk += 1) {
+ const a_val = self.data[ii * K + kk];
+ const b_vec: VecType = blk: {
+ var tmp: [vec_width]DataType = undefined;
+ for (0..vec_width) |v| {
+ if (jj + v < j_end) {
+ tmp[v] = other.data[kk * N + (jj + v)];
+ } else {
+ tmp[v] = 0;
+ }
+ }
+ break :blk tmp;
+ };
+ sum += @splat(a_val) * b_vec;
+ }
+
+ // Store result
+ for (0..vec_width) |v| {
+ if (jj + v < j_end) {
+ result.data[ii * N + (jj + v)] += sum[v];
+ }
+ }
+ } else {
+ // Handle remaining columns (tail)
+ while (jj < j_end) : (jj += 1) {
+ var sum: DataType = 0;
+ var kk: usize = k;
+ while (kk < k_end) : (kk += 1) {
+ sum += self.data[ii * K + kk] * other.data[kk * N + jj];
+ }
+ result.data[ii * N + jj] += sum;
+ }
+ }
+ }
+ }
+ }
+ }
+ }
+ } else {
+ // Fallback for non-contiguous tensors
+ var i: usize = 0;
+ while (i < M) : (i += 1) {
+ var j: usize = 0;
+ while (j < N) : (j += 1) {
+ var sum: DataType = 0;
+ var k: usize = 0;
+ while (k < K) : (k += 1) {
+ sum += self.at(.{i, k}) * other.at(.{k, j});
+ }
+ try result.set(.{i, j}, sum);
+ }
+ }
+ }
+
+ return result;
+ }
+
+ // Access element at specific indices
+ pub fn at(self: Self, indices: [dimensions]usize) DataType {
+ var offset: usize = 0;
+ inline for (0..dimensions) |i| {
+ offset += indices[i] * self.strides[i];
+ }
+ return self.data[offset];
+ }
+
+ // Set element at specific indices
+ pub fn set(self: *Self, indices: [dimensions]usize, value: DataType) !void {
+ var offset: usize = 0;
+ inline for (0..dimensions) |i| {
+ offset += indices[i] * self.strides[i];
+ }
+ self.data[offset] = value;
+ }
+
+ // Apply element-wise operations with SIMD acceleration
+ pub fn map(self: Self, comptime op: fn (DataType) DataType, allocator: std.mem.Allocator) !Self {
+ var result = try Self.init(allocator, self.shape);
+
+ // Use SIMD operations for contiguous data
+ if (self.is_contiguous) {
+ var i: usize = 0;
+ const vec_chunks = self.data.len / vec_width;
+
+ // Process in SIMD chunks
+ while (i < vec_chunks) : (i += 1) {
+ const base_idx = i * vec_width;
+ var vec: VecType = undefined;
+
+ // Load vector
+ for (0..vec_width) |j| {
+ vec[j] = self.data[base_idx + j];
+ }
+
+ // Apply operation on each vector element
+ for (0..vec_width) |j| {
+ vec[j] = op(vec[j]);
+ }
+
+ // Store result
+ for (0..vec_width) |j| {
+ result.data[base_idx + j] = vec[j];
+ }
+ }
+
+ // Process remaining elements
+ const remaining_start = vec_chunks * vec_width;
+ for (remaining_start..self.data.len) |j| {
+ result.data[j] = op(self.data[j]);
+ }
+ } else {
+ // Fallback for non-contiguous data
+ var indices: [dimensions]usize = .{0} ** dimensions;
+ var done = false;
+
+ while (!done) {
+ const val = self.at(indices);
+ try result.set(indices, op(val));
+
+ // Increment indices
+ var d = dimensions - 1;
+ while (true) {
+ indices[d] += 1;
+ if (indices[d] < self.shape[d]) break;
+ indices[d] = 0;
+ if (d == 0) {
+ done = true;
+ break;
+ }
+ d -= 1;
+ }
+ }
+ }
+
+ return result;
+ }
+ };
+}
+
+// Specialized tensor types for common uses
+const FloatTensor1D = Tensor(f32, 1);
+const FloatTensor2D = Tensor(f32, 2);
+const FloatTensor4D = Tensor(f32, 4); // Common for batch x height x width x channels
+const QuantizedTensor4D = Tensor(i8, 4); // For quantized operations
+```
+
+**Key Features:**
+- **Hardware-Aware SIMD Vectorization**: Automatically selects optimal vector width based on CPU capabilities (AVX, SSE)
+- **Cache-Optimized Algorithms**: Blocked matrix multiplication designed for L1/L2 cache efficiency
+- **Aligned Memory Allocation**: Ensures data is properly aligned for SIMD operations
+- **Specialized Tensor Types**: Pre-defined tensor configurations for common use cases
+- **Automatic Fallbacks**: Graceful degradation for non-contiguous tensors or unsupported operations
+- **Compile-Time Optimization**: Tensor dimensions and data types resolved at compile time for maximum performance
+- **Zero-Runtime Overhead**: SIMD operations with no dynamic dispatch or virtual function calls
+
+#### 1.3 Error Handling Framework
+
+Zig's error handling system provides a powerful foundation for creating robust, high-performance software. Unlike exceptions in languages like C++ or Python, Zig's error handling is explicit and deterministic, making it particularly well-suited for large-scale machine learning applications:
+
+```zig
+// Define a comprehensive set of potential errors with clear semantic meaning
+const ModelError = error{
+ ModelLoadFailed,
+ InvalidDimension,
+ InvalidShape,
+ OutOfMemory,
+ ComputeBackendError,
+ InvalidWeight,
+ UnsupportedOperation,
+ UnsupportedDataType,
+ DeviceNotAvailable,
+ TensorShapeMismatch,
+ QuantizationError,
+ InvalidConfiguration,
+ ModelTooLarge,
+ UnsupportedArchitecture,
+ InvalidTokenization,
+ ContextLengthExceeded,
+ DeviceMemoryExhausted,
+};
+
+// Union error sets for comprehensive error handling
+const DeepSeekError = ModelError || TensorError || AllocationError || IoError;
+
+// Example function demonstrating Zig's error handling with defer for cleanup
+fn loadModel(allocator: std.mem.Allocator, path: []const u8) DeepSeekError!*Model {
+ var file = try std.fs.cwd().openFile(path, .{});
+ defer file.close(); // Ensures file is closed even if an error occurs
+
+ var buffer = std.ArrayList(u8).init(allocator);
+ defer buffer.deinit(); // Clean up buffer regardless of success/failure
+
+ try buffer.ensureTotalCapacity(file.getEndPos() catch return ModelError.ModelLoadFailed);
+
+ const bytes_read = try file.readAll(buffer.items);
+ if (bytes_read == 0) return ModelError.ModelLoadFailed;
+
+ var model = try allocator.create(Model);
+ errdefer allocator.destroy(model); // Only called if an error occurs after this point
+
+ model.* = Model.init(allocator);
+ errdefer model.deinit(); // Only called if an error occurs after this point
+
+ // Parse weights and initialize model...
+ if (!try parseWeights(model, buffer.items)) {
+ return ModelError.InvalidWeight;
+ }
+
+ return model;
+}
+
+// Demonstrate error handling in caller code
+pub fn main() !void {
+ var gpa = std.heap.GeneralPurposeAllocator(.{}){};
+ defer _ = gpa.deinit();
+ const allocator = gpa.allocator();
+
+ // Handle errors explicitly with try/catch blocks
+ const model = loadModel(allocator, "model.bin") catch |err| {
+ switch (err) {
+ ModelError.ModelLoadFailed => {
+ std.debug.print("Failed to load model file\n", .{});
+ return err;
+ },
+ ModelError.InvalidWeight => {
+ std.debug.print("Model contains invalid weights\n", .{});
+ return err;
+ },
+ else => {
+ std.debug.print("Unexpected error: {}\n", .{err});
+ return err;
+ },
+ }
+ };
+ defer model.deinit();
+
+ // Example of handling errors with fallbacks
+ const modelVersion = getModelVersion(model.path) catch |err| switch (err) {
+ ModelError.InvalidConfiguration => "unknown",
+ else => return err,
+ };
+
+ // Example of collecting and reporting multiple errors
+ var errors = std.ArrayList(ModelError).init(allocator);
+ defer errors.deinit();
+
+ if (validateModelStructure(model)) |_| {
+ // Structure is valid
+ } else |err| {
+ try errors.append(err);
+ }
+
+ if (validateModelWeights(model)) |_| {
+ // Weights are valid
+ } else |err| {
+ try errors.append(err);
+ }
+
+ if (errors.items.len > 0) {
+ std.debug.print("Found {d} errors in model validation\n", .{errors.items.len});
+ return ModelError.InvalidConfiguration;
+ }
+
+ // Continue with model usage...
+ try initializeModelBackend(model);
+
+ std.debug.print("Model version: {s} loaded successfully\n", .{modelVersion});
+ std.debug.print("Model has {d} parameters, {d} activated\n",
+ .{model.totalParameters(), model.activatedParameters()});
+}
+```
+
+**Key Features:**
+- **Explicit Error Types**: Clearly defined error sets that precisely describe what can go wrong
+- **No Exceptions**: Deterministic error handling with no hidden control flow
+- **Resource Safety**: Automatic cleanup with `defer` and `errdefer` ensures resources are properly managed
+- **Performance Optimization**: Error handling doesn't rely on stack unwinding or dynamic dispatch
+- **Composable Error Sets**: Error types can be combined using the `||` operator
+- **Try-Catch Blocks**: For selective error handling when needed
+- **Error Tracing**: Built-in error return trace capability for debugging
+
+#### 1.4 Concurrency Model
+
+Zig's concurrency model will be leveraged to parallelize computation-intensive operations in DeepSeek. Zig's async/await syntax provides a structured approach to concurrency without the overhead of traditional threading:
+
+```zig
+const std = @import("std");
+
+// Thread pool for CPU-bound parallel tasks
+pub const ComputeThreadPool = struct {
+ pool: std.Thread.Pool,
+ completion_count: std.atomic.Atomic(usize),
+
+ pub fn init(thread_count: usize) !ComputeThreadPool {
+ var pool: std.Thread.Pool = undefined;
+ try pool.init(.{
+ .allocator = std.heap.c_allocator,
+ .n_jobs = thread_count,
+ });
+
+ return ComputeThreadPool{
+ .pool = pool,
+ .completion_count = std.atomic.Atomic(usize).init(0),
+ };
+ }
+
+ pub fn deinit(self: *ComputeThreadPool) void {
+ self.pool.deinit();
+ }
+
+ // Execute a compute task asynchronously
+ pub fn compute(self: *ComputeThreadPool, task: *const fn(*anyopaque) void, context: *anyopaque) !void {
+ try self.pool.spawn(task, context);
+ }
+
+ // Wait for all compute tasks to complete
+ pub fn waitAll(self: *ComputeThreadPool) void {
+ // Process tasks in the event loop until all are complete
+ while (self.completion_count.load(.Acquire) > 0) {
+ std.time.sleep(1 * std.time.millisecond);
+ }
+ }
+};
+
+// Note: Zig's async/await is still under development and may change
+// This example shows the current Thread.Pool-based approach which is stable
+// Future versions may leverage async/await for more elegant concurrency
+
+// Example of how we might use async in the future when it's stable
+pub fn asyncMatMulExample(allocator: std.mem.Allocator, a: *Tensor(f32, 2), b: *Tensor(f32, 2)) !*Tensor(f32, 2) {
+ // This is an example of potential future API design
+ // Not recommended for production use until async is stabilized
+
+ const M = a.shape[0];
+ const K = a.shape[1];
+ const N = b.shape[1];
+
+ var result = try Tensor(f32, 2).init(allocator, .{M, N});
+ errdefer result.deinit();
+
+ @memset(result.data, 0);
+
+ // Process rows concurrently
+ var row_jobs = try allocator.alloc(@Frame(processRow), M);
+ defer allocator.free(row_jobs);
+
+ for (0..M) |i| {
+ row_jobs[i] = async processRow(i, a, b, &result);
+ }
+
+ // Wait for all rows to complete
+ for (row_jobs) |*job| {
+ await job;
+ }
+
+ return result;
+}
+
+fn processRow(row: usize, a: *Tensor(f32, 2), b: *Tensor(f32, 2), result: *Tensor(f32, 2)) !void {
+ // Process a single row of the matrix multiplication
+ const K = a.shape[1];
+ const N = b.shape[1];
+
+ for (0..N) |j| {
+ var sum: f32 = 0.0;
+ for (0..K) |k| {
+ sum += a.at(.{row, k}) * b.at(.{k, j});
+ }
+ try result.set(.{row, j}, sum);
+ }
+}
+
+// Parallel tensor operation example with async/await
+pub fn parallelMatMul(allocator: std.mem.Allocator, a: *Tensor(f32, 2), b: *Tensor(f32, 2)) !*Tensor(f32, 2) {
+ const M = a.shape[0];
+ const K = a.shape[1];
+ const N = b.shape[1];
+
+ var result = try Tensor(f32, 2).init(allocator, .{M, N});
+ errdefer result.deinit();
+
+ @memset(result.data, 0);
+
+ // Create thread pool with optimal number of threads
+ const cpu_count = try std.Thread.getCpuCount();
+ var thread_pool = try ComputeThreadPool.init(cpu_count);
+ defer thread_pool.deinit();
+
+ // Split work based on number of available cores
+ const rows_per_thread = (M + cpu_count - 1) / cpu_count;
+
+ // Define the worker task
+ const WorkContext = struct {
+ a: *const Tensor(f32, 2),
+ b: *const Tensor(f32, 2),
+ result: *Tensor(f32, 2),
+ start_row: usize,
+ end_row: usize,
+ thread_pool: *ComputeThreadPool,
+ };
+
+ // Worker function for computing a subset of rows
+ const workerFn = struct {
+ fn compute(context_ptr: *anyopaque) void {
+ const context = @ptrCast(*WorkContext, @alignCast(@alignOf(WorkContext), context_ptr));
+ const a = context.a;
+ const b = context.b;
+ const result = context.result;
+ const start_row = context.start_row;
+ const end_row = context.end_row;
+
+ // Compute assigned rows
+ for (start_row..end_row) |i| {
+ if (i >= a.shape[0]) break;
+
+ for (0..b.shape[1]) |j| {
+ var sum: f32 = 0.0;
+ for (0..a.shape[1]) |k| {
+ sum += a.at(.{i, k}) * b.at(.{k, j});
+ }
+ result.set(.{i, j}, sum) catch {};
+ }
+ }
+
+ // Mark task as complete
+ _ = context.thread_pool.completion_count.fetchSub(1, .Release);
+ }
+ };
+
+ // Spawn workers for each section of the matrix
+ for (0..cpu_count) |i| {
+ const start_row = i * rows_per_thread;
+ const end_row = std.math.min(start_row + rows_per_thread, M);
+
+ if (start_row >= M) break;
+
+ // Create context for this worker
+ var context = try allocator.create(WorkContext);
+ context.* = .{
+ .a = a,
+ .b = b,
+ .result = result,
+ .start_row = start_row,
+ .end_row = end_row,
+ .thread_pool = &thread_pool,
+ };
+
+ // Increment completion counter before spawning task
+ _ = thread_pool.completion_count.fetchAdd(1, .Release);
+
+ // Spawn the worker task
+ try thread_pool.compute(workerFn.compute, context);
+ }
+
+ // Wait for all tasks to complete
+ thread_pool.waitAll();
+
+ return result;
+}
+```
+
+**Key Features:**
+- **Thread Pool Management**: Efficient worker thread allocation based on available CPU cores
+- **Work Partitioning**: Automatic division of work across available cores
+- **Minimal Synchronization**: Lock-free atomic counters for synchronization when needed
+- **Resource Safety**: Proper cleanup with `defer` and `errdefer` even during concurrent execution
+- **Structured Concurrency**: Clear task dependencies and lifecycle management
+- **Zero Runtime Overhead**: No garbage collection or runtime dependencies
+
+### 2. Model Architecture
+
+#### 2.1 Transformer Core
+
+The transformer architecture is the foundation of DeepSeek V3. Our Zig implementation will leverage compile-time metaprogramming and advanced memory optimizations for maximum performance:
+
+```zig
+const std = @import("std");
+
+// Precomputed type variants for different data precisions
+pub const DataType = enum {
+ f32, // 32-bit floating point (for debugging/development)
+ bf16, // BFloat16 (for training/default inference)
+ f16, // Float16 (for hardware with native f16 support)
+ i8, // 8-bit integer (for quantized inference)
+ i4, // 4-bit integer (for extreme quantization)
+};
+
+// Configuration struct with default values matching DeepSeek V3
+pub const ModelArgs = struct {
+ // Core model parameters
+ max_batch_size: usize = 8,
+ max_seq_len: usize = 4096 * 32, // 128K context window
+ data_type: DataType = .bf16,
+ vocab_size: usize = 102400,
+ dim: usize = 2048,
+ inter_dim: usize = 10944,
+ moe_inter_dim: usize = 1408,
+ n_layers: usize = 27,
+ n_dense_layers: usize = 1,
+ n_heads: usize = 16,
+
+ // MoE configuration
+ n_routed_experts: usize = 64,
+ n_shared_experts: usize = 2,
+ n_activated_experts: usize = 6,
+ n_expert_groups: usize = 1,
+ n_limited_groups: usize = 1,
+ score_func: enum { softmax, sigmoid } = .softmax,
+ route_scale: f32 = 1.0,
+
+ // MLA configuration
+ q_lora_rank: usize = 0,
+ kv_lora_rank: usize = 512,
+ qk_nope_head_dim: usize = 128,
+ qk_rope_head_dim: usize = 64,
+ v_head_dim: usize = 128,
+
+ // Positional encoding
+ original_seq_len: usize = 4096,
+ rope_theta: f32 = 10000.0,
+ rope_factor: f32 = 40,
+ beta_fast: usize = 32,
+ beta_slow: usize = 1,
+ mscale: f32 = 1.0,
+
+ // Runtime options
+ use_flash_attention: bool = true, // Use optimized attention implementation
+ use_parallel_experts: bool = true, // Run experts in parallel
+ max_token_limit: ?usize = null, // Optional token generation limit
+ enable_kv_cache: bool = true, // Use KV cache for inference
+ use_multi_token_prediction: bool = false, // Enable multi-token prediction
+
+ // Hardware optimization flags
+ target_specific_optimizations: bool = true, // Enable target-specific optimizations
+ enable_low_precision_computation: bool = true, // Enable mixed-precision computation
+ use_tensor_cores: bool = true, // Use tensor cores if available
+
+ // Generate optimized implementations based on config parameters
+ pub fn getModelType(self: @This()) type {
+ return struct {
+ const ModelType = @This();
+ const config = self;
+
+ // Select optimal types based on data_type
+ pub const StorageType = switch (config.data_type) {
+ .f32 => f32,
+ .bf16 => std.packed_bf16,
+ .f16 => f16,
+ .i8 => i8,
+ .i4 => i4,
+ };
+
+ // Define tensor types for different dimensions
+ pub const WeightTensor = Tensor(StorageType, 2);
+ pub const ActivationTensor = Tensor(f32, 3); // Always use f32 for activations
+ pub const EmbeddingTensor = Tensor(StorageType, 2);
+ pub const KVCacheTensor = Tensor(f32, 4); // [batch, seq_len, heads, dim]
+
+ // Generate layer configuration
+ pub const layer_config = struct {
+ pub const head_dim = (config.dim / config.n_heads);
+ pub const moe_layers_start = config.n_dense_layers;
+ pub const total_params = calculateTotalParameters(config);
+ pub const activated_params = calculateActivatedParameters(config);
+ };
+
+ fn calculateTotalParameters(config: ModelArgs) usize {
+ // This would be a more detailed calculation in reality
+ const embedding_params = config.vocab_size * config.dim;
+ const attention_params = config.n_layers * (config.dim * config.dim * 4);
+ const moe_params = (config.n_layers - config.n_dense_layers) *
+ config.n_routed_experts *
+ (config.dim * config.moe_inter_dim * 2);
+ const dense_ffn_params = config.n_dense_layers * (config.dim * config.inter_dim * 2);
+
+ return embedding_params + attention_params + moe_params + dense_ffn_params;
+ }
+
+ fn calculateActivatedParameters(config: ModelArgs) usize {
+ // This would be a more detailed calculation in reality
+ const embedding_params = config.vocab_size * config.dim;
+ const attention_params = config.n_layers * (config.dim * config.dim * 4);
+ const moe_activated_params = (config.n_layers - config.n_dense_layers) *
+ config.n_activated_experts *
+ (config.dim * config.moe_inter_dim * 2);
+ const dense_ffn_params = config.n_dense_layers * (config.dim * config.inter_dim * 2);
+
+ return embedding_params + attention_params + moe_activated_params + dense_ffn_params;
+ }
+ };
+ }
+};
+
+// Main transformer model implementation
+pub fn TransformerModel(comptime args: ModelArgs) type {
+ // Use comptime to generate a specialized model implementation based on args
+ return struct {
+ const Self = @This();
+ const ModelType = args.getModelType();
+
+ // Model components
+ allocator: std.mem.Allocator,
+ embedding: Embedding(args),
+ layers: []TransformerBlock(args),
+ norm: RMSNorm(args.dim),
+ head: Linear(args.dim, args.vocab_size),
+ freqs_cis: Tensor(f32, 3), // [max_seq_len, 2, qk_rope_head_dim]
+
+ // KV cache for optimized inference
+ kv_cache: ?ModelType.KVCacheTensor,
+
+ pub fn init(allocator: std.mem.Allocator) !Self {
+ // Initialize components
+ var embedding = try Embedding(args).init(allocator);
+ errdefer embedding.deinit();
+
+ var layers = try allocator.alloc(TransformerBlock(args), args.n_layers);
+ errdefer allocator.free(layers);
+
+ // Create layers with appropriate configurations
+ for (layers, 0..) |*layer, i| {
+ const is_moe = i >= args.n_dense_layers;
+ layer.* = try TransformerBlock(args).init(allocator, i, is_moe);
+ }
+
+ var norm = try RMSNorm(args.dim).init(allocator);
+ errdefer norm.deinit();
+
+ var head = try Linear(args.dim, args.vocab_size).init(allocator, false);
+ errdefer head.deinit();
+
+ // Precompute positional encoding frequencies
+ var freqs_cis = try precomputeFreqsCis(allocator, args);
+
+ return Self{
+ .allocator = allocator,
+ .embedding = embedding,
+ .layers = layers,
+ .norm = norm,
+ .head = head,
+ .freqs_cis = freqs_cis,
+ .kv_cache = null,
+ };
+ }
+
+ pub fn deinit(self: *Self) void {
+ self.embedding.deinit();
+
+ for (self.layers) |*layer| {
+ layer.deinit();
+ }
+ self.allocator.free(self.layers);
+
+ self.norm.deinit();
+ self.head.deinit();
+ self.freqs_cis.deinit();
+
+ if (self.kv_cache) |*cache| {
+ cache.deinit();
+ }
+ }
+
+ // Initialize KV cache for efficient inference
+ pub fn initKVCache(self: *Self) !void {
+ if (self.kv_cache != null) return;
+
+ const batch_size = args.max_batch_size;
+ const seq_len = args.max_seq_len;
+ const n_heads = args.n_heads;
+ const head_dim = ModelType.layer_config.head_dim;
+
+ self.kv_cache = try ModelType.KVCacheTensor.init(
+ self.allocator,
+ .{batch_size, seq_len, n_heads, head_dim * 2}
+ );
+
+ // Zero-initialize cache
+ @memset(self.kv_cache.?.data, 0);
+ }
+
+ // Forward pass through the transformer model
+ pub fn forward(self: *Self, token_ids: []const usize, start_pos: usize) !Tensor(f32, 2) {
+ const batch_size = 1; // Currently supporting batch_size=1 for inference
+ const seq_len = token_ids.len;
+
+ // Create tensor from token_ids
+ var input_tensor = try ModelType.ActivationTensor.init(
+ self.allocator,
+ .{batch_size, seq_len, args.dim}
+ );
+ defer input_tensor.deinit();
+
+ // Get embeddings for input tokens
+ try self.embedding.embed(token_ids, &input_tensor);
+
+ // Process through each transformer layer
+ var x = input_tensor;
+ const freqs_cis_slice = try self.freqs_cis.slice(.{start_pos, 0, 0}, .{start_pos + seq_len, 2, args.qk_rope_head_dim});
+
+ // Create attention mask for causal attention
+ var mask: ?Tensor(f32, 2) = null;
+ if (seq_len > 1) {
+ mask = try createCausalMask(self.allocator, seq_len);
+ defer if (mask) |*m| m.deinit();
+ }
+
+ // Process through transformer layers
+ for (self.layers) |*layer| {
+ x = try layer.forward(x, start_pos, freqs_cis_slice, mask);
+ }
+
+ // Apply final normalization
+ var normalized = try self.norm.forward(x);
+ defer normalized.deinit();
+
+ // Extract last token for prediction
+ var last_token = try normalized.slice(
+ .{0, seq_len - 1, 0},
+ .{batch_size, seq_len, args.dim}
+ );
+ defer last_token.deinit();
+
+ // Project to vocabulary
+ return try self.head.forward(last_token);
+ }
+
+ // Helper to create causal attention mask
+ fn createCausalMask(allocator: std.mem.Allocator, seq_len: usize) !Tensor(f32, 2) {
+ var mask = try Tensor(f32, 2).init(allocator, .{seq_len, seq_len});
+ errdefer mask.deinit();
+
+ for (0..seq_len) |i| {
+ for (0..seq_len) |j| {
+ const value: f32 = if (j <= i) 0.0 else -10000.0;
+ try mask.set(.{i, j}, value);
+ }
+ }
+
+ return mask;
+ }
+ };
+}
+
+// Generate specialized transformer based on configuration
+pub fn createTransformer(allocator: std.mem.Allocator, args: ModelArgs) !*TransformerModel(args) {
+ var model = try allocator.create(TransformerModel(args));
+ errdefer allocator.destroy(model);
+
+ model.* = try TransformerModel(args).init(allocator);
+ return model;
+}
+```
+
+This implementation leverages Zig's compile-time features to generate specialized model implementations based on the provided configuration parameters. The use of generic types and comptime evaluation allows for maximum performance optimization while maintaining code flexibility.
+
+#### 2.2 Attention Mechanism
+
+The Multi-Head Latent Attention (MLA) mechanism is a critical component of DeepSeek V3's performance. Our Zig implementation leverages compile-time specialization, SIMD vectorization, and cache-friendly algorithms for maximum efficiency:
+
+```zig
+// Generic MLA implementation with compile-time specialization
+pub fn MLA(comptime args: ModelArgs) type {
+ return struct {
+ const Self = @This();
+ const ModelType = args.getModelType();
+
+ // Attention configuration
+ dim: usize,
+ n_heads: usize,
+ head_dim: usize,
+ q_lora_rank: usize,
+ kv_lora_rank: usize,
+ qk_nope_head_dim: usize,
+ qk_rope_head_dim: usize,
+ qk_head_dim: usize,
+ v_head_dim: usize,
+ softmax_scale: f32,
+ use_flash_attention: bool,
+
+ // Projection matrices
+ allocator: std.mem.Allocator,
+ wq: ?ColumnParallelLinear(args) = null, // Regular query projection
+ wq_a: ?Linear(args.dim, args.q_lora_rank) = null, // LoRA decomposition
+ q_norm: ?RMSNorm(args.q_lora_rank) = null, // LoRA normalization
+ wq_b: ?ColumnParallelLinear(args) = null, // LoRA decomposition
+ wkv_a: Linear(args.dim, args.kv_lora_rank + args.qk_rope_head_dim),
+ kv_norm: RMSNorm(args.kv_lora_rank),
+ wkv_b: ColumnParallelLinear(args),
+ wo: RowParallelLinear(args),
+
+ // KV caching - optimized for memory access patterns
+ kv_cache: ?Tensor(f32, 4) = null, // [batch, seq_len, heads, head_dim*2]
+ rope_cache: ?Tensor(f32, 3) = null, // [batch, seq_len, rope_dim]
+
+ // Initialize MLA with appropriate configuration
+ pub fn init(allocator: std.mem.Allocator) !Self {
+ const head_dim = args.dim / args.n_heads;
+ var softmax_scale = 1.0 / std.math.sqrt(@as(f32, @floatFromInt(args.qk_nope_head_dim + args.qk_rope_head_dim)));
+
+ // Apply scaling for extended context if needed
+ if (args.max_seq_len > args.original_seq_len) {
+ const mscale = 0.1 * args.mscale * std.math.log(args.rope_factor) + 1.0;
+ softmax_scale *= mscale * mscale;
+ }
+
+ // Initialize query projection (either direct or with LoRA)
+ var wq: ?ColumnParallelLinear(args) = null;
+ var wq_a: ?Linear(args.dim, args.q_lora_rank) = null;
+ var q_norm: ?RMSNorm(args.q_lora_rank) = null;
+ var wq_b: ?ColumnParallelLinear(args) = null;
+
+ if (args.q_lora_rank == 0) {
+ // Standard query projection
+ wq = try ColumnParallelLinear(args).init(
+ allocator,
+ args.dim,
+ args.n_heads * (args.qk_nope_head_dim + args.qk_rope_head_dim),
+ false
+ );
+ } else {
+ // Low-rank adaptation for query
+ wq_a = try Linear(args.dim, args.q_lora_rank).init(allocator, false);
+ q_norm = try RMSNorm(args.q_lora_rank).init(allocator);
+ wq_b = try ColumnParallelLinear(args).init(
+ allocator,
+ args.q_lora_rank,
+ args.n_heads * (args.qk_nope_head_dim + args.qk_rope_head_dim),
+ false
+ );
+ }
+
+ // Key-value projections
+ var wkv_a = try Linear(args.dim, args.kv_lora_rank + args.qk_rope_head_dim).init(allocator, false);
+ var kv_norm = try RMSNorm(args.kv_lora_rank).init(allocator);
+ var wkv_b = try ColumnParallelLinear(args).init(
+ allocator,
+ args.kv_lora_rank,
+ args.n_heads * (args.qk_nope_head_dim + args.v_head_dim),
+ false
+ );
+
+ // Output projection
+ var wo = try RowParallelLinear(args).init(
+ allocator,
+ args.n_heads * args.v_head_dim,
+ args.dim,
+ false
+ );
+
+ return Self{
+ .allocator = allocator,
+ .dim = args.dim,
+ .n_heads = args.n_heads,
+ .head_dim = head_dim,
+ .q_lora_rank = args.q_lora_rank,
+ .kv_lora_rank = args.kv_lora_rank,
+ .qk_nope_head_dim = args.qk_nope_head_dim,
+ .qk_rope_head_dim = args.qk_rope_head_dim,
+ .qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim,
+ .v_head_dim = args.v_head_dim,
+ .softmax_scale = softmax_scale,
+ .use_flash_attention = args.use_flash_attention,
+ .wq = wq,
+ .wq_a = wq_a,
+ .q_norm = q_norm,
+ .wq_b = wq_b,
+ .wkv_a = wkv_a,
+ .kv_norm = kv_norm,
+ .wkv_b = wkv_b,
+ .wo = wo,
+ };
+ }
+
+ pub fn deinit(self: *Self) void {
+ if (self.wq) |*w| w.deinit();
+ if (self.wq_a) |*w| w.deinit();
+ if (self.q_norm) |*n| n.deinit();
+ if (self.wq_b) |*w| w.deinit();
+
+ self.wkv_a.deinit();
+ self.kv_norm.deinit();
+ self.wkv_b.deinit();
+ self.wo.deinit();
+
+ if (self.kv_cache) |*cache| cache.deinit();
+ if (self.rope_cache) |*cache| cache.deinit();
+ }
+
+ // Initialize KV cache for efficient inference
+ pub fn initKVCache(self: *Self, batch_size: usize, seq_len: usize) !void {
+ if (self.kv_cache != null) return;
+
+ // Allocate KV cache
+ self.kv_cache = try Tensor(f32, 4).init(
+ self.allocator,
+ .{batch_size, seq_len, self.n_heads, self.head_dim * 2}
+ );
+
+ // Zero-initialize
+ @memset(self.kv_cache.?.data, 0);
+
+ // Allocate rotary positional encoding cache
+ self.rope_cache = try Tensor(f32, 3).init(
+ self.allocator,
+ .{batch_size, seq_len, self.qk_rope_head_dim}
+ );
+
+ @memset(self.rope_cache.?.data, 0);
+ }
+
+ // Forward pass implementation with multiple specialized paths
+ pub fn forward(
+ self: *Self,
+ x: Tensor(f32, 3),
+ start_pos: usize,
+ freqs_cis: Tensor(f32, 3),
+ mask: ?Tensor(f32, 2)
+ ) !Tensor(f32, 3) {
+ const batch_size = x.shape[0];
+ const seq_len = x.shape[1];
+ const end_pos = start_pos + seq_len;
+
+ // Initialize KV cache if not already done
+ if (start_pos > 0 and self.kv_cache == null) {
+ try self.initKVCache(batch_size, args.max_seq_len);
+ }
+
+ // Compute query vectors
+ var q: Tensor(f32, 4) = undefined;
+ if (self.q_lora_rank == 0) {
+ // Standard query projection
+ var q_flat = try self.wq.?.forward(x);
+ defer q_flat.deinit();
+
+ // Reshape to [batch, seq_len, heads, head_dim]
+ q = try q_flat.reshape(.{batch_size, seq_len, self.n_heads, self.qk_head_dim});
+ } else {
+ // Low-rank adaptation
+ var q_a = try self.wq_a.?.forward(x);
+ defer q_a.deinit();
+
+ var q_norm = try self.q_norm.?.forward(q_a);
+ defer q_norm.deinit();
+
+ var q_b = try self.wq_b.?.forward(q_norm);
+ defer q_b.deinit();
+
+ // Reshape
+ q = try q_b.reshape(.{batch_size, seq_len, self.n_heads, self.qk_head_dim});
+ }
+ defer q.deinit();
+
+ // Split query into regular and positional parts
+ var q_slices = try q.split(3, .{self.qk_nope_head_dim, self.qk_rope_head_dim});
+ defer for (q_slices) |*slice| slice.deinit();
+
+ var q_nope = q_slices[0];
+ var q_pe = q_slices[1];
+
+ // Apply rotary embeddings to position-dependent part
+ try applyRotaryEmbeddings(&q_pe, freqs_cis);
+
+ // Compute key-value vectors
+ var kv_raw = try self.wkv_a.forward(x);
+ defer kv_raw.deinit();
+
+ // Split into KV features and positional features
+ var kv_slices = try kv_raw.split(2, .{self.kv_lora_rank, self.qk_rope_head_dim});
+ defer for (kv_slices) |*slice| slice.deinit();
+
+ var kv_features = kv_slices[0];
+ var k_pe_features = kv_slices[1];
+
+ // Add batch and heads dimension to positional features
+ var k_pe = try k_pe_features.reshape(.{batch_size, seq_len, 1, self.qk_rope_head_dim});
+ defer k_pe.deinit();
+
+ // Apply rotary embeddings
+ try applyRotaryEmbeddings(&k_pe, freqs_cis);
+
+ // Process main KV branch
+ var kv_norm_features = try self.kv_norm.forward(kv_features);
+ defer kv_norm_features.deinit();
+
+ var kv_proj = try self.wkv_b.forward(kv_norm_features);
+ defer kv_proj.deinit();
+
+ // Reshape to separate K and V
+ var kv_reshaped = try kv_proj.reshape(
+ .{batch_size, seq_len, self.n_heads, self.qk_nope_head_dim + self.v_head_dim}
+ );
+ defer kv_reshaped.deinit();
+
+ // Split into K and V
+ var kv_parts = try kv_reshaped.split(3, .{self.qk_nope_head_dim, self.v_head_dim});
+ defer for (kv_parts) |*part| part.deinit();
+
+ var k_nope = kv_parts[0];
+ var v = kv_parts[1];
+
+ // Combine positional and non-positional key parts
+ var k = try combineTensors(k_nope, k_pe, 3);
+ defer k.deinit();
+
+ // Store in KV cache if available
+ if (self.kv_cache != null) {
+ try self.updateKVCache(k, v, start_pos, end_pos);
+ }
+
+ // Choose attention implementation based on settings
+ var attention_output: Tensor(f32, 4) = undefined;
+ if (self.use_flash_attention and seq_len > 1) {
+ attention_output = try self.computeFlashAttention(
+ q_nope,
+ q_pe,
+ self.kv_cache.?,
+ self.rope_cache.?,
+ mask,
+ batch_size,
+ seq_len,
+ end_pos
+ );
+ } else {
+ attention_output = try self.computeStandardAttention(
+ q,
+ k,
+ v,
+ mask,
+ batch_size,
+ seq_len,
+ end_pos
+ );
+ }
+ defer attention_output.deinit();
+
+ // Final projection
+ var attention_flat = try attention_output.reshape(
+ .{batch_size, seq_len, self.n_heads * self.v_head_dim}
+ );
+ defer attention_flat.deinit();
+
+ return self.wo.forward(attention_flat);
+ }
+
+ // Flash attention implementation optimized for large contexts
+ fn computeFlashAttention(
+ self: *const Self,
+ q_nope: Tensor(f32, 4),
+ q_pe: Tensor(f32, 4),
+ kv_cache: Tensor(f32, 4),
+ rope_cache: Tensor(f32, 3),
+ mask: ?Tensor(f32, 2),
+ batch_size: usize,
+ seq_len: usize,
+ end_pos: usize
+ ) !Tensor(f32, 4) {
+ // Flash attention implementation with tiling to maximize cache efficiency
+ // This function would include a highly optimized SIMD implementation
+ // specializing in memory-efficient attention computation
+
+ // Note: This would be a substantial implementation with memory-efficient
+ // blocked matrix multiplication and careful SIMD optimization
+ // We're providing a simplified structure here
+
+ // For a full implementation, see the FlashAttention algorithm paper
+ const block_size = 32; // Block size tuned for L1 cache
+
+ // Output tensor
+ var output = try Tensor(f32, 4).init(
+ self.allocator,
+ .{batch_size, seq_len, self.n_heads, self.v_head_dim}
+ );
+
+ // Implement blocked attention algorithm...
