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server: handle multiple completions with cam

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FSSRepo 2023-10-03 17:39:24 -04:00
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commit e44cef8144
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1
examples/CMakeLists.txt
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@ -35,6 +35,7 @@ else()
endif()
if (LLAMA_BUILD_SERVER)
add_subdirectory(server)
add_subdirectory(server-parallel)
endif()
add_subdirectory(export-lora)
endif()

15
examples/server-parallel/CMakeLists.txt Normal file
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set(TARGET server-parallel)
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
add_executable(${TARGET} server.cpp ../server/json.hpp ../server/httplib.h)
install(TARGETS ${TARGET} RUNTIME)
target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
if (WIN32)
TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
endif()
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

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examples/server-parallel/README.md Normal file
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@ -0,0 +1,23 @@
# llama.cpp/example/server-parallel
This example demonstrates a PoC HTTP API server that handles simulataneus requests.
## Quick Start
To get started right away, run the following command, making sure to use the correct path for the model you have:
### Unix-based systems (Linux, macOS, etc.):
```bash
./server-parallel -m models/7B/ggml-model.gguf --ctx_size 2048 -t 4 -ngl 33 --batch-size 512 --parallel 3 -n 512 --cont-batching --reverse-prompt "User:"
```
### Windows:
```powershell
server-parallel.exe -m models\7B\ggml-model.gguf --ctx_size 2048 -t 4 -ngl 33 --batch-size 512 --parallel 3 -n 512 --cont-batching --reverse-prompt "User:"
```
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.
# This example is a Proof of Concept, have bugs and unexpected behaivors

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examples/server-parallel/index.h Normal file
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const auto index_html = R"(
<!DOCTYPE html>
<html>
<head>
<title>llama.cpp - server parallel PoC</title>
</head>
<body>
<div style="width: 90%;margin: auto;">
<h2>Server parallel - Proof of Concept</h2>
<form id="myForm">
<label for="client_slot">Client Slot (-1 load in a idle client)</label>
<input type="number" id="client_slot" value="-1" required>
<br><br>
<label for="message">Message</label>
<input id="message" style="width: 80%;" required>
<br><br>
<button type="button" id="btn_send" onclick="perform() " >Send</button>
<button type="button" onclick="reset() " >Reset</button>
</form>
<div id="conversation_view">
</div>
</div>
<script>
let conversation = [];
let current_message = -1;
const questions = ["Who is Elon Musk?", "Who is Jeff Bezos?", "How to get a job at google?", "What are you?", "When was born Abraham Lincoln?"];
window.onload = function() {
document.getElementById("message").value = questions[Math.floor(Math.random() * questions.length)];
};
function updateView() {
let conv_view = document.getElementById("conversation_view");
// build view
conv_view.innerHTML = "";
for(let index in conversation) {
conversation[index].assistant = conversation[index].assistant.replace("User:", "");
conv_view.innerHTML += `
<p><span style="font-weight: bold">User:</span> ${conversation[index].user}<p>
<p style="white-space: pre-line;"><span style="font-weight: bold">Assistant:</span> ${conversation[index].assistant}<p>`;
}
}
async function call_llama(options) {
const response = await fetch("/completion", {
method: 'POST',
body: JSON.stringify(options),
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream'
}
});
const reader = response.body.getReader();
let cont = true;
const decoder = new TextDecoder();
let leftover = ""; // Buffer for partially read lines
try {
let cont = true;
while (cont) {
const result = await reader.read();
if (result.done) {
break;
}
// Add any leftover data to the current chunk of data
const text = leftover + decoder.decode(result.value);
// Check if the last character is a line break
const endsWithLineBreak = text.endsWith('\n');
// Split the text into lines
let lines = text.split('\n');
// If the text doesn't end with a line break, then the last line is incomplete
// Store it in leftover to be added to the next chunk of data
if (!endsWithLineBreak) {
leftover = lines.pop();
} else {
leftover = ""; // Reset leftover if we have a line break at the end
}
// Parse all sse events and add them to result
const regex = /^(\S+):\s(.*)$/gm;
for (const line of lines) {
const match = regex.exec(line);
if (match) {
result[match[1]] = match[2]
// since we know this is llama.cpp, let's just decode the json in data
if (result.data) {
result.data = JSON.parse(result.data);
conversation[current_message].assistant += result.data.token;
updateView();
}
}
}
}
} catch (e) {
if (e.name !== 'AbortError') {
console.error("llama error: ", e);
}
throw e;
}
}
function generatePrompt() {
let prompt = '';
for(let index in conversation) {
if(index == 0) {
prompt += conversation[index].user + "\n";
} else {
prompt += "User: " + conversation[index].user + "\n";
}
if(index == current_message) {
prompt += "Assistant:";
} else {
prompt += "Assistant: " + conversation[index].assistant;
}
}
return prompt;
}
function reset() {
conversation = [];
document.getElementById("client_slot").value = "-1";
document.getElementById("message").value = "";
document.getElementById("conversation_view").innerHTML = "";
}
async function perform() {
var client_slot = parseFloat(document.getElementById("client_slot").value);
var prompt = document.getElementById("message").value;
if (!isNaN(client_slot) && prompt.length > 0) {
current_message++;
conversation.push({
user: prompt,
assistant: ''
});
updateView();
await call_llama({
client_slot,
prompt: generatePrompt()
});
} else {
document.getElementById("conversation_view").innerText = "please, insert valid props.";
}
}
</script>
</body>
</html>
)";

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examples/server-parallel/server.cpp Normal file
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@ -0,0 +1,809 @@
#include <chrono>
#include "../server/httplib.h"
#include "../server/json.hpp"
#include <iostream>
#include <sstream>
#include <thread>
#include <vector>
#include "index.h"
#include "common.h"
#include "llama.h"
using namespace httplib;
using namespace std;
using namespace nlohmann;
struct server_params
{
std::string hostname = "127.0.0.1";
std::string public_path = "examples/server/public";
int32_t port = 8080;
int32_t read_timeout = 600;
int32_t write_timeout = 600;
};
// utils functions taken of examples/server
static bool ends_with(const std::string &str, const std::string &suffix)
{
return str.size() >= suffix.size() &&
0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
}
static size_t find_partial_stop_string(const std::string &stop,
const std::string &text)
{
if (!text.empty() && !stop.empty())
{
const char text_last_char = text.back();
for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--)
{
if (stop[char_index] == text_last_char)
{
const std::string current_partial = stop.substr(0, char_index + 1);
if (ends_with(text, current_partial))
{
return text.size() - char_index - 1;
}
}
}
}
return std::string::npos;
}
enum stop_type
{
STOP_FULL,
STOP_PARTIAL,
};
enum slot_state
{
BUSY,
IDLE,
NEXT_TOKEN
};
static std::string system_prompt =
R"(Transcript of a never ending dialog, where the User interacts with an Assistant.
The Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
User: Recommend a nice restaurant in the area.
Assistant: I recommend the restaurant "The Golden Duck". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.
User: Who is Richard Feynman?
Assistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including "Surely You're Joking, Mr. Feynman!" and "What Do You Care What Other People Think?".
User:)";
struct llama_client_slot
{
int id;
int32_t n_prompt = 0;
int32_t n_decoded = 0;
int32_t i_batch = -1;
bool process_prompt = false;
bool release_slot = false;
bool forced_release = false;
string prompt = "";
string sampled_token_str;
string generated_text;
llama_token sampled;
std::vector<llama_token> tokens_prev;
slot_state state = IDLE;
};
struct server_parallel_context {
// example props
vector<llama_client_slot> slots;
// llama native props
gpt_params params;
llama_model *model = NULL;
llama_context *ctx = NULL;
int n_ctx;
int n_vocab;
std::vector<llama_token_data> candidates;
std::vector<llama_token> tokens_system;
int32_t n_tokens_system;
llama_batch batch;
bool request_clean_kv = true;
bool loadModel(gpt_params params_) {
params = params_;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == nullptr)
{
LOG_TEE("unable to load model: %s", params.model);
return false;
}
n_ctx = llama_n_ctx(ctx);
n_vocab = llama_n_vocab(model);
candidates.reserve(n_vocab);
return true;
}
void initializeSlots() {
LOG_TEE("Available slots:\n");
for (int i = 0; i < params.n_parallel; i++)
{
llama_client_slot slot;
slot.id = i;
slot.prompt = "default";
slot.state = IDLE;
slot.tokens_prev.resize(std::max(256, params.n_predict));
std::fill(slot.tokens_prev.begin(), slot.tokens_prev.end(), 0);
LOG_TEE(" -> client slot: %i\n", slot.id);
slots.push_back(slot);
}
}
bool loadSystemPrompt() {
tokens_system = ::llama_tokenize(ctx, system_prompt, true);
n_tokens_system = tokens_system.size();
batch = llama_batch_init(params.n_ctx, 0);
{
LOG_TEE("Evaluating the system prompt ...\n");
batch.n_tokens = n_tokens_system;
for (int32_t i = 0; i < batch.n_tokens; ++i)
{
batch.token[i] = tokens_system[i];
batch.pos[i] = i;
batch.seq_id[i] = 0;
batch.logits[i] = false;
}
if (llama_decode(ctx, batch) != 0)
{
LOG_TEE("%s: llama_decode() failed\n", __func__);
return false;
}
// assign the system KV cache to all parallel sequences
for (int32_t i = 1; i < params.n_parallel; ++i)
{
llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system);
}
}
return true;
}
llama_client_slot* loadPrompt(int slot_id, string prompt) {
for (llama_client_slot & slot : slots)
{
if (
slot_id == -1 && slot.state == IDLE ||
slot.id == slot_id)
{
slot.prompt = prompt;
slot.process_prompt = true;
LOG_TEE("client %i is workloaded\n", slot.id);
return &slot; // return a pointer to slot (thread safe?)
