vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Restore faulty merge p.2

Browse source
This commit is contained in:
Michael Klimenko 2024-01-28 17:44:08 +01:00
parent 92f8f64332
commit e1349fb4b0
1 changed files with 157 additions and 37 deletions
Download patch file Download diff file Expand all files Collapse all files

194
examples/server/server.cpp
View file

@ -184,6 +184,12 @@ struct llama_client_slot
struct llama_sampling_params sparams;
llama_sampling_context *ctx_sampling = nullptr;
int32_t ga_i = 0; // group-attention state
int32_t ga_n = 1;// group-attention factor
int32_t ga_w = 512; // group-attention width
int32_t n_past_se = 0; // self-extend
// multimodal
std::vector<slot_image> images;
@ -212,7 +218,8 @@ struct llama_client_slot
sent_count = 0;
sent_token_probs_index = 0;
infill = false;
ga_i = 0;
n_past_se = 0;
generated_token_probs.clear();
for (slot_image & img : images)
@ -399,9 +406,26 @@ struct llama_server_context
slot.id = i;
slot.n_ctx = n_ctx_slot;
slot.reset();
LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
const int ga_n = params.grp_attn_n;
const int ga_w = params.grp_attn_w;
if (ga_n != 1) {
GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT
GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
LOG_TEE(" -> Slot %i - self-extend: ga_n = %d, ga_w = %d\n", slot.id, ga_n, ga_w);
}
slot.ga_i = 0;
slot.ga_n = ga_n;
slot.ga_w = ga_w;
slot.reset();
slots.push_back(slot);
}
@ -1202,7 +1226,8 @@ struct llama_server_context
(json)(slot.images[image_idx].prefix_prompt);
std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
for (int append_token : append_tokens) {
for (int append_token : append_tokens)
{
llama_batch_add(batch, append_token, slot.n_past, { slot.id }, true);
slot.n_past += 1;
}
@ -1221,12 +1246,12 @@ struct llama_server_context
void split_multiprompt_task(int multitask_id, task_server& multiprompt_task)
{
std::size_t prompt_count = multiprompt_task.data.at("prompt").size();
int prompt_count = int(multiprompt_task.data.at("prompt").size());
assert(prompt_count > 1);
// generate all the ID for subtask
std::vector<int> subtask_ids(prompt_count);
for (std::size_t i = 0; i < prompt_count; i++)
for (int i = 0; i < prompt_count; i++)
{
subtask_ids[i] = queue_tasks.get_new_id();
}
@ -1235,7 +1260,7 @@ struct llama_server_context
queue_tasks.add_multitask(multitask_id, subtask_ids);
// add subtasks
for (std::size_t i = 0; i < prompt_count; i++)
for (int i = 0; i < prompt_count; i++)
{
json subtask_data = multiprompt_task.data;
subtask_data["prompt"] = subtask_data["prompt"][i];
@ -1349,32 +1374,35 @@ struct llama_server_context
for (llama_client_slot &slot : slots)
{
if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
if (slot.ga_n == 1)
{
// Shift context
const int n_left = slot.n_past - slot.params.n_keep - 1;
const int n_discard = n_left / 2;
LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard);
for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
{
slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
// Shift context
const int n_left = slot.n_past - slot.params.n_keep - 1;
const int n_discard = n_left / 2;
LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard);
for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
{
slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
}
slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
slot.n_past -= n_discard;
slot.truncated = true;
LOG_VERBOSE("context shift", {
{ "n_ctx", n_ctx },
{ "n_keep", params.n_keep },
{ "n_left", n_left },
});
}
slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
slot.n_past -= n_discard;
slot.truncated = true;
LOG_VERBOSE("context shift", {
{"n_ctx", n_ctx},
{"n_keep", params.n_keep},
{"n_left", n_left},
});
}
}
@ -1401,7 +1429,8 @@ struct llama_server_context
slot.i_batch = batch.n_tokens;
llama_batch_add(batch, slot.sampled, llama_pos(system_tokens.size() + slot.n_past), { slot.id }, true);
const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
llama_batch_add(batch, slot.sampled, int(system_tokens.size() + slot_npast), {slot.id}, true);
slot.n_past += 1;
}
@ -1499,6 +1528,8 @@ struct llama_server_context
llama_sampling_reset(slot.ctx_sampling);
slot.n_past = 0;
slot.n_past_se = 0;
slot.ga_i = 0;
slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
}
else
@ -1512,6 +1543,25 @@ struct llama_server_context
slot.n_past = int32_t(common_part(slot.cache_tokens, prompt_tokens));
slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
if (slot.ga_n != 1)
{
int ga_i = 0;
int32_t ga_n = slot.ga_n;
int32_t ga_w = slot.ga_w;
int32_t slot_npast = 0;
for (int k = 0; k < slot.n_past; ++k)
{
while (slot_npast >= ga_i + ga_w) {
const int bd = (ga_w/ga_n)*(ga_n - 1);
slot_npast -= bd;
ga_i += ga_w/ga_n;
}
slot_npast++;
}
slot.n_past_se = slot_npast;
slot.ga_i = ga_i;
}
LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
}
@ -1526,6 +1576,10 @@ struct llama_server_context
// we have to evaluate at least 1 token to generate logits.
LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
slot.n_past--;
if (slot.ga_i > 0)
{
slot.n_past_se--;
}
}
LOG_VERBOSE("prompt ingested", {
@ -1538,9 +1592,22 @@ struct llama_server_context
// process the prefix of first image
std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
int ga_i = slot.ga_i;
int32_t ga_n = slot.ga_n;
int32_t ga_w = slot.ga_w;
for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
{
llama_batch_add(batch, prefix_tokens[slot.n_past], llama_pos(system_tokens.size() + slot.n_past), { slot.id }, false);
if (slot.ga_n != 1)
{
while (slot_npast >= ga_i + ga_w) {
const int bd = (ga_w/ga_n)*(ga_n - 1);
slot_npast -= bd;
ga_i += ga_w/ga_n;
}
}
llama_batch_add(batch, prefix_tokens[slot.n_past], llama_pos(system_tokens.size() + slot_npast), {slot.id }, false);
slot_npast += 1;
}
if (has_images && !ingest_images(slot, n_batch))
@ -1570,6 +1637,36 @@ struct llama_server_context
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
{
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
for (auto & slot : slots)
{
if (slot.ga_n != 1)
{
// context extension via Self-Extend
while (slot.n_past_se >= slot.ga_i + slot.ga_w)
{
const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
LOG_TEE("\n");
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
slot.n_past_se -= bd;
slot.ga_i += slot.ga_w / slot.ga_n;
LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
}
slot.n_past_se += n_tokens;
}
}
llama_batch batch_view =
{
n_tokens,
@ -1583,6 +1680,7 @@ struct llama_server_context
};
const int ret = llama_decode(ctx, batch_view);
if (ret != 0)
{
if (n_batch == 1 || ret < 0)
@ -1728,6 +1826,8 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf(" -gan N, --grp-attn-n N Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`");
printf(" -gaw N, --grp-attn-w N Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`");
printf("\n");
}
@ -1913,6 +2013,25 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
params.n_threads = std::stoi(argv[i]);
}
else if (arg == "--grp-attn-n" || arg == "-gan")
{
if (++i >= argc) {
invalid_param = true;
break;
}
params.grp_attn_n = std::stoi(argv[i]);
}
else if (arg == "--grp-attn-w" || arg == "-gaw")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.grp_attn_w = std::stoi(argv[i]);
}
else if (arg == "--threads-batch" || arg == "-tb")
{
if (++i >= argc)
@ -2033,7 +2152,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
invalid_param = true;
break;
}
params.lora_adapter.emplace_back(argv[i], 1.0f);
params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
params.use_mmap = false;
}
else if (arg == "--lora-scaled")
@ -2049,7 +2168,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
invalid_param = true;
break;
}
params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
params.use_mmap = false;
}
else if (arg == "--lora-base")
@ -2191,7 +2310,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
}
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back();
params.kv_overrides.emplace_back(llama_model_kv_override());
params.kv_overrides.back().key[0] = 0;
}
@ -2625,11 +2744,12 @@ int main(int argc, char **argv)
if (!llama_result.error) {
std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
for (auto& it : result_array) {
if (!it.empty()) {
for (auto it = result_array.begin(); it != result_array.end(); ++it)
{
if (!it->empty()) {
const std::string str =
"data: " +
it.dump(-1, ' ', false, json::error_handler_t::replace) +
it->dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {