ikawrakow/ik_llama.cpp
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A few server commits from mainline. (#872)

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server : handle models with missing EOS token (#8997)

server : fix segfault on long system prompt (#8987)
* server : fix segfault on long system prompt
* server : fix parallel generation with very small batch sizes
* server : fix typo in comment

server : init stop and error fields of the result struct (#9026)

server : fix duplicated n_predict key in the generation_settings (#8994)

server : support reading arguments from environment variables (#9105)
* server : support reading arguments from environment variables
* add -fa and -dt
* readme : specify non-arg env var

server : add some missing env variables (#9116)
* server : add some missing env variables
* add LLAMA_ARG_HOST to server dockerfile
* also add LLAMA_ARG_CONT_BATCHING

Credits are to the respective authors.
Not a single merge conflict occurred.
Compiled, then tested without bug.
This commit is contained in:
Nexes the Elder
2025-10-28 08:58:31 +01:00
committed by GitHub
parent 0a80135392
commit e68dabc242
9 changed files with 142 additions and 33 deletions
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47
examples/server/server.cpp
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@@ -1146,6 +1146,7 @@ struct server_context {
bool clean_kv_cache = true;
bool add_bos_token = true;
bool has_eos_token = false;
// For speculative decoding
llama_model * model_draft = nullptr;
@@ -1232,7 +1233,7 @@ struct server_context {
n_ctx = llama_n_ctx(ctx);
add_bos_token = llama_should_add_bos_token(model);
GGML_ASSERT(llama_add_eos_token(model) != 1);
has_eos_token = llama_add_eos_token(model) != 1;
chat_templates = common_chat_templates_init(model, params.chat_template);
try {
@@ -1350,13 +1351,13 @@ struct server_context {
default_generation_settings_for_props = get_formated_generation(slots.front());
default_generation_settings_for_props["seed"] = -1;
// the update_slots() logic will always submit a maximum of n_batch tokens
// the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens
// note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
{
const int32_t n_batch = llama_n_batch(ctx);
// only a single seq_id per token is needed
batch = llama_batch_init(n_batch, 0, 1);
batch = llama_batch_init(std::max(n_batch, params.n_parallel), 0, 1);
}
metrics.init();
@@ -1784,7 +1785,7 @@ struct server_context {
{
slot.sparams.logit_bias.clear();
if (json_value(data, "ignore_eos", false)) {
if (json_value(data, "ignore_eos", false) && has_eos_token) {
slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
}
@@ -1889,28 +1890,19 @@ struct server_context {
if (!system_prompt.empty()) {
system_tokens = ::llama_tokenize(ctx, system_prompt, true);
llama_batch_clear(batch);
for (int i = 0; i < (int)system_tokens.size(); ++i) {
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
}
const int32_t n_batch = llama_n_batch(ctx);
const int32_t n_tokens_prompt = system_tokens.size();
for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i);
if (llama_decode(ctx, batch_view) != 0) {
llama_batch_clear(batch);
for (int32_t j = 0; j < n_tokens; ++j) {
llama_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false);
}
if (llama_decode(ctx, batch) != 0) {
LOG_ERROR("llama_decode() failed", {});
return;
}
@@ -2114,7 +2106,7 @@ struct server_context {
return json {
{"n_ctx", slot.n_ctx},
{"n_predict", slot.n_predict},
{"n_predict", slot.n_predict}, // Server configured n_predict
{"model", params.model_alias},
{"seed", slot.sparams.seed},
{"temperature", slot.sparams.temp},
@@ -2141,7 +2133,7 @@ struct server_context {
{"mirostat_eta", slot.sparams.mirostat_eta},
{"penalize_nl", slot.sparams.penalize_nl},
{"stop", slot.params.antiprompt},
{"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict
{"max_tokens", slot.params.n_predict}, // User configured n_predict
{"n_keep", slot.params.n_keep},
{"n_discard", slot.params.n_discard},
{"ignore_eos", ignore_eos},
@@ -2673,6 +2665,8 @@ struct server_context {
llama_lora_adapters_apply(ctx, lora_adapters);
server_task_result result;
result.id = task.id;
result.stop = true;
result.error = false;
result.data = json{{ "success", true }};
queue_results.send(result);
} break;
@@ -3619,6 +3613,9 @@ int main(int argc, char ** argv) {
return 1;
}
// parse arguments from environment variables
gpt_params_parse_from_env(params);
// TODO: not great to use extern vars
server_log_json = params.log_json;
server_verbose = params.verbosity > 0;