}
}
return nullptr;
}
size_t findStoppingStrings(const std::string &text, const size_t last_token_size,
const stop_type type)
{
size_t stop_pos = std::string::npos;
for (const std::string &word : params.antiprompt)
{
size_t pos;
if (type == STOP_FULL)
{
const size_t tmp = word.size() + last_token_size;
const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
pos = text.find(word, from_pos);
}
else
{
pos = find_partial_stop_string(word, text);
}
if (pos != std::string::npos &&
(stop_pos == std::string::npos || pos < stop_pos))
{
stop_pos = pos;
}
}
return stop_pos;
}
bool updateSlots() {
batch.n_tokens = 0;
// decode any currently ongoing sequences
for (auto & slot : slots) {
if(slot.release_slot && slot.state == BUSY || slot.forced_release) {
if(slot.forced_release) {
llama_kv_cache_seq_rm(ctx, slot.id, n_tokens_system, n_ctx);
slot.forced_release = false;
}
LOG_TEE("client %i is released\n", slot.id);
slot.state = IDLE;
slot.release_slot = false;
}
if (slot.state == IDLE) {
continue;
}
batch.token [batch.n_tokens] = slot.sampled;
batch.pos [batch.n_tokens] = n_tokens_system + slot.n_prompt + slot.n_decoded;
batch.seq_id[batch.n_tokens] = slot.id;
batch.logits[batch.n_tokens] = true;
slot.n_decoded += 1;
slot.i_batch = batch.n_tokens;
batch.n_tokens += 1;
}
if (batch.n_tokens == 0 && request_clean_kv) {
// all sequences have ended - clear the entire KV cache
for (int i = 0; i < params.n_parallel; ++i) {
llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1);
}
request_clean_kv = false;
LOG_TEE("%s: clearing the KV cache\n", __func__);
}
// assign workload to the slots
if (params.cont_batching || batch.n_tokens == 0) {
for (llama_client_slot & slot : slots) {
if (slot.state == IDLE && slot.process_prompt) {
slot.state = BUSY;
slot.process_prompt = false;
//LOG_TEE("client %i process prompt:\n%s'------------------------------\n", slot.id, slot.prompt.c_str());
std::fill(slot.tokens_prev.begin(), slot.tokens_prev.end(), 0);
// do not prepend BOS because we have a system prompt!
std::vector<llama_token> tokens_prompt;
tokens_prompt = ::llama_tokenize(ctx, slot.prompt, false);
for (size_t i = 0; i < tokens_prompt.size(); ++i) {
batch.token [batch.n_tokens] = tokens_prompt[i];
batch.pos [batch.n_tokens] = i + n_tokens_system;
batch.seq_id[batch.n_tokens] = slot.id;
batch.logits[batch.n_tokens] = false;
batch.n_tokens += 1;
}
// extract the logits only for the last token
if (batch.n_tokens > 0) {
batch.logits[batch.n_tokens - 1] = true;
}
slot.n_prompt = tokens_prompt.size();
slot.n_decoded = 0;
slot.i_batch = batch.n_tokens - 1;
// insert new requests one-by-one
//if (cont_batching) {
// break;
//}
}
}
}
if (batch.n_tokens == 0) {
return true;
}
// process in chunks of params.n_batch
int32_t n_batch = params.n_batch;
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
// experiment: process in powers of 2
//if (i + n_batch > (int32_t) batch.n_tokens && n_batch > 32) {
// n_batch /= 2;
// i -= n_batch;
// continue;
//}
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
const int ret = llama_decode(ctx, batch_view);
if (ret != 0) {
if (n_batch == 1 || ret < 0) {
// if you get here, it means the KV cache is full - try increasing it via the context size
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
return false;
}
LOG("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2);
// retry with half the batch size to try to find a free slot in the KV cache
n_batch /= 2;
i -= n_batch;
continue;
}
for (auto & slot : slots) {
if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
continue;
}
const llama_token id = llama_sample_token(ctx, NULL, NULL, params, slot.tokens_prev, candidates, slot.i_batch - i);
// remember which tokens were sampled - used for repetition penalties during sampling
slot.tokens_prev.erase(slot.tokens_prev.begin());
slot.tokens_prev.push_back(id);
const std::string token_str = llama_token_to_piece(ctx, id);
slot.generated_text += token_str;
slot.sampled = id;
size_t pos = 0;
size_t stop_pos =
findStoppingStrings(slot.generated_text, token_str.size(), STOP_FULL);
slot.sampled_token_str = token_str;
slot.state = NEXT_TOKEN;
if (slot.n_decoded > 2 &&
(id == llama_token_eos(ctx) ||
(params.n_predict > 0 &&
slot.n_decoded + slot.n_prompt >=
params.n_predict) ||
stop_pos != std::string::npos)) {
// delete only the generated part of the sequence, i.e. keep the system prompt in the cache
llama_kv_cache_seq_rm(ctx, slot.id, n_tokens_system, n_ctx);
