vbatts/llama.cpp
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gritlm example using llama_get_embeddings_mean_pooled

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This commit is contained in:
Matt Grosso 2024-04-17 17:49:29 -07:00
parent 2a24f71497
commit 798c29d6b9
1 changed files with 4 additions and 17 deletions
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21
examples/gritlm/gritlm.cpp
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@ -54,24 +54,11 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
// allocate embedding output // allocate embedding output
std::vector<float> emb_unorm(n_embd, 0.0f); std::vector<float> emb_unorm(n_embd, 0.0f);
float * raw_embed = emb_unorm.data();
// sum up all token embeddings // retrieve summed up token embeddings, skipping instruction tokens,
for (int32_t k = n_inst; k < n_toks; k++) { // and writes the result to raw_embed
float * emb = llama_get_embeddings_ith(ctx, k); llama_get_embeddings_mean_pooled(ctx, n_inst, n_toks, raw_embed);
for (uint64_t j = 0; j < n_embd; j++) {
emb_unorm[j] += emb[j];
}
}
// divide by number of tokens (mean pooling)
{
const uint64_t n_sent = n_toks - n_inst;
for (uint64_t j = 0; j < n_embd; j++) {
emb_unorm[j] /= n_sent;
}
}
std::vector<float> emb_norm(emb_unorm.size()); std::vector<float> emb_norm(emb_unorm.size());
llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd); llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
result.push_back(emb_norm); result.push_back(emb_norm);