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//! Fused Q4_K_M dequant + GEMV — Metal compute kernel + Rust glue.
//!
//! Companion to `q4_k.rs` (the standalone dequant kernel). Use this
//! whenever `m == 1` (decode path): one kernel dispatch reads the Q4
//! super-blocks, decodes 256 weights per super-block on the fly, and
//! reduces against `A` — no fp16 transient written or read. Drops
//! ~64 MB of memory traffic per 4K×4K matmul vs. the dequant→transient→
//! gemv pipeline.
//!
//! For prefill (`m > 1`) the model still goes through the standalone
//! dequant + `gemm_v2_f16w` path; a fused gemm is a future optimisation.
//!
//! Layout assumptions (must hold; otherwise fall back to dequant path):
//! - `K` (in_features) is a multiple of 256 (Q4_K_M super-block size)
//! - `W` is row-major over `[N, K/256]` super-blocks (matches what
//! `MetalQuantStore::Q4K { blocks, .. }` already stores)
#![cfg(all(target_os = "macos", feature = "metal"))]
use std::ffi::c_void;
use std::sync::OnceLock;
use metal::{
Buffer, CompileOptions, ComputeCommandEncoderRef, ComputePipelineState, Device, MTLSize,
};
const SHADER_SRC: &str = include_str!("q4_k_gemv.metal");
const KERNEL_NAME: &str = "gemv_f32a_q4kw";
static PIPELINE: OnceLock<ComputePipelineState> = OnceLock::new();
fn pipeline(device: &Device) -> &'static ComputePipelineState {
PIPELINE.get_or_init(|| {
let lib = device
.new_library_with_source(SHADER_SRC, &CompileOptions::new())
.expect("compile q4_k_gemv.metal");
let function = lib
.get_function(KERNEL_NAME, None)
.expect("find gemv_f32a_q4kw function");
device
.new_compute_pipeline_state_with_function(&function)
.expect("build gemv_f32a_q4kw pipeline")
})
}
/// Dispatch fused GEMV on an existing compute encoder.
///
/// Computes `c[n] = a[k] @ w[n, k]^T` where `w` is `block_q4_K[n * (k/256)]`.
///
/// `w_byte_offset` is the byte offset into the `w` buffer where this
/// tensor's super-blocks start. Set to 0 when `w` is a private
/// allocation; non-zero when binding into a shared zero-copy mmap buffer.
///
/// Caller is responsible for `enc.end_encoding()` after the dispatch
/// (or chaining further dispatches).
pub fn dispatch_gemv_q4k_on_encoder(
device: &Device,
enc: &ComputeCommandEncoderRef,
a: &Buffer,
w: &Buffer,
w_byte_offset: u64,
c: &Buffer,
n: usize,
k: usize,
) {
debug_assert!(
k % 256 == 0,
"gemv_q4k requires K divisible by 256 (got K={k})"
);
#[repr(C)]
struct P {
n: i32,
k: i32,
}
let params = P {
n: n as i32,
k: k as i32,
};
let pipe = pipeline(device);
enc.set_compute_pipeline_state(pipe);
enc.set_buffer(0, Some(a), 0);
enc.set_buffer(1, Some(w), w_byte_offset);
enc.set_buffer(2, Some(c), 0);
// setBytes inlines small (<=4KB) params into the encoder argument
// table — no MTLBuffer allocation per call. With 145 quant matmuls
// per token, this is real money on Apple Silicon (alloc takes ~ms).
enc.set_bytes(
3,
std::mem::size_of::<P>() as u64,
¶ms as *const _ as *const c_void,
);
// 1 threadgroup per output column, exactly 1 SIMD group (32 threads)
// per threadgroup — the kernel stripes `tiitg` ∈ [0, 32) over K with
// step 32 and reduces with `simd_sum`.
let grid = MTLSize::new(n as u64, 1, 1);
let tg = MTLSize::new(32, 1, 1);
enc.dispatch_thread_groups(grid, tg);
}
#[cfg(test)]
mod tests {
use super::*;
use candle_core::quantized::{GgmlDType, QTensor};
use candle_core::{Device as CandleDevice, Tensor};
use metal::MTLResourceOptions;
/// Compare the fused GEMV against candle's CPU `dequantize → matmul`
/// reference within fp16 quantisation tolerance. Tests a 4K×4K shape
/// (matches Qwen3-8B o_proj exactly).
#[test]
fn fused_gemv_q4k_matches_cpu_reference_4096x4096() {
let n: usize = 4096;
let k: usize = 4096;
// Synthetic weight, sin/cos pattern so quantisation is non-trivial.
let raw_w: Vec<f32> = (0..n * k)
.map(|i| {
(((i % 313) as f32) * 0.0173).sin() * 0.5
+ (((i % 251) as f32) * 0.0091).cos() * 0.5
})
.collect();
let cpu = CandleDevice::Cpu;
let t_w = Tensor::from_vec(raw_w, (n, k), &cpu).unwrap();
let qt_w = QTensor::quantize(&t_w, GgmlDType::Q4K).unwrap();
let dense_w = qt_w.dequantize(&cpu).unwrap();
let raw_a: Vec<f32> = (0..k).map(|i| ((i as f32) * 0.0007).sin()).collect();
let t_a = Tensor::from_vec(raw_a.clone(), (1, k), &cpu).unwrap();
// Reference: A @ W^T via candle CPU.
let ref_t = t_a.matmul(&dense_w.transpose(0, 1).unwrap()).unwrap();
let ref_c: Vec<f32> = ref_t.flatten_all().unwrap().to_vec1::<f32>().unwrap();
assert_eq!(ref_c.len(), n);
let bytes = qt_w.data().unwrap();
let Some(device) = Device::system_default() else {
eprintln!("no Metal device — skipping");
return;
};
let queue = device.new_command_queue();
let a_buf = device.new_buffer_with_data(
raw_a.as_ptr() as *const _,
(raw_a.len() * 4) as u64,
MTLResourceOptions::StorageModeShared,
);
let w_buf = device.new_buffer_with_data(
bytes.as_ptr() as *const _,
bytes.len() as u64,
MTLResourceOptions::StorageModeShared,
);
let c_buf = device.new_buffer((n * 4) as u64, MTLResourceOptions::StorageModeShared);
let cmd = queue.new_command_buffer();
let enc = cmd.new_compute_command_encoder();
dispatch_gemv_q4k_on_encoder(&device, enc, &a_buf, &w_buf, 0, &c_buf, n, k);
enc.end_encoding();
cmd.commit();
cmd.wait_until_completed();
let our_ptr = c_buf.contents() as *const f32;
let our_c: &[f32] = unsafe { std::slice::from_raw_parts(our_ptr, n) };
// GEMM accumulates K=4096 multiplies — fp16 weight quant adds ~0.1
// relative error per element after that many additions. Loose
// tolerance: 1e-1 absolute on values typically in [-1, 1].
let mut max_abs = 0.0_f32;
let mut mismatches = 0usize;
for (i, (&our, &refv)) in our_c.iter().zip(ref_c.iter()).enumerate() {
let diff = (our - refv).abs();
if diff > max_abs {
max_abs = diff;
}
let denom = our.abs().max(refv.abs()).max(1e-3);
let rel = diff / denom;
if diff > 0.5 && rel > 0.05 {
mismatches += 1;
if mismatches < 5 {
eprintln!("[{i}] our={our} ref={refv} diff={diff} rel={rel}");
}
}
}
eprintln!("max_abs={max_abs:.4} mismatches={mismatches}/{n}");
assert!(
mismatches == 0,
"{mismatches}/{n} elements outside fp16 tolerance"
);
}
}