CS348Project/llama.cpp
5
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-11-03 09:22:01 +00:00
Code Issues Packages Projects Releases Wiki Activity

ggml : refactor quantized processing functions (#509)

Browse Source 
* Refactor quantized processing functions

* ggml : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Stephan Walter
2023-03-28 17:13:01 +00:00
committed by GitHub
parent 692ce3164e
commit 99c5b27654
1 changed files with 53 additions and 254 deletions
Download Patch File Download Diff File Expand all files Collapse all files

307
ggml.c
View File

@@ -1540,7 +1540,7 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t
*s = sumf;
}
inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
const int nb = n / QK;
assert(n % QK == 0);
@@ -1824,7 +1824,7 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void
*s = sumf;
}
inline static void ggml_vec_dot_q4_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
static void ggml_vec_dot_q4_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
const int nb = n / QK;
const block_q4_1 * restrict x = vx;
@@ -6106,7 +6106,30 @@ static void ggml_compute_forward_mul_mat_f16_f32(
//}
}
static void ggml_compute_forward_mul_mat_q4_0_f32(
typedef void (*dequantize_row_q_t)(const void * restrict x, float * restrict y, int k);
typedef void (*quantize_row_q_t)(const float * restrict x, void * restrict y, int k);
typedef void (*vec_dot_q_t)(const int n, float * restrict s, const void * restrict x, const void * restrict y);
typedef struct {
dequantize_row_q_t dequantize_row_q;
quantize_row_q_t quantize_row_q;
vec_dot_q_t vec_dot_q;
} quantize_fns_t;
static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = {
.dequantize_row_q = dequantize_row_q4_0,
.quantize_row_q = quantize_row_q4_0,
.vec_dot_q = ggml_vec_dot_q4_0,
},
[GGML_TYPE_Q4_1] = {
.dequantize_row_q = dequantize_row_q4_1,
.quantize_row_q = quantize_row_q4_1,
.vec_dot_q = ggml_vec_dot_q4_1,
},
};
static void ggml_compute_forward_mul_mat_q_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
@@ -6152,8 +6175,12 @@ static void ggml_compute_forward_mul_mat_q4_0_f32(
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
const enum ggml_type type = src0->type;
quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q;
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[GGML_TYPE_Q4_0]);
GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]);
GGML_ASSERT(nb10 == sizeof(float));
// dst cannot be transposed or permuted
@@ -6185,194 +6212,14 @@ static void ggml_compute_forward_mul_mat_q4_0_f32(
}
float * const wdata = params->wdata;
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
{
size_t id = 0;
for (int i01 = 0; i01 < ne01; ++i01) {
dequantize_row_q4_0((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
id += ne00;
}
}
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne10,
x, ne10,
0.0f, d, ne01);
}
}
/*printf("CBLAS Q4_0 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/
return;
}
#endif
if (params->type == GGML_TASK_INIT) {
char * wdata = params->wdata;
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
quantize_row_q4_0((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
wdata += (ne10*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
}
}
}
return;
}
if (params->type == GGML_TASK_FINALIZE) {
return;
}
// parallelize by src0 rows using ggml_vec_dot_q4_0
// total rows in src0
const int nr = ne01*ne02*ne03;
// rows per thread
const int dr = (nr + nth - 1)/nth;
// row range for this thread
const int ir0 = dr*ith;
const int ir1 = MIN(ir0 + dr, nr);
void * wdata = params->wdata;
for (int ir = ir0; ir < ir1; ++ir) {
// src0 indices
const int i03 = ir/(ne02*ne01);
const int i02 = (ir - i03*ne02*ne01)/ne01;
const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
const int i13 = i03;
const int i12 = i02;
const int i0 = i01;
const int i2 = i02;
const int i3 = i03;
void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0]);
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
assert(ne00 % 32 == 0);
for (int ic = 0; ic < ne11; ++ic) {
ggml_vec_dot_q4_0(ne00, &dst_col[ic*ne0], src0_row, ((void *) (src1_col + (ic*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0])));
}
}
//int64_t t1 = ggml_time_us();
//static int64_t acc = 0;
//acc += t1 - t0;
//if (t1 - t0 > 10) {
// printf("\n");
// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
//}
}
static void ggml_compute_forward_mul_mat_q4_1_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
int64_t t0 = ggml_perf_time_us();
UNUSED(t0);
const int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne02 = src0->ne[2];
const int ne03 = src0->ne[3];
const int ne10 = src1->ne[0];
const int ne11 = src1->ne[1];
const int ne12 = src1->ne[2];
const int ne13 = src1->ne[3];
const int ne0 = dst->ne[0];
const int ne1 = dst->ne[1];
const int ne2 = dst->ne[2];
const int ne3 = dst->ne[3];
const int nb00 = src0->nb[0];
const int nb01 = src0->nb[1];
const int nb02 = src0->nb[2];
const int nb03 = src0->nb[3];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
const int nb12 = src1->nb[2];
const int nb13 = src1->nb[3];
const int nb0 = dst->nb[0];
const int nb1 = dst->nb[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const int ith = params->ith;
const int nth = params->nth;
GGML_ASSERT(ne02 == ne12);
GGML_ASSERT(ne03 == ne13);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[GGML_TYPE_Q4_1]);
GGML_ASSERT(nb10 == sizeof(float));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne02);
GGML_ASSERT(ne3 == ne03);
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
if (params->ith != 0) {
return;
}
if (params->type == GGML_TASK_INIT) {
return;
}
