ikawrakow/ik_llama.cpp
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Faster MoE inference (#112)

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* multi_sdd: WIP

* multi_sdd: CPU works

* multi_add: CUDA

* multi_add: simplify

* multi_add: Metal

* Metal: speed up mul_mat_id

For the Granite-1B MoE model PP-512 goes from
156 t/s to 890 t/s, so nearly a 6X speedup!

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-10-31 12:05:27 +01:00
committed by GitHub
parent 5ad6439486
commit 52874c5d21
8 changed files with 332 additions and 33 deletions
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51
src/llama.cpp
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@@ -8351,25 +8351,40 @@ static struct ggml_tensor * llm_build_moe_ffn(
experts = ggml_mul(ctx, experts, weights);
// aggregate experts
ggml_tensor * moe_out = nullptr;
for (int i = 0; i < n_expert_used; ++i) {
ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
experts->nb[2], i*experts->nb[1]);
if (i == 0) {
moe_out = cur_expert;
} else {
moe_out = ggml_add(ctx, moe_out, cur_expert);
}
}
if (n_expert_used == 1) {
// avoid returning a non-contiguous tensor
moe_out = ggml_cont(ctx, moe_out);
return ggml_cont(ctx, ggml_view_2d(ctx, experts, n_embd, n_tokens, experts->nb[2], 0));
}
if (n_expert_used == 2) {
return ggml_add(ctx, ggml_view_2d(ctx, experts, n_embd, n_tokens, experts->nb[2], 0),
ggml_view_2d(ctx, experts, n_embd, n_tokens, experts->nb[2], experts->nb[1]));
}
return ggml_multi_add(ctx, ggml_view_2d(ctx, experts, n_embd, n_tokens, experts->nb[2], 0), n_expert_used);
return moe_out;
//// aggregate experts
//ggml_tensor * moe_out = nullptr;
////ggml_tensor * first_expert = nullptr;
//for (int i = 0; i < n_expert_used; ++i) {
// ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
// experts->nb[2], i*experts->nb[1]);
// if (i == 0) {
// moe_out = cur_expert;
// //first_expert = cur_expert;
// //printf("%s: %d: %d x %d x %d x %d | %d x %d x %d x %d\n", __func__, ggml_is_contiguous(first_expert),
// // (int)cur_expert->ne[0], (int)cur_expert->ne[1], (int)cur_expert->ne[2], (int)cur_expert->ne[3],
// // (int)cur_expert->nb[0], (int)cur_expert->nb[1], (int)cur_expert->nb[2], (int)cur_expert->nb[3]);
// } else {
// moe_out = ggml_add(ctx, moe_out, cur_expert);
// //printf("%s: %d %d\n", __func__, ggml_is_contiguous(cur_expert), ggml_are_same_shape(cur_expert, first_expert));
// }
//}
//if (n_expert_used == 1) {
// // avoid returning a non-contiguous tensor
// moe_out = ggml_cont(ctx, moe_out);
//}
//return moe_out;
}
static struct ggml_tensor * llm_build_kqv(
@@ -9011,6 +9026,7 @@ struct llm_build_context {
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
if (hparams.f_attention_scale != 0) {
// Why is hparams.f_attention_scale not simply absorbed into model.layers[il].wq ?
Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
}
cb(Qcur, "Qcur", il);
@@ -9062,6 +9078,7 @@ struct llm_build_context {
// For Granite architecture
if (hparams.f_residual_scale) {
// Why is hparams.f_residual_scale not simply absorbed into model.layers[il].wv ?
cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
}
@@ -9103,6 +9120,7 @@ struct llm_build_context {
// For Granite architecture
if (hparams.f_residual_scale) {
// Why is hparams.f_residual_scale not simply absorbed into model.layers[il].ffn_down_exps ?
cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
}
@@ -9128,6 +9146,7 @@ struct llm_build_context {
// For Granite architecture
if (hparams.f_logit_scale) {
// Why is hparams.f_logit_scale not simply absorbed into model.output ?
cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
}