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ggml : refactor online repacking (#10446)

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* rename ggml-cpu-aarch64.c to .cpp

* reformat extra cpu backend.

- clean Q4_0_N_M and IQ4_0_N_M
  - remove from "file" tensor type
  - allow only with dynamic repack

- extract cpu extra bufts and convert to C++
  - hbm
  - "aarch64"

- more generic use of extra buffer
  - generalise extra_supports_op
  - new API for "cpu-accel":
     - amx
     - aarch64

* clang-format

* Clean Q4_0_N_M ref

Enable restrict on C++

* add op GGML_OP_MUL_MAT_ID for Q4_0_N_M with runtime repack

* added/corrected control on tensor size for Q4 repacking.

* Update ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* add debug logs on repacks.

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Djip007 2024-12-07 13:37:50 +01:00 • committed by GitHub
parent c2a16c0bdb
commit 19d8762ab6
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33 changed files with 1136 additions and 1049 deletions
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436
ggml/src/ggml-cpu/ggml-cpu.c
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@ -3,7 +3,7 @@
#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-cpu-aarch64.h"
#include "ggml-cpu-traits.h"
#include "ggml-cpu-impl.h"
#include "ggml-cpu.h"
#include "ggml-impl.h"
@ -224,10 +224,6 @@ typedef void * thread_ret_t;
typedef pthread_t ggml_thread_t;
#ifdef GGML_USE_CPU_HBM
#include <hbwmalloc.h>
#endif
#if defined(__APPLE__)
#include <unistd.h>
#include <mach/mach.h>
@ -301,7 +297,6 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
},
[GGML_TYPE_Q8_0] = {
.from_float = quantize_row_q8_0,
.from_float_to_mat = quantize_mat_q8_0,
.vec_dot = ggml_vec_dot_q8_0_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
#if defined (__ARM_FEATURE_MATMUL_INT8)
@ -409,33 +404,6 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_BF16,
.nrows = 1,
},
[GGML_TYPE_Q4_0_4_4] = {
.from_float = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 4,
.gemv = ggml_gemv_q4_0_4x4_q8_0,
.gemm = ggml_gemm_q4_0_4x4_q8_0,
},
[GGML_TYPE_Q4_0_4_8] = {
.from_float = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 4,
.gemv = ggml_gemv_q4_0_4x8_q8_0,
.gemm = ggml_gemm_q4_0_4x8_q8_0,
},
[GGML_TYPE_Q4_0_8_8] = {
.from_float = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 8,
.gemv = ggml_gemv_q4_0_8x8_q8_0,
.gemm = ggml_gemm_q4_0_8x8_q8_0,
},
[GGML_TYPE_TQ1_0] = {
.from_float = quantize_row_tq1_0,
.vec_dot = ggml_vec_dot_tq1_0_q8_K,
@ -448,15 +416,6 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ4_NL_4_4] = {
.from_float = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 4,
.gemv = ggml_gemv_iq4_nl_4x4_q8_0,
.gemm = ggml_gemm_iq4_nl_4x4_q8_0,
},
};
const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) {
@ -4509,9 +4468,6 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
{
ggml_compute_forward_add_q_f32(params, dst);
} break;
@ -4889,9 +4845,6 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
{
ggml_compute_forward_add1_q_f32(params, dst);
} break;
@ -5019,9 +4972,6 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
default:
{
GGML_ABORT("fatal error");
@ -7437,27 +7387,9 @@ static void ggml_compute_forward_mul_mat(
const int ith = params->ith;
const int nth = params->nth;
enum ggml_type type = src0->type;
if (src0->buffer && ggml_backend_cpu_buft_is_aarch64(src0->buffer->buft)) {
type = (enum ggml_type)(intptr_t)src0->extra;
}
#if defined(__AMX_INT8__) && defined(__AVX512VNNI__)
if (src0->buffer && ggml_backend_amx_buft_is_amx(src0->buffer->buft)) {
ggml_backend_amx_mul_mat(params, dst);
return;
}
#endif
enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
enum ggml_type const vec_dot_type = type_traits_cpu[src0->type].vec_dot_type;
ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float;
ggml_from_float_to_mat_t const from_float_to_mat = type_traits_cpu[vec_dot_type].from_float_to_mat;
int64_t const vec_dot_num_rows = type_traits_cpu[type].nrows;
int64_t const matmul_num_cols = type_traits_cpu[type].ncols;
int64_t const blck_size_interleave = ggml_get_type_traits(type)->blck_size_interleave;
ggml_gemv_t const gemv = type_traits_cpu[type].gemv;
ggml_gemm_t const gemm = type_traits_cpu[type].gemm;
