vbatts/llama.cpp
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server : construct batch with size of llama_n_batch

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Georgi Gerganov 2024-03-13 16:16:20 +02:00
parent 015e1bfe64
commit 0d934ee517
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GPG key ID: BF970631944C16B7
1 changed files with 13 additions and 5 deletions
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examples/server/server.cpp
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@ -147,7 +147,7 @@ struct server_slot {
int32_t n_decoded = 0;
int32_t n_remaining = -1;
int32_t i_batch = -1;
int32_t n_predict = -1;
int32_t n_predict = -1; // TODO: disambiguate from params.n_predict
int32_t n_prompt_tokens = 0;
int32_t n_prompt_tokens_processed = 0;
@ -739,7 +739,13 @@ struct server_context {
default_generation_settings_for_props = get_formated_generation(slots.front());
default_generation_settings_for_props["seed"] = -1;
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
// the update_slots() logic will always submit a maximum of n_batch 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);
batch = llama_batch_init(n_batch, 0, params.n_parallel);
}
metrics.init();
}
@ -1036,8 +1042,10 @@ struct server_context {
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
}
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) {
const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i));
const int32_t n_batch = llama_n_batch(ctx);
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,
@ -1226,7 +1234,7 @@ 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},
{"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict
{"n_keep", params.n_keep},
{"ignore_eos", ignore_eos},
{"stream", slot.params.stream},