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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/docker.yml
#	CMakeLists.txt
#	CONTRIBUTING.md
#	docs/android.md
#	docs/docker.md
#	examples/embedding/embedding.cpp
#	examples/imatrix/imatrix.cpp
#	examples/infill/infill.cpp
#	examples/llama-bench/llama-bench.cpp
#	examples/main/README.md
#	examples/parallel/parallel.cpp
#	examples/perplexity/perplexity.cpp
#	examples/quantize-stats/quantize-stats.cpp
#	examples/save-load-state/save-load-state.cpp
#	examples/server/README.md
#	examples/simple/CMakeLists.txt
#	examples/speculative/speculative.cpp
#	flake.lock
#	ggml/src/CMakeLists.txt
#	ggml/src/ggml-blas.cpp
#	pocs/vdot/q8dot.cpp
#	pocs/vdot/vdot.cpp
#	scripts/debug-test.sh
#	scripts/sync-ggml.last
#	src/llama.cpp
#	tests/test-backend-ops.cpp
#	tests/test-chat-template.cpp
#	tests/test-quantize-fns.cpp
#	tests/test-quantize-perf.cpp
#	tests/test-tokenizer-0.cpp
#	tests/test-tokenizer-1-bpe.cpp
#	tests/test-tokenizer-1-spm.cpp
This commit is contained in:
Concedo 2024-10-11 11:59:59 +08:00
commit e692a79aab
61 changed files with 2579 additions and 1949 deletions
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26
.devops/full-musa.Dockerfile Normal file
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@ -0,0 +1,26 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG MUSA_VERSION=rc3.1.0
# Target the MUSA build image
ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
FROM ${BASE_MUSA_DEV_CONTAINER} AS build
RUN apt-get update && \
apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
COPY requirements.txt requirements.txt
COPY requirements requirements
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release -j$(nproc) && \
cp build/bin/* .
ENTRYPOINT ["/app/.devops/tools.sh"]

30
.devops/llama-cli-musa.Dockerfile Normal file
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@ -0,0 +1,30 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG MUSA_VERSION=rc3.1.0
# Target the MUSA build image
ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
# Target the MUSA runtime image
ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
FROM ${BASE_MUSA_DEV_CONTAINER} AS build
RUN apt-get update && \
apt-get install -y build-essential git cmake
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-cli -j$(nproc)
FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libgomp1
COPY --from=build /app/build/ggml/src/libggml.so /libggml.so
COPY --from=build /app/build/src/libllama.so /libllama.so
COPY --from=build /app/build/bin/llama-cli /llama-cli
ENTRYPOINT [ "/llama-cli" ]

35
.devops/llama-server-musa.Dockerfile Normal file
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@ -0,0 +1,35 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG MUSA_VERSION=rc3.1.0
# Target the MUSA build image
ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
# Target the MUSA runtime image
ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
FROM ${BASE_MUSA_DEV_CONTAINER} AS build
RUN apt-get update && \
apt-get install -y build-essential git cmake libcurl4-openssl-dev
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-server -j$(nproc)
FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev libgomp1 curl
COPY --from=build /app/build/ggml/src/libggml.so /libggml.so
COPY --from=build /app/build/src/libllama.so /libllama.so
COPY --from=build /app/build/bin/llama-server /llama-server
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/llama-server" ]

880
common/arg.cpp
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44
common/arg.h
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@ -10,7 +10,7 @@
// CLI argument parsing
//
struct llama_arg {
struct common_arg {
std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
std::vector<const char *> args;
const char * value_hint = nullptr; // help text or example for arg value
@ -18,60 +18,60 @@ struct llama_arg {
const char * env = nullptr;
std::string help;
bool is_sparam = false; // is current arg a sampling param?
void (*handler_void) (gpt_params & params) = nullptr;
void (*handler_string) (gpt_params & params, const std::string &) = nullptr;
void (*handler_str_str)(gpt_params & params, const std::string &, const std::string &) = nullptr;
void (*handler_int) (gpt_params & params, int) = nullptr;
void (*handler_void) (common_params & params) = nullptr;
void (*handler_string) (common_params & params, const std::string &) = nullptr;
void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr;
void (*handler_int) (common_params & params, int) = nullptr;
llama_arg(
common_arg(
const std::initializer_list<const char *> & args,
const char * value_hint,
const std::string & help,
void (*handler)(gpt_params & params, const std::string &)
void (*handler)(common_params & params, const std::string &)
) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
llama_arg(
common_arg(
const std::initializer_list<const char *> & args,
const char * value_hint,
const std::string & help,
void (*handler)(gpt_params & params, int)
void (*handler)(common_params & params, int)
) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
llama_arg(
common_arg(
const std::initializer_list<const char *> & args,
const std::string & help,
void (*handler)(gpt_params & params)
void (*handler)(common_params & params)
) : args(args), help(help), handler_void(handler) {}
// support 2 values for arg
llama_arg(
common_arg(
const std::initializer_list<const char *> & args,
const char * value_hint,
const char * value_hint_2,
const std::string & help,
void (*handler)(gpt_params & params, const std::string &, const std::string &)
void (*handler)(common_params & params, const std::string &, const std::string &)
) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
llama_arg & set_examples(std::initializer_list<enum llama_example> examples);
llama_arg & set_env(const char * env);
llama_arg & set_sparam();
common_arg & set_examples(std::initializer_list<enum llama_example> examples);
common_arg & set_env(const char * env);
common_arg & set_sparam();
bool in_example(enum llama_example ex);
bool get_value_from_env(std::string & output);
bool has_value_from_env();
std::string to_string();
};
struct gpt_params_context {
struct common_params_context {
enum llama_example ex = LLAMA_EXAMPLE_COMMON;
gpt_params & params;
std::vector<llama_arg> options;
common_params & params;
std::vector<common_arg> options;
void(*print_usage)(int, char **) = nullptr;
gpt_params_context(gpt_params & params) : params(params) {}
common_params_context(common_params & params) : params(params) {}
};
// parse input arguments from CLI
// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
bool gpt_params_parse(int argc, char ** argv, gpt_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
// function to be used by test-arg-parser
gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

116
common/common.cpp
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@ -364,10 +364,10 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
return true;
}
void gpt_init() {
void common_init() {
llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
if (LOG_DEFAULT_LLAMA <= gpt_log_verbosity_thold) {
gpt_log_add(gpt_log_main(), level, "%s", text);
if (LOG_DEFAULT_LLAMA <= common_log_verbosity_thold) {
common_log_add(common_log_main(), level, "%s", text);
}
}, NULL);
@ -380,7 +380,7 @@ void gpt_init() {
LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type);
}
std::string gpt_params_get_system_info(const gpt_params & params) {
std::string common_params_get_system_info(const common_params & params) {
std::ostringstream os;
os << "system_info: n_threads = " << params.cpuparams.n_threads;
@ -495,7 +495,7 @@ std::string string_from(const struct llama_context * ctx, const std::vector<llam
first = false;
}
auto detokenized = llama_token_to_piece(ctx, token);
auto detokenized = common_token_to_piece(ctx, token);
detokenized.erase(
std::remove_if(
@ -526,7 +526,7 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat
first = false;
}
auto detokenized = llama_token_to_piece(ctx, batch.token[i]);
auto detokenized = common_token_to_piece(ctx, batch.token[i]);
detokenized.erase(
std::remove_if(
@ -821,16 +821,16 @@ std::string fs_get_cache_file(const std::string & filename) {
//
// Model utils
//
struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
llama_init_result iparams;
auto mparams = llama_model_params_from_gpt_params(params);
struct common_init_result common_init_from_params(common_params & params) {
common_init_result iparams;
auto mparams = common_model_params_to_llama(params);
llama_model * model = nullptr;
if (!params.hf_repo.empty() && !params.hf_file.empty()) {
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
model = common_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else if (!params.model_url.empty()) {
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
model = common_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
} else {
model = llama_load_model_from_file(params.model.c_str(), mparams);
}
@ -865,7 +865,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
}
}
auto cparams = llama_context_params_from_gpt_params(params);
auto cparams = common_context_params_to_llama(params);
llama_context * lctx = llama_new_context_with_model(model, cparams);
if (lctx == NULL) {
@ -878,7 +878,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
const auto cvec = llama_control_vector_load(params.control_vectors);
const auto cvec = common_control_vector_load(params.control_vectors);
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_free_model(model);
@ -902,7 +902,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
// load and optionally apply lora adapters
for (auto & la : params.lora_adapters) {
llama_lora_adapter_container loaded_la;
common_lora_adapter_container loaded_la;
loaded_la.path = la.path;
loaded_la.scale = la.scale;
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
@ -915,7 +915,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
}
if (!params.lora_init_without_apply) {
llama_lora_adapters_apply(lctx, iparams.lora_adapters);
common_lora_adapters_apply(lctx, iparams.lora_adapters);
}
if (params.sparams.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
@ -963,7 +963,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
return iparams;
}
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters) {
llama_lora_adapter_clear(ctx);
for (auto & la : lora_adapters) {
if (la.scale != 0.0f) {
@ -972,7 +972,7 @@ void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lor
}
}
struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
struct llama_model_params common_model_params_to_llama(const common_params & params) {
auto mparams = llama_model_default_params();
if (params.n_gpu_layers != -1) {
@ -1024,7 +1024,7 @@ static ggml_type kv_cache_type_from_str(const std::string & s) {
throw std::runtime_error("Invalid cache type: " + s);
}
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
struct llama_context_params common_context_params_to_llama(const common_params & params) {
auto cparams = llama_context_default_params();
cparams.n_ctx = params.n_ctx;
@ -1114,7 +1114,7 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_
return false;
}
static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
// Initialize libcurl
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
@ -1184,15 +1184,15 @@ static bool llama_download_file(const std::string & url, const std::string & pat
}
// Send a HEAD request to retrieve the etag and last-modified headers
struct llama_load_model_from_url_headers {
struct common_load_model_from_url_headers {
std::string etag;
std::string last_modified;
};
llama_load_model_from_url_headers headers;
common_load_model_from_url_headers headers;
{
typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
common_load_model_from_url_headers *headers = (common_load_model_from_url_headers *) userdata;
static std::regex header_regex("([^:]+): (.*)\r\n");
static std::regex etag_regex("ETag", std::regex_constants::icase);
@ -1328,7 +1328,7 @@ static bool llama_download_file(const std::string & url, const std::string & pat
return true;
}
struct llama_model * llama_load_model_from_url(
struct llama_model * common_load_model_from_url(
const char * model_url,
const char * path_model,
const char * hf_token,
@ -1339,7 +1339,7 @@ struct llama_model * llama_load_model_from_url(
return NULL;
}
if (!llama_download_file(model_url, path_model, hf_token)) {
if (!common_download_file(model_url, path_model, hf_token)) {
return NULL;
}
@ -1392,7 +1392,7 @@ struct llama_model * llama_load_model_from_url(
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
return llama_download_file(split_url, split_path, hf_token);
return common_download_file(split_url, split_path, hf_token);
}, idx));
}
@ -1407,7 +1407,7 @@ struct llama_model * llama_load_model_from_url(
return llama_load_model_from_file(path_model, params);
}
struct llama_model * llama_load_model_from_hf(
struct llama_model * common_load_model_from_hf(
const char * repo,
const char * model,
const char * path_model,
@ -1427,12 +1427,12 @@ struct llama_model * llama_load_model_from_hf(
model_url += "/resolve/main/";
model_url += model;
return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
return common_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
}
#else
struct llama_model * llama_load_model_from_url(
struct llama_model * common_load_model_from_url(
const char * /*model_url*/,
const char * /*path_model*/,
const char * /*hf_token*/,
@ -1441,7 +1441,7 @@ struct llama_model * llama_load_model_from_url(
return nullptr;
}
struct llama_model * llama_load_model_from_hf(
struct llama_model * common_load_model_from_hf(
const char * /*repo*/,
const char * /*model*/,
const char * /*path_model*/,
@ -1457,11 +1457,11 @@ struct llama_model * llama_load_model_from_hf(
// Batch utils
//
void llama_batch_clear(struct llama_batch & batch) {
void common_batch_clear(struct llama_batch & batch) {
batch.n_tokens = 0;
}
void llama_batch_add(
void common_batch_add(
struct llama_batch & batch,
llama_token id,
llama_pos pos,
@ -1484,15 +1484,15 @@ void llama_batch_add(
// Vocab utils
//
std::vector<llama_token> llama_tokenize(
std::vector<llama_token> common_tokenize(
const struct llama_context * ctx,
const std::string & text,
bool add_special,
bool parse_special) {
return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
}
std::vector<llama_token> llama_tokenize(
std::vector<llama_token> common_tokenize(
const struct llama_model * model,
const std::string & text,
bool add_special,
@ -1511,7 +1511,7 @@ std::vector<llama_token> llama_tokenize(
return result;
}
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
@ -1527,7 +1527,7 @@ std::string llama_token_to_piece(const struct llama_context * ctx, llama_token t
return piece;
}
std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
std::string common_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
std::string text;
text.resize(std::max(text.capacity(), tokens.size()));
int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
@ -1547,15 +1547,15 @@ std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token>
// Chat template utils
//
bool llama_chat_verify_template(const std::string & tmpl) {
bool common_chat_verify_template(const std::string & tmpl) {
llama_chat_message chat[] = {{"user", "test"}};
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
return res >= 0;
}
std::string llama_chat_apply_template(const struct llama_model * model,
std::string common_chat_apply_template(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & msgs,
const std::vector<common_chat_msg> & msgs,
bool add_ass) {
int alloc_size = 0;
bool fallback = false; // indicate if we must fallback to default chatml
@ -1597,42 +1597,42 @@ std::string llama_chat_apply_template(const struct llama_model * model,
return formatted_chat;
}
std::string llama_chat_format_single(const struct llama_model * model,
std::string common_chat_format_single(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & past_msg,
const llama_chat_msg & new_msg,
const std::vector<common_chat_msg> & past_msg,
const common_chat_msg & new_msg,
bool add_ass) {
std::ostringstream ss;
auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
std::vector<llama_chat_msg> chat_new(past_msg);
auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(model, tmpl, past_msg, false);
std::vector<common_chat_msg> chat_new(past_msg);
// if the past_msg ends with a newline, we must preserve it in the formatted version
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
ss << "\n";
};
// format chat with new_msg
chat_new.push_back(new_msg);
auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
auto fmt_new_msg = common_chat_apply_template(model, tmpl, chat_new, add_ass);
// get the diff part
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
return ss.str();
}
std::string llama_chat_format_example(const struct llama_model * model,
std::string common_chat_format_example(const struct llama_model * model,
const std::string & tmpl) {
std::vector<llama_chat_msg> msgs = {
std::vector<common_chat_msg> msgs = {
{"system", "You are a helpful assistant"},
{"user", "Hello"},
{"assistant", "Hi there"},
{"user", "How are you?"},
};
return llama_chat_apply_template(model, tmpl, msgs, true);
return common_chat_apply_template(model, tmpl, msgs, true);
}
//
// KV cache utils
//
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
@ -1655,7 +1655,7 @@ void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
printf("\n=== Done dumping\n");
}
void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
@ -1707,7 +1707,7 @@ void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_siz
// Embedding utils
//
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
double sum = 0.0;
switch (embd_norm) {
@ -1741,7 +1741,7 @@ void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm)
}
}
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){
double sum = 0.0;
double sum1 = 0.0;
double sum2 = 0.0;
@ -1767,8 +1767,8 @@ float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n)
// Control vector utils
//
static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
llama_control_vector_data result = { -1, {} };
static common_control_vector_data common_control_vector_load_one(const common_control_vector_load_info & load_info) {
common_control_vector_data result = { -1, {} };
ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = {
@ -1852,11 +1852,11 @@ static llama_control_vector_data llama_control_vector_load_one(const llama_contr
return result;
}
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
llama_control_vector_data result = { -1, {} };
common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos) {
common_control_vector_data result = { -1, {} };
for (const auto & info : load_infos) {
auto cur = llama_control_vector_load_one(info);
auto cur = common_control_vector_load_one(info);
if (cur.n_embd == -1) {
result.n_embd = -1;
@ -1948,7 +1948,7 @@ void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const cha
}
}
void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
void yaml_dump_non_result_info(FILE * stream, const common_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
const auto & sparams = params.sparams;

