Add --no-op-offload to improve -ot pp perf in MoE models like llama4 400B (llama/13386)

This commit is contained in:
David Huang 2025-05-11 20:18:39 +08:00 committed by Georgi Gerganov
parent 6db0e01db6
commit e1b2ace0f8
2 changed files with 8 additions and 4 deletions

View File

@ -248,7 +248,7 @@ extern "C" {
// preferrably to run on the same backend as the buffer
ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false, true);
// initialize buffers from a max size graph (optional)
reserve_graph = build_graph(sched, max_batch_size);
@ -289,7 +289,7 @@ extern "C" {
typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
// Initialize a backend scheduler, backends with low index are given priority over backends with high index
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel, bool op_offload);
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
// Initialize backend buffers from a measure graph

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@ -674,6 +674,8 @@ struct ggml_backend_sched {
char * context_buffer;
size_t context_buffer_size;
bool op_offload;
int debug;
};
@ -766,7 +768,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
// check if a backend with higher prio wants to offload the op
if (src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) {
if (sched->op_offload && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) {
for (int b = 0; b < src_backend_id; b++) {
if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
SET_CAUSE(tensor, "1.off");
@ -1452,7 +1454,8 @@ ggml_backend_sched_t ggml_backend_sched_new(
ggml_backend_buffer_type_t * bufts,
int n_backends,
size_t graph_size,
bool parallel) {
bool parallel,
bool op_offload) {
GGML_ASSERT(n_backends > 0);
GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
GGML_ASSERT(ggml_backend_dev_type(ggml_backend_get_device(backends[n_backends - 1])) == GGML_BACKEND_DEVICE_TYPE_CPU);
@ -1497,6 +1500,7 @@ ggml_backend_sched_t ggml_backend_sched_new(
}
sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
sched->op_offload = op_offload;
ggml_backend_sched_reset(sched);