ikawrakow/ik_llama.cpp
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FlashMLA-2 (CPU): faster and smaller compute buffer size (#253)

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* FlashMLA-2: eliminate intermediate f32 tensors

This works on the CPU. PP performance is ~13% better for 16k tokens
and compute buffer is quite a bit smaller.

* FlashMLA-2: enable fast path only on the CPU for now

I did implement the necessary ops on CUDA, but something is
still wrong there, so for now we only use it when running
CPU-only.

* FlashMLA-2: slightly smaller computer buffer size

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2025-03-13 12:07:43 +02:00
committed by GitHub
parent 3f23ed68f1
commit 305fabfc3b
5 changed files with 225 additions and 48 deletions
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111
src/llama.cpp
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@@ -13630,45 +13630,94 @@ struct llm_build_context {
if (lctx.cparams.mla_attn > 1 && lctx.cparams.flash_attn && (pp_opt || lctx.cparams.mla_attn > 2)) {
// Hahaha, we need to convert the KV cache for this layer to f32 because the general purpose ML library ggml does not
// provide ops on (almost) anything other than f32. In this case, the cache will be the second operand to a matrix
// multiplication, which *must* be f32.
auto kv_cache_view = ggml_view_2d(ctx0, kv_self.kv_l[il], kv_self.kv_l[il]->ne[0], n_kv, kv_self.kv_l[il]->nb[1], 0);
auto kv_cache_view_f32 = ggml_cast(ctx0, kv_cache_view, GGML_TYPE_F32);
cb(kv_cache_view_f32, "kv_cache_view_f32", il);
// The no- and rotational position encoding portions of the KV cache
auto kv_cache_nope = ggml_view_2d(ctx0, kv_cache_view_f32, kv_lora_rank, n_kv, kv_cache_view_f32->nb[1], 0);
auto kv_cache_rope = ggml_view_3d(ctx0, kv_cache_view_f32, n_embd_head_qk_rope, 1, n_kv,
kv_cache_view_f32->nb[1], kv_cache_view_f32->nb[1], ggml_row_size(kv_cache_view_f32->type, kv_lora_rank));
ggml_tensor * k;
ggml_tensor * v;
auto kv_f32 = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cache_nope);
cb(kv_f32, "kv_f32", il);
// For now this only works in the CPU implementation, so we only use it if there is just the CPU backend.
// If the code was compiled with CUDA (and/or Metal, Vulkan, whatever) support, this branch will not
// be taken even if no layers were offloaded to the GPU.
if (lctx.backends.size() == 1 && lctx.backends.front() == lctx.backend_cpu) {
auto k_nope_f32 = ggml_view_3d(ctx0, kv_f32, n_embd_head_qk_nope, n_kv, n_head,
ggml_row_size(kv_f32->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope + hparams.n_embd_head_v), 0);
cb(k_nope_f32, "k_nope_f32", il);
auto kv_cache_nope = ggml_view_2d(ctx0, kv_self.kv_l[il], kv_lora_rank, n_kv, kv_self.kv_l[il]->nb[1], 0);
ggml_tensor repeater;
repeater.ne[0] = n_embd_head_qk_rope; repeater.ne[1] = n_head; repeater.ne[2] = n_kv; repeater.ne[3] = 1;
auto k_rope_f32 = ggml_permute(ctx0, ggml_repeat(ctx0, kv_cache_rope, &repeater), 0, 2, 1, 3);
cb(k_rope_f32, "k_rope_f32", il);
auto kv_f32 = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cache_nope);
cb(kv_f32, "kv_f32", il);
auto k_f32 = ggml_concat(ctx0, k_nope_f32, k_rope_f32, 0);
cb(k_f32, "k_f32", il);
auto v_f32 = ggml_view_3d(ctx0, kv_f32, hparams.n_embd_head_v, n_kv, n_head,
ggml_row_size(kv_f32->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope));