115
common/common.h
View file

@ -24,18 +24,18 @@
#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
struct llama_lora_adapter_info {
struct common_lora_adapter_info {
std::string path;
float scale;
};
struct llama_lora_adapter_container : llama_lora_adapter_info {
struct common_lora_adapter_container : common_lora_adapter_info {
struct llama_lora_adapter * adapter;
};
// build info
struct llama_control_vector_load_info;
struct common_control_vector_load_info;
//
// CPU utils
@ -78,14 +78,14 @@ enum llama_example {
LLAMA_EXAMPLE_COUNT,
};
enum gpt_sampler_type {
GPT_SAMPLER_TYPE_NONE = 0,
GPT_SAMPLER_TYPE_TOP_K = 1,
GPT_SAMPLER_TYPE_TOP_P = 2,
GPT_SAMPLER_TYPE_MIN_P = 3,
GPT_SAMPLER_TYPE_TFS_Z = 4,
GPT_SAMPLER_TYPE_TYPICAL_P = 5,
GPT_SAMPLER_TYPE_TEMPERATURE = 6,
enum common_sampler_type {
COMMON_SAMPLER_TYPE_NONE = 0,
COMMON_SAMPLER_TYPE_TOP_K = 1,
COMMON_SAMPLER_TYPE_TOP_P = 2,
COMMON_SAMPLER_TYPE_MIN_P = 3,
COMMON_SAMPLER_TYPE_TFS_Z = 4,
COMMON_SAMPLER_TYPE_TYPICAL_P = 5,
COMMON_SAMPLER_TYPE_TEMPERATURE = 6,
};
// dimensionality reduction methods, used by cvector-generator
@ -95,7 +95,7 @@ enum dimre_method {
};
// sampler parameters
struct gpt_sampler_params {
struct common_sampler_params {
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
int32_t n_prev = 64; // number of previous tokens to remember
@ -120,13 +120,13 @@ struct gpt_sampler_params {
bool ignore_eos = false;
bool no_perf = false; // disable performance metrics
std::vector<enum gpt_sampler_type> samplers = {
GPT_SAMPLER_TYPE_TOP_K,
GPT_SAMPLER_TYPE_TFS_Z,
GPT_SAMPLER_TYPE_TYPICAL_P,
GPT_SAMPLER_TYPE_TOP_P,
GPT_SAMPLER_TYPE_MIN_P,
GPT_SAMPLER_TYPE_TEMPERATURE
std::vector<enum common_sampler_type> samplers = {
COMMON_SAMPLER_TYPE_TOP_K,
COMMON_SAMPLER_TYPE_TFS_Z,
COMMON_SAMPLER_TYPE_TYPICAL_P,
COMMON_SAMPLER_TYPE_TOP_P,
COMMON_SAMPLER_TYPE_MIN_P,
COMMON_SAMPLER_TYPE_TEMPERATURE
};
std::string grammar; // optional BNF-like grammar to constrain sampling
@ -137,7 +137,7 @@ struct gpt_sampler_params {
std::string print() const;
};
struct gpt_params {
struct common_params {
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 0; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
@ -179,7 +179,7 @@ struct gpt_params {
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
struct gpt_sampler_params sparams;
struct common_sampler_params sparams;
std::string model = ""; // model path // NOLINT
std::string model_draft = ""; // draft model for speculative decoding // NOLINT
@ -204,9 +204,9 @@ struct gpt_params {
std::vector<llama_model_kv_override> kv_overrides;
bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply)
std::vector<llama_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
std::vector<common_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
int32_t verbosity = 0;
int32_t control_vector_layer_start = -1; // layer range for control vector
@ -286,7 +286,10 @@ struct gpt_params {
std::string ssl_file_key = ""; // NOLINT
std::string ssl_file_cert = ""; // NOLINT
bool endpoint_slots = true;
// "advanced" endpoints are disabled by default for better security
bool webui = true;
bool endpoint_slots = false;
bool endpoint_props = false; // only control POST requests, not GET
bool endpoint_metrics = false;
bool log_json = false;
@ -341,9 +344,9 @@ struct gpt_params {
// call once at the start of a program if it uses libcommon
// initializes the logging system and prints info about the build
void gpt_init();
void common_init();
std::string gpt_params_get_system_info(const gpt_params & params);
std::string common_params_get_system_info(const common_params & params);
bool parse_cpu_range(const std::string& range, bool(&boolmask)[GGML_MAX_N_THREADS]);
bool parse_cpu_mask(const std::string& mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
@ -397,29 +400,29 @@ std::string fs_get_cache_file(const std::string & filename);
// Model utils
//
struct llama_init_result {
struct common_init_result {
struct llama_model * model = nullptr;
struct llama_context * context = nullptr;
std::vector<llama_lora_adapter_container> lora_adapters;
std::vector<common_lora_adapter_container> lora_adapters;
};
struct llama_init_result llama_init_from_gpt_params(gpt_params & params);
struct common_init_result common_init_from_params(common_params & params);
struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params);
struct llama_context_params llama_context_params_from_gpt_params (const gpt_params & params);
struct llama_model_params common_model_params_to_llama (const common_params & params);
struct llama_context_params common_context_params_to_llama(const common_params & params);
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params);
struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params);
struct llama_model * common_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params);
struct llama_model * common_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params);
// clear LoRA adapters from context, then apply new list of adapters
void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters);
void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters);
// Batch utils
void llama_batch_clear(struct llama_batch & batch);
void common_batch_clear(struct llama_batch & batch);
void llama_batch_add(
void common_batch_add(
struct llama_batch & batch,
llama_token id,
llama_pos pos,
@ -432,13 +435,13 @@ void llama_batch_add(
// tokenizes a string into a vector of tokens
// should work similar to Python's `tokenizer.encode`
std::vector<llama_token> llama_tokenize(
std::vector<llama_token> common_tokenize(
const struct llama_context * ctx,
const std::string & text,
bool add_special,
bool parse_special = false);
std::vector<llama_token> llama_tokenize(
std::vector<llama_token> common_tokenize(
const struct llama_model * model,
const std::string & text,
bool add_special,
@ -446,7 +449,7 @@ std::vector<llama_token> llama_tokenize(
// tokenizes a token into a piece, optionally renders special/control tokens
// should work similar to Python's `tokenizer.id_to_piece`
std::string llama_token_to_piece(
std::string common_token_to_piece(
const struct llama_context * ctx,
llama_token token,
bool special = true);
@ -454,7 +457,7 @@ std::string llama_token_to_piece(
// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
// optionally renders special/control tokens
std::string llama_detokenize(
std::string common_detokenize(
llama_context * ctx,
const std::vector<llama_token> & tokens,
bool special = true);
@ -464,31 +467,31 @@ std::string llama_detokenize(
//
// same with llama_chat_message, but uses std::string
struct llama_chat_msg {
struct common_chat_msg {
std::string role;
std::string content;
};
// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
bool llama_chat_verify_template(const std::string & tmpl);
bool common_chat_verify_template(const std::string & tmpl);
// CPP wrapper for llama_chat_apply_template
// If the built-in template is not supported, we default to chatml
// If the custom "tmpl" is not supported, we throw an error
std::string llama_chat_apply_template(const struct llama_model * model,
std::string common_chat_apply_template(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & chat,
const std::vector<common_chat_msg> & chat,
bool add_ass);
// Format single message, while taking into account the position of that message in chat history
std::string llama_chat_format_single(const struct llama_model * model,
std::string common_chat_format_single(const struct llama_model * model,
const std::string & tmpl,
const std::vector<llama_chat_msg> & past_msg,
const llama_chat_msg & new_msg,
const std::vector<common_chat_msg> & past_msg,
const common_chat_msg & new_msg,
bool add_ass);
// Returns an example of formatted chat
std::string llama_chat_format_example(const struct llama_model * model,
std::string common_chat_format_example(const struct llama_model * model,
const std::string & tmpl);
//
@ -496,31 +499,31 @@ std::string llama_chat_format_example(const struct llama_model * model,
//
// Dump the KV cache view with the number of sequences per cell.
void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
void common_kv_cache_dump_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 llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
//
// Embedding utils
//
void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);
//
// Control vector utils
//
struct llama_control_vector_data {
struct common_control_vector_data {
int n_embd;
// stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
std::vector<float> data;
};
struct llama_control_vector_load_info {
struct common_control_vector_load_info {
float strength;
std::string fname;
@ -528,7 +531,7 @@ struct llama_control_vector_load_info {
// Load control vectors, scale each by strength, and add them together.
// On error, returns {-1, empty}
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos);
common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos);
//
// Split utils
@ -547,5 +550,5 @@ void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data);
void yaml_dump_non_result_info(
FILE * stream, const gpt_params & params, const llama_context * lctx,
FILE * stream, const common_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);

100
common/log.cpp
View file

@ -8,10 +8,10 @@
#include <thread>
#include <vector>
int gpt_log_verbosity_thold = LOG_DEFAULT_LLAMA;
int common_log_verbosity_thold = LOG_DEFAULT_LLAMA;
void gpt_log_set_verbosity_thold(int verbosity) {
gpt_log_verbosity_thold = verbosity;
void common_log_set_verbosity_thold(int verbosity) {
common_log_verbosity_thold = verbosity;
}
#define LOG_COL_DEFAULT "\033[0m"
@ -29,16 +29,16 @@ static int64_t t_us() {
}
// colors
enum gpt_log_col : int {
GPT_LOG_COL_DEFAULT = 0,
GPT_LOG_COL_BOLD,
GPT_LOG_COL_RED,
GPT_LOG_COL_GREEN,
GPT_LOG_COL_YELLOW,
GPT_LOG_COL_BLUE,
GPT_LOG_COL_MAGENTA,
GPT_LOG_COL_CYAN,
GPT_LOG_COL_WHITE,
enum common_log_col : int {
COMMON_LOG_COL_DEFAULT = 0,
COMMON_LOG_COL_BOLD,
COMMON_LOG_COL_RED,
COMMON_LOG_COL_GREEN,
COMMON_LOG_COL_YELLOW,
COMMON_LOG_COL_BLUE,
COMMON_LOG_COL_MAGENTA,
COMMON_LOG_COL_CYAN,
COMMON_LOG_COL_WHITE,
};
// disable colors by default
@ -54,7 +54,7 @@ static std::vector<const char *> g_col = {
"",
};
struct gpt_log_entry {
struct common_log_entry {
enum ggml_log_level level;
bool prefix;
@ -71,7 +71,7 @@ struct gpt_log_entry {
if (!fcur) {
// stderr displays DBG messages only when their verbosity level is not higher than the threshold
// these messages will still be logged to a file
if (level == GGML_LOG_LEVEL_DEBUG && gpt_log_verbosity_thold < LOG_DEFAULT_DEBUG) {
if (level == GGML_LOG_LEVEL_DEBUG && common_log_verbosity_thold < LOG_DEFAULT_DEBUG) {
return;
}
@ -86,19 +86,19 @@ struct gpt_log_entry {
if (timestamp) {
// [M.s.ms.us]
fprintf(fcur, "%s%d.%02d.%03d.%03d%s ",
g_col[GPT_LOG_COL_BLUE],
g_col[COMMON_LOG_COL_BLUE],
(int) (timestamp / 1000000 / 60),
(int) (timestamp / 1000000 % 60),
(int) (timestamp / 1000 % 1000),
(int) (timestamp % 1000),
g_col[GPT_LOG_COL_DEFAULT]);
g_col[COMMON_LOG_COL_DEFAULT]);
}
switch (level) {
case GGML_LOG_LEVEL_INFO: fprintf(fcur, "%sI %s", g_col[GPT_LOG_COL_GREEN], g_col[GPT_LOG_COL_DEFAULT]); break;
case GGML_LOG_LEVEL_WARN: fprintf(fcur, "%sW %s", g_col[GPT_LOG_COL_MAGENTA], "" ); break;
case GGML_LOG_LEVEL_ERROR: fprintf(fcur, "%sE %s", g_col[GPT_LOG_COL_RED], "" ); break;
case GGML_LOG_LEVEL_DEBUG: fprintf(fcur, "%sD %s", g_col[GPT_LOG_COL_YELLOW], "" ); break;
case GGML_LOG_LEVEL_INFO: fprintf(fcur, "%sI %s", g_col[COMMON_LOG_COL_GREEN], g_col[COMMON_LOG_COL_DEFAULT]); break;
case GGML_LOG_LEVEL_WARN: fprintf(fcur, "%sW %s", g_col[COMMON_LOG_COL_MAGENTA], "" ); break;
case GGML_LOG_LEVEL_ERROR: fprintf(fcur, "%sE %s", g_col[COMMON_LOG_COL_RED], "" ); break;
case GGML_LOG_LEVEL_DEBUG: fprintf(fcur, "%sD %s", g_col[COMMON_LOG_COL_YELLOW], "" ); break;
default:
break;
}
@ -107,18 +107,18 @@ struct gpt_log_entry {
fprintf(fcur, "%s", msg.data());
if (level == GGML_LOG_LEVEL_WARN || level == GGML_LOG_LEVEL_ERROR || level == GGML_LOG_LEVEL_DEBUG) {
fprintf(fcur, "%s", g_col[GPT_LOG_COL_DEFAULT]);
fprintf(fcur, "%s", g_col[COMMON_LOG_COL_DEFAULT]);
}
fflush(fcur);
}
};
struct gpt_log {
struct common_log {
// default capacity - will be expanded if needed
gpt_log() : gpt_log(256) {}
common_log() : common_log(256) {}
gpt_log(size_t capacity) {
common_log(size_t capacity) {
file = nullptr;
prefix = false;
timestamps = false;
@ -137,7 +137,7 @@ struct gpt_log {
resume();
}
~gpt_log() {
~common_log() {
pause();
if (file) {
fclose(file);
@ -158,12 +158,12 @@ private:
int64_t t_start;
// ring buffer of entries
std::vector<gpt_log_entry> entries;
std::vector<common_log_entry> entries;
size_t head;
size_t tail;
// worker thread copies into this
gpt_log_entry cur;
common_log_entry cur;
public:
void add(enum ggml_log_level level, const char * fmt, va_list args) {
@ -219,7 +219,7 @@ public:
tail = (tail + 1) % entries.size();
if (tail == head) {
// expand the buffer
std::vector<gpt_log_entry> new_entries(2*entries.size());
std::vector<common_log_entry> new_entries(2*entries.size());
size_t new_tail = 0;
@ -320,15 +320,15 @@ public:
pause();
if (colors) {
g_col[GPT_LOG_COL_DEFAULT] = LOG_COL_DEFAULT;
g_col[GPT_LOG_COL_BOLD] = LOG_COL_BOLD;
g_col[GPT_LOG_COL_RED] = LOG_COL_RED;
g_col[GPT_LOG_COL_GREEN] = LOG_COL_GREEN;
g_col[GPT_LOG_COL_YELLOW] = LOG_COL_YELLOW;
g_col[GPT_LOG_COL_BLUE] = LOG_COL_BLUE;
g_col[GPT_LOG_COL_MAGENTA] = LOG_COL_MAGENTA;
g_col[GPT_LOG_COL_CYAN] = LOG_COL_CYAN;
g_col[GPT_LOG_COL_WHITE] = LOG_COL_WHITE;
g_col[COMMON_LOG_COL_DEFAULT] = LOG_COL_DEFAULT;
g_col[COMMON_LOG_COL_BOLD] = LOG_COL_BOLD;
g_col[COMMON_LOG_COL_RED] = LOG_COL_RED;
g_col[COMMON_LOG_COL_GREEN] = LOG_COL_GREEN;
g_col[COMMON_LOG_COL_YELLOW] = LOG_COL_YELLOW;
g_col[COMMON_LOG_COL_BLUE] = LOG_COL_BLUE;
g_col[COMMON_LOG_COL_MAGENTA] = LOG_COL_MAGENTA;
g_col[COMMON_LOG_COL_CYAN] = LOG_COL_CYAN;
g_col[COMMON_LOG_COL_WHITE] = LOG_COL_WHITE;
} else {
for (size_t i = 0; i < g_col.size(); i++) {
g_col[i] = "";
@ -355,47 +355,47 @@ public:
// public API
//
struct gpt_log * gpt_log_init() {
return new gpt_log;
struct common_log * common_log_init() {
return new common_log;
}
struct gpt_log * gpt_log_main() {
static struct gpt_log log;
struct common_log * common_log_main() {
static struct common_log log;
return &log;
}
void gpt_log_pause(struct gpt_log * log) {
void common_log_pause(struct common_log * log) {
log->pause();
}
void gpt_log_resume(struct gpt_log * log) {
void common_log_resume(struct common_log * log) {
log->resume();
}
void gpt_log_free(struct gpt_log * log) {
void common_log_free(struct common_log * log) {
delete log;
}
void gpt_log_add(struct gpt_log * log, enum ggml_log_level level, const char * fmt, ...) {
void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...) {
va_list args;
va_start(args, fmt);
log->add(level, fmt, args);
va_end(args);
}
void gpt_log_set_file(struct gpt_log * log, const char * file) {
void common_log_set_file(struct common_log * log, const char * file) {
log->set_file(file);
}
void gpt_log_set_colors(struct gpt_log * log, bool colors) {
void common_log_set_colors(struct common_log * log, bool colors) {
log->set_colors(colors);
}
void gpt_log_set_prefix(struct gpt_log * log, bool prefix) {
void common_log_set_prefix(struct common_log * log, bool prefix) {
log->set_prefix(prefix);
}
void gpt_log_set_timestamps(struct gpt_log * log, bool timestamps) {
void common_log_set_timestamps(struct common_log * log, bool timestamps) {
log->set_timestamps(timestamps);
}