+ // This would contain optimized SIMD code for tiled attention computation
+
+ return output;
+ }
+
+ // Standard attention for shorter sequences or when flash attention is disabled
+ fn computeStandardAttention(
+ self: *const Self,
+ q: Tensor(f32, 4),
+ k: Tensor(f32, 4),
+ v: Tensor(f32, 4),
+ mask: ?Tensor(f32, 2),
+ batch_size: usize,
+ seq_len: usize,
+ end_pos: usize
+ ) !Tensor(f32, 4) {
+ // Compute QK attention scores
+ var scores = try computeAttentionScores(q, k, self.softmax_scale);
+ defer scores.deinit();
+
+ // Apply causal mask if provided
+ if (mask) |m| {
+ try applyAttentionMask(&scores, m);
+ }
+
+ // Apply softmax
+ try applySoftmax(&scores, -1);
+
+ // Compute attention output (scores @ v)
+ return computeAttentionOutput(scores, v);
+ }
+
+ // Update KV cache with new values
+ fn updateKVCache(
+ self: *Self,
+ k: Tensor(f32, 4),
+ v: Tensor(f32, 4),
+ start_pos: usize,
+ end_pos: usize
+ ) !void {
+ const batch_size = k.shape[0];
+ const seq_len = k.shape[1];
+
+ // Update key cache
+ for (0..batch_size) |b| {
+ for (0..seq_len) |s| {
+ const cache_pos = start_pos + s;
+ for (0..self.n_heads) |h| {
+ // Copy K values
+ for (0..self.qk_head_dim) |d| {
+ const k_val = try k.at(.{b, s, h, d});
+ try self.kv_cache.?.set(.{b, cache_pos, h, d}, k_val);
+ }
+
+ // Copy V values
+ for (0..self.v_head_dim) |d| {
+ const v_val = try v.at(.{b, s, h, d});
+ try self.kv_cache.?.set(.{b, cache_pos, h, self.qk_head_dim + d}, v_val);
+ }
+ }
+ }
+ }
+ }
+ };
+}
+```
+
+**Key Optimizations:**
+- **Compile-Time Specialization**: Generated attention routines are tailored to model dimensions at compile time
+- **Flash Attention Algorithm**: Memory-efficient attention computation for long sequences
+- **SIMD-Optimized Matrix Operations**: Vectorized attention score calculation and softmax
+- **Optimized KV-Cache Layout**: Cache-friendly memory layout for efficient sequence generation
+- **Sparse Attention Patterns**: Support for different attention patterns beyond standard causal attention
+- **Memory Reuse**: Careful tensor management to minimize allocations during inference
+- **Specialized Attention Paths**: Different implementations optimized for inference vs. training
+- **Low-Rank Adaptation**: LoRA support for more efficient fine-tuning
+
+#### 2.3 Mixture of Experts (MoE)
+
+The Mixture of Experts (MoE) architecture is a key innovation in DeepSeek V3 that enables scaling model capacity without proportionally increasing computation cost. Our Zig implementation leverages compile-time specialization and parallel execution for maximum efficiency:
+
+```zig
+// Generic MoE implementation with compile-time specialization
+pub fn MixtureOfExperts(comptime args: ModelArgs) type {
+ return struct {
+ const Self = @This();
+ const ModelType = args.getModelType();
+
+ // Configuration
+ allocator: std.mem.Allocator,
+ dim: usize,
+ n_routed_experts: usize,
+ n_local_experts: usize,
+ n_activated_experts: usize,
+ experts_start_idx: usize,
+ experts_end_idx: usize,
+ use_parallel_execution: bool,
+
+ // Components
+ gate: RouterGate(args),
+ experts: []Expert(args),
+ shared_experts: MLP(args),
+ thread_pool: ?*ComputeThreadPool = null,
+
+ // Initialize MoE with appropriate configuration
+ pub fn init(allocator: std.mem.Allocator) !Self {
+ // Determine expert distribution across processes
+ const world_size = 1; // Set to actual world size for distributed training
+ const rank = 0; // Set to actual rank for distributed training
+
+ std.debug.assert(args.n_routed_experts % world_size == 0,
+ "Number of experts must be divisible by world size");
+
+ const n_local_experts = args.n_routed_experts / world_size;
+ const experts_start_idx = rank * n_local_experts;
+ const experts_end_idx = experts_start_idx + n_local_experts;
+
+ // Initialize routing gate
+ var gate = try RouterGate(args).init(allocator);
+ errdefer gate.deinit();
+
+ // Initialize experts
+ var experts = try allocator.alloc(Expert(args), args.n_routed_experts);
+ errdefer allocator.free(experts);
+
+ // Only initialize experts that belong to this process
+ for (experts, 0..) |*expert, i| {
+ if (experts_start_idx <= i and i < experts_end_idx) {
+ expert.* = try Expert(args).init(allocator);
+ } else {
+ expert.* = undefined; // Not used on this process
+ }
+ }
+
+ // Initialize shared experts (always executed)
+ var shared_experts = try MLP(args).init(
+ allocator,
+ args.dim,
+ args.n_shared_experts * args.moe_inter_dim
+ );
+ errdefer shared_experts.deinit();
+
+ // Initialize thread pool for parallel execution if needed
+ var thread_pool: ?*ComputeThreadPool = null;
+ if (args.use_parallel_experts) {
+ thread_pool = try allocator.create(ComputeThreadPool);
+ const cpu_count = try std.Thread.getCpuCount();
+ const optimal_threads = std.math.min(
+ cpu_count,
+ args.n_activated_experts + args.n_shared_experts
+ );
+ thread_pool.?.* = try ComputeThreadPool.init(optimal_threads);
+ }
+
+ return Self{
+ .allocator = allocator,
+ .dim = args.dim,
+ .n_routed_experts = args.n_routed_experts,
+ .n_local_experts = n_local_experts,
+ .n_activated_experts = args.n_activated_experts,
+ .experts_start_idx = experts_start_idx,
+ .experts_end_idx = experts_end_idx,
+ .use_parallel_execution = args.use_parallel_experts,
+ .gate = gate,
+ .experts = experts,
+ .shared_experts = shared_experts,
+ .thread_pool = thread_pool,
+ };
+ }
+
+ pub fn deinit(self: *Self) void {
+ self.gate.deinit();
+
+ // Only deinit experts that belong to this process
+ for (self.experts, 0..) |*expert, i| {
+ if (self.experts_start_idx <= i and i < self.experts_end_idx) {
+ expert.deinit();
+ }
+ }
+ self.allocator.free(self.experts);
+
+ self.shared_experts.deinit();
+
+ if (self.thread_pool) |pool| {
+ pool.deinit();
+ self.allocator.destroy(pool);
+ }
+ }
+
+ // Forward pass implementation with parallel expert execution
+ pub fn forward(self: *Self, x: Tensor(f32, 3)) !Tensor(f32, 3) {
+ const batch_size = x.shape[0];
+ const seq_len = x.shape[1];
+
+ // Reshape input for routing
+ var x_flat = try x.reshape(.{batch_size * seq_len, self.dim});
+ defer x_flat.deinit();
+
+ // Router computation
+ var router_output = try self.gate.forward(x_flat);
+ defer {
+ router_output.weights.deinit();
+ router_output.indices.deinit();
+ }
+
+ // Get routing weights and indices
+ const weights = router_output.weights;
+ const indices = router_output.indices;
+
+ // Initialize result tensor with zeros
+ var result = try Tensor(f32, 2).init(
+ self.allocator,
+ .{batch_size * seq_len, self.dim}
+ );
+ errdefer result.deinit();
+
+ @memset(result.data, 0);
+
+ // Count expert assignments for load balancing analysis
+ var expert_counts = try self.allocator.alloc(usize, self.n_routed_experts);
+ defer self.allocator.free(expert_counts);
+ @memset(expert_counts, 0);
+
+ for (indices.data) |idx| {
+ expert_counts[idx] += 1;
+ }
+
+ // Process each expert
+ if (self.use_parallel_execution and self.thread_pool != null) {
+ try self.parallelExpertExecution(
+ x_flat,
+ weights,
+ indices,
+ expert_counts,
+ &result
+ );
+ } else {
+ try self.sequentialExpertExecution(
+ x_flat,
+ weights,
+ indices,
+ expert_counts,
+ &result
+ );
+ }
+
+ // Always execute shared experts
+ var shared_output = try self.shared_experts.forward(x_flat);
+ defer shared_output.deinit();
+
+ // Add shared expert output to result
+ try addTensors(&result, shared_output);
+
+ // Reshape back to original dimensions
+ return result.reshape(.{batch_size, seq_len, self.dim});
+ }
+
+ // Parallel execution of experts using thread pool
+ fn parallelExpertExecution(
+ self: *Self,
+ x: Tensor(f32, 2),
+ weights: Tensor(f32, 2),
+ indices: Tensor(usize, 2),
+ expert_counts: []usize,
+ result: *Tensor(f32, 2)
+ ) !void {
+ const thread_pool = self.thread_pool.?;
+ var work_queue = std.ArrayList(ExpertWorkItem).init(self.allocator);
+ defer work_queue.deinit();
+
+ // Create work items for each expert
+ for (0..self.n_routed_experts) |expert_idx| {
+ if (expert_counts[expert_idx] == 0) continue;
+
+ if (expert_idx < self.experts_start_idx or expert_idx >= self.experts_end_idx) {
+ // Skip experts not assigned to this process
+ continue;
+ }
+
+ // Extract tokens routed to this expert
+ var token_indices = try self.allocator.alloc(usize, expert_counts[expert_idx]);
+ var token_weights = try self.allocator.alloc(f32, expert_counts[expert_idx]);
+
+ var token_count: usize = 0;
+ for (0..x.shape[0]) |i| {
+ for (0..self.n_activated_experts) |j| {
+ const index_offset = i * self.n_activated_experts + j;
+ if (indices.data[index_offset] == expert_idx) {
+ token_indices[token_count] = i;
+ token_weights[token_count] = weights.data[index_offset];
+ token_count += 1;
+ }
+ }
+ }
+
+ // Create work item
+ try work_queue.append(.{
+ .allocator = self.allocator,
+ .expert = &self.experts[expert_idx],
+ .x = x,
+ .token_indices = token_indices,
+ .token_weights = token_weights,
+ .result = result,
+ .thread_pool = thread_pool,
+ });
+ }
+
+ // Schedule parallel expert execution
+ for (work_queue.items) |*work_item| {
+ // Increment completion counter
+ _ = thread_pool.completion_count.fetchAdd(1, .Release);
+
+ // Submit task to thread pool
+ try thread_pool.compute(processExpertWork, work_item);
+ }
+
+ // Wait for all expert computations to complete
+ thread_pool.waitAll();
+ }
+
+ // Sequential execution of experts
+ fn sequentialExpertExecution(
+ self: *Self,
+ x: Tensor(f32, 2),
+ weights: Tensor(f32, 2),
+ indices: Tensor(usize, 2),
+ expert_counts: []usize,
+ result: *Tensor(f32, 2)
+ ) !void {
+ // Process each expert sequentially
+ for (0..self.n_routed_experts) |expert_idx| {
+ if (expert_counts[expert_idx] == 0) continue;
+
+ if (expert_idx < self.experts_start_idx or expert_idx >= self.experts_end_idx) {
+ // Skip experts not assigned to this process
+ continue;
+ }
+
+ // Get tokens assigned to this expert
+ for (0..x.shape[0]) |i| {
+ for (0..self.n_activated_experts) |j| {
+ const index_offset = i * self.n_activated_experts + j;
+ if (indices.data[index_offset] == expert_idx) {
+ // Process token with this expert
+ const token_weight = weights.data[index_offset];
+
+ // Extract input token
+ var token_input = try x.slice(.{i, 0}, .{i + 1, self.dim});
+ defer token_input.deinit();
+
+ // Process through expert
+ var expert_output = try self.experts[expert_idx].forward(token_input);
+ defer expert_output.deinit();
+
+ // Scale by routing weight
+ try scaleTensor(&expert_output, token_weight);
+
+ // Add to result
+ for (0..self.dim) |d| {
+ result.data[i * self.dim + d] += expert_output.data[d];
+ }
+ }
+ }
+ }
+ }
+ }
+
+ // Worker task for parallel expert execution
+ const ExpertWorkItem = struct {
+ allocator: std.mem.Allocator,
+ expert: *Expert(args),
+ x: Tensor(f32, 2),
+ token_indices: []usize,
+ token_weights: []f32,
+ result: *Tensor(f32, 2),
+ thread_pool: *ComputeThreadPool,
+ };
+
+ fn processExpertWork(ctx_ptr: *anyopaque) void {
+ const ctx = @ptrCast(*ExpertWorkItem, @alignCast(@alignOf(ExpertWorkItem), ctx_ptr));
+ defer {
+ ctx.allocator.free(ctx.token_indices);
+ ctx.allocator.free(ctx.token_weights);
+ _ = ctx.thread_pool.completion_count.fetchSub(1, .Release);
+ }
+
+ // Process each token assigned to this expert
+ for (ctx.token_indices, ctx.token_weights, 0..) |token_idx, weight, i| {
+ // Extract input token
+ var token_input = ctx.x.slice(.{token_idx, 0}, .{token_idx + 1, ctx.x.shape[1]}) catch return;
+ defer token_input.deinit();
+
+ // Process through expert
+ var expert_output = ctx.expert.forward(token_input) catch return;
+ defer expert_output.deinit();
+
+ // Scale by routing weight
+ scaleTensor(&expert_output, weight) catch return;
+
+ // Add to result (using atomic operations to avoid race conditions)
+ for (0..expert_output.shape[1]) |d| {
+ const offset = token_idx * expert_output.shape[1] + d;
+ const old_val = @atomicLoad(f32, &ctx.result.data[offset], .Acquire);
+ const new_val = old_val + expert_output.data[d];
+ @atomicStore(f32, &ctx.result.data[offset], new_val, .Release);
+ }
+ }
+ }
+ };
+}
+
+// Router gate for MoE that determines which experts to use for each token
+pub fn RouterGate(comptime args: ModelArgs) type {
+ return struct {
+ const Self = @This();
+
+ allocator: std.mem.Allocator,
+ dim: usize,
+ n_experts: usize,
+ n_groups: usize,
+ n_limited_groups: usize,
+ topk: usize,
+ score_func: enum { softmax, sigmoid },
+ route_scale: f32,
+
+ // Router weights
+ weight: Tensor(f32, 2),
+ bias: ?Tensor(f32, 1) = null,
+
+ pub fn init(allocator: std.mem.Allocator) !Self {
+ var weight = try Tensor(f32, 2).init(
+ allocator,
+ .{args.n_routed_experts, args.dim}
+ );
+
+ // Initialize with appropriate distribution
+ try initializeParameters(&weight, 0.0, 0.02);
+
+ // Create optional bias
+ var bias: ?Tensor(f32, 1) = null;
+ if (args.dim == 7168) { // Special case for bias
+ bias = try Tensor(f32, 1).init(allocator, .{args.n_routed_experts});
+ @memset(bias.?.data, 0);
+ }
+
+ return Self{
+ .allocator = allocator,
+ .dim = args.dim,
+ .n_experts = args.n_routed_experts,
+ .n_groups = args.n_expert_groups,
+ .n_limited_groups = args.n_limited_groups,
+ .topk = args.n_activated_experts,
+ .score_func = args.score_func,
+ .route_scale = args.route_scale,
+ .weight = weight,
+ .bias = bias,
+ };
+ }
+
+ pub fn deinit(self: *Self) void {
+ self.weight.deinit();
+ if (self.bias) |*b| b.deinit();
+ }
+
+ // Router forward pass to determine expert assignment
+ pub fn forward(self: *const Self, x: Tensor(f32, 2)) !RouterOutput {
+ // Compute routing scores
+ var scores = try linearProjection(x, self.weight, self.bias);
+ defer scores.deinit();
+
+ // Apply scoring function
+ var routing_probs: Tensor(f32, 2) = undefined;
+ if (self.score_func == .softmax) {
+ routing_probs = try applySoftmax(scores, 1);
+ } else {
+ routing_probs = try applySigmoid(scores);
+ }
+ defer routing_probs.deinit();
+
+ // Save original scores for later
+ var original_scores = try routing_probs.clone();
+
+ // Expert group handling
+ if (self.n_groups > 1) {
+ try self.applyGroupFiltering(&routing_probs);
+ }
+
+ // Select top-k experts
+ var indices = try Tensor(usize, 2).init(
+ self.allocator,
+ .{x.shape[0], self.topk}
+ );
+
+ var weights = try Tensor(f32, 2).init(
+ self.allocator,
+ .{x.shape[0], self.topk}
+ );
+
+ try self.selectTopkExperts(routing_probs, original_scores, &indices, &weights);
+
+ // Apply routing scale
+ if (self.route_scale != 1.0) {
+ try scaleTensor(&weights, self.route_scale);
+ }
+
+ return RouterOutput{
+ .weights = weights,
+ .indices = indices,
+ };
+ }
+
+ // Apply expert group filtering
+ fn applyGroupFiltering(self: *const Self, scores: *Tensor(f32, 2)) !void {
+ // Reshape scores for group processing
+ const batch_size = scores.shape[0];
+ const experts_per_group = self.n_experts / self.n_groups;
+
+ var reshaped_scores = try scores.reshape(
+ .{batch_size, self.n_groups, experts_per_group}
+ );
+ defer reshaped_scores.deinit();
+
+ // Compute group scores
+ var group_scores = try Tensor(f32, 2).init(
+ self.allocator,
+ .{batch_size, self.n_groups}
+ );
+ defer group_scores.deinit();
+
+ // Calculate score for each group
+ if (self.bias == null) {
+ // Use max score as group score
+ for (0..batch_size) |b| {
+ for (0..self.n_groups) |g| {
+ var max_score: f32 = -std.math.inf_f32;
+ for (0..experts_per_group) |e| {
+ const score = try reshaped_scores.at(.{b, g, e});
+ if (score > max_score) max_score = score;
+ }
+ try group_scores.set(.{b, g}, max_score);
+ }
+ }
+ } else {
+ // Use sum of top-2 scores as group score
+ for (0..batch_size) |b| {
+ for (0..self.n_groups) |g| {
+ var scores_arr = try self.allocator.alloc(f32, experts_per_group);
+ defer self.allocator.free(scores_arr);
+
+ // Extract scores for this group
+ for (0..experts_per_group) |e| {
+ scores_arr[e] = try reshaped_scores.at(.{b, g, e});
+ }
+
+ // Sort to find top-2
+ std.sort.sort(f32, scores_arr, {}, std.sort.desc(f32));
+
+ // Sum top-2 scores
+ const group_score = scores_arr[0] + scores_arr[1];
+ try group_scores.set(.{b, g}, group_score);
+ }
+ }
+ }
+
+ // Find top-k groups
+ var top_groups = try Tensor(usize, 2).init(
+ self.allocator,
+ .{batch_size, self.n_limited_groups}
+ );
+ defer top_groups.deinit();
+
+ // Select top-k groups
+ for (0..batch_size) |b| {
+ var scores_arr = try self.allocator.alloc(struct { score: f32, idx: usize }, self.n_groups);
+ defer self.allocator.free(scores_arr);
+
+ // Prepare for sorting
+ for (0..self.n_groups) |g| {
+ scores_arr[g] = .{
+ .score = try group_scores.at(.{b, g}),
+ .idx = g,
+ };
+ }
+
+ // Sort by score
+ const Sort = struct {
+ fn desc(context: void, a: anytype, b: anytype) bool {
+ return a.score > b.score;
+ }
+ };
+ std.sort.sort(struct { score: f32, idx: usize }, scores_arr, {}, Sort.desc);
+
+ // Store top-k group indices
+ for (0..self.n_limited_groups) |i| {
+ try top_groups.set(.{b, i}, scores_arr[i].idx);
+ }
+ }
+
+ // Create mask for filtering
+ var mask = try Tensor(bool, 3).init(
+ self.allocator,
+ .{batch_size, self.n_groups, 1}
+ );
+ defer mask.deinit();
+
+ // Initialize all groups as masked (excluded)
+ @memset(mask.data, true);
+
+ // Unmask top groups
+ for (0..batch_size) |b| {
+ for (0..self.n_limited_groups) |i| {
+ const g = try top_groups.at(.{b, i});
+ try mask.set(.{b, g, 0}, false);
+ }
+ }
+
+ // Apply mask
+ for (0..batch_size) |b| {
+ for (0..self.n_groups) |g| {
+ const is_masked = try mask.at(.{b, g, 0});
+ if (is_masked) {
+ // Mask out this group by setting scores to -inf
+ for (0..experts_per_group) |e| {
+ try reshaped_scores.set(.{b, g, e}, -std.math.inf_f32);
+ }
+ }
+ }
+ }
+
+ // Reshape back to original shape
+ try scores.copyFrom(reshaped_scores.reshape(.{batch_size, self.n_experts}) catch unreachable);
+ }
+
+ // Select top-k experts based on routing scores
+ fn selectTopkExperts(
+ self: *const Self,
+ scores: Tensor(f32, 2),
+ original_scores: Tensor(f32, 2),
+ indices: *Tensor(usize, 2),
+ weights: *Tensor(f32, 2)
+ ) !void {
+ const batch_size = scores.shape[0];
+
+ for (0..batch_size) |b| {
+ var scores_arr = try self.allocator.alloc(struct { score: f32, idx: usize }, self.n_experts);
+ defer self.allocator.free(scores_arr);
+
+ // Prepare for sorting
+ for (0..self.n_experts) |e| {
+ scores_arr[e] = .{
+ .score = try scores.at(.{b, e}),
+ .idx = e,
+ };
+ }
+
+ // Sort by score
+ const Sort = struct {
+ fn desc(context: void, a: anytype, b: anytype) bool {
+ return a.score > b.score;
+ }
+ };
+ std.sort.sort(struct { score: f32, idx: usize }, scores_arr, {}, Sort.desc);
+
+ // Store top-k indices and get weights from original scores
+ for (0..self.topk) |i| {
+ const expert_idx = scores_arr[i].idx;
+ try indices.set(.{b, i}, expert_idx);
+
+ // Get weight from original scores
+ const weight = try original_scores.at(.{b, expert_idx});
+ try weights.set(.{b, i}, weight);
+ }
+
+ // Normalize weights for sigmoid scoring
+ if (self.score_func == .sigmoid) {
+ var sum: f32 = 0.0;
+ for (0..self.topk) |i| {
+ sum += try weights.at(.{b, i});
+ }
+
+ if (sum > 0.0) {
+ for (0..self.topk) |i| {
+ const w = try weights.at(.{b, i});
+ try weights.set(.{b, i}, w / sum);
+ }
+ }
+ }
+ }
+ }
+ };
+}
+
+// Output from router gate
+pub const RouterOutput = struct {
+ weights: Tensor(f32, 2), // [batch_size, topk]
+ indices: Tensor(usize, 2), // [batch_size, topk]
+};
+```
+
+**Key Features:**
+- **Compile-Time Specialization**: Generated MoE implementation tailored to model dimensions and configuration
+- **Parallel Expert Execution**: Efficient multi-threading with work distribution and load balancing
+- **Atomic Operations**: Thread-safe updates to shared tensors
+- **Group-Based Routing**: Optimized implementation of expert groups for more efficient routing
+- **Memory-Efficient Tensor Management**: Careful handling of temporary allocations
+- **Flexible Scoring Functions**: Support for both softmax and sigmoid routing
+- **Expert Load Balancing**: Runtime tracking of expert utilization
+- **Distributed Expert Sharding**: Support for distributing experts across multiple processes
+
+### 3. Computation Backend
+
+Outlining the computation backend architecture for the DeepSeek-V3 project implemented in Zig. The design emphasizes performance, modularity, and hardware portability.
+
+#### 3.1 Backend Interface
+
+The backend interface provides a unified abstraction layer for all computation targets while maintaining Zig's zero-cost abstraction philosophy.
+
+```zig
+pub const ComputeError = error{
+ MatrixDimensionMismatch,
+ OutOfMemory,
+ UnsupportedOperation,
+ HardwareAccelerationFailed,
+ DeviceError,
+ InvalidParameter,
+ UnsupportedDataType,
+ KernelExecutionFailed,
+ QuantizationError,
+};
+
+pub const ComputeBackend = struct {
+ const Self = @This();
+
+ // Function pointers for backend operations
+ matmulFn: *const fn(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) ComputeError!void,
+ addFn: *const fn(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) ComputeError!void,
+ activationFn: *const fn(x: anytype, y: *anytype, act_type: ActivationType, allocator: std.mem.Allocator) ComputeError!void,
+ softmaxFn: *const fn(x: anytype, y: *anytype, dim: ?usize, allocator: std.mem.Allocator) ComputeError!void,
+
+ // Device management
+ initDeviceFn: *const fn(device_id: ?usize) ComputeError!void,
+ releaseDeviceFn: *const fn() void,
+
+ // Memory management
+ allocateDeviceMemoryFn: *const fn(size: usize) ComputeError!*anyopaque,
+ freeDeviceMemoryFn: *const fn(ptr: *anyopaque) void,
+ copyHostToDeviceFn: *const fn(host_ptr: *const anyopaque, device_ptr: *anyopaque, size: usize) ComputeError!void,
+ copyDeviceToHostFn: *const fn(device_ptr: *const anyopaque, host_ptr: *anyopaque, size: usize) ComputeError!void,
+
+ // Backend info
+ getBackendInfoFn: *const fn() BackendInfo,
+
+ // Backend factory functions
+ pub fn createCpuBackend(config: CpuBackendConfig) !*Self {
+ const allocator = config.allocator orelse std.heap.page_allocator;
+
+ var backend = try allocator.create(Self);
+ errdefer allocator.destroy(backend);
+
+ backend.* = .{
+ .matmulFn = if (config.use_simd) simdMatmul else scalarMatmul,
+ .addFn = if (config.use_simd) simdAdd else scalarAdd,
+ .activationFn = genericActivation,
+ .softmaxFn = genericSoftmax,
+ .initDeviceFn = initCpuDevice,
+ .releaseDeviceFn = releaseCpuDevice,
+ .allocateDeviceMemoryFn = allocateCpuMemory,
+ .freeDeviceMemoryFn = freeCpuMemory,
+ .copyHostToDeviceFn = cpuMemcpy,
+ .copyDeviceToHostFn = cpuMemcpy,
+ .getBackendInfoFn = getCpuBackendInfo,
+ };
+
+ return backend;
+ }
+
+ pub fn createMetalBackend(config: MetalBackendConfig) !*Self {
+ // Implementation details for Metal backend would go here
+ @compileError("Metal backend not implemented yet");
+ }
+
+ pub fn createCudaBackend(config: CudaBackendConfig) !*Self {
+ // Implementation details for CUDA backend would go here
+ @compileError("CUDA backend not implemented yet");
+ }
+};
+```
+
+#### 3.2 Cross-Platform Compilation
+
+One of the key advantages of implementing DeepZig V3 in Zig is the language's exceptional cross-compilation capabilities. Zig includes the compiler and standard libraries for all supported targets, making it trivial to compile for different platforms without additional toolchains.
+
+#### 3.2.1 Cross-Compilation Support
+
+```zig
+// Example of how to build for different target platforms
+pub fn build(b: *std.Build) void {
+ // Standard x86_64 Linux build
+ const linux_x86_64 = b.standardTargetOptions(.{
+ .default_target = .{
+ .cpu_arch = .x86_64,
+ .os_tag = .linux,
+ .cpu_features_add = std.Target.x86.Feature.avx2_featureset,
+ },
+ });
+
+ // Apple Silicon build
+ const macos_aarch64 = b.standardTargetOptions(.{
+ .default_target = .{
+ .cpu_arch = .aarch64,
+ .os_tag = .macos,
+ .cpu_features_add = std.Target.aarch64.Feature.apple_a14_featureset,
+ },
+ });
+
+ // Windows x86_64 build
+ const windows_x86_64 = b.standardTargetOptions(.{
+ .default_target = .{
+ .cpu_arch = .x86_64,
+ .os_tag = .windows,
+ .abi = .msvc,
+ },
+ });
+
+ // WASM build for browser deployment
+ const wasm = b.standardTargetOptions(.{
+ .default_target = .{
+ .cpu_arch = .wasm32,
+ .os_tag = .freestanding,
+ },
+ });
+
+ // Create libs/executables for each target
+ createBuild(b, linux_x86_64, "linux-x86_64");
+ createBuild(b, macos_aarch64, "macos-arm64");
+ createBuild(b, windows_x86_64, "windows-x86_64");
+ createBuild(b, wasm, "web");
+}
+
+fn createBuild(b: *std.Build, target: std.zig.CrossTarget, name: []const u8) void {
+ // Create optimized and debug builds
+ const optimize = b.standardOptimizeOption(.{});
+
+ // Create library
+ const lib = b.addStaticLibrary(.{
+ .name = std.fmt.allocPrint(
+ b.allocator,
+ "deepzig-{s}",
+ .{name}
+ ) catch unreachable,
+ .root_source_file = .{ .path = "src/main.zig" },
+ .target = target,
+ .optimize = optimize,
+ });
+
+ // Install in the appropriate location
+ b.installArtifact(lib);
+
+ // Create a CLI tool using the library
+ const exe = b.addExecutable(.{
+ .name = std.fmt.allocPrint(
+ b.allocator,
+ "deepzig-cli-{s}",
+ .{name}
+ ) catch unreachable,
+ .root_source_file = .{ .path = "src/cli.zig" },
+ .target = target,
+ .optimize = optimize,
+ });
+
+ exe.linkLibrary(lib);
+ b.installArtifact(exe);
+}
+```
+
+#### 3.2.2 C ABI Compatibility
+
+DeepZig V3 leverages Zig's seamless interoperability with C to interface with existing ML libraries:
+
+```zig
+// Example of interfacing with C libraries
+const c = @cImport({
+ @cInclude("cublas_v2.h"); // For NVIDIA GPU acceleration
+ @cInclude("mkl.h"); // For Intel CPU optimization
+});
+
+pub fn createOptimizedBackend() !*ComputeBackend {
+ // Try to use hardware-specific libraries in order of preference
+ if (hasCudaSupport()) {
+ return createCudaBackend();
+ } else if (hasMklSupport()) {
+ return createMklBackend();
+ } else {
+ return createNativeBackend();
+ }
+}
+
+fn hasCudaSupport() bool {
+ // Check if CUDA is available
+ var device_count: c_int = 0;
+ const status = c.cudaGetDeviceCount(&device_count);
+ return (status == c.cudaSuccess and device_count > 0);
+}
+
+fn hasMklSupport() bool {
+ // Check if MKL is available
+ return c.mkl_get_version(null) != 0;
+}
+```
+
+This cross-platform approach ensures DeepZig V3 can run efficiently on virtually any hardware platform, from high-end GPU servers to consumer devices, with appropriate performance optimizations for each target.
+
+#### 3.3 Platform-Specific Implementations
+
+```zig
+pub const CPUBackend = struct {
+ allocator: std.mem.Allocator,
+ thread_pool: ?*ThreadPool,
+
+ pub fn init(allocator: std.mem.Allocator, thread_count: ?usize) !ComputeBackend {
+ const thread_pool = if (thread_count) |count| {
+ try ThreadPool.init(allocator, .{ .thread_count = count });
+ } else null;
+
+ return ComputeBackend{
+ .matmulFn = cpuMatmul,
+ .softmaxFn = cpuSoftmax,
+ .rmsnormFn = cpuRmsnorm,
+ .attentionFn = cpuAttention,
+ // Other operations...
+ .config = BackendConfig{
+ .backend_type = .Cpu,
+ .max_threads = thread_count,
+ // Other CPU-specific config...
+ },
+ };
+ }
+
+ fn cpuMatmul(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) !void {
+ // Dynamically select the optimal implementation based on matrix dimensions and CPU features
+ if (c.rows * c.cols > 1024 * 1024 and detectCpuFeatures().use_avx2) {
+ return cpuMatmulParallel(a, b, c, allocator);
+ }
+ return cpuMatmulSIMD(a, b, c, allocator);
+ }
+
+ fn cpuSoftmax(x: anytype, dim: usize, allocator: std.mem.Allocator) !void {
+ // Optimized CPU implementation using SIMD
+ // Implementation details...
+ }
+
+ // Other CPU-specific implementations...
+};
+
+pub const MetalBackend = struct {
+ device: *MTLDevice,
+ command_queue: *MTLCommandQueue,
+ library: *MTLLibrary,
+ allocator: std.mem.Allocator,
+ pipelines: PipelineCache,
+
+ pub fn init(allocator: std.mem.Allocator) !ComputeBackend {
+ // Initialize Metal device, command queue, and library
+ const device = MTLCreateSystemDefaultDevice() orelse return error.MetalDeviceNotAvailable;
+ const command_queue = device.newCommandQueue() orelse return error.CommandQueueCreationFailed;
+
+ // Load compute shaders from embedded metal code or compiled library
+ const library = try loadDefaultLibrary(device);
+
+ // Initialize pipeline cache
+ var pipelines = PipelineCache.init(allocator);
+ try pipelines.precompileEssentialPipelines(device, library);
+
+ return ComputeBackend{
+ .matmulFn = metalMatmul,
+ .softmaxFn = metalSoftmax,
+ .rmsnormFn = metalRmsnorm,
+ .attentionFn = metalAttention,
+ // Other operations...
+ .config = BackendConfig{
+ .backend_type = .Metal,
+ .workgroup_size = .{16, 16, 1},
+ .shared_memory_size = 32 * 1024,
+ // Other Metal-specific config...
+ },
+ };
+ }
+
+ fn metalMatmul(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) !void {
+ // Implementation using Metal Performance Shaders when available
+ // Fallback to custom compute kernel for specialized operations
+ // Implementation details...
+ }
+
+ fn metalSoftmax(x: anytype, dim: usize, allocator: std.mem.Allocator) !void {
+ // Metal implementation
+ // Implementation details...
+ }
+
+ // Other Metal-specific implementations...
+};
+```
+
+**Key Features:**
+- Abstract interface with compile-time type safety
+- Proper error handling with Zig's error system
+- Zero-cost abstraction for backend dispatch
+- Dynamic backend selection based on available hardware
+- Specialized implementations for different hardware architectures
+- Thread pool integration for CPU parallelism
+- Resource management for GPU backends
+- Pipeline caching for improved performance
+
+
+#### 3.4 SIMD Vectorization
+
+DeepSeek-V3 leverages Zig's built-in vector types to achieve high-performance computation across different architectures.
+
+```zig
+// Define vector types with architecture-specific sizes
+pub fn VectorType(comptime T: type, comptime len: usize) type {
+ return @Vector(len, T);
+}
+
+// Compile-time determination of optimal vector size
+pub fn getOptimalVectorSize(comptime T: type) usize {
+ const target = @import("builtin").target;
+
+ // Determine vector size based on architecture and data type
+ if (T == f32) {
+ if (target.cpu.arch == .x86_64 or target.cpu.arch == .x86) {
+ if (target.cpu.features.isEnabled(.avx512f)) {
+ return 16; // 512 bits / 32 bits = 16 elements
+ } else if (target.cpu.features.isEnabled(.avx2)) {
+ return 8; // 256 bits / 32 bits = 8 elements
+ } else if (target.cpu.features.isEnabled(.sse4_1)) {
+ return 4; // 128 bits / 32 bits = 4 elements
+ }
+ } else if (target.cpu.arch == .aarch64) {
+ if (target.cpu.features.isEnabled(.neon)) {
+ return 4; // 128 bits / 32 bits = 4 elements
+ }
+ }
+ } else if (T == f16) {
+ // Similar logic for f16 with doubled vector sizes
+ // ...
+ }
+
+ // Default fallback
+ return 4;
+}
+
+// Example of SIMD matrix multiplication
+pub fn matrixMultiplySIMD(comptime T: type, a: []const T, b: []const T, c: []T, m: usize, n: usize, k: usize) void {
+ const vec_size = comptime getOptimalVectorSize(T);
+ const Vec = VectorType(T, vec_size);
+
+ // Process blocks that align with vector size
+ const k_vec = k / vec_size * vec_size;
+
+ for (0..m) |i| {
+ for (0..n) |j| {
+ var sum: T = 0;
+ var vec_sum: Vec = @splat(0);
+
+ // Vector part
+ var kv: usize = 0;
+ while (kv < k_vec) : (kv += vec_size) {
+ const a_vec = blk: {
+ var tmp: Vec = undefined;
+ for (0..vec_size) |v| {
+ tmp[v] = a[i * k + kv + v];
+ }
+ break :blk tmp;
+ };
+
+ const b_vec = blk: {
+ var tmp: Vec = undefined;
+ for (0..vec_size) |v| {
+ tmp[v] = b[kv + v + j * k];
+ }
+ break :blk tmp;
+ };
+
+ vec_sum += a_vec * b_vec;
+ }
+
+ // Reduce vector
+ for (0..vec_size) |v| {
+ sum += vec_sum[v];
+ }
+
+ // Remaining elements
+ for (k_vec..k) |kk| {
+ sum += a[i * k + kk] * b[kk + j * k];
+ }
+
+ c[i * n + j] = sum;
+ }
+ }
+}
+```
+
+#### 3.5 Runtime CPU Feature Detection
+
+```zig
+pub fn detectCpuFeatures() BackendConfig {
+ var config = BackendConfig{
+ .backend_type = BackendType.Cpu,
+ };
+
+ // Try to detect CPU features at runtime
+ const cpu_info = std.zig.system.getCpuInfo() catch {
+ // Fallback to safe defaults if detection fails
+ return config;
+ };
+
+ // Configure based on detected features
+ config.use_avx512 = cpu_info.features.isEnabled(.avx512f);
+ config.use_avx2 = cpu_info.features.isEnabled(.avx2);
+ config.use_sse4_1 = cpu_info.features.isEnabled(.sse4_1);
+ config.use_neon = cpu_info.features.isEnabled(.neon);
+
+ return config;
+}
+```
+
+#### 3.6 Backend Configuration
+
+Backend configuration allows fine-tuning performance characteristics based on hardware capabilities and workload requirements.
+
+```zig
+pub const BackendType = enum {
+ Cpu,
+ Cuda,
+ Metal,
+ Vulkan,
+ WebGPU,
+};
+
+pub const BackendConfig = struct {
+ backend_type: BackendType,
+ max_threads: ?usize = null,
+ cache_line_size: usize = 64, // Default x86-64 cache line size
+ use_avx512: bool = false, // Use AVX-512 when available
+ use_avx2: bool = true, // Use AVX2 when available
+ use_sse4_1: bool = true, // Use SSE4.1 when available
+ use_neon: bool = false, // Use ARM NEON when available
+ prefetch_distance: usize = 8, // Prefetch N cache lines ahead
+ tiling_size: ?[2]usize = null, // Matrix tiling dimensions
+ batch_size: ?usize = null, // Batch size for kernel operations
+ memory_pool_size: ?usize = null, // Size of pre-allocated memory pool
+ use_half_precision: bool = false, // Use FP16 where appropriate
+ use_mixed_precision: bool = true, // Use mixed precision for matmul
+
+ // GPU-specific options
+ workgroup_size: ?[3]usize = null, // GPU workgroup dimensions
+ shared_memory_size: ?usize = null, // GPU shared memory allocation
+ compute_queue_depth: usize = 3, // Maximum concurrent compute operations
+};
+```
+
+#### 3.7 GPU Integration
+
+DeepSeek-V3 supports multiple GPU backends, with specialized implementations for each platform.
+
+#### 3.7.1 CUDA Backend
+
+```zig
+pub const CudaBackend = struct {
+ allocator: std.mem.Allocator,
+ device: i32,
+ stream: ?*anyopaque,
+ handles: CudaHandles,
+ module_cache: ModuleCache,
+
+ pub fn init(allocator: std.mem.Allocator, device_id: ?i32) !ComputeBackend {
+ // Initialize CUDA device, context, and stream
+ const device = if (device_id) |id| id else try getOptimalCudaDevice();
+ try cudaSetDevice(device);
+
+ var stream: ?*anyopaque = null;
+ try checkCudaStatus(cudaStreamCreate(&stream));
+
+ // Initialize cuBLAS and cuDNN handles
+ var handles = try CudaHandles.init(stream);
+
+ // Compile and cache essential CUDA kernels
+ var module_cache = try ModuleCache.init(allocator);
+ try module_cache.compileEssentialKernels();
+
+ return ComputeBackend{
+ .matmulFn = cudaMatmul,
+ .softmaxFn = cudaSoftmax,
+ .rmsnormFn = cudaRmsnorm,
+ .attentionFn = cudaAttention,
+ // Other operations...
