mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 15:29:43 +00:00
metal : add Q2_K implementation (#1762)
* metal : add Q2_K implementation 27.1 ms / token on M2 Max 30-core GPU, so about the same speed as Q4_0. Memory throughput is ~156 GB/s. The access pattern used in the Q2_K CUDA implementation resulted in significantly lower performance (~31 ms/token). * Fixing merge conflicts --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
GitHub
parent
0bf7cf1b29
commit
72ff5282bf
2 changed files with 200 additions and 18 deletions
17
ggml-metal.m
17
ggml-metal.m
|
|
@ -49,11 +49,13 @@ struct ggml_metal_context {
|
|||
GGML_METAL_DECL_KERNEL(diag_mask_inf);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_f16);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q4_0);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q2_k);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q4_k);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q6_k);
|
||||
GGML_METAL_DECL_KERNEL(rms_norm);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_q2_k_f32);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_q6_k_f32);
|
||||
GGML_METAL_DECL_KERNEL(rope);
|
||||
|
|
@ -137,11 +139,13 @@ struct ggml_metal_context * ggml_metal_init(void) {
|
|||
GGML_METAL_ADD_KERNEL(diag_mask_inf);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_f16);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q4_0);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q2_k);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q4_k);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q6_k);
|
||||
GGML_METAL_ADD_KERNEL(rms_norm);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_q2_k_f32);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_q6_k_f32);
|
||||
GGML_METAL_ADD_KERNEL(rope);
|
||||
|
|
@ -525,6 +529,15 @@ void ggml_metal_graph_compute(
|
|||
nth1 = 4;
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32];
|
||||
} break;
|
||||
case GGML_TYPE_Q2_K:
|
||||
{
|
||||
GGML_ASSERT(ne02 == 1);
|
||||
GGML_ASSERT(ne12 == 1);
|
||||
|
||||
nth0 = 4;
|
||||
nth1 = 16;
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_k_f32];
|
||||
} break;
|
||||
case GGML_TYPE_Q4_K:
|
||||
{
|
||||
GGML_ASSERT(ne02 == 1);
|
||||
|
|
@ -570,6 +583,9 @@ void ggml_metal_graph_compute(
|
|||
if (src0t == GGML_TYPE_Q4_0) {
|
||||
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
} else if (src0t == GGML_TYPE_Q2_K) {
|
||||
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
} else if (src0t == GGML_TYPE_Q4_K) {
|
||||
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
|
|
@ -591,6 +607,7 @@ void ggml_metal_graph_compute(
|
|||
switch (src0->type) {
|
||||
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
|
||||
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
|
||||
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_k]; break;
|
||||
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break;
|
||||
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_k]; break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
|
|
|
|||
201
ggml-metal.metal
201
ggml-metal.metal
|
|
@ -527,6 +527,13 @@ kernel void kernel_cpy_f32_f32(
|
|||
|
||||
#define QK_K 256
|
||||
|
||||
typedef struct {
|
||||
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
|
||||
uint8_t qs[QK_K/4]; // quants
|
||||
half d; // super-block scale for quantized scales
|
||||
half dmin; // super-block scale for quantized mins
|
||||
} block_q2_k;
|
||||
|
||||
typedef struct {
|
||||
half d; // super-block scale for quantized scales
|
||||
half dmin; // super-block scale for quantized mins
|
||||
|
|
@ -555,6 +562,41 @@ static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) {
|
|||
return r;
|
||||
}
|
||||
|
||||
//========================================== dequantization =============================
|
||||
|
||||
static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, int k) {
|
||||
assert(k % QK_K == 0);
|
||||
const int nb = k / QK_K;
|
||||
|
||||
for (int i = 0; i < nb; i++) {
|
||||
|
||||
const float d = x[i].d;
|
||||
const float min = x[i].dmin;
|
||||
|
||||
device const uint8_t * q = x[i].qs;
|
||||
|
||||
int is = 0;
|
||||
float dl, ml;
|
||||
for (int n = 0; n < QK_K; n += 128) {
|
||||
int shift = 0;
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
|
||||
uint8_t sc = x[i].scales[is++];
|
||||
dl = d * (sc & 0xF); ml = min * (sc >> 4);
|
||||
for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l] >> shift) & 3)) - ml;
|
||||
|
||||
sc = x[i].scales[is++];
|
||||
dl = d * (sc & 0xF); ml = min * (sc >> 4);
|
||||
for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l+16] >> shift) & 3)) - ml;
|
||||
|
||||
shift += 2;
|
||||
}
|
||||
q += 32;
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, int k) {
|
||||
assert(k % QK_K == 0);
|
||||
const int nb = k / QK_K;
|
||||
|
|
@ -586,12 +628,12 @@ static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, i
|
|||
|
||||
for (int i = 0; i < nb; i++) {
|
||||
|
||||
const float d = x[i].d;
|
||||
|
||||
device const uint8_t * ql = x[i].ql;
|
||||
device const uint8_t * qh = x[i].qh;
|
||||
device const int8_t * sc = x[i].scales;
|
||||
|
||||
const float d = x[i].d;
|
||||
|
||||
for (int n = 0; n < QK_K; n += 128) {
|
||||
for (int l = 0; l < 32; ++l) {
|
||||
int is = l/16;
|
||||
|
|
@ -612,6 +654,22 @@ static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, i
|
|||
}
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_q2_k(
|
||||
device const void * src0,
|
||||
device const int * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb1,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
const int i = tpig;
|
||||
const int r = ((device int32_t *) src1)[i];
|
||||
|
||||
dequantize_row_q2_k(
|
||||
(device const block_q2_k *) ((device char *) src0 + r*nb01),
|
||||
(device float *) ((device char *) dst + i*nb1), ne00);
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_q4_k(
|
||||
device const void * src0,
|
||||
device const int * src1,
|
||||
|
|
@ -628,6 +686,129 @@ kernel void kernel_get_rows_q4_k(
|
|||
(device float *) ((device char *) dst + i*nb1), ne00);
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_q6_k(
|
||||
device const void * src0,
|
||||
device const int * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb1,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
const int i = tpig;
|
||||
const int r = ((device int32_t *) src1)[i];
|
||||
|
||||
dequantize_row_q6_k(
|
||||
(device const block_q6_k *) ((device char *) src0 + r*nb01),
|
||||
(device float *) ((device char *) dst + i*nb1), ne00);
|
||||
}
|
||||
|
||||
//====================================== dot products =========================
|
||||
|
||||
kernel void kernel_mul_mat_q2_k_f32(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
threadgroup float * sum [[threadgroup(0)]],
|
||||
uint2 tgpig[[threadgroup_position_in_grid]],
|
||||
uint2 tpig[[thread_position_in_grid]], // we don't use this for now
|
||||
uint2 tpitg[[thread_position_in_threadgroup]],
|
||||
uint2 tptg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int nb = ne00/QK_K;
|
||||
|
||||
const int64_t r0 = tgpig.x;
|
||||
const int64_t r1 = tgpig.y;
|
||||
|
||||
device const block_q2_k * x = (device const block_q2_k *) src0 + r0*nb;
|
||||
device const float * yy = (device const float *) src1 + r1*ne10;
|
||||
|
||||
const int nth = tptg.x*tptg.y;
|
||||
const int ith = tptg.y*tpitg.x + tpitg.y;
|
||||
|
||||
|
||||
const int tid = tpitg.y; // 0...16
|
||||
const int il = tid/4; // 0...3
|
||||
const int ir = tid%4; // 0...3
|
||||
const int ip = il/2; // 0 or 1
|
||||
const int shift1 = 4*(il%2);// 0 or 4
|
||||
const int shift2 = shift1+2;// 2 or 6
|
||||
const int n = 8;
|
||||
const int is = 4*il + (n*ir)/16;
|
||||
|
||||
sum[ith] = 0.0f;
|
||||
|
||||
float sumf = 0;
|
||||
for (int i = tpitg.x; i < nb; i += tptg.x) {
|
||||
|
||||
device const uint8_t * q = x[i].qs + 32*ip + n*ir;
|
||||
device const uint8_t * scales = x[i].scales + is;
|
||||
|
||||
uint8_t d1 = scales[0] & 0xF;
|
||||
uint8_t m1 = scales[0] >> 4;
|
||||
uint8_t d2 = scales[2] & 0xF;
|
||||
uint8_t m2 = scales[2] >> 4;
|
||||
|
||||
device const float * y = yy + i*QK_K + 64*il + n*ir;
|
||||
|
||||
const float dall = (float)x[i].d;
|
||||
const float dmin = (float)x[i].dmin;
|
||||
|
||||
float4 s = {0.f, 0.f, 0.f, 0.f};
|
||||
for (int l = 0; l < n; ++l) {
|
||||
s[0] += y[l+ 0] * ((q[l] >> shift1) & 3); s[1] += y[l+ 0];
|
||||
s[2] += y[l+32] * ((q[l] >> shift2) & 3); s[3] += y[l+32];
|
||||
}
|
||||
sumf += dall * (s[0] * d1 + s[2] * d2) - dmin * (s[1] * m1 + s[3] * m2);
|
||||
|
||||
|
||||
}
|
||||
sum[ith] = sumf;
|
||||
|
||||
//
|
||||
// Accumulate the sum from all threads in the threadgroup
|
||||
// This version is slightly faster than the commented out one below,
|
||||
// which I copy-pasted from ggerganov's q4_0 dot product for metal.
|
||||
//
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
if (ith%4 == 0) {
|
||||
for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i];
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
if (ith%16 == 0) {
|
||||
for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i];
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
if (ith == 0) {
|
||||
for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
|
||||
dst[r1*ne0 + r0] = sum[0];
|
||||
}
|
||||
|
||||
//// accumulate the sum from all threads in the threadgroup
|
||||
//threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
//for (uint i = nth/2; i > 0; i /= 2) {
|
||||
// if (ith < i) {
|
||||
// sum[ith] += sum[ith + i];
|
||||
// }
|
||||
// threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
//}
|
||||
|
||||
//if (ith == 0) {
|
||||
// dst[r1*ne0 + r0] = sum[0];
|
||||
//}
|
||||
}
|
||||
|
||||
kernel void kernel_mul_mat_q4_k_f32(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
|
|
@ -724,22 +905,6 @@ kernel void kernel_mul_mat_q4_k_f32(
|
|||
//}
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_q6_k(
|
||||
device const void * src0,
|
||||
device const int * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb1,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
const int i = tpig;
|
||||
const int r = ((device int32_t *) src1)[i];
|
||||
|
||||
dequantize_row_q6_k(
|
||||
(device const block_q6_k *) ((device char *) src0 + r*nb01),
|
||||
(device float *) ((device char *) dst + i*nb1), ne00);
|
||||
}
|
||||
|
||||
kernel void kernel_mul_mat_q6_k_f32(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
|
|
|
|||
Loading…
Reference in a new issue