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+ DeepSeek V3 in Zig +
+
+
+ Language: Zig + License: DeepSeek + Status: Proposal +
+ Performance: High Efficiency + Platform: Cross Platform +
+ Feature: SIMD Optimized + Architecture: MoE + Backend: Customizable +
+
+ +

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 +
DeepSeek V3 in Zig
@@ -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 \ No newline at end of file +**Status**: 🎯 Seeking feedback on initial idea +**Target**: Production-ready LLM inference in Zig \ No newline at end of file