+ .config = BackendConfig{
+ .backend_type = .Cuda,
+ .workgroup_size = .{16, 16, 1},
+ .shared_memory_size = 48 * 1024,
+ // Other CUDA-specific config...
+ },
+ };
+ }
+
+ fn cudaMatmul(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) !void {
+ // Use cuBLAS for large matrices
+ // Fall back to custom kernels for specialized operations
+ // Implementation details...
+ }
+
+ // Other CUDA-specific implementations...
+};
+```
+
+#### 3.7.2 Vulkan Backend
+
+```zig
+pub const VulkanBackend = struct {
+ allocator: std.mem.Allocator,
+ instance: vk.Instance,
+ physical_device: vk.PhysicalDevice,
+ device: vk.Device,
+ compute_queue: vk.Queue,
+ command_pool: vk.CommandPool,
+ pipeline_cache: vk.PipelineCache,
+ shader_modules: ShaderModuleCache,
+
+ pub fn init(allocator: std.mem.Allocator) !ComputeBackend {
+ // Initialize Vulkan instance, device, and queues
+ // Implementation details...
+
+ return ComputeBackend{
+ .matmulFn = vulkanMatmul,
+ .softmaxFn = vulkanSoftmax,
+ .rmsnormFn = vulkanRmsnorm,
+ .attentionFn = vulkanAttention,
+ // Other operations...
+ .config = BackendConfig{
+ .backend_type = .Vulkan,
+ // Vulkan-specific config...
+ },
+ };
+ }
+
+ // Vulkan-specific implementations...
+};
+```
+
+#### 3.8 Quantization Framework
+
+The quantization framework enables efficient model deployment through reduced precision arithmetic.
+
+```zig
+// Supported quantization methods
+pub const QuantizationMethod = enum {
+ None,
+ FP16, // Half precision
+ Int8, // 8-bit integer quantization
+ Int4, // 4-bit integer quantization
+ NF4, // NormalFloat4 quantization
+ GPTQ, // GPTQ quantization
+ AWQ, // Activation-aware weight quantization
+};
+
+// Quantization configuration
+pub const QuantConfig = struct {
+ method: QuantizationMethod = .None,
+ scale_type: ?type = null, // Type for quantization scales
+ group_size: usize = 128, // Size of quantization groups
+ bits: u8 = 8, // Bits per quantized value
+ symmetric: bool = false, // Symmetric vs asymmetric quantization
+
+ // Calibration parameters
+ calibration_dataset: ?[]const u8 = null,
+ num_calibration_samples: usize = 128,
+
+ // Sparsity options
+ use_sparse: bool = false,
+ sparsity_threshold: f32 = 0.01,
+};
+
+// Abstract quantizer interface
+pub const Quantizer = struct {
+ const Self = @This();
+
+ quantizeFn: *const fn(self: *Self, tensor: Tensor, config: QuantConfig, allocator: std.mem.Allocator) anyerror!Tensor,
+ dequantizeFn: *const fn(self: *Self, tensor: Tensor, allocator: std.mem.Allocator) anyerror!Tensor,
+
+ pub fn quantize(self: *Self, tensor: Tensor, config: QuantConfig, allocator: std.mem.Allocator) !Tensor {
+ return self.quantizeFn(self, tensor, config, allocator);
+ }
+
+ pub fn dequantize(self: *Self, tensor: Tensor, allocator: std.mem.Allocator) !Tensor {
+ return self.dequantizeFn(self, tensor, allocator);
+ }
+};
+```
+
+#### 3.9 Memory Management
+
+Efficient memory management is crucial for large language model inference.
+
+```zig
+// Memory allocation strategy
+pub const AllocStrategy = enum {
+ Default, // Standard allocator
+ Arena, // Arena allocator for bulk allocations
+ Pool, // Memory pool for fixed-size allocations
+ Streaming, // Streaming allocator for pipelined operations
+ Pinned, // Pinned memory for efficient host-device transfers
+};
+
+// Memory pool for efficient tensor allocations
+pub const TensorMemoryPool = struct {
+ const Self = @This();
+
+ parent_allocator: std.mem.Allocator,
+ pool: std.heap.MemoryPool,
+ block_sizes: []const usize,
+ blocks: std.AutoArrayHashMap(usize, std.ArrayList(*anyopaque)),
+ mutex: std.Thread.Mutex,
+ stats: MemoryStats,
+
+ pub fn init(allocator: std.mem.Allocator, config: MemoryPoolConfig) !Self {
+ // Initialize memory pool with predefined block sizes
+ // Implementation details...
+ }
+
+ pub fn allocate(self: *Self, size: usize, alignment: usize) ![]u8 {
+ // Find the appropriate block size or allocate directly
+ // Implementation details...
+ }
+
+ pub fn free(self: *Self, ptr: []u8) void {
+ // Return to pool or free directly
+ // Implementation details...
+ }
+
+ // Memory management utilities
+ pub fn preallocate(self: *Self, block_size: usize, count: usize) !void {
+ // Preallocate multiple blocks of the specified size
+ // Implementation details...
+ }
+
+ pub fn reclaim(self: *Self) void {
+ // Reclaim unused memory blocks
+ // Implementation details...
+ }
+};
+
+// Key-Value cache management for efficient inference
+pub const KVCache = struct {
+ allocator: std.mem.Allocator,
+ k_cache: Tensor,
+ v_cache: Tensor,
+ capacity: usize,
+ size: usize,
+ head_dim: usize,
+ num_heads: usize,
+
+ pub fn init(allocator: std.mem.Allocator, batch_size: usize, num_heads: usize, head_dim: usize, max_seq_len: usize) !Self {
+ // Initialize key-value cache with appropriate dimensions
+ // Implementation details...
+ }
+
+ pub fn append(self: *Self, k: Tensor, v: Tensor, pos: usize) !void {
+ // Append new key-value pairs to the cache
+ // Implementation details...
+ }
+
+ pub fn prefill(self: *Self, k: Tensor, v: Tensor) !void {
+ // Prefill the cache with initial key-value pairs
+ // Implementation details...
+ }
+
+ pub fn rotatePositions(self: *Self, positions: []const usize) !void {
+ // Rearrange cache entries based on position IDs (for speculative decoding)
+ // Implementation details...
+ }
+
+ pub fn clear(self: *Self) void {
+ // Reset the cache size without deallocating memory
+ // Implementation details...
+ }
+};
+```
+
+#### 3.10 Metal Integration for Apple Silicon
+
+Modern Apple Silicon devices offer exceptional compute performance, and our Zig implementation takes full advantage of these capabilities through direct Metal API integration:
+
+```zig
+pub const MetalBackend = struct {
+ const Self = @This();
+
+ // Core Metal resources
+ device: *MTLDevice,
+ command_queue: *MTLCommandQueue,
+ library: *MTLLibrary,
+
+ // Pipeline cache for reusing compiled compute pipelines
+ pipeline_cache: std.AutoHashMap(u64, *MTLComputePipelineState),
+
+ // Memory management
+ allocator: std.mem.Allocator,
+ buffer_pool: BufferPool,
+
+ // Configuration and statistics
+ config: BackendConfig,
+ stats: MetalStatistics,
+
+ pub fn init(allocator: std.mem.Allocator) !*Self {
+ // Get the default Metal device
+ var device = MTLCreateSystemDefaultDevice();
+ if (device == null) return error.MetalDeviceNotAvailable;
+
+ // Create a command queue for submitting work to the GPU
+ var command_queue = device.?.newCommandQueue();
+ if (command_queue == null) return error.MetalCommandQueueCreationFailed;
+
+ // Compile our Metal shader library from source or load precompiled metallib
+ var library: ?*MTLLibrary = null;
+ if (comptime @import("builtin").mode == .Debug) {
+ // Compile from source for easier debugging
+ library = try compileLibraryFromSource(device.?, shader_source);
+ } else {
+ // Use precompiled metallib for release builds
+ const metallib_path = try findMetalLibPath(allocator);
+ defer allocator.free(metallib_path);
+
+ library = try loadCompiledLibrary(device.?, metallib_path);
+ }
+
+ // Create the Metal backend
+ var self = try allocator.create(Self);
+ errdefer allocator.destroy(self);
+
+ // Initialize the pipeline cache
+ var pipeline_cache = std.AutoHashMap(u64, *MTLComputePipelineState).init(allocator);
+ errdefer pipeline_cache.deinit();
+
+ // Initialize the buffer pool for efficient memory reuse
+ var buffer_pool = try BufferPool.init(allocator, device.?);
+ errdefer buffer_pool.deinit();
+
+ // Get optimal configuration based on the device capabilities
+ var config = try getMetalOptimalConfig(device.?);
+
+ self.* = .{
+ .device = device.?,
+ .command_queue = command_queue.?,
+ .library = library.?,
+ .pipeline_cache = pipeline_cache,
+ .allocator = allocator,
+ .buffer_pool = buffer_pool,
+ .config = config,
+ .stats = MetalStatistics.init(),
+ };
+
+ return self;
+ }
+
+ pub fn deinit(self: *Self) void {
+ // Release all cached pipelines
+ var it = self.pipeline_cache.valueIterator();
+ while (it.next()) |pipeline| {
+ pipeline.*.release();
+ }
+ self.pipeline_cache.deinit();
+
+ // Clean up buffer pool
+ self.buffer_pool.deinit();
+
+ // Release Metal resources
+ self.library.release();
+ self.command_queue.release();
+ self.device.release();
+
+ // Free memory
+ self.allocator.destroy(self);
+ }
+
+ // Get or create a compute pipeline for a function
+ pub fn getPipeline(self: *Self, function_name: []const u8) !*MTLComputePipelineState {
+ // Hash the function name for quick lookup
+ const hash = std.hash.CityHash64.hash(function_name);
+
+ // Check if we already have a cached pipeline
+ if (self.pipeline_cache.get(hash)) |pipeline| {
+ return pipeline;
+ }
+
+ // Create a new pipeline if not found
+ var function = self.library.newFunctionWithName(function_name);
+ if (function == null) return error.MetalFunctionNotFound;
+ defer function.?.release();
+
+ // Create the compute pipeline
+ var pipeline_desc = MTLComputePipelineDescriptor.alloc().init();
+ defer pipeline_desc.release();
+
+ pipeline_desc.setComputeFunction(function.?);
+
+ // Enable buffer mutability tracking in debug mode
+ if (comptime @import("builtin").mode == .Debug) {
+ pipeline_desc.setMutabilityOptions(.{
+ .MTLPipelineBufferMutabilityAccessTracking = true,
+ });
+ }
+
+ // Enable threadgroup memory length optimization
+ pipeline_desc.setThreadGroupSizeIsMultipleOfThreadExecutionWidth(true);
+
+ // Create the pipeline state
+ var error_ptr: ?*NSError = null;
+ var pipeline = self.device.newComputePipelineStateWithDescriptor(
+ pipeline_desc,
+ .MTLPipelineOptionArgumentInfo,
+ null,
+ &error_ptr
+ );
+
+ if (pipeline == null) {
+ if (error_ptr != null) {
+ // Log the error details
+ const error_str = error_ptr.?.localizedDescription().UTF8String();
+ std.log.err("Failed to create pipeline for {s}: {s}", .{
+ function_name, error_str,
+ });
+ error_ptr.?.release();
+ }
+ return error.MetalPipelineCreationFailed;
+ }
+
+ // Cache the pipeline for future use
+ try self.pipeline_cache.put(hash, pipeline.?);
+
+ return pipeline.?;
+ }
+
+ // Execute a compute kernel with the given parameters
+ pub fn executeKernel(
+ self: *Self,
+ kernel_name: []const u8,
+ grid_size: [3]u32,
+ block_size: [3]u32,
+ buffers: []const MetalBuffer,
+ wait_until_completed: bool,
+ ) !void {
+ // Get the pipeline for this kernel
+ var pipeline = try self.getPipeline(kernel_name);
+
+ // Create a command buffer
+ var command_buffer = self.command_queue.commandBuffer();
+ if (command_buffer == null) return error.MetalCommandBufferCreationFailed;
+
+ // Create a compute command encoder
+ var encoder = command_buffer.?.computeCommandEncoder();
+ if (encoder == null) return error.MetalComputeEncoderCreationFailed;
+
+ // Set the compute pipeline
+ encoder.?.setComputePipelineState(pipeline);
+
+ // Bind buffers
+ for (buffers, 0..) |buffer, i| {
+ encoder.?.setBuffer(buffer.handle, buffer.offset, @intCast(i));
+ }
+
+ // Calculate threadgroup size
+ var threadgroup_size = MTLSize{
+ .width = block_size[0],
+ .height = block_size[1],
+ .depth = block_size[2],
+ };
+
+ // Calculate grid size
+ var grid = MTLSize{
+ .width = grid_size[0],
+ .height = grid_size[1],
+ .depth = grid_size[2],
+ };
+
+ // Dispatch the compute work
+ encoder.?.dispatchThreadgroups(grid, threadgroup_size);
+
+ // End encoding
+ encoder.?.endEncoding();
+
+ // Commit the command buffer
+ command_buffer.?.commit();
+
+ // Wait for completion if requested
+ if (wait_until_completed) {
+ command_buffer.?.waitUntilCompleted();
+ }
+
+ // Update statistics
+ self.stats.kernel_executions += 1;
+ }
+
+ // Create a buffer and copy data to it
+ pub fn createBuffer(
+ self: *Self,
+ data: []const u8,
+ options: MTLResourceOptions,
+ ) !*MTLBuffer {
+ // Get a buffer from the pool or create a new one
+ var buffer = try self.buffer_pool.getBuffer(data.len, options);
+
+ // Copy data to the buffer
+ @memcpy(buffer.contents()[0..data.len], data);
+
+ return buffer;
+ }
+
+ // Create a tensor in Metal memory
+ pub fn createTensor(self: *Self, tensor: Tensor(f32, 2)) !MetalTensor {
+ // Calculate size in bytes
+ const size_bytes = tensor.data.len * @sizeOf(f32);
+
+ // Create a buffer
+ var buffer = try self.createBuffer(
+ @ptrCast([*]const u8, tensor.data.ptr)[0..size_bytes],
+ .StorageModeShared
+ );
+
+ return MetalTensor{
+ .buffer = buffer,
+ .shape = tensor.shape,
+ .element_type = .f32,
+ };
+ }
+
+ // Example implementation of matrix multiplication using Metal
+ pub fn matmul(
+ self: *Self,
+ a: Tensor(f32, 2),
+ b: Tensor(f32, 2),
+ ) !Tensor(f32, 2) {
+ // Validate dimensions
+ std.debug.assert(a.shape[1] == b.shape[0], "Incompatible matrix dimensions");
+
+ const m = a.shape[0];
+ const k = a.shape[1];
+ const n = b.shape[1];
+
+ // Create result tensor
+ var result = try Tensor(f32, 2).init(self.allocator, .{m, n});
+ errdefer result.deinit();
+
+ // Create Metal tensors
+ var a_metal = try self.createTensor(a);
+ defer a_metal.buffer.release();
+
+ var b_metal = try self.createTensor(b);
+ defer b_metal.buffer.release();
+
+ var result_metal = try self.createTensor(result);
+ defer result_metal.buffer.release();
+
+ // Create dimension buffer
+ const dims = [_]u32{@intCast(m), @intCast(k), @intCast(n)};
+ var dims_buffer = try self.createBuffer(
+ @ptrCast([*]const u8, &dims)[0..dims.len * @sizeOf(u32)],
+ .StorageModeShared
+ );
+ defer dims_buffer.release();
+
+ // Set up buffers
+ const buffers = [_]MetalBuffer{
+ .{ .handle = a_metal.buffer, .offset = 0 },
+ .{ .handle = b_metal.buffer, .offset = 0 },
+ .{ .handle = result_metal.buffer, .offset = 0 },
+ .{ .handle = dims_buffer, .offset = 0 },
+ };
+
+ // Calculate optimal workgroup size
+ const workgroup_size: [3]u32 = if (self.config.workgroup_size) |ws|
+ .{ @intCast(ws[0]), @intCast(ws[1]), 1 }
+ else
+ .{ 16, 16, 1 };
+
+ // Calculate grid size
+ const grid_size: [3]u32 = .{
+ (n + workgroup_size[0] - 1) / workgroup_size[0],
+ (m + workgroup_size[1] - 1) / workgroup_size[1],
+ 1,
+ };
+
+ // Execute the kernel
+ try self.executeKernel(
+ "matmul",
+ grid_size,
+ workgroup_size,
+ &buffers,
+ true
+ );
+
+ // Copy data back from Metal
+ @memcpy(
+ result.data,
+ @ptrCast([*]const f32, result_metal.buffer.contents())[0..result.data.len]
+ );
+
+ return result;
+ }
+};
+
+// Efficient buffer pooling to avoid frequent allocations
+pub const BufferPool = struct {
+ const Self = @This();
+
+ allocator: std.mem.Allocator,
+ device: *MTLDevice,
+ free_buffers: std.AutoHashMap(u64, std.ArrayList(*MTLBuffer)),
+
+ pub fn init(allocator: std.mem.Allocator, device: *MTLDevice) !Self {
+ return Self{
+ .allocator = allocator,
+ .device = device,
+ .free_buffers = std.AutoHashMap(u64, std.ArrayList(*MTLBuffer)).init(allocator),
+ };
+ }
+
+ pub fn deinit(self: *Self) void {
+ // Release all buffers
+ var it = self.free_buffers.valueIterator();
+ while (it.next()) |buffer_list| {
+ for (buffer_list.items) |buffer| {
+ buffer.release();
+ }
+ buffer_list.deinit();
+ }
+ self.free_buffers.deinit();
+ }
+
+ // Get a buffer of at least the requested size
+ pub fn getBuffer(self: *Self, size: usize, options: MTLResourceOptions) !*MTLBuffer {
+ // Round up to power of 2 for better reuse
+ const aligned_size = nextPowerOfTwo(size);
+
+ // Check if we have a free buffer of appropriate size
+ if (self.free_buffers.getPtr(aligned_size)) |buffer_list| {
+ if (buffer_list.items.len > 0) {
+ // Reuse an existing buffer
+ return buffer_list.pop();
+ }
+ }
+
+ // Create a new buffer if none available
+ var buffer = self.device.newBufferWithLength(aligned_size, options);
+ if (buffer == null) return error.MetalBufferAllocationFailed;
+
+ return buffer.?;
+ }
+
+ // Return a buffer to the pool for reuse
+ pub fn releaseBuffer(self: *Self, buffer: *MTLBuffer) !void {
+ const size = buffer.length();
+ const aligned_size = nextPowerOfTwo(size);
+
+ // Add to the appropriate size list
+ if (self.free_buffers.getPtr(aligned_size)) |buffer_list| {
+ try buffer_list.append(buffer);
+ } else {
+ // Create a new list if this is the first buffer of this size
+ var buffer_list = std.ArrayList(*MTLBuffer).init(self.allocator);
+ try buffer_list.append(buffer);
+ try self.free_buffers.put(aligned_size, buffer_list);
+ }
+ }
+
+ // Utility to find next power of two
+ fn nextPowerOfTwo(n: usize) usize {
+ var v = n;
+ v -= 1;
+ v |= v >> 1;
+ v |= v >> 2;
+ v |= v >> 4;
+ v |= v >> 8;
+ v |= v >> 16;
+ v |= v >> 32;
+ v += 1;
+ return v;
+ }
+};
+
+// Representation of a tensor in Metal memory
+pub const MetalTensor = struct {
+ buffer: *MTLBuffer,
+ shape: []const usize,
+ element_type: enum {
+ f16,
+ f32,
+ },
+};
+
+// Helper for buffer binding
+pub const MetalBuffer = struct {
+ handle: *MTLBuffer,
+ offset: u64 = 0,
+};
+
+// Statistics for performance monitoring
+pub const MetalStatistics = struct {
+ kernel_executions: usize = 0,
+ bytes_transferred: usize = 0,
+ peak_memory_usage: usize = 0,
+
+ pub fn init() MetalStatistics {
+ return .{};
+ }
+};
+
+// Example Metal shader source for matrix multiplication
+const shader_source =
+ \\#include
+ \\using namespace metal;
+ \\
+ \\kernel void matmul(
+ \\ const device float* a [[buffer(0)]],
+ \\ const device float* b [[buffer(1)]],
+ \\ device float* result [[buffer(2)]],
+ \\ const device uint* dims [[buffer(3)]],
+ \\ uint2 gid [[thread_position_in_grid]],
+ \\ uint2 lid [[thread_position_in_threadgroup]],
+ \\ uint2 lsize [[threads_per_threadgroup]])
+ \\{
+ \\ const uint m = dims[0];
+ \\ const uint k = dims[1];
+ \\ const uint n = dims[2];
+ \\
+ \\ // Check if within bounds
+ \\ if (gid.x >= n || gid.y >= m) return;
+ \\
+ \\ // Calculate result[gid.y][gid.x]
+ \\ float sum = 0.0f;
+ \\ for (uint i = 0; i < k; i++) {
+ \\ sum += a[gid.y * k + i] * b[i * n + gid.x];
+ \\ }
+ \\
+ \\ result[gid.y * n + gid.x] = sum;
+ \\}
+ \\
+ \\kernel void matmul_optimized(
+ \\ const device float* a [[buffer(0)]],
+ \\ const device float* b [[buffer(1)]],
+ \\ device float* result [[buffer(2)]],
+ \\ const device uint* dims [[buffer(3)]],
+ \\ uint2 gid [[thread_position_in_grid]],
+ \\ uint2 lid [[thread_position_in_threadgroup]],
+ \\ uint2 lsize [[threads_per_threadgroup]])
+ \\{
+ \\ const uint m = dims[0];
+ \\ const uint k = dims[1];
+ \\ const uint n = dims[2];
+ \\
+ \\ // Check if within bounds
+ \\ if (gid.x >= n || gid.y >= m) return;
+ \\
+ \\ // Use threadgroup memory for caching
+ \\ threadgroup float a_cache[16][16];
+ \\ threadgroup float b_cache[16][16];
+ \\
+ \\ float sum = 0.0f;
+ \\
+ \\ // Process in tiles
+ \\ for (uint tile = 0; tile < (k + 15) / 16; tile++) {
+ \\ // Load a tile into threadgroup memory
+ \\ const uint tile_idx = tile * 16;
+ \\
+ \\ if (tile_idx + lid.x < k && gid.y < m) {
+ \\ a_cache[lid.y][lid.x] = a[gid.y * k + tile_idx + lid.x];
+ \\ } else {
+ \\ a_cache[lid.y][lid.x] = 0.0f;
+ \\ }
+ \\
+ \\ if (tile_idx + lid.y < k && gid.x < n) {
+ \\ b_cache[lid.y][lid.x] = b[(tile_idx + lid.y) * n + gid.x];
+ \\ } else {
+ \\ b_cache[lid.y][lid.x] = 0.0f;
+ \\ }
+ \\
+ \\ // Wait for all threads to load data
+ \\ threadgroup_barrier(mem_flags::mem_threadgroup);
+ \\
+ \\ // Compute partial dot product for this tile
+ \\ for (uint i = 0; i < 16; i++) {
+ \\ sum += a_cache[lid.y][i] * b_cache[i][lid.x];
+ \\ }
+ \\
+ \\ // Wait for all threads to finish using the cached data
+ \\ threadgroup_barrier(mem_flags::mem_threadgroup);
+ \\ }
+ \\
+ \\ // Write result
+ \\ if (gid.x < n && gid.y < m) {
+ \\ result[gid.y * n + gid.x] = sum;
+ \\ }
+ \\}
+;
+```
+
+**Apple-Specific Optimizations:**
+
+1. **Metal Shader Integration**
+ - Direct compilation of Metal shaders from Zig source code
+ - Runtime shader compilation in debug mode for easier iteration
+ - Precompiled metallib loading for optimized release builds
+
+2. **Memory Management**
+ - Buffer pooling to minimize allocations and deallocations
+ - Shared memory mode for zero-copy between CPU and GPU
+ - Explicit control over resource storage options
+
+3. **Performance Optimizations**
+ - Tile-based computation for optimal cache utilization
+ - Threadgroup memory usage for shared data access
+ - Work distribution based on detected GPU characteristics
+ - Pipeline state caching for faster kernel dispatching
+
+4. **AMX Acceleration**
+ - Support for Apple Matrix extensions (AMX)
+ - Specialized matrix multiplication operations for M-series chips
+ - Custom shader variants optimized for different Apple Silicon generations
+
+5. **Neural Engine Integration**
+ - Optional ANE (Apple Neural Engine) offloading for supported operations
+ - Hybrid execution strategies combining GPU and Neural Engine
+ - Automatic fallback to Metal for unsupported operations
+
+
+### 4. Inference Pipeline
+
+The inference pipeline is the core execution flow for running the DeepSeek V3 model. Our Zig implementation focuses on efficiency, flexibility, and streaming capabilities.
+
+#### 4.1 Model Loading
+
+```zig
+// The ModelLoader handles loading and initializing DeepSeek V3 models
+pub const ModelLoader = struct {
+ const Self = @This();
+
+ allocator: std.mem.Allocator,
+ config: LoaderConfig,
+
+ // Configuration for model loading
+ pub const LoaderConfig = struct {
+ // Number of threads to use for weight loading
+ loading_threads: ?usize = null,
+
+ // Optional cache directory for model weights
+ cache_dir: ?[]const u8 = null,
+
+ // How to handle safetensors format
+ safetensors_memory_map: bool = true,
+
+ // Validation level for loaded weights
+ validation: enum {
+ none,
+ basic,
+ full
+ } = .basic,
+
+ // Device to place model on after loading
+ target_device: BackendType = .Cpu,
+ };
+
+ pub fn init(allocator: std.mem.Allocator, config: LoaderConfig) Self {
+ return .{
+ .allocator = allocator,
+ .config = config,
+ };
+ }
+
+ // Load a model from file
+ pub fn loadModel(
+ self: *Self,
+ path: []const u8,
+ model_args: ?ModelArgs,
+ ) !*TransformerModel {
+ const extension = std.fs.path.extension(path);
+
+ // Determine model format from file extension
+ if (std.mem.eql(u8, extension, ".safetensors")) {
+ return try self.loadFromSafetensors(path, model_args);
+ } else if (std.mem.eql(u8, extension, ".ckpt")) {
+ return try self.loadFromCheckpoint(path, model_args);
+ } else if (std.mem.eql(u8, extension, ".bin")) {
+ return try self.loadFromBinary(path, model_args);
+ } else if (std.fs.cwd().accessZ(path, .{}) == .AccessDenied) {
+ // Could be a Hugging Face model ID, try to download it
+ return try self.loadFromHuggingFace(path, model_args);
+ }
+
+ return error.UnsupportedModelFormat;
+ }
+
+ // Load model from SafeTensors format (optimized for memory mapping)
+ fn loadFromSafetensors(
+ self: *Self,
+ path: []const u8,
+ model_args: ?ModelArgs,
+ ) !*TransformerModel {
+ // Open the safetensors file
+ var file = try std.fs.cwd().openFile(path, .{});
+ defer file.close();
+
+ // Memory map the file for zero-copy access if configured
+ if (self.config.safetensors_memory_map) {
+ const file_size = try file.getEndPos();
+
+ // Memory map the file
+ const mapped_memory = try std.os.mmap(
+ null,
+ file_size,
+ std.os.PROT.READ,
+ std.os.MAP.PRIVATE,
+ file.handle,
+ 0,
+ );
+
+ // Process the memory-mapped safetensors
+ return try self.processSafetensorsMemoryMapped(
+ mapped_memory,
+ file_size,
+ model_args,
+ );
+ } else {
+ // If memory mapping is disabled, read the file conventionally
+ return try self.processSafetensorsFile(file, model_args);
+ }
+ }
+
+ // Process a memory-mapped SafeTensors file
+ fn processSafetensorsMemoryMapped(
+ self: *Self,
+ memory: []const u8,
+ file_size: usize,
+ model_args: ?ModelArgs,
+ ) !*TransformerModel {
+ // Parse the header which contains tensor metadata
+ const header_size = std.mem.readIntLittle(u64, memory[0..8]);
+ const header_json = memory[8..8+header_size];
+
+ // Parse the JSON header
+ var parsed = try std.json.parseFromSlice(
+ std.json.Value,
+ self.allocator,
+ header_json,
+ .{},
+ );
+ defer parsed.deinit();
+
+ // Get the model configuration from arguments or try to infer it
+ const args = try self.determineModelArgs(model_args, parsed.value);
+
+ // Create the model with the determined configuration
+ var model = try TransformerModel.create(self.allocator, args);
+ errdefer model.destroy();
+
+ // Create a tensor mapping for zero-copy loading
+ try self.loadTensorsFromSafetensorsMemory(
+ model,
+ memory,
+ header_size,
+ parsed.value,
+ );
+
+ // Validate the loaded model if configured
+ if (self.config.validation != .none) {
+ try self.validateModel(model, parsed.value);
+ }
+
+ return model;
+ }
+
+ // Load a model from Hugging Face
+ fn loadFromHuggingFace(
+ self: *Self,
+ model_id: []const u8,
+ model_args: ?ModelArgs,
+ ) !*TransformerModel {
+ // Get cache directory or create a temporary one
+ const cache_dir = self.config.cache_dir orelse
+ try std.fs.getAppDataDir(self.allocator, "deepseek-zig");
+
+ // Create HF client
+ var hf_client = try HuggingFaceClient.init(self.allocator, cache_dir);
+ defer hf_client.deinit();
+
+ // Download the model
+ const model_path = try hf_client.downloadModel(model_id);
+
+ // Load the downloaded model
+ return try self.loadModel(model_path, model_args);
+ }
+
+ // Infer model arguments if not explicitly provided
+ fn determineModelArgs(
+ self: *Self,
+ model_args: ?ModelArgs,
+ header: std.json.Value,
+ ) !ModelArgs {
+ if (model_args) |args| {
+ return args;
+ }
+
+ // Try to infer model configuration from the weight shapes
+ if (header.Object.get("metadata")) |metadata| {
+ if (metadata.Object.get("model_type")) |model_type| {
+ if (std.mem.eql(u8, model_type.String, "deepseek")) {
+ // Extract dimensions from metadata
+ return try self.parseDeepSeekConfig(metadata);
+ }
+ }
+ }
+
+ // Infer from weight shapes if metadata is not available
+ return try self.inferArgsFromWeights(header);
+ }
+
+ // ... more implementation details ...
+};
+
+// Implementation of TransformerModel
+pub const TransformerModel = struct {
+ const Self = @This();
+
+ allocator: std.mem.Allocator,
+ args: ModelArgs,
+
+ // Tokenizer for text processing
+ tokenizer: *Tokenizer,
+
+ // Model components
+ embedding: *Embedding,
+ layers: []TransformerLayer,
+ norm: *LayerNorm,
+ lm_head: *Linear,
+
+ // KV cache for efficient inference
+ kv_cache: ?*KVCache,
+
+ // Backend for computation
+ backend: *ComputeBackend,
+
+ // Create a model with the given configuration
+ pub fn create(
+ allocator: std.mem.Allocator,
+ args: ModelArgs,
+ ) !*Self {
+ // Create model components
+ var embedding = try Embedding.create(allocator, args);
+ errdefer embedding.destroy();
+
+ var layers = try allocator.alloc(TransformerLayer, args.num_layers);
+ errdefer allocator.free(layers);
+
+ for (layers, 0..) |*layer, i| {
+ layer.* = try TransformerLayer.create(allocator, args, i);
+ }
+
+ var norm = try LayerNorm.create(allocator, args.dim);
+ errdefer norm.destroy();
+
+ var lm_head = try Linear.create(allocator, args.dim, args.vocab_size);
+ errdefer lm_head.destroy();
+
+ // Initialize compute backend
+ var backend = try ComputeBackend.create(allocator);
+ errdefer backend.destroy();
+
+ // Initialize tokenizer
+ var tokenizer = try Tokenizer.create(allocator, args.vocab_size);
+ errdefer tokenizer.destroy();
+
+ // Create the model
+ var model = try allocator.create(Self);
+ errdefer allocator.destroy(model);
+
+ model.* = .{
+ .allocator = allocator,
+ .args = args,
+ .tokenizer = tokenizer,
+ .embedding = embedding,
+ .layers = layers,
+ .norm = norm,
+ .lm_head = lm_head,
+ .kv_cache = null,
+ .backend = backend,
+ };
+
+ return model;
+ }
+
+ // Clean up resources
+ pub fn destroy(self: *Self) void {
+ // Free all components
+ self.tokenizer.destroy();
+ self.embedding.destroy();
+
+ for (self.layers) |*layer| {
+ layer.deinit();
+ }
+ self.allocator.free(self.layers);
+
+ self.norm.destroy();
+ self.lm_head.destroy();
+
+ if (self.kv_cache) |kv_cache| {
+ kv_cache.destroy();
+ }
+
+ self.backend.destroy();
+ self.allocator.destroy(self);
+ }
+
+ // Load a model from a specific path
+ pub fn loadFromPath(
+ allocator: std.mem.Allocator,
+ path: []const u8,
+ args: ?ModelArgs,
+ ) !*Self {
+ var loader = ModelLoader.init(allocator, .{});
+ return try loader.loadModel(path, args);
+ }
+
+ // Forward pass for a single token
+ pub fn forward(
+ self: *Self,
+ token_id: usize,
+ position: usize,
+ ) !Tensor(f32, 2) {
+ // Get the token embedding
+ var x = try self.embedding.forward(token_id);
+
+ // Process through all transformer layers
+ for (self.layers, 0..) |*layer, i| {
+ x = try layer.forward(x, position, self.kv_cache);
+ }
+
+ // Apply final layer norm
+ x = try self.norm.forward(x);
+
+ // Project to vocabulary
+ return try self.lm_head.forward(x);
+ }
+
+ // Prepare the model for generation
+ pub fn prepareForGeneration(
+ self: *Self,
+ max_seq_len: usize,
+ batch_size: usize,
+ ) !void {
+ // Create KV cache if not already created
+ if (self.kv_cache == null) {
+ self.kv_cache = try KVCache.create(
+ self.allocator,
+ self.args,
+ max_seq_len,
+ batch_size,
+ );
+ } else {
+ // Reset the cache if it already exists
+ try self.kv_cache.?.reset(max_seq_len, batch_size);
+ }
+ }
+
+ // Load tokenizer from vocabulary file
+ pub fn loadTokenizer(
+ self: *Self,
+ path: []const u8,
+ ) !void {
+ try self.tokenizer.loadFromFile(path);
+ }
+};
+```
+
+#### 4.2 Generation Strategies
+
+```zig
+// Configuration for text generation
+pub const GenerationConfig = struct {
+ // Maximum new tokens to generate
+ max_new_tokens: usize = 128,
+
+ // Sampling temperature (higher = more random)
+ temperature: f32 = 1.0,
+
+ // Top-p sampling parameter (0.0-1.0)
+ top_p: f32 = 1.0,
+
+ // Top-k sampling parameter (0 = disabled)
+ top_k: usize = 0,
+
+ // Repetition penalty to prevent looping
+ repetition_penalty: f32 = 1.0,
+
+ // Whether to use sampling or greedy decoding
+ do_sample: bool = true,
+
+ // Frequency penalty for repeated tokens
+ frequency_penalty: f32 = 0.0,
+
+ // Presence penalty for token occurrence
+ presence_penalty: f32 = 0.0,
+
+ // Stop sequences to terminate generation
+ stop_sequences: ?[]const []const u8 = null,
+
+ // Minimum number of tokens to generate
+ min_new_tokens: ?usize = null,
+
+ // Beam search width (1 = greedy)
+ num_beams: usize = 1,
+
+ // Random seed for reproducibility
+ seed: ?u64 = null,
+
+ // Whether to use speculative decoding
+ use_speculative: bool = false,
+
+ // Draft model for speculative decoding
+ draft_model: ?*TransformerModel = null,
+
+ // Number of speculative tokens to generate at once
+ speculative_tokens: usize = 5,
+};
+
+// Generate text from a model given input tokens
+pub fn generate(
+ model: *TransformerModel,
+ input_ids: []const usize,
+ config: GenerationConfig,
+ callback: ?fn ([]const u8) void,
+) ![]usize {
+ // Initialize RNG with seed if provided
+ var rng = if (config.seed) |seed|
+ std.rand.DefaultPrng.init(seed)
+ else
+ std.rand.DefaultPrng.init(@bitCast(u64, std.time.milliTimestamp()));
+
+ // Allocate result buffer
+ var result = try model.allocator.alloc(
+ usize,
+ input_ids.len + config.max_new_tokens,
+ );
+ errdefer model.allocator.free(result);
+
+ // Copy input tokens
+ @memcpy(result[0..input_ids.len], input_ids);
+ var token_count = input_ids.len;
+
+ // Prepare model for generation
+ try model.prepareForGeneration(
+ input_ids.len + config.max_new_tokens,
+ 1, // Batch size
+ );
+
+ // Process all input tokens to fill KV cache
+ var position: usize = 0;
+ for (input_ids) |token_id| {
+ _ = try model.forward(token_id, position);
+ position += 1;
+ }
+
+ // Check if we should use speculative decoding
+ if (config.use_speculative and config.draft_model != null) {
+ return try speculativeGenerate(
+ model,
+ config.draft_model.?,
+ result,
+ token_count,
+ position,
+ config,
+ callback,
+ );
+ }
+
+ // Set up logit processors based on config
+ var logit_processors = LogitProcessorList.init(model.allocator);
+ defer logit_processors.deinit();
+
+ if (config.temperature != 1.0) {
+ try logit_processors.add(TemperatureLogitProcessor.init(config.temperature));
+ }
+
+ if (config.repetition_penalty != 1.0) {
+ try logit_processors.add(RepetitionPenaltyLogitProcessor.init(
+ config.repetition_penalty,
+ result[0..token_count],
+ ));
+ }
+
+ if (config.frequency_penalty != 0.0 or config.presence_penalty != 0.0) {
+ try logit_processors.add(FrequencyPenaltyLogitProcessor.init(
+ config.frequency_penalty,
+ config.presence_penalty,
+ ));
+ }
+
+ // Main generation loop
+ while (token_count < result.len) {
+ // Get next token logits
+ var logits = try model.forward(result[token_count - 1], position);
+ defer logits.deinit();
+
+ // Apply logit processors
+ try logit_processors.process(&logits, result[0..token_count]);
+
+ // Sample next token
+ const next_token = if (config.do_sample)
+ try sampleNextToken(
+ model.allocator,
+ logits,
+ config.top_p,
+ config.top_k,
+ &rng.random(),
+ )
+ else
+ try greedyNextToken(logits);
+
+ // Add token to result
+ result[token_count] = next_token;
+ token_count += 1;
+ position += 1;
+
+ // Check for stop sequences
+ if (config.stop_sequences) |stop_seqs| {
+ if (checkStopSequences(
+ model.tokenizer,
+ result[0..token_count],
+ stop_seqs,
+ )) {
+ break;
+ }
+ }
+
+ // Call callback with generated token if provided
+ if (callback != null) {
+ var token_text = try model.tokenizer.decodeTokens(
+ model.allocator,
+ result[token_count-1..token_count],
+ );
+ defer model.allocator.free(token_text);
+
+ callback.?(token_text);
+ }
+
+ // Check if we've reached minimum token count
+ if (config.min_new_tokens) |min_tokens| {
+ if (token_count >= input_ids.len + min_tokens) {
+ // Check if we're at an EOS token
+ if (next_token == model.tokenizer.eos_token_id) {
+ break;
+ }
+ }
+ } else if (next_token == model.tokenizer.eos_token_id) {
+ // Otherwise just stop at EOS
+ break;
+ }
+ }
+
+ // Resize result to actual number of tokens
+ result = try model.allocator.realloc(result, token_count);
+ return result;
+}
+
+// Speculative decoding implementation
+fn speculativeGenerate(
+ model: *TransformerModel,
+ draft_model: *TransformerModel,
+ result: []usize,
+ token_count: usize,
+ position: usize,
+ config: GenerationConfig,
+ callback: ?fn ([]const u8) void,
+) ![]usize {
+ // Implementation of speculative decoding algorithm
+ // This generates multiple tokens using a smaller draft model
+ // and verifies them with the main model for faster generation
+
+ // ... implementation details ...
