vrr/koboldcpp
2
0
Fork 0
mirror of https://github.com/LostRuins/koboldcpp.git synced 2025-09-11 01:24:36 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'master' into concedo_experimental

Browse source 
# Conflicts:
#	.github/workflows/build.yml
#	.github/workflows/docker.yml
#	CMakeLists.txt
#	Makefile
#	README.md
#	ci/README.md
#	ci/run.sh
#	flake.lock
#	ggml-metal.m
#	ggml-opencl.cpp
#	ggml-vulkan-shaders.hpp
#	ggml-vulkan.cpp
#	ggml-vulkan.h
#	ggml.c
#	ggml_vk_generate_shaders.py
#	llama.cpp
#	llama.h
#	pocs/vdot/vdot.cpp
#	tests/test-llama-grammar.cpp
#	tests/test-sampling.cpp
This commit is contained in:
Concedo 2024-01-29 23:12:09 +08:00
commit f73de33f74
37 changed files with 37432 additions and 21072 deletions
Download patch file Download diff file Expand all files Collapse all files

11
examples/server/README.md
View file

@ -30,7 +30,8 @@ Command line options:
- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)
- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
- `--grp-attn-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`
- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`
## Build
server is build alongside everything else from the root of the project
@ -65,6 +66,14 @@ server.exe -m models\7B\ggml-model.gguf -c 2048
The above command will start a server that by default listens on `127.0.0.1:8080`.
You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.
### Docker:
```bash
docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
# or, with CUDA:
docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
```
## Testing with CURL
Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.

180
examples/server/server.cpp
View file

@ -185,6 +185,12 @@ 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;
@ -213,7 +219,8 @@ 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)
@ -400,9 +407,26 @@ 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);
}
@ -658,7 +682,7 @@ struct llama_server_context
while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
size_t end_prefix = pos;
pos += pattern.length();
size_t end_pos = prompt.find("]", pos);
size_t end_pos = prompt.find(']', pos);
if (end_pos != std::string::npos)
{
std::string image_id = prompt.substr(pos, end_pos - pos);
@ -1350,32 +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},
});
}
}
@ -1402,7 +1429,8 @@ struct llama_server_context
slot.i_batch = batch.n_tokens;
llama_batch_add(batch, slot.sampled, 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, system_tokens.size() + slot_npast, { slot.id }, true);
slot.n_past += 1;
}
@ -1500,6 +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
@ -1513,6 +1543,25 @@ struct llama_server_context
slot.n_past = 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);
}
@ -1527,6 +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", {
@ -1539,9 +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], 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], system_tokens.size() + slot_npast, {slot.id }, false);
slot_npast += 1;
}
if (has_images && !ingest_images(slot, n_batch))
@ -1571,6 +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,
@ -1584,6 +1680,7 @@ struct llama_server_context
};
const int ret = llama_decode(ctx, batch_view);
if (ret != 0)
{
if (n_batch == 1 || ret < 0)
@ -1729,6 +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");
}
@ -1914,6 +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)
@ -1982,7 +2100,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
invalid_param = true;
break;
}
#ifdef GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
std::string arg_next = argv[i];
// split string by , and /
@ -2008,7 +2126,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
else if (arg == "--no-mul-mat-q" || arg == "-nommq")
{
#ifdef GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
params.mul_mat_q = false;
#else
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
@ -2021,7 +2139,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
invalid_param = true;
break;
}
#ifdef GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
params.main_gpu = std::stoi(argv[i]);
#else
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});

1
examples/server/utils.hpp
View file

@ -249,6 +249,7 @@ struct llama_server_queue {
}
// Start the main loop. This call is blocking
[[noreturn]]
void start_loop() {
while (true) {
// new task arrived