//LOG_TEE("client %i generated text:\n%s'------------------------------\n", slot.id, slot.generated_text.c_str());
slot.generated_text.clear();
slot.release_slot = true;
}
slot.i_batch = -1;
}
return true;
}
}
};
static void server_print_usage(const char *argv0, const gpt_params &params,
const server_params &sparams)
{
printf("usage: %s [options]\n", argv0);
printf("\n");
printf("options:\n");
printf(" -h, --help show this help message and exit\n");
printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n");
printf(" --rope-freq-scale N RoPE frequency scaling factor (default: loaded from model)\n");
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
if (llama_mlock_supported())
{
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
if (llama_mmap_supported())
{
printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
printf(" --numa attempt optimizations that help on some NUMA systems\n");
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
printf(" -ngl N, --n-gpu-layers N\n");
printf(" number of layers to store in VRAM\n");
printf(" -ts SPLIT --tensor-split SPLIT\n");
printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
printf(" -nommq, --no-mul-mat-q\n");
printf(" use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
printf(" Not recommended since this is both slower and uses more VRAM.\n");
#endif
printf(" -m FNAME, --model FNAME\n");
printf(" model path (default: %s)\n", params.model.c_str());
printf(" -a ALIAS, --alias ALIAS\n");
printf(" set an alias for the model, will be added as `model` field in completion response\n");
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
printf(" --port PORT port to listen (default (default: %d)\n", sparams.port);
printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
// new arguments
printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel);
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
printf(" -f FNAME, --file FNAME\n");
printf(" load a system prompt from a file.\n");
printf("\n");
}
static void server_params_parse(int argc, char **argv, server_params &sparams,
gpt_params &params)
{
gpt_params default_params;
server_params default_sparams;
std::string arg;
bool invalid_param = false;
for (int i = 1; i < argc; i++)
{
arg = argv[i];
if (arg == "--port")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.port = std::stoi(argv[i]);
}
else if (arg == "--host")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.hostname = argv[i];
}
else if (arg == "--path")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.public_path = argv[i];
}
else if (arg == "--timeout" || arg == "-to")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.read_timeout = std::stoi(argv[i]);
sparams.write_timeout = std::stoi(argv[i]);
}
else if (arg == "-m" || arg == "--model")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.model = argv[i];
}
else if (arg == "-a" || arg == "--alias")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.model_alias = argv[i];
}
else if (arg == "-h" || arg == "--help")
{
server_print_usage(argv[0], default_params, default_sparams);
exit(0);
}
else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_ctx = std::stoi(argv[i]);
}
else if (arg == "--rope-freq-base")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.rope_freq_base = std::stof(argv[i]);
}
else if (arg == "--rope-freq-scale")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.rope_freq_scale = std::stof(argv[i]);
}
else if (arg == "--memory-f32" || arg == "--memory_f32")
{
params.memory_f16 = false;
}
else if (arg == "--threads" || arg == "-t")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_threads = std::stoi(argv[i]);
}
else if (arg == "-b" || arg == "--batch-size")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_batch = std::stoi(argv[i]);
params.n_batch = std::min(512, params.n_batch);
}
else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
params.n_gpu_layers = std::stoi(argv[i]);
#else
LOG_TEE("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
"See main README.md for information on enabling GPU BLAS support\n");
#endif
}
else if (arg == "--tensor-split" || arg == "-ts")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
#ifdef GGML_USE_CUBLAS
std::string arg_next = argv[i];
// split string by , and /
const std::regex regex{R"([,/]+)"};
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
std::vector<std::string> split_arg{it, {}};
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device)
{
if (i_device < split_arg.size())
{