+ return result;
+}
+
+// Sample next token using top-p (nucleus) and top-k sampling
+fn sampleNextToken(
+ allocator: std.mem.Allocator,
+ logits: Tensor(f32, 2),
+ top_p: f32,
+ top_k: usize,
+ random: *std.rand.Random,
+) !usize {
+ const vocab_size = logits.shape[1];
+
+ // Create a sorted list of (token_id, probability) pairs
+ var token_probs = try allocator.alloc(
+ struct { token_id: usize, prob: f32 },
+ vocab_size,
+ );
+ defer allocator.free(token_probs);
+
+ // Apply softmax to get probabilities
+ var probs = try softmax(allocator, logits);
+ defer probs.deinit();
+
+ // Fill token_probs array
+ for (0..vocab_size) |i| {
+ token_probs[i] = .{
+ .token_id = i,
+ .prob = probs.data[i],
+ };
+ }
+
+ // Sort by probability (descending)
+ std.sort.sort(
+ struct { token_id: usize, prob: f32 },
+ token_probs,
+ {},
+ struct {
+ fn lessThan(_: void, a: struct { token_id: usize, prob: f32 }, b: struct { token_id: usize, prob: f32 }) bool {
+ return b.prob < a.prob;
+ }
+ }.lessThan,
+ );
+
+ // Apply top-k filtering if enabled
+ const k = if (top_k > 0)
+ @min(top_k, vocab_size)
+ else
+ vocab_size;
+
+ // Apply top-p filtering
+ var cumulative_prob: f32 = 0.0;
+ var last_idx: usize = 0;
+
+ for (token_probs[0..k], 0..) |tp, i| {
+ cumulative_prob += tp.prob;
+ if (cumulative_prob >= top_p) {
+ last_idx = i;
+ break;
+ }
+ }
+
+ // Sample from the filtered distribution
+ const rand_val = random.float(f32);
+ var curr_prob: f32 = 0.0;
+
+ for (token_probs[0..last_idx+1]) |tp| {
+ curr_prob += tp.prob;
+ if (rand_val < curr_prob) {
+ return tp.token_id;
+ }
+ }
+
+ // Fallback to the highest probability token
+ return token_probs[0].token_id;
+}
+```
+
+**Advanced Features:**
+
+1. **Speculative Decoding**
+ - Implementation of speculative decoding using a smaller draft model
+ - Verification and acceptance/rejection of speculated tokens
+ - Significant speedup in generation throughput
+
+2. **Streaming Token Output**
+ - Callback-based token streaming for real-time results
+ - Zero-copy token decoding for minimal overhead
+ - Support for incremental UI updates
+
+3. **Custom Sampling Strategies**
+ - Top-p (nucleus) sampling with dynamic probability mass cutoff
+ - Top-k sampling with configurable k value
+ - Temperature scaling for controlling randomness
+ - Repetition penalty to prevent loops and repetitive text
+ - Frequency and presence penalties for more diverse output
+
+4. **Stop Sequence Detection**
+ - Efficient detection of multiple stop sequences
+ - Support for subword token matching across boundaries
+ - Early termination based on generated content
+
+5. **Beam Search Implementation**
+ - Configurable beam width for exploring multiple generation paths
+ - Length normalization for balancing short and long outputs
+ - Diverse beam groups to prevent similar outputs
+
+6. **Memory Efficiency**
+ - KV-cache memory management for long context handling
+ - Incremental cache updates for streaming inference
+ - Automatic cache pruning for memory optimization
+
+7. **Performance Optimizations**
+ - Batched token processing for higher throughput
+ - Parallel sampling for multi-sequence generation
+ - SIMD-accelerated logit processing
+ - Compile-time specialization for common configuration patterns
+
+### 5. Optimization Layer
+
+The optimization layer leverages Zig's unique features to maximise performance across different hardware targets.
+
+#### 5.1 Compile-Time Optimizations
+
+Zig's powerful compile-time metaprogramming enables us to generate highly specialized code for specific hardware and model configurations:
+
+```zig
+// Specialized matrix multiplication kernels generated at compile-time
+pub fn generateMatmulKernel(comptime config: KernelConfig) type {
+ return struct {
+ const Self = @This();
+
+ // Compile-time configuration
+ const M = config.M;
+ const N = config.N;
+ const K = config.K;
+ const block_size = config.block_size;
+ const vector_width = config.vector_width;
+ const use_fma = config.use_fma;
+
+ // Vector type based on configuration
+ const Vec = @Vector(vector_width, f32);
+
+ // Matmul implementation specialized for the given dimensions
+ pub fn matmul(
+ a: *const [M][K]f32,
+ b: *const [K][N]f32,
+ c: *[M][N]f32,
+ ) void {
+ // Use specialized implementation for small matrices
+ if (comptime M <= 4 and N <= 4 and K <= 4) {
+ return smallMatmul(a, b, c);
+ }
+
+ // Use blocked implementation for larger matrices
+ return blockedMatmul(a, b, c);
+ }
+
+ // Specialized implementation for small matrices
+ // Fully unrolled at compile time
+ fn smallMatmul(
+ a: *const [M][K]f32,
+ b: *const [K][N]f32,
+ c: *[M][N]f32,
+ ) void {
+ inline for (0..M) |i| {
+ inline for (0..N) |j| {
+ var sum: f32 = 0;
+ inline for (0..K) |k| {
+ sum += a[i][k] * b[k][j];
+ }
+ c[i][j] = sum;
+ }
+ }
+ }
+
+ // Cache-blocked implementation for larger matrices
+ fn blockedMatmul(
+ a: *const [M][K]f32,
+ b: *const [K][N]f32,
+ c: *[M][N]f32,
+ ) void {
+ // Compute using blocks for better cache utilization
+ comptime var i_block: usize = 0;
+ inline while (i_block < M) : (i_block += block_size) {
+ comptime var j_block: usize = 0;
+ inline while (j_block < N) : (j_block += block_size) {
+ comptime var k_block: usize = 0;
+ inline while (k_block < K) : (k_block += block_size) {
+ const i_end = @min(i_block + block_size, M);
+ const j_end = @min(j_block + block_size, N);
+ const k_end = @min(k_block + block_size, K);
+
+ // Process current block
+ for (i_block..i_end) |i| {
+ for (j_block..j_end) |j| {
+ var sum: f32 = c[i][j];
+
+ // Vectorized inner loop when possible
+ if (comptime vector_width > 1 and (k_end - k_block) >= vector_width) {
+ var k_vec: usize = k_block;
+ var acc: Vec = @splat(0.0);
+
+ while (k_vec + vector_width <= k_end) : (k_vec += vector_width) {
+ const a_vec: Vec = blk: {
+ var tmp: [vector_width]f32 = undefined;
+ for (0..vector_width) |vi| {
+ tmp[vi] = a[i][k_vec + vi];
+ }
+ break :blk tmp;
+ };
+
+ const b_vec: Vec = blk: {
+ var tmp: [vector_width]f32 = undefined;
+ for (0..vector_width) |vi| {
+ tmp[vi] = b[k_vec + vi][j];
+ }
+ break :blk tmp;
+ };
+
+ // Use FMA instruction if available
+ if (comptime use_fma) {
+ acc = @mulAdd(Vec, a_vec, b_vec, acc);
+ } else {
+ acc += a_vec * b_vec;
+ }
+ }
+
+ // Reduce vector to scalar
+ for (0..vector_width) |vi| {
+ sum += acc[vi];
+ }
+
+ // Handle remaining elements
+ for (k_vec..k_end) |k| {
+ sum += a[i][k] * b[k][j];
+ }
+ } else {
+ // Scalar fallback
+ for (k_block..k_end) |k| {
+ sum += a[i][k] * b[k][j];
+ }
+ }
+
+ c[i][j] = sum;
+ }
+ }
+ }
+ }
+ }
+ }
+ };
+}
+
+// Configuration for kernel generation
+pub const KernelConfig = struct {
+ // Matrix dimensions (can be comptime_int or dynamic)
+ M: comptime_int,
+ N: comptime_int,
+ K: comptime_int,
+
+ // Blocking configuration for cache optimization
+ block_size: comptime_int = 32,
+
+ // Vector width for SIMD operations
+ vector_width: comptime_int = 4,
+
+ // Whether to use FMA instructions when available
+ use_fma: bool = true,
+};
+
+// Usage: Create specialized kernels at compile time
+// Fully unrolled 4x4 matrix multiplication
+const Kernel4x4 = generateMatmulKernel(.{
+ .M = 4,
+ .N = 4,
+ .K = 4,
+ .vector_width = 4,
+});
+
+// Cache-friendly 128x128 matrix multiplication
+const Kernel128x128 = generateMatmulKernel(.{
+ .M = 128,
+ .N = 128,
+ .K = 128,
+ .block_size = 32,
+ .vector_width = 8,
+});
+
+// Runtime dispatch to select the best kernel based on matrix dimensions
+pub fn dispatchMatmul(
+ allocator: std.mem.Allocator,
+ a: Tensor(f32, 2),
+ b: Tensor(f32, 2),
+) !Tensor(f32, 2) {
+ // Check dimensions
+ const m = a.shape[0];
+ const k = a.shape[1];
+ const n = b.shape[1];
+
+ std.debug.assert(k == b.shape[0], "Incompatible matrix dimensions");
+
+ // Create result tensor
+ var result = try Tensor(f32, 2).init(allocator, .{m, n});
+ errdefer result.deinit();
+
+ // Initialize result to zeros
+ @memset(result.data, 0);
+
+ // Dispatch to specialized kernels if dimensions match exactly
+ if (m == 4 and n == 4 and k == 4) {
+ // Use specialized 4x4 kernel
+ Kernel4x4.matmul(
+ @ptrCast(*const [4][4]f32, a.data),
+ @ptrCast(*const [4][4]f32, b.data),
+ @ptrCast(*[4][4]f32, result.data),
+ );
+ } else if (m == 128 and n == 128 and k == 128) {
+ // Use specialized 128x128 kernel
+ Kernel128x128.matmul(
+ @ptrCast(*const [128][128]f32, a.data),
+ @ptrCast(*const [128][128]f32, b.data),
+ @ptrCast(*[128][128]f32, result.data),
+ );
+ } else {
+ // Use generic implementation for arbitrary dimensions
+ try genericMatmul(a, b, &result);
+ }
+
+ return result;
+}
+
+// Apply compile-time metaprogramming to optimize data layouts
+pub fn optimizedTensorLayout(comptime T: type, comptime dims: []const usize) type {
+ return struct {
+ const Self = @This();
+
+ // Determine optimal memory layout at compile time
+ const optimal_layout = optimizeMemoryLayout(T, dims);
+
+ // Data storage with optimized layout
+ data: [product(dims)]T align(optimal_layout.alignment),
+ shape: [dims.len]usize,
+ strides: [dims.len]usize,
+
+ // Tensor initialization with optimal layout
+ pub fn init(allocator: std.mem.Allocator) !Self {
+ const data = try allocator.alignedAlloc(
+ T,
+ optimal_layout.alignment,
+ product(dims),
+ );
+
+ // Calculate optimal strides based on layout
+ var strides: [dims.len]usize = undefined;
+ if (optimal_layout.row_major) {
+ // Row-major strides
+ var stride: usize = 1;
+ var i: usize = dims.len;
+ while (i > 0) {
+ i -= 1;
+ strides[i] = stride;
+ stride *= dims[i];
+ }
+ } else {
+ // Column-major strides
+ var stride: usize = 1;
+ for (0..dims.len) |i| {
+ strides[i] = stride;
+ stride *= dims[i];
+ }
+ }
+
+ return Self{
+ .data = data,
+ .shape = dims,
+ .strides = strides,
+ };
+ }
+
+ // Helper function to calculate optimal memory layout
+ fn optimizeMemoryLayout(comptime T: type, comptime dims: []const usize) struct {
+ row_major: bool,
+ alignment: u29,
+ } {
+ // Use column-major for matrices where the first dimension is much larger
+ // This often improves cache locality for common access patterns
+ const row_major = if (dims.len == 2)
+ dims[0] <= dims[1] * 2
+ else
+ true;
+
+ // Determine optimal alignment based on vector units
+ const alignment = if (@sizeOf(T) == 4 and comptime std.Target.current.cpu.arch == .x86_64)
+ if (comptime std.Target.current.cpu.features.isEnabled(.avx512f))
+ 64 // 512-bit alignment for AVX-512
+ else if (comptime std.Target.current.cpu.features.isEnabled(.avx2))
+ 32 // 256-bit alignment for AVX2
+ else if (comptime std.Target.current.cpu.features.isEnabled(.sse2))
+ 16 // 128-bit alignment for SSE2
+ else
+ @alignOf(T)
+ else
+ @alignOf(T);
+
+ return .{
+ .row_major = row_major,
+ .alignment = alignment,
+ };
+ }
+
+ // Helper to calculate the product of dimensions
+ fn product(comptime dims: []const usize) usize {
+ var result: usize = 1;
+ for (dims) |dim| {
+ result *= dim;
+ }
+ return result;
+ }
+ };
+}
+```
+
+**Key Compile-Time Techniques:**
+
+1. **Matrix Operation Specialization**
+ - Specialized kernels generated at compile-time for common dimensions
+ - Full loop unrolling for small matrices
+ - Compile-time configurable blocking strategies for cache optimization
+
+2. **Data Layout Optimization**
+ - Automatic selection of row-major or column-major layout based on dimensions
+ - Optimal memory alignment for target architecture's vector units
+ - Compile-time stride calculation for fast indexing
+
+3. **Architecture-Specific Optimizations**
+ - Vector width specialization based on target CPU features
+ - Automatic use of FMA instructions when available
+ - SIMD instruction generation tailored to the target architecture
+
+4. **Kernel Selection**
+ - Runtime dispatch to specialized kernels based on input dimensions
+ - Fallback to generic implementation for arbitrary dimensions
+ - Compile-time branch elimination for performance-critical paths
+
+#### 5.2 Quantization Framework
+
+Our quantization framework allows for efficient low-precision inference while maintaining accuracy:
+
+```zig
+// Quantization configuration
+pub const QuantizationConfig = struct {
+ // Precision of quantized values
+ bits: u8 = 8,
+
+ // Quantization scheme
+ scheme: enum {
+ symmetric, // Zero-point is always 0, simplifies arithmetic
+ asymmetric, // Allows representing the full range more precisely
+ } = .symmetric,
+
+ // Quantization granularity
+ granularity: enum {
+ per_tensor, // One scale for the entire tensor
+ per_channel, // Different scale for each output channel
+ } = .per_tensor,
+
+ // Whether to use integer or float16 quantization
+ use_float16: bool = false,
+
+ // Calibration strategy
+ calibration: enum {
+ minmax, // Simple min/max scaling
+ entropy, // Entropy-based quantization
+ percentile, // Clip to percentile range for outliers
+ } = .minmax,
+
+ // Percentile value for calibration (0.0-1.0)
+ percentile: f32 = 0.99995,
+};
+
+// Quantized tensor type that tracks quantization parameters
+pub fn QuantizedTensor(comptime original_type: type, comptime bits: u8) type {
+ return struct {
+ const Self = @This();
+
+ // Determine the appropriate integer type based on bit width
+ const IntType = std.meta.Int(.unsigned, bits);
+
+ // Original element type for reference
+ pub const OriginalType = original_type;
+
+ // Quantized data
+ data: []IntType,
+
+ // Original tensor shape
+ shape: []const usize,
+
+ // Quantization parameters
+ scale: []f32,
+ zero_point: []IntType,
+
+ // Whether scale/zero_point are per-tensor or per-channel
+ per_channel: bool,
+
+ // For asymmetric quantization: minimum representable value
+ qmin: IntType,
+
+ // For asymmetric quantization: maximum representable value
+ qmax: IntType,
+
+ // Channel dimension for per-channel quantization
+ channel_dim: ?usize,
+
+ // Memory allocator for cleanup
+ allocator: std.mem.Allocator,
+
+ // Initialize a quantized tensor
+ pub fn init(
+ allocator: std.mem.Allocator,
+ shape: []const usize,
+ per_channel: bool,
+ channel_dim: ?usize,
+ ) !Self {
+ // Calculate total size
+ var total_size: usize = 1;
+ for (shape) |dim| {
+ total_size *= dim;
+ }
+
+ // Determine number of scales/zero_points needed
+ const param_size = if (per_channel)
+ shape[channel_dim.?]
+ else
+ 1;
+
+ // Allocate memory
+ const data = try allocator.alloc(IntType, total_size);
+ errdefer allocator.free(data);
+
+ const scale = try allocator.alloc(f32, param_size);
+ errdefer allocator.free(scale);
+
+ const zero_point = try allocator.alloc(IntType, param_size);
+ errdefer allocator.free(zero_point);
+
+ // Calculate quantization range
+ const qmin: IntType = 0;
+ const qmax: IntType = (1 << bits) - 1;
+
+ // Create shape copy
+ const shape_copy = try allocator.dupe(usize, shape);
+ errdefer allocator.free(shape_copy);
+
+ return Self{
+ .data = data,
+ .shape = shape_copy,
+ .scale = scale,
+ .zero_point = zero_point,
+ .per_channel = per_channel,
+ .qmin = qmin,
+ .qmax = qmax,
+ .channel_dim = channel_dim,
+ .allocator = allocator,
+ };
+ }
+
+ // Free allocated memory
+ pub fn deinit(self: *Self) void {
+ self.allocator.free(self.data);
+ self.allocator.free(self.scale);
+ self.allocator.free(self.zero_point);
+ self.allocator.free(self.shape);
+ }
+ };
+}
+
+// Quantize a floating-point tensor to integer precision
+pub fn quantize(
+ tensor: anytype,
+ config: QuantizationConfig,
+ allocator: std.mem.Allocator,
+) !QuantizedTensor(
+ @TypeOf(tensor.data[0]),
+ config.bits,
+) {
+ const T = @TypeOf(tensor.data[0]);
+
+ // Validate input
+ if (config.bits > 16) {
+ return error.UnsupportedQuantizationBits;
+ }
+
+ if (config.granularity == .per_channel and config.calibration != .minmax) {
+ return error.UnsupportedCombination;
+ }
+
+ // Create quantized tensor
+ var channel_dim: ?usize = null;
+ if (config.granularity == .per_channel) {
+ // For per-channel quantization, use dimension 0 for vectors,
+ // dimension 1 for matrices (assuming CHW layout)
+ channel_dim = if (tensor.shape.len == 1) 0 else 1;
+ }
+
+ var qtensor = try QuantizedTensor(T, config.bits).init(
+ allocator,
+ tensor.shape,
+ config.granularity == .per_channel,
+ channel_dim,
+ );
+ errdefer qtensor.deinit();
+
+ // Different calibration strategies
+ switch (config.calibration) {
+ .minmax => try calibrateMinMax(&qtensor, tensor, config),
+ .entropy => try calibrateEntropy(&qtensor, tensor, config),
+ .percentile => try calibratePercentile(&qtensor, tensor, config),
+ }
+
+ // Perform actual quantization
+ try quantizeTensor(&qtensor, tensor, config);
+
+ return qtensor;
+}
+
+// Dequantize a tensor back to floating point
+pub fn dequantize(
+ qtensor: anytype,
+ allocator: std.mem.Allocator,
+) !Tensor(@TypeOf(qtensor).OriginalType, qtensor.shape.len) {
+ const T = @TypeOf(qtensor).OriginalType;
+
+ // Create tensor to hold dequantized values
+ var tensor = try Tensor(T, qtensor.shape.len).init(
+ allocator,
+ qtensor.shape,
+ );
+ errdefer tensor.deinit();
+
+ // Dequantize values
+ if (qtensor.per_channel) {
+ const channel_dim = qtensor.channel_dim.?;
+ const channels = qtensor.shape[channel_dim];
+
+ // Calculate strides for traversing channels
+ var strides: []usize = try allocator.alloc(usize, qtensor.shape.len);
+ defer allocator.free(strides);
+
+ var stride: usize = 1;
+ var i: usize = qtensor.shape.len;
+ while (i > 0) {
+ i -= 1;
+ strides[i] = stride;
+ stride *= qtensor.shape[i];
+ }
+
+ // Dequantize each element based on its channel
+ for (0..tensor.data.len) |idx| {
+ const channel_idx = (idx / strides[channel_dim]) % channels;
+ const scale = qtensor.scale[channel_idx];
+ const zero_point = qtensor.zero_point[channel_idx];
+
+ tensor.data[idx] = @floatCast(T,
+ @intToFloat(f32, qtensor.data[idx] - zero_point) * scale
+ );
+ }
+ } else {
+ // Per-tensor dequantization (simpler)
+ const scale = qtensor.scale[0];
+ const zero_point = qtensor.zero_point[0];
+
+ for (0..tensor.data.len) |i| {
+ tensor.data[i] = @floatCast(T,
+ @intToFloat(f32, qtensor.data[i] - zero_point) * scale
+ );
+ }
+ }
+
+ return tensor;
+}
+
+// Calibrate using simple min/max strategy
+fn calibrateMinMax(
+ qtensor: anytype,
+ tensor: anytype,
+ config: QuantizationConfig,
+) !void {
+ if (config.granularity == .per_tensor) {
+ // Find min/max across entire tensor
+ var min_val: f32 = std.math.inf_f32;
+ var max_val: f32 = -std.math.inf_f32;
+
+ for (tensor.data) |val| {
+ const fval = @floatCast(f32, val);
+ min_val = @min(min_val, fval);
+ max_val = @max(max_val, fval);
+ }
+
+ // Handle symmetric quantization
+ if (config.scheme == .symmetric) {
+ const abs_max = @max(@abs(min_val), @abs(max_val));
+ min_val = -abs_max;
+ max_val = abs_max;
+ }
+
+ // Calculate scale and zero_point
+ const range = max_val - min_val;
+ qtensor.scale[0] = range / @intToFloat(f32, qtensor.qmax - qtensor.qmin);
+
+ if (config.scheme == .symmetric) {
+ qtensor.zero_point[0] = @divFloor(qtensor.qmax - qtensor.qmin, 2) + qtensor.qmin;
+ } else {
+ qtensor.zero_point[0] = @floatToInt(
+ @TypeOf(qtensor.zero_point[0]),
+ @round(qtensor.qmin - min_val / qtensor.scale[0])
+ );
+ }
+ } else {
+ // Per-channel quantization
+ // ... implementation details ...
+ }
+}
+
+// Perform actual quantization
+fn quantizeTensor(
+ qtensor: anytype,
+ tensor: anytype,
+ config: QuantizationConfig,
+) !void {
+ if (qtensor.per_channel) {
+ // Per-channel quantization
+ // ... implementation details ...
+ } else {
+ // Per-tensor quantization
+ const scale = qtensor.scale[0];
+ const zero_point = qtensor.zero_point[0];
+ const qmin = qtensor.qmin;
+ const qmax = qtensor.qmax;
+
+ for (0..tensor.data.len) |i| {
+ const val = @floatCast(f32, tensor.data[i]);
+
+ // Quantize: x_q = round(x / scale) + zero_point
+ var q_val = @floatToInt(
+ @TypeOf(qtensor.data[0]),
+ @round(val / scale) + @intToFloat(f32, zero_point)
+ );
+
+ // Clamp to quantization range
+ q_val = @max(@min(q_val, qmax), qmin);
+
+ qtensor.data[i] = q_val;
+ }
+ }
+}
+```
+
+**Quantization Features:**
+
+1. **Multiple Precision Options**
+ - 8-bit quantization for maximum throughput
+ - 4-bit quantization for model compression
+ - 3-bit quantization for extreme size reduction
+ - FP16 quantization for memory bandwidth reduction with minimal accuracy loss
+
+2. **Flexible Quantization Schemes**
+ - Symmetric quantization for simpler arithmetic
+ - Asymmetric quantization for better range utilization
+ - Per-tensor quantization for speed
+ - Per-channel quantization for accuracy
+
+3. **Advanced Calibration Methods**
+ - Min/max calibration for simplicity
+ - Entropy-based calibration for better distribution representation
+ - Percentile-based calibration for outlier handling
+
+4. **Mixed-Precision Execution**
+ - Critical layers in higher precision for accuracy
+ - Non-critical layers in lower precision for speed
+ - Automatic precision selection based on sensitivity analysis
+
+5. **Hardware Acceleration**
+ - Optimized integer SIMD operations for quantized execution
+ - Specialized kernels for common quantized operations
+ - Hardware-specific optimizations for quantized compute
+
+## Platform-Specific Optimizations
+
+### Apple Silicon (M-Series)
+
+The DeepSeek V3 Zig implementation is highly optimized for Apple Silicon's unique architecture:
+
+1. **Metal Performance Shaders (MPS) Integration**
+ - Direct integration with Apple's Metal Performance Shaders for matrix operations
+ - Custom Metal compute kernels optimized for M-series chips
+ - Efficient memory sharing between CPU and GPU with zero-copy transfers
+
+2. **Tensor Core Utilization**
+ - Leveraging Matrix multiplication units in M-series chips
+ - Mixed-precision operations optimized for Apple Silicon
+ - Native FP16 support for improved throughput
+
+3. **AMX Instruction Set Access**
+ - Direct use of Apple Matrix extensions for accelerated linear algebra
+ - Low-level optimization of critical matrix operations
+ - Custom assembly routines for maximum performance
+
+4. **Memory Bandwidth Optimization**
+ - Unified memory architecture exploitation
+ - Cache-friendly memory access patterns
+ - Optimal tile sizes for M-series cache hierarchy
+
+5. **Power Efficiency Tuning**
+ - Dynamic performance/power scaling
+ - Efficient core utilization across P and E cores
+ - Background inference optimizations
+
+### x86_64 Architecture
+
+For x86_64 platforms, our implementation focuses on leveraging the latest instruction sets:
+
+1. **AVX-512 Vectorization**
+ - Full utilization of 512-bit vector operations
+ - Masked operations for efficient boundary handling
+ - FMA instruction usage for maximum throughput
+
+2. **Cache-Friendly Memory Layouts**
+ - Cache line aligned data structures
+ - Blocked algorithms optimized for typical L1/L2/L3 cache sizes
+ - Software prefetching for critical data paths
+
+3. **Thread Pool Optimization**
+ - Work-stealing scheduler for balanced multicore utilization
+ - NUMA-aware memory allocation and thread assignment
+ - Adaptive parallelism based on available cores
+
+4. **Dynamic Dispatch**
+ - Runtime CPU feature detection
+ - Specialized code paths for different instruction sets
+ - Fallback implementations for compatibility
+
+### NVIDIA GPUs
+
+NVIDIA GPU acceleration is implemented through an efficient CUDA integration:
+
+1. **CUDA Integration via FFI**
+ - Zero-overhead bindings to CUDA runtime
+ - Asynchronous kernel execution and memory transfers
+ - Efficient stream management for overlapping operations
+
+2. **Custom CUDA Kernels**
+ - Specialized kernels for attention mechanisms
+ - Optimized matrix multiplication for transformer layers
+ - Fused operations for reduced kernel launch overhead
+
+3. **Memory Management**
+ - Pinned memory for efficient transfers
+ - Memory pool for reduced allocation overhead
+ - Smart prefetching for predictable memory access patterns
+
+4. **Tensor Core Utilization**
+ - Mixed-precision operations using TensorCores
+ - Automatic kernel selection for tensor-core eligible operations
+ - Tensor Core compatible memory layouts
+
+## Development Roadmap
+
+### Phase 1: Core Infrastructure
+
+The initial phase focuses on establishing the foundational components:
+
+- **Memory Management System**
+ - Custom tensor allocator implementation
+ - Arena-based allocation strategies
+ - Error handling framework
+
+- **Tensor Implementation**
+ - Basic tensor operations and utilities
+ - SIMD-accelerated implementations
+ - Platform detection and optimization
+
+- **Computation Backend Interfaces**
+ - Abstract backend interfaces
+ - CPU backend implementation
+ - Initial Metal backend for Apple Silicon
+
+- **Error Handling Framework**
+ - Robust error propagation
+ - Detailed error reporting
+ - Resource cleanup guarantees
+
+### Phase 2: Model Architecture
+
+Building on the infrastructure, we implement the core model components:
+
+- **Transformer Layers**
+ - Multi-head attention implementation
+ - Feed-forward networks
+ - Layer normalization
+
+- **Attention Mechanisms**
+ - Standard attention implementation
+ - Flash attention optimizations
+ - Memory-efficient attention variants
+
+- **Mixture of Experts**
+ - Router implementation
+ - Parallel expert execution
+ - Load balancing mechanisms
+
+- **Embedding Systems**
+ - Token embeddings
+ - Position embeddings
+ - Rotary position embeddings
+
+### Phase 3: Backend Integration
+
+This phase extends compute capabilities across different hardware:
+
+- **CPU Backend**
+ - AVX-512 optimizations
+ - Thread pool implementation
+ - Cache-optimized algorithms
+
+- **Metal Backend**
+ - Complete Metal shader library
+ - Apple Neural Engine integration
+ - M-series specific optimizations
+
+- **CUDA Backend**
+ - NVIDIA GPU support
+ - Tensor Core optimizations
+ - Multi-GPU scaling
+
+- **Vulkan Backend**
+ - Cross-platform GPU support
+ - AMD GPU optimizations
+ - Intel GPU support
+
+### Phase 4: Inference Pipeline
+
+Creating the end-to-end inference system:
+
+- **Model Loading**
+ - SafeTensors format support
+ - Checkpoint loading
+ - Weight quantization
+
+- **Tokenization**
+ - Efficient tokenizer implementation
+ - Streaming tokenization
+ - Special token handling
+
+- **Generation Strategies**
+ - Sampling methods implementation
+ - Beam search
+ - Speculative decoding
+
+- **Output Processing**
+ - Token streaming
+ - Stop sequence handling
+ - Result formatting
+
+### Phase 5: Optimization
+
+Comprehensive optimization across the entire stack:
+
+- **Compile-Time Optimizations**
+ - Template specialization
+ - Kernel generation
+ - Custom data layouts
+
+- **Runtime Optimizations**
+ - Dynamic kernel selection
+ - Adaptive compute strategies
+ - Memory access optimizations
+
+- **Architecture-Specific Tuning**
+ - Platform-specific parameter tuning
+ - Hardware-specific kernel variants
+ - Feature detection and adaptation
+
+- **Quantization Framework**
+ - 8-bit quantization
+ - 4-bit quantization
+ - Mixed precision execution
+
+### Phase 6: Testing and Benchmarking
+
+Ensuring correctness and measuring performance:
+
+- **Comprehensive Test Suite**
+ - Unit tests for all components
+ - Integration tests for end-to-end validation
+ - Conformance tests against reference implementation
+
+- **Benchmarking Framework**
+ - Performance measurement tools
+ - Comparison with PyTorch implementation
+ - Memory usage analysis
+
+- **Platform Benchmarks**
+ - Apple Silicon performance
+ - x86_64 performance
+ - NVIDIA GPU performance
+
+- **Fine-Tuning**
+ - Performance bottleneck identification
+ - Targeted optimizations
+ - Final parameter tuning
\ No newline at end of file
diff --git a/README.md b/README.md
index 7f22b6d..f07a6b8 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,5 @@
+# DeepSeek V3 in Zig - Project Proposal
+
@@ -20,4941 +22,162 @@
## Overview
-This document outlines the initial architecture proposal for implementing DeepSeek V3 in the Zig programming language. The focus is on leveraging Zig's unique features to create a high-performance, memory-efficient, and robust implementation of the DeepSeek V3 architecture.
+A proposal for implementing DeepSeek V3 in Zig to create a high-performance, web-ready LLM inference engine. This would leverage Zig's unique advantages for systems programming while targeting modern deployment scenarios.
-1. **Superior Performance**: Leverage Zig's compile-time metaprogramming, SIMD vectorization, and low-level control to achieve optimal performance across platforms
-2. **Memory Efficiency**: Utilize Zig's explicit allocator system and arena allocation patterns for precise resource management
-3. **Concurrent Processing**: Implement efficient parallel execution using Zig's advanced async/await framework and evented I/O
-4. **Type Safety & Reliability**: Employ Zig's strong type system, comptime checks, and explicit error handling to prevent runtime errors
-5. **Cross-Platform Support**: Create a portable implementation with seamless support across architectures (x86_64, ARM64, etc.)
+## Why This Matters
-## Why DeepSeek V3 in Zig?
+Current LLM inference is dominated by Python/PyTorch, which introduces:
+- **Garbage collection pauses** during generation
+- **Runtime overhead** from dynamic dispatch
+- **Complex deployment** with heavy runtimes
+- **Platform lock-in** due to dependency complexity
-The migration of DeepSeek V3 to Zig represents a significant advancement in language model implementation. By leveraging Zig's unique features, particularly compile-time metaprogramming and fine-grained memory control, we aim to create a highly optimized implementation that outperforms the original Python/PyTorch version significantly while maintaining flexibility and ease of use.
+## The Zig Advantage
-Key advantages of the Zig implementation include:
+**Performance**: Zero-cost abstractions, compile-time optimization, direct hardware access
+**Simplicity**: Single static binary, no runtime dependencies, cross-compilation built-in
+**Web-First**: Native HTTP server, WebAssembly compilation, efficient memory management
-1. **Superior Performance**
- - Compile-time specialization eliminates runtime overhead
- - Direct hardware access for maximum efficiency
- - Zero-cost abstractions for clean yet fast code
- - SIMD vectorization through native vector types
- - Cache-aware memory layout optimization
-
-2. **Memory Efficiency**
- - Explicit allocation strategies tailored to LLM workloads
- - Reduced memory fragmentation through custom allocators
- - Lower overall memory footprint through data structure optimization
- - Precise control over tensor memory layouts
- - Arena allocation for temporary computations
-
-3. **Reliability**
- - Comprehensive error handling with explicit error sets
- - No runtime exceptions, all errors are explicitly handled
- - Deterministic resource cleanup through defer and errdefer
- - Compile-time correctness guarantees
- - Clear separation of error paths from happy paths
-
-4. **Portability**
- - Integrated cross-compilation for all supported platforms
- - No external dependencies for core functionality
- - C ABI compatibility for integration with existing libraries
- - Consistent behavior across environments
- - WebAssembly target support for browser deployment
-
-5. **Scalability**
- - Explicit threading model for compute-intensive operations
- - Efficient parallel execution of independent tensor operations
- - Multi-token prediction support
- - Quantization-aware data structures
- - Optimized KV-cache for efficient sequence generation
-
-The resulting system will be particularly well-suited for deployment on resource-constrained devices and will provide superior performance on all platforms. This architectural approach sets the foundation for future innovations in large language model deployment.
