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sched : copy only the used experts when offloading prompt processing

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slaren
2025-08-15 15:57:51 +02:00
parent 7aeee88cfe
commit 497474b135
1 changed files with 66 additions and 2 deletions
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68
ggml/src/ggml-backend.cpp
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@@ -19,9 +19,9 @@
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <string.h> #include <string.h>
#include <string>
#include <vector>
#include <algorithm> #include <algorithm>
#include <vector>
#include <set>
#ifdef __APPLE__ #ifdef __APPLE__
#include <sys/types.h> #include <sys/types.h>
@@ -1378,6 +1378,70 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
} else { } else {
ggml_backend_synchronize(split_backend); ggml_backend_synchronize(split_backend);
} }
#if 1
ggml_tensor * node = split->graph.nodes[0];
if (split->graph.n_nodes > 0 &&
ggml_backend_buffer_get_usage(input->buffer) == GGML_BACKEND_BUFFER_USAGE_WEIGHTS &&
ggml_backend_buffer_is_host(input->buffer) && (
(node->src[0] == input_cpy && node->op == GGML_OP_MUL_MAT_ID)
/*|| (node->src[1] == input_cpy && node->op == GGML_OP_ADD_ID) */)) {
ggml_backend_synchronize(input_backend);
// find the ids
ggml_tensor * ids_tensor = node->src[2];
std::vector<int32_t> ids(ggml_nbytes(ids_tensor) / sizeof(int32_t));
ggml_backend_tensor_get_async(split_backend, ids_tensor, ids.data(), 0, ggml_nbytes(ids_tensor));
ggml_backend_synchronize(split_backend);
std::set<int32_t> unique_ids;
for (int64_t i1 = 0; i1 < ids_tensor->ne[1]; i1++) {
for (int64_t i0 = 0; i0 < ids_tensor->ne[0]; i0++) {
int32_t id = ids[i1 * ids_tensor->nb[1]/sizeof(int32_t) + i0 * ids_tensor->nb[0]/sizeof(int32_t)];
unique_ids.insert(id);
}
}
// group consecutive experts and copy them together
GGML_ASSERT(!unique_ids.empty());
auto it = unique_ids.begin();
int32_t first_id = *it;
int32_t last_id = first_id;
auto copy_experts = [&](int32_t first_id, int32_t last_id) {
const size_t expert_size = node->op == GGML_OP_MUL_MAT_ID ? input->nb[2] : input->nb[1];
const size_t expert_offset = first_id * expert_size;
const size_t expert_size_copy = (last_id - first_id + 1) * expert_size;
const size_t padding = 512;
const size_t padding_end = last_id < input->ne[2] - 1 ? std::min<size_t>(expert_size, padding) : 0;
ggml_backend_tensor_set_async(split_backend,
input_cpy,
(const uint8_t *)input->data + expert_offset, expert_offset,
// copy a bit extra to ensure there are no NaNs in the padding
expert_size_copy + padding_end);
};
for (++it; it != unique_ids.end(); ++it) {
const int32_t id = *it;
if (id == last_id + 1) {
last_id = id;
continue;
}
copy_experts(first_id, last_id);
first_id = id;
last_id = id;
}
copy_experts(first_id, last_id);
} else
#endif
// try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events // try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events
// TODO: add public function to facilitate this, since applications do not have direct access to the backend interface // TODO: add public function to facilitate this, since applications do not have direct access to the backend interface
if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) { if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) {