tqcq/llama.cpp
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apply various in places

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This commit is contained in:
Xuan Son Nguyen
2025-03-01 20:42:18 +01:00
parent 1d6ba97789
commit 46596caf6d
12 changed files with 142 additions and 133 deletions
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29
examples/embedding/embedding.cpp
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@ -25,14 +25,14 @@ static std::vector<std::string> split_lines(const std::string & s, const std::st
return lines;
}
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
static void batch_add_seq(common_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) {
common_batch_add(batch, tokens[i], i, { seq_id }, true);
batch.add_text(tokens[i], i, seq_id, true);
}
}
static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
static void batch_decode(llama_context * ctx, common_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
const struct llama_model * model = llama_get_model(ctx);
@ -40,21 +40,21 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
llama_kv_cache_clear(ctx);
// run model
LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, llama_batch_ext_get_n_tokens(batch.get()), n_seq);
if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) {
// encoder-only model
if (llama_encode(ctx, batch) < 0) {
if (llama_encode_ext(ctx, batch.get()) < 0) {
LOG_ERR("%s : failed to encode\n", __func__);
}
} else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
// decoder-only model
if (llama_decode(ctx, batch) < 0) {
if (llama_decode_ext(ctx, batch.get()) < 0) {
LOG_ERR("%s : failed to decode\n", __func__);
}
}
for (int i = 0; i < batch.n_tokens; i++) {
if (!batch.logits[i]) {
for (int i = 0; i < llama_batch_ext_get_n_tokens(batch.get()); i++) {
if (!batch.tokens[i].logits) {
continue;
}
@ -68,8 +68,8 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
GGML_ASSERT(embd != NULL && "failed to get token embeddings");
} else {
// try to get sequence embeddings - supported only when pooling_type is not NONE
embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
embd_pos = batch.seq_id[i][0];
embd = llama_get_embeddings_seq(ctx, batch.tokens[i].seq_id);
embd_pos = batch.tokens[i].seq_id;
GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
}
@ -170,7 +170,7 @@ int main(int argc, char ** argv) {
// initialize batch
const int n_prompts = prompts.size();
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
struct common_batch batch = common_batch(n_batch, 1);
// count number of embeddings
int n_embd_count = 0;
@ -197,12 +197,12 @@ int main(int argc, char ** argv) {
const uint64_t n_toks = inp.size();
// encode if at capacity
if (batch.n_tokens + n_toks > n_batch) {
if (batch.get_n_tokens() + n_toks > n_batch) {
float * out = emb + e * n_embd;
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.get_n_tokens() : s;
s = 0;
common_batch_clear(batch);
batch.clear();
}
// add to batch
@ -318,7 +318,6 @@ int main(int argc, char ** argv) {
llama_perf_context_print(ctx);
// clean up
llama_batch_free(batch);
llama_backend_free();
return 0;