Files
llama.cpp/ggml/src/ggml-metal/kernels/norm.metal
T
b615f5b4bd metal: per-op source split + parallel compile (#26561)
* metal : per-op source split + parallel compile (#24021)

* preliminary extract common header

* op source split

* split metallib into 8 libs && load in parallel

* derive kernel->library routing from functionNames

* x-macro lib list + underscore filenames, dedup QK_NL, MRC fixes

* op source split 8 to 20

* improve robustness of source fallback

* clean up

* change bool -> atomic_bool

* only prepend headers that source actually includes

* no semaphore, use GCD global queue

* dedup library compile path, fix NSError lifetime, rename gla

* relocate upstream concat/rope_back/repeat kernel changes into split files

* move ggml-common.h from common.h into dequantize.h to shrink binary size

---------

Co-authored-by: lvyichen <lvyichen@stepfun.com>

* metal: add col2im_1d op (f32/f16/bf16) (#25176)

* metal : add set_rows with src0 f16 (#25434)

* metal : add CONV_2D_DW (depthwise convolution) support (#21565)

* metal : add Q2_0 support (#25419)

* metal: fuse snake activation (mul, sin, sqr, mul, add) (#25459)

* ggml-metal: FWHT kernel for metal backend (#25924)

* metal : port new kernels into the split sources

Move the kernels added on master after the split (lightning indexer,
DSv4 hyper-connections, silu_back, f16 bin ops, TQ2_0, the flash-attn KV
dequantization pass, rope offset/inplace, ssm_scan rollback, packed q8_0
dequantization and the tensor-API mat-mat K clamp) into the corresponding
kernels/*.metal sources. Copied verbatim, no functional change.

---------

Co-authored-by: lvyichen <lvyichen@stepfun.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2026-08-24 19:16:13 +03:00

309 lines
9.8 KiB
Metal

#include "common.h"
// F == 1 : norm (no fuse)
// F == 2 : norm + mul
// F == 3 : norm + mul + add
template <typename T, short F>
kernel void kernel_norm_fuse_impl(
constant ggml_metal_kargs_norm & args,
device const char * src0,
device const char * src1_0,
device const char * src1_1,
device char * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
if (sgitg == 0) {
shmem_f32[tiisg] = 0.0f;
}
const int i01 = tgpig.x;
const int i02 = tgpig.y;
const int i03 = tgpig.z;
device const T * x = (device const T *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
device const T * f0 = (device const T *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
device const T * f1 = (device const T *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
T sumft(0.0f);
float sumf = 0.0f;
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
sumft += x[i00];
}
sumf = dot(sumft, T(1.0f));
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
const float mean = sumf/args.ne00;
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
sumf = 0.0f;
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
y[i00] = x[i00] - mean;
sumf += dot(y[i00], y[i00]);
}
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
const float variance = sumf/args.ne00;
const float scale = 1.0f/sqrt(variance + args.eps);
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
if (F == 1) {
y[i00] = (y[i00]*scale);
}
if (F == 2) {
y[i00] = (y[i00]*scale)*f0[i00];
}
if (F == 3) {
y[i00] = (y[i00]*scale)*f0[i00] + f1[i00];
}
}
}
typedef decltype(kernel_norm_fuse_impl<float4, 1>) kernel_norm_fuse_t;
template [[host_name("kernel_norm_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 1>;
template [[host_name("kernel_norm_mul_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 2>;
template [[host_name("kernel_norm_mul_add_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 3>;
template [[host_name("kernel_norm_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 1>;
template [[host_name("kernel_norm_mul_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 2>;
template [[host_name("kernel_norm_mul_add_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 3>;
// F == 1 : rms_norm (no fuse)
// F == 2 : rms_norm + mul
// F == 3 : rms_norm + mul + add
template <typename T, short F>
kernel void kernel_rms_norm_fuse_impl(
constant ggml_metal_kargs_norm & args,
device const char * src0,
device const char * src1_0,
device const char * src1_1,
device char * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
if (sgitg == 0) {
shmem_f32[tiisg] = 0.0f;
}
const int i01 = tgpig.x;
const int i02 = tgpig.y;
const int i03 = tgpig.z;
device const T * x = (device const T *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
device const T * f0 = (device const T *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
device const T * f1 = (device const T *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
float sumf = 0.0f;
// parallel sum
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
sumf += dot(x[i00], x[i00]);
}
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
const float mean = sumf/args.ne00;
const float scale = 1.0f/sqrt(mean + args.eps);
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
if (F == 1) {
y[i00] = (x[i00]*scale);
}
if (F == 2) {
y[i00] = (x[i00]*scale)*f0[i00];
}
if (F == 3) {
y[i00] = (x[i00]*scale)*f0[i00] + f1[i00];
}
}
}
typedef decltype(kernel_rms_norm_fuse_impl<float4, 1>) kernel_rms_norm_fuse_t;
template [[host_name("kernel_rms_norm_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 1>;
template [[host_name("kernel_rms_norm_mul_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 2>;
template [[host_name("kernel_rms_norm_mul_add_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 3>;
template [[host_name("kernel_rms_norm_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 1>;
template [[host_name("kernel_rms_norm_mul_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 2>;
template [[host_name("kernel_rms_norm_mul_add_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 3>;
template <typename T0, typename T>
kernel void kernel_l2_norm_impl(
constant ggml_metal_kargs_l2_norm & args,
device const char * src0,
device char * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i03 = tgpig.z;
const int i02 = tgpig.y;
const int i01 = tgpig.x;
if (sgitg == 0) {
shmem_f32[tiisg] = 0.0f;
}
device const T0 * x = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
float sumf = 0.0f;
// parallel sum
for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
sumf += dot(x[i00], x[i00]);
}
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
shmem_f32[sgitg] = sumf;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
const float scale = 1.0f/max(sqrt(sumf), args.eps);
for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
y[i00] = x[i00] * scale;
}
}
typedef decltype(kernel_l2_norm_impl<float, float>) kernel_l2_norm_t;
template [[host_name("kernel_l2_norm_f32_f32")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float, float>;
template [[host_name("kernel_l2_norm_f32_f32_4")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float4, float4>;
kernel void kernel_group_norm_f32(
constant ggml_metal_kargs_group_norm & args,
device const float * src0,
device float * dst,
threadgroup float * buf [[threadgroup(0)]],
uint tgpig[[threadgroup_position_in_grid]],
uint tpitg[[thread_position_in_threadgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]],
uint tiisg[[thread_index_in_simdgroup]],
uint ntg[[threads_per_threadgroup]]) {
const int64_t ne = args.ne00*args.ne01*args.ne02;
const int64_t gs = args.ne00*args.ne01*((args.ne02 + args.ngrp - 1) / args.ngrp);
int start = tgpig * gs;
int end = start + gs;
start += tpitg;
if (end >= ne) {
end = ne;
}
float tmp = 0.0f; // partial sum for thread in warp
for (int j = start; j < end; j += ntg) {
tmp += src0[j];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
tmp = simd_sum(tmp);
if (ntg > N_SIMDWIDTH) {
if (sgitg == 0) {
buf[tiisg] = 0.0f;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
buf[sgitg] = tmp;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
tmp = buf[tiisg];
tmp = simd_sum(tmp);
}
const float mean = tmp / gs;
tmp = 0.0f;
for (int j = start; j < end; j += ntg) {
float xi = src0[j] - mean;
dst[j] = xi;
tmp += xi * xi;
}
tmp = simd_sum(tmp);
if (ntg > N_SIMDWIDTH) {
if (sgitg == 0) {
buf[tiisg] = 0.0f;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (tiisg == 0) {
buf[sgitg] = tmp;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
tmp = buf[tiisg];
tmp = simd_sum(tmp);
}
const float variance = tmp / gs;
const float scale = 1.0f/sqrt(variance + args.eps);
for (int j = start; j < end; j += ntg) {
dst[j] *= scale;
}
}