36
common/log.h
View file

@ -14,23 +14,23 @@
#define LOG_DEFAULT_LLAMA 0
// needed by the LOG_TMPL macro to avoid computing log arguments if the verbosity lower
// set via gpt_log_set_verbosity()
extern int gpt_log_verbosity_thold;
// set via common_log_set_verbosity()
extern int common_log_verbosity_thold;
void gpt_log_set_verbosity_thold(int verbosity); // not thread-safe
void common_log_set_verbosity_thold(int verbosity); // not thread-safe
// the gpt_log uses an internal worker thread to print/write log messages
// the common_log uses an internal worker thread to print/write log messages
// when the worker thread is paused, incoming log messages are discarded
struct gpt_log;
struct common_log;
struct gpt_log * gpt_log_init();
struct gpt_log * gpt_log_main(); // singleton, automatically destroys itself on exit
void gpt_log_pause (struct gpt_log * log); // pause the worker thread, not thread-safe
void gpt_log_resume(struct gpt_log * log); // resume the worker thread, not thread-safe
void gpt_log_free (struct gpt_log * log);
struct common_log * common_log_init();
struct common_log * common_log_main(); // singleton, automatically destroys itself on exit
void common_log_pause (struct common_log * log); // pause the worker thread, not thread-safe
void common_log_resume(struct common_log * log); // resume the worker thread, not thread-safe
void common_log_free (struct common_log * log);
LOG_ATTRIBUTE_FORMAT(3, 4)
void gpt_log_add(struct gpt_log * log, enum ggml_log_level level, const char * fmt, ...);
void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...);
// defaults: file = NULL, colors = false, prefix = false, timestamps = false
//
@ -54,10 +54,10 @@ void gpt_log_add(struct gpt_log * log, enum ggml_log_level level, const char * f
// D - debug (stderr, V = LOG_DEFAULT_DEBUG)
//
void gpt_log_set_file (struct gpt_log * log, const char * file); // not thread-safe
void gpt_log_set_colors (struct gpt_log * log, bool colors); // not thread-safe
void gpt_log_set_prefix (struct gpt_log * log, bool prefix); // whether to output prefix to each log
void gpt_log_set_timestamps(struct gpt_log * log, bool timestamps); // whether to output timestamps in the prefix
void common_log_set_file (struct common_log * log, const char * file); // not thread-safe
void common_log_set_colors (struct common_log * log, bool colors); // not thread-safe
void common_log_set_prefix (struct common_log * log, bool prefix); // whether to output prefix to each log
void common_log_set_timestamps(struct common_log * log, bool timestamps); // whether to output timestamps in the prefix
// helper macros for logging
// use these to avoid computing log arguments if the verbosity of the log is higher than the threshold
@ -66,13 +66,13 @@ void gpt_log_set_timestamps(struct gpt_log * log, bool timestamps); // w
//
// LOG_DBG("this is a debug message: %d\n", expensive_function());
//
// this will avoid calling expensive_function() if LOG_DEFAULT_DEBUG > gpt_log_verbosity_thold
// this will avoid calling expensive_function() if LOG_DEFAULT_DEBUG > common_log_verbosity_thold
//
#define LOG_TMPL(level, verbosity, ...) \
do { \
if ((verbosity) <= gpt_log_verbosity_thold) { \
gpt_log_add(gpt_log_main(), (level), __VA_ARGS__); \
if ((verbosity) <= common_log_verbosity_thold) { \
common_log_add(common_log_main(), (level), __VA_ARGS__); \
} \
} while (0)

72
common/ngram-cache.cpp
View file

@ -8,7 +8,7 @@
#include <fstream>
#include <thread>
void llama_ngram_cache_update(llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max,
void common_ngram_cache_update(common_ngram_cache & ngram_cache, int ngram_min, int ngram_max,
std::vector<llama_token> & inp, int nnew, bool print_progress) {
const int64_t t_start_ms = ggml_time_ms();
const int64_t inp_size = inp.size();
@ -20,16 +20,16 @@ void llama_ngram_cache_update(llama_ngram_cache & ngram_cache, int ngram_min, in
const int64_t i_start = std::max(inp_size - nnew, ngram_size);
for (int64_t i = i_start; i < inp_size; ++i) {
const int64_t ngram_start = i - ngram_size;
llama_ngram ngram(&inp[ngram_start], ngram_size);
common_ngram ngram(&inp[ngram_start], ngram_size);
const llama_token token = inp[i];
llama_ngram_cache::iterator part_it = ngram_cache.find(ngram);
common_ngram_cache::iterator part_it = ngram_cache.find(ngram);
if (part_it == ngram_cache.end()) {
llama_ngram_cache_part part;
common_ngram_cache_part part;
part.emplace(token, 1);
ngram_cache.emplace(ngram, part);
} else {
llama_ngram_cache_part::iterator token_count_it = part_it->second.find(token);
common_ngram_cache_part::iterator token_count_it = part_it->second.find(token);
if (token_count_it == part_it->second.end()) {
part_it->second.emplace(token, 1);
} else {
@ -62,12 +62,12 @@ constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4, 3, 2, 2};
constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
// Helper function that tries to draft a token from only the static ngram cache:
static llama_token try_draft(llama_ngram_cache & nc_static, const llama_ngram ngram_static) {
llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) {
common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
if (part_static_it == nc_static.end()) {
return -1;
}
const llama_ngram_cache_part part_static = part_static_it->second;
const common_ngram_cache_part part_static = part_static_it->second;
int max_count_static = 0;
int sum_count_static = 0;
@ -95,19 +95,19 @@ static llama_token try_draft(llama_ngram_cache & nc_static, const llama_ngram ng
// Try to draft a token from primary cache (context/dynamic), validate with static cache:
static llama_token try_draft(
llama_ngram_cache & nc_primary, const std::vector<llama_ngram> & ngrams_primary, llama_ngram_cache_part & part_static,
common_ngram_cache & nc_primary, const std::vector<common_ngram> & ngrams_primary, common_ngram_cache_part & part_static,
const int * min_sample_size, const int * min_percent) {
llama_token drafted_token = -1;
for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
const llama_ngram ngram_primary = ngrams_primary[i];
const common_ngram ngram_primary = ngrams_primary[i];
llama_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
if (part_primary_it == nc_primary.end()) {
continue;
}
const llama_ngram_cache_part part_primary = part_primary_it->second;
const common_ngram_cache_part part_primary = part_primary_it->second;
int max_count_primary = 0;
int max_count_static = 0;
@ -117,7 +117,7 @@ static llama_token try_draft(
for (std::pair<llama_token, int> token_count_primary : part_primary) {
const llama_token token = token_count_primary.first;
llama_ngram_cache_part::iterator token_count_static_it = part_static.find(token);
common_ngram_cache_part::iterator token_count_static_it = part_static.find(token);
const int32_t count_primary = token_count_primary.second;
const int32_t count_static = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1;
@ -142,9 +142,9 @@ static llama_token try_draft(
return drafted_token;
}
void llama_ngram_cache_draft(
void common_ngram_cache_draft(
std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static
common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static
) {
GGML_ASSERT(draft.size() == 1);
const int inp_size = inp.size();
@ -157,21 +157,21 @@ void llama_ngram_cache_draft(
llama_token drafted_token = -1;
const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
llama_ngram ngram_static;
common_ngram ngram_static;
for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) {
ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j);
}
llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
llama_ngram_cache_part part_static;
common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
common_ngram_cache_part part_static;
if (part_static_it != nc_static.end()) {
part_static = part_static_it->second;
}
// cd = context + dynamic
std::vector<llama_ngram> ngrams_cd;
std::vector<common_ngram> ngrams_cd;
for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) {
const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1;
llama_ngram ngram_cd;
common_ngram ngram_cd;
for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) {
ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j);
}
@ -196,16 +196,16 @@ void llama_ngram_cache_draft(
}
}
void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename) {
void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename) {
std::ofstream file_out(filename, std::ios::binary);
for (std::pair<llama_ngram, llama_ngram_cache_part> item : ngram_cache) {
const llama_ngram ngram = item.first;
llama_ngram_cache_part token_counts = item.second;
for (std::pair<common_ngram, common_ngram_cache_part> item : ngram_cache) {
const common_ngram ngram = item.first;
common_ngram_cache_part token_counts = item.second;
GGML_ASSERT(!token_counts.empty());
const int32_t ntokens = token_counts.size();
GGML_ASSERT(ntokens > 0);
file_out.write(reinterpret_cast<const char *>(&ngram), sizeof(llama_ngram));
file_out.write(reinterpret_cast<const char *>(&ngram), sizeof(common_ngram));
file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t));
for (std::pair<llama_token, int32_t> item2 : token_counts) {
const llama_token token = item2.first;
@ -219,14 +219,14 @@ void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filen
}
llama_ngram_cache llama_ngram_cache_load(std::string & filename) {
common_ngram_cache common_ngram_cache_load(std::string & filename) {
std::ifstream hashmap_file(filename, std::ios::binary);
if (!hashmap_file) {
throw std::ifstream::failure("Unable to open file " + filename);
}
llama_ngram_cache ngram_cache;
common_ngram_cache ngram_cache;
llama_ngram ngram;
common_ngram ngram;
int32_t ntokens;
llama_token token;
int32_t count;
@ -235,11 +235,11 @@ llama_ngram_cache llama_ngram_cache_load(std::string & filename) {
char * ntokensc = reinterpret_cast<char*>(&ntokens);
char * tokenc = reinterpret_cast<char*>(&token);
char * countc = reinterpret_cast<char*>(&count);
while(hashmap_file.read(ngramc, sizeof(llama_ngram))) {
while(hashmap_file.read(ngramc, sizeof(common_ngram))) {
GGML_ASSERT(!hashmap_file.eof());
GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t)));
GGML_ASSERT(ntokens > 0);
llama_ngram_cache_part token_counts;
common_ngram_cache_part token_counts;
for (int i = 0; i < ntokens; ++i) {
GGML_ASSERT(!hashmap_file.eof());
@ -257,12 +257,12 @@ llama_ngram_cache llama_ngram_cache_load(std::string & filename) {
return ngram_cache;
}
void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add) {
for (std::pair<llama_ngram, llama_ngram_cache_part> ngram_part : ngram_cache_add) {
const llama_ngram ngram = ngram_part.first;
llama_ngram_cache_part part = ngram_part.second;
void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add) {
for (std::pair<common_ngram, common_ngram_cache_part> ngram_part : ngram_cache_add) {
const common_ngram ngram = ngram_part.first;
common_ngram_cache_part part = ngram_part.second;
llama_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram);
common_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram);
if (part_merged_it == ngram_cache_target.end()) {
ngram_cache_target.emplace(ngram, part);
continue;
@ -273,7 +273,7 @@ void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram
const int32_t count = token_count.second;
GGML_ASSERT(count > 0);
llama_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token);
common_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token);
if (token_count_merged_it == part_merged_it->second.end()) {
part_merged_it->second.emplace(token, count);
continue;

