mirror of
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i have a feeling this might need to be mirrored
| .github | ||
| figures | ||
| inference | ||
| .gitignore | ||
| dzv3-logo.svg | ||
| LICENSE-CODE | ||
| LICENSE-MODEL | ||
| MACBOOK_SETUP.md | ||
| README_WEIGHTS.md | ||
| README-DEEPSEEK_LEGACY.md | ||
| README.md | ||
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.
- Superior Performance: Leverage Zig's compile-time metaprogramming, SIMD vectorization, and low-level control to achieve optimal performance across platforms
- Memory Efficiency: Utilize Zig's explicit allocator system and arena allocation patterns for precise resource management
- Concurrent Processing: Implement efficient parallel execution using Zig's advanced async/await framework and evented I/O
- Type Safety & Reliability: Employ Zig's strong type system, comptime checks, and explicit error handling to prevent runtime errors
- Cross-Platform Support: Create a portable implementation with seamless support across architectures (x86_64, ARM64, etc.)
Table of Contents
- Overview
- System Architecture
- Component Design
- Platform-Specific Optimizations
- Development Roadmap
- 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:
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:
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:
// 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
deferanderrdeferensures 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:
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
deferanderrdefereven 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:
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:
// 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:
// 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.
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
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.
// 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
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.
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
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
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.
// 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.
// 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:
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 <metal_stdlib>
\\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:
-
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
-
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
-
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
-
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
-
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
// 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
// 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:
-
Speculative Decoding
- Implementation of speculative decoding using a smaller draft model
- Verification and acceptance/rejection of speculated tokens
- Significant speedup in generation throughput
-
Streaming Token Output
- Callback-based token streaming for real-time results
- Zero-copy token decoding for minimal overhead
- Support for incremental UI updates
-
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
-
Stop Sequence Detection
- Efficient detection of multiple stop sequences
- Support for subword token matching across boundaries
- Early termination based on generated content
-
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
-
Memory Efficiency
- KV-cache memory management for long context handling
- Incremental cache updates for streaming inference
- Automatic cache pruning for memory optimization
-
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:
// 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:
-
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
-
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
-
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
-
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:
// 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:
-
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
-
Flexible Quantization Schemes
- Symmetric quantization for simpler arithmetic
- Asymmetric quantization for better range utilization
- Per-tensor quantization for speed
- Per-channel quantization for accuracy
-
Advanced Calibration Methods
- Min/max calibration for simplicity
- Entropy-based calibration for better distribution representation
- Percentile-based calibration for outlier handling
-
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
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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:
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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
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Tensor Core Utilization
- Leveraging Matrix multiplication units in M-series chips
- Mixed-precision operations optimized for Apple Silicon
- Native FP16 support for improved throughput
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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
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Memory Bandwidth Optimization
- Unified memory architecture exploitation
- Cache-friendly memory access patterns
- Optimal tile sizes for M-series cache hierarchy
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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:
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AVX-512 Vectorization
- Full utilization of 512-bit vector operations
- Masked operations for efficient boundary handling
- FMA instruction usage for maximum throughput
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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
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Thread Pool Optimization
- Work-stealing scheduler for balanced multicore utilization
- NUMA-aware memory allocation and thread assignment
- Adaptive parallelism based on available cores
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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:
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CUDA Integration via FFI
- Zero-overhead bindings to CUDA runtime
- Asynchronous kernel execution and memory transfers
- Efficient stream management for overlapping operations
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Custom CUDA Kernels
- Specialized kernels for attention mechanisms
- Optimized matrix multiplication for transformer layers
- Fused operations for reduced kernel launch overhead
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Memory Management
- Pinned memory for efficient transfers
- Memory pool for reduced allocation overhead
- Smart prefetching for predictable memory access patterns
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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:
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Memory Management System
- Custom tensor allocator implementation
- Arena-based allocation strategies
- Error handling framework
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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:
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Transformer Layers
- Multi-head attention implementation
- Feed-forward networks
- Layer normalization
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Attention Mechanisms
- Standard attention implementation
- Flash attention optimizations
- Memory-efficient attention variants
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Mixture of Experts
- Router implementation
- Parallel expert execution
- Load balancing mechanisms
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Embedding Systems
- Token embeddings
- Position embeddings
- Rotary position embeddings
Phase 3: Backend Integration
This phase extends compute capabilities across different hardware:
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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:
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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:
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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:
-
Superior Performance
- Compile-time specialization eliminates runtime overhead
- Direct hardware access for maximum efficiency
- Zero-cost abstractions for clean yet fast code
-
Memory Efficiency
- Explicit allocation strategies tailored to LLM workloads
- Reduced memory fragmentation
- Lower overall memory footprint
-
Reliability
- Comprehensive error handling
- No runtime exceptions
- Deterministic resource cleanup
-
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.