ikawrakow/ik_llama.cpp
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Warn user when disabling MLA

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Iwan Kawrakow
2025-02-13 08:40:24 +02:00
parent 00063b7d99
commit 91db234fb5
1 changed files with 19 additions and 0 deletions
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19
src/llama.cpp
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@@ -3168,6 +3168,8 @@ static bool llama_kv_cache_init(
cache.kv_l.reserve(n_layer); cache.kv_l.reserve(n_layer);
cache.kvt_l.reserve(n_layer); cache.kvt_l.reserve(n_layer);
bool warn = true;
int n_mla = 0;
for (int i = 0; i < (int) n_layer; i++) { for (int i = 0; i < (int) n_layer; i++) {
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s(); const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
@@ -3175,6 +3177,17 @@ static bool llama_kv_cache_init(
struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front(); struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
ggml_tensor * k; ggml_tensor * k;
ggml_tensor * v; ggml_tensor * v;
if (cparams.mla_attn) {
if (!model.layers[i].wk_b || !model.layers[i].wv_b) {
if (warn) {
LLAMA_LOG_WARN("=======================================================================================\n");
LLAMA_LOG_WARN("%s: missing MLA tensors => disabling MLA\n", __func__);
LLAMA_LOG_WARN("%s: you need to reconvert your model in order to use MLA\n", __func__);
LLAMA_LOG_WARN("=======================================================================================\n");
warn = false;
}
}
}
if (cparams.mla_attn && model.layers[i].wk_b && model.layers[i].wv_b) { if (cparams.mla_attn && model.layers[i].wk_b && model.layers[i].wv_b) {
// DeepSeek MLA // DeepSeek MLA
const uint32_t n_embd_head_qk_rope = hparams.n_rot; const uint32_t n_embd_head_qk_rope = hparams.n_rot;
@@ -3186,6 +3199,7 @@ static bool llama_kv_cache_init(
ggml_format_name(kvt, "cache_kvt_l%d", i); ggml_format_name(kvt, "cache_kvt_l%d", i);
cache.kv_l.push_back(kv); cache.kv_l.push_back(kv);
cache.kvt_l.push_back(kvt); cache.kvt_l.push_back(kvt);
n_mla++;
} }
else { else {
k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
@@ -3196,6 +3210,11 @@ static bool llama_kv_cache_init(
cache.v_l.push_back(v); cache.v_l.push_back(v);
} }
} }
if (cparams.mla_attn && n_mla < n_layer && n_mla > 0) {
LLAMA_LOG_ERROR("%s: unexpected situation with %d out of %d layers having MLA enabled\n", __func__, n_mla, int(n_layer));
LLAMA_LOG_ERROR("%s: bailing out\n", __func__);
GGML_ABORT("fatal error");
}
// allocate tensors and initialize the buffers to avoid NaNs in the padding // allocate tensors and initialize the buffers to avoid NaNs in the padding
for (auto it : ctx_map) { for (auto it : ctx_map) {