38
common/ngram-cache.h
View file

@ -12,22 +12,22 @@
// Data structures to map n-grams to empirical token probabilities:
struct llama_ngram {
struct common_ngram {
llama_token tokens[LLAMA_NGRAM_MAX];
llama_ngram() {
common_ngram() {
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
tokens[i] = -1;
}
}
llama_ngram(const llama_token * input, const int ngram_size) {
common_ngram(const llama_token * input, const int ngram_size) {
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
tokens[i] = i < ngram_size ? input[i] : -1;
}
}
bool operator==(const llama_ngram & other) const {
bool operator==(const common_ngram & other) const {
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
if (tokens[i] != other.tokens[i]) {
return false;
@ -37,28 +37,28 @@ struct llama_ngram {
}
};
struct llama_token_hash_function {
struct common_token_hash_function {
size_t operator()(const llama_token token) const {
// see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/
return token * 11400714819323198485llu;
}
};
struct llama_ngram_hash_function {
size_t operator()(const llama_ngram & ngram) const {
size_t hash = llama_token_hash_function{}(ngram.tokens[0]);
struct common_ngram_hash_function {
size_t operator()(const common_ngram & ngram) const {
size_t hash = common_token_hash_function{}(ngram.tokens[0]);
for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) {
hash ^= llama_token_hash_function{}(ngram.tokens[i]);
hash ^= common_token_hash_function{}(ngram.tokens[i]);
}
return hash;
}
};
// token -> number of times token has been seen
typedef std::unordered_map<llama_token, int32_t> llama_ngram_cache_part;
typedef std::unordered_map<llama_token, int32_t> common_ngram_cache_part;
// n-gram -> empirical distribution of following tokens
typedef std::unordered_map<llama_ngram, llama_ngram_cache_part, llama_ngram_hash_function> llama_ngram_cache;
typedef std::unordered_map<common_ngram, common_ngram_cache_part, common_ngram_hash_function> common_ngram_cache;
// Update an ngram cache with tokens.
@ -70,8 +70,8 @@ typedef std::unordered_map<llama_ngram, llama_ngram_cache_part, llama_ngram_hash
//
// In order to get correct results inp_data can ONLY BE APPENDED TO.
// Changes in the middle need a complete rebuild.
void llama_ngram_cache_update(
llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress);
void common_ngram_cache_update(
common_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress);
// Try to draft tokens from ngram caches.
// inp: the tokens generated so far.
@ -81,21 +81,21 @@ void llama_ngram_cache_update(
// nc_context: ngram cache based on current context.
// nc_dynamic: ngram cache based on previous user generations.
// nc_static: ngram cache generated from a large text corpus, used for validation.
void llama_ngram_cache_draft(
void common_ngram_cache_draft(
std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static);
common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static);
// Save an ngram cache to a file.
// ngram_cache: the ngram cache to save.
// filename: the path under which to save the ngram cache.
void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename);
void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename);
// Load an ngram cache saved with llama_ngram_cache_save.
// Load an ngram cache saved with common_ngram_cache_save.
// filename: the path from which to load the ngram cache.
// returns: an ngram cache containing the information saved to filename.
llama_ngram_cache llama_ngram_cache_load(std::string & filename);
common_ngram_cache common_ngram_cache_load(std::string & filename);
// Merge two ngram caches.
// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add.
// ngram_cache_add: the ngram cache to add to ngram_cache_target.
void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add);
void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add);

136
common/sampling.cpp
View file

@ -98,8 +98,8 @@ struct ring_buffer {
std::vector<T> data;
};
struct gpt_sampler {
gpt_sampler_params params;
struct common_sampler {
common_sampler_params params;
struct llama_sampler * grmr;
struct llama_sampler * chain;
@ -125,7 +125,7 @@ struct gpt_sampler {
}
};
std::string gpt_sampler_params::print() const {
std::string common_sampler_params::print() const {
char result[1024];
snprintf(result, sizeof(result),
@ -139,12 +139,12 @@ std::string gpt_sampler_params::print() const {
return std::string(result);
}
struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const struct gpt_sampler_params & params) {
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params) {
llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
lparams.no_perf = params.no_perf;
auto * result = new gpt_sampler {
auto * result = new common_sampler {
/* .params = */ params,
/* .grmr = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"),
/* .chain = */ llama_sampler_chain_init(lparams),
@ -175,22 +175,22 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
if (params.mirostat == 0) {
for (const auto & cnstr : params.samplers) {
switch (cnstr) {
case GPT_SAMPLER_TYPE_TOP_K:
case COMMON_SAMPLER_TYPE_TOP_K:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k));
break;
case GPT_SAMPLER_TYPE_TOP_P:
case COMMON_SAMPLER_TYPE_TOP_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep));
break;
case GPT_SAMPLER_TYPE_MIN_P:
case COMMON_SAMPLER_TYPE_MIN_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep));
break;
case GPT_SAMPLER_TYPE_TFS_Z:
case COMMON_SAMPLER_TYPE_TFS_Z:
llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep));
break;
case GPT_SAMPLER_TYPE_TYPICAL_P:
case COMMON_SAMPLER_TYPE_TYPICAL_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep));
break;
case GPT_SAMPLER_TYPE_TEMPERATURE:
case COMMON_SAMPLER_TYPE_TEMPERATURE:
llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
break;
default:
@ -224,7 +224,7 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
return result;
}
void gpt_sampler_free(struct gpt_sampler * gsmpl) {
void common_sampler_free(struct common_sampler * gsmpl) {
if (gsmpl) {
llama_sampler_free(gsmpl->grmr);
@ -234,7 +234,7 @@ void gpt_sampler_free(struct gpt_sampler * gsmpl) {
}
}
void gpt_sampler_accept(struct gpt_sampler * gsmpl, llama_token token, bool accept_grammar) {
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
if (accept_grammar) {
llama_sampler_accept(gsmpl->grmr, token);
}
@ -244,14 +244,14 @@ void gpt_sampler_accept(struct gpt_sampler * gsmpl, llama_token token, bool acce
gsmpl->prev.push_back(token);
}
void gpt_sampler_reset(struct gpt_sampler * gsmpl) {
void common_sampler_reset(struct common_sampler * gsmpl) {
llama_sampler_reset(gsmpl->grmr);
llama_sampler_reset(gsmpl->chain);
}
struct gpt_sampler * gpt_sampler_clone(gpt_sampler * gsmpl) {
return new gpt_sampler {
struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
return new common_sampler {
/* .params = */ gsmpl->params,
/* .grmr = */ llama_sampler_clone(gsmpl->grmr),
/* .chain = */ llama_sampler_clone(gsmpl->chain),
@ -261,7 +261,7 @@ struct gpt_sampler * gpt_sampler_clone(gpt_sampler * gsmpl) {
};
}
void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler * gsmpl) {
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
// TODO: measure grammar performance
if (gsmpl) {
@ -272,7 +272,7 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
}
}
llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
gsmpl->set_logits(ctx, idx);
auto & grmr = gsmpl->grmr;
@ -318,21 +318,21 @@ llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context
return cur_p.data[cur_p.selected].id;
}
uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl) {
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
return llama_sampler_get_seed(gsmpl->chain);
}
// helpers
llama_token_data_array * gpt_sampler_get_candidates(struct gpt_sampler * gsmpl) {
llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) {
return &gsmpl->cur_p;
}
llama_token gpt_sampler_last(const struct gpt_sampler * gsmpl) {
llama_token common_sampler_last(const struct common_sampler * gsmpl) {
return gsmpl->prev.rat(0);
}
std::string gpt_sampler_print(const struct gpt_sampler * gsmpl) {
std::string common_sampler_print(const struct common_sampler * gsmpl) {
std::string result = "logits ";
for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
@ -343,7 +343,7 @@ std::string gpt_sampler_print(const struct gpt_sampler * gsmpl) {
return result;
}
std::string gpt_sampler_prev_str(gpt_sampler * gsmpl, llama_context * ctx_main, int n) {
std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_main, int n) {
n = std::min(n, (int) gsmpl->prev.size());
if (n <= 0) {
@ -358,63 +358,63 @@ std::string gpt_sampler_prev_str(gpt_sampler * gsmpl, llama_context * ctx_main,
GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen");
result += llama_token_to_piece(ctx_main, id);
result += common_token_to_piece(ctx_main, id);
}
return result;
}
char gpt_sampler_type_to_chr(enum gpt_sampler_type cnstr) {
char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
switch (cnstr) {
case GPT_SAMPLER_TYPE_TOP_K: return 'k';
case GPT_SAMPLER_TYPE_TFS_Z: return 'f';
case GPT_SAMPLER_TYPE_TYPICAL_P: return 'y';
case GPT_SAMPLER_TYPE_TOP_P: return 'p';
case GPT_SAMPLER_TYPE_MIN_P: return 'm';
case GPT_SAMPLER_TYPE_TEMPERATURE: return 't';
case COMMON_SAMPLER_TYPE_TOP_K: return 'k';
case COMMON_SAMPLER_TYPE_TFS_Z: return 'f';
case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y';
case COMMON_SAMPLER_TYPE_TOP_P: return 'p';
case COMMON_SAMPLER_TYPE_MIN_P: return 'm';
case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
default : return '?';
}
}
std::string gpt_sampler_type_to_str(enum gpt_sampler_type cnstr) {
std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
switch (cnstr) {
case GPT_SAMPLER_TYPE_TOP_K: return "top_k";
case GPT_SAMPLER_TYPE_TFS_Z: return "tfs_z";
case GPT_SAMPLER_TYPE_TYPICAL_P: return "typ_p";
case GPT_SAMPLER_TYPE_TOP_P: return "top_p";
case GPT_SAMPLER_TYPE_MIN_P: return "min_p";
case GPT_SAMPLER_TYPE_TEMPERATURE: return "temperature";
case COMMON_SAMPLER_TYPE_TOP_K: return "top_k";
case COMMON_SAMPLER_TYPE_TFS_Z: return "tfs_z";
case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p";
case COMMON_SAMPLER_TYPE_TOP_P: return "top_p";
case COMMON_SAMPLER_TYPE_MIN_P: return "min_p";
case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
default : return "";
}
}
std::vector<gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
std::unordered_map<std::string, gpt_sampler_type> sampler_canonical_name_map {
{ "top_k", GPT_SAMPLER_TYPE_TOP_K },
{ "top_p", GPT_SAMPLER_TYPE_TOP_P },
{ "typ_p", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "min_p", GPT_SAMPLER_TYPE_MIN_P },
{ "tfs_z", GPT_SAMPLER_TYPE_TFS_Z },
{ "temperature", GPT_SAMPLER_TYPE_TEMPERATURE },
std::vector<common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
std::unordered_map<std::string, common_sampler_type> sampler_canonical_name_map {
{ "top_k", COMMON_SAMPLER_TYPE_TOP_K },
{ "top_p", COMMON_SAMPLER_TYPE_TOP_P },
{ "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min_p", COMMON_SAMPLER_TYPE_MIN_P },
{ "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z },
{ "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
};
// since samplers names are written multiple ways
// make it ready for both system names and input names
std::unordered_map<std::string, gpt_sampler_type> sampler_alt_name_map {
{ "top-k", GPT_SAMPLER_TYPE_TOP_K },
{ "top-p", GPT_SAMPLER_TYPE_TOP_P },
{ "nucleus", GPT_SAMPLER_TYPE_TOP_P },
{ "typical-p", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "typical", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "typ-p", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "typ", GPT_SAMPLER_TYPE_TYPICAL_P },
{ "min-p", GPT_SAMPLER_TYPE_MIN_P },
{ "tfs-z", GPT_SAMPLER_TYPE_TFS_Z },
{ "tfs", GPT_SAMPLER_TYPE_TFS_Z },
{ "temp", GPT_SAMPLER_TYPE_TEMPERATURE },
std::unordered_map<std::string, common_sampler_type> sampler_alt_name_map {
{ "top-k", COMMON_SAMPLER_TYPE_TOP_K },
{ "top-p", COMMON_SAMPLER_TYPE_TOP_P },
{ "nucleus", COMMON_SAMPLER_TYPE_TOP_P },
{ "typical-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "typical", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "typ", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min-p", COMMON_SAMPLER_TYPE_MIN_P },
{ "tfs-z", COMMON_SAMPLER_TYPE_TFS_Z },
{ "tfs", COMMON_SAMPLER_TYPE_TFS_Z },
{ "temp", COMMON_SAMPLER_TYPE_TEMPERATURE },
};
std::vector<gpt_sampler_type> samplers;
std::vector<common_sampler_type> samplers;
samplers.reserve(names.size());
for (const auto & name : names) {
@ -434,17 +434,17 @@ std::vector<gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std
return samplers;
}
std::vector<gpt_sampler_type> gpt_sampler_types_from_chars(const std::string & chars) {
std::unordered_map<char, gpt_sampler_type> sampler_name_map = {
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TOP_K), GPT_SAMPLER_TYPE_TOP_K },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TFS_Z), GPT_SAMPLER_TYPE_TFS_Z },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TYPICAL_P), GPT_SAMPLER_TYPE_TYPICAL_P },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TOP_P), GPT_SAMPLER_TYPE_TOP_P },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_MIN_P), GPT_SAMPLER_TYPE_MIN_P },
{ gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TEMPERATURE), GPT_SAMPLER_TYPE_TEMPERATURE }
std::vector<common_sampler_type> common_sampler_types_from_chars(const std::string & chars) {
std::unordered_map<char, common_sampler_type> sampler_name_map = {
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TFS_Z), COMMON_SAMPLER_TYPE_TFS_Z },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }
};
std::vector<gpt_sampler_type> samplers;
std::vector<common_sampler_type> samplers;
samplers.reserve(chars.size());
for (const auto & c : chars) {

