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ggml : add more generic custom op, remove deprecated custom ops (ggml/1183)

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* ggml : add more generic ggml_custom op

* ggml : remove deprecated custom ops
This commit is contained in:
Diego Devesa 2025-04-09 12:31:34 +02:00 committed by Georgi Gerganov
parent e4bf72d631
commit 459895c326
6 changed files with 132 additions and 485 deletions
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110
ggml/include/ggml.h
View File

@ -507,17 +507,12 @@ extern "C" {
GGML_OP_UNARY,
GGML_OP_MAP_UNARY,
GGML_OP_MAP_BINARY,
GGML_OP_MAP_CUSTOM1_F32,
GGML_OP_MAP_CUSTOM2_F32,
GGML_OP_MAP_CUSTOM3_F32,
GGML_OP_MAP_CUSTOM1,
GGML_OP_MAP_CUSTOM2,
GGML_OP_MAP_CUSTOM3,
GGML_OP_CUSTOM,
GGML_OP_CROSS_ENTROPY_LOSS,
GGML_OP_CROSS_ENTROPY_LOSS_BACK,
GGML_OP_OPT_STEP_ADAMW,
@ -1916,83 +1911,6 @@ extern "C" {
// custom operators
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun),
"use ggml_map_custom1 instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun),
"use ggml_map_custom1_inplace instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun),
"use ggml_map_custom2 instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun),
"use ggml_map_custom2_inplace instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun),
"use ggml_map_custom1 instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun),
"use ggml_map_custom1_inplace instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun),
"use ggml_map_custom2 instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun),
"use ggml_map_custom2_inplace instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun),
"use ggml_map_custom3 instead");
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun),
"use ggml_map_custom3_inplace instead");
// custom operators v2
typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
@ -2048,6 +1966,30 @@ extern "C" {
int n_tasks,
void * userdata);
typedef void (*ggml_custom_op_t)(struct ggml_tensor * dst , int ith, int nth, void * userdata);
GGML_API struct ggml_tensor * ggml_custom_4d(
struct ggml_context * ctx,
enum ggml_type type,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3,
struct ggml_tensor ** args,
int n_args,
ggml_custom_op_t fun,
int n_tasks,
void * userdata);
GGML_API struct ggml_tensor * ggml_custom_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor ** args,
int n_args,
ggml_custom_op_t fun,
int n_tasks,
void * userdata);
// loss function
GGML_API struct ggml_tensor * ggml_cross_entropy_loss(

55
ggml/src/ggml-cpu/ggml-cpu.c
View File

@ -2027,41 +2027,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_rwkv_wkv7(params, tensor);
} break;
case GGML_OP_MAP_UNARY:
{
ggml_unary_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_unary(params, tensor, fun);
}
break;
case GGML_OP_MAP_BINARY:
{
ggml_binary_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_binary(params, tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM1_F32:
{
ggml_custom1_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_custom1_f32(params, tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM2_F32:
{
ggml_custom2_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_custom2_f32(params, tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM3_F32:
{
ggml_custom3_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_custom3_f32(params, tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM1:
{
ggml_compute_forward_map_custom1(params, tensor);
@ -2077,6 +2042,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
ggml_compute_forward_map_custom3(params, tensor);
}
break;
case GGML_OP_CUSTOM:
{
ggml_compute_forward_custom(params, tensor);
}
break;
case GGML_OP_CROSS_ENTROPY_LOSS:
{
ggml_compute_forward_cross_entropy_loss(params, tensor);
@ -2328,11 +2298,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
case GGML_OP_WIN_PART:
case GGML_OP_WIN_UNPART:
case GGML_OP_GET_REL_POS:
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
case GGML_OP_MAP_CUSTOM1_F32:
case GGML_OP_MAP_CUSTOM2_F32:
case GGML_OP_MAP_CUSTOM3_F32:
{
n_tasks = 1;
} break;
@ -2366,6 +2331,16 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_CUSTOM:
{
struct ggml_custom_op_params p;
memcpy(&p, node->op_params, sizeof(p));
if (p.n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_CROSS_ENTROPY_LOSS:
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
case GGML_OP_OPT_STEP_ADAMW:

158
ggml/src/ggml-cpu/ops.cpp
View File

@ -8268,152 +8268,6 @@ void ggml_compute_forward_rwkv_wkv7(
}
}
// ggml_compute_forward_map_unary
static void ggml_compute_forward_map_unary_f32(
const ggml_compute_params * params,
ggml_tensor * dst,
const ggml_unary_op_f32_t fun) {
const ggml_tensor * src0 = dst->src[0];
if (params->ith != 0) {
return;
}
assert(ggml_is_contiguous_1(src0));
assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
for (int i = 0; i < n; i++) {
fun(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
(float *) ((char *) src0->data + i*(src0->nb[1])));
}
}
void ggml_compute_forward_map_unary(
const ggml_compute_params * params,
ggml_tensor * dst,
const ggml_unary_op_f32_t fun) {
const ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_unary_f32(params, dst, fun);
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
// ggml_compute_forward_map_binary
static void ggml_compute_forward_map_binary_f32(
const ggml_compute_params * params,
ggml_tensor * dst,
const ggml_binary_op_f32_t fun) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
if (params->ith != 0) {
return;
}
assert(ggml_is_contiguous_1(src0));
assert(ggml_is_contiguous_1(src1));
assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
for (int i = 0; i < n; i++) {
fun(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
(float *) ((char *) src0->data + i*(src0->nb[1])),
(float *) ((char *) src1->data + i*(src1->nb[1])));
}
}
void ggml_compute_forward_map_binary(
const ggml_compute_params * params,
ggml_tensor * dst,
const ggml_binary_op_f32_t fun) {
const ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_binary_f32(params, dst, fun);
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
// ggml_compute_forward_map_custom1
void ggml_compute_forward_map_custom1_f32(
const ggml_compute_params * params,
ggml_tensor * dst,
const ggml_custom1_op_f32_t fun) {
const ggml_tensor * a = dst->src[0];
if (params->ith != 0) {
return;
}
fun(dst, a);
}
// ggml_compute_forward_map_custom2
void ggml_compute_forward_map_custom2_f32(
const ggml_compute_params * params,
ggml_tensor * dst,
const ggml_custom2_op_f32_t fun) {
const ggml_tensor * a = dst->src[0];
const ggml_tensor * b = dst->src[1];
if (params->ith != 0) {
return;
}
fun(dst, a, b);
}
// ggml_compute_forward_map_custom3
void ggml_compute_forward_map_custom3_f32(
const ggml_compute_params * params,
ggml_tensor * dst,
const ggml_custom3_op_f32_t fun) {
const ggml_tensor * a = dst->src[0];
const ggml_tensor * b = dst->src[1];
const ggml_tensor * c = dst->src[1];
if (params->ith != 0) {
return;
}
fun(dst, a, b, c);
}
// ggml_compute_forward_map_custom1
void ggml_compute_forward_map_custom1(
@ -8459,6 +8313,18 @@ void ggml_compute_forward_map_custom3(
p.fun(dst, a, b, c, params->ith, params->nth, p.userdata);
}
// ggml_compute_forward_custom
void ggml_compute_forward_custom(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
struct ggml_custom_op_params p;
memcpy(&p, dst->op_params, sizeof(p));
p.fun(dst, params->ith, params->nth, p.userdata);
}
// ggml_compute_forward_cross_entropy_loss
static void ggml_compute_forward_cross_entropy_loss_f32(