-
-
-## Table of Contents
-1. [Overview](#overview)
-2. [Why DeepSeek V3 in Zig?](#why-deepseek-v3-in-zig)
-3. [System Architecture](#system-architecture)
- - [High-Level Component Overview](#high-level-component-overview)
-4. [Detailed Component Design](#detailed-component-design)
- 1. [Core Systems](#1-core-systems)
- - [1.1 Memory Management System](#11-memory-management-system)
- - [1.2 Tensor Implementation](#12-tensor-implementation)
- - [1.3 Error Handling Framework](#13-error-handling-framework)
- - [1.4 Concurrency Model](#14-concurrency-model)
- 2. [Model Architecture](#2-model-architecture)
- - [2.1 Transformer Core](#21-transformer-core)
- - [2.2 Attention Mechanism](#22-attention-mechanism)
- - [2.3 Mixture of Experts (MoE)](#23-mixture-of-experts-moe)
- 3. [Computation Backend](#3-computation-backend)
- - [3.1 Backend Interface](#31-backend-interface)
- - [3.2 Cross-Platform Compilation](#32-cross-platform-compilation)
- - [3.2.1 Cross-Compilation Support](#321-cross-compilation-support)
- - [3.2.2 C ABI Compatibility](#322-c-abi-compatibility)
- - [3.3 Platform-Specific Implementations](#33-platform-specific-implementations)
- - [3.4 SIMD Vectorization](#34-simd-vectorization)
- - [3.5 Runtime CPU Feature Detection](#35-runtime-cpu-feature-detection)
- - [3.6 Backend Configuration](#36-backend-configuration)
- - [3.7 GPU Integration](#37-gpu-integration)
- - [3.7.1 CUDA Backend](#371-cuda-backend)
- - [3.7.2 Vulkan Backend](#372-vulkan-backend)
- - [3.8 Quantization Framework](#38-quantization-framework)
- - [3.9 Memory Management](#39-memory-management)
- - [3.10 Metal Integration for Apple Silicon](#310-metal-integration-for-apple-silicon)
- 4. [Inference Pipeline](#4-inference-pipeline)
- - [4.1 Model Loading](#41-model-loading)
- - [4.2 Generation Strategies](#42-generation-strategies)
- 5. [Optimization Layer](#5-optimization-layer)
- - [5.1 Compile-Time Optimizations](#51-compile-time-optimizations)
- - [5.2 Quantization Framework](#52-quantization-framework)
-5. [Platform-Specific Optimizations](#platform-specific-optimizations)
- - [Apple Silicon (M-Series)](#apple-silicon-m-series)
- - [x86_64 Architecture](#x86_64-architecture)
- - [NVIDIA GPUs](#nvidia-gpus)
-6. [Development Roadmap](#development-roadmap)
- - [Phase 1: Core Infrastructure](#phase-1-core-infrastructure)
- - [Phase 2: Model Architecture](#phase-2-model-architecture)
- - [Phase 3: Backend Integration](#phase-3-backend-integration)
- - [Phase 4: Inference Pipeline](#phase-4-inference-pipeline)
- - [Phase 5: Optimization](#phase-5-optimization)
- - [Phase 6: Testing and Benchmarking](#phase-6-testing-and-benchmarking)
-
-## System Architecture
-
-### High-Level Component Overview
-
-The DeepSeek V3 Zig implementation consists of the following major components:
+## Proposed Architecture
```
-DeepSeek V3 Zig
-│
-├── Core
-│ ├── Memory Management System
-│ │ ├── Custom Allocator Framework
-│ │ ├── Arena Allocation Strategy
-│ │ └── Memory Pool Implementation
-│ ├── Tensor Implementation
-│ │ ├── SIMD-Optimized Operations
-│ │ ├── Compile-Time Specialization
-│ │ └── Zero-Cost Abstractions
-│ └── Error Handling Framework
-│ ├── Comprehensive Error Types
-│ └── Performance-Optimized Error Paths
-│
-├── Model Architecture
-│ ├── Transformer Layers
-│ │ ├── Comptime-Generated Layer Variants
-│ │ └── Optimized Forward Pass
-│ ├── Attention Mechanisms
-│ │ ├── Vectorized Multi-Head Attention
-│ │ └── Efficient KV-Cache Management
-│ ├── MoE (Mixture of Experts)
-│ │ ├── Parallel Expert Execution
-│ │ └── Optimized Router Implementation
-│ └── Embedding Systems
-│ ├── Memory-Efficient Token Embeddings
-│ └── Positional Encoding Optimizations
-│
-├── Computation Backend
-│ ├── CPU Implementation
-│ │ ├── SIMD Vectorization
-│ │ └── Multi-Threaded Execution
-│ ├── GPU Integration (Optional)
-│ │ ├── CUDA Support (NVIDIA)
-│ │ ├── Metal Support (Apple)
-│ │ └── ROCm Support (AMD)
-│ └── Backend Interface Layer
-│ ├── Zero-Cost Abstraction
-│ └── Compile-Time Dispatch
-│
-├── Inference Pipeline
-│ ├── Model Loading & Weight Management
-│ ├── Tokenization System
-│ ├── Advanced Generation Strategies
-│ │ ├── Speculative Decoding
-│ │ └── Beam Search
-│ └── Streaming Output Processing
-│
-└── Optimization Layer
- ├── Compile-Time Specialization
- │ ├── Architecture-Specific Code Gen
- │ └── Tensor Operation Optimization
- ├── Runtime Performance Tuning
- │ ├── Cache-Aware Memory Layout
- │ └── Workload Balancing
- └── Quantization Framework
- ├── Mixed-Precision Support
- └── Hardware-Accelerated Execution
+┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
+│ Web Layer │ │ Core Engine │ │ Backends │
+│ │ │ │ │ │
+│ ├─ HTTP API │◄──►│ ├─ Transformer │◄──►│ ├─ CPU (SIMD) │
+│ ├─ WebSocket │ │ ├─ Attention │ │ ├─ Metal (macOS)│
+│ ├─ Rate Limit │ │ ├─ MoE Routing │ │ ├─ CUDA (Linux) │
+│ └─ Auth │ │ └─ Tokenizer │ │ └─ WebGPU │
+└─────────────────┘ └──────────────────┘ └─────────────────┘
```
-## Detailed Component Design
+## Proposed Web API
-### 1. Core Systems
+### Target Endpoints
+- `POST /v1/chat/completions` - OpenAI-compatible chat API
+- `POST /v1/completions` - Text completion
+- `GET /v1/models` - List available models
+- `GET /health` - Service health check
+- `WebSocket /ws` - Streaming inference
-#### 1.1 Memory Management System
+### Deployment Vision
+- **Docker containers** for cloud deployment
+- **Static binaries** for edge devices
+- **WebAssembly** for browser inference
+- **Serverless functions** for auto-scaling
-Memory management in Zig represents a significant advancement over Python's garbage collection. Zig provides explicit allocator interfaces that give fine-grained control over memory allocation and deallocation strategies:
+## Implementation Plan
-```zig
-const std = @import("std");
+### Phase 1: Foundation
+- [ ] Set up Zig project structure
+- [ ] Implement basic tensor operations with SIMD
+- [ ] Create memory management system (arena allocators)
+- [ ] Build HTTP server framework
-// Define a custom tensor allocator that combines multiple strategies
-pub const TensorAllocator = struct {
- // Use arena for temporary tensor operations during inference
- arena: std.heap.ArenaAllocator,
- // Use a fixed buffer for small activations
- fixed_buffer: [1024 * 1024]u8 = undefined,
- fixed_allocator: std.heap.FixedBufferAllocator,
- // General purpose allocator for long-lived objects
- gpa: std.heap.GeneralPurposeAllocator(.{}),
-
- pub fn init(backing_allocator: std.mem.Allocator) !*TensorAllocator {
- var self = try backing_allocator.create(TensorAllocator);
- self.* = .{
- .arena = std.heap.ArenaAllocator.init(backing_allocator),
- .fixed_allocator = std.heap.FixedBufferAllocator.init(&self.fixed_buffer),
- .gpa = std.heap.GeneralPurposeAllocator(.{}){},
- };
- return self;
- }
-
- pub fn deinit(self: *TensorAllocator) void {
- self.arena.deinit();
- _ = self.gpa.deinit();
- // backing allocator will free self
- }
+### Phase 2: Core Model
+- [ ] Implement transformer layers
+- [ ] Add Multi-Head Latent Attention (MLA)
+- [ ] Build Mixture of Experts (MoE) routing
+- [ ] Create tokenizer integration
- // Create a stack fallback allocator for small tensors that can be stack-allocated
- pub fn smallTensorAllocator(self: *TensorAllocator, comptime size: usize) std.heap.StackFallbackAllocator(size) {
- return std.heap.stackFallbackAllocator(size, self.arena.allocator());
- }
-
- // Get a leak-detecting allocator for debugging builds
- pub fn debugAllocator(self: *TensorAllocator) std.mem.Allocator {
- if (builtin.mode == .Debug) {
- return self.gpa.allocator(); // GPA tracks leaks in debug mode
- } else {
- return self.persistentAllocator();
- }
- }
-
- // Specialized allocator for model weights that need to be memory-mapped
- pub fn weightAllocator(self: *TensorAllocator, path: []const u8) !std.mem.Allocator {
- // In real implementation, this would return a memory-mapped allocator
- // For now, just use the persistent allocator
- return self.persistentAllocator();
- }
-
- // Get the right allocator for specific tensor use cases
- pub fn temporaryAllocator(self: *TensorAllocator) std.mem.Allocator {
- return self.arena.allocator();
- }
-
- pub fn smallActivationAllocator(self: *TensorAllocator) std.mem.Allocator {
- return self.fixed_allocator.allocator();
- }
-
- pub fn persistentAllocator(self: *TensorAllocator) std.mem.Allocator {
- return self.gpa.allocator();
- }
-};
+### Phase 3: Backends
+- [ ] Optimize CPU backend with AVX/NEON
+- [ ] Integrate Metal for Apple Silicon
+- [ ] Add CUDA support for NVIDIA GPUs
+- [ ] Implement WebGPU for browsers
-// Inference function example with specialized memory allocation
-pub fn performInference(model: *Model, input: Tensor) !Tensor {
- var allocator = try TensorAllocator.init(std.heap.page_allocator);
- defer allocator.deinit();
-
- // Use different allocators for different tensor operations
- var activations = try computeActivations(model, input, allocator.temporaryAllocator());
- var weights = try loadModelWeights(model, allocator.persistentAllocator());
-
- // Results are automatically freed when the arena is deinitialized
- return try generateOutput(activations, weights, allocator.temporaryAllocator());
-}
+### Phase 4: Web Integration
+- [ ] Complete HTTP API implementation
+- [ ] Add WebSocket streaming
+- [ ] Build authentication/rate limiting
+- [ ] Create deployment tooling
+
+## Expected Benefits
+
+| Aspect | Current (PyTorch) | Proposed (Zig) |
+|--------|------------------|----------------|
+| Cold start | 10-30s | **< 2s** |
+| Memory usage | 20-40GB | **< 16GB** |
+| Dependencies | ~2GB runtime | **Single binary** |
+| Deployment | Complex | **Copy & run** |
+
+## Technical Challenges
+
+**Model Complexity**: DeepSeek V3's MoE architecture requires careful memory management
+**Backend Integration**: Need efficient FFI to CUDA/Metal while maintaining performance
+**Web Scale**: Handle concurrent requests without blocking inference
+**Accuracy**: Match PyTorch numerical precision
+
+## Getting Started
+
+**Current Status**: This repository contains the original Python DeepSeek V3 implementation. The Zig implementation is proposed future work.
+
+### For the Current Python Implementation:
+```bash
+# Clone this repository
+git clone https://github.com/[current-repo-path]
+cd DeepSeek-V3-Zig
+
+# Follow existing Python setup instructions
+# (see original DeepSeek V3 documentation)
```
-**Key Features:**
-- **Tiered Allocation Strategy**: Different allocators for different memory usage patterns
-- **Arena Allocation**: Bulk allocation and freeing for intermediate tensors, dramatically reducing memory management overhead
-- **Fixed Buffer Allocation**: Zero-heap-allocation path for small, predictable tensor operations
-- **Memory Pool Implementation**: Custom pools for tensor data to minimize fragmentation
-- **Explicit Error Handling**: All allocation failures are explicitly handled with Zig's error system
+### For the Proposed Zig Implementation:
+```bash
+# This would be the future workflow once implemented:
-#### 1.2 Tensor Implementation
+# 1. Set up new Zig project structure
+zig init-exe deepseek-v3-zig
-Tensors are the fundamental data structure for DeepSeek. Our implementation leverages Zig's advanced compile-time features, SIMD capabilities, and memory layout optimizations for maximum performance:
+# 2. Implement core components
+# - Tensor operations with SIMD
+# - HTTP server framework
+# - Model architecture
-```zig
-pub fn Tensor(comptime DataType: type, comptime dimensions: usize) type {
- return struct {
- const Self = @This();
-
- data: []DataType,
- shape: [dimensions]usize,
- strides: [dimensions]usize,
- allocator: std.mem.Allocator,
- is_contiguous: bool,
-
- // Vector types for SIMD operations based on hardware capabilities
- pub const VecType = switch (DataType) {
- f32 => if (std.Target.x86.featureSetHas(builtin.cpu.features, .avx512f))
- @Vector(16, f32) // AVX-512
- else if (std.Target.x86.featureSetHas(builtin.cpu.features, .avx2))
- @Vector(8, f32) // AVX2
- else if (std.Target.x86.featureSetHas(builtin.cpu.features, .sse4_1))
- @Vector(4, f32) // SSE4.1
- else
- @Vector(4, f32), // Fallback for non-x86 or basic x86
- f16 => if (std.Target.aarch64.featureSetHas(builtin.cpu.features, .fp16))
- @Vector(8, f16) // ARM with FP16 support
- else
- @Vector(4, f16), // Default for f16
- i32 => @Vector(8, i32),
- i8 => @Vector(16, i8),
- i4 => @Vector(32, i4), // Support for 4-bit quantization
- else => @compileError("Unsupported data type for SIMD"),
- };
-
- // Number of elements in the SIMD vector
- pub const vec_width = @sizeOf(VecType) / @sizeOf(DataType);
-
- pub fn init(allocator: std.mem.Allocator, shape: [dimensions]usize) !Self {
- var strides: [dimensions]usize = undefined;
- var total_size: usize = 1;
-
- // Calculate C-contiguous (row-major) strides for optimal memory access
- var i: usize = dimensions;
- while (i > 0) {
- i -= 1;
- strides[i] = total_size;
- total_size *= shape[i];
- }
-
- // Align memory for optimal SIMD access
- const alignment = @alignOf(VecType);
- const data = try allocator.alignedAlloc(DataType, alignment, total_size);
-
- return Self{
- .data = data,
- .shape = shape,
- .strides = strides,
- .allocator = allocator,
- .is_contiguous = true,
- };
- }
-
- pub fn deinit(self: *Self) void {
- self.allocator.free(self.data);
- }
-
- // Optimized SIMD matrix multiplication for 2D tensors
- pub fn matmul(self: *Self, other: *Self, allocator: std.mem.Allocator) !Self {
- std.debug.assert(dimensions == 2 and other.dimensions == 2);
- std.debug.assert(self.shape[1] == other.shape[0]);
-
- const M = self.shape[0];
- const K = self.shape[1];
- const N = other.shape[1];
-
- var result = try Self.init(allocator, .{ M, N });
-
- // Zero initialization
- @memset(result.data, 0);
-
- // Check if both tensors are contiguous for optimal performance
- if (self.is_contiguous and other.is_contiguous) {
- // Cache-aware blocked matrix multiplication with SIMD
- const block_size = 64; // Tuned for L1 cache
-
- // For each block
- var i: usize = 0;
- while (i < M) : (i += block_size) {
- const i_end = @min(i + block_size, M);
- var j: usize = 0;
- while (j < N) : (j += block_size) {
- const j_end = @min(j + block_size, N);
- var k: usize = 0;
- while (k < K) : (k += block_size) {
- const k_end = @min(k + block_size, K);
-
- // Process each block
- var ii: usize = i;
- while (ii < i_end) : (ii += 1) {
- var jj: usize = j;
- while (jj < j_end) : (jj += vec_width) {
- // SIMD-optimized inner loop
- if (jj + vec_width <= j_end) {
- var sum: VecType = @splat(0);
- var kk: usize = k;
- while (kk < k_end) : (kk += 1) {
- const a_val = self.data[ii * K + kk];
- const b_vec: VecType = blk: {
- var tmp: [vec_width]DataType = undefined;
- for (0..vec_width) |v| {
- if (jj + v < j_end) {
- tmp[v] = other.data[kk * N + (jj + v)];
- } else {
- tmp[v] = 0;
- }
- }
- break :blk tmp;
- };
- sum += @splat(a_val) * b_vec;
- }
-
- // Store result
- for (0..vec_width) |v| {
- if (jj + v < j_end) {
- result.data[ii * N + (jj + v)] += sum[v];
- }
- }
- } else {
- // Handle remaining columns (tail)
- while (jj < j_end) : (jj += 1) {
- var sum: DataType = 0;
- var kk: usize = k;
- while (kk < k_end) : (kk += 1) {
- sum += self.data[ii * K + kk] * other.data[kk * N + jj];
- }
- result.data[ii * N + jj] += sum;
- }
- }
- }
- }
- }
- }
- }
- } else {
- // Fallback for non-contiguous tensors
- var i: usize = 0;
- while (i < M) : (i += 1) {
- var j: usize = 0;
- while (j < N) : (j += 1) {
- var sum: DataType = 0;
- var k: usize = 0;
- while (k < K) : (k += 1) {
- sum += self.at(.{i, k}) * other.at(.{k, j});
- }
- try result.set(.{i, j}, sum);
- }
- }
- }
-
- return result;
- }
-
- // Access element at specific indices
- pub fn at(self: Self, indices: [dimensions]usize) DataType {
- var offset: usize = 0;
- inline for (0..dimensions) |i| {
- offset += indices[i] * self.strides[i];
- }
- return self.data[offset];
- }
-
- // Set element at specific indices
- pub fn set(self: *Self, indices: [dimensions]usize, value: DataType) !void {
- var offset: usize = 0;
- inline for (0..dimensions) |i| {
- offset += indices[i] * self.strides[i];
- }
- self.data[offset] = value;
- }
-
- // Apply element-wise operations with SIMD acceleration
- pub fn map(self: Self, comptime op: fn (DataType) DataType, allocator: std.mem.Allocator) !Self {
- var result = try Self.init(allocator, self.shape);
-
- // Use SIMD operations for contiguous data
- if (self.is_contiguous) {
- var i: usize = 0;
- const vec_chunks = self.data.len / vec_width;
-
- // Process in SIMD chunks
- while (i < vec_chunks) : (i += 1) {
- const base_idx = i * vec_width;
- var vec: VecType = undefined;
-
- // Load vector
- for (0..vec_width) |j| {
- vec[j] = self.data[base_idx + j];
- }
-
- // Apply operation on each vector element
- for (0..vec_width) |j| {
- vec[j] = op(vec[j]);
- }
-
- // Store result
- for (0..vec_width) |j| {
- result.data[base_idx + j] = vec[j];
- }
- }
-
- // Process remaining elements
- const remaining_start = vec_chunks * vec_width;
- for (remaining_start..self.data.len) |j| {
- result.data[j] = op(self.data[j]);
- }
- } else {
- // Fallback for non-contiguous data
- var indices: [dimensions]usize = .{0} ** dimensions;
- var done = false;
-
- while (!done) {
- const val = self.at(indices);
- try result.set(indices, op(val));
-
- // Increment indices
- var d = dimensions - 1;
- while (true) {
- indices[d] += 1;
- if (indices[d] < self.shape[d]) break;
- indices[d] = 0;
- if (d == 0) {
- done = true;
- break;
- }
- d -= 1;
- }
- }
- }
-
- return result;
- }
- };
-}
+# 3. Test and benchmark
+zig build test
+zig build bench
-// Specialized tensor types for common uses
-const FloatTensor1D = Tensor(f32, 1);
-const FloatTensor2D = Tensor(f32, 2);
-const FloatTensor4D = Tensor(f32, 4); // Common for batch x height x width x channels
-const QuantizedTensor4D = Tensor(i8, 4); // For quantized operations
+# 4. Run web server
+zig build run -- --port 8080
```
-**Key Features:**
-- **Hardware-Aware SIMD Vectorization**: Automatically selects optimal vector width based on CPU capabilities (AVX, SSE)
-- **Cache-Optimized Algorithms**: Blocked matrix multiplication designed for L1/L2 cache efficiency
-- **Aligned Memory Allocation**: Ensures data is properly aligned for SIMD operations
-- **Specialized Tensor Types**: Pre-defined tensor configurations for common use cases
-- **Automatic Fallbacks**: Graceful degradation for non-contiguous tensors or unsupported operations
-- **Compile-Time Optimization**: Tensor dimensions and data types resolved at compile time for maximum performance
-- **Zero-Runtime Overhead**: SIMD operations with no dynamic dispatch or virtual function calls
+**Want to contribute to making this real?** See [Seeking Contributors](#seeking-contributors) below.
-#### 1.3 Error Handling Framework
+## Development Approach
-Zig's error handling system provides a powerful foundation for creating robust, high-performance software. Unlike exceptions in languages like C++ or Python, Zig's error handling is explicit and deterministic, making it particularly well-suited for large-scale machine learning applications:
+Following established [Zig patterns](https://github.com/SuperAuguste/zig-patterns):
+- **Arena allocators** for request-scoped memory
+- **Error unions** for explicit error handling
+- **Comptime generics** for zero-cost abstractions
+- **SIMD vectors** for numerical computation
-```zig
-// Define a comprehensive set of potential errors with clear semantic meaning
-const ModelError = error{
- ModelLoadFailed,
- InvalidDimension,
- InvalidShape,
- OutOfMemory,
- ComputeBackendError,
- InvalidWeight,
- UnsupportedOperation,
- UnsupportedDataType,
- DeviceNotAvailable,
- TensorShapeMismatch,
- QuantizationError,
- InvalidConfiguration,
- ModelTooLarge,
- UnsupportedArchitecture,
- InvalidTokenization,
- ContextLengthExceeded,
- DeviceMemoryExhausted,
-};
+Reference: [Zig Cookbook](https://zigcc.github.io/zig-cookbook/) for implementation patterns.
-// Union error sets for comprehensive error handling
-const DeepSeekError = ModelError || TensorError || AllocationError || IoError;
+## Seeking Contributors
-// Example function demonstrating Zig's error handling with defer for cleanup
-fn loadModel(allocator: std.mem.Allocator, path: []const u8) DeepSeekError!*Model {
- var file = try std.fs.cwd().openFile(path, .{});
- defer file.close(); // Ensures file is closed even if an error occurs
-
- var buffer = std.ArrayList(u8).init(allocator);
- defer buffer.deinit(); // Clean up buffer regardless of success/failure
-
- try buffer.ensureTotalCapacity(file.getEndPos() catch return ModelError.ModelLoadFailed);
-
- const bytes_read = try file.readAll(buffer.items);
- if (bytes_read == 0) return ModelError.ModelLoadFailed;
-
- var model = try allocator.create(Model);
- errdefer allocator.destroy(model); // Only called if an error occurs after this point
-
- model.* = Model.init(allocator);
- errdefer model.deinit(); // Only called if an error occurs after this point
-
- // Parse weights and initialize model...
- if (!try parseWeights(model, buffer.items)) {
- return ModelError.InvalidWeight;
- }
-
- return model;
-}
+This is an ambitious project that would benefit from expertise in:
+- **Zig systems programming**
+- **GPU kernel optimization** (CUDA/Metal)
+- **ML model implementation**
+- **Web server development**
+- **Performance optimization**
-// Demonstrate error handling in caller code
-pub fn main() !void {
- var gpa = std.heap.GeneralPurposeAllocator(.{}){};
- defer _ = gpa.deinit();
- const allocator = gpa.allocator();
-
- // Handle errors explicitly with try/catch blocks
- const model = loadModel(allocator, "model.bin") catch |err| {
- switch (err) {
- ModelError.ModelLoadFailed => {
- std.debug.print("Failed to load model file\n", .{});
- return err;
- },
- ModelError.InvalidWeight => {
- std.debug.print("Model contains invalid weights\n", .{});
- return err;
- },
- else => {
- std.debug.print("Unexpected error: {}\n", .{err});
- return err;
- },
- }
- };
- defer model.deinit();
-
- // Example of handling errors with fallbacks
- const modelVersion = getModelVersion(model.path) catch |err| switch (err) {
- ModelError.InvalidConfiguration => "unknown",
- else => return err,
- };
-
- // Example of collecting and reporting multiple errors
- var errors = std.ArrayList(ModelError).init(allocator);
- defer errors.deinit();
-
- if (validateModelStructure(model)) |_| {
- // Structure is valid
- } else |err| {
- try errors.append(err);
- }
-
- if (validateModelWeights(model)) |_| {
- // Weights are valid
- } else |err| {
- try errors.append(err);
- }
-
- if (errors.items.len > 0) {
- std.debug.print("Found {d} errors in model validation\n", .{errors.items.len});
- return ModelError.InvalidConfiguration;
- }
-
- // Continue with model usage...
- try initializeModelBackend(model);
-
- std.debug.print("Model version: {s} loaded successfully\n", .{modelVersion});
- std.debug.print("Model has {d} parameters, {d} activated\n",
- .{model.totalParameters(), model.activatedParameters()});
-}
-```
+## Project Timeline
-**Key Features:**
-- **Explicit Error Types**: Clearly defined error sets that precisely describe what can go wrong
-- **No Exceptions**: Deterministic error handling with no hidden control flow
-- **Resource Safety**: Automatic cleanup with `defer` and `errdefer` ensures resources are properly managed
-- **Performance Optimization**: Error handling doesn't rely on stack unwinding or dynamic dispatch
-- **Composable Error Sets**: Error types can be combined using the `||` operator
-- **Try-Catch Blocks**: For selective error handling when needed
-- **Error Tracing**: Built-in error return trace capability for debugging
+- Foundation and basic tensor ops
+- Core transformer implementation
+- Backend optimization and web API
+- Testing, benchmarking, deployment tools
-#### 1.4 Concurrency Model
+## References
-Zig's concurrency model will be leveraged to parallelize computation-intensive operations in DeepSeek. Zig's async/await syntax provides a structured approach to concurrency without the overhead of traditional threading:
+- [DeepSeek V3 Paper](https://arxiv.org/abs/2412.19437) - Original model architecture
+- [Zig Language](https://ziglang.org/) - Language documentation
+- [Awesome Zig](https://github.com/C-BJ/awesome-zig) - Community resources
+- [Zig Patterns](https://github.com/SuperAuguste/zig-patterns) - Common idioms
-```zig
-const std = @import("std");
+---
-// Thread pool for CPU-bound parallel tasks
-pub const ComputeThreadPool = struct {
- pool: std.Thread.Pool,
- completion_count: std.atomic.Atomic(usize),
-
- pub fn init(thread_count: usize) !ComputeThreadPool {
- var pool: std.Thread.Pool = undefined;
- try pool.init(.{
- .allocator = std.heap.c_allocator,
- .n_jobs = thread_count,
- });
-
- return ComputeThreadPool{
- .pool = pool,
- .completion_count = std.atomic.Atomic(usize).init(0),
- };
- }
-
- pub fn deinit(self: *ComputeThreadPool) void {
- self.pool.deinit();
- }
-
- // Execute a compute task asynchronously
- pub fn compute(self: *ComputeThreadPool, task: *const fn(*anyopaque) void, context: *anyopaque) !void {
- try self.pool.spawn(task, context);
- }
-
- // Wait for all compute tasks to complete
- pub fn waitAll(self: *ComputeThreadPool) void {
- // Process tasks in the event loop until all are complete
- while (self.completion_count.load(.Acquire) > 0) {
- std.time.sleep(1 * std.time.millisecond);
- }
- }
-};
-
-// Note: Zig's async/await is still under development and may change
-// This example shows the current Thread.Pool-based approach which is stable
-// Future versions may leverage async/await for more elegant concurrency
-
-// Example of how we might use async in the future when it's stable
-pub fn asyncMatMulExample(allocator: std.mem.Allocator, a: *Tensor(f32, 2), b: *Tensor(f32, 2)) !*Tensor(f32, 2) {
- // This is an example of potential future API design
- // Not recommended for production use until async is stabilized
-
- const M = a.shape[0];
- const K = a.shape[1];
- const N = b.shape[1];
-
- var result = try Tensor(f32, 2).init(allocator, .{M, N});
- errdefer result.deinit();
-
- @memset(result.data, 0);
-
- // Process rows concurrently
- var row_jobs = try allocator.alloc(@Frame(processRow), M);
- defer allocator.free(row_jobs);
-
- for (0..M) |i| {
- row_jobs[i] = async processRow(i, a, b, &result);
- }
-
- // Wait for all rows to complete
- for (row_jobs) |*job| {
- await job;
- }
-
- return result;
-}
-
-fn processRow(row: usize, a: *Tensor(f32, 2), b: *Tensor(f32, 2), result: *Tensor(f32, 2)) !void {
- // Process a single row of the matrix multiplication
- const K = a.shape[1];
- const N = b.shape[1];
-
- for (0..N) |j| {
- var sum: f32 = 0.0;
- for (0..K) |k| {
- sum += a.at(.{row, k}) * b.at(.{k, j});
- }
- try result.set(.{row, j}, sum);
- }
-}
-
-// Parallel tensor operation example with async/await
-pub fn parallelMatMul(allocator: std.mem.Allocator, a: *Tensor(f32, 2), b: *Tensor(f32, 2)) !*Tensor(f32, 2) {
- const M = a.shape[0];
- const K = a.shape[1];
- const N = b.shape[1];
-
- var result = try Tensor(f32, 2).init(allocator, .{M, N});
- errdefer result.deinit();
-
- @memset(result.data, 0);
-
- // Create thread pool with optimal number of threads
- const cpu_count = try std.Thread.getCpuCount();
- var thread_pool = try ComputeThreadPool.init(cpu_count);
- defer thread_pool.deinit();
-
- // Split work based on number of available cores
- const rows_per_thread = (M + cpu_count - 1) / cpu_count;
-
- // Define the worker task
- const WorkContext = struct {
- a: *const Tensor(f32, 2),
- b: *const Tensor(f32, 2),
- result: *Tensor(f32, 2),
- start_row: usize,
- end_row: usize,
- thread_pool: *ComputeThreadPool,
- };
-
- // Worker function for computing a subset of rows
- const workerFn = struct {
- fn compute(context_ptr: *anyopaque) void {
- const context = @ptrCast(*WorkContext, @alignCast(@alignOf(WorkContext), context_ptr));
- const a = context.a;
- const b = context.b;
- const result = context.result;
- const start_row = context.start_row;
- const end_row = context.end_row;
-
- // Compute assigned rows
- for (start_row..end_row) |i| {
- if (i >= a.shape[0]) break;
-
- for (0..b.shape[1]) |j| {
- var sum: f32 = 0.0;
- for (0..a.shape[1]) |k| {
- sum += a.at(.{i, k}) * b.at(.{k, j});
- }
- result.set(.{i, j}, sum) catch {};
- }
- }
-
- // Mark task as complete
- _ = context.thread_pool.completion_count.fetchSub(1, .Release);
- }
- };
-
- // Spawn workers for each section of the matrix
- for (0..cpu_count) |i| {
- const start_row = i * rows_per_thread;
- const end_row = std.math.min(start_row + rows_per_thread, M);
-
- if (start_row >= M) break;
-
- // Create context for this worker
- var context = try allocator.create(WorkContext);
- context.* = .{
- .a = a,
- .b = b,
- .result = result,
- .start_row = start_row,
- .end_row = end_row,
- .thread_pool = &thread_pool,
- };
-
- // Increment completion counter before spawning task
- _ = thread_pool.completion_count.fetchAdd(1, .Release);
-
- // Spawn the worker task
- try thread_pool.compute(workerFn.compute, context);
- }
-
- // Wait for all tasks to complete
- thread_pool.waitAll();
-
- return result;
-}
-```
-
-**Key Features:**
-- **Thread Pool Management**: Efficient worker thread allocation based on available CPU cores
-- **Work Partitioning**: Automatic division of work across available cores
-- **Minimal Synchronization**: Lock-free atomic counters for synchronization when needed
-- **Resource Safety**: Proper cleanup with `defer` and `errdefer` even during concurrent execution
-- **Structured Concurrency**: Clear task dependencies and lifecycle management
-- **Zero Runtime Overhead**: No garbage collection or runtime dependencies
-
-### 2. Model Architecture
-
-#### 2.1 Transformer Core
-
-The transformer architecture is the foundation of DeepSeek V3. Our Zig implementation will leverage compile-time metaprogramming and advanced memory optimizations for maximum performance:
-
-```zig
-const std = @import("std");
-
-// Precomputed type variants for different data precisions
-pub const DataType = enum {
- f32, // 32-bit floating point (for debugging/development)
- bf16, // BFloat16 (for training/default inference)
- f16, // Float16 (for hardware with native f16 support)
- i8, // 8-bit integer (for quantized inference)
- i4, // 4-bit integer (for extreme quantization)
-};
-
-// Configuration struct with default values matching DeepSeek V3
-pub const ModelArgs = struct {
- // Core model parameters
- max_batch_size: usize = 8,
- max_seq_len: usize = 4096 * 32, // 128K context window
- data_type: DataType = .bf16,
- vocab_size: usize = 102400,
- dim: usize = 2048,
- inter_dim: usize = 10944,
- moe_inter_dim: usize = 1408,
- n_layers: usize = 27,
- n_dense_layers: usize = 1,
- n_heads: usize = 16,
-
- // MoE configuration
- n_routed_experts: usize = 64,
- n_shared_experts: usize = 2,
- n_activated_experts: usize = 6,
- n_expert_groups: usize = 1,
- n_limited_groups: usize = 1,
- score_func: enum { softmax, sigmoid } = .softmax,
- route_scale: f32 = 1.0,
-
- // MLA configuration
- q_lora_rank: usize = 0,
- kv_lora_rank: usize = 512,
- qk_nope_head_dim: usize = 128,
- qk_rope_head_dim: usize = 64,
- v_head_dim: usize = 128,
-
- // Positional encoding
- original_seq_len: usize = 4096,
- rope_theta: f32 = 10000.0,
- rope_factor: f32 = 40,
- beta_fast: usize = 32,
- beta_slow: usize = 1,
- mscale: f32 = 1.0,
-
- // Runtime options
- use_flash_attention: bool = true, // Use optimized attention implementation
- use_parallel_experts: bool = true, // Run experts in parallel
- max_token_limit: ?usize = null, // Optional token generation limit
- enable_kv_cache: bool = true, // Use KV cache for inference
- use_multi_token_prediction: bool = false, // Enable multi-token prediction
-
- // Hardware optimization flags
- target_specific_optimizations: bool = true, // Enable target-specific optimizations
- enable_low_precision_computation: bool = true, // Enable mixed-precision computation
- use_tensor_cores: bool = true, // Use tensor cores if available
-
- // Generate optimized implementations based on config parameters
- pub fn getModelType(self: @This()) type {
- return struct {
- const ModelType = @This();
- const config = self;
-
- // Select optimal types based on data_type
- pub const StorageType = switch (config.data_type) {
- .f32 => f32,
- .bf16 => std.packed_bf16,
- .f16 => f16,
- .i8 => i8,
- .i4 => i4,
- };
-
- // Define tensor types for different dimensions
- pub const WeightTensor = Tensor(StorageType, 2);
- pub const ActivationTensor = Tensor(f32, 3); // Always use f32 for activations
- pub const EmbeddingTensor = Tensor(StorageType, 2);
- pub const KVCacheTensor = Tensor(f32, 4); // [batch, seq_len, heads, dim]
-
- // Generate layer configuration
- pub const layer_config = struct {
- pub const head_dim = (config.dim / config.n_heads);
- pub const moe_layers_start = config.n_dense_layers;
- pub const total_params = calculateTotalParameters(config);
- pub const activated_params = calculateActivatedParameters(config);
- };
-
- fn calculateTotalParameters(config: ModelArgs) usize {
- // This would be a more detailed calculation in reality
- const embedding_params = config.vocab_size * config.dim;
- const attention_params = config.n_layers * (config.dim * config.dim * 4);
- const moe_params = (config.n_layers - config.n_dense_layers) *
- config.n_routed_experts *
- (config.dim * config.moe_inter_dim * 2);
- const dense_ffn_params = config.n_dense_layers * (config.dim * config.inter_dim * 2);
-
- return embedding_params + attention_params + moe_params + dense_ffn_params;
- }
-
- fn calculateActivatedParameters(config: ModelArgs) usize {
- // This would be a more detailed calculation in reality
- const embedding_params = config.vocab_size * config.dim;
- const attention_params = config.n_layers * (config.dim * config.dim * 4);
- const moe_activated_params = (config.n_layers - config.n_dense_layers) *
- config.n_activated_experts *
- (config.dim * config.moe_inter_dim * 2);
- const dense_ffn_params = config.n_dense_layers * (config.dim * config.inter_dim * 2);
-
- return embedding_params + attention_params + moe_activated_params + dense_ffn_params;
- }
- };
- }
-};
-
-// Main transformer model implementation
-pub fn TransformerModel(comptime args: ModelArgs) type {
- // Use comptime to generate a specialized model implementation based on args
- return struct {
- const Self = @This();
- const ModelType = args.getModelType();
-
- // Model components
- allocator: std.mem.Allocator,
- embedding: Embedding(args),
- layers: []TransformerBlock(args),
- norm: RMSNorm(args.dim),
- head: Linear(args.dim, args.vocab_size),
- freqs_cis: Tensor(f32, 3), // [max_seq_len, 2, qk_rope_head_dim]
-
- // KV cache for optimized inference
- kv_cache: ?ModelType.KVCacheTensor,
-
- pub fn init(allocator: std.mem.Allocator) !Self {
- // Initialize components
- var embedding = try Embedding(args).init(allocator);
- errdefer embedding.deinit();
-
- var layers = try allocator.alloc(TransformerBlock(args), args.n_layers);
- errdefer allocator.free(layers);
-
- // Create layers with appropriate configurations
- for (layers, 0..) |*layer, i| {
- const is_moe = i >= args.n_dense_layers;
- layer.* = try TransformerBlock(args).init(allocator, i, is_moe);
- }
-
- var norm = try RMSNorm(args.dim).init(allocator);
- errdefer norm.deinit();
-
- var head = try Linear(args.dim, args.vocab_size).init(allocator, false);
- errdefer head.deinit();
-
- // Precompute positional encoding frequencies
- var freqs_cis = try precomputeFreqsCis(allocator, args);
-
- return Self{
- .allocator = allocator,
- .embedding = embedding,
- .layers = layers,
- .norm = norm,
- .head = head,
- .freqs_cis = freqs_cis,
- .kv_cache = null,
- };
- }
-
- pub fn deinit(self: *Self) void {
- self.embedding.deinit();
-
- for (self.layers) |*layer| {
- layer.deinit();
- }
- self.allocator.free(self.layers);
-
- self.norm.deinit();
- self.head.deinit();
- self.freqs_cis.deinit();
-
- if (self.kv_cache) |*cache| {
- cache.deinit();
- }
- }
-
- // Initialize KV cache for efficient inference
- pub fn initKVCache(self: *Self) !void {
- if (self.kv_cache != null) return;
-
- const batch_size = args.max_batch_size;
- const seq_len = args.max_seq_len;
- const n_heads = args.n_heads;
- const head_dim = ModelType.layer_config.head_dim;
-
- self.kv_cache = try ModelType.KVCacheTensor.init(
- self.allocator,
- .{batch_size, seq_len, n_heads, head_dim * 2}
- );
-
- // Zero-initialize cache
- @memset(self.kv_cache.?.data, 0);
- }
-
- // Forward pass through the transformer model
- pub fn forward(self: *Self, token_ids: []const usize, start_pos: usize) !Tensor(f32, 2) {
- const batch_size = 1; // Currently supporting batch_size=1 for inference
- const seq_len = token_ids.len;
-
- // Create tensor from token_ids
- var input_tensor = try ModelType.ActivationTensor.init(
- self.allocator,
- .{batch_size, seq_len, args.dim}
- );
- defer input_tensor.deinit();
-
- // Get embeddings for input tokens
- try self.embedding.embed(token_ids, &input_tensor);
-
- // Process through each transformer layer
- var x = input_tensor;
- const freqs_cis_slice = try self.freqs_cis.slice(.{start_pos, 0, 0}, .{start_pos + seq_len, 2, args.qk_rope_head_dim});
-
- // Create attention mask for causal attention
- var mask: ?Tensor(f32, 2) = null;
- if (seq_len > 1) {
- mask = try createCausalMask(self.allocator, seq_len);
- defer if (mask) |*m| m.deinit();
- }
-
- // Process through transformer layers
- for (self.layers) |*layer| {
- x = try layer.forward(x, start_pos, freqs_cis_slice, mask);
- }
-
- // Apply final normalization
- var normalized = try self.norm.forward(x);
- defer normalized.deinit();
-
- // Extract last token for prediction
- var last_token = try normalized.slice(
- .{0, seq_len - 1, 0},
- .{batch_size, seq_len, args.dim}
- );
- defer last_token.deinit();
-
- // Project to vocabulary
- return try self.head.forward(last_token);
- }
-
- // Helper to create causal attention mask
- fn createCausalMask(allocator: std.mem.Allocator, seq_len: usize) !Tensor(f32, 2) {
- var mask = try Tensor(f32, 2).init(allocator, .{seq_len, seq_len});
- errdefer mask.deinit();
-
- for (0..seq_len) |i| {
- for (0..seq_len) |j| {
- const value: f32 = if (j <= i) 0.0 else -10000.0;
- try mask.set(.{i, j}, value);
- }
- }
-
- return mask;
- }
- };
-}
-
-// Generate specialized transformer based on configuration
-pub fn createTransformer(allocator: std.mem.Allocator, args: ModelArgs) !*TransformerModel(args) {
- var model = try allocator.create(TransformerModel(args));
- errdefer allocator.destroy(model);
-
- model.* = try TransformerModel(args).init(allocator);
- return model;
-}
-```
-
-This implementation leverages Zig's compile-time features to generate specialized model implementations based on the provided configuration parameters. The use of generic types and comptime evaluation allows for maximum performance optimization while maintaining code flexibility.
-
-#### 2.2 Attention Mechanism
-
-The Multi-Head Latent Attention (MLA) mechanism is a critical component of DeepSeek V3's performance. Our Zig implementation leverages compile-time specialization, SIMD vectorization, and cache-friendly algorithms for maximum efficiency:
-
-```zig
-// Generic MLA implementation with compile-time specialization
-pub fn MLA(comptime args: ModelArgs) type {
- return struct {
- const Self = @This();
- const ModelType = args.getModelType();
-
- // Attention configuration
- dim: usize,
- n_heads: usize,
- head_dim: usize,
- q_lora_rank: usize,
- kv_lora_rank: usize,
- qk_nope_head_dim: usize,
- qk_rope_head_dim: usize,
- qk_head_dim: usize,
- v_head_dim: usize,
- softmax_scale: f32,
- use_flash_attention: bool,
-
- // Projection matrices
- allocator: std.mem.Allocator,
- wq: ?ColumnParallelLinear(args) = null, // Regular query projection
- wq_a: ?Linear(args.dim, args.q_lora_rank) = null, // LoRA decomposition
- q_norm: ?RMSNorm(args.q_lora_rank) = null, // LoRA normalization
- wq_b: ?ColumnParallelLinear(args) = null, // LoRA decomposition
- wkv_a: Linear(args.dim, args.kv_lora_rank + args.qk_rope_head_dim),
- kv_norm: RMSNorm(args.kv_lora_rank),
- wkv_b: ColumnParallelLinear(args),
- wo: RowParallelLinear(args),
-
- // KV caching - optimized for memory access patterns
- kv_cache: ?Tensor(f32, 4) = null, // [batch, seq_len, heads, head_dim*2]
- rope_cache: ?Tensor(f32, 3) = null, // [batch, seq_len, rope_dim]
-
- // Initialize MLA with appropriate configuration
- pub fn init(allocator: std.mem.Allocator) !Self {
- const head_dim = args.dim / args.n_heads;
- var softmax_scale = 1.0 / std.math.sqrt(@as(f32, @floatFromInt(args.qk_nope_head_dim + args.qk_rope_head_dim)));
-
- // Apply scaling for extended context if needed
- if (args.max_seq_len > args.original_seq_len) {
- const mscale = 0.1 * args.mscale * std.math.log(args.rope_factor) + 1.0;
- softmax_scale *= mscale * mscale;
- }
-
- // Initialize query projection (either direct or with LoRA)
- var wq: ?ColumnParallelLinear(args) = null;
- var wq_a: ?Linear(args.dim, args.q_lora_rank) = null;
- var q_norm: ?RMSNorm(args.q_lora_rank) = null;
- var wq_b: ?ColumnParallelLinear(args) = null;
-
- if (args.q_lora_rank == 0) {
- // Standard query projection
- wq = try ColumnParallelLinear(args).init(
- allocator,
- args.dim,
- args.n_heads * (args.qk_nope_head_dim + args.qk_rope_head_dim),
- false
- );
- } else {
- // Low-rank adaptation for query
- wq_a = try Linear(args.dim, args.q_lora_rank).init(allocator, false);
- q_norm = try RMSNorm(args.q_lora_rank).init(allocator);
- wq_b = try ColumnParallelLinear(args).init(
- allocator,
- args.q_lora_rank,
- args.n_heads * (args.qk_nope_head_dim + args.qk_rope_head_dim),
- false
- );
- }
-
- // Key-value projections
- var wkv_a = try Linear(args.dim, args.kv_lora_rank + args.qk_rope_head_dim).init(allocator, false);
- var kv_norm = try RMSNorm(args.kv_lora_rank).init(allocator);
- var wkv_b = try ColumnParallelLinear(args).init(
- allocator,
- args.kv_lora_rank,
- args.n_heads * (args.qk_nope_head_dim + args.v_head_dim),
- false
- );
-
- // Output projection
- var wo = try RowParallelLinear(args).init(
- allocator,
- args.n_heads * args.v_head_dim,
- args.dim,
- false
- );
-
- return Self{
- .allocator = allocator,
- .dim = args.dim,
- .n_heads = args.n_heads,
- .head_dim = head_dim,
- .q_lora_rank = args.q_lora_rank,
- .kv_lora_rank = args.kv_lora_rank,
- .qk_nope_head_dim = args.qk_nope_head_dim,
- .qk_rope_head_dim = args.qk_rope_head_dim,
- .qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim,
- .v_head_dim = args.v_head_dim,
- .softmax_scale = softmax_scale,
- .use_flash_attention = args.use_flash_attention,
- .wq = wq,
- .wq_a = wq_a,
- .q_norm = q_norm,
- .wq_b = wq_b,
- .wkv_a = wkv_a,
- .kv_norm = kv_norm,
- .wkv_b = wkv_b,
- .wo = wo,
- };
- }
-
- pub fn deinit(self: *Self) void {
- if (self.wq) |*w| w.deinit();
- if (self.wq_a) |*w| w.deinit();
- if (self.q_norm) |*n| n.deinit();
- if (self.wq_b) |*w| w.deinit();
-
- self.wkv_a.deinit();
- self.kv_norm.deinit();
- self.wkv_b.deinit();
- self.wo.deinit();
-
- if (self.kv_cache) |*cache| cache.deinit();
- if (self.rope_cache) |*cache| cache.deinit();
- }
-
- // Initialize KV cache for efficient inference
- pub fn initKVCache(self: *Self, batch_size: usize, seq_len: usize) !void {
- if (self.kv_cache != null) return;
-
- // Allocate KV cache
- self.kv_cache = try Tensor(f32, 4).init(
- self.allocator,
- .{batch_size, seq_len, self.n_heads, self.head_dim * 2}
- );
-
- // Zero-initialize
- @memset(self.kv_cache.?.data, 0);
-
- // Allocate rotary positional encoding cache
- self.rope_cache = try Tensor(f32, 3).init(
- self.allocator,
- .{batch_size, seq_len, self.qk_rope_head_dim}
- );
-
- @memset(self.rope_cache.?.data, 0);
- }
-
- // Forward pass implementation with multiple specialized paths
- pub fn forward(
- self: *Self,
- x: Tensor(f32, 3),
- start_pos: usize,
- freqs_cis: Tensor(f32, 3),
- mask: ?Tensor(f32, 2)
- ) !Tensor(f32, 3) {
- const batch_size = x.shape[0];
- const seq_len = x.shape[1];
- const end_pos = start_pos + seq_len;
-
- // Initialize KV cache if not already done
- if (start_pos > 0 and self.kv_cache == null) {
- try self.initKVCache(batch_size, args.max_seq_len);
- }
-
- // Compute query vectors
- var q: Tensor(f32, 4) = undefined;
- if (self.q_lora_rank == 0) {
- // Standard query projection
- var q_flat = try self.wq.?.forward(x);
- defer q_flat.deinit();
-
- // Reshape to [batch, seq_len, heads, head_dim]
- q = try q_flat.reshape(.{batch_size, seq_len, self.n_heads, self.qk_head_dim});
- } else {
- // Low-rank adaptation
- var q_a = try self.wq_a.?.forward(x);
- defer q_a.deinit();
-
- var q_norm = try self.q_norm.?.forward(q_a);
- defer q_norm.deinit();
-
- var q_b = try self.wq_b.?.forward(q_norm);
- defer q_b.deinit();
-
- // Reshape
- q = try q_b.reshape(.{batch_size, seq_len, self.n_heads, self.qk_head_dim});
- }
- defer q.deinit();
-
- // Split query into regular and positional parts
- var q_slices = try q.split(3, .{self.qk_nope_head_dim, self.qk_rope_head_dim});
- defer for (q_slices) |*slice| slice.deinit();
-
- var q_nope = q_slices[0];
- var q_pe = q_slices[1];
-
- // Apply rotary embeddings to position-dependent part
- try applyRotaryEmbeddings(&q_pe, freqs_cis);
-
- // Compute key-value vectors
- var kv_raw = try self.wkv_a.forward(x);
- defer kv_raw.deinit();
-
- // Split into KV features and positional features
- var kv_slices = try kv_raw.split(2, .{self.kv_lora_rank, self.qk_rope_head_dim});
- defer for (kv_slices) |*slice| slice.deinit();
-
- var kv_features = kv_slices[0];
- var k_pe_features = kv_slices[1];
-
- // Add batch and heads dimension to positional features
- var k_pe = try k_pe_features.reshape(.{batch_size, seq_len, 1, self.qk_rope_head_dim});
- defer k_pe.deinit();
-
- // Apply rotary embeddings
- try applyRotaryEmbeddings(&k_pe, freqs_cis);
-
- // Process main KV branch
- var kv_norm_features = try self.kv_norm.forward(kv_features);
- defer kv_norm_features.deinit();
-
- var kv_proj = try self.wkv_b.forward(kv_norm_features);
- defer kv_proj.deinit();
-
- // Reshape to separate K and V
- var kv_reshaped = try kv_proj.reshape(
- .{batch_size, seq_len, self.n_heads, self.qk_nope_head_dim + self.v_head_dim}
- );
- defer kv_reshaped.deinit();
-
- // Split into K and V
- var kv_parts = try kv_reshaped.split(3, .{self.qk_nope_head_dim, self.v_head_dim});
- defer for (kv_parts) |*part| part.deinit();
-
- var k_nope = kv_parts[0];
- var v = kv_parts[1];
-
- // Combine positional and non-positional key parts
- var k = try combineTensors(k_nope, k_pe, 3);
- defer k.deinit();
-
- // Store in KV cache if available
- if (self.kv_cache != null) {
- try self.updateKVCache(k, v, start_pos, end_pos);
- }
-
- // Choose attention implementation based on settings
- var attention_output: Tensor(f32, 4) = undefined;
- if (self.use_flash_attention and seq_len > 1) {
- attention_output = try self.computeFlashAttention(
- q_nope,
- q_pe,
- self.kv_cache.?,
- self.rope_cache.?,
- mask,
- batch_size,
- seq_len,
- end_pos
- );
- } else {
- attention_output = try self.computeStandardAttention(
- q,
- k,
- v,
- mask,
- batch_size,
- seq_len,
- end_pos
- );
- }
- defer attention_output.deinit();
-
- // Final projection
- var attention_flat = try attention_output.reshape(
- .{batch_size, seq_len, self.n_heads * self.v_head_dim}
- );
- defer attention_flat.deinit();
-
- return self.wo.forward(attention_flat);
- }
-
- // Flash attention implementation optimized for large contexts
- fn computeFlashAttention(
- self: *const Self,
- q_nope: Tensor(f32, 4),
- q_pe: Tensor(f32, 4),
- kv_cache: Tensor(f32, 4),
- rope_cache: Tensor(f32, 3),
- mask: ?Tensor(f32, 2),
- batch_size: usize,
- seq_len: usize,
- end_pos: usize
- ) !Tensor(f32, 4) {
- // Flash attention implementation with tiling to maximize cache efficiency
- // This function would include a highly optimized SIMD implementation
- // specializing in memory-efficient attention computation
-
- // Note: This would be a substantial implementation with memory-efficient
- // blocked matrix multiplication and careful SIMD optimization
- // We're providing a simplified structure here
-
- // For a full implementation, see the FlashAttention algorithm paper
- const block_size = 32; // Block size tuned for L1 cache
-
- // Output tensor
- var output = try Tensor(f32, 4).init(
- self.allocator,
- .{batch_size, seq_len, self.n_heads, self.v_head_dim}
- );
-
- // Implement blocked attention algorithm...
- // This would contain optimized SIMD code for tiled attention computation
-
- return output;
- }
-
- // Standard attention for shorter sequences or when flash attention is disabled
- fn computeStandardAttention(
- self: *const Self,
- q: Tensor(f32, 4),
- k: Tensor(f32, 4),
- v: Tensor(f32, 4),
- mask: ?Tensor(f32, 2),
- batch_size: usize,
- seq_len: usize,
- end_pos: usize
- ) !Tensor(f32, 4) {
- // Compute QK attention scores
- var scores = try computeAttentionScores(q, k, self.softmax_scale);
- defer scores.deinit();
-
- // Apply causal mask if provided
- if (mask) |m| {
- try applyAttentionMask(&scores, m);
- }
-
- // Apply softmax
- try applySoftmax(&scores, -1);
-
- // Compute attention output (scores @ v)
- return computeAttentionOutput(scores, v);
- }
-
- // Update KV cache with new values
- fn updateKVCache(
- self: *Self,
- k: Tensor(f32, 4),
- v: Tensor(f32, 4),
- start_pos: usize,
- end_pos: usize
- ) !void {
- const batch_size = k.shape[0];
- const seq_len = k.shape[1];
-
- // Update key cache
- for (0..batch_size) |b| {
- for (0..seq_len) |s| {
- const cache_pos = start_pos + s;
- for (0..self.n_heads) |h| {
- // Copy K values
- for (0..self.qk_head_dim) |d| {
- const k_val = try k.at(.{b, s, h, d});
- try self.kv_cache.?.set(.{b, cache_pos, h, d}, k_val);
- }
-
- // Copy V values
- for (0..self.v_head_dim) |d| {
- const v_val = try v.at(.{b, s, h, d});
- try self.kv_cache.?.set(.{b, cache_pos, h, self.qk_head_dim + d}, v_val);
- }
- }
- }
- }
- }
- };
-}
-```
-
-**Key Optimizations:**
-- **Compile-Time Specialization**: Generated attention routines are tailored to model dimensions at compile time
-- **Flash Attention Algorithm**: Memory-efficient attention computation for long sequences
-- **SIMD-Optimized Matrix Operations**: Vectorized attention score calculation and softmax
-- **Optimized KV-Cache Layout**: Cache-friendly memory layout for efficient sequence generation
-- **Sparse Attention Patterns**: Support for different attention patterns beyond standard causal attention
-- **Memory Reuse**: Careful tensor management to minimize allocations during inference
-- **Specialized Attention Paths**: Different implementations optimized for inference vs. training
-- **Low-Rank Adaptation**: LoRA support for more efficient fine-tuning
-
-#### 2.3 Mixture of Experts (MoE)
-
-The Mixture of Experts (MoE) architecture is a key innovation in DeepSeek V3 that enables scaling model capacity without proportionally increasing computation cost. Our Zig implementation leverages compile-time specialization and parallel execution for maximum efficiency:
-
-```zig
-// Generic MoE implementation with compile-time specialization
-pub fn MixtureOfExperts(comptime args: ModelArgs) type {
- return struct {
- const Self = @This();
- const ModelType = args.getModelType();
-
- // Configuration
- allocator: std.mem.Allocator,
- dim: usize,
- n_routed_experts: usize,
- n_local_experts: usize,
- n_activated_experts: usize,
- experts_start_idx: usize,
- experts_end_idx: usize,
- use_parallel_execution: bool,
-
- // Components
- gate: RouterGate(args),
- experts: []Expert(args),
- shared_experts: MLP(args),
- thread_pool: ?*ComputeThreadPool = null,
-
- // Initialize MoE with appropriate configuration
- pub fn init(allocator: std.mem.Allocator) !Self {
- // Determine expert distribution across processes
- const world_size = 1; // Set to actual world size for distributed training
- const rank = 0; // Set to actual rank for distributed training
-
- std.debug.assert(args.n_routed_experts % world_size == 0,
- "Number of experts must be divisible by world size");
-
- const n_local_experts = args.n_routed_experts / world_size;
- const experts_start_idx = rank * n_local_experts;
- const experts_end_idx = experts_start_idx + n_local_experts;
-
- // Initialize routing gate
- var gate = try RouterGate(args).init(allocator);
- errdefer gate.deinit();
-
- // Initialize experts
- var experts = try allocator.alloc(Expert(args), args.n_routed_experts);
- errdefer allocator.free(experts);
-
- // Only initialize experts that belong to this process
- for (experts, 0..) |*expert, i| {
- if (experts_start_idx <= i and i < experts_end_idx) {
- expert.* = try Expert(args).init(allocator);
- } else {
- expert.* = undefined; // Not used on this process
- }
- }
-
- // Initialize shared experts (always executed)
- var shared_experts = try MLP(args).init(
- allocator,
- args.dim,
- args.n_shared_experts * args.moe_inter_dim
- );
- errdefer shared_experts.deinit();
-
- // Initialize thread pool for parallel execution if needed
- var thread_pool: ?*ComputeThreadPool = null;
- if (args.use_parallel_experts) {
- thread_pool = try allocator.create(ComputeThreadPool);
- const cpu_count = try std.Thread.getCpuCount();
- const optimal_threads = std.math.min(
- cpu_count,
- args.n_activated_experts + args.n_shared_experts
- );
- thread_pool.?.* = try ComputeThreadPool.init(optimal_threads);
- }
-
- return Self{
- .allocator = allocator,
- .dim = args.dim,
- .n_routed_experts = args.n_routed_experts,
- .n_local_experts = n_local_experts,
- .n_activated_experts = args.n_activated_experts,
- .experts_start_idx = experts_start_idx,
- .experts_end_idx = experts_end_idx,
- .use_parallel_execution = args.use_parallel_experts,
- .gate = gate,
- .experts = experts,
- .shared_experts = shared_experts,
- .thread_pool = thread_pool,
- };
- }
-
- pub fn deinit(self: *Self) void {
- self.gate.deinit();
-
- // Only deinit experts that belong to this process
- for (self.experts, 0..) |*expert, i| {
- if (self.experts_start_idx <= i and i < self.experts_end_idx) {
- expert.deinit();
- }
- }
- self.allocator.free(self.experts);
-
- self.shared_experts.deinit();
-
- if (self.thread_pool) |pool| {
- pool.deinit();
- self.allocator.destroy(pool);
- }
- }
-
- // Forward pass implementation with parallel expert execution
- pub fn forward(self: *Self, x: Tensor(f32, 3)) !Tensor(f32, 3) {
- const batch_size = x.shape[0];
- const seq_len = x.shape[1];
-
- // Reshape input for routing
- var x_flat = try x.reshape(.{batch_size * seq_len, self.dim});
- defer x_flat.deinit();
-
- // Router computation
- var router_output = try self.gate.forward(x_flat);
- defer {
- router_output.weights.deinit();
- router_output.indices.deinit();
- }
-
- // Get routing weights and indices
- const weights = router_output.weights;
- const indices = router_output.indices;
-
- // Initialize result tensor with zeros
- var result = try Tensor(f32, 2).init(
- self.allocator,
- .{batch_size * seq_len, self.dim}
- );
- errdefer result.deinit();
-
- @memset(result.data, 0);
-
- // Count expert assignments for load balancing analysis
- var expert_counts = try self.allocator.alloc(usize, self.n_routed_experts);
- defer self.allocator.free(expert_counts);
- @memset(expert_counts, 0);
-
- for (indices.data) |idx| {
- expert_counts[idx] += 1;
- }
-
- // Process each expert
- if (self.use_parallel_execution and self.thread_pool != null) {
- try self.parallelExpertExecution(
- x_flat,
- weights,
- indices,
- expert_counts,
- &result
- );
- } else {
- try self.sequentialExpertExecution(
- x_flat,
- weights,
- indices,
- expert_counts,
- &result
- );
- }
-
- // Always execute shared experts
- var shared_output = try self.shared_experts.forward(x_flat);
- defer shared_output.deinit();
-
- // Add shared expert output to result
- try addTensors(&result, shared_output);
-
- // Reshape back to original dimensions
- return result.reshape(.{batch_size, seq_len, self.dim});
- }
-
- // Parallel execution of experts using thread pool
- fn parallelExpertExecution(
- self: *Self,
- x: Tensor(f32, 2),
- weights: Tensor(f32, 2),
- indices: Tensor(usize, 2),
- expert_counts: []usize,
- result: *Tensor(f32, 2)
- ) !void {
- const thread_pool = self.thread_pool.?;
- var work_queue = std.ArrayList(ExpertWorkItem).init(self.allocator);
- defer work_queue.deinit();
-
- // Create work items for each expert
- for (0..self.n_routed_experts) |expert_idx| {
- if (expert_counts[expert_idx] == 0) continue;
-
- if (expert_idx < self.experts_start_idx or expert_idx >= self.experts_end_idx) {
- // Skip experts not assigned to this process
- continue;
- }
-
- // Extract tokens routed to this expert
- var token_indices = try self.allocator.alloc(usize, expert_counts[expert_idx]);
- var token_weights = try self.allocator.alloc(f32, expert_counts[expert_idx]);
-
- var token_count: usize = 0;
- for (0..x.shape[0]) |i| {
- for (0..self.n_activated_experts) |j| {
- const index_offset = i * self.n_activated_experts + j;
- if (indices.data[index_offset] == expert_idx) {
- token_indices[token_count] = i;
- token_weights[token_count] = weights.data[index_offset];
- token_count += 1;
- }
- }
- }
-
- // Create work item
- try work_queue.append(.{
- .allocator = self.allocator,
- .expert = &self.experts[expert_idx],
- .x = x,
- .token_indices = token_indices,
- .token_weights = token_weights,
- .result = result,
- .thread_pool = thread_pool,
- });
- }
-
- // Schedule parallel expert execution
- for (work_queue.items) |*work_item| {
- // Increment completion counter
- _ = thread_pool.completion_count.fetchAdd(1, .Release);
-
- // Submit task to thread pool
- try thread_pool.compute(processExpertWork, work_item);
- }
-
- // Wait for all expert computations to complete
- thread_pool.waitAll();
- }
-
- // Sequential execution of experts
- fn sequentialExpertExecution(
- self: *Self,
- x: Tensor(f32, 2),
- weights: Tensor(f32, 2),
- indices: Tensor(usize, 2),
- expert_counts: []usize,
- result: *Tensor(f32, 2)
- ) !void {
- // Process each expert sequentially
- for (0..self.n_routed_experts) |expert_idx| {
- if (expert_counts[expert_idx] == 0) continue;
-
- if (expert_idx < self.experts_start_idx or expert_idx >= self.experts_end_idx) {
- // Skip experts not assigned to this process
- continue;
- }
-
- // Get tokens assigned to this expert
- for (0..x.shape[0]) |i| {
- for (0..self.n_activated_experts) |j| {
- const index_offset = i * self.n_activated_experts + j;
- if (indices.data[index_offset] == expert_idx) {
- // Process token with this expert
- const token_weight = weights.data[index_offset];
-
- // Extract input token
- var token_input = try x.slice(.{i, 0}, .{i + 1, self.dim});
- defer token_input.deinit();
-
- // Process through expert
- var expert_output = try self.experts[expert_idx].forward(token_input);
- defer expert_output.deinit();
-
- // Scale by routing weight
- try scaleTensor(&expert_output, token_weight);
-
- // Add to result
- for (0..self.dim) |d| {
- result.data[i * self.dim + d] += expert_output.data[d];
- }
- }
- }
- }
- }
- }
-
- // Worker task for parallel expert execution
- const ExpertWorkItem = struct {
- allocator: std.mem.Allocator,
- expert: *Expert(args),
- x: Tensor(f32, 2),
- token_indices: []usize,
- token_weights: []f32,
- result: *Tensor(f32, 2),
- thread_pool: *ComputeThreadPool,
- };
-
- fn processExpertWork(ctx_ptr: *anyopaque) void {
- const ctx = @ptrCast(*ExpertWorkItem, @alignCast(@alignOf(ExpertWorkItem), ctx_ptr));
- defer {
- ctx.allocator.free(ctx.token_indices);
- ctx.allocator.free(ctx.token_weights);
- _ = ctx.thread_pool.completion_count.fetchSub(1, .Release);
- }
-
- // Process each token assigned to this expert
- for (ctx.token_indices, ctx.token_weights, 0..) |token_idx, weight, i| {
- // Extract input token
- var token_input = ctx.x.slice(.{token_idx, 0}, .{token_idx + 1, ctx.x.shape[1]}) catch return;
- defer token_input.deinit();
-
- // Process through expert
- var expert_output = ctx.expert.forward(token_input) catch return;
- defer expert_output.deinit();
-
- // Scale by routing weight
- scaleTensor(&expert_output, weight) catch return;
-
- // Add to result (using atomic operations to avoid race conditions)
- for (0..expert_output.shape[1]) |d| {
- const offset = token_idx * expert_output.shape[1] + d;
- const old_val = @atomicLoad(f32, &ctx.result.data[offset], .Acquire);
- const new_val = old_val + expert_output.data[d];
- @atomicStore(f32, &ctx.result.data[offset], new_val, .Release);
- }
- }
- }
- };
-}
-
-// Router gate for MoE that determines which experts to use for each token
-pub fn RouterGate(comptime args: ModelArgs) type {
- return struct {
- const Self = @This();
-
- allocator: std.mem.Allocator,
- dim: usize,
- n_experts: usize,
- n_groups: usize,
- n_limited_groups: usize,
- topk: usize,
- score_func: enum { softmax, sigmoid },
- route_scale: f32,
-
- // Router weights
- weight: Tensor(f32, 2),
- bias: ?Tensor(f32, 1) = null,
-
- pub fn init(allocator: std.mem.Allocator) !Self {
- var weight = try Tensor(f32, 2).init(
- allocator,
- .{args.n_routed_experts, args.dim}
- );
-
- // Initialize with appropriate distribution
- try initializeParameters(&weight, 0.0, 0.02);
-
- // Create optional bias
- var bias: ?Tensor(f32, 1) = null;
- if (args.dim == 7168) { // Special case for bias
- bias = try Tensor(f32, 1).init(allocator, .{args.n_routed_experts});
- @memset(bias.?.data, 0);
- }
-
- return Self{
- .allocator = allocator,
- .dim = args.dim,
- .n_experts = args.n_routed_experts,
- .n_groups = args.n_expert_groups,
- .n_limited_groups = args.n_limited_groups,
- .topk = args.n_activated_experts,
- .score_func = args.score_func,
- .route_scale = args.route_scale,
- .weight = weight,
- .bias = bias,
- };
- }
-
- pub fn deinit(self: *Self) void {
- self.weight.deinit();
- if (self.bias) |*b| b.deinit();
- }
-
- // Router forward pass to determine expert assignment
- pub fn forward(self: *const Self, x: Tensor(f32, 2)) !RouterOutput {
- // Compute routing scores
- var scores = try linearProjection(x, self.weight, self.bias);
- defer scores.deinit();
-
- // Apply scoring function
- var routing_probs: Tensor(f32, 2) = undefined;
- if (self.score_func == .softmax) {
- routing_probs = try applySoftmax(scores, 1);
- } else {
- routing_probs = try applySigmoid(scores);
- }
- defer routing_probs.deinit();
-
- // Save original scores for later
- var original_scores = try routing_probs.clone();
-
- // Expert group handling
- if (self.n_groups > 1) {
- try self.applyGroupFiltering(&routing_probs);
- }
-
- // Select top-k experts
- var indices = try Tensor(usize, 2).init(
- self.allocator,
- .{x.shape[0], self.topk}
- );
-
- var weights = try Tensor(f32, 2).init(
- self.allocator,
- .{x.shape[0], self.topk}
- );
-
- try self.selectTopkExperts(routing_probs, original_scores, &indices, &weights);
-
- // Apply routing scale
- if (self.route_scale != 1.0) {
- try scaleTensor(&weights, self.route_scale);
- }
-
- return RouterOutput{
- .weights = weights,
- .indices = indices,
- };
- }
-
- // Apply expert group filtering
- fn applyGroupFiltering(self: *const Self, scores: *Tensor(f32, 2)) !void {
- // Reshape scores for group processing
- const batch_size = scores.shape[0];
- const experts_per_group = self.n_experts / self.n_groups;
-
- var reshaped_scores = try scores.reshape(
- .{batch_size, self.n_groups, experts_per_group}
- );
- defer reshaped_scores.deinit();
-
- // Compute group scores
- var group_scores = try Tensor(f32, 2).init(
- self.allocator,
- .{batch_size, self.n_groups}
- );
- defer group_scores.deinit();
-
- // Calculate score for each group
- if (self.bias == null) {
- // Use max score as group score
- for (0..batch_size) |b| {
- for (0..self.n_groups) |g| {
- var max_score: f32 = -std.math.inf_f32;
- for (0..experts_per_group) |e| {
- const score = try reshaped_scores.at(.{b, g, e});
- if (score > max_score) max_score = score;
- }
- try group_scores.set(.{b, g}, max_score);
- }
- }
- } else {
- // Use sum of top-2 scores as group score
- for (0..batch_size) |b| {
- for (0..self.n_groups) |g| {
- var scores_arr = try self.allocator.alloc(f32, experts_per_group);
- defer self.allocator.free(scores_arr);
-
- // Extract scores for this group
- for (0..experts_per_group) |e| {
- scores_arr[e] = try reshaped_scores.at(.{b, g, e});
- }
-
- // Sort to find top-2
- std.sort.sort(f32, scores_arr, {}, std.sort.desc(f32));
-
- // Sum top-2 scores
- const group_score = scores_arr[0] + scores_arr[1];
- try group_scores.set(.{b, g}, group_score);
- }
- }
- }
-
- // Find top-k groups
- var top_groups = try Tensor(usize, 2).init(
- self.allocator,
- .{batch_size, self.n_limited_groups}
- );
- defer top_groups.deinit();
-
- // Select top-k groups
- for (0..batch_size) |b| {
- var scores_arr = try self.allocator.alloc(struct { score: f32, idx: usize }, self.n_groups);
- defer self.allocator.free(scores_arr);
-
- // Prepare for sorting
- for (0..self.n_groups) |g| {
- scores_arr[g] = .{
- .score = try group_scores.at(.{b, g}),
- .idx = g,
- };
- }
-
- // Sort by score
- const Sort = struct {
- fn desc(context: void, a: anytype, b: anytype) bool {
- return a.score > b.score;
- }
- };
- std.sort.sort(struct { score: f32, idx: usize }, scores_arr, {}, Sort.desc);
-
- // Store top-k group indices
- for (0..self.n_limited_groups) |i| {
- try top_groups.set(.{b, i}, scores_arr[i].idx);
- }
- }
-
- // Create mask for filtering
- var mask = try Tensor(bool, 3).init(
- self.allocator,
- .{batch_size, self.n_groups, 1}
- );
- defer mask.deinit();
-
- // Initialize all groups as masked (excluded)
- @memset(mask.data, true);
-
- // Unmask top groups
- for (0..batch_size) |b| {
- for (0..self.n_limited_groups) |i| {
- const g = try top_groups.at(.{b, i});
- try mask.set(.{b, g, 0}, false);
- }
- }
-
- // Apply mask
- for (0..batch_size) |b| {
- for (0..self.n_groups) |g| {
- const is_masked = try mask.at(.{b, g, 0});
- if (is_masked) {
- // Mask out this group by setting scores to -inf
- for (0..experts_per_group) |e| {
- try reshaped_scores.set(.{b, g, e}, -std.math.inf_f32);
- }
- }
- }
- }
-
- // Reshape back to original shape
- try scores.copyFrom(reshaped_scores.reshape(.{batch_size, self.n_experts}) catch unreachable);
- }
-
- // Select top-k experts based on routing scores
- fn selectTopkExperts(
- self: *const Self,
- scores: Tensor(f32, 2),
- original_scores: Tensor(f32, 2),
- indices: *Tensor(usize, 2),
- weights: *Tensor(f32, 2)
- ) !void {
- const batch_size = scores.shape[0];
-
- for (0..batch_size) |b| {
- var scores_arr = try self.allocator.alloc(struct { score: f32, idx: usize }, self.n_experts);
- defer self.allocator.free(scores_arr);
-
- // Prepare for sorting
- for (0..self.n_experts) |e| {
- scores_arr[e] = .{
- .score = try scores.at(.{b, e}),
- .idx = e,
- };
- }
-
- // Sort by score
- const Sort = struct {
- fn desc(context: void, a: anytype, b: anytype) bool {
- return a.score > b.score;
- }
- };
- std.sort.sort(struct { score: f32, idx: usize }, scores_arr, {}, Sort.desc);
-
- // Store top-k indices and get weights from original scores
- for (0..self.topk) |i| {
- const expert_idx = scores_arr[i].idx;
- try indices.set(.{b, i}, expert_idx);
-
- // Get weight from original scores
- const weight = try original_scores.at(.{b, expert_idx});
- try weights.set(.{b, i}, weight);
- }
-
- // Normalize weights for sigmoid scoring
- if (self.score_func == .sigmoid) {
- var sum: f32 = 0.0;
- for (0..self.topk) |i| {
- sum += try weights.at(.{b, i});
- }
-
- if (sum > 0.0) {
- for (0..self.topk) |i| {
- const w = try weights.at(.{b, i});
- try weights.set(.{b, i}, w / sum);
- }
- }
- }
- }
- }
- };
-}
-
-// Output from router gate
-pub const RouterOutput = struct {
- weights: Tensor(f32, 2), // [batch_size, topk]
- indices: Tensor(usize, 2), // [batch_size, topk]
-};
-```
-
-**Key Features:**
-- **Compile-Time Specialization**: Generated MoE implementation tailored to model dimensions and configuration
-- **Parallel Expert Execution**: Efficient multi-threading with work distribution and load balancing
-- **Atomic Operations**: Thread-safe updates to shared tensors
-- **Group-Based Routing**: Optimized implementation of expert groups for more efficient routing
-- **Memory-Efficient Tensor Management**: Careful handling of temporary allocations
-- **Flexible Scoring Functions**: Support for both softmax and sigmoid routing
-- **Expert Load Balancing**: Runtime tracking of expert utilization
-- **Distributed Expert Sharding**: Support for distributing experts across multiple processes
-
-### 3. Computation Backend
-
-Outlining the computation backend architecture for the DeepSeek-V3 project implemented in Zig. The design emphasizes performance, modularity, and hardware portability.
-
-#### 3.1 Backend Interface
-
-The backend interface provides a unified abstraction layer for all computation targets while maintaining Zig's zero-cost abstraction philosophy.
-
-```zig
-pub const ComputeError = error{
- MatrixDimensionMismatch,
- OutOfMemory,
- UnsupportedOperation,
- HardwareAccelerationFailed,
- DeviceError,
- InvalidParameter,
- UnsupportedDataType,
- KernelExecutionFailed,
- QuantizationError,
-};
-
-pub const ComputeBackend = struct {
- const Self = @This();
-
- // Function pointers for backend operations
- matmulFn: *const fn(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) ComputeError!void,
- addFn: *const fn(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) ComputeError!void,
- activationFn: *const fn(x: anytype, y: *anytype, act_type: ActivationType, allocator: std.mem.Allocator) ComputeError!void,
- softmaxFn: *const fn(x: anytype, y: *anytype, dim: ?usize, allocator: std.mem.Allocator) ComputeError!void,
-
- // Device management
- initDeviceFn: *const fn(device_id: ?usize) ComputeError!void,
- releaseDeviceFn: *const fn() void,
-
- // Memory management
- allocateDeviceMemoryFn: *const fn(size: usize) ComputeError!*anyopaque,
- freeDeviceMemoryFn: *const fn(ptr: *anyopaque) void,
- copyHostToDeviceFn: *const fn(host_ptr: *const anyopaque, device_ptr: *anyopaque, size: usize) ComputeError!void,
- copyDeviceToHostFn: *const fn(device_ptr: *const anyopaque, host_ptr: *anyopaque, size: usize) ComputeError!void,
-
- // Backend info
- getBackendInfoFn: *const fn() BackendInfo,
-
- // Backend factory functions
- pub fn createCpuBackend(config: CpuBackendConfig) !*Self {
- const allocator = config.allocator orelse std.heap.page_allocator;
-
- var backend = try allocator.create(Self);
- errdefer allocator.destroy(backend);
-
- backend.* = .{
- .matmulFn = if (config.use_simd) simdMatmul else scalarMatmul,
- .addFn = if (config.use_simd) simdAdd else scalarAdd,
- .activationFn = genericActivation,
- .softmaxFn = genericSoftmax,
- .initDeviceFn = initCpuDevice,
- .releaseDeviceFn = releaseCpuDevice,
- .allocateDeviceMemoryFn = allocateCpuMemory,
- .freeDeviceMemoryFn = freeCpuMemory,
- .copyHostToDeviceFn = cpuMemcpy,
- .copyDeviceToHostFn = cpuMemcpy,
- .getBackendInfoFn = getCpuBackendInfo,
- };
-
- return backend;
- }
-
- pub fn createMetalBackend(config: MetalBackendConfig) !*Self {
- // Implementation details for Metal backend would go here
- @compileError("Metal backend not implemented yet");
- }
-
- pub fn createCudaBackend(config: CudaBackendConfig) !*Self {
- // Implementation details for CUDA backend would go here
- @compileError("CUDA backend not implemented yet");
- }
-};
-```
-
-#### 3.2 Cross-Platform Compilation
-
-One of the key advantages of implementing DeepZig V3 in Zig is the language's exceptional cross-compilation capabilities. Zig includes the compiler and standard libraries for all supported targets, making it trivial to compile for different platforms without additional toolchains.
-
-#### 3.2.1 Cross-Compilation Support
-
-```zig
-// Example of how to build for different target platforms
-pub fn build(b: *std.Build) void {
- // Standard x86_64 Linux build
- const linux_x86_64 = b.standardTargetOptions(.{
- .default_target = .{
- .cpu_arch = .x86_64,
- .os_tag = .linux,
- .cpu_features_add = std.Target.x86.Feature.avx2_featureset,
- },
- });
-
- // Apple Silicon build
- const macos_aarch64 = b.standardTargetOptions(.{
- .default_target = .{
- .cpu_arch = .aarch64,
- .os_tag = .macos,
- .cpu_features_add = std.Target.aarch64.Feature.apple_a14_featureset,
- },
- });
-
- // Windows x86_64 build
- const windows_x86_64 = b.standardTargetOptions(.{
- .default_target = .{
- .cpu_arch = .x86_64,
- .os_tag = .windows,
- .abi = .msvc,
- },
- });
-
- // WASM build for browser deployment
- const wasm = b.standardTargetOptions(.{
- .default_target = .{
- .cpu_arch = .wasm32,
- .os_tag = .freestanding,
- },
- });
-
- // Create libs/executables for each target
- createBuild(b, linux_x86_64, "linux-x86_64");
- createBuild(b, macos_aarch64, "macos-arm64");
- createBuild(b, windows_x86_64, "windows-x86_64");
- createBuild(b, wasm, "web");
-}
-
-fn createBuild(b: *std.Build, target: std.zig.CrossTarget, name: []const u8) void {
- // Create optimized and debug builds
- const optimize = b.standardOptimizeOption(.{});
-
- // Create library
- const lib = b.addStaticLibrary(.{
- .name = std.fmt.allocPrint(
- b.allocator,
- "deepzig-{s}",
- .{name}
- ) catch unreachable,
- .root_source_file = .{ .path = "src/main.zig" },
- .target = target,
- .optimize = optimize,
- });
-
- // Install in the appropriate location
- b.installArtifact(lib);
-
- // Create a CLI tool using the library
- const exe = b.addExecutable(.{
- .name = std.fmt.allocPrint(
- b.allocator,
- "deepzig-cli-{s}",
- .{name}
- ) catch unreachable,
- .root_source_file = .{ .path = "src/cli.zig" },
- .target = target,
- .optimize = optimize,
- });
-
- exe.linkLibrary(lib);
- b.installArtifact(exe);
-}
-```
-
-#### 3.2.2 C ABI Compatibility
-
-DeepZig V3 leverages Zig's seamless interoperability with C to interface with existing ML libraries:
-
-```zig
-// Example of interfacing with C libraries
-const c = @cImport({
- @cInclude("cublas_v2.h"); // For NVIDIA GPU acceleration
- @cInclude("mkl.h"); // For Intel CPU optimization
-});
-
-pub fn createOptimizedBackend() !*ComputeBackend {
- // Try to use hardware-specific libraries in order of preference
- if (hasCudaSupport()) {
- return createCudaBackend();
- } else if (hasMklSupport()) {
- return createMklBackend();
- } else {
- return createNativeBackend();
- }
-}
-
-fn hasCudaSupport() bool {
- // Check if CUDA is available
- var device_count: c_int = 0;
- const status = c.cudaGetDeviceCount(&device_count);
- return (status == c.cudaSuccess and device_count > 0);
-}
-
-fn hasMklSupport() bool {
- // Check if MKL is available
- return c.mkl_get_version(null) != 0;
-}
-```
-
-This cross-platform approach ensures DeepZig V3 can run efficiently on virtually any hardware platform, from high-end GPU servers to consumer devices, with appropriate performance optimizations for each target.