int64_t const vec_dot_num_rows = type_traits_cpu[src0->type].nrows;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
@ -7465,7 +7397,7 @@ static void ggml_compute_forward_mul_mat(
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb00 == ggml_type_size(src0->type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
@ -7477,6 +7409,7 @@ static void ggml_compute_forward_mul_mat(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
// TODO: extract to "extra_op"
#if GGML_USE_LLAMAFILE
// broadcast factors
const int64_t r2 = ne12 / ne02;
@ -7487,15 +7420,15 @@ static void ggml_compute_forward_mul_mat(
if (src1_cont) {
for (int64_t i13 = 0; i13 < ne13; i13++)
for (int64_t i12 = 0; i12 < ne12; i12++)
if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(type),
if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
(const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
nb01/ggml_type_size(type),
nb01/ggml_type_size(src0->type),
(const char *)src1->data + i12*nb12 + i13*nb13,
nb11/ggml_type_size(src1->type),
(char *)dst->data + i12*nb2 + i13*nb3,
nb1/ggml_type_size(dst->type),
ith, nth,
type,
src0->type,
src1->type,
dst->type))
goto UseGgmlGemm1;
@ -7516,19 +7449,10 @@ UseGgmlGemm1:;
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
int64_t i11_processed = 0;
if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) {
for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
4, ne10, blck_size_interleave);
}
i11_processed = ne11 - ne11 % 4;
}
for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
}
}
}
@ -7548,15 +7472,15 @@ UseGgmlGemm1:;
for (int64_t i13 = 0; i13 < ne13; i13++)
for (int64_t i12 = 0; i12 < ne12; i12++)
if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(type),
if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
(const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
nb01/ggml_type_size(type),
nb01/ggml_type_size(src0->type),
(const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size,
row_size/ggml_type_size(vec_dot_type),
(char *)dst->data + i12*nb2 + i13*nb3,
nb1/ggml_type_size(dst->type),
ith, nth,
type,
src0->type,
vec_dot_type,
dst->type))
goto UseGgmlGemm2;
@ -7598,28 +7522,6 @@ UseGgmlGemm2:;
const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;
if ((ggml_n_dims(src0) == 2) && gemv) {
const void * src1_wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11;
int64_t src0_start = (ith * ne01) / nth;
int64_t src0_end = ((ith + 1) * ne01) / nth;
src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start;
src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end;
if (src0_start >= src0_end) return;
// If there are more than three rows in src1, use gemm; otherwise, use gemv.
if (gemm && (ne11 > 3)) {
gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01,
(const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
}
for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) {
gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01,
(const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1,
src0_end - src0_start);
}
return;
}
// The first chunk comes from our thread_id, the rest will get auto-assigned.
int current_chunk = ith;
@ -7642,7 +7544,7 @@ UseGgmlGemm2:;
num_rows_per_vec_dot = 1;
}
ggml_compute_forward_mul_mat_one_chunk(params, dst, type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end);
ggml_compute_forward_mul_mat_one_chunk(params, dst, src0->type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end);
if (nth >= nchunk0 * nchunk1) {
break;
@ -7674,8 +7576,6 @@ static void ggml_compute_forward_mul_mat_id(
ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float;
int64_t const matmul_num_cols = type_traits_cpu[type].ncols;
ggml_gemv_t const gemv = type_traits_cpu[type].gemv;
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
@ -7761,34 +7661,6 @@ static void ggml_compute_forward_mul_mat_id(
const int64_t nr0 = ne01; // src0 rows
const int64_t nr1 = cne1; // src1 rows
if (((ggml_n_dims(src0) - 1) == 2) && gemv) {
int64_t src0_cur_start = (ith * ne01) / nth;
int64_t src0_cur_end = ((ith + 1) * ne01) / nth;
src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start;
src0_cur_end = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end;
if (src0_cur_start >= src0_cur_end) return;
for (int ir1 = 0; ir1 < nr1; ir1++) {
struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1);
const int id = row_mapping.i1; // selected expert index
