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10 Commits

Author SHA1 Message Date
Georgi Gerganov
f86b9d152c lookup : minor 2023-12-17 17:25:28 +02:00
Georgi Gerganov
5b27975479 lookup : fix token positions in the draft batch 2023-12-17 16:47:26 +02:00
Leon Ericsson
1b26d7151a Added colors to distinguish drafted tokens (--color). Updated README 2023-12-17 13:04:46 +01:00
Leon Ericsson
45b8032b9c Merge branch 'prompt-lookup' of github.com:LeonEricsson/llama.cpp into prompt-lookup 2023-12-16 12:13:50 +01:00
Leon Ericsson
21431197a1 kv_cache management 2023-12-16 12:12:33 +01:00
LeonEricsson
340484161f Merge branch 'ggerganov:master' into prompt-lookup 2023-12-15 14:15:04 +01:00
Leon Ericsson
1665ad8bf1 BUG: generates gibberish/repeating tokens after a while 2023-12-15 14:14:17 +01:00
ShadovvBeast
88ae8952b6 server : add optional API Key Authentication example (#4441)
* Add API key authentication for enhanced server-client security

* server : to snake_case

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-15 13:49:01 +02:00
Leon Ericsson
0ec5fdb5ce main loop finished, starting to debug 2023-12-10 20:20:01 +01:00
Leon Ericsson
cae8f50b1a initial commit, going through initializations 2023-12-04 21:52:17 +01:00
10 changed files with 324 additions and 11 deletions

1
.gitignore vendored
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@@ -48,6 +48,7 @@ models-mnt
/llama-bench
/llava-cli
/lookahead
/lookup
/main
/metal
/perplexity

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@@ -2,7 +2,7 @@
BUILD_TARGETS = \
main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup tests/test-c.o
# Binaries only useful for tests
TEST_TARGETS = \
@@ -639,6 +639,9 @@ parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
ifdef LLAMA_METAL
metal: examples/metal/metal.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)

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@@ -240,3 +240,4 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
// Dump the KV cache view showing individual sequences in each cell (long output).
void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);

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@@ -33,6 +33,7 @@ else()
add_subdirectory(simple)
add_subdirectory(speculative)
add_subdirectory(lookahead)
add_subdirectory(lookup)
add_subdirectory(train-text-from-scratch)
if (LLAMA_METAL)
add_subdirectory(metal)

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@@ -0,0 +1,5 @@
set(TARGET lookup)
add_executable(${TARGET} lookup.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

13
examples/lookup/README.md Normal file
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@@ -0,0 +1,13 @@
# llama.cpp/examples/lookup
Demonstration of Prompt Lookup Decoding
https://github.com/apoorvumang/prompt-lookup-decoding
The two key parameters for lookup decoding are `max_ngram_size` and `n_draft`. The first, determines how many ngrams to use when searching through the prompt for a match and the second specifies how many subsequent tokens to draft if a match is found.
More info:
https://github.com/ggerganov/llama.cpp/pull/4484
https://github.com/ggerganov/llama.cpp/issues/4226

229
examples/lookup/lookup.cpp Normal file
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@@ -0,0 +1,229 @@
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
// maximum n-grams to search for in prompt
const int max_ngram_size = 3;
// length of the candidate / draft sequence, if match is found
const int n_draft = 10;
const bool dump_kv_cache = params.dump_kv_cache;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("lookup", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
const int max_context_size = llama_n_ctx(ctx);
const int max_tokens_list_size = max_context_size - 4;
if ((int) inp.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
return 1;
}
fprintf(stderr, "\n\n");
for (auto id : inp) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
const int n_input = inp.size();
const auto t_enc_start = ggml_time_us();
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
const auto t_enc_end = ggml_time_us();
int n_predict = 0;
int n_drafted = 0;
int n_accept = 0;
int n_past = inp.size();
bool has_eos = false;
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
std::vector<llama_token> draft;
llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1);
// debug
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, 1);
const auto t_dec_start = ggml_time_us();
while (true) {
// debug
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
dump_kv_cache_view_seqs(kvc_view, 40);
}
// print current draft sequence
LOG("drafted %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, draft).c_str());
int i_dft = 0;
while (true) {
// sample from the target model
llama_token id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_dft);
llama_sampling_accept(ctx_sampling, ctx, id, true);
const std::string token_str = llama_token_to_piece(ctx, id);
if (!params.use_color) {
printf("%s", token_str.c_str());
}
if (id == llama_token_eos(model)) {
has_eos = true;
}
++n_predict;
// check if the target token matches the draft
if (i_dft < (int) draft.size() && id == draft[i_dft]) {
LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
++n_accept;
++n_past;
++i_dft;
inp.push_back(id);
if (params.use_color) {
// color accepted draft token
printf("\033[34m%s\033[0m", token_str.c_str());
fflush(stdout);
}
continue;
}
if (params.use_color) {
printf("%s", token_str.c_str());
}
fflush(stdout);
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
draft.clear();
draft.push_back(id);
inp.push_back(id);
break;
}
if ((params.n_predict > 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// KV cache management
// clean the cache of draft tokens that weren't accepted
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
llama_batch_clear(batch_tgt);
llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
// generate n_pred tokens through prompt lookup
auto prompt_lookup = [&]() -> void {
int inp_size = inp.size();
for (int ngram_size = max_ngram_size ; ngram_size > 0; --ngram_size){
const llama_token * ngram = &inp[inp_size - ngram_size];
for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) {
bool match = true;
for (int j = 0; j < ngram_size; ++j) {
if (inp[i + j] != ngram[j]) {
match = false;
break;
}
}
if (match) {
const int startIdx = i + ngram_size;
const int endIdx = startIdx + n_draft;
if (endIdx < inp_size) {
for (int j = startIdx; j < endIdx; ++j) {
LOG(" - draft candidate %d: %d\n", j, inp[j]);
draft.push_back(inp[j]);
llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true);
++n_drafted;
}
return;
}
}
}
}
return;
};
prompt_lookup();
llama_decode(ctx, batch_tgt);
++n_past;
draft.erase(draft.begin());
}
auto t_dec_end = ggml_time_us();
LOG_TEE("\n\n");
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
LOG_TEE("\n");
LOG_TEE("n_draft = %d\n", n_draft);
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_drafted = %d\n", n_drafted);
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_TEE("\ntarget:\n");
llama_print_timings(ctx);
llama_sampling_free(ctx_sampling);
llama_batch_free(batch_tgt);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}

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@@ -34,7 +34,8 @@ export async function* llama(prompt, params = {}, config = {}) {
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream'
'Accept': 'text/event-stream',
...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
},
signal: controller.signal,
});

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@@ -235,7 +235,8 @@
grammar: '',
n_probs: 0, // no completion_probabilities,
image_data: [],
cache_prompt: true
cache_prompt: true,
api_key: ''
})
/* START: Support for storing prompt templates and parameters in browsers LocalStorage */
@@ -790,6 +791,10 @@
<fieldset>
${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
</fieldset>
<fieldset>
<label for="api_key">API Key</label>
<input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
</fieldset>
</details>
</form>
`

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@@ -36,6 +36,7 @@ using json = nlohmann::json;
struct server_params
{
std::string hostname = "127.0.0.1";
std::string api_key;
std::string public_path = "examples/server/public";
int32_t port = 8080;
int32_t read_timeout = 600;
@@ -1953,6 +1954,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
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(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n");
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
@@ -2002,6 +2004,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
sparams.public_path = argv[i];
}
else if (arg == "--api-key")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.api_key = argv[i];
}
else if (arg == "--timeout" || arg == "-to")
{
if (++i >= argc)
@@ -2669,6 +2680,32 @@ int main(int argc, char **argv)
httplib::Server svr;
// Middleware for API key validation
auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
// If API key is not set, skip validation
if (sparams.api_key.empty()) {
return true;
}
// Check for API key in the header
auto auth_header = req.get_header_value("Authorization");
std::string prefix = "Bearer ";
if (auth_header.substr(0, prefix.size()) == prefix) {
std::string received_api_key = auth_header.substr(prefix.size());
if (received_api_key == sparams.api_key) {
return true; // API key is valid
}
}
// API key is invalid or not provided
res.set_content("Unauthorized: Invalid API Key", "text/plain");
res.status = 401; // Unauthorized
LOG_WARNING("Unauthorized: Invalid API Key", {});
return false;
};
svr.set_default_headers({{"Server", "llama.cpp"},
{"Access-Control-Allow-Origin", "*"},
{"Access-Control-Allow-Headers", "content-type"}});
@@ -2711,8 +2748,11 @@ int main(int argc, char **argv)
res.set_content(data.dump(), "application/json");
});
svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = json::parse(req.body);
const int task_id = llama.request_completion(data, false, false, -1);
if (!json_value(data, "stream", false)) {
@@ -2799,8 +2839,11 @@ int main(int argc, char **argv)
});
// TODO: add mount point without "/v1" prefix -- how?
svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = oaicompat_completion_params_parse(json::parse(req.body));
const int task_id = llama.request_completion(data, false, false, -1);
@@ -2869,8 +2912,11 @@ int main(int argc, char **argv)
}
});
svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = json::parse(req.body);
const int task_id = llama.request_completion(data, true, false, -1);
if (!json_value(data, "stream", false)) {
@@ -3005,11 +3051,15 @@ int main(int argc, char **argv)
svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
{
if (res.status == 401)
{
res.set_content("Unauthorized", "text/plain");
}
if (res.status == 400)
{
res.set_content("Invalid request", "text/plain");
}
else if (res.status != 500)
else if (res.status == 404)
{
res.set_content("File Not Found", "text/plain");
res.status = 404;
@@ -3032,11 +3082,15 @@ int main(int argc, char **argv)
// to make it ctrl+clickable:
LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
LOG_INFO("HTTP server listening", {
{"hostname", sparams.hostname},
{"port", sparams.port},
});
std::unordered_map<std::string, std::string> log_data;
log_data["hostname"] = sparams.hostname;
log_data["port"] = std::to_string(sparams.port);
if (!sparams.api_key.empty()) {
log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4);
}
LOG_INFO("HTTP server listening", log_data);
// run the HTTP server in a thread - see comment below
std::thread t([&]()
{