-
-#### 3.3 Platform-Specific Implementations
-
-```zig
-pub const CPUBackend = struct {
- allocator: std.mem.Allocator,
- thread_pool: ?*ThreadPool,
-
- pub fn init(allocator: std.mem.Allocator, thread_count: ?usize) !ComputeBackend {
- const thread_pool = if (thread_count) |count| {
- try ThreadPool.init(allocator, .{ .thread_count = count });
- } else null;
-
- return ComputeBackend{
- .matmulFn = cpuMatmul,
- .softmaxFn = cpuSoftmax,
- .rmsnormFn = cpuRmsnorm,
- .attentionFn = cpuAttention,
- // Other operations...
- .config = BackendConfig{
- .backend_type = .Cpu,
- .max_threads = thread_count,
- // Other CPU-specific config...
- },
- };
- }
-
- fn cpuMatmul(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) !void {
- // Dynamically select the optimal implementation based on matrix dimensions and CPU features
- if (c.rows * c.cols > 1024 * 1024 and detectCpuFeatures().use_avx2) {
- return cpuMatmulParallel(a, b, c, allocator);
- }
- return cpuMatmulSIMD(a, b, c, allocator);
- }
-
- fn cpuSoftmax(x: anytype, dim: usize, allocator: std.mem.Allocator) !void {
- // Optimized CPU implementation using SIMD
- // Implementation details...
- }
-
- // Other CPU-specific implementations...
-};
-
-pub const MetalBackend = struct {
- device: *MTLDevice,
- command_queue: *MTLCommandQueue,
- library: *MTLLibrary,
- allocator: std.mem.Allocator,
- pipelines: PipelineCache,
-
- pub fn init(allocator: std.mem.Allocator) !ComputeBackend {
- // Initialize Metal device, command queue, and library
- const device = MTLCreateSystemDefaultDevice() orelse return error.MetalDeviceNotAvailable;
- const command_queue = device.newCommandQueue() orelse return error.CommandQueueCreationFailed;
-
- // Load compute shaders from embedded metal code or compiled library
- const library = try loadDefaultLibrary(device);
-
- // Initialize pipeline cache
- var pipelines = PipelineCache.init(allocator);
- try pipelines.precompileEssentialPipelines(device, library);
-
- return ComputeBackend{
- .matmulFn = metalMatmul,
- .softmaxFn = metalSoftmax,
- .rmsnormFn = metalRmsnorm,
- .attentionFn = metalAttention,
- // Other operations...
- .config = BackendConfig{
- .backend_type = .Metal,
- .workgroup_size = .{16, 16, 1},
- .shared_memory_size = 32 * 1024,
- // Other Metal-specific config...
- },
- };
- }
-
- fn metalMatmul(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) !void {
- // Implementation using Metal Performance Shaders when available
- // Fallback to custom compute kernel for specialized operations
- // Implementation details...
- }
-
- fn metalSoftmax(x: anytype, dim: usize, allocator: std.mem.Allocator) !void {
- // Metal implementation
- // Implementation details...
- }
-
- // Other Metal-specific implementations...
-};
-```
-
-**Key Features:**
-- Abstract interface with compile-time type safety
-- Proper error handling with Zig's error system
-- Zero-cost abstraction for backend dispatch
-- Dynamic backend selection based on available hardware
-- Specialized implementations for different hardware architectures
-- Thread pool integration for CPU parallelism
-- Resource management for GPU backends
-- Pipeline caching for improved performance
-
-
-#### 3.4 SIMD Vectorization
-
-DeepSeek-V3 leverages Zig's built-in vector types to achieve high-performance computation across different architectures.
-
-```zig
-// Define vector types with architecture-specific sizes
-pub fn VectorType(comptime T: type, comptime len: usize) type {
- return @Vector(len, T);
-}
-
-// Compile-time determination of optimal vector size
-pub fn getOptimalVectorSize(comptime T: type) usize {
- const target = @import("builtin").target;
-
- // Determine vector size based on architecture and data type
- if (T == f32) {
- if (target.cpu.arch == .x86_64 or target.cpu.arch == .x86) {
- if (target.cpu.features.isEnabled(.avx512f)) {
- return 16; // 512 bits / 32 bits = 16 elements
- } else if (target.cpu.features.isEnabled(.avx2)) {
- return 8; // 256 bits / 32 bits = 8 elements
- } else if (target.cpu.features.isEnabled(.sse4_1)) {
- return 4; // 128 bits / 32 bits = 4 elements
- }
- } else if (target.cpu.arch == .aarch64) {
- if (target.cpu.features.isEnabled(.neon)) {
- return 4; // 128 bits / 32 bits = 4 elements
- }
- }
- } else if (T == f16) {
- // Similar logic for f16 with doubled vector sizes
- // ...
- }
-
- // Default fallback
- return 4;
-}
-
-// Example of SIMD matrix multiplication
-pub fn matrixMultiplySIMD(comptime T: type, a: []const T, b: []const T, c: []T, m: usize, n: usize, k: usize) void {
- const vec_size = comptime getOptimalVectorSize(T);
- const Vec = VectorType(T, vec_size);
-
- // Process blocks that align with vector size
- const k_vec = k / vec_size * vec_size;
-
- for (0..m) |i| {
- for (0..n) |j| {
- var sum: T = 0;
- var vec_sum: Vec = @splat(0);
-
- // Vector part
- var kv: usize = 0;
- while (kv < k_vec) : (kv += vec_size) {
- const a_vec = blk: {
- var tmp: Vec = undefined;
- for (0..vec_size) |v| {
- tmp[v] = a[i * k + kv + v];
- }
- break :blk tmp;
- };
-
- const b_vec = blk: {
- var tmp: Vec = undefined;
- for (0..vec_size) |v| {
- tmp[v] = b[kv + v + j * k];
- }
- break :blk tmp;
- };
-
- vec_sum += a_vec * b_vec;
- }
-
- // Reduce vector
- for (0..vec_size) |v| {
- sum += vec_sum[v];
- }
-
- // Remaining elements
- for (k_vec..k) |kk| {
- sum += a[i * k + kk] * b[kk + j * k];
- }
-
- c[i * n + j] = sum;
- }
- }
-}
-```
-
-#### 3.5 Runtime CPU Feature Detection
-
-```zig
-pub fn detectCpuFeatures() BackendConfig {
- var config = BackendConfig{
- .backend_type = BackendType.Cpu,
- };
-
- // Try to detect CPU features at runtime
- const cpu_info = std.zig.system.getCpuInfo() catch {
- // Fallback to safe defaults if detection fails
- return config;
- };
-
- // Configure based on detected features
- config.use_avx512 = cpu_info.features.isEnabled(.avx512f);
- config.use_avx2 = cpu_info.features.isEnabled(.avx2);
- config.use_sse4_1 = cpu_info.features.isEnabled(.sse4_1);
- config.use_neon = cpu_info.features.isEnabled(.neon);
-
- return config;
-}
-```
-
-#### 3.6 Backend Configuration
-
-Backend configuration allows fine-tuning performance characteristics based on hardware capabilities and workload requirements.
-
-```zig
-pub const BackendType = enum {
- Cpu,
- Cuda,
- Metal,
- Vulkan,
- WebGPU,
-};
-
-pub const BackendConfig = struct {
- backend_type: BackendType,
- max_threads: ?usize = null,
- cache_line_size: usize = 64, // Default x86-64 cache line size
- use_avx512: bool = false, // Use AVX-512 when available
- use_avx2: bool = true, // Use AVX2 when available
- use_sse4_1: bool = true, // Use SSE4.1 when available
- use_neon: bool = false, // Use ARM NEON when available
- prefetch_distance: usize = 8, // Prefetch N cache lines ahead
- tiling_size: ?[2]usize = null, // Matrix tiling dimensions
- batch_size: ?usize = null, // Batch size for kernel operations
- memory_pool_size: ?usize = null, // Size of pre-allocated memory pool
- use_half_precision: bool = false, // Use FP16 where appropriate
- use_mixed_precision: bool = true, // Use mixed precision for matmul
-
- // GPU-specific options
- workgroup_size: ?[3]usize = null, // GPU workgroup dimensions
- shared_memory_size: ?usize = null, // GPU shared memory allocation
- compute_queue_depth: usize = 3, // Maximum concurrent compute operations
-};
-```
-
-#### 3.7 GPU Integration
-
-DeepSeek-V3 supports multiple GPU backends, with specialized implementations for each platform.
-
-#### 3.7.1 CUDA Backend
-
-```zig
-pub const CudaBackend = struct {
- allocator: std.mem.Allocator,
- device: i32,
- stream: ?*anyopaque,
- handles: CudaHandles,
- module_cache: ModuleCache,
-
- pub fn init(allocator: std.mem.Allocator, device_id: ?i32) !ComputeBackend {
- // Initialize CUDA device, context, and stream
- const device = if (device_id) |id| id else try getOptimalCudaDevice();
- try cudaSetDevice(device);
-
- var stream: ?*anyopaque = null;
- try checkCudaStatus(cudaStreamCreate(&stream));
-
- // Initialize cuBLAS and cuDNN handles
- var handles = try CudaHandles.init(stream);
-
- // Compile and cache essential CUDA kernels
- var module_cache = try ModuleCache.init(allocator);
- try module_cache.compileEssentialKernels();
-
- return ComputeBackend{
- .matmulFn = cudaMatmul,
- .softmaxFn = cudaSoftmax,
- .rmsnormFn = cudaRmsnorm,
- .attentionFn = cudaAttention,
- // Other operations...
- .config = BackendConfig{
- .backend_type = .Cuda,
- .workgroup_size = .{16, 16, 1},
- .shared_memory_size = 48 * 1024,
- // Other CUDA-specific config...
- },
- };
- }
-
- fn cudaMatmul(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) !void {
- // Use cuBLAS for large matrices
- // Fall back to custom kernels for specialized operations
- // Implementation details...
- }
-
- // Other CUDA-specific implementations...
-};
-```
-
-#### 3.7.2 Vulkan Backend
-
-```zig
-pub const VulkanBackend = struct {
- allocator: std.mem.Allocator,
- instance: vk.Instance,
- physical_device: vk.PhysicalDevice,
- device: vk.Device,
- compute_queue: vk.Queue,
- command_pool: vk.CommandPool,
- pipeline_cache: vk.PipelineCache,
- shader_modules: ShaderModuleCache,
-
- pub fn init(allocator: std.mem.Allocator) !ComputeBackend {
- // Initialize Vulkan instance, device, and queues
- // Implementation details...
-
- return ComputeBackend{
- .matmulFn = vulkanMatmul,
- .softmaxFn = vulkanSoftmax,
- .rmsnormFn = vulkanRmsnorm,
- .attentionFn = vulkanAttention,
- // Other operations...
- .config = BackendConfig{
- .backend_type = .Vulkan,
- // Vulkan-specific config...
- },
- };
- }
-
- // Vulkan-specific implementations...
-};
-```
-
-#### 3.8 Quantization Framework
-
-The quantization framework enables efficient model deployment through reduced precision arithmetic.
-
-```zig
-// Supported quantization methods
-pub const QuantizationMethod = enum {
- None,
- FP16, // Half precision
- Int8, // 8-bit integer quantization
- Int4, // 4-bit integer quantization
- NF4, // NormalFloat4 quantization
- GPTQ, // GPTQ quantization
- AWQ, // Activation-aware weight quantization
-};
-
-// Quantization configuration
-pub const QuantConfig = struct {
- method: QuantizationMethod = .None,
- scale_type: ?type = null, // Type for quantization scales
- group_size: usize = 128, // Size of quantization groups
- bits: u8 = 8, // Bits per quantized value
- symmetric: bool = false, // Symmetric vs asymmetric quantization
-
- // Calibration parameters
- calibration_dataset: ?[]const u8 = null,
- num_calibration_samples: usize = 128,
-
- // Sparsity options
- use_sparse: bool = false,
- sparsity_threshold: f32 = 0.01,
-};
-
-// Abstract quantizer interface
-pub const Quantizer = struct {
- const Self = @This();
-
- quantizeFn: *const fn(self: *Self, tensor: Tensor, config: QuantConfig, allocator: std.mem.Allocator) anyerror!Tensor,
- dequantizeFn: *const fn(self: *Self, tensor: Tensor, allocator: std.mem.Allocator) anyerror!Tensor,
-
- pub fn quantize(self: *Self, tensor: Tensor, config: QuantConfig, allocator: std.mem.Allocator) !Tensor {
- return self.quantizeFn(self, tensor, config, allocator);
- }
-
- pub fn dequantize(self: *Self, tensor: Tensor, allocator: std.mem.Allocator) !Tensor {
- return self.dequantizeFn(self, tensor, allocator);
- }
-};
-```
-
-#### 3.9 Memory Management
-
-Efficient memory management is crucial for large language model inference.
-
-```zig
-// Memory allocation strategy
-pub const AllocStrategy = enum {
- Default, // Standard allocator
- Arena, // Arena allocator for bulk allocations
- Pool, // Memory pool for fixed-size allocations
- Streaming, // Streaming allocator for pipelined operations
- Pinned, // Pinned memory for efficient host-device transfers
-};
-
-// Memory pool for efficient tensor allocations
-pub const TensorMemoryPool = struct {
- const Self = @This();
-
- parent_allocator: std.mem.Allocator,
- pool: std.heap.MemoryPool,
- block_sizes: []const usize,
- blocks: std.AutoArrayHashMap(usize, std.ArrayList(*anyopaque)),
- mutex: std.Thread.Mutex,
- stats: MemoryStats,
-
- pub fn init(allocator: std.mem.Allocator, config: MemoryPoolConfig) !Self {
- // Initialize memory pool with predefined block sizes
- // Implementation details...
- }
-
- pub fn allocate(self: *Self, size: usize, alignment: usize) ![]u8 {
- // Find the appropriate block size or allocate directly
- // Implementation details...
- }
-
- pub fn free(self: *Self, ptr: []u8) void {
- // Return to pool or free directly
- // Implementation details...
- }
-
- // Memory management utilities
- pub fn preallocate(self: *Self, block_size: usize, count: usize) !void {
- // Preallocate multiple blocks of the specified size
- // Implementation details...
- }
-
- pub fn reclaim(self: *Self) void {
- // Reclaim unused memory blocks
- // Implementation details...
- }
-};
-
-// Key-Value cache management for efficient inference
-pub const KVCache = struct {
- allocator: std.mem.Allocator,
- k_cache: Tensor,
- v_cache: Tensor,
- capacity: usize,
- size: usize,
- head_dim: usize,
- num_heads: usize,
-
- pub fn init(allocator: std.mem.Allocator, batch_size: usize, num_heads: usize, head_dim: usize, max_seq_len: usize) !Self {
- // Initialize key-value cache with appropriate dimensions
- // Implementation details...
- }
-
- pub fn append(self: *Self, k: Tensor, v: Tensor, pos: usize) !void {
- // Append new key-value pairs to the cache
- // Implementation details...
- }
-
- pub fn prefill(self: *Self, k: Tensor, v: Tensor) !void {
- // Prefill the cache with initial key-value pairs
- // Implementation details...
- }
-
- pub fn rotatePositions(self: *Self, positions: []const usize) !void {
- // Rearrange cache entries based on position IDs (for speculative decoding)
- // Implementation details...
- }
-
- pub fn clear(self: *Self) void {
- // Reset the cache size without deallocating memory
- // Implementation details...
- }
-};
-```
-
-#### 3.10 Metal Integration for Apple Silicon
-
-Modern Apple Silicon devices offer exceptional compute performance, and our Zig implementation takes full advantage of these capabilities through direct Metal API integration:
-
-```zig
-pub const MetalBackend = struct {
- const Self = @This();
-
- // Core Metal resources
- device: *MTLDevice,
- command_queue: *MTLCommandQueue,
- library: *MTLLibrary,
-
- // Pipeline cache for reusing compiled compute pipelines
- pipeline_cache: std.AutoHashMap(u64, *MTLComputePipelineState),
-
- // Memory management
- allocator: std.mem.Allocator,
- buffer_pool: BufferPool,
-
- // Configuration and statistics
- config: BackendConfig,
- stats: MetalStatistics,
-
- pub fn init(allocator: std.mem.Allocator) !*Self {
- // Get the default Metal device
- var device = MTLCreateSystemDefaultDevice();
- if (device == null) return error.MetalDeviceNotAvailable;
-
- // Create a command queue for submitting work to the GPU
- var command_queue = device.?.newCommandQueue();
- if (command_queue == null) return error.MetalCommandQueueCreationFailed;
-
- // Compile our Metal shader library from source or load precompiled metallib
- var library: ?*MTLLibrary = null;
- if (comptime @import("builtin").mode == .Debug) {
- // Compile from source for easier debugging
- library = try compileLibraryFromSource(device.?, shader_source);
- } else {
- // Use precompiled metallib for release builds
- const metallib_path = try findMetalLibPath(allocator);
- defer allocator.free(metallib_path);
-
- library = try loadCompiledLibrary(device.?, metallib_path);
- }
-
- // Create the Metal backend
- var self = try allocator.create(Self);
- errdefer allocator.destroy(self);
-
- // Initialize the pipeline cache
- var pipeline_cache = std.AutoHashMap(u64, *MTLComputePipelineState).init(allocator);
- errdefer pipeline_cache.deinit();
-
- // Initialize the buffer pool for efficient memory reuse
- var buffer_pool = try BufferPool.init(allocator, device.?);
- errdefer buffer_pool.deinit();
-
- // Get optimal configuration based on the device capabilities
- var config = try getMetalOptimalConfig(device.?);
-
- self.* = .{
- .device = device.?,
- .command_queue = command_queue.?,
- .library = library.?,
- .pipeline_cache = pipeline_cache,
- .allocator = allocator,
- .buffer_pool = buffer_pool,
- .config = config,
- .stats = MetalStatistics.init(),
- };
-
- return self;
- }
-
- pub fn deinit(self: *Self) void {
- // Release all cached pipelines
- var it = self.pipeline_cache.valueIterator();
- while (it.next()) |pipeline| {
- pipeline.*.release();
- }
- self.pipeline_cache.deinit();
-
- // Clean up buffer pool
- self.buffer_pool.deinit();
-
- // Release Metal resources
- self.library.release();
- self.command_queue.release();
- self.device.release();
-
- // Free memory
- self.allocator.destroy(self);
- }
-
- // Get or create a compute pipeline for a function
- pub fn getPipeline(self: *Self, function_name: []const u8) !*MTLComputePipelineState {
- // Hash the function name for quick lookup
- const hash = std.hash.CityHash64.hash(function_name);
-
- // Check if we already have a cached pipeline
- if (self.pipeline_cache.get(hash)) |pipeline| {
- return pipeline;
- }
-
- // Create a new pipeline if not found
- var function = self.library.newFunctionWithName(function_name);
- if (function == null) return error.MetalFunctionNotFound;
- defer function.?.release();
-
- // Create the compute pipeline
- var pipeline_desc = MTLComputePipelineDescriptor.alloc().init();
- defer pipeline_desc.release();
-
- pipeline_desc.setComputeFunction(function.?);
-
- // Enable buffer mutability tracking in debug mode
- if (comptime @import("builtin").mode == .Debug) {
- pipeline_desc.setMutabilityOptions(.{
- .MTLPipelineBufferMutabilityAccessTracking = true,
- });
- }
-
- // Enable threadgroup memory length optimization
- pipeline_desc.setThreadGroupSizeIsMultipleOfThreadExecutionWidth(true);
-
- // Create the pipeline state
- var error_ptr: ?*NSError = null;
- var pipeline = self.device.newComputePipelineStateWithDescriptor(
- pipeline_desc,
- .MTLPipelineOptionArgumentInfo,
- null,
- &error_ptr
- );
-
- if (pipeline == null) {
- if (error_ptr != null) {
- // Log the error details
- const error_str = error_ptr.?.localizedDescription().UTF8String();
- std.log.err("Failed to create pipeline for {s}: {s}", .{
- function_name, error_str,
- });
- error_ptr.?.release();
- }
- return error.MetalPipelineCreationFailed;
- }
-
- // Cache the pipeline for future use
- try self.pipeline_cache.put(hash, pipeline.?);
-
- return pipeline.?;
- }
-
- // Execute a compute kernel with the given parameters
- pub fn executeKernel(
- self: *Self,
- kernel_name: []const u8,
- grid_size: [3]u32,
- block_size: [3]u32,
- buffers: []const MetalBuffer,
- wait_until_completed: bool,
- ) !void {
- // Get the pipeline for this kernel
- var pipeline = try self.getPipeline(kernel_name);
-
- // Create a command buffer
- var command_buffer = self.command_queue.commandBuffer();
- if (command_buffer == null) return error.MetalCommandBufferCreationFailed;
-
- // Create a compute command encoder
- var encoder = command_buffer.?.computeCommandEncoder();
- if (encoder == null) return error.MetalComputeEncoderCreationFailed;
-
- // Set the compute pipeline
- encoder.?.setComputePipelineState(pipeline);
-
- // Bind buffers
- for (buffers, 0..) |buffer, i| {
- encoder.?.setBuffer(buffer.handle, buffer.offset, @intCast(i));
- }
-
- // Calculate threadgroup size
- var threadgroup_size = MTLSize{
- .width = block_size[0],
- .height = block_size[1],
- .depth = block_size[2],
- };
-
- // Calculate grid size
- var grid = MTLSize{
- .width = grid_size[0],
- .height = grid_size[1],
- .depth = grid_size[2],
- };
-
- // Dispatch the compute work
- encoder.?.dispatchThreadgroups(grid, threadgroup_size);
-
- // End encoding
- encoder.?.endEncoding();
-
- // Commit the command buffer
- command_buffer.?.commit();
-
- // Wait for completion if requested
- if (wait_until_completed) {
- command_buffer.?.waitUntilCompleted();
- }
-
- // Update statistics
- self.stats.kernel_executions += 1;
- }
-
- // Create a buffer and copy data to it
- pub fn createBuffer(
- self: *Self,
- data: []const u8,
- options: MTLResourceOptions,
- ) !*MTLBuffer {
- // Get a buffer from the pool or create a new one
- var buffer = try self.buffer_pool.getBuffer(data.len, options);
-
- // Copy data to the buffer
- @memcpy(buffer.contents()[0..data.len], data);
-
- return buffer;
- }
-
- // Create a tensor in Metal memory
- pub fn createTensor(self: *Self, tensor: Tensor(f32, 2)) !MetalTensor {
- // Calculate size in bytes
- const size_bytes = tensor.data.len * @sizeOf(f32);
-
- // Create a buffer
- var buffer = try self.createBuffer(
- @ptrCast([*]const u8, tensor.data.ptr)[0..size_bytes],
- .StorageModeShared
- );
-
- return MetalTensor{
- .buffer = buffer,
- .shape = tensor.shape,
- .element_type = .f32,
- };
- }
-
- // Example implementation of matrix multiplication using Metal
- pub fn matmul(
- self: *Self,
- a: Tensor(f32, 2),
- b: Tensor(f32, 2),
- ) !Tensor(f32, 2) {
- // Validate dimensions
- std.debug.assert(a.shape[1] == b.shape[0], "Incompatible matrix dimensions");
-
- const m = a.shape[0];
- const k = a.shape[1];
- const n = b.shape[1];
-
- // Create result tensor
- var result = try Tensor(f32, 2).init(self.allocator, .{m, n});
- errdefer result.deinit();
-
- // Create Metal tensors
- var a_metal = try self.createTensor(a);
- defer a_metal.buffer.release();
-
- var b_metal = try self.createTensor(b);
- defer b_metal.buffer.release();
-
- var result_metal = try self.createTensor(result);
- defer result_metal.buffer.release();
-
- // Create dimension buffer
- const dims = [_]u32{@intCast(m), @intCast(k), @intCast(n)};
- var dims_buffer = try self.createBuffer(
- @ptrCast([*]const u8, &dims)[0..dims.len * @sizeOf(u32)],
- .StorageModeShared
- );
- defer dims_buffer.release();
-
- // Set up buffers
- const buffers = [_]MetalBuffer{
- .{ .handle = a_metal.buffer, .offset = 0 },
- .{ .handle = b_metal.buffer, .offset = 0 },
- .{ .handle = result_metal.buffer, .offset = 0 },
- .{ .handle = dims_buffer, .offset = 0 },
- };
-
- // Calculate optimal workgroup size
- const workgroup_size: [3]u32 = if (self.config.workgroup_size) |ws|
- .{ @intCast(ws[0]), @intCast(ws[1]), 1 }
- else
- .{ 16, 16, 1 };
-
- // Calculate grid size
- const grid_size: [3]u32 = .{
- (n + workgroup_size[0] - 1) / workgroup_size[0],
- (m + workgroup_size[1] - 1) / workgroup_size[1],
- 1,
- };
-
- // Execute the kernel
- try self.executeKernel(
- "matmul",
- grid_size,
- workgroup_size,
- &buffers,
- true
- );
-
- // Copy data back from Metal
- @memcpy(
- result.data,
- @ptrCast([*]const f32, result_metal.buffer.contents())[0..result.data.len]
- );
-
- return result;
- }
-};
-
-// Efficient buffer pooling to avoid frequent allocations
-pub const BufferPool = struct {
- const Self = @This();
-
- allocator: std.mem.Allocator,
- device: *MTLDevice,
- free_buffers: std.AutoHashMap(u64, std.ArrayList(*MTLBuffer)),
-
- pub fn init(allocator: std.mem.Allocator, device: *MTLDevice) !Self {
- return Self{
- .allocator = allocator,
- .device = device,
- .free_buffers = std.AutoHashMap(u64, std.ArrayList(*MTLBuffer)).init(allocator),
- };
- }
-
- pub fn deinit(self: *Self) void {
- // Release all buffers
- var it = self.free_buffers.valueIterator();
- while (it.next()) |buffer_list| {
- for (buffer_list.items) |buffer| {
- buffer.release();
- }
- buffer_list.deinit();
- }
- self.free_buffers.deinit();
- }
-
- // Get a buffer of at least the requested size
- pub fn getBuffer(self: *Self, size: usize, options: MTLResourceOptions) !*MTLBuffer {
- // Round up to power of 2 for better reuse
- const aligned_size = nextPowerOfTwo(size);
-
- // Check if we have a free buffer of appropriate size
- if (self.free_buffers.getPtr(aligned_size)) |buffer_list| {
- if (buffer_list.items.len > 0) {
- // Reuse an existing buffer
- return buffer_list.pop();
- }
- }
-
- // Create a new buffer if none available
- var buffer = self.device.newBufferWithLength(aligned_size, options);
- if (buffer == null) return error.MetalBufferAllocationFailed;
-
- return buffer.?;
- }
-
- // Return a buffer to the pool for reuse
- pub fn releaseBuffer(self: *Self, buffer: *MTLBuffer) !void {
- const size = buffer.length();
- const aligned_size = nextPowerOfTwo(size);
-
- // Add to the appropriate size list
- if (self.free_buffers.getPtr(aligned_size)) |buffer_list| {
- try buffer_list.append(buffer);
- } else {
- // Create a new list if this is the first buffer of this size
- var buffer_list = std.ArrayList(*MTLBuffer).init(self.allocator);
- try buffer_list.append(buffer);
- try self.free_buffers.put(aligned_size, buffer_list);
- }
- }
-
- // Utility to find next power of two
- fn nextPowerOfTwo(n: usize) usize {
- var v = n;
- v -= 1;
- v |= v >> 1;
- v |= v >> 2;
- v |= v >> 4;
- v |= v >> 8;
- v |= v >> 16;
- v |= v >> 32;
- v += 1;
- return v;
- }
-};
-
-// Representation of a tensor in Metal memory
-pub const MetalTensor = struct {
- buffer: *MTLBuffer,
- shape: []const usize,
- element_type: enum {
- f16,
- f32,
- },
-};
-
-// Helper for buffer binding
-pub const MetalBuffer = struct {
- handle: *MTLBuffer,
- offset: u64 = 0,
-};
-
-// Statistics for performance monitoring
-pub const MetalStatistics = struct {
- kernel_executions: usize = 0,
- bytes_transferred: usize = 0,
- peak_memory_usage: usize = 0,
-
- pub fn init() MetalStatistics {
- return .{};
- }
-};
-
-// Example Metal shader source for matrix multiplication
-const shader_source =
- \\#include
- \\using namespace metal;
- \\
- \\kernel void matmul(
- \\ const device float* a [[buffer(0)]],
- \\ const device float* b [[buffer(1)]],
- \\ device float* result [[buffer(2)]],
- \\ const device uint* dims [[buffer(3)]],
- \\ uint2 gid [[thread_position_in_grid]],
- \\ uint2 lid [[thread_position_in_threadgroup]],
- \\ uint2 lsize [[threads_per_threadgroup]])
- \\{
- \\ const uint m = dims[0];
- \\ const uint k = dims[1];
- \\ const uint n = dims[2];
- \\
- \\ // Check if within bounds
- \\ if (gid.x >= n || gid.y >= m) return;
- \\
- \\ // Calculate result[gid.y][gid.x]
- \\ float sum = 0.0f;
- \\ for (uint i = 0; i < k; i++) {
- \\ sum += a[gid.y * k + i] * b[i * n + gid.x];
- \\ }
- \\
- \\ result[gid.y * n + gid.x] = sum;
- \\}
- \\
- \\kernel void matmul_optimized(
- \\ const device float* a [[buffer(0)]],
- \\ const device float* b [[buffer(1)]],
- \\ device float* result [[buffer(2)]],
- \\ const device uint* dims [[buffer(3)]],
- \\ uint2 gid [[thread_position_in_grid]],
- \\ uint2 lid [[thread_position_in_threadgroup]],
- \\ uint2 lsize [[threads_per_threadgroup]])
- \\{
- \\ const uint m = dims[0];
- \\ const uint k = dims[1];
- \\ const uint n = dims[2];
- \\
- \\ // Check if within bounds
- \\ if (gid.x >= n || gid.y >= m) return;
- \\
- \\ // Use threadgroup memory for caching
- \\ threadgroup float a_cache[16][16];
- \\ threadgroup float b_cache[16][16];
- \\
- \\ float sum = 0.0f;
- \\
- \\ // Process in tiles
- \\ for (uint tile = 0; tile < (k + 15) / 16; tile++) {
- \\ // Load a tile into threadgroup memory
- \\ const uint tile_idx = tile * 16;
- \\
- \\ if (tile_idx + lid.x < k && gid.y < m) {
- \\ a_cache[lid.y][lid.x] = a[gid.y * k + tile_idx + lid.x];
- \\ } else {
- \\ a_cache[lid.y][lid.x] = 0.0f;
- \\ }
- \\
- \\ if (tile_idx + lid.y < k && gid.x < n) {
- \\ b_cache[lid.y][lid.x] = b[(tile_idx + lid.y) * n + gid.x];
- \\ } else {
- \\ b_cache[lid.y][lid.x] = 0.0f;
- \\ }
- \\
- \\ // Wait for all threads to load data
- \\ threadgroup_barrier(mem_flags::mem_threadgroup);
- \\
- \\ // Compute partial dot product for this tile
- \\ for (uint i = 0; i < 16; i++) {
- \\ sum += a_cache[lid.y][i] * b_cache[i][lid.x];
- \\ }
- \\
- \\ // Wait for all threads to finish using the cached data
- \\ threadgroup_barrier(mem_flags::mem_threadgroup);
- \\ }
- \\
- \\ // Write result
- \\ if (gid.x < n && gid.y < m) {
- \\ result[gid.y * n + gid.x] = sum;
- \\ }
- \\}
-;
-```
-
-**Apple-Specific Optimizations:**
-
-1. **Metal Shader Integration**
- - Direct compilation of Metal shaders from Zig source code
- - Runtime shader compilation in debug mode for easier iteration
- - Precompiled metallib loading for optimized release builds
-
-2. **Memory Management**
- - Buffer pooling to minimize allocations and deallocations
- - Shared memory mode for zero-copy between CPU and GPU
- - Explicit control over resource storage options
-
-3. **Performance Optimizations**
- - Tile-based computation for optimal cache utilization
- - Threadgroup memory usage for shared data access
- - Work distribution based on detected GPU characteristics
- - Pipeline state caching for faster kernel dispatching
-
-4. **AMX Acceleration**
- - Support for Apple Matrix extensions (AMX)
- - Specialized matrix multiplication operations for M-series chips
- - Custom shader variants optimized for different Apple Silicon generations
-
-5. **Neural Engine Integration**
- - Optional ANE (Apple Neural Engine) offloading for supported operations
- - Hybrid execution strategies combining GPU and Neural Engine
- - Automatic fallback to Metal for unsupported operations
-
-
-### 4. Inference Pipeline
-
-The inference pipeline is the core execution flow for running the DeepSeek V3 model. Our Zig implementation focuses on efficiency, flexibility, and streaming capabilities.
-
-#### 4.1 Model Loading
-
-```zig
-// The ModelLoader handles loading and initializing DeepSeek V3 models
-pub const ModelLoader = struct {
- const Self = @This();
-
- allocator: std.mem.Allocator,
- config: LoaderConfig,
-
- // Configuration for model loading
- pub const LoaderConfig = struct {
- // Number of threads to use for weight loading
- loading_threads: ?usize = null,
-
- // Optional cache directory for model weights
- cache_dir: ?[]const u8 = null,
-
- // How to handle safetensors format
- safetensors_memory_map: bool = true,
-
- // Validation level for loaded weights
- validation: enum {
- none,
- basic,
- full
- } = .basic,
-
- // Device to place model on after loading
- target_device: BackendType = .Cpu,
- };
-
- pub fn init(allocator: std.mem.Allocator, config: LoaderConfig) Self {
- return .{
- .allocator = allocator,
- .config = config,
- };
- }
-
- // Load a model from file
- pub fn loadModel(
- self: *Self,
- path: []const u8,
- model_args: ?ModelArgs,
- ) !*TransformerModel {
- const extension = std.fs.path.extension(path);
-
- // Determine model format from file extension
- if (std.mem.eql(u8, extension, ".safetensors")) {
- return try self.loadFromSafetensors(path, model_args);
- } else if (std.mem.eql(u8, extension, ".ckpt")) {
- return try self.loadFromCheckpoint(path, model_args);
- } else if (std.mem.eql(u8, extension, ".bin")) {
- return try self.loadFromBinary(path, model_args);
- } else if (std.fs.cwd().accessZ(path, .{}) == .AccessDenied) {
- // Could be a Hugging Face model ID, try to download it
- return try self.loadFromHuggingFace(path, model_args);
- }
-
- return error.UnsupportedModelFormat;
- }
-
- // Load model from SafeTensors format (optimized for memory mapping)
- fn loadFromSafetensors(
- self: *Self,
- path: []const u8,
- model_args: ?ModelArgs,
- ) !*TransformerModel {
- // Open the safetensors file
- var file = try std.fs.cwd().openFile(path, .{});
- defer file.close();
-
- // Memory map the file for zero-copy access if configured
- if (self.config.safetensors_memory_map) {
- const file_size = try file.getEndPos();
-
- // Memory map the file
- const mapped_memory = try std.os.mmap(
- null,
- file_size,
- std.os.PROT.READ,
- std.os.MAP.PRIVATE,
- file.handle,
- 0,
- );
-
- // Process the memory-mapped safetensors
- return try self.processSafetensorsMemoryMapped(
- mapped_memory,
- file_size,
- model_args,
- );
- } else {
- // If memory mapping is disabled, read the file conventionally
- return try self.processSafetensorsFile(file, model_args);
- }
- }
-
- // Process a memory-mapped SafeTensors file
- fn processSafetensorsMemoryMapped(
- self: *Self,
- memory: []const u8,
- file_size: usize,
- model_args: ?ModelArgs,
- ) !*TransformerModel {
- // Parse the header which contains tensor metadata
- const header_size = std.mem.readIntLittle(u64, memory[0..8]);
- const header_json = memory[8..8+header_size];
-
- // Parse the JSON header
- var parsed = try std.json.parseFromSlice(
- std.json.Value,
- self.allocator,
- header_json,
- .{},
- );
- defer parsed.deinit();
-
- // Get the model configuration from arguments or try to infer it
- const args = try self.determineModelArgs(model_args, parsed.value);
-
- // Create the model with the determined configuration
- var model = try TransformerModel.create(self.allocator, args);
- errdefer model.destroy();
-
- // Create a tensor mapping for zero-copy loading
- try self.loadTensorsFromSafetensorsMemory(
- model,
- memory,
- header_size,
- parsed.value,
- );
-
- // Validate the loaded model if configured
- if (self.config.validation != .none) {
- try self.validateModel(model, parsed.value);
- }
-
- return model;
- }
-
- // Load a model from Hugging Face
- fn loadFromHuggingFace(
- self: *Self,
- model_id: []const u8,
- model_args: ?ModelArgs,
- ) !*TransformerModel {
- // Get cache directory or create a temporary one
- const cache_dir = self.config.cache_dir orelse
- try std.fs.getAppDataDir(self.allocator, "deepseek-zig");
-
- // Create HF client
- var hf_client = try HuggingFaceClient.init(self.allocator, cache_dir);
- defer hf_client.deinit();
-
- // Download the model
- const model_path = try hf_client.downloadModel(model_id);
-
- // Load the downloaded model
- return try self.loadModel(model_path, model_args);
- }
-
- // Infer model arguments if not explicitly provided
- fn determineModelArgs(
- self: *Self,
- model_args: ?ModelArgs,
- header: std.json.Value,
- ) !ModelArgs {
- if (model_args) |args| {
- return args;
- }
-
- // Try to infer model configuration from the weight shapes
- if (header.Object.get("metadata")) |metadata| {
- if (metadata.Object.get("model_type")) |model_type| {
- if (std.mem.eql(u8, model_type.String, "deepseek")) {
- // Extract dimensions from metadata
- return try self.parseDeepSeekConfig(metadata);
- }
- }
- }
-
- // Infer from weight shapes if metadata is not available
- return try self.inferArgsFromWeights(header);
- }
-
- // ... more implementation details ...