40
common/sampling.h
View file

@ -7,7 +7,7 @@
#include <string>
#include <vector>
// gpt_sampler extends llama_sampler with additional functionality:
// common_sampler extends llama_sampler with additional functionality:
//
// - grammar support
// - custom sampler logic based on the parameters
@ -23,30 +23,30 @@
// token in order to verify if it fits the grammar. And only if the token doesn't fit the grammar, the
// grammar constraints are applied to the full vocabulary and the token is resampled.
//
// The gpt_sampler also maintains a container with the last accepted tokens. In the future, this can
// The common_sampler also maintains a container with the last accepted tokens. In the future, this can
// be moved into the core llama library.
//
// For convenience, the gpt_sampler also maintains a container with the current candidate tokens.
// For convenience, the common_sampler also maintains a container with the current candidate tokens.
// This can be used to access the probabilities of the rest of the non-sampled tokens.
//
// TODO: measure grammar performance
//
struct gpt_sampler;
struct common_sampler;
// llama_sampler API overloads
struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const struct gpt_sampler_params & params);
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params);
void gpt_sampler_free(struct gpt_sampler * gsmpl);
void common_sampler_free(struct common_sampler * gsmpl);
// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar
void gpt_sampler_accept(struct gpt_sampler * gsmpl, llama_token token, bool accept_grammar);
void gpt_sampler_reset (struct gpt_sampler * gsmpl);
struct gpt_sampler * gpt_sampler_clone (struct gpt_sampler * gsmpl);
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar);
void common_sampler_reset (struct common_sampler * gsmpl);
struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
// arguments can be nullptr to skip printing
void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler * gsmpl);
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
// extended sampling implementation:
//
@ -58,26 +58,26 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
// if grammar_first is true, the grammar is applied before the samplers (slower)
// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
//
llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl);
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
// helpers
// access the internal list of current candidate tokens
llama_token_data_array * gpt_sampler_get_candidates(struct gpt_sampler * gsmpl);
llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl);
// get the last accepted token
llama_token gpt_sampler_last(const struct gpt_sampler * gsmpl);
llama_token common_sampler_last(const struct common_sampler * gsmpl);
// print the sampler chain into a string
std::string gpt_sampler_print(const struct gpt_sampler * gsmpl);
std::string common_sampler_print(const struct common_sampler * gsmpl);
// get a string representation of the last accepted tokens
std::string gpt_sampler_prev_str(gpt_sampler * gsmpl, llama_context * ctx, int n);
std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx, int n);
char gpt_sampler_type_to_chr(enum gpt_sampler_type cnstr);
std::string gpt_sampler_type_to_str(enum gpt_sampler_type cnstr);
char common_sampler_type_to_chr(enum common_sampler_type cnstr);
std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
std::vector<enum gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
std::vector<enum gpt_sampler_type> gpt_sampler_types_from_chars(const std::string & chars);
std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);

20
examples/batched-bench/batched-bench.cpp
View file

@ -15,13 +15,13 @@ static void print_usage(int, char ** argv) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
return 1;
}
gpt_init();
common_init();
int is_pp_shared = params.is_pp_shared;
@ -36,7 +36,7 @@ int main(int argc, char ** argv) {
// initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model_params model_params = common_model_params_to_llama(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -45,7 +45,7 @@ int main(int argc, char ** argv) {
return 1;
}
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context_params ctx_params = common_context_params_to_llama(params);
// ensure enough sequences are available
ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end());
@ -92,7 +92,7 @@ int main(int argc, char ** argv) {
// warm up
{
for (int i = 0; i < 16; ++i) {
llama_batch_add(batch, 0, i, { 0 }, false);
common_batch_add(batch, 0, i, { 0 }, false);
}
if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
@ -122,11 +122,11 @@ int main(int argc, char ** argv) {
continue;
}
llama_batch_clear(batch);
common_batch_clear(batch);
for (int i = 0; i < pp; ++i) {
for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
llama_batch_add(batch, 0, i, { j }, false);
common_batch_add(batch, 0, i, { j }, false);
}
}
batch.logits[batch.n_tokens - 1] = true;
@ -151,10 +151,10 @@ int main(int argc, char ** argv) {
const auto t_tg_start = ggml_time_us();
for (int i = 0; i < tg; ++i) {
llama_batch_clear(batch);
common_batch_clear(batch);
for (int j = 0; j < pl; ++j) {
llama_batch_add(batch, 0, pp + i, { j }, true);
common_batch_add(batch, 0, pp + i, { j }, true);
}
if (!decode_helper(ctx, batch, ctx_params.n_batch)) {

28
examples/batched/batched.cpp
View file

@ -15,16 +15,16 @@ static void print_usage(int, char ** argv) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.prompt = "Hello my name is";
params.n_predict = 32;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
return 1;
}
gpt_init();
common_init();
// number of parallel batches
int n_parallel = params.n_parallel;
@ -39,7 +39,7 @@ int main(int argc, char ** argv) {
// initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model_params model_params = common_model_params_to_llama(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -51,13 +51,13 @@ int main(int argc, char ** argv) {
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(model, params.prompt, true);
tokens_list = common_tokenize(model, params.prompt, true);
const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
// initialize the context
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context_params ctx_params = common_context_params_to_llama(params);
ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_predict, n_parallel);
@ -94,7 +94,7 @@ int main(int argc, char ** argv) {
LOG("\n");
for (auto id : tokens_list) {
LOG("%s", llama_token_to_piece(ctx, id).c_str());
LOG("%s", common_token_to_piece(ctx, id).c_str());
}
// create a llama_batch
@ -108,7 +108,7 @@ int main(int argc, char ** argv) {
// evaluate the initial prompt
for (size_t i = 0; i < tokens_list.size(); ++i) {
llama_batch_add(batch, tokens_list[i], i, seq_ids, false);
common_batch_add(batch, tokens_list[i], i, seq_ids, false);
}
GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
@ -123,8 +123,8 @@ int main(int argc, char ** argv) {
decoder_start_token_id = llama_token_bos(model);
}
llama_batch_clear(batch);
llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
common_batch_clear(batch);
common_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
}
// llama_decode will output logits only for the last token of the prompt
@ -161,7 +161,7 @@ int main(int argc, char ** argv) {
while (n_cur <= n_predict) {
// prepare the next batch
llama_batch_clear(batch);
common_batch_clear(batch);
// sample the next token for each parallel sequence / stream
for (int32_t i = 0; i < n_parallel; ++i) {
@ -185,15 +185,15 @@ int main(int argc, char ** argv) {
// if there is only one stream, we print immediately to stdout
if (n_parallel == 1) {
LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str());
LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
}
streams[i] += llama_token_to_piece(ctx, new_token_id);
streams[i] += common_token_to_piece(ctx, new_token_id);
i_batch[i] = batch.n_tokens;
// push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_cur, { i }, true);
common_batch_add(batch, new_token_id, n_cur, { i }, true);
n_decode += 1;
}

2
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -872,7 +872,7 @@ static std::string basename(const std::string &path) {
}
int main(int argc, char ** argv) {
gpt_init();
common_init();
struct train_params params = get_default_train_params();
if (!params_parse(argc, argv, &params)) {

16
examples/cvector-generator/cvector-generator.cpp
View file

@ -31,7 +31,7 @@ template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret;
for (; begin != end; ++begin) {
ret += llama_token_to_piece(ctx, *begin);
ret += common_token_to_piece(ctx, *begin);
}
return ret;
@ -272,8 +272,8 @@ struct tokenized_prompt {
tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
tokens_pos = ::llama_tokenize(ctx, pos, add_bos, true);
tokens_neg = ::llama_tokenize(ctx, neg, add_bos, true);
tokens_pos = common_tokenize(ctx, pos, add_bos, true);
tokens_neg = common_tokenize(ctx, neg, add_bos, true);
max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
padding_seq(ctx, tokens_pos, max_seq_len);
padding_seq(ctx, tokens_neg, max_seq_len);
@ -281,7 +281,7 @@ struct tokenized_prompt {
void padding_seq(llama_context * ctx, std::vector<llama_token> & tokens, size_t len) {
// TODO: customize padding token
std::vector<llama_token> pad_tokens = ::llama_tokenize(ctx, " ", false);
std::vector<llama_token> pad_tokens = common_tokenize(ctx, " ", false);
llama_token pad_tok = pad_tokens.back();
while (tokens.size() < len) {
tokens.push_back(pad_tok);
@ -370,7 +370,7 @@ static void export_gguf(const std::vector<struct ggml_tensor *> & v_ctrl, const
* Load prompt files and completion file.
* Then format each pair of prompt + completion to make an entry.
*/
static int prepare_entries(gpt_params & params, train_context & ctx_train) {
static int prepare_entries(common_params & params, train_context & ctx_train) {
// load prompts
std::vector<std::string> positive_prompts = ctrlvec_load_prompt_file(params.cvector_positive_file, true);
std::vector<std::string> negative_prompts = ctrlvec_load_prompt_file(params.cvector_negative_file, true);
@ -388,9 +388,9 @@ static int prepare_entries(gpt_params & params, train_context & ctx_train) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
return 1;
}
@ -413,7 +413,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the model to get hparams
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;

14
examples/eval-callback/eval-callback.cpp
View file

@ -126,10 +126,10 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
return true;
}
static bool run(llama_context * ctx, const gpt_params & params) {
static bool run(llama_context * ctx, const common_params & params) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
LOG_ERR("%s : failed to eval\n", __func__);
@ -142,13 +142,13 @@ static bool run(llama_context * ctx, const gpt_params & params) {
int main(int argc, char ** argv) {
callback_data cb_data;
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
gpt_init();
common_init();
llama_backend_init();
llama_numa_init(params.numa);
@ -160,7 +160,7 @@ int main(int argc, char ** argv) {
params.warmup = false;
// init
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
@ -172,7 +172,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
}

10
examples/export-lora/export-lora.cpp
View file

@ -128,7 +128,7 @@ struct lora_merge_ctx {
lora_merge_ctx(
std::string & base_fname,
std::vector<llama_lora_adapter_info> & lora_files,
std::vector<common_lora_adapter_info> & lora_files,
std::string & outfile,
int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) {
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
@ -314,9 +314,9 @@ struct lora_merge_ctx {
// optionally dequantize it
printf("%s : + dequantize base tensor from %s to F32\n", __func__, ggml_type_name(base->type));
auto nels = ggml_nelements(inp_base);
ggml_type_traits_t qtype = ggml_internal_get_type_traits(base->type);
const auto * qtype = ggml_get_type_traits(base->type);
std::vector<uint8_t> dequant_buf(nels * sizeof(float));
qtype.to_float(read_buf.data(), (float *)dequant_buf.data(), nels);
qtype->to_float(read_buf.data(), (float *)dequant_buf.data(), nels);
ggml_backend_tensor_set(inp_base, dequant_buf.data(), 0, dequant_buf.size());
} else {
ggml_backend_tensor_set(inp_base, read_buf.data(), 0, ggml_nbytes(inp_base));
@ -400,9 +400,9 @@ static void print_usage(int, char ** argv) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
return 1;
}

14
examples/gen-docs/gen-docs.cpp
View file

@ -11,7 +11,7 @@ static void write_table_header(std::ofstream & file) {
file << "| -------- | ----------- |\n";
}
static void write_table_entry(std::ofstream & file, const llama_arg & opt) {
static void write_table_entry(std::ofstream & file, const common_arg & opt) {
file << "| `";
// args
for (const auto & arg : opt.args) {
@ -40,7 +40,7 @@ static void write_table_entry(std::ofstream & file, const llama_arg & opt) {
file << "` | " << md_help << " |\n";
}
static void write_table(std::ofstream & file, std::vector<llama_arg *> & opts) {
static void write_table(std::ofstream & file, std::vector<common_arg *> & opts) {
write_table_header(file);
for (const auto & opt : opts) {
write_table_entry(file, *opt);
@ -50,12 +50,12 @@ static void write_table(std::ofstream & file, std::vector<llama_arg *> & opts) {
static void export_md(std::string fname, llama_example ex) {
std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
gpt_params params;
auto ctx_arg = gpt_params_parser_init(params, ex);
common_params params;
auto ctx_arg = common_params_parser_init(params, ex);
std::vector<llama_arg *> common_options;
std::vector<llama_arg *> sparam_options;
std::vector<llama_arg *> specific_options;
std::vector<common_arg *> common_options;
std::vector<common_arg *> sparam_options;
std::vector<common_arg *> specific_options;
for (auto & opt : ctx_arg.options) {
// in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
if (opt.is_sparam) {

36
examples/gritlm/gritlm.cpp
View file

@ -15,11 +15,11 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
for (uint64_t i = 0; i < sentences.size(); i++) {
llama_batch_clear(batch);
common_batch_clear(batch);
const std::string input_string = instruction + sentences[i];
std::vector<llama_token> inputs = llama_tokenize(model, input_string, true, false);
std::vector<llama_token> inputs = common_tokenize(model, input_string, true, false);
const int32_t n_toks = inputs.size();
@ -28,7 +28,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
// inputs.push_back(llama_token_eos(model));
// we want to ignore instruction tokens for mean pooling
const int32_t n_inst = llama_tokenize(model, instruction, true, false).size();
const int32_t n_inst = common_tokenize(model, instruction, true, false).size();
#ifdef GRIT_DEBUG
// debug tokens - should be matching as referenced in the GritLM sample
@ -40,7 +40,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
// add input to batch (this increments n_tokens)
for (int32_t j = 0; j < n_toks; j++) {
llama_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst);
common_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst);
}
// clear previous kv_cache values (irrelevant for embeddings)
@ -75,7 +75,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
}
std::vector<float> emb_norm(emb_unorm.size());
llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
result.push_back(emb_norm);
#ifdef GRIT_DEBUG
@ -105,16 +105,16 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
std::vector<llama_token> inputs = llama_tokenize(model, prompt, false, true);
std::vector<llama_token> inputs = common_tokenize(model, prompt, false, true);
int32_t i_current_token = 0;
while (true) {
llama_batch_clear(bat);
common_batch_clear(bat);
{
const int32_t n_inputs = inputs.size();
for (int32_t i = 0; i < n_inputs; i++) {
llama_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
common_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
}
}
inputs.clear();
@ -127,7 +127,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
break;
}
std::string piece = llama_token_to_piece(ctx, token);
std::string piece = common_token_to_piece(ctx, token);
if (stream) {
std::printf("%s", piece.c_str());
std::fflush(stdout);
@ -152,16 +152,16 @@ static std::string gritlm_instruction(const std::string & instruction) {
}
int main(int argc, char * argv[]) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
gpt_init();
common_init();
llama_model_params mparams = llama_model_params_from_gpt_params(params);
llama_context_params cparams = llama_context_params_from_gpt_params(params);
llama_model_params mparams = common_model_params_to_llama(params);
llama_context_params cparams = common_context_params_to_llama(params);
llama_backend_init();
@ -199,10 +199,10 @@ int main(int argc, char * argv[]) {
const int n_embd = llama_n_embd(model);
const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
const float cosine_sim_q1_d0 = common_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q1_d1 = common_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);

