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retrieval : avoid common_batch

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ggml-ci
This commit is contained in:
Georgi Gerganov
2025-03-19 13:50:15 +02:00
parent 32c2c41d5e
commit 6f54ee660c
2 changed files with 24 additions and 39 deletions
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57
examples/retrieval/retrieval.cpp
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@@ -74,55 +74,38 @@ static std::vector<chunk> chunk_file(const std::string & filename, int chunk_siz
return chunks; return chunks;
} }
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(); const size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) { 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 = 2) { static void batch_decode(llama_context * ctx, llama_batch_ext * batch, float * output, int n_seq, int n_embd, int embd_norm = 2) {
const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
const struct llama_model * model = llama_get_model(ctx); const struct llama_model * model = llama_get_model(ctx);
// clear previous kv_cache values (irrelevant for embeddings) // clear previous kv_cache values (irrelevant for embeddings)
llama_kv_self_clear(ctx); llama_kv_self_clear(ctx);
// run model // 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__, llama_batch_ext_get_n_tokens(batch), n_seq);
if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) { if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) {
// encoder-only 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__); LOG_ERR("%s : failed to encode\n", __func__);
} }
} else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) { } else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
// decoder-only 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__); LOG_ERR("%s : failed to decode\n", __func__);
} }
} }
for (int i = 0; i < llama_batch_ext_get_n_tokens(batch.get()); i++) { for (int s = 0; s < n_seq; s++) {
if (!batch.tokens[i].logits) { const float * embd = llama_get_embeddings_seq(ctx, s);
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;
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"); GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
}
float * out = output + embd_pos * n_embd; float * out = output + s * n_embd;
common_embd_normalize(embd, out, n_embd, embd_norm); common_embd_normalize(embd, out, n_embd, embd_norm);
} }
} }
@@ -230,7 +213,7 @@ int main(int argc, char ** argv) {
// initialize batch // initialize batch
const int n_chunks = chunks.size(); const int n_chunks = chunks.size();
struct common_batch batch = common_batch(n_batch, 1); llama_batch_ext * batch = llama_batch_ext_init(n_batch, 1);
// allocate output // allocate output
const int n_embd = llama_model_n_embd(model); const int n_embd = llama_model_n_embd(model);
@@ -247,10 +230,10 @@ int main(int argc, char ** argv) {
const uint64_t n_toks = inp.size(); const uint64_t n_toks = inp.size();
// encode if at capacity // encode if at capacity
if (llama_batch_ext_get_n_tokens(batch.get()) + n_toks > n_batch) { if (llama_batch_ext_get_n_tokens(batch) + n_toks > n_batch) {
float * out = emb + p * n_embd; batch_decode(ctx, batch, emb + p * n_embd, s, n_embd);
batch_decode(ctx, batch, out, s, n_embd); llama_batch_ext_clear(batch);
batch.clear();
p += s; p += s;
s = 0; s = 0;
} }
@@ -261,8 +244,7 @@ int main(int argc, char ** argv) {
} }
// final batch // final batch
float * out = emb + p * n_embd; batch_decode(ctx, batch, emb + p * n_embd, s, n_embd);
batch_decode(ctx, batch, out, s, n_embd);
// save embeddings to chunks // save embeddings to chunks
for (int i = 0; i < n_chunks; i++) { for (int i = 0; i < n_chunks; i++) {
@@ -271,7 +253,7 @@ int main(int argc, char ** argv) {
chunks[i].tokens.clear(); chunks[i].tokens.clear();
} }
struct common_batch query_batch = common_batch(n_batch, 1); llama_batch_ext * query_batch = llama_batch_ext_init(n_batch, 1);
// start loop, receive query and return top k similar chunks based on cosine similarity // start loop, receive query and return top k similar chunks based on cosine similarity
std::string query; std::string query;
@@ -285,7 +267,7 @@ int main(int argc, char ** argv) {
std::vector<float> query_emb(n_embd, 0); std::vector<float> query_emb(n_embd, 0);
batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd); batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
query_batch.clear(); llama_batch_ext_clear(query_batch);
// compute cosine similarities // compute cosine similarities
{ {
@@ -314,6 +296,9 @@ int main(int argc, char ** argv) {
LOG("\n"); LOG("\n");
llama_perf_context_print(ctx); llama_perf_context_print(ctx);
llama_batch_ext_free(batch);
llama_batch_ext_free(query_batch);
// clean up // clean up
llama_backend_free(); llama_backend_free();
} }