DeepSeek V3 in Zig: `DeepZig V3` Proposal
## Overview
This document outlines the architecture 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.)
## Table of Contents
1. [Overview](#overview)
2. [System Architecture](#system-architecture)
3. [Component Design](#component-design)
1. [Core System](#core-system)
1. [Memory Management System](#memory-management-system)
2. [Tensor Implementation](#tensor-implementation)
3. [Error Handling Framework](#error-handling-framework)
4. [Concurrency Model](#concurrency-model)
2. [Model Architecture](#model-architecture)
1. [Transformer Core](#transformer-core)
2. [Attention Mechanisms](#attention-mechanisms)
3. [Mixture of Experts (MoE)](#mixture-of-experts-moe)
3. [Computation Backend](#computation-backend)
1. [Backend Interface](#backend-interface)
2. [Metal Integration for Apple Silicon](#metal-integration-for-apple-silicon)
4. [Inference Pipeline](#inference-pipeline)
1. [Model Loading](#model-loading)
2. [Generation Strategies](#generation-strategies)
5. [Optimization Layer](#optimization-layer)
1. [Compile-Time Optimizations](#compile-time-optimizations)
2. [Quantization Framework](#quantization-framework)
4. [Platform-Specific Optimizations](#platform-specific-optimizations)
5. [Development Roadmap](#development-roadmap)
6. [Why Propose DeepSeek V3 in Zig?](#why-propose-deepseek-v3-in-zig)
## 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
}
// 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 (@hasDecl(builtin, "cpu") and @hasDecl(builtin.cpu, "avx"))
@Vector(8, f32) // AVX
else if (@hasDecl(builtin, "cpu") and @hasDecl(builtin.cpu, "sse"))
@Vector(4, f32) // SSE
else
@Vector(4, f32), // Fallback
f16 => @Vector(8, f16),
i32 => @Vector(8, i32),
i8 => @Vector(16, i8),
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,
};
// 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();
// Continue with model usage...
}
```
**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);
}
}
};
// 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 * 4,
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
// 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;
};
};
}
};
// 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 ComputeBackend = struct {
const Self = @This();
// Function pointers for backend-specific operations with proper type safety
matmulFn: *const fn(a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) anyerror!void,
softmaxFn: *const fn(x: anytype, dim: usize, allocator: std.mem.Allocator) anyerror!void,
rmsnormFn: *const fn(x: anytype, weight: anytype, eps: f32, allocator: std.mem.Allocator) anyerror!void,
attentionFn: *const fn(q: anytype, k: anytype, v: anytype, mask: ?anytype, scale: f32, allocator: std.mem.Allocator) anyerror!void,
// Other operations...
// Configuration for the backend
config: BackendConfig,
// Dispatch based on backend type with proper error handling
pub fn matmul(self: *const Self, a: anytype, b: anytype, c: *anytype, allocator: std.mem.Allocator) !void {
return self.matmulFn(a, b, c, allocator);
}
pub fn softmax(self: *const Self, x: anytype, dim: usize, allocator: std.mem.Allocator) !void {
return self.softmaxFn(x, dim, allocator);
}
pub fn rmsnorm(self: *const Self, x: anytype, weight: anytype, eps: f32, allocator: std.mem.Allocator) !void {
return self.rmsnormFn(x, weight, eps, allocator);
}
pub fn attention(self: *const Self, q: anytype, k: anytype, v: anytype, mask: ?anytype, scale: f32, allocator: std.mem.Allocator) !void {
return self.attentionFn(q, k, v, mask, scale, allocator);
}
};
```
#### 3.2 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
#### 3.3 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;
}
}
}
```
#### 3.4 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;
}
```
#### 3.5 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
};
```
#### 3.6 GPU Integration
DeepSeek-V3 supports multiple GPU backends, with specialized implementations for each platform.
#### 3.6.1 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...
};
```
#### 3.6.2 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...
};
```
#### 3.7 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);
}
};
```
#### 3.8 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...
}
};
```
#### 3.9 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
## 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
2. **Memory Efficiency**
- Explicit allocation strategies tailored to LLM workloads
- Reduced memory fragmentation
- Lower overall memory footprint
3. **Reliability**
- Comprehensive error handling
- No runtime exceptions
- Deterministic resource cleanup
4. **Portability**
- Cross-platform support with optimized backends
- Consistent behavior across environments
- Single codebase for all target platforms
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.
