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llama: use FA + max. GPU layers by default (#15434)

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* llama: use max. GPU layers by default, auto -fa

* ggml-backend: abort instead of segfault
This commit is contained in:
Johannes Gäßler
2025-08-30 16:32:10 +02:00
committed by GitHub
parent 38ad381f9f
commit e81b8e4b7f
19 changed files with 235 additions and 72 deletions
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24
src/llama-graph.cpp
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@@ -1221,7 +1221,8 @@ ggml_tensor * llm_graph_context::build_attn_mha(
ggml_tensor * kq_mask,
ggml_tensor * sinks,
ggml_tensor * v_mla,
float kq_scale) const {
float kq_scale,
int il) const {
const bool v_trans = v->nb[1] > v->nb[2];
// split the batch into streams if needed
@@ -1256,6 +1257,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
cb(cur, LLAMA_TENSOR_NAME_FATTN, il);
ggml_flash_attn_ext_add_sinks(cur, sinks);
ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32);
@@ -1271,6 +1273,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
// The permutations are noops and only change how the tensor data is interpreted.
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
cur = ggml_mul_mat(ctx0, v_mla, cur);
cb(cur, "fattn_mla", il);
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
cur = ggml_cont(ctx0, cur); // Needed because ggml_reshape_2d expects contiguous inputs.
#endif
@@ -1279,6 +1282,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
} else {
ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
cb(kq, "kq", il);
// note: this op tends to require high floating point range
// while for some models F16 is enough, for others it is not, so we default to F32 here
@@ -1292,32 +1296,42 @@ ggml_tensor * llm_graph_context::build_attn_mha(
// before the softmax below
kq = ggml_tanh(ctx0, ggml_scale(ctx0, kq, 0.08838834764831845f/30.0f));
cb(kq, "kq_tanh", il);
kq = ggml_scale(ctx0, kq, 30);
cb(kq, "kq_scaled", il);
}
if (hparams.attn_soft_cap) {
kq = ggml_scale(ctx0, kq, 1.0f / hparams.f_attn_logit_softcapping);
cb(kq, "kq_scaled_1", il);
kq = ggml_tanh (ctx0, kq);
cb(kq, "kq_tanh", il);
kq = ggml_scale(ctx0, kq, hparams.f_attn_logit_softcapping);
cb(kq, "kq_scaled_2", il);
}
if (kq_b) {
kq = ggml_add(ctx0, kq, kq_b);
cb(kq, "kq_plus_kq_b", il);
}
kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
ggml_soft_max_add_sinks(kq, sinks);
cb(kq, "kq_soft_max", il);
if (!v_trans) {
// note: avoid this branch
v = ggml_cont(ctx0, ggml_transpose(ctx0, v));
cb(v, "v_cont", il);
}
ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
cb(kqv, "kqv", il);
// for MLA with the absorption optimization, we need to "decompress" from MQA back to MHA
if (v_mla) {
kqv = ggml_mul_mat(ctx0, v_mla, kqv);
cb(kqv, "kqv_mla", il);
}
cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
@@ -1378,7 +1392,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k = k_cur;
ggml_tensor * v = v_cur;
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
cb(cur, "kqv_out", il);
if (wo) {
@@ -1467,7 +1481,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
cb(cur, "kqv_out", il);
if (wo) {
@@ -1534,7 +1548,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
cb(cur, "kqv_out", il);
if (wo) {
@@ -1589,7 +1603,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * k = k_cur;
ggml_tensor * v = v_cur;
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale);
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
cb(cur, "kqv_out", il);
if (wo) {