-};
-
-// Implementation of TransformerModel
-pub const TransformerModel = struct {
- const Self = @This();
-
- allocator: std.mem.Allocator,
- args: ModelArgs,
-
- // Tokenizer for text processing
- tokenizer: *Tokenizer,
-
- // Model components
- embedding: *Embedding,
- layers: []TransformerLayer,
- norm: *LayerNorm,
- lm_head: *Linear,
-
- // KV cache for efficient inference
- kv_cache: ?*KVCache,
-
- // Backend for computation
- backend: *ComputeBackend,
-
- // Create a model with the given configuration
- pub fn create(
- allocator: std.mem.Allocator,
- args: ModelArgs,
- ) !*Self {
- // Create model components
- var embedding = try Embedding.create(allocator, args);
- errdefer embedding.destroy();
-
- var layers = try allocator.alloc(TransformerLayer, args.num_layers);
- errdefer allocator.free(layers);
-
- for (layers, 0..) |*layer, i| {
- layer.* = try TransformerLayer.create(allocator, args, i);
- }
-
- var norm = try LayerNorm.create(allocator, args.dim);
- errdefer norm.destroy();
-
- var lm_head = try Linear.create(allocator, args.dim, args.vocab_size);
- errdefer lm_head.destroy();
-
- // Initialize compute backend
- var backend = try ComputeBackend.create(allocator);
- errdefer backend.destroy();
-
- // Initialize tokenizer
- var tokenizer = try Tokenizer.create(allocator, args.vocab_size);
- errdefer tokenizer.destroy();
-
- // Create the model
- var model = try allocator.create(Self);
- errdefer allocator.destroy(model);
-
- model.* = .{
- .allocator = allocator,
- .args = args,
- .tokenizer = tokenizer,
- .embedding = embedding,
- .layers = layers,
- .norm = norm,
- .lm_head = lm_head,
- .kv_cache = null,
- .backend = backend,
- };
-
- return model;
- }
-
- // Clean up resources
- pub fn destroy(self: *Self) void {
- // Free all components
- self.tokenizer.destroy();
- self.embedding.destroy();
-
- for (self.layers) |*layer| {
- layer.deinit();
- }
- self.allocator.free(self.layers);
-
- self.norm.destroy();
- self.lm_head.destroy();
-
- if (self.kv_cache) |kv_cache| {
- kv_cache.destroy();
- }
-
- self.backend.destroy();
- self.allocator.destroy(self);
- }
-
- // Load a model from a specific path
- pub fn loadFromPath(
- allocator: std.mem.Allocator,
- path: []const u8,
- args: ?ModelArgs,
- ) !*Self {
- var loader = ModelLoader.init(allocator, .{});
- return try loader.loadModel(path, args);
- }
-
- // Forward pass for a single token
- pub fn forward(
- self: *Self,
- token_id: usize,
- position: usize,
- ) !Tensor(f32, 2) {
- // Get the token embedding
- var x = try self.embedding.forward(token_id);
-
- // Process through all transformer layers
- for (self.layers, 0..) |*layer, i| {
- x = try layer.forward(x, position, self.kv_cache);
- }
-
- // Apply final layer norm
- x = try self.norm.forward(x);
-
- // Project to vocabulary
- return try self.lm_head.forward(x);
- }
-
- // Prepare the model for generation
- pub fn prepareForGeneration(
- self: *Self,
- max_seq_len: usize,
- batch_size: usize,
- ) !void {
- // Create KV cache if not already created
- if (self.kv_cache == null) {
- self.kv_cache = try KVCache.create(
- self.allocator,
- self.args,
- max_seq_len,
- batch_size,
- );
- } else {
- // Reset the cache if it already exists
- try self.kv_cache.?.reset(max_seq_len, batch_size);
- }
- }
-
- // Load tokenizer from vocabulary file
- pub fn loadTokenizer(
- self: *Self,
- path: []const u8,
- ) !void {
- try self.tokenizer.loadFromFile(path);
- }
-};
-```
-
-#### 4.2 Generation Strategies
-
-```zig
-// Configuration for text generation
-pub const GenerationConfig = struct {
- // Maximum new tokens to generate
- max_new_tokens: usize = 128,
-
- // Sampling temperature (higher = more random)
- temperature: f32 = 1.0,
-
- // Top-p sampling parameter (0.0-1.0)
- top_p: f32 = 1.0,
-
- // Top-k sampling parameter (0 = disabled)
- top_k: usize = 0,
-
- // Repetition penalty to prevent looping
- repetition_penalty: f32 = 1.0,
-
- // Whether to use sampling or greedy decoding
- do_sample: bool = true,
-
- // Frequency penalty for repeated tokens
- frequency_penalty: f32 = 0.0,
-
- // Presence penalty for token occurrence
- presence_penalty: f32 = 0.0,
-
- // Stop sequences to terminate generation
- stop_sequences: ?[]const []const u8 = null,
-
- // Minimum number of tokens to generate
- min_new_tokens: ?usize = null,
-
- // Beam search width (1 = greedy)
- num_beams: usize = 1,
-
- // Random seed for reproducibility
- seed: ?u64 = null,
-
- // Whether to use speculative decoding
- use_speculative: bool = false,
-
- // Draft model for speculative decoding
- draft_model: ?*TransformerModel = null,
-
- // Number of speculative tokens to generate at once
- speculative_tokens: usize = 5,
-};
-
-// Generate text from a model given input tokens
-pub fn generate(
- model: *TransformerModel,
- input_ids: []const usize,
- config: GenerationConfig,
- callback: ?fn ([]const u8) void,
-) ![]usize {
- // Initialize RNG with seed if provided
- var rng = if (config.seed) |seed|
- std.rand.DefaultPrng.init(seed)
- else
- std.rand.DefaultPrng.init(@bitCast(u64, std.time.milliTimestamp()));
-
- // Allocate result buffer
- var result = try model.allocator.alloc(
- usize,
- input_ids.len + config.max_new_tokens,
- );
- errdefer model.allocator.free(result);
-
- // Copy input tokens
- @memcpy(result[0..input_ids.len], input_ids);
- var token_count = input_ids.len;
-
- // Prepare model for generation
- try model.prepareForGeneration(
- input_ids.len + config.max_new_tokens,
- 1, // Batch size
- );
-
- // Process all input tokens to fill KV cache
- var position: usize = 0;
- for (input_ids) |token_id| {
- _ = try model.forward(token_id, position);
- position += 1;
- }
-
- // Check if we should use speculative decoding
- if (config.use_speculative and config.draft_model != null) {
- return try speculativeGenerate(
- model,
- config.draft_model.?,
- result,
- token_count,
- position,
- config,
- callback,
- );
- }
-
- // Set up logit processors based on config
- var logit_processors = LogitProcessorList.init(model.allocator);
- defer logit_processors.deinit();
-
- if (config.temperature != 1.0) {
- try logit_processors.add(TemperatureLogitProcessor.init(config.temperature));
- }
-
- if (config.repetition_penalty != 1.0) {
- try logit_processors.add(RepetitionPenaltyLogitProcessor.init(
- config.repetition_penalty,
- result[0..token_count],
- ));
- }
-
- if (config.frequency_penalty != 0.0 or config.presence_penalty != 0.0) {
- try logit_processors.add(FrequencyPenaltyLogitProcessor.init(
- config.frequency_penalty,
- config.presence_penalty,
- ));
- }
-
- // Main generation loop
- while (token_count < result.len) {
- // Get next token logits
- var logits = try model.forward(result[token_count - 1], position);
- defer logits.deinit();
-
- // Apply logit processors
- try logit_processors.process(&logits, result[0..token_count]);
-
- // Sample next token
- const next_token = if (config.do_sample)
- try sampleNextToken(
- model.allocator,
- logits,
- config.top_p,
- config.top_k,
- &rng.random(),
- )
- else
- try greedyNextToken(logits);
-
- // Add token to result
- result[token_count] = next_token;
- token_count += 1;
- position += 1;
-
- // Check for stop sequences
- if (config.stop_sequences) |stop_seqs| {
- if (checkStopSequences(
- model.tokenizer,
- result[0..token_count],
- stop_seqs,
- )) {
- break;
- }
- }
-
- // Call callback with generated token if provided
- if (callback != null) {
- var token_text = try model.tokenizer.decodeTokens(
- model.allocator,
- result[token_count-1..token_count],
- );
- defer model.allocator.free(token_text);
-
- callback.?(token_text);
- }
-
- // Check if we've reached minimum token count
- if (config.min_new_tokens) |min_tokens| {
- if (token_count >= input_ids.len + min_tokens) {
- // Check if we're at an EOS token
- if (next_token == model.tokenizer.eos_token_id) {
- break;
- }
- }
- } else if (next_token == model.tokenizer.eos_token_id) {
- // Otherwise just stop at EOS
- break;
- }
- }
-
- // Resize result to actual number of tokens
- result = try model.allocator.realloc(result, token_count);
- return result;
-}
-
-// Speculative decoding implementation
-fn speculativeGenerate(
- model: *TransformerModel,
- draft_model: *TransformerModel,
- result: []usize,
- token_count: usize,
- position: usize,
- config: GenerationConfig,
- callback: ?fn ([]const u8) void,
-) ![]usize {
- // Implementation of speculative decoding algorithm
- // This generates multiple tokens using a smaller draft model
- // and verifies them with the main model for faster generation
-
- // ... implementation details ...
- return result;
-}
-
-// Sample next token using top-p (nucleus) and top-k sampling
-fn sampleNextToken(
- allocator: std.mem.Allocator,
- logits: Tensor(f32, 2),
- top_p: f32,
- top_k: usize,
- random: *std.rand.Random,
-) !usize {
- const vocab_size = logits.shape[1];
-
- // Create a sorted list of (token_id, probability) pairs
- var token_probs = try allocator.alloc(
- struct { token_id: usize, prob: f32 },
- vocab_size,
- );
- defer allocator.free(token_probs);
-
- // Apply softmax to get probabilities
- var probs = try softmax(allocator, logits);
- defer probs.deinit();
-
- // Fill token_probs array
- for (0..vocab_size) |i| {
- token_probs[i] = .{
- .token_id = i,
- .prob = probs.data[i],
- };
- }
-
- // Sort by probability (descending)
- std.sort.sort(
- struct { token_id: usize, prob: f32 },
- token_probs,
- {},
- struct {
- fn lessThan(_: void, a: struct { token_id: usize, prob: f32 }, b: struct { token_id: usize, prob: f32 }) bool {
- return b.prob < a.prob;
- }
- }.lessThan,
- );
-
- // Apply top-k filtering if enabled
- const k = if (top_k > 0)
- @min(top_k, vocab_size)
- else
- vocab_size;
-
- // Apply top-p filtering
- var cumulative_prob: f32 = 0.0;
- var last_idx: usize = 0;
-
- for (token_probs[0..k], 0..) |tp, i| {
- cumulative_prob += tp.prob;
- if (cumulative_prob >= top_p) {
- last_idx = i;
- break;
- }
- }
-
- // Sample from the filtered distribution
- const rand_val = random.float(f32);
- var curr_prob: f32 = 0.0;
-
- for (token_probs[0..last_idx+1]) |tp| {
- curr_prob += tp.prob;
- if (rand_val < curr_prob) {
- return tp.token_id;
- }
- }
-
- // Fallback to the highest probability token
- return token_probs[0].token_id;
-}
-```
-
-**Advanced Features:**
-
-1. **Speculative Decoding**
- - Implementation of speculative decoding using a smaller draft model
- - Verification and acceptance/rejection of speculated tokens
- - Significant speedup in generation throughput
-
-2. **Streaming Token Output**
- - Callback-based token streaming for real-time results
- - Zero-copy token decoding for minimal overhead
- - Support for incremental UI updates
-
-3. **Custom Sampling Strategies**
- - Top-p (nucleus) sampling with dynamic probability mass cutoff
- - Top-k sampling with configurable k value
- - Temperature scaling for controlling randomness
- - Repetition penalty to prevent loops and repetitive text
- - Frequency and presence penalties for more diverse output
-
-4. **Stop Sequence Detection**
- - Efficient detection of multiple stop sequences
- - Support for subword token matching across boundaries
- - Early termination based on generated content
-
-5. **Beam Search Implementation**
- - Configurable beam width for exploring multiple generation paths
- - Length normalization for balancing short and long outputs
- - Diverse beam groups to prevent similar outputs
-
-6. **Memory Efficiency**
- - KV-cache memory management for long context handling
- - Incremental cache updates for streaming inference
- - Automatic cache pruning for memory optimization
-
-7. **Performance Optimizations**
- - Batched token processing for higher throughput
- - Parallel sampling for multi-sequence generation
- - SIMD-accelerated logit processing
- - Compile-time specialization for common configuration patterns
-
-### 5. Optimization Layer
-
-The optimization layer leverages Zig's unique features to maximise performance across different hardware targets.
-
-#### 5.1 Compile-Time Optimizations
-
-Zig's powerful compile-time metaprogramming enables us to generate highly specialized code for specific hardware and model configurations:
-
-```zig
-// Specialized matrix multiplication kernels generated at compile-time
-pub fn generateMatmulKernel(comptime config: KernelConfig) type {
- return struct {
- const Self = @This();
-
- // Compile-time configuration
- const M = config.M;
- const N = config.N;
- const K = config.K;
- const block_size = config.block_size;
- const vector_width = config.vector_width;
- const use_fma = config.use_fma;
-
- // Vector type based on configuration
- const Vec = @Vector(vector_width, f32);
-
- // Matmul implementation specialized for the given dimensions
- pub fn matmul(
- a: *const [M][K]f32,
- b: *const [K][N]f32,
- c: *[M][N]f32,
- ) void {
- // Use specialized implementation for small matrices
- if (comptime M <= 4 and N <= 4 and K <= 4) {
- return smallMatmul(a, b, c);
- }
-
- // Use blocked implementation for larger matrices
- return blockedMatmul(a, b, c);
- }
-
- // Specialized implementation for small matrices
- // Fully unrolled at compile time
- fn smallMatmul(
- a: *const [M][K]f32,
- b: *const [K][N]f32,
- c: *[M][N]f32,
- ) void {
- inline for (0..M) |i| {
- inline for (0..N) |j| {
- var sum: f32 = 0;
- inline for (0..K) |k| {
- sum += a[i][k] * b[k][j];
- }
- c[i][j] = sum;
- }
- }
- }
-
- // Cache-blocked implementation for larger matrices
- fn blockedMatmul(
- a: *const [M][K]f32,
- b: *const [K][N]f32,
- c: *[M][N]f32,
- ) void {
- // Compute using blocks for better cache utilization
- comptime var i_block: usize = 0;
- inline while (i_block < M) : (i_block += block_size) {
- comptime var j_block: usize = 0;
- inline while (j_block < N) : (j_block += block_size) {
- comptime var k_block: usize = 0;
- inline while (k_block < K) : (k_block += block_size) {
- const i_end = @min(i_block + block_size, M);
- const j_end = @min(j_block + block_size, N);
- const k_end = @min(k_block + block_size, K);
-
- // Process current block
- for (i_block..i_end) |i| {
- for (j_block..j_end) |j| {
- var sum: f32 = c[i][j];
-
- // Vectorized inner loop when possible
- if (comptime vector_width > 1 and (k_end - k_block) >= vector_width) {
- var k_vec: usize = k_block;
- var acc: Vec = @splat(0.0);
-
- while (k_vec + vector_width <= k_end) : (k_vec += vector_width) {
- const a_vec: Vec = blk: {
- var tmp: [vector_width]f32 = undefined;
- for (0..vector_width) |vi| {
- tmp[vi] = a[i][k_vec + vi];
- }
- break :blk tmp;
- };
-
- const b_vec: Vec = blk: {
- var tmp: [vector_width]f32 = undefined;
- for (0..vector_width) |vi| {
- tmp[vi] = b[k_vec + vi][j];
- }
- break :blk tmp;
- };
-
- // Use FMA instruction if available
- if (comptime use_fma) {
- acc = @mulAdd(Vec, a_vec, b_vec, acc);
- } else {
- acc += a_vec * b_vec;
- }
- }
-
- // Reduce vector to scalar
- for (0..vector_width) |vi| {
- sum += acc[vi];
- }
-
- // Handle remaining elements
- for (k_vec..k_end) |k| {
- sum += a[i][k] * b[k][j];
- }
- } else {
- // Scalar fallback
- for (k_block..k_end) |k| {
- sum += a[i][k] * b[k][j];
- }
- }
-
- c[i][j] = sum;
- }
- }
- }
- }
- }
- }
- };
-}
-
-// Configuration for kernel generation
-pub const KernelConfig = struct {
- // Matrix dimensions (can be comptime_int or dynamic)
- M: comptime_int,
- N: comptime_int,
- K: comptime_int,
-
- // Blocking configuration for cache optimization
- block_size: comptime_int = 32,
-
- // Vector width for SIMD operations
- vector_width: comptime_int = 4,
-
- // Whether to use FMA instructions when available
- use_fma: bool = true,
-};
-
-// Usage: Create specialized kernels at compile time
-// Fully unrolled 4x4 matrix multiplication
-const Kernel4x4 = generateMatmulKernel(.{
- .M = 4,
- .N = 4,
- .K = 4,
- .vector_width = 4,
-});
-
-// Cache-friendly 128x128 matrix multiplication
-const Kernel128x128 = generateMatmulKernel(.{
- .M = 128,
- .N = 128,
- .K = 128,
- .block_size = 32,
- .vector_width = 8,
-});
-
-// Runtime dispatch to select the best kernel based on matrix dimensions
-pub fn dispatchMatmul(
- allocator: std.mem.Allocator,
- a: Tensor(f32, 2),
- b: Tensor(f32, 2),
-) !Tensor(f32, 2) {
- // Check dimensions
- const m = a.shape[0];
- const k = a.shape[1];
- const n = b.shape[1];
-
- std.debug.assert(k == b.shape[0], "Incompatible matrix dimensions");
-
- // Create result tensor
- var result = try Tensor(f32, 2).init(allocator, .{m, n});
- errdefer result.deinit();
-
- // Initialize result to zeros
- @memset(result.data, 0);
-
- // Dispatch to specialized kernels if dimensions match exactly
- if (m == 4 and n == 4 and k == 4) {
- // Use specialized 4x4 kernel
- Kernel4x4.matmul(
- @ptrCast(*const [4][4]f32, a.data),
- @ptrCast(*const [4][4]f32, b.data),
- @ptrCast(*[4][4]f32, result.data),
- );
- } else if (m == 128 and n == 128 and k == 128) {
- // Use specialized 128x128 kernel
- Kernel128x128.matmul(
- @ptrCast(*const [128][128]f32, a.data),
- @ptrCast(*const [128][128]f32, b.data),
- @ptrCast(*[128][128]f32, result.data),
- );
- } else {
- // Use generic implementation for arbitrary dimensions
- try genericMatmul(a, b, &result);
- }
-
- return result;
-}
-
-// Apply compile-time metaprogramming to optimize data layouts
-pub fn optimizedTensorLayout(comptime T: type, comptime dims: []const usize) type {
- return struct {
- const Self = @This();
-
- // Determine optimal memory layout at compile time
- const optimal_layout = optimizeMemoryLayout(T, dims);
-
- // Data storage with optimized layout
- data: [product(dims)]T align(optimal_layout.alignment),
- shape: [dims.len]usize,
- strides: [dims.len]usize,
-
- // Tensor initialization with optimal layout
- pub fn init(allocator: std.mem.Allocator) !Self {
- const data = try allocator.alignedAlloc(
- T,
- optimal_layout.alignment,
- product(dims),
- );
-
- // Calculate optimal strides based on layout
- var strides: [dims.len]usize = undefined;
- if (optimal_layout.row_major) {
- // Row-major strides
- var stride: usize = 1;
- var i: usize = dims.len;
- while (i > 0) {
- i -= 1;
- strides[i] = stride;
- stride *= dims[i];
- }
- } else {
- // Column-major strides
- var stride: usize = 1;
- for (0..dims.len) |i| {
- strides[i] = stride;
- stride *= dims[i];
- }
- }
-
- return Self{
- .data = data,
- .shape = dims,
- .strides = strides,
- };
- }
-
- // Helper function to calculate optimal memory layout
- fn optimizeMemoryLayout(comptime T: type, comptime dims: []const usize) struct {
- row_major: bool,
- alignment: u29,
- } {
- // Use column-major for matrices where the first dimension is much larger
- // This often improves cache locality for common access patterns
- const row_major = if (dims.len == 2)
- dims[0] <= dims[1] * 2
- else
- true;
-
- // Determine optimal alignment based on vector units
- const alignment = if (@sizeOf(T) == 4 and comptime std.Target.current.cpu.arch == .x86_64)
- if (comptime std.Target.current.cpu.features.isEnabled(.avx512f))
- 64 // 512-bit alignment for AVX-512
- else if (comptime std.Target.current.cpu.features.isEnabled(.avx2))
- 32 // 256-bit alignment for AVX2
- else if (comptime std.Target.current.cpu.features.isEnabled(.sse2))
- 16 // 128-bit alignment for SSE2
- else
- @alignOf(T)
- else
- @alignOf(T);
-
- return .{
- .row_major = row_major,
- .alignment = alignment,
- };
- }
-
- // Helper to calculate the product of dimensions
- fn product(comptime dims: []const usize) usize {
- var result: usize = 1;
- for (dims) |dim| {
- result *= dim;
- }
- return result;
- }
- };
-}
-```
-
-**Key Compile-Time Techniques:**
-
-1. **Matrix Operation Specialization**
- - Specialized kernels generated at compile-time for common dimensions
- - Full loop unrolling for small matrices
- - Compile-time configurable blocking strategies for cache optimization
-
-2. **Data Layout Optimization**
- - Automatic selection of row-major or column-major layout based on dimensions
- - Optimal memory alignment for target architecture's vector units
- - Compile-time stride calculation for fast indexing
-
-3. **Architecture-Specific Optimizations**
- - Vector width specialization based on target CPU features
- - Automatic use of FMA instructions when available
- - SIMD instruction generation tailored to the target architecture
-
-4. **Kernel Selection**
- - Runtime dispatch to specialized kernels based on input dimensions
- - Fallback to generic implementation for arbitrary dimensions
- - Compile-time branch elimination for performance-critical paths
-
-#### 5.2 Quantization Framework
-
-Our quantization framework allows for efficient low-precision inference while maintaining accuracy:
-
-```zig
-// Quantization configuration
-pub const QuantizationConfig = struct {
- // Precision of quantized values
- bits: u8 = 8,
-
- // Quantization scheme
- scheme: enum {
- symmetric, // Zero-point is always 0, simplifies arithmetic
- asymmetric, // Allows representing the full range more precisely
- } = .symmetric,
-
- // Quantization granularity
- granularity: enum {
- per_tensor, // One scale for the entire tensor
- per_channel, // Different scale for each output channel
- } = .per_tensor,
-
- // Whether to use integer or float16 quantization
- use_float16: bool = false,
-
- // Calibration strategy
- calibration: enum {
- minmax, // Simple min/max scaling
- entropy, // Entropy-based quantization
- percentile, // Clip to percentile range for outliers
- } = .minmax,
-
- // Percentile value for calibration (0.0-1.0)
- percentile: f32 = 0.99995,
-};
-
-// Quantized tensor type that tracks quantization parameters
-pub fn QuantizedTensor(comptime original_type: type, comptime bits: u8) type {
- return struct {
- const Self = @This();
-
- // Determine the appropriate integer type based on bit width
- const IntType = std.meta.Int(.unsigned, bits);
-
- // Original element type for reference
- pub const OriginalType = original_type;
-
- // Quantized data
- data: []IntType,
-
- // Original tensor shape
- shape: []const usize,
-
- // Quantization parameters
- scale: []f32,
- zero_point: []IntType,
-
- // Whether scale/zero_point are per-tensor or per-channel
- per_channel: bool,
-
- // For asymmetric quantization: minimum representable value
- qmin: IntType,
-
- // For asymmetric quantization: maximum representable value
- qmax: IntType,
-
- // Channel dimension for per-channel quantization
- channel_dim: ?usize,
-
- // Memory allocator for cleanup
- allocator: std.mem.Allocator,
-
- // Initialize a quantized tensor
- pub fn init(
- allocator: std.mem.Allocator,
- shape: []const usize,
- per_channel: bool,
- channel_dim: ?usize,
- ) !Self {
- // Calculate total size
- var total_size: usize = 1;
- for (shape) |dim| {
- total_size *= dim;
- }
-
- // Determine number of scales/zero_points needed
- const param_size = if (per_channel)
- shape[channel_dim.?]
- else
- 1;
-
- // Allocate memory
- const data = try allocator.alloc(IntType, total_size);
- errdefer allocator.free(data);
-
- const scale = try allocator.alloc(f32, param_size);
- errdefer allocator.free(scale);
-
- const zero_point = try allocator.alloc(IntType, param_size);
- errdefer allocator.free(zero_point);
-
- // Calculate quantization range
- const qmin: IntType = 0;
- const qmax: IntType = (1 << bits) - 1;
-
- // Create shape copy
- const shape_copy = try allocator.dupe(usize, shape);
- errdefer allocator.free(shape_copy);
-
- return Self{
- .data = data,
- .shape = shape_copy,
- .scale = scale,
- .zero_point = zero_point,
- .per_channel = per_channel,
- .qmin = qmin,
- .qmax = qmax,
- .channel_dim = channel_dim,
- .allocator = allocator,
- };
- }
-
- // Free allocated memory
- pub fn deinit(self: *Self) void {
- self.allocator.free(self.data);
- self.allocator.free(self.scale);
- self.allocator.free(self.zero_point);
- self.allocator.free(self.shape);
- }
- };
-}
-
-// Quantize a floating-point tensor to integer precision
-pub fn quantize(
- tensor: anytype,
- config: QuantizationConfig,
- allocator: std.mem.Allocator,
-) !QuantizedTensor(
- @TypeOf(tensor.data[0]),
- config.bits,
-) {
- const T = @TypeOf(tensor.data[0]);
-
- // Validate input
- if (config.bits > 16) {
- return error.UnsupportedQuantizationBits;
- }
-
- if (config.granularity == .per_channel and config.calibration != .minmax) {
- return error.UnsupportedCombination;
- }
-
- // Create quantized tensor
- var channel_dim: ?usize = null;
- if (config.granularity == .per_channel) {
- // For per-channel quantization, use dimension 0 for vectors,
- // dimension 1 for matrices (assuming CHW layout)
- channel_dim = if (tensor.shape.len == 1) 0 else 1;
- }
-
- var qtensor = try QuantizedTensor(T, config.bits).init(
- allocator,
- tensor.shape,
- config.granularity == .per_channel,
- channel_dim,
- );
- errdefer qtensor.deinit();
-
- // Different calibration strategies
- switch (config.calibration) {
- .minmax => try calibrateMinMax(&qtensor, tensor, config),
- .entropy => try calibrateEntropy(&qtensor, tensor, config),
- .percentile => try calibratePercentile(&qtensor, tensor, config),
- }
-
- // Perform actual quantization
- try quantizeTensor(&qtensor, tensor, config);
-
- return qtensor;
-}
-
-// Dequantize a tensor back to floating point
-pub fn dequantize(
- qtensor: anytype,
- allocator: std.mem.Allocator,
-) !Tensor(@TypeOf(qtensor).OriginalType, qtensor.shape.len) {
- const T = @TypeOf(qtensor).OriginalType;
-
- // Create tensor to hold dequantized values
- var tensor = try Tensor(T, qtensor.shape.len).init(
- allocator,
- qtensor.shape,
- );
- errdefer tensor.deinit();
-
- // Dequantize values
- if (qtensor.per_channel) {
- const channel_dim = qtensor.channel_dim.?;
- const channels = qtensor.shape[channel_dim];
-
- // Calculate strides for traversing channels
- var strides: []usize = try allocator.alloc(usize, qtensor.shape.len);
- defer allocator.free(strides);
-
- var stride: usize = 1;
- var i: usize = qtensor.shape.len;
- while (i > 0) {
- i -= 1;
- strides[i] = stride;
- stride *= qtensor.shape[i];
- }
-
- // Dequantize each element based on its channel
- for (0..tensor.data.len) |idx| {
- const channel_idx = (idx / strides[channel_dim]) % channels;
- const scale = qtensor.scale[channel_idx];
- const zero_point = qtensor.zero_point[channel_idx];
-
- tensor.data[idx] = @floatCast(T,
- @intToFloat(f32, qtensor.data[idx] - zero_point) * scale
- );
- }
- } else {
- // Per-tensor dequantization (simpler)
- const scale = qtensor.scale[0];
- const zero_point = qtensor.zero_point[0];
-
- for (0..tensor.data.len) |i| {
- tensor.data[i] = @floatCast(T,
- @intToFloat(f32, qtensor.data[i] - zero_point) * scale
- );
- }
- }
-
- return tensor;
-}
-
-// Calibrate using simple min/max strategy
-fn calibrateMinMax(
- qtensor: anytype,
- tensor: anytype,
- config: QuantizationConfig,
-) !void {
- if (config.granularity == .per_tensor) {
- // Find min/max across entire tensor
- var min_val: f32 = std.math.inf_f32;
- var max_val: f32 = -std.math.inf_f32;
-
- for (tensor.data) |val| {
- const fval = @floatCast(f32, val);
- min_val = @min(min_val, fval);
- max_val = @max(max_val, fval);
- }
-
- // Handle symmetric quantization
- if (config.scheme == .symmetric) {
- const abs_max = @max(@abs(min_val), @abs(max_val));
- min_val = -abs_max;
- max_val = abs_max;
- }
-
- // Calculate scale and zero_point
- const range = max_val - min_val;
- qtensor.scale[0] = range / @intToFloat(f32, qtensor.qmax - qtensor.qmin);
-
- if (config.scheme == .symmetric) {
- qtensor.zero_point[0] = @divFloor(qtensor.qmax - qtensor.qmin, 2) + qtensor.qmin;
- } else {
- qtensor.zero_point[0] = @floatToInt(
- @TypeOf(qtensor.zero_point[0]),
- @round(qtensor.qmin - min_val / qtensor.scale[0])
- );
- }
- } else {
- // Per-channel quantization
- // ... implementation details ...
- }
-}
-
-// Perform actual quantization
-fn quantizeTensor(
- qtensor: anytype,
- tensor: anytype,
- config: QuantizationConfig,
-) !void {
- if (qtensor.per_channel) {
- // Per-channel quantization
- // ... implementation details ...
- } else {
- // Per-tensor quantization
- const scale = qtensor.scale[0];
- const zero_point = qtensor.zero_point[0];
- const qmin = qtensor.qmin;
- const qmax = qtensor.qmax;
-
- for (0..tensor.data.len) |i| {
- const val = @floatCast(f32, tensor.data[i]);
-
- // Quantize: x_q = round(x / scale) + zero_point
- var q_val = @floatToInt(
- @TypeOf(qtensor.data[0]),
- @round(val / scale) + @intToFloat(f32, zero_point)
- );
-
- // Clamp to quantization range
- q_val = @max(@min(q_val, qmax), qmin);
-
- qtensor.data[i] = q_val;
- }
- }
-}
-```
-
-**Quantization Features:**
-
-1. **Multiple Precision Options**
- - 8-bit quantization for maximum throughput
- - 4-bit quantization for model compression
- - 3-bit quantization for extreme size reduction
- - FP16 quantization for memory bandwidth reduction with minimal accuracy loss
-
-2. **Flexible Quantization Schemes**
- - Symmetric quantization for simpler arithmetic
- - Asymmetric quantization for better range utilization
- - Per-tensor quantization for speed
- - Per-channel quantization for accuracy
-
-3. **Advanced Calibration Methods**
- - Min/max calibration for simplicity
- - Entropy-based calibration for better distribution representation
- - Percentile-based calibration for outlier handling
-
-4. **Mixed-Precision Execution**
- - Critical layers in higher precision for accuracy
- - Non-critical layers in lower precision for speed
- - Automatic precision selection based on sensitivity analysis
-
-5. **Hardware Acceleration**
- - Optimized integer SIMD operations for quantized execution
- - Specialized kernels for common quantized operations
- - Hardware-specific optimizations for quantized compute
-
-## Platform-Specific Optimizations
-
-### Apple Silicon (M-Series)
-
-The DeepSeek V3 Zig implementation is highly optimized for Apple Silicon's unique architecture:
-
-1. **Metal Performance Shaders (MPS) Integration**
- - Direct integration with Apple's Metal Performance Shaders for matrix operations
- - Custom Metal compute kernels optimized for M-series chips
- - Efficient memory sharing between CPU and GPU with zero-copy transfers
-
-2. **Tensor Core Utilization**
- - Leveraging Matrix multiplication units in M-series chips
- - Mixed-precision operations optimized for Apple Silicon
- - Native FP16 support for improved throughput
-
-3. **AMX Instruction Set Access**
- - Direct use of Apple Matrix extensions for accelerated linear algebra
- - Low-level optimization of critical matrix operations
- - Custom assembly routines for maximum performance
-
-4. **Memory Bandwidth Optimization**
- - Unified memory architecture exploitation
- - Cache-friendly memory access patterns
- - Optimal tile sizes for M-series cache hierarchy
-
-5. **Power Efficiency Tuning**
- - Dynamic performance/power scaling
- - Efficient core utilization across P and E cores
- - Background inference optimizations
-
-### x86_64 Architecture
-
-For x86_64 platforms, our implementation focuses on leveraging the latest instruction sets:
-
-1. **AVX-512 Vectorization**
- - Full utilization of 512-bit vector operations
- - Masked operations for efficient boundary handling
- - FMA instruction usage for maximum throughput
-
-2. **Cache-Friendly Memory Layouts**
- - Cache line aligned data structures
- - Blocked algorithms optimized for typical L1/L2/L3 cache sizes
- - Software prefetching for critical data paths
-
-3. **Thread Pool Optimization**
- - Work-stealing scheduler for balanced multicore utilization
- - NUMA-aware memory allocation and thread assignment
- - Adaptive parallelism based on available cores
-
-4. **Dynamic Dispatch**
- - Runtime CPU feature detection
- - Specialized code paths for different instruction sets
- - Fallback implementations for compatibility
-
-### NVIDIA GPUs
-
-NVIDIA GPU acceleration is implemented through an efficient CUDA integration:
-
-1. **CUDA Integration via FFI**
- - Zero-overhead bindings to CUDA runtime
- - Asynchronous kernel execution and memory transfers
- - Efficient stream management for overlapping operations
-
-2. **Custom CUDA Kernels**
- - Specialized kernels for attention mechanisms
- - Optimized matrix multiplication for transformer layers
- - Fused operations for reduced kernel launch overhead
-
-3. **Memory Management**
- - Pinned memory for efficient transfers
- - Memory pool for reduced allocation overhead
- - Smart prefetching for predictable memory access patterns
-
-4. **Tensor Core Utilization**
- - Mixed-precision operations using TensorCores
- - Automatic kernel selection for tensor-core eligible operations
- - Tensor Core compatible memory layouts
-
-## Development Roadmap
-
-### Phase 1: Core Infrastructure
-
-The initial phase focuses on establishing the foundational components:
-
-- **Memory Management System**
- - Custom tensor allocator implementation
- - Arena-based allocation strategies
- - Error handling framework
-
-- **Tensor Implementation**
- - Basic tensor operations and utilities
- - SIMD-accelerated implementations
- - Platform detection and optimization
-
-- **Computation Backend Interfaces**
- - Abstract backend interfaces
- - CPU backend implementation
- - Initial Metal backend for Apple Silicon
-
-- **Error Handling Framework**
- - Robust error propagation
- - Detailed error reporting
- - Resource cleanup guarantees
-
-### Phase 2: Model Architecture
-
-Building on the infrastructure, we implement the core model components:
-
-- **Transformer Layers**
- - Multi-head attention implementation
- - Feed-forward networks
- - Layer normalization
-
-- **Attention Mechanisms**
- - Standard attention implementation
- - Flash attention optimizations
- - Memory-efficient attention variants
-
-- **Mixture of Experts**
- - Router implementation
- - Parallel expert execution
- - Load balancing mechanisms
-
-- **Embedding Systems**
- - Token embeddings
- - Position embeddings
- - Rotary position embeddings
-
-### Phase 3: Backend Integration
-
-This phase extends compute capabilities across different hardware:
-
-- **CPU Backend**
- - AVX-512 optimizations
- - Thread pool implementation
- - Cache-optimized algorithms
-
-- **Metal Backend**
- - Complete Metal shader library
- - Apple Neural Engine integration
- - M-series specific optimizations
-
-- **CUDA Backend**
- - NVIDIA GPU support
- - Tensor Core optimizations
- - Multi-GPU scaling
-
-- **Vulkan Backend**
- - Cross-platform GPU support
- - AMD GPU optimizations
- - Intel GPU support
-
-### Phase 4: Inference Pipeline
-
-Creating the end-to-end inference system:
-
-- **Model Loading**
- - SafeTensors format support
- - Checkpoint loading
- - Weight quantization
-
-- **Tokenization**
- - Efficient tokenizer implementation
- - Streaming tokenization
- - Special token handling
-
-- **Generation Strategies**
- - Sampling methods implementation
- - Beam search
- - Speculative decoding
-
-- **Output Processing**
- - Token streaming
- - Stop sequence handling
- - Result formatting
-
-### Phase 5: Optimization
-
-Comprehensive optimization across the entire stack:
-
-- **Compile-Time Optimizations**
- - Template specialization
- - Kernel generation
- - Custom data layouts
-
-- **Runtime Optimizations**
- - Dynamic kernel selection
- - Adaptive compute strategies
- - Memory access optimizations
-
-- **Architecture-Specific Tuning**
- - Platform-specific parameter tuning
- - Hardware-specific kernel variants
- - Feature detection and adaptation
-
-- **Quantization Framework**
- - 8-bit quantization
- - 4-bit quantization
- - Mixed precision execution
-
-### Phase 6: Testing and Benchmarking
-
-Ensuring correctness and measuring performance:
-
-- **Comprehensive Test Suite**
- - Unit tests for all components
- - Integration tests for end-to-end validation
- - Conformance tests against reference implementation
-
-- **Benchmarking Framework**
- - Performance measurement tools
- - Comparison with PyTorch implementation
- - Memory usage analysis
-
-- **Platform Benchmarks**
- - Apple Silicon performance
- - x86_64 performance
- - NVIDIA GPU performance
-
-- **Fine-Tuning**
- - Performance bottleneck identification
- - Targeted optimizations
- - Final parameter tuning
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+**Status**: 🎯 Seeking feedback on initial idea
+**Target**: Production-ready LLM inference in Zig
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