1
examples/llama.android/llama/build.gradle.kts
View file

@ -18,6 +18,7 @@ android {
}
externalNativeBuild {
cmake {
arguments += "-DLLAMA_BUILD_COMMON=ON"
arguments += "-DCMAKE_BUILD_TYPE=Release"
cppFlags += listOf()
arguments += listOf()

22
examples/llama.android/llama/src/main/cpp/llama-android.cpp
View file

@ -186,11 +186,11 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(
for (nri = 0; nri < nr; nri++) {
LOGi("Benchmark prompt processing (pp)");
llama_batch_clear(*batch);
common_batch_clear(*batch);
const int n_tokens = pp;
for (i = 0; i < n_tokens; i++) {
llama_batch_add(*batch, 0, i, { 0 }, false);
common_batch_add(*batch, 0, i, { 0 }, false);
}
batch->logits[batch->n_tokens - 1] = true;
@ -210,9 +210,9 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(
const auto t_tg_start = ggml_time_us();
for (i = 0; i < tg; i++) {
llama_batch_clear(*batch);
common_batch_clear(*batch);
for (j = 0; j < pl; j++) {
llama_batch_add(*batch, 0, i, { j }, true);
common_batch_add(*batch, 0, i, { j }, true);
}
LOGi("llama_decode() text generation: %d", i);
@ -357,7 +357,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
const auto context = reinterpret_cast<llama_context *>(context_pointer);
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
const auto tokens_list = llama_tokenize(context, text, 1);
const auto tokens_list = common_tokenize(context, text, 1);
auto n_ctx = llama_n_ctx(context);
auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
@ -369,14 +369,14 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
}
for (auto id : tokens_list) {
LOGi("%s", llama_token_to_piece(context, id).c_str());
LOGi("%s", common_token_to_piece(context, id).c_str());
}
llama_batch_clear(*batch);
common_batch_clear(*batch);
// evaluate the initial prompt
for (auto i = 0; i < tokens_list.size(); i++) {
llama_batch_add(*batch, tokens_list[i], i, { 0 }, false);
common_batch_add(*batch, tokens_list[i], i, { 0 }, false);
}
// llama_decode will output logits only for the last token of the prompt
@ -419,7 +419,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
return nullptr;
}
auto new_token_chars = llama_token_to_piece(context, new_token_id);
auto new_token_chars = common_token_to_piece(context, new_token_id);
cached_token_chars += new_token_chars;
jstring new_token = nullptr;
@ -431,8 +431,8 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
new_token = env->NewStringUTF("");
}
llama_batch_clear(*batch);
llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
common_batch_clear(*batch);
common_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
env->CallVoidMethod(intvar_ncur, la_int_var_inc);

135
examples/llama.vim
View file

@ -1,135 +0,0 @@
" Requires an already running llama.cpp server
" To install either copy or symlink to ~/.vim/autoload/llama.vim
" Then start with either :call llama#doLlamaGen(),
" or add a keybind to your vimrc such as
" nnoremap Z :call llama#doLlamaGen()<CR>
" Similarly, you could add an insert mode keybind with
" inoremap <C-B> <Cmd>call llama#doLlamaGen()<CR>
"
" g:llama_api_url, g:llama_api_key and g:llama_overrides can be configured in your .vimrc
" let g:llama_api_url = "192.168.1.10:8080"
" llama_overrides can also be set through buffer/window scopes. For instance
" autocmd filetype python let b:llama_overrides = {"temp": 0.2}
" Could be added to your .vimrc to automatically set a lower temperature when
" editing a python script
" Additionally, an override dict can be stored at the top of a file
" !*{"stop": ["User:"]}
" Could be added to the start of your chatlog.txt to set the stopping token
" These parameter dicts are merged together from lowest to highest priority:
" server default -> g:llama_overrides -> w:llama_overrides ->
" b:llama_overrides -> in file (!*) overrides
"
" Sublists (like logit_bias and stop) are overridden, not merged
" Example override:
" !*{"logit_bias": [[13, -5], [2, false]], "temperature": 1, "top_k": 5, "top_p": 0.5, "n_predict": 256, "repeat_last_n": 256, "repeat_penalty": 1.17647}
if !exists("g:llama_api_url")
let g:llama_api_url= "127.0.0.1:8080"
endif
if !exists("g:llama_overrides")
let g:llama_overrides = {}
endif
const s:querydata = {"n_predict": 256, "stop": [ "\n" ], "stream": v:true }
const s:curlcommand = ['curl','--data-raw', "{\"prompt\":\"### System:\"}", '--silent', '--no-buffer', '--request', 'POST', '--url', g:llama_api_url .. '/completion', '--header', "Content-Type: application/json"]
let s:linedict = {}
func s:callbackHandler(bufn, channel, msg)
if len(a:msg) < 3
return
elseif a:msg[0] == "d"
let l:msg = a:msg[6:-1]
else
let l:msg = a:msg
endif
let l:decoded_msg = json_decode(l:msg)
let l:newtext = split(l:decoded_msg['content'], "\n", 1)
if len(l:newtext) > 0
call setbufline(a:bufn, s:linedict[a:bufn], getbufline(a:bufn, s:linedict[a:bufn])[0] .. newtext[0])
else
echo "nothing genned"
endif
if len(newtext) > 1
let l:failed = appendbufline(a:bufn, s:linedict[a:bufn], newtext[1:-1])
let s:linedict[a:bufn] = s:linedict[a:bufn] + len(newtext)-1
endif
if has_key(l:decoded_msg, "stop") && l:decoded_msg.stop
echo "Finished generation"
endif
endfunction
func llama#doLlamaGen()
if exists("b:job")
if job_status(b:job) == "run"
call job_stop(b:job)
return
endif
endif
let l:cbuffer = bufnr("%")
let s:linedict[l:cbuffer] = line('$')
let l:buflines = getbufline(l:cbuffer, 1, 1000)
let l:querydata = copy(s:querydata)
call extend(l:querydata, g:llama_overrides)
if exists("w:llama_overrides")
call extend(l:querydata, w:llama_overrides)
endif
if exists("b:llama_overrides")
call extend(l:querydata, b:llama_overrides)
endif
if l:buflines[0][0:1] == '!*'
let l:userdata = json_decode(l:buflines[0][2:-1])
call extend(l:querydata, l:userdata)
let l:buflines = l:buflines[1:-1]
endif
let l:querydata.prompt = join(l:buflines, "\n")
let l:curlcommand = copy(s:curlcommand)
if exists("g:llama_api_key")
call extend(l:curlcommand, ['--header', 'Authorization: Bearer ' .. g:llama_api_key])
endif
let l:curlcommand[2] = json_encode(l:querydata)
let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])})
endfunction
" Echos the tokkenization of the provided string , or cursor to end of word
" Onus is placed on the user to include the preceding space
func llama#tokenizeWord(...)
if (a:0 > 0)
let l:input = a:1
else
exe "normal \"*ye"
let l:input = @*
endif
let l:querydata = {"content": l:input}
let l:curlcommand = copy(s:curlcommand)
let l:curlcommand[2] = json_encode(l:querydata)
let l:curlcommand[8] = g:llama_api_url .. "/tokenize"
let s:token_job = job_start(l:curlcommand, {"callback": function("s:tokenizeWordCallback", [l:input])})
endfunction
func s:tokenizeWordCallback(plaintext, channel, msg)
echo '"' .. a:plaintext ..'" - ' .. string(json_decode(a:msg).tokens)
endfunction
" Echos the token count of the entire buffer (or provided string)
" Example usage :echo llama#tokenCount()
func llama#tokenCount(...)
if (a:0 > 0)
let l:buflines = a:1
else
let l:buflines = getline(1,1000)
if l:buflines[0][0:1] == '!*'
let l:buflines = l:buflines[1:-1]
endif
let l:buflines = join(l:buflines, "\n")
endif
let l:querydata = {"content": l:buflines}
let l:curlcommand = copy(s:curlcommand)
let l:curlcommand[2] = json_encode(l:querydata)
let l:curlcommand[8] = g:llama_api_url .. "/tokenize"
let s:token_job = job_start(l:curlcommand, {"callback": "s:tokenCountCallback"})
endfunction
func s:tokenCountCallback(channel, msg)
let resp = json_decode(a:msg)
echo len(resp.tokens)
endfunction

44
examples/llava/llava-cli.cpp
View file

@ -37,21 +37,21 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true);
std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
return true;
}
static const char * sample(struct gpt_sampler * smpl,
static const char * sample(struct common_sampler * smpl,
struct llama_context * ctx_llama,
int * n_past) {
const llama_token id = gpt_sampler_sample(smpl, ctx_llama, -1);
gpt_sampler_accept(smpl, id, true);
const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
common_sampler_accept(smpl, id, true);
static std::string ret;
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
ret = "</s>";
} else {
ret = llama_token_to_piece(ctx_llama, id);
ret = common_token_to_piece(ctx_llama, id);
}
eval_id(ctx_llama, id, n_past);
return ret.c_str();
@ -120,7 +120,7 @@ static void print_usage(int, char ** argv) {
LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
}
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {
static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
// load and preprocess the image
llava_image_embed * embed = NULL;
@ -146,7 +146,7 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
return embed;
}
static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, gpt_params * params, const std::string & prompt) {
static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
int n_past = 0;
const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
@ -159,16 +159,16 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
user_prompt = prompt.substr(image_pos + std::string("<image>").length());
LOG_INF("system_prompt: %s\n", system_prompt.c_str());
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
LOG_INF("user_prompt: %s\n", user_prompt.c_str());
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
} else {
@ -176,9 +176,9 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:";
user_prompt = prompt + "\nASSISTANT:";
if (params->verbose_prompt) {
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
}
@ -191,7 +191,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
LOG("\n");
struct gpt_sampler * smpl = gpt_sampler_init(ctx_llava->model, params->sparams);
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams);
if (!smpl) {
LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
exit(1);
@ -211,15 +211,15 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
fflush(stdout);
}
gpt_sampler_free(smpl);
common_sampler_free(smpl);
LOG("\n");
}
static struct llama_model * llava_init(gpt_params * params) {
static struct llama_model * llava_init(common_params * params) {
llama_backend_init();
llama_numa_init(params->numa);
llama_model_params model_params = llama_model_params_from_gpt_params(*params);
llama_model_params model_params = common_model_params_to_llama(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
if (model == NULL) {
@ -229,7 +229,7 @@ static struct llama_model * llava_init(gpt_params * params) {
return model;
}
static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
const char * clip_path = params->mmproj.c_str();
auto prompt = params->prompt;
@ -240,7 +240,7 @@ static struct llava_context * llava_init_context(gpt_params * params, llama_mode
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
llama_context_params ctx_params = common_context_params_to_llama(*params);
ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
@ -272,13 +272,13 @@ static void llava_free(struct llava_context * ctx_llava) {
int main(int argc, char ** argv) {
ggml_time_init();
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
return 1;
}
gpt_init();
common_init();
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
print_usage(argc, argv);

40
examples/llava/minicpmv-cli.cpp
View file

@ -25,11 +25,11 @@ static void show_additional_info(int /*argc*/, char ** argv) {
LOG("\nnote: a lower temperature value like 0.1 is recommended for better quality.\n");
}
static struct llama_model * llava_init(gpt_params * params) {
static struct llama_model * llava_init(common_params * params) {
llama_backend_init();
llama_numa_init(params->numa);
llama_model_params model_params = llama_model_params_from_gpt_params(*params);
llama_model_params model_params = common_model_params_to_llama(*params);
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
if (model == NULL) {
@ -39,13 +39,13 @@ static struct llama_model * llava_init(gpt_params * params) {
return model;
}
static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
auto prompt = params->prompt;
if (prompt.empty()) {
prompt = "describe the image in detail.";
}
llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
llama_context_params ctx_params = common_context_params_to_llama(*params);
if (params->n_ctx < 2048) {
// warn user here, "Image processing requires at least 2048 context, setting context to 2048"
LOG_WRN("%s: Image processing requires at least 2048 context, setting context to 2048\n" , __func__);
@ -79,7 +79,7 @@ static void llava_free(struct llava_context * ctx_llava) {
llama_backend_free();
}
static struct clip_ctx * clip_init_context(gpt_params * params) {
static struct clip_ctx * clip_init_context(common_params * params) {
const char * clip_path = params->mmproj.c_str();
auto prompt = params->prompt;
@ -114,7 +114,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true);
std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
return eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
}
@ -129,7 +129,7 @@ static void process_eval_image_embed(struct llava_context * ctx_llava, const str
llava_image_embed_free(slice_embed);
}
static void process_image(struct llava_context * ctx_llava, struct llava_image_embed * embeds, gpt_params * params, int &n_past) {
static void process_image(struct llava_context * ctx_llava, struct llava_image_embed * embeds, common_params * params, int &n_past) {
std::string system_prompt;
int idx = 0;
int num_image_embeds = embeds->n_image_pos / clip_n_patches(ctx_llava->ctx_clip);
@ -162,22 +162,22 @@ static void process_image(struct llava_context * ctx_llava, struct llava_image_e
LOG_INF("%s: image token past: %d\n", __func__, n_past);
}
static const char * sample(struct gpt_sampler * smpl,
static const char * sample(struct common_sampler * smpl,
struct llama_context * ctx_llama,
int * n_past) {
const llama_token id = gpt_sampler_sample(smpl, ctx_llama, -1);
gpt_sampler_accept(smpl, id, true);
const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
common_sampler_accept(smpl, id, true);
static std::string ret;
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
ret = "</s>";
} else {
ret = llama_token_to_piece(ctx_llama, id);
ret = common_token_to_piece(ctx_llama, id);
}
eval_id(ctx_llama, id, n_past);
return ret.c_str();
}
static struct llava_context * minicpmv_init(gpt_params * params, const std::string & fname, int &n_past){
static struct llava_context * minicpmv_init(common_params * params, const std::string & fname, int &n_past){
auto * ctx_clip = clip_init_context(params);
auto * embeds = llava_image_embed_make_with_filename(ctx_clip, params->cpuparams.n_threads, fname.c_str());
if (!embeds) {
@ -213,7 +213,7 @@ static struct llava_context * minicpmv_init(gpt_params * params, const std::stri
return ctx_llava;
}
static struct gpt_sampler * llama_init(struct llava_context * ctx_llava, gpt_params * params, const std::string & prompt, int & n_past, bool is_first = false){
static struct common_sampler * llama_init(struct llava_context * ctx_llava, common_params * params, const std::string & prompt, int & n_past, bool is_first = false){
std::string user_prompt = prompt;
int has_minicpmv_projector = clip_is_minicpmv(ctx_llava->ctx_clip);
if (!is_first) {
@ -237,11 +237,11 @@ static struct gpt_sampler * llama_init(struct llava_context * ctx_llava, gpt_par
LOG_INF("\n");
struct gpt_sampler * smpl = gpt_sampler_init(ctx_llava->model, params->sparams);
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams);
return smpl;
}
static const char * llama_loop(struct llava_context * ctx_llava,struct gpt_sampler * smpl, int &n_past){
static const char * llama_loop(struct llava_context * ctx_llava,struct common_sampler * smpl, int &n_past){
const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past);
return tmp;
@ -250,13 +250,13 @@ static const char * llama_loop(struct llava_context * ctx_llava,struct gpt_sampl
int main(int argc, char ** argv) {
ggml_time_init();
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
return 1;
}
gpt_init();
common_init();
if (params.mmproj.empty() || (params.image.empty())) {
show_additional_info(argc, argv);
@ -290,7 +290,7 @@ int main(int argc, char ** argv) {
fflush(stdout);
}
gpt_sampler_free(smpl);
common_sampler_free(smpl);
}else {
while (true) {
LOG("<user>");
@ -309,7 +309,7 @@ int main(int argc, char ** argv) {
if (strstr(response.c_str(), "<user>")) break; // minicpm-v
fflush(stdout);
}
gpt_sampler_free(smpl);
common_sampler_free(smpl);
}
}
printf("\n");