const int64_t i11 = id % ne11;
const int64_t i12 = row_mapping.i2; // row index in src1
const int64_t i1 = id; // selected expert index
const int64_t i2 = i12; // row
const char * src1_col = (const char *) wdata +
(src1_cont || src1->type != vec_dot_type
? (i11 + i12 * ne11) * row_size
: (i11 * nb11 + i12 * nb12));
gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01,
(const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start);
}
continue;
}
// distribute the thread work across the inner or outer loop based on which one is larger
const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
@ -8096,9 +7968,6 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
{
ggml_compute_forward_out_prod_q_f32(params, dst);
} break;
@ -8361,9 +8230,6 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
default:
{
GGML_ABORT("fatal error");
@ -8625,9 +8491,6 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
{
ggml_compute_forward_get_rows_q(params, dst);
} break;
@ -9217,10 +9080,6 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q8_K:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
case GGML_TYPE_IQ4_NL_4_4:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
@ -12426,6 +12285,9 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
return;
}
// extra_buffer op?
if (ggml_cpu_extra_compute_forward(params, tensor)) return;
switch (tensor->op) {
case GGML_OP_DUP:
{
@ -13373,146 +13235,142 @@ struct ggml_cplan ggml_graph_plan(
size_t cur = 0;
switch (node->op) {
case GGML_OP_CPY:
case GGML_OP_DUP:
{
if (ggml_is_quantized(node->type) ||
// F16 -> BF16 and BF16 -> F16 copies go through intermediate F32
(node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) ||
(node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) {
if (!ggml_cpu_extra_work_size(n_threads, node, &cur)) {
switch (node->op) {
case GGML_OP_CPY:
case GGML_OP_DUP:
{
if (ggml_is_quantized(node->type) ||
// F16 -> BF16 and BF16 -> F16 copies go through intermediate F32
(node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) ||
(node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
}
} break;
case GGML_OP_ADD:
case GGML_OP_ADD1:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
case GGML_OP_ACC:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
}
} break;
case GGML_OP_COUNT_EQUAL:
{
cur = ggml_type_size(node->type)*n_tasks;
} break;
case GGML_OP_MUL_MAT:
{
const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type;
if (node->src[1]->type != vec_dot_type) {
cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1]));
}
} break;
case GGML_OP_MUL_MAT_ID:
{
cur = 0;
const struct ggml_tensor * src0 = node->src[0];
const struct ggml_tensor * src1 = node->src[1];
const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type;
if (src1->type != vec_dot_type) {
cur += ggml_row_size(vec_dot_type, ggml_nelements(src1));
}
const int n_as = src0->ne[2];
cur += GGML_PAD(cur, sizeof(int64_t)); // align
cur += n_as * sizeof(int64_t); // matrix_row_counts
cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows
} break;
case GGML_OP_OUT_PROD:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
case GGML_OP_SOFT_MAX:
case GGML_OP_ROPE:
{
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
}
} break;
case GGML_OP_ADD:
case GGML_OP_ADD1:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
case GGML_OP_ACC:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
}
} break;
case GGML_OP_COUNT_EQUAL:
{
cur = ggml_type_size(node->type)*n_tasks;
} break;
case GGML_OP_MUL_MAT:
{
#if defined(__AMX_INT8__) && defined(__AVX512VNNI__)
if (node->src[0]->buffer && ggml_backend_amx_buft_is_amx(node->src[0]->buffer->buft)) {
cur = ggml_backend_amx_desired_wsize(node);
}
#endif
const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type;
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
{
GGML_ASSERT(node->src[0]->ne[3] == 1);
GGML_ASSERT(node->src[1]->ne[2] == 1);
GGML_ASSERT(node->src[1]->ne[3] == 1);
if (node->src[1]->type != vec_dot_type) {
size_t cur2 = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1]));
cur = MAX(cur, cur2);
}
} break;
case GGML_OP_MUL_MAT_ID:
{
cur = 0;
const struct ggml_tensor * src0 = node->src[0];
const struct ggml_tensor * src1 = node->src[1];
const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type;
if (src1->type != vec_dot_type) {
