jdelony/llama.cpp
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spec: fix segfault error on long prompts for eagle3 (#24707)

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Ruixiang Wang 2026-06-17 16:29:49 +02:00 committed by GitHub
parent 74a80dd9c0
commit 1a2dea29b9
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4 changed files with 16 additions and 5 deletions
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2
src/llama-context.cpp
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@ -1382,7 +1382,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
const auto & hparams = model.hparams;
// eagle3/DFlash: features as encoder input, and non-draft paths fall back to model's input dim
const int64_t n_embd = hparams.n_embd_inp();
const int64_t n_embd = hparams.n_embd_inp_enc();
const int64_t n_vocab = model.vocab.n_tokens();
// note: during encode, we always pass the full sequence starting from pos = 0

4
src/llama-hparams.cpp
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@ -104,6 +104,10 @@ uint32_t llama_hparams::n_embd_inp() const {
return n_embd_inp;
}
uint32_t llama_hparams::n_embd_inp_enc() const {
return n_embd_inp_enc_impl > 0 ? n_embd_inp_enc_impl : n_embd_inp();
}
uint32_t llama_hparams::n_embd_out() const {
return n_embd_out_impl > 0 ? n_embd_out_impl : n_embd;
}

7
src/llama-hparams.h
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@ -189,6 +189,10 @@ struct llama_hparams {
// input embedding dimension (0 = use n_embd)
uint32_t n_embd_inp_impl = 0;
// encoder input embedding dimension (0 = use n_embd_inp())
// e.g. the eagle3 encoder fuses target_layers * target_hidden features
uint32_t n_embd_inp_enc_impl = 0;
// output embedding dimension (0 = use n_embd)
uint32_t n_embd_out_impl = 0;
@ -305,6 +309,9 @@ struct llama_hparams {
// dimension of main + auxiliary input embeddings
uint32_t n_embd_inp() const;
// dimension of the encoder input embeddings
uint32_t n_embd_inp_enc() const;
// dimension of output embeddings
uint32_t n_embd_out() const;

8
src/models/eagle3.cpp
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@ -19,7 +19,7 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_TARGET_HIDDEN_SIZE, n_embd_tgt);
LLAMA_LOG_INFO("%s: EAGLE3 n_embd_tgt = %u (draft n_embd = %u)\n", __func__, n_embd_tgt, hparams.n_embd);
hparams.n_embd_inp_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
hparams.n_embd_inp_enc_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
// eagle3 norm_before_residual (optional, default false)
// compatible with Readhat eagle3 speculator model
@ -34,7 +34,7 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
void llama_model_eagle3::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_embd_inp = hparams.n_embd_inp();
const int64_t n_embd_inp = hparams.n_embd_inp_enc();
const int64_t n_embd_attn_input = 2 * n_embd;
// Get vocab size from the d2t tensor in the GGUF file (optional - only needed if eagle3 has different vocab_size than target)
@ -109,8 +109,8 @@ ggml_tensor * llama_model_eagle3::graph<true>::build_inp_embd_enc() const {
// Input: Target model features (3 layers concatenated: low, mid, high)
// Data will be provided via ubatch->embd in encode_eagle3_features()
auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp());
inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32,hparams.n_embd_inp(), n_tokens);
auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp_enc());
inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp_enc(), n_tokens);
ggml_set_input(inp_target->embd);
cur = inp_target->embd;