48
examples/lookahead/lookahead.cpp
View file

@ -37,13 +37,13 @@ struct ngram_container {
};
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
gpt_init();
common_init();
const int W = 15; // lookahead window
const int N = 5; // n-gram size
@ -56,7 +56,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the target model
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
@ -65,7 +65,7 @@ int main(int argc, char ** argv) {
std::vector<llama_token> inp;
std::vector<llama_token> all;
inp = ::llama_tokenize(ctx, params.prompt, true, true);
inp = common_tokenize(ctx, params.prompt, true, true);
all = inp;
const int max_context_size = llama_n_ctx(ctx);
@ -79,7 +79,7 @@ int main(int argc, char ** argv) {
LOG("\n\n");
for (auto id : inp) {
LOG("%s", llama_token_to_piece(ctx, id).c_str());
LOG("%s", common_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
@ -115,7 +115,7 @@ int main(int argc, char ** argv) {
llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
// target model sampling context
struct gpt_sampler * smpl = gpt_sampler_init(model, params.sparams);
struct common_sampler * smpl = common_sampler_init(model, params.sparams);
// verification n-grams
std::vector<ngram_data> ngrams_cur(G);
@ -156,12 +156,12 @@ int main(int argc, char ** argv) {
// sample first token
{
id = gpt_sampler_sample(smpl, ctx, 0);
id = common_sampler_sample(smpl, ctx, 0);
gpt_sampler_accept(smpl, id, true);
common_sampler_accept(smpl, id, true);
{
const std::string token_str = llama_token_to_piece(ctx, id);
const std::string token_str = common_token_to_piece(ctx, id);
LOG("%s", token_str.c_str());
fflush(stdout);
@ -172,7 +172,7 @@ int main(int argc, char ** argv) {
// debug
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
llama_kv_cache_dump_view_seqs(kvc_view, 40);
common_kv_cache_dump_view_seqs(kvc_view, 40);
}
// build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
@ -201,10 +201,10 @@ int main(int argc, char ** argv) {
// V V V V V V
// id
{
llama_batch_clear(batch);
common_batch_clear(batch);
// current token - first token of the first level
llama_batch_add(batch, id, n_past, seq_id_all, true);
common_batch_add(batch, id, n_past, seq_id_all, true);
// verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
{
@ -229,7 +229,7 @@ int main(int argc, char ** argv) {
ngrams_cur[g].tokens [j + 1] = t;
ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
common_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
}
}
}
@ -241,13 +241,13 @@ int main(int argc, char ** argv) {
seq_id_look[j] = i + j + 1;
}
llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
common_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
}
// fill the rest of the levels
for (int j = 1; j < N - 1; j++) {
for (int i = 0; i < W; i++) {
llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
common_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
}
}
}
@ -281,13 +281,13 @@ int main(int argc, char ** argv) {
}
// sample the next token
id = gpt_sampler_sample(smpl, ctx, i_batch);
id = common_sampler_sample(smpl, ctx, i_batch);
gpt_sampler_accept(smpl, id, true);
common_sampler_accept(smpl, id, true);
// print
{
const std::string token_str = llama_token_to_piece(ctx, id);
const std::string token_str = common_token_to_piece(ctx, id);
if (v == 0) {
LOG("%s", token_str.c_str());
@ -327,7 +327,7 @@ int main(int argc, char ** argv) {
// print known n-grams starting with token id (debug)
if (0 && v == 0) {
if (ngrams_observed.cnt[id] > 0) {
LOG("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str());
LOG("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], common_token_to_piece(ctx, id).c_str());
}
for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
@ -336,7 +336,7 @@ int main(int argc, char ** argv) {
const int idx = id*(N - 1)*G + i*(N - 1);
for (int j = 0; j < N - 1; j++) {
const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
const std::string token_str = common_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
LOG("%s", token_str.c_str());
}
@ -358,7 +358,7 @@ int main(int argc, char ** argv) {
if (v == 0) {
// sample from the last level
for (int i = 0; i < W; i++) {
tokens_j[N - 2][i] = gpt_sampler_sample(smpl, ctx, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
tokens_j[N - 2][i] = common_sampler_sample(smpl, ctx, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
}
} else {
for (int i = 0; i < W; i++) {
@ -466,9 +466,9 @@ int main(int argc, char ** argv) {
LOG_INF("n_accept = %d\n", n_accept);
LOG_INF("\n");
gpt_perf_print(ctx, smpl);
common_perf_print(ctx, smpl);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
llama_kv_cache_view_free(&kvc_view);

14
examples/lookup/lookup-create.cpp
View file

@ -12,9 +12,9 @@
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
@ -23,7 +23,7 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa);
// load the model
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
@ -31,15 +31,15 @@ int main(int argc, char ** argv){
// tokenize the prompt
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, true, true);
inp = common_tokenize(ctx, params.prompt, true, true);
fprintf(stderr, "%s: tokenization done\n", __func__);
llama_ngram_cache ngram_cache;
llama_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
common_ngram_cache ngram_cache;
common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
llama_ngram_cache_save(ngram_cache, params.lookup_cache_static);
common_ngram_cache_save(ngram_cache, params.lookup_cache_static);
return 0;
}

8
examples/lookup/lookup-merge.cpp
View file

@ -33,15 +33,15 @@ int main(int argc, char ** argv){
}
fprintf(stderr, "lookup-merge: loading file %s\n", args[0].c_str());
llama_ngram_cache ngram_cache_merged = llama_ngram_cache_load(args[0]);
common_ngram_cache ngram_cache_merged = common_ngram_cache_load(args[0]);
for (size_t i = 1; i < args.size()-1; ++i) {
fprintf(stderr, "lookup-merge: loading file %s\n", args[i].c_str());
llama_ngram_cache ngram_cache = llama_ngram_cache_load(args[i]);
common_ngram_cache ngram_cache = common_ngram_cache_load(args[i]);
llama_ngram_cache_merge(ngram_cache_merged, ngram_cache);
common_ngram_cache_merge(ngram_cache_merged, ngram_cache);
}
fprintf(stderr, "lookup-merge: saving file %s\n", args.back().c_str());
llama_ngram_cache_save(ngram_cache_merged, args.back());
common_ngram_cache_save(ngram_cache_merged, args.back());
}

28
examples/lookup/lookup-stats.cpp
View file

@ -13,13 +13,13 @@
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
gpt_init();
common_init();
const int n_draft = params.n_draft;
@ -28,18 +28,18 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa);
// load the model
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
// tokenize the prompt
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, true, true);
inp = common_tokenize(ctx, params.prompt, true, true);
llama_ngram_cache ngram_cache_context;
llama_ngram_cache ngram_cache_dynamic;
llama_ngram_cache ngram_cache_static;
common_ngram_cache ngram_cache_context;
common_ngram_cache ngram_cache_dynamic;
common_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;
@ -48,7 +48,7 @@ int main(int argc, char ** argv){
if (!params.lookup_cache_static.empty()) {
try {
ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static);
} catch (std::ifstream::failure const &) {
LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
exit(1);
@ -57,7 +57,7 @@ int main(int argc, char ** argv){
if (!params.lookup_cache_dynamic.empty()) {
try {
ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic);
} catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
}
@ -86,7 +86,7 @@ int main(int argc, char ** argv){
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
common_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
@ -105,7 +105,7 @@ int main(int argc, char ** argv){
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
}
@ -115,7 +115,7 @@ int main(int argc, char ** argv){
pseudo_output.push_back(inp_slice[pseudo_output.size()]);
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
}
@ -133,7 +133,7 @@ int main(int argc, char ** argv){
}
// After each chunk, update the dynamic ngram cache with the context ngram cache:
llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
ngram_cache_context.clear();
}

54
examples/lookup/lookup.cpp
View file

@ -13,13 +13,13 @@
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
return 1;
}
gpt_init();
common_init();
// max. number of additional tokens to draft if match is found
const int n_draft = params.n_draft;
@ -31,29 +31,29 @@ int main(int argc, char ** argv){
llama_numa_init(params.numa);
// load the model
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
// tokenize the prompt
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, true, true);
inp = common_tokenize(ctx, params.prompt, true, true);
llama_ngram_cache ngram_cache_context;
llama_ngram_cache ngram_cache_dynamic;
llama_ngram_cache ngram_cache_static;
common_ngram_cache ngram_cache_context;
common_ngram_cache ngram_cache_dynamic;
common_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;
{
// Fill up context ngram cache with tokens from user input:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
if (!params.lookup_cache_static.empty()) {
try {
ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static);
} catch (std::ifstream::failure const &) {
LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
exit(1);
@ -62,7 +62,7 @@ int main(int argc, char ** argv){
if (!params.lookup_cache_dynamic.empty()) {
try {
ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic);
} catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
}
@ -80,7 +80,7 @@ int main(int argc, char ** argv){
LOG("\n\n");
for (auto id : inp) {
LOG("%s", llama_token_to_piece(ctx, id).c_str());
LOG("%s", common_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
@ -102,7 +102,7 @@ int main(int argc, char ** argv){
bool has_eos = false;
struct gpt_sampler * smpl = gpt_sampler_init(model, params.sparams);
struct common_sampler * smpl = common_sampler_init(model, params.sparams);
std::vector<llama_token> draft;
@ -117,7 +117,7 @@ int main(int argc, char ** argv){
// debug
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
llama_kv_cache_dump_view_seqs(kvc_view, 40);
common_kv_cache_dump_view_seqs(kvc_view, 40);
}
// print current draft sequence
@ -126,11 +126,11 @@ int main(int argc, char ** argv){
int i_dft = 0;
while (true) {
// sample from the target model
llama_token id = gpt_sampler_sample(smpl, ctx, i_dft);
llama_token id = common_sampler_sample(smpl, ctx, i_dft);
gpt_sampler_accept(smpl, id, true);
common_sampler_accept(smpl, id, true);
const std::string token_str = llama_token_to_piece(ctx, id);
const std::string token_str = common_token_to_piece(ctx, id);
if (!params.use_color) {
LOG("%s", token_str.c_str());
@ -152,7 +152,7 @@ int main(int argc, char ** argv){
{
// Update context ngram cache with the newly accepted token:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
@ -178,7 +178,7 @@ int main(int argc, char ** argv){
{
// Update context ngram cache with the newly accepted token:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
break;
@ -192,18 +192,18 @@ int main(int argc, char ** argv){
// 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);
common_batch_clear(batch_tgt);
common_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
// Draft already contains a single token sampled from the model:
GGML_ASSERT(draft.size() == 1);
GGML_ASSERT(draft[0] == inp.back());
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
common_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
for (size_t i = 1; i < draft.size(); ++i) {
llama_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
}
t_draft_us += ggml_time_us() - t_start_draft_us;
@ -218,8 +218,8 @@ int main(int argc, char ** argv){
auto t_dec_end = ggml_time_us();
// Update dynamic ngram cache with context ngram cache and save it to disk:
llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
llama_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
common_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
LOG("\n\n");
@ -237,9 +237,9 @@ int main(int argc, char ** argv){
LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_INF("\ntarget:\n\n");
gpt_perf_print(ctx, smpl);
common_perf_print(ctx, smpl);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
llama_batch_free(batch_tgt);