21
ggml/src/ggml-cpu/ops.h
View File

@ -96,29 +96,10 @@ void ggml_compute_forward_add_rel_pos(const struct ggml_compute_params * params,
void ggml_compute_forward_rwkv_wkv6(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_rwkv_wkv7(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_gla(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_map_unary(
const struct ggml_compute_params * params,
struct ggml_tensor * dst,
const ggml_unary_op_f32_t fun);
void ggml_compute_forward_map_binary(
const struct ggml_compute_params * params,
struct ggml_tensor * dst,
const ggml_binary_op_f32_t fun);
void ggml_compute_forward_map_custom1_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst,
const ggml_custom1_op_f32_t fun);
void ggml_compute_forward_map_custom2_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst,
const ggml_custom2_op_f32_t fun);
void ggml_compute_forward_map_custom3_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst,
const ggml_custom3_op_f32_t fun);
void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_custom(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_cross_entropy_loss(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_cross_entropy_loss_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_opt_step_adamw(const struct ggml_compute_params * params, struct ggml_tensor * dst);

10
ggml/src/ggml-impl.h
View File

@ -140,8 +140,14 @@ struct ggml_map_custom2_op_params {
struct ggml_map_custom3_op_params {
ggml_custom3_op_t fun;
int n_tasks;
void * userdata;
int n_tasks;
void * userdata;
};
struct ggml_custom_op_params {
ggml_custom_op_t fun;
int n_tasks;
void * userdata;
};
// bitset

