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ggml : ggml_compute_forward_concat() for arbitrary tensor type (ggml/1118)

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* ggml_compute_forward_concat() for arbitrary tensor type

* Check that tensors' type match

* ggml-cpu.c: check type of source tensors

* ggml-cpu.c: move tensor type check to ggml_compute_forward_concat()

* ggml.c: check concatenated tensor type

* Remove tensor type check from ggml_compute_forward_concat() in ggml-cpu.c

..., as it was moved to ggml.c.
This commit is contained in:
vmobilis 2025-03-07 11:11:40 +03:00 committed by Georgi Gerganov
parent 54a54faee4
commit cc03608e78
2 changed files with 142 additions and 2 deletions
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143
ggml/src/ggml-cpu/ggml-cpu.c
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@ -6648,6 +6648,135 @@ static void ggml_compute_forward_repeat_back(
// ggml_compute_forward_concat
static void ggml_compute_forward_concat_any(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const size_t len = ggml_type_size(src0->type);
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_BINARY_OP_LOCALS
const int32_t dim = ggml_get_op_params_i32(dst, 0);
GGML_ASSERT(dim >= 0 && dim < 4);
int64_t o[4] = {0, 0, 0, 0};
o[dim] = src0->ne[dim];
const char * x;
// TODO: smarter multi-theading
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2 += nth) {
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03;
} else {
x = (const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13;
}
char * y = (char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3;
memcpy(y, x, len);
}
}
}
}
}
static void ggml_compute_forward_concat_i8(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(ggml_type_size(src0->type) == sizeof(int8_t));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_BINARY_OP_LOCALS
const int32_t dim = ggml_get_op_params_i32(dst, 0);
GGML_ASSERT(dim >= 0 && dim < 4);
int64_t o[4] = {0, 0, 0, 0};
o[dim] = src0->ne[dim];
const int8_t * x;
// TODO: smarter multi-theading
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2 += nth) {
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const int8_t *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03);
} else {
x = (const int8_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13);
}
int8_t * y = (int8_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
*y = *x;
}
}
}
}
}
static void ggml_compute_forward_concat_f16(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(ggml_type_size(src0->type) == sizeof(ggml_fp16_t));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_BINARY_OP_LOCALS
const int32_t dim = ggml_get_op_params_i32(dst, 0);
GGML_ASSERT(dim >= 0 && dim < 4);
int64_t o[4] = {0, 0, 0, 0};
o[dim] = src0->ne[dim];
const ggml_fp16_t * x;
// TODO: smarter multi-theading
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2 += nth) {
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const ggml_fp16_t *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03);
} else {
x = (const ggml_fp16_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13);
}
ggml_fp16_t * y = (ggml_fp16_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
*y = *x;
}
}
}
}
}
static void ggml_compute_forward_concat_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
@ -6655,7 +6784,7 @@ static void ggml_compute_forward_concat_f32(
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->nb[0] == sizeof(float));
GGML_ASSERT(ggml_type_size(src0->type) == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
@ -6698,6 +6827,16 @@ static void ggml_compute_forward_concat(
const struct ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F16:
case GGML_TYPE_BF16:
case GGML_TYPE_I16:
{
ggml_compute_forward_concat_f16(params, dst);
} break;
case GGML_TYPE_I8:
{
ggml_compute_forward_concat_i8(params, dst);
} break;
case GGML_TYPE_F32:
case GGML_TYPE_I32:
{
@ -6705,7 +6844,7 @@ static void ggml_compute_forward_concat(
} break;
default:
{
GGML_ABORT("fatal error");
ggml_compute_forward_concat_any(params, dst);
}
}
}

1
ggml/src/ggml.c
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@ -2332,6 +2332,7 @@ struct ggml_tensor * ggml_concat(
struct ggml_tensor * b,
int dim) {
GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS);
GGML_ASSERT(a->type == b->type);
int64_t ne[GGML_MAX_DIMS];
for (int d = 0; d < GGML_MAX_DIMS; ++d) {