LOG_VERBOSE("eos token found", {});
}
+ auto n_ctx_train = llama_n_ctx_train(model);
+ if (slot.params.n_predict < 1 && slot.ga_n == 1
+ && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
+ LOG_WARNING("n_predict is not set and self-context extend is disabled."
+ " Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", {
+ { "id_slot", slot.id },
+ { "params.n_predict", slot.params.n_predict },
+ { "slot.n_prompt_tokens", slot.n_prompt_tokens },
+ { "slot.n_decoded", slot.n_decoded },
+ { "slot.n_predict", slot.n_predict },
+ { "n_slots", params.n_parallel },
+ { "slot.n_ctx", slot.n_ctx },
+ { "n_ctx", n_ctx },
+ { "n_ctx_train", n_ctx_train },
+ { "ga_n", slot.ga_n },
+ });
+ slot.truncated = true;
+ slot.stopped_limit = true;
+ slot.has_next_token = false; // stop prediction
+ }
+
LOG_VERBOSE("next token", {
{"id_slot", slot.id},
{"id_task", slot.id_task},
});
// process the created batch of tokens
- for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
+ for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
for (auto & slot : slots) {