tqcq/llama.cpp
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embedding : avoid common_batch

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ggml-ci
This commit is contained in:
Georgi Gerganov
2025-03-19 14:29:04 +02:00
parent 6f54ee660c
commit 8b80d68338
2 changed files with 33 additions and 36 deletions
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63
examples/embedding/embedding.cpp
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@ -26,57 +26,53 @@ static std::vector<std::string> split_lines(const std::string & s, const std::st
return lines;
}
static void batch_add_seq(common_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
static void batch_add_seq(llama_batch_ext * 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++) {
batch.add_text(tokens[i], i, seq_id, true);
llama_batch_ext_add_text(batch, tokens[i], i, &seq_id, 1, true);
}
}
static void batch_decode(llama_context * ctx, common_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
static void batch_decode(llama_context * ctx, llama_batch_ext * 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);
const llama_model * model = llama_get_model(ctx);
// clear previous kv_cache values (irrelevant for embeddings)
llama_kv_self_clear(ctx);
const int n_tokens = llama_batch_ext_get_n_tokens(batch);
// run model
LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, llama_batch_ext_get_n_tokens(batch.get()), n_seq);
LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, n_tokens, n_seq);
if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) {
// encoder-only model
if (llama_encode_ext(ctx, batch.get()) < 0) {
if (llama_encode_ext(ctx, batch) < 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_ext(ctx, batch.get()) < 0) {
if (llama_decode_ext(ctx, batch) < 0) {
LOG_ERR("%s : failed to decode\n", __func__);
}
}
for (int i = 0; i < llama_batch_ext_get_n_tokens(batch.get()); i++) {
if (!batch.tokens[i].logits) {
continue;
}
const float * embd = nullptr;
int embd_pos = 0;
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
// try to get token embeddings
embd = llama_get_embeddings_ith(ctx, i);
embd_pos = i;
for (int i = 0; i < n_tokens; i++) {
const float * embd = llama_get_embeddings_ith(ctx, i);
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.tokens[i].seq_id);
embd_pos = batch.tokens[i].seq_id;
GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
}
float * out = output + embd_pos * n_embd;
float * out = output + i * n_embd;
common_embd_normalize(embd, out, n_embd, embd_norm);
}
} else {
for (int s = 0; s < n_seq; s++) {
const float * embd = llama_get_embeddings_seq(ctx, s);
GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
float * out = output + s * n_embd;
common_embd_normalize(embd, out, n_embd, embd_norm);
}
}
}
int main(int argc, char ** argv) {
@ -171,7 +167,7 @@ int main(int argc, char ** argv) {
// initialize batch
const int n_prompts = prompts.size();
struct common_batch batch = common_batch(n_batch, 1);
llama_batch_ext * batch = llama_batch_ext_init(n_batch, 1);
// count number of embeddings
int n_embd_count = 0;
@ -198,12 +194,12 @@ int main(int argc, char ** argv) {
const uint64_t n_toks = inp.size();
// encode if at capacity
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.get_n_tokens() : s;
if (llama_batch_ext_get_n_tokens(batch) + n_toks > n_batch) {
batch_decode(ctx, batch, emb + e * n_embd, s, n_embd, params.embd_normalize);
llama_batch_ext_clear(batch);
e += pooling_type == LLAMA_POOLING_TYPE_NONE ? llama_batch_ext_get_n_tokens(batch) : s;
s = 0;
batch.clear();
}
// add to batch
@ -212,8 +208,7 @@ int main(int argc, char ** argv) {
}
// final batch
float * out = emb + e * n_embd;
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
batch_decode(ctx, batch, emb + e * n_embd, s, n_embd, params.embd_normalize);
if (params.embd_out.empty()) {
LOG("\n");
@ -318,6 +313,8 @@ int main(int argc, char ** argv) {
LOG("\n");
llama_perf_context_print(ctx);
llama_batch_ext_free(batch);
// clean up
llama_backend_free();

2
examples/retrieval/retrieval.cpp
View File

@ -82,7 +82,7 @@ static void batch_add_seq(llama_batch_ext * batch, const std::vector<int32_t> &
}
static void batch_decode(llama_context * ctx, llama_batch_ext * batch, float * output, int n_seq, int n_embd, int embd_norm = 2) {
const struct llama_model * model = llama_get_model(ctx);
const llama_model * model = llama_get_model(ctx);
// clear previous kv_cache values (irrelevant for embeddings)
llama_kv_self_clear(ctx);