if (params->type == GGML_TASK_FINALIZE) {
return;
}
float * const wdata = params->wdata;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
{
size_t id = 0;
for (int i01 = 0; i01 < ne01; ++i01) {
dequantize_row_q4_1((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
id += ne00;
}
}
@@ -6399,15 +6246,13 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
if (params->type == GGML_TASK_INIT) {
char * wdata = params->wdata;
const size_t row_size = ne10*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type];
for (int i13 = 0; i13 < ne13; ++i13) {
for (int i12 = 0; i12 < ne12; ++i12) {
for (int i11 = 0; i11 < ne11; ++i11) {
//for (int i10 = 0; i10 < ne10; ++i10) {
// wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
//}
quantize_row_q4_1((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
wdata += (ne10*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
quantize_row_q((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
wdata += row_size;
}
}
}
@@ -6419,7 +6264,7 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
return;
}
// parallelize by src0 rows using ggml_vec_dot_q4_1
// parallelize by src0 rows using ggml_vec_dot_q
// total rows in src0
const int nr = ne01*ne02*ne03;
@@ -6432,6 +6277,7 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
const int ir1 = MIN(ir0 + dr, nr);
void * wdata = params->wdata;
const size_t row_size = ne00*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type];
for (int ir = ir0; ir < ir1; ++ir) {
// src0 indices
@@ -6447,14 +6293,14 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
const int i3 = i03;
void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1]);
char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*row_size));
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
assert(ne00 % 32 == 0);
for (int ic = 0; ic < ne11; ++ic) {
ggml_vec_dot_q4_1(ne00, &dst_col[ic*ne0], src0_row, ((void *) (src1_col + (ic*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1])));
vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
}
}
@@ -6478,12 +6324,9 @@ static void ggml_compute_forward_mul_mat(
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_Q4_0:
{
ggml_compute_forward_mul_mat_q4_0_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_1:
{
ggml_compute_forward_mul_mat_q4_1_f32(params, src0, src1, dst);
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_F16:
{
@@ -6644,7 +6487,7 @@ static void ggml_compute_forward_transpose(
// ggml_compute_forward_get_rows
static void ggml_compute_forward_get_rows_q4_0(
static void ggml_compute_forward_get_rows_q(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
@@ -6657,42 +6500,17 @@ static void ggml_compute_forward_get_rows_q4_0(
const int nc = src0->ne[0];
const int nr = ggml_nelements(src1);
const enum ggml_type type = src0->type;
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
assert( dst->ne[0] == nc);
assert( dst->ne[1] == nr);
assert(src0->nb[0] == GGML_TYPE_SIZE[GGML_TYPE_Q4_0]);
assert(src0->nb[0] == GGML_TYPE_SIZE[type]);
for (int i = 0; i < nr; ++i) {
const int r = ((int32_t *) src1->data)[i];
dequantize_row_q4_0(
(const void *) ((char *) src0->data + r*src0->nb[1]),
(float *) ((char *) dst->data + i*dst->nb[1]), nc);
}
}
static void ggml_compute_forward_get_rows_q4_1(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(params->ith == 0);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int nc = src0->ne[0];
const int nr = ggml_nelements(src1);
assert( dst->ne[0] == nc);
assert( dst->ne[1] == nr);
assert(src0->nb[0] == GGML_TYPE_SIZE[GGML_TYPE_Q4_1]);
for (int i = 0; i < nr; ++i) {
const int r = ((int32_t *) src1->data)[i];
dequantize_row_q4_1(
dequantize_row_q(
(const void *) ((char *) src0->data + r*src0->nb[1]),
(float *) ((char *) dst->data + i*dst->nb[1]), nc);
}
@@ -6760,12 +6578,9 @@ static void ggml_compute_forward_get_rows(
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_Q4_0:
{
ggml_compute_forward_get_rows_q4_0(params, src0, src1, dst);
} break;
case GGML_TYPE_Q4_1:
{
ggml_compute_forward_get_rows_q4_1(params, src0, src1, dst);
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
} break;
case GGML_TYPE_F16:
{
@@ -9098,8 +8913,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
size_t cur = 0;
if (node->src0->type == GGML_TYPE_F16 &&
node->src1->type == GGML_TYPE_F32) {
if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1; // TODO: this actually is doing nothing
@@ -9114,33 +8928,18 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
#else
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
#endif
} else if (node->src0->type == GGML_TYPE_F32 &&
node->src1->type == GGML_TYPE_F32) {
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
cur = 0;
} else if (node->src0->type == GGML_TYPE_Q4_0 &&
node->src1->type == GGML_TYPE_F32) {
} else if (quantize_fns[node->src0->type].vec_dot_q && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
} else {
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_0]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
}
#else
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_0]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
} else
#endif
} else if (node->src0->type == GGML_TYPE_Q4_1 &&
node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
} else {
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_1]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
{
cur = GGML_TYPE_SIZE[node->src0->type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[node->src0->type];
}
#else
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_1]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
#endif
} else {
GGML_ASSERT(false);
}