263
ggml/src/ggml.c
View File

@ -982,23 +982,18 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"UNARY",
"MAP_UNARY",
"MAP_BINARY",
"MAP_CUSTOM1_F32",
"MAP_CUSTOM2_F32",
"MAP_CUSTOM3_F32",
"MAP_CUSTOM1",
"MAP_CUSTOM2",
"MAP_CUSTOM3",
"CUSTOM",
"CROSS_ENTROPY_LOSS",
"CROSS_ENTROPY_LOSS_BACK",
"OPT_STEP_ADAMW",
};
static_assert(GGML_OP_COUNT == 85, "GGML_OP_COUNT != 85");
static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -1081,23 +1076,18 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"unary(x)",
"f(x)",
"f(x,y)",
"custom_f32(x)",
"custom_f32(x,y)",
"custom_f32(x,y,z)",
"map_custom(x)",
"map_custom(x,y)",
"map_custom(x,y,z)",
"custom(x)",
"custom(x,y)",
"custom(x,y,z)",
"cross_entropy_loss(x,y)",
"cross_entropy_loss_back(x,y)",
"adamw(x)",
};
static_assert(GGML_OP_COUNT == 85, "GGML_OP_COUNT != 85");
static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -4842,179 +4832,6 @@ struct ggml_tensor * ggml_unary_inplace(
return ggml_unary_impl(ctx, a, op, true);
}
// ggml_map_unary
static struct ggml_tensor * ggml_map_unary_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_unary_op_f32_t fun,
bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
result->op = GGML_OP_MAP_UNARY;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_unary_op_f32_t fun) {
return ggml_map_unary_impl_f32(ctx, a, fun, false);
}
struct ggml_tensor * ggml_map_unary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_unary_op_f32_t fun) {
return ggml_map_unary_impl_f32(ctx, a, fun, true);
}
// ggml_map_binary
static struct ggml_tensor * ggml_map_binary_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_binary_op_f32_t fun,
bool inplace) {
GGML_ASSERT(ggml_are_same_shape(a, b));
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
result->op = GGML_OP_MAP_BINARY;
result->src[0] = a;
result->src[1] = b;
return result;
}
struct ggml_tensor * ggml_map_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_binary_op_f32_t fun) {
return ggml_map_binary_impl_f32(ctx, a, b, fun, false);
}
struct ggml_tensor * ggml_map_binary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_binary_op_f32_t fun) {
return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
}
// ggml_map_custom1_f32
static struct ggml_tensor * ggml_map_custom1_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_f32_t fun,
bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
result->op = GGML_OP_MAP_CUSTOM1_F32;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_map_custom1_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_f32_t fun) {
return ggml_map_custom1_impl_f32(ctx, a, fun, false);
}
struct ggml_tensor * ggml_map_custom1_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_f32_t fun) {
return ggml_map_custom1_impl_f32(ctx, a, fun, true);
}
// ggml_map_custom2_f32
static struct ggml_tensor * ggml_map_custom2_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_f32_t fun,
bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
result->op = GGML_OP_MAP_CUSTOM2_F32;
result->src[0] = a;
result->src[1] = b;
return result;
}
struct ggml_tensor * ggml_map_custom2_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_f32_t fun) {
return ggml_map_custom2_impl_f32(ctx, a, b, fun, false);
}
struct ggml_tensor * ggml_map_custom2_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_f32_t fun) {
return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
}
// ggml_map_custom3_f32
static struct ggml_tensor * ggml_map_custom3_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_f32_t fun,
bool inplace) {
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
result->op = GGML_OP_MAP_CUSTOM3_F32;
result->src[0] = a;
result->src[1] = b;
result->src[2] = c;
return result;
}
struct ggml_tensor * ggml_map_custom3_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_f32_t fun) {
return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false);
}
struct ggml_tensor * ggml_map_custom3_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_f32_t fun) {
return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
}
// ggml_map_custom1
static struct ggml_tensor * ggml_map_custom1_impl(
@ -5033,7 +4850,7 @@ static struct ggml_tensor * ggml_map_custom1_impl(
/*.n_tasks =*/ n_tasks,
/*.userdata =*/ userdata
};
ggml_set_op_params(result, (const void *) &params, sizeof(params));
ggml_set_op_params(result, &params, sizeof(params));
result->op = GGML_OP_MAP_CUSTOM1;
result->src[0] = a;
@ -5078,7 +4895,7 @@ static struct ggml_tensor * ggml_map_custom2_impl(
/*.n_tasks =*/ n_tasks,
/*.userdata =*/ userdata
};
ggml_set_op_params(result, (const void *) &params, sizeof(params));
ggml_set_op_params(result, &params, sizeof(params));
result->op = GGML_OP_MAP_CUSTOM2;
result->src[0] = a;
@ -5127,7 +4944,7 @@ static struct ggml_tensor * ggml_map_custom3_impl(
/*.n_tasks =*/ n_tasks,
/*.userdata =*/ userdata
};
ggml_set_op_params(result, (const void *) &params, sizeof(params));
ggml_set_op_params(result, &params, sizeof(params));
result->op = GGML_OP_MAP_CUSTOM3;
result->src[0] = a;
@ -5159,6 +4976,66 @@ struct ggml_tensor * ggml_map_custom3_inplace(
return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
}
struct ggml_tensor * ggml_custom_4d(
struct ggml_context * ctx,
enum ggml_type type,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3,
struct ggml_tensor ** args,
int n_args,
ggml_custom_op_t fun,
int n_tasks,
void * userdata) {
GGML_ASSERT(n_args < GGML_MAX_SRC);
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, ne0, ne1, ne2, ne3);
struct ggml_custom_op_params params = {
/*.fun =*/ fun,
/*.n_tasks =*/ n_tasks,
/*.userdata =*/ userdata
};
ggml_set_op_params(result, &params, sizeof(params));
result->op = GGML_OP_CUSTOM;
for (int i = 0; i < n_args; i++) {
result->src[i] = args[i];
}
return result;
}
struct ggml_tensor * ggml_custom_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor ** args,
int n_args,
ggml_custom_op_t fun,
int n_tasks,
void * userdata) {
GGML_ASSERT(n_args < GGML_MAX_SRC - 1);
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
struct ggml_custom_op_params params = {
/*.fun =*/ fun,
/*.n_tasks =*/ n_tasks,
/*.userdata =*/ userdata
};
ggml_set_op_params(result, &params, sizeof(params));
result->op = GGML_OP_CUSTOM;
result->src[0] = a;
for (int i = 0; i < n_args; i++) {
result->src[i + 1] = args[i];
}
return result;
}
// ggml_cross_entropy_loss
struct ggml_tensor * ggml_cross_entropy_loss(