ikawrakow/ik_llama.cpp
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Faster Gemma2 (#27)

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* soft_cap_max: initial CPU version of fused softcap + soft_max

With this vanilla CPU implementation I'm already getting a ~3% speedup
for Gemma-2-9b and a prompt of 8192 tokens.

* soft_cap_max: WIP - something is wrong with CUDA

* soft_cap_max: looks good on CPU and CUDA

* Add softcap to flash attention

Just CPU and CUDA for now (but, as we know, flash attention
on the CPU is useless in llama.cpp).

On CUDA this improves PP performance quite a bit, especially for
long contexts. E.g., for PP-16384, I now get 3777 t/s.
Without this change, one cannot use FA, and one gets 2300 t/s
(after fusing softcap and softmax), or 2000 t/s without the
fused softcap+softmax.

In comparison, mainline llama.cpp has PP-16384 = 1549 t/s before
PR-8542 (where Johannes Gaessler has also added softcap to FA),
and PP-16384 = 3097 t/s after this PR.

* soft_cap_max: Metal

* Flash attention with softcap: Metal

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-08-27 17:40:59 +03:00
committed by GitHub
parent bd99ed7d0a
commit c7e99c88a2
16 changed files with 896 additions and 133 deletions
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71
src/llama.cpp
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@@ -8290,7 +8290,8 @@ static struct ggml_tensor * llm_build_kqv(
0);
cb(v, "v", il);
cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias);
cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) {
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
@@ -8324,10 +8325,12 @@ static struct ggml_tensor * llm_build_kqv(
}
if (hparams.attn_soft_cap) {
kq = ggml_softcap(ctx, kq, 1.0f / hparams.f_attn_logit_softcapping, hparams.f_attn_logit_softcapping);
//kq = ggml_softcap(ctx, kq, 1.0f / hparams.f_attn_logit_softcapping, hparams.f_attn_logit_softcapping);
kq = ggml_softcap_max(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias,
1.0f / hparams.f_attn_logit_softcapping, hparams.f_attn_logit_softcapping);
} else {
kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
}
kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
GGML_ASSERT(kv.size == n_ctx);
@@ -13220,47 +13223,31 @@ struct llm_build_context {
0);
cb(k, "k", il);
if (cparams.flash_attn) {
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
cb(kq, "kq", il);
// split cached v into n_head heads (not transposed)
struct ggml_tensor * v =
ggml_view_3d(ctx0, kv_self.v_l[il],
n_embd_head_v, n_kv, n_head_kv,
ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
ggml_row_size(kv_self.v_l[il]->type, n_embd_head_v),
0);
cb(v, "v", il);
kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
cur_attn = ggml_flash_attn_ext(ctx0, q, k, v, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
GGML_ASSERT(kv_self.size == n_ctx);
cur_attn = ggml_reshape_2d(ctx0, cur, n_embd_head_v*n_head, n_tokens);
} else {
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
cb(kq, "kq", il);
// split cached v into n_head heads
struct ggml_tensor * v =
ggml_view_3d(ctx0, kv_self.v_l[il],
n_kv, n_embd_head_v, n_head_kv,
ggml_element_size(kv_self.v_l[il])*n_ctx,
ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v,
0);
cb(v, "v", il);
kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
cb(kqv, "kqv", il);
GGML_ASSERT(kv_self.size == n_ctx);
struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
cb(kqv_merged, "kqv_merged", il);
// split cached v into n_head heads
struct ggml_tensor * v =
ggml_view_3d(ctx0, kv_self.v_l[il],
n_kv, n_embd_head_v, n_head_kv,
ggml_element_size(kv_self.v_l[il])*n_ctx,
ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v,
0);
cb(v, "v", il);
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
cb(kqv, "kqv", il);
struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
cb(kqv_merged, "kqv_merged", il);
cur_attn = ggml_cont_2d(ctx0, kqv_merged, n_embd_head_v*n_head, n_tokens);
cb(cur_attn, "kqv_merged_cont", il);
}
cur_attn = ggml_cont_2d(ctx0, kqv_merged, n_embd_head_v*n_head, n_tokens);
cb(cur_attn, "kqv_merged_cont", il);
cur_attn = llm_build_norm(ctx0, cur_attn, hparams,
model.layers[il].attn_sub_norm, NULL,
@@ -16811,12 +16798,6 @@ struct llama_context * llama_new_context_with_model(
params.flash_attn = false;
}
if (params.flash_attn && model->hparams.attn_soft_cap) {
LLAMA_LOG_WARN("%s: flash_attn is not compatible with attn_soft_cap - forcing off\n", __func__);
params.flash_attn = false;
}
if (params.flash_attn && model->hparams.n_embd_head_k != model->hparams.n_embd_head_v) {
LLAMA_LOG_WARN("%s: flash_attn requires n_embd_head_k == n_embd_head_v - forcing off\n", __func__);
params.flash_attn = false;