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// https://github.com/cmp-nct/ggllm.cpp/blob/master/ggml.c
// ggml_repeat2
struct ggml_tensor *ggml_repeat2(struct ggml_context *ctx,
struct ggml_tensor *a, struct ggml_tensor *b) {
GGML_ASSERT(ggml_can_repeat(a, b));
bool is_node = false;
if (a->grad) {
is_node = true;
}
if (ggml_are_same_shape(a, b) && !is_node) {
return a;
}
struct ggml_tensor *result = ggml_new_tensor(ctx, a->type, b->n_dims, b->ne);
result->op = GGML_OP_REPEAT2;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// ggml_compute_forward_repeat2
static void ggml_compute_forward_repeat2_f32(
const struct ggml_compute_params *params, const struct ggml_tensor *src0,
struct ggml_tensor *dst) {
GGML_ASSERT(params->ith == 0);
GGML_ASSERT(ggml_can_repeat(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
const int64_t ne3 = dst->ne[3];
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const size_t nb0 = dst->nb[0];
const size_t nb1 = dst->nb[1];
const size_t nb2 = dst->nb[2];
const size_t nb3 = dst->nb[3];
const size_t nb00 = src0->nb[0];
const size_t nb01 = src0->nb[1];
const size_t nb02 = src0->nb[2];
const size_t nb03 = src0->nb[3];
// guaranteed to be an integer due to the check in ggml_can_repeat
const int nr0 = (int)(ne0 / ne00);
const int nr1 = (int)(ne1 / ne01);
const int nr2 = (int)(ne2 / ne02);
const int nr3 = (int)(ne3 / ne03);
// TODO: support for transposed / permuted tensors
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
int i2k2 = 0;
// TODO: maybe this is not optimal?
for (int i3 = 0; i3 < nr3; i3++) {
for (int k3 = 0; k3 < ne03; k3++, i2k2 = 0) {
for (int i2 = 0; i2 < nr2; i2++) {
for (int k2 = 0; k2 < ne02; k2++, i2k2++) {
for (int i1 = 0; i1 < nr1; i1++) {
for (int k1 = 0; k1 < ne01; k1++) {
for (int i0 = 0; i0 < nr0; i0++) {
ggml_vec_cpy_f32(
ne00,
(float *)((char *)dst->data + (i3 * ne03 + k3) * nb3 +
(i2 * ne02 + k2) * nb2 + (i1 * ne01 + k1) * nb1 +
(i0 * ne00) * nb0),
(float *)((char *)src0->data + (k3)*nb03 +
(i2k2 / nr2) * nb02 + (k1)*nb01));
}
}
}
}
}
}
}
}
static void ggml_compute_forward_repeat2(
const struct ggml_compute_params *params, const struct ggml_tensor *src0,
struct ggml_tensor *dst) {
switch (src0->type) {
case GGML_TYPE_F32: {
ggml_compute_forward_repeat2_f32(params, src0, dst);
} break;
default: {
GGML_ASSERT(false);
} break;
}
}
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