cb(v_f32, "v_f32", il);
auto k = ggml_cast(ctx0, k_f32, kv_self.kv_l[il]->type);
cb(k, "k", il);
v = ggml_cast(ctx0, v_f32, kv_self.kv_l[il]->type);
cb(v, "v", il);
auto v_f32 = ggml_view_3d(ctx0, kv_f32, hparams.n_embd_head_v, n_kv, n_head,
ggml_row_size(kv_f32->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope));
cb(v_f32, "v_f32", il);
auto k_nope_f32 = ggml_view_3d(ctx0, kv_f32, n_embd_head_qk_nope, n_kv, n_head,
ggml_row_size(kv_f32->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope + hparams.n_embd_head_v), 0);
cb(k_nope_f32, "k_nope_f32", il);
auto v = ggml_cast(ctx0, v_f32, kv_self.kv_l[il]->type);
cb(v, "v", il);
auto k_nope = ggml_cast(ctx0, k_nope_f32, kv_self.kv_l[il]->type);
cb(k_nope, "k_nope", il);
ggml_build_forward_expand(gf, k_nope);
ggml_build_forward_expand(gf, v);
auto kv_cache_rope = ggml_view_3d(ctx0, kv_self.kv_l[il], n_embd_head_qk_rope, n_kv, 1,
kv_self.kv_l[il]->nb[1], kv_self.kv_l[il]->nb[2], ggml_row_size(kv_self.kv_l[il]->type, kv_lora_rank));
ggml_tensor repeater;
repeater.ne[0] = n_embd_head_qk_rope; repeater.ne[1] = n_kv; repeater.ne[2] = n_head; repeater.ne[3] = 1;
auto k_rope = ggml_repeat(ctx0, kv_cache_rope, &repeater);
cb(k_rope, "k_rope", il);
k = ggml_concat(ctx0, k_nope, k_rope, 0);
cb(k, "k", il);
ggml_build_forward_expand(gf, k);
}
else {
// Hahaha, we need to convert the KV cache for this layer to f32 because the general purpose ML library ggml does not
// provide ops on (almost) anything other than f32. In this case, the cache will be the second operand to a matrix
// multiplication, which *must* be f32.
auto kv_cache_view = ggml_view_2d(ctx0, kv_self.kv_l[il], kv_self.kv_l[il]->ne[0], n_kv, kv_self.kv_l[il]->nb[1], 0);
auto kv_cache_view_f32 = ggml_cast(ctx0, kv_cache_view, GGML_TYPE_F32);
cb(kv_cache_view_f32, "kv_cache_view_f32", il);
// The no- and rotational position encoding portions of the KV cache
auto kv_cache_nope = ggml_view_2d(ctx0, kv_cache_view_f32, kv_lora_rank, n_kv, kv_cache_view_f32->nb[1], 0);
auto kv_cache_rope = ggml_view_3d(ctx0, kv_cache_view_f32, n_embd_head_qk_rope, 1, n_kv,
kv_cache_view_f32->nb[1], kv_cache_view_f32->nb[1], ggml_row_size(kv_cache_view_f32->type, kv_lora_rank));
auto kv_f32 = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cache_nope);
cb(kv_f32, "kv_f32", il);
auto k_nope_f32 = ggml_view_3d(ctx0, kv_f32, n_embd_head_qk_nope, n_kv, n_head,
ggml_row_size(kv_f32->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope + hparams.n_embd_head_v), 0);
cb(k_nope_f32, "k_nope_f32", il);
ggml_tensor repeater;
repeater.ne[0] = n_embd_head_qk_rope; repeater.ne[1] = n_head; repeater.ne[2] = n_kv; repeater.ne[3] = 1;
auto k_rope_f32 = ggml_permute(ctx0, ggml_repeat(ctx0, kv_cache_rope, &repeater), 0, 2, 1, 3);
cb(k_rope_f32, "k_rope_f32", il);
auto k_f32 = ggml_concat(ctx0, k_nope_f32, k_rope_f32, 0);
cb(k_f32, "k_f32", il);
k = ggml_cast(ctx0, k_f32, kv_self.kv_l[il]->type);
cb(k, "k", il);
auto v_f32 = ggml_view_3d(ctx0, kv_f32, hparams.n_embd_head_v, n_kv, n_head,
ggml_row_size(kv_f32->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
ggml_row_size(kv_f32->type, n_embd_head_qk_nope));
cb(v_f32, "v_f32", il);
v = ggml_cast(ctx0, v_f32, kv_self.kv_l[il]->type);
cb(v, "v", il);
}
auto q = ggml_concat(ctx0, q_nope, q_rope, 0);
q = ggml_permute(ctx0, q, 0, 2, 1, 3);