cur += ggml_row_size(vec_dot_type, ggml_nelements(src1));
}
const int n_as = src0->ne[2];
cur += GGML_PAD(cur, sizeof(int64_t)); // align
cur += n_as * sizeof(int64_t); // matrix_row_counts
cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows
} break;
case GGML_OP_OUT_PROD:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
case GGML_OP_SOFT_MAX:
case GGML_OP_ROPE:
{
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
{
GGML_ASSERT(node->src[0]->ne[3] == 1);
GGML_ASSERT(node->src[1]->ne[2] == 1);
GGML_ASSERT(node->src[1]->ne[3] == 1);
const int64_t ne00 = node->src[0]->ne[0]; // K
const int64_t ne01 = node->src[0]->ne[1]; // Cout
const int64_t ne02 = node->src[0]->ne[2]; // Cin
const int64_t ne10 = node->src[1]->ne[0]; // L
const int64_t ne11 = node->src[1]->ne[1]; // Cin
const int64_t ne00 = node->src[0]->ne[0]; // K
const int64_t ne01 = node->src[0]->ne[1]; // Cout
const int64_t ne02 = node->src[0]->ne[2]; // Cin
if ((node->src[0]->type == GGML_TYPE_F16 ||
node->src[0]->type == GGML_TYPE_BF16) &&
node->src[1]->type == GGML_TYPE_F32) {
cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02;
cur += sizeof(ggml_fp16_t)*ne10*ne11;
} else if (node->src[0]->type == GGML_TYPE_F32 &&
node->src[1]->type == GGML_TYPE_F32) {
cur += sizeof(float)*ne00*ne01*ne02;
cur += sizeof(float)*ne10*ne11;
} else {
GGML_ABORT("fatal error");
}
} break;
case GGML_OP_CONV_TRANSPOSE_2D:
{
const int64_t ne00 = node->src[0]->ne[0]; // W
const int64_t ne01 = node->src[0]->ne[1]; // H
const int64_t ne02 = node->src[0]->ne[2]; // Channels Out
const int64_t ne03 = node->src[0]->ne[3]; // Channels In
const int64_t ne10 = node->src[1]->ne[0]; // L
const int64_t ne11 = node->src[1]->ne[1]; // Cin
const int64_t ne10 = node->src[1]->ne[0]; // W
const int64_t ne11 = node->src[1]->ne[1]; // H
const int64_t ne12 = node->src[1]->ne[2]; // Channels In
if ((node->src[0]->type == GGML_TYPE_F16 ||
node->src[0]->type == GGML_TYPE_BF16) &&
node->src[1]->type == GGML_TYPE_F32) {
cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02;
cur += sizeof(ggml_fp16_t)*ne10*ne11;
} else if (node->src[0]->type == GGML_TYPE_F32 &&
node->src[1]->type == GGML_TYPE_F32) {
cur += sizeof(float)*ne00*ne01*ne02;
cur += sizeof(float)*ne10*ne11;
} else {
cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
} break;
case GGML_OP_FLASH_ATTN_EXT:
{
const int64_t ne00 = node->src[0]->ne[0]; // D
cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread
} break;
case GGML_OP_FLASH_ATTN_BACK:
{
const int64_t D = node->src[0]->ne[0];
const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back
if (node->src[1]->type == GGML_TYPE_F32) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
} else if (node->src[1]->type == GGML_TYPE_F16) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
} else if (node->src[1]->type == GGML_TYPE_BF16) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
}
} break;
case GGML_OP_CROSS_ENTROPY_LOSS:
{
cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks);
} break;
case GGML_OP_COUNT:
{
GGML_ABORT("fatal error");
}
} break;
case GGML_OP_CONV_TRANSPOSE_2D:
{
const int64_t ne00 = node->src[0]->ne[0]; // W
const int64_t ne01 = node->src[0]->ne[1]; // H
const int64_t ne02 = node->src[0]->ne[2]; // Channels Out
const int64_t ne03 = node->src[0]->ne[3]; // Channels In
const int64_t ne10 = node->src[1]->ne[0]; // W
const int64_t ne11 = node->src[1]->ne[1]; // H
const int64_t ne12 = node->src[1]->ne[2]; // Channels In
cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
} break;
case GGML_OP_FLASH_ATTN_EXT:
{
const int64_t ne00 = node->src[0]->ne[0]; // D
cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread
} break;
case GGML_OP_FLASH_ATTN_BACK:
{
const int64_t D = node->src[0]->ne[0];
const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back
if (node->src[1]->type == GGML_TYPE_F32) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
} else if (node->src[1]->type == GGML_TYPE_F16) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
} else if (node->src[1]->type == GGML_TYPE_BF16) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
}
} break;
case GGML_OP_CROSS_ENTROPY_LOSS:
{
cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks);
} break;
case GGML_OP_COUNT:
{
GGML_ABORT("fatal error");
}
default:
break;
default:
break;
}
}
work_size = MAX(work_size, cur);