params.tensor_split[i_device] = std::stof(split_arg[i_device]);
}
else
{
params.tensor_split[i_device] = 0.0f;
}
}
#else
LOG_TEE("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
#endif // GGML_USE_CUBLAS
}
else if (arg == "--no-mul-mat-q" || arg == "-nommq")
{
#ifdef GGML_USE_CUBLAS
params.mul_mat_q = false;
#else
LOG_TEE("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n");
#endif // GGML_USE_CUBLAS
}
else if (arg == "--main-gpu" || arg == "-mg")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
#ifdef GGML_USE_CUBLAS
params.main_gpu = std::stoi(argv[i]);
#else
LOG_TEE("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.");
#endif
}
else if (arg == "--lora")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.lora_adapter.push_back({argv[i], 1.0f});
params.use_mmap = false;
}
else if (arg == "--lora-scaled")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
const char * lora_adapter = argv[i];
if (++i >= argc)
{
invalid_param = true;
break;
}
params.lora_adapter.push_back({lora_adapter, std::stof(argv[i])});
params.use_mmap = false;
}
else if (arg == "--lora-base")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.lora_base = argv[i];
}
else if (arg == "--mlock")
{
params.use_mlock = true;
}
else if (arg == "--no-mmap")
{
params.use_mmap = false;
}
else if (arg == "--numa")
{
params.numa = true;
} else if (arg == "-cb" || arg == "--cont-batching") {
params.cont_batching = true;
} else if (arg == "-np" || arg == "--parallel") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_parallel = std::stoi(argv[i]);
} else if (arg == "-n" || arg == "--n-predict") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_predict = std::stoi(argv[i]);
} else if (arg == "-f" || arg == "--file") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
break;
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(system_prompt));
if (system_prompt.back() == '\n') {
system_prompt.pop_back();
}
} else if (arg == "-r" || arg == "--reverse-prompt") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.antiprompt.push_back(argv[i]);
}
else
{
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
server_print_usage(argv[0], default_params, default_sparams);
exit(1);
}
}
if (invalid_param)
{
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
server_print_usage(argv[0], default_params, default_sparams);
exit(1);
}
}
void processClient(server_parallel_context* ctx)
{
bool running = true;
while (running)
{
running = ctx->updateSlots();
}
}
int main(int argc, char **argv)
{
gpt_params params;
server_params sparams;
server_params_parse(argc, argv, sparams, params);
llama_backend_init(params.numa);
// load the target model
params.logits_all = true;
server_parallel_context llama;
if(!llama.loadModel(params)) {
return 1;
}
// create slots
llama.initializeSlots();
// process system prompt
llama.loadSystemPrompt();
Server svr;
svr.Get("/", [&](const Request & /*req*/, Response &res)
{ res.set_content(index_html, "text/html"); });
svr.Post("/completion", [&llama](const Request &req, Response &res)
{
json data = json::parse(req.body);
int slot_id = data.value("client_slot", -1);
string prompt = data.value("prompt", "");
llama_client_slot* slot_client = llama.loadPrompt(slot_id, prompt);
// Verify if the slot exist
if (slot_client) {
res.set_chunked_content_provider("text/event-stream",
[slot_client](size_t /*offset*/, DataSink &sink) {
if(slot_client->state == IDLE && !slot_client->process_prompt) { // slot has been released
sink.done();
return false;
}
if(slot_client->state == NEXT_TOKEN) { // new token notification
stringstream ss;
json res_d = {{"token", slot_client->sampled_token_str}};
ss << "data: " << res_d.dump() << "\n\n";
string result = ss.str();
if(!sink.write(result.c_str(), result.size())) { // user request release
slot_client->forced_release = true;
return false;
}
slot_client->state = BUSY; // process next token
}
return true;
});
} else {
LOG_TEE("slot unavailable\n");
res.status = 404;
res.set_content("slot_error", "text/plain");
} });
thread t(processClient, &llama);
svr.set_read_timeout(sparams.read_timeout);
svr.set_write_timeout(sparams.write_timeout);
if (!svr.bind_to_port(sparams.hostname, sparams.port))
{
fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
return 1;
}
// Set the base directory for serving static files
svr.set_base_dir(sparams.public_path);
// to make it ctrl+clickable:
printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
if (!svr.listen_after_bind())
{
return 1;
}
}