92
examples/main/main.cpp
View file

@ -34,8 +34,8 @@
static llama_context ** g_ctx;
static llama_model ** g_model;
static gpt_sampler ** g_smpl;
static gpt_params * g_params;
static common_sampler ** g_smpl;
static common_params * g_params;
static std::vector<llama_token> * g_input_tokens;
static std::ostringstream * g_output_ss;
static std::vector<llama_token> * g_output_tokens;
@ -64,7 +64,7 @@ static bool file_is_empty(const std::string & path) {
}
static void write_logfile(
const llama_context * ctx, const gpt_params & params, const llama_model * model,
const llama_context * ctx, const common_params & params, const llama_model * model,
const std::vector<llama_token> & input_tokens, const std::string & output,
const std::vector<llama_token> & output_tokens
) {
@ -115,12 +115,12 @@ static void sigint_handler(int signo) {
} else {
console::cleanup();
LOG("\n");
gpt_perf_print(*g_ctx, *g_smpl);
common_perf_print(*g_ctx, *g_smpl);
write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
// make sure all logs are flushed
LOG("Interrupted by user\n");
gpt_log_pause(gpt_log_main());
common_log_pause(common_log_main());
_exit(130);
}
@ -128,22 +128,22 @@ static void sigint_handler(int signo) {
}
#endif
static std::string chat_add_and_format(struct llama_model * model, std::vector<llama_chat_msg> & chat_msgs, const std::string & role, const std::string & content) {
llama_chat_msg new_msg{role, content};
auto formatted = llama_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user");
static std::string chat_add_and_format(struct llama_model * model, std::vector<common_chat_msg> & chat_msgs, const std::string & role, const std::string & content) {
common_chat_msg new_msg{role, content};
auto formatted = common_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user");
chat_msgs.push_back({role, content});
LOG_DBG("formatted: '%s'\n", formatted.c_str());
return formatted;
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
g_params = &params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
return 1;
}
gpt_init();
common_init();
auto & sparams = params.sparams;
@ -188,9 +188,9 @@ int main(int argc, char ** argv) {
llama_model * model = nullptr;
llama_context * ctx = nullptr;
gpt_sampler * smpl = nullptr;
common_sampler * smpl = nullptr;
std::vector<llama_chat_msg> chat_msgs;
std::vector<common_chat_msg> chat_msgs;
g_model = &model;
g_ctx = &ctx;
@ -198,7 +198,7 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any
LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
model = llama_init.model;
ctx = llama_init.context;
@ -247,7 +247,7 @@ int main(int argc, char ** argv) {
// print chat template example in conversation mode
if (params.conversation) {
if (params.enable_chat_template) {
LOG_INF("%s: chat template example:\n%s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str());
LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(model, params.chat_template).c_str());
} else {
LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
}
@ -256,7 +256,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
}
@ -297,7 +297,7 @@ int main(int argc, char ** argv) {
: params.prompt;
if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
LOG_DBG("tokenize the prompt\n");
embd_inp = ::llama_tokenize(ctx, prompt, true, true);
embd_inp = common_tokenize(ctx, prompt, true, true);
} else {
LOG_DBG("use session tokens\n");
embd_inp = session_tokens;
@ -380,13 +380,13 @@ int main(int argc, char ** argv) {
LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
LOG_INF("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str());
}
if (params.n_keep > add_bos) {
LOG_INF("%s: static prompt based on n_keep: '", __func__);
for (int i = 0; i < params.n_keep; i++) {
LOG_CNT("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str());
}
LOG_CNT("'\n");
}
@ -416,9 +416,9 @@ int main(int argc, char ** argv) {
for (const auto & antiprompt : params.antiprompt) {
LOG_INF("Reverse prompt: '%s'\n", antiprompt.c_str());
if (params.verbose_prompt) {
auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);
auto tmp = common_tokenize(ctx, antiprompt, false, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
}
}
}
@ -431,9 +431,9 @@ int main(int argc, char ** argv) {
if (!params.input_prefix.empty()) {
LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str());
if (params.verbose_prompt) {
auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);
auto tmp = common_tokenize(ctx, params.input_prefix, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
}
}
}
@ -441,23 +441,23 @@ int main(int argc, char ** argv) {
if (!params.input_suffix.empty()) {
LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
if (params.verbose_prompt) {
auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);
auto tmp = common_tokenize(ctx, params.input_suffix, false, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
}
}
}
}
smpl = gpt_sampler_init(model, sparams);
smpl = common_sampler_init(model, sparams);
if (!smpl) {
LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
return 1;
}
LOG_INF("sampler seed: %u\n", gpt_sampler_get_seed(smpl));
LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl));
LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
LOG_INF("sampler chain: %s\n", gpt_sampler_print(smpl).c_str());
LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str());
LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
@ -522,7 +522,7 @@ int main(int argc, char ** argv) {
antiprompt_ids.reserve(params.antiprompt.size());
for (const std::string & antiprompt : params.antiprompt) {
antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
antiprompt_ids.emplace_back(::common_tokenize(ctx, antiprompt, false, true));
}
if (llama_model_has_encoder(model)) {
@ -680,9 +680,9 @@ int main(int argc, char ** argv) {
LOG_DBG("saved session to %s\n", path_session.c_str());
}
const llama_token id = gpt_sampler_sample(smpl, ctx, -1);
const llama_token id = common_sampler_sample(smpl, ctx, -1);
gpt_sampler_accept(smpl, id, /* accept_grammar= */ true);
common_sampler_accept(smpl, id, /* accept_grammar= */ true);
// LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str());
@ -703,7 +703,7 @@ int main(int argc, char ** argv) {
// push the prompt in the sampling context in order to apply repetition penalties later
// for the prompt, we don't apply grammar rules
gpt_sampler_accept(smpl, embd_inp[n_consumed], /* accept_grammar= */ false);
common_sampler_accept(smpl, embd_inp[n_consumed], /* accept_grammar= */ false);
++n_consumed;
if ((int) embd.size() >= params.n_batch) {
@ -715,7 +715,7 @@ int main(int argc, char ** argv) {
// display text
if (input_echo && display) {
for (auto id : embd) {
const std::string token_str = llama_token_to_piece(ctx, id, params.special);
const std::string token_str = common_token_to_piece(ctx, id, params.special);
// Console/Stream Output
LOG("%s", token_str.c_str());
@ -744,7 +744,7 @@ int main(int argc, char ** argv) {
// check for reverse prompt in the last n_prev tokens
if (!params.antiprompt.empty()) {
const int n_prev = 32;
const std::string last_output = gpt_sampler_prev_str(smpl, ctx, n_prev);
const std::string last_output = common_sampler_prev_str(smpl, ctx, n_prev);
is_antiprompt = false;
// Check if each of the reverse prompts appears at the end of the output.
@ -766,7 +766,7 @@ int main(int argc, char ** argv) {
}
// check for reverse prompt using special tokens
llama_token last_token = gpt_sampler_last(smpl);
llama_token last_token = common_sampler_last(smpl);
for (std::vector<llama_token> ids : antiprompt_ids) {
if (ids.size() == 1 && last_token == ids[0]) {
if (params.interactive) {
@ -783,13 +783,13 @@ int main(int argc, char ** argv) {
}
// deal with end of generation tokens in interactive mode
if (llama_token_is_eog(model, gpt_sampler_last(smpl))) {
if (llama_token_is_eog(model, common_sampler_last(smpl))) {
LOG_DBG("found an EOG token\n");
if (params.interactive) {
if (!params.antiprompt.empty()) {
// tokenize and inject first reverse prompt
const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true);
const auto first_antiprompt = common_tokenize(ctx, params.antiprompt.front(), false, true);
embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
is_antiprompt = true;
}
@ -804,8 +804,8 @@ int main(int argc, char ** argv) {
// if current token is not EOG, we add it to current assistant message
if (params.conversation) {
const auto id = gpt_sampler_last(smpl);
assistant_ss << llama_token_to_piece(ctx, id, false);
const auto id = common_sampler_last(smpl);
assistant_ss << common_token_to_piece(ctx, id, false);
}
if (n_past > 0 && is_interacting) {
@ -863,9 +863,9 @@ int main(int argc, char ** argv) {
? chat_add_and_format(model, chat_msgs, "user", std::move(buffer))
: std::move(buffer);
// TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
const auto line_inp = ::llama_tokenize(ctx, user_inp, false, format_chat);
const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
const auto line_pfx = common_tokenize(ctx, params.input_prefix, false, true);
const auto line_inp = common_tokenize(ctx, user_inp, false, format_chat);
const auto line_sfx = common_tokenize(ctx, params.input_suffix, false, true);
LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str());
@ -883,7 +883,7 @@ int main(int argc, char ** argv) {
for (size_t i = original_size; i < embd_inp.size(); ++i) {
const llama_token token = embd_inp[i];
output_tokens.push_back(token);
output_ss << llama_token_to_piece(ctx, token);
output_ss << common_token_to_piece(ctx, token);
}
// reset assistant message
@ -900,7 +900,7 @@ int main(int argc, char ** argv) {
if (n_past > 0) {
if (is_interacting) {
gpt_sampler_reset(smpl);
common_sampler_reset(smpl);
}
is_interacting = false;
}
@ -926,10 +926,10 @@ int main(int argc, char ** argv) {
}
LOG("\n\n");
gpt_perf_print(ctx, smpl);
common_perf_print(ctx, smpl);
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
llama_free(ctx);
llama_free_model(model);

28
examples/passkey/passkey.cpp
View file

@ -15,17 +15,17 @@ static void print_usage(int, char ** argv) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.n_junk = 250;
params.n_keep = 32;
params.i_pos = -1;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
return 1;
}
gpt_init();
common_init();
int n_junk = params.n_junk;
int n_keep = params.n_keep;
@ -61,7 +61,7 @@ int main(int argc, char ** argv) {
// initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model_params model_params = common_model_params_to_llama(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -72,7 +72,7 @@ int main(int argc, char ** argv) {
// initialize the context
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context_params ctx_params = common_context_params_to_llama(params);
ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep;
@ -92,10 +92,10 @@ int main(int argc, char ** argv) {
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
tokens_list = common_tokenize(ctx, params.prompt, true);
// tokenize the prefix and use it as a sink
const int n_tokens_prefix = ::llama_tokenize(ctx, prompt_prefix, true).size();
const int n_tokens_prefix = common_tokenize(ctx, prompt_prefix, true).size();
const int n_tokens_all = tokens_list.size();
@ -137,10 +137,10 @@ int main(int argc, char ** argv) {
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
}
llama_batch_clear(batch);
common_batch_clear(batch);
for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
}
if (i + n_batch >= n_tokens_all) {
@ -171,10 +171,10 @@ int main(int argc, char ** argv) {
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
llama_batch_clear(batch);
common_batch_clear(batch);
for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
}
if (i + n_batch >= n_tokens_all) {
@ -229,15 +229,15 @@ int main(int argc, char ** argv) {
break;
}
LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str());
LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
n_decode += 1;
// prepare the next batch
llama_batch_clear(batch);
common_batch_clear(batch);
// push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_past++, { 0 }, true);
common_batch_add(batch, new_token_id, n_past++, { 0 }, true);
}
n_cur += 1;

26
examples/retrieval/retrieval.cpp
View file

@ -77,7 +77,7 @@ static std::vector<chunk> chunk_file(const std::string & filename, int chunk_siz
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, true);
common_batch_add(batch, tokens[i], i, { seq_id }, true);
}
}
@ -107,18 +107,18 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
}
float * out = output + batch.seq_id[i][0] * n_embd;
llama_embd_normalize(embd, out, n_embd);
common_embd_normalize(embd, out, n_embd);
}
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
return 1;
}
gpt_init();
common_init();
// For BERT models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
@ -149,7 +149,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the model
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
@ -176,7 +176,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
// max batch size
@ -185,7 +185,7 @@ int main(int argc, char ** argv) {
// tokenize the prompts and trim
for (auto & chunk : chunks) {
auto inp = ::llama_tokenize(ctx, chunk.textdata, true, false);
auto inp = common_tokenize(ctx, chunk.textdata, true, false);
if (inp.size() > n_batch) {
LOG_ERR("%s: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
@ -204,7 +204,7 @@ int main(int argc, char ** argv) {
LOG_INF("%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
LOG_INF("%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
LOG_INF("%6d -> '%s'\n", chunks[i].tokens[j], common_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
}
LOG_INF("\n\n");
}
@ -232,7 +232,7 @@ int main(int argc, char ** argv) {
if (batch.n_tokens + n_toks > n_batch) {
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
llama_batch_clear(batch);
common_batch_clear(batch);
p += s;
s = 0;
}
@ -260,20 +260,20 @@ int main(int argc, char ** argv) {
while (true) {
LOG("Enter query: ");
std::getline(std::cin, query);
std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
std::vector<int32_t> query_tokens = common_tokenize(ctx, query, true);
batch_add_seq(query_batch, query_tokens, 0);
std::vector<float> query_emb(n_embd, 0);
batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
llama_batch_clear(query_batch);
common_batch_clear(query_batch);
// compute cosine similarities
{
std::vector<std::pair<int, float>> similarities;
for (int i = 0; i < n_chunks; i++) {
float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
float sim = common_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
similarities.push_back(std::make_pair(i, sim));
}

2
examples/rpc/rpc-server.cpp
View file

@ -151,7 +151,7 @@ int main(int argc, char * argv[]) {
get_backend_memory(&free_mem, &total_mem);
}
printf("Starting RPC server on %s, backend memory: %zu MB\n", endpoint.c_str(), free_mem / (1024 * 1024));
start_rpc_server(backend, endpoint.c_str(), free_mem, total_mem);
ggml_backend_rpc_start_server(backend, endpoint.c_str(), free_mem, total_mem);
ggml_backend_free(backend);
return 0;
}

169
examples/server/server.cpp
View file

@ -189,8 +189,8 @@ struct server_slot {
// sampling
json json_schema;
struct gpt_sampler_params sparams;
struct gpt_sampler * smpl = nullptr;
struct common_sampler_params sparams;
struct common_sampler * smpl = nullptr;
llama_token sampled;
@ -232,7 +232,7 @@ struct server_slot {
generated_token_probs.clear();
}
bool has_budget(gpt_params &global_params) {
bool has_budget(common_params &global_params) {
if (params.n_predict == -1 && global_params.n_predict == -1) {
return true; // limitless
}
@ -612,9 +612,9 @@ struct server_response {
struct server_context {
llama_model * model = nullptr;
llama_context * ctx = nullptr;
std::vector<llama_lora_adapter_container> loras;
std::vector<common_lora_adapter_container> loras;
gpt_params params;
common_params params;
llama_batch batch = {};
@ -656,20 +656,20 @@ struct server_context {
// Clear any sampling context
for (server_slot & slot : slots) {
if (slot.smpl != nullptr) {
gpt_sampler_free(slot.smpl);
common_sampler_free(slot.smpl);
}
}
llama_batch_free(batch);
}
bool load_model(const gpt_params & params_) {
bool load_model(const common_params & params_) {
params = params_;
// dedicate one sequence to the system prompt
params.n_parallel += 1;
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
model = llama_init.model;
ctx = llama_init.context;
@ -772,10 +772,10 @@ struct server_context {
std::vector<llama_token> p;
if (first) {
p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
p = common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
first = false;
} else {
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
p = common_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
}
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
@ -789,7 +789,7 @@ struct server_context {
}
} else {
auto s = json_prompt.template get<std::string>();
prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
prompt_tokens = common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
}
return prompt_tokens;
@ -1000,7 +1000,7 @@ struct server_context {
slot.sparams.logit_bias.push_back({tok, bias});
}
} else if (el[0].is_string()) {
auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
auto toks = common_tokenize(model, el[0].get<std::string>(), false);
for (auto tok : toks) {
slot.sparams.logit_bias.push_back({tok, bias});
}
@ -1032,7 +1032,7 @@ struct server_context {
sampler_names.emplace_back(name);
}
}
slot.sparams.samplers = gpt_sampler_types_from_names(sampler_names, false);
slot.sparams.samplers = common_sampler_types_from_names(sampler_names, false);
} else {
slot.sparams.samplers = default_sparams.samplers;
}
@ -1040,10 +1040,10 @@ struct server_context {
{
if (slot.smpl != nullptr) {
gpt_sampler_free(slot.smpl);
common_sampler_free(slot.smpl);
}
slot.smpl = gpt_sampler_init(model, slot.sparams);
slot.smpl = common_sampler_init(model, slot.sparams);
if (slot.smpl == nullptr) {
// for now, the only error that may happen here is invalid grammar
send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
@ -1074,7 +1074,7 @@ struct server_context {
system_tokens.clear();
if (!system_prompt.empty()) {
system_tokens = ::llama_tokenize(ctx, system_prompt, true);
system_tokens = common_tokenize(ctx, system_prompt, true);
const int32_t n_batch = llama_n_batch(ctx);
const int32_t n_tokens_prompt = system_tokens.size();
@ -1082,10 +1082,10 @@ struct server_context {
for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i);
llama_batch_clear(batch);
common_batch_clear(batch);
for (int32_t j = 0; j < n_tokens; ++j) {
llama_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false);
common_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false);
}
