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metal : Q3_K support

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This commit is contained in:
Iwan Kawrakow 2023-06-09 08:35:49 +03:00
parent 355e8c6e95
commit f5b6ed315e
3 changed files with 224 additions and 30 deletions
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25
ggml-metal.m
View file

@ -52,6 +52,7 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(get_rows_q4_0);
GGML_METAL_DECL_KERNEL(get_rows_q4_1);
GGML_METAL_DECL_KERNEL(get_rows_q2_k);
GGML_METAL_DECL_KERNEL(get_rows_q3_k);
GGML_METAL_DECL_KERNEL(get_rows_q4_k);
GGML_METAL_DECL_KERNEL(get_rows_q6_k);
GGML_METAL_DECL_KERNEL(rms_norm);
@ -59,6 +60,7 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q2_k_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q3_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);
@ -152,6 +154,7 @@ struct ggml_metal_context * ggml_metal_init(void) {
GGML_METAL_ADD_KERNEL(get_rows_q4_0);
GGML_METAL_ADD_KERNEL(get_rows_q4_1);
GGML_METAL_ADD_KERNEL(get_rows_q2_k);
GGML_METAL_ADD_KERNEL(get_rows_q3_k);
GGML_METAL_ADD_KERNEL(get_rows_q4_k);
GGML_METAL_ADD_KERNEL(get_rows_q6_k);
GGML_METAL_ADD_KERNEL(rms_norm);
@ -159,6 +162,7 @@ struct ggml_metal_context * ggml_metal_init(void) {
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q2_k_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q3_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);
@ -574,6 +578,15 @@ void ggml_metal_graph_compute(
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_k_f32];
} break;
case GGML_TYPE_Q3_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_k_f32];
} break;
case GGML_TYPE_Q4_K:
{
GGML_ASSERT(ne02 == 1);
@ -619,15 +632,12 @@ void ggml_metal_graph_compute(
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1) {
[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) {
} else if (src0t == GGML_TYPE_Q2_K ||
src0t == GGML_TYPE_Q3_K ||
src0t == GGML_TYPE_Q4_K ||
src0t == GGML_TYPE_Q6_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)];
} else if (src0t == GGML_TYPE_Q6_K) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -645,6 +655,7 @@ void ggml_metal_graph_compute(
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_k]; break;
case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_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");

219
ggml-metal.metal
View file

@ -318,8 +318,8 @@ kernel void kernel_mul_mat_q4_0_f32(
device const block_q4_0 * x = (device const block_q4_0 *) src0 + r0*nb;
device const float * y = (device const float *) src1 + r1*ne10;
const uint nth = tptg.x*tptg.y;
const uint ith = tptg.y*tpitg.x + tpitg.y;
const int nth = tptg.x*tptg.y;
const int ith = tptg.y*tpitg.x + tpitg.y;
const int ix = tpitg.y/4; // 0 or 1
const int iy = tpitg.y - 4*ix; // 0...3
@ -635,6 +635,13 @@ typedef struct {
half dmin; // super-block scale for quantized mins
} block_q2_k;
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
half d; // super-block scale
} block_q3_k;
typedef struct {
half d; // super-block scale for quantized scales
half dmin; // super-block scale for quantized mins
@ -698,6 +705,64 @@ static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, i
}
}
static void dequantize_row_q3_k(device const block_q3_k * x, device float * y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
const uint32_t kmask1 = 0x03030303;
const uint32_t kmask2 = 0x0f0f0f0f;
uint32_t aux[4];
for (int i = 0; i < nb; i++) {
const float d_all = (float)x[i].d;
device const uint8_t * q = x[i].qs;
device const uint8_t * hm = x[i].hmask;
uint8_t m = 1;
device const uint32_t * a = (device const uint32_t *)x[i].scales;
uint32_t tmp = a[2];
aux[2] = ((a[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
aux[3] = ((a[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
aux[0] = (a[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
aux[1] = (a[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
char4 scales;
int ia = 0;
int is = 4;
float dl;
for (int n = 0; n < QK_K; n += 128) {
int shift = 0;
for (int j = 0; j < 4; ++j) {
if (is == 4) {
scales = as_type<char4>(aux[ia++]);
is = 0;
}
dl = d_all * (scales[is++] - 32);
for (int l = 0; l < 16; ++l) {
*y++ = dl * ((int8_t)((q[l+ 0] >> shift) & 3) - ((hm[l+ 0] & m) ? 0 : 4));
}
dl = d_all * (scales[is++] - 32);
for (int l = 0; l < 16; ++l) {
*y++ = dl * ((int8_t)((q[l+16] >> shift) & 3) - ((hm[l+16] & m) ? 0 : 4));
}
shift += 2;
m <<= 1;
}
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;
@ -771,6 +836,22 @@ kernel void kernel_get_rows_q2_k(
(device float *) ((device char *) dst + i*nb1), ne00);
}
kernel void kernel_get_rows_q3_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_q3_k(
(device const block_q3_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,
@ -903,19 +984,123 @@ kernel void kernel_mul_mat_q2_k_f32(
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);
//}
kernel void kernel_mul_mat_q3_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_q3_k * x = (device const block_q3_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 step = QK_K / tptg.y; // we expect this to be 16
const int iqs = step * tpitg.y; // 0...240 in steps of 16
const int ip = iqs / 128; // 0 or 1
const int il = (iqs - 128*ip)/16; // 0...7
const int n = 4;
const int l0 = n * il;
const int is = l0/16;
const uint8_t m = 1 << (4*ip);
//const int shift1 = 4*ip;
//const int shift2 = 4*ip + 2;
int8_t sc[4];
float sumf = 0;
for (int i = tpitg.x; i < nb; i += tptg.x) {
device const uint8_t * q = x[i].qs + 32*ip + l0;
device const uint8_t * hm = x[i].hmask + l0;
device const uint8_t * scales = x[i].scales + is;
device const float * y = yy + i * QK_K + 128*ip + l0;
const float dall = x[i].d;
//sc[0] = ((scales[ 8] >> shift1) & 3) << 4;
//sc[1] = ((scales[10] >> shift1) & 3) << 4;
//sc[2] = ((scales[ 8] >> shift2) & 3) << 4;
//sc[3] = ((scales[10] >> shift2) & 3) << 4;
//if (ip == 0) {
// sc[0] |= (scales[0] & 0xF);
// sc[1] |= (scales[2] & 0xF);
// sc[2] |= (scales[4] & 0xF);
// sc[3] |= (scales[6] & 0xF);
//} else {
// sc[0] |= (scales[0] >> 4);
// sc[1] |= (scales[2] >> 4);
// sc[2] |= (scales[4] >> 4);
// sc[3] |= (scales[6] >> 4);
//}
if (ip == 0) {
sc[0] = (scales[0] & 0xF) | (((scales[ 8] >> 0) & 3) << 4);
sc[1] = (scales[2] & 0xF) | (((scales[10] >> 0) & 3) << 4);
sc[2] = (scales[4] & 0xF) | (((scales[ 8] >> 2) & 3) << 4);
sc[3] = (scales[6] & 0xF) | (((scales[10] >> 2) & 3) << 4);
} else {
sc[0] = (scales[0] >> 4) | (((scales[ 8] >> 4) & 3) << 4);
sc[1] = (scales[2] >> 4) | (((scales[10] >> 4) & 3) << 4);
sc[2] = (scales[4] >> 4) | (((scales[ 8] >> 6) & 3) << 4);
sc[3] = (scales[6] >> 4) | (((scales[10] >> 6) & 3) << 4);
}
float4 sums = {0.f, 0.f, 0.f, 0.f};
for (int l = 0; l < n; ++l) {
sums[0] += y[l+ 0] * ((int8_t)((q[l] >> 0) & 3) - (hm[l] & (m << 0) ? 0 : 4));
sums[1] += y[l+32] * ((int8_t)((q[l] >> 2) & 3) - (hm[l] & (m << 1) ? 0 : 4));
sums[2] += y[l+64] * ((int8_t)((q[l] >> 4) & 3) - (hm[l] & (m << 2) ? 0 : 4));
sums[3] += y[l+96] * ((int8_t)((q[l] >> 6) & 3) - (hm[l] & (m << 3) ? 0 : 4));
}
sumf += dall * (sums[0] * (sc[0] - 32) + sums[1] * (sc[1] - 32) + sums[2] * (sc[2] - 32) + sums[3] * (sc[3] - 32));
}
sum[ith] = sumf;
//
// Accumulate the sum from all threads in the threadgroup
//
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];
}
//if (ith == 0) {
// dst[r1*ne0 + r0] = sum[0];
//}
}
kernel void kernel_mul_mat_q4_k_f32(
@ -942,8 +1127,8 @@ kernel void kernel_mul_mat_q4_k_f32(
device const block_q4_k * x = (device const block_q4_k *) src0 + r0*nb;
device const float * yy = (device const float *) src1 + r1*ne10;
const uint nth = tptg.x*tptg.y;
const uint ith = tptg.y*tpitg.x + tpitg.y;
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
@ -1051,8 +1236,8 @@ kernel void kernel_mul_mat_q6_k_f32(
device const block_q6_k * x = (device const block_q6_k *) src0 + r0*nb;
device const float * yy = (device const float *) src1 + r1*ne10;
const uint nth = tptg.x*tptg.y;
const uint ith = tptg.y*tpitg.x + tpitg.y;
const int nth = tptg.x*tptg.y;
const int ith = tptg.y*tpitg.x + tpitg.y;
// Note: we absolutely assume that tptg.y = 16 and QK_K = 256!
const int iqs = 16 * tpitg.y;

10
llama.cpp
View file

@ -2390,12 +2390,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
} else {
new_type = quantized_type;
// TODO: temporary disabled until Metal / OpenCL support is available
// ref: https://github.com/ggerganov/llama.cpp/issues/1711
//if (tensor.name == "output.weight") {
// new_type = GGML_TYPE_Q6_K;
//}
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
if (tensor.name == "output.weight") {
new_type = GGML_TYPE_Q6_K;
}
else if (tensor.name.find("attention.wv.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&