const int ith = params->ith; // thread index
const int nth = params->nth; // number of threads
- // parallelize by elements
- const int ne = ggml_nelements(dst);
- const int dr = (ne + nth - 1) / nth;
- const int ie0 = dr * ith;
- const int ie1 = MIN(ie0 + dr, ne);
+ // parallelize by blocks
+ const int nk = ggml_nelements(src0)/ggml_blck_size(src0->type);
+ const int dr = (nk + nth - 1) / nth;
+ const int k0 = dr * ith;
+ const int k1 = MIN(k0 + dr, nk);
- if (ie0 < ie1) {
+ if (k0 < k1) {
memcpy(
- ((char *) dst->data + ie0*nb0),
- ((char *) src0->data + ie0*nb0),
- (ie1 - ie0) * nb0);
+ ((char *) dst->data + k0*nb0),
+ ((char *) src0->data + k0*nb0),
+ (k1 - k0) * nb0);
}
}
static void ggml_compute_forward_dup_bytes(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
-
const struct ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
}
const size_t type_size = ggml_type_size(src0->type);
+
const int ith = params->ith; // thread index
const int nth = params->nth; // number of threads
-
// parallelize by rows
const int nr = ne01;
// number of rows per thread
const int ir1 = MIN(ir0 + dr, nr);
if (src0->type == dst->type &&
- ne00 == ne0 &&
+ ggml_are_same_shape(src0, dst) &&
nb00 == type_size && nb0 == type_size) {
// copy by rows
- const size_t rs = ne00 * type_size;
+ const size_t rs = ggml_row_size(src0->type, ne00);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
for (int64_t i01 = ir0; i01 < ir1; i01++) {
}
// dst counters
-
- int64_t i10 = 0;
+ int64_t k10 = 0;
int64_t i11 = 0;
int64_t i12 = 0;
int64_t i13 = 0;
+ // number of blocks in a row
+ const int64_t nk00 = ne00 / ggml_blck_size(src0->type);
+ const int64_t nk0 = ne0 / ggml_blck_size(dst->type);
+
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
- i10 += ne00 * ir0;
- while (i10 >= ne0) {
- i10 -= ne0;
+ k10 += nk00 * ir0;
+ while (k10 >= nk0) {
+ k10 -= nk0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
}
}
for (int64_t i01 = ir0; i01 < ir1; i01++) {
- for (int64_t i00 = 0; i00 < ne00; i00++) {
- const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
- char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3);
+ for (int64_t k00 = 0; k00 < nk00; k00++) {
+ const char * src0_ptr = ((char *) src0->data + k00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+ char * dst_ptr = ((char *) dst->data + k10*nb0 + i11*nb1 + i12*nb2 + i13*nb3);
memcpy(dst_ptr, src0_ptr, type_size);
- if (++i10 == ne0) {
- i10 = 0;
+ if (++k10 == nk0) {
+ k10 = 0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
}
}
}
- i10 += ne00 * (ne01 - ir1);
- while (i10 >= ne0) {
- i10 -= ne0;
+ k10 += nk00 * (ne01 - ir1);
+ while (k10 >= nk0) {
+ k10 -= nk0;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
}
// extra_buffer op?
- if (ggml_cpu_extra_compute_forward(params, tensor)) return;
+ if (ggml_cpu_extra_compute_forward(params, tensor)) {
+ return;
+ }
switch (tensor->op) {
case GGML_OP_DUP: