tqcq/llama.cpp
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remove token_info API

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This commit is contained in:
Xuan Son Nguyen
2025-03-01 16:21:16 +01:00
parent 1170135dfb
commit 1d6ba97789
3 changed files with 82 additions and 69 deletions
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126
examples/server/server.cpp
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@ -1205,6 +1205,47 @@ struct server_task_result_apply_lora : server_task_result {
}
};
struct server_batch {
llama_batch_ext_ptr batch;
struct batch_token {
llama_token token;
llama_seq_id seq_id;
bool logits;
};
std::vector<batch_token> tokens;
server_batch() = default;
server_batch(int32_t n_tokens, int32_t n_seq_max) {
batch.reset(llama_batch_ext_init(n_tokens, n_seq_max));
tokens.reserve(n_tokens);
}
void clear() {
llama_batch_ext_clear(batch.get());
tokens.clear();
}
void add_text(llama_token token, llama_pos pos, llama_seq_id seq_id, bool logits) {
llama_batch_ext_add_text(batch.get(), token, pos, &seq_id, 1, logits);
tokens.push_back({token, seq_id, logits});
}
void set_logits_last() {
if (!tokens.empty()) {
llama_batch_ext_set_logits_last(batch.get());
tokens.back().logits = true;
}
}
int32_t get_n_tokens() const {
return (int32_t)tokens.size();
}
server_batch get_view(int32_t offset, int32_t n_tokens) {
server_batch view;
view.batch = llama_batch_ext_ptr(llama_batch_ext_get_view(batch.get(), offset, n_tokens));
view.tokens.reserve(n_tokens);
for (int32_t i = 0; i < n_tokens; i++) {
view.tokens.push_back(tokens[offset + i]);
}
return view;
}
};
struct server_slot {
int id;
int id_task = -1;
@ -1212,7 +1253,7 @@ struct server_slot {
// only used for completion/embedding/infill/rerank
server_task_type task_type = SERVER_TASK_TYPE_COMPLETION;
llama_batch_ext_ptr batch_spec;
server_batch batch_spec;
llama_context * ctx = nullptr;
llama_context * ctx_dft = nullptr;
@ -1784,7 +1825,7 @@ struct server_context {
llama_context_params cparams_dft;
llama_batch_ext_ptr batch;
server_batch batch;
bool clean_kv_cache = true;
bool add_bos_token = true;
@ -1909,7 +1950,7 @@ struct server_context {
slot.n_predict = params_base.n_predict;
if (model_dft) {
slot.batch_spec.reset(llama_batch_ext_init(params_base.speculative.n_max + 1, 1));
slot.batch_spec = server_batch(params_base.speculative.n_max + 1, 1);
slot.ctx_dft = llama_init_from_model(model_dft, cparams_dft);
if (slot.ctx_dft == nullptr) {
@ -1945,7 +1986,7 @@ struct server_context {
const int32_t n_batch = llama_n_batch(ctx);
// only a single seq_id per token is needed
batch.reset(llama_batch_ext_init(std::max(n_batch, params_base.n_parallel), 1));
batch = server_batch(std::max(n_batch, params_base.n_parallel), 1);
}
metrics.init();
@ -2063,7 +2104,7 @@ struct server_context {
}
if (slot.ctx_dft) {
slot.batch_spec.reset(llama_batch_ext_init(slot.params.speculative.n_max + 1, 1));
slot.batch_spec = server_batch(slot.params.speculative.n_max + 1, 1);
}
slot.state = SLOT_STATE_STARTED;
@ -2371,7 +2412,7 @@ struct server_context {
queue_results.send(std::move(res));
}
void send_embedding(const server_slot & slot, llama_batch_ext_ptr & batch) {
void send_embedding(const server_slot & slot, server_batch & batch) {
auto res = std::make_unique<server_task_result_embd>();
res->id = slot.id_task;
res->index = slot.index;
@ -2382,19 +2423,19 @@ struct server_context {
std::vector<float> embd_res(n_embd, 0.0f);
for (int i = 0; i < llama_batch_ext_get_n_tokens(batch.get()); ++i) {
llama_batch_ext_token_info tok = llama_batch_ext_get_token_info(batch.get(), i);
if (!tok.logits || tok.seq_id[0] != slot.id) {
for (int i = 0; i < batch.get_n_tokens(); ++i) {
auto tok = batch.tokens[i];
if (!tok.logits || tok.seq_id != slot.id) {
continue;
}
const float * embd = llama_get_embeddings_seq(ctx, tok.seq_id[0]);
const float * embd = llama_get_embeddings_seq(ctx, tok.seq_id);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
}
if (embd == NULL) {
SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", tok.token, tok.seq_id[0]);
SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", tok.token, tok.seq_id);
res->embedding.push_back(std::vector<float>(n_embd, 0.0f));
continue;
@ -2415,25 +2456,25 @@ struct server_context {
queue_results.send(std::move(res));
}
void send_rerank(const server_slot & slot, llama_batch_ext_ptr & batch) {
void send_rerank(const server_slot & slot, server_batch & batch) {
auto res = std::make_unique<server_task_result_rerank>();
res->id = slot.id_task;
res->index = slot.index;
res->n_tokens = slot.n_prompt_tokens;
for (int i = 0; i < llama_batch_ext_get_n_tokens(batch.get()); ++i) {
llama_batch_ext_token_info tok = llama_batch_ext_get_token_info(batch.get(), i);
if (!tok.logits || tok.seq_id[0] != slot.id) {
for (int i = 0; i < batch.get_n_tokens(); ++i) {
auto tok = batch.tokens[i];
if (!tok.logits || tok.seq_id != slot.id) {
continue;
}
const float * embd = llama_get_embeddings_seq(ctx, tok.seq_id[0]);
const float * embd = llama_get_embeddings_seq(ctx, tok.seq_id);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
}
if (embd == NULL) {
SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", tok.token, tok.seq_id[0]);
SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", tok.token, tok.seq_id);
res->score = -1e6;
continue;
@ -2824,7 +2865,7 @@ struct server_context {
}
// start populating the batch for this iteration
llama_batch_ext_clear(batch.get());
batch.clear();
// track if given slot can be batched with slots already in the batch
server_slot * slot_batched = nullptr;
@ -2846,10 +2887,9 @@ struct server_context {
continue;
}
slot.i_batch = llama_batch_ext_get_n_tokens(batch.get());
slot.i_batch = batch.get_n_tokens();
std::array<llama_token, 1> seq_id = { slot.id };
llama_batch_ext_add_text(batch.get(), slot.sampled, slot.n_past, seq_id.data(), seq_id.size(), true);
batch.add_text(slot.sampled, slot.n_past, slot.id, true);
slot.n_past += 1;
@ -2866,7 +2906,7 @@ struct server_context {
int32_t n_ubatch = llama_n_ubatch(ctx);
// next, batch any pending prompts without exceeding n_batch
if (params_base.cont_batching || llama_batch_ext_get_n_tokens(batch.get()) == 0) {
if (params_base.cont_batching || batch.get_n_tokens() == 0) {
for (auto & slot : slots) {
// check if we can batch this slot with the previous one
if (slot.is_processing()) {
@ -3032,7 +3072,7 @@ struct server_context {
// non-causal tasks require to fit the entire prompt in the physical batch
if (slot.is_non_causal()) {
// cannot fit the prompt in the current batch - will try next iter
if (llama_batch_ext_get_n_tokens(batch.get()) + slot.n_prompt_tokens > n_batch) {
if (batch.get_n_tokens() + slot.n_prompt_tokens > n_batch) {
continue;
}
}
@ -3052,12 +3092,11 @@ struct server_context {
slot.cache_tokens.resize(slot.n_past);
// add prompt tokens for processing in the current batch
while (slot.n_past < slot.n_prompt_tokens && llama_batch_ext_get_n_tokens(batch.get()) < n_batch) {
while (slot.n_past < slot.n_prompt_tokens && batch.get_n_tokens() < n_batch) {
// without pooling, we want to output the embeddings for all the tokens in the batch
const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE;
std::array<llama_token, 1> seq_id = { slot.id };
llama_batch_ext_add_text(batch.get(), prompt_tokens[slot.n_past], slot.n_past, seq_id.data(), seq_id.size(), need_embd);
batch.add_text(prompt_tokens[slot.n_past], slot.n_past, slot.id, need_embd);
if (slot.params.cache_prompt) {
slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
@ -3067,13 +3106,13 @@ struct server_context {
slot.n_past++;
}
SLT_INF(slot, "prompt processing progress, n_past = %d, n_tokens = %d, progress = %f\n", slot.n_past, llama_batch_ext_get_n_tokens(batch.get()), (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens);
SLT_INF(slot, "prompt processing progress, n_past = %d, n_tokens = %d, progress = %f\n", slot.n_past, batch.get_n_tokens(), (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens);
// entire prompt has been processed
if (slot.n_past == slot.n_prompt_tokens) {
slot.state = SLOT_STATE_DONE_PROMPT;
GGML_ASSERT(llama_batch_ext_get_n_tokens(batch.get()) > 0);
GGML_ASSERT(batch.get_n_tokens() > 0);
common_sampler_reset(slot.smpl);
@ -3083,27 +3122,27 @@ struct server_context {
}
// extract the logits only for the last token
llama_batch_ext_set_logits_last(batch.get());
batch.set_logits_last();
slot.n_decoded = 0;
slot.i_batch = llama_batch_ext_get_n_tokens(batch.get()) - 1;
slot.i_batch = batch.get_n_tokens() - 1;
SLT_INF(slot, "prompt done, n_past = %d, n_tokens = %d\n", slot.n_past, llama_batch_ext_get_n_tokens(batch.get()));
SLT_INF(slot, "prompt done, n_past = %d, n_tokens = %d\n", slot.n_past, batch.get_n_tokens());
}
}
if (llama_batch_ext_get_n_tokens(batch.get()) >= n_batch) {
if (batch.get_n_tokens() >= n_batch) {
break;
}
}
}
if (llama_batch_ext_get_n_tokens(batch.get()) == 0) {
if (batch.get_n_tokens() == 0) {
SRV_WRN("%s", "no tokens to decode\n");
return;
}
SRV_DBG("decoding batch, n_tokens = %d\n", llama_batch_ext_get_n_tokens(batch.get()));
SRV_DBG("decoding batch, n_tokens = %d\n", batch.get_n_tokens());
if (slot_batched) {
// make sure we're in the right embedding mode
@ -3113,12 +3152,12 @@ struct server_context {
}
// process the created batch of tokens
for (int32_t i = 0; i < llama_batch_ext_get_n_tokens(batch.get()); i += n_batch) {
const int32_t n_tokens = std::min(n_batch, llama_batch_ext_get_n_tokens(batch.get()) - i);
for (int32_t i = 0; i < batch.get_n_tokens(); i += n_batch) {
const int32_t n_tokens = std::min(n_batch, batch.get_n_tokens() - i);
llama_batch_ext_ptr batch_view(llama_batch_ext_get_view(batch.get(), i, n_tokens));
server_batch batch_view = batch.get_view(i, n_tokens);
const int ret = llama_decode_ext(ctx, batch_view.get());
const int ret = llama_decode_ext(ctx, batch_view.batch.get());
metrics.on_decoded(slots);
if (ret != 0) {
@ -3253,17 +3292,16 @@ struct server_context {
}
// construct the speculation batch
llama_batch_ext_clear(slot.batch_spec.get());
std::array<llama_token, 1> seq_id = { slot.id };
llama_batch_ext_add_text(slot.batch_spec.get(), id, slot.n_past, seq_id.data(), seq_id.size(), true);
slot.batch_spec.clear();
slot.batch_spec.add_text(id, slot.n_past, slot.id, true);
for (size_t i = 0; i < draft.size(); ++i) {
llama_batch_ext_add_text(slot.batch_spec.get(), draft[i], slot.n_past + 1, seq_id.data(), seq_id.size(), true);
slot.batch_spec.add_text(draft[i], slot.n_past + 1 + i, slot.id, true);
}
SLT_DBG(slot, "decoding speculative batch, size = %d\n", llama_batch_ext_get_n_tokens(slot.batch_spec.get()));
SLT_DBG(slot, "decoding speculative batch, size = %d\n", slot.batch_spec.get_n_tokens());
llama_decode_ext(ctx, slot.batch_spec.get());
llama_decode_ext(ctx, slot.batch_spec.batch.get());
// the accepted tokens from the speculation
const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, draft);

12
include/llama.h
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@ -263,14 +263,6 @@ extern "C" {
// It can contain text tokens and embeddings for one or many sequences
struct llama_batch_ext;
struct llama_batch_ext_token_info {
llama_token token;
llama_pos pos;
int32_t n_seq_id;
llama_seq_id * seq_id;
int8_t logits;
};
enum llama_model_kv_override_type {
LLAMA_KV_OVERRIDE_TYPE_INT,
LLAMA_KV_OVERRIDE_TYPE_FLOAT,
@ -896,10 +888,6 @@ extern "C" {
// Get the number of tokens in the batch
LLAMA_API int32_t llama_batch_ext_get_n_tokens(const struct llama_batch_ext * batch);
LLAMA_API struct llama_batch_ext_token_info llama_batch_ext_get_token_info(
struct llama_batch_ext * batch,
int32_t i);
// Add text tokens to the batch
// Return values:
// 0 : success

13
src/llama-batch.cpp
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@ -480,19 +480,6 @@ struct llama_batch_ext * llama_batch_ext_get_view(
return batch_view;
}
struct llama_batch_ext_token_info llama_batch_ext_get_token_info(
struct llama_batch_ext * batch,
int32_t i) {
GGML_ASSERT(i >= 0 && i < batch->n_tokens);
return llama_batch_ext_token_info{
/*token =*/ batch->token [i],
/*pos =*/ batch->pos [i],
/*n_seq_id =*/ batch->n_seq_id[i],
/*seq_id =*/ batch->seq_id [i],
/*logits =*/ batch->logits [i],
};
}
void llama_batch_ext_free(struct llama_batch_ext * batch) {
// do not free the members if it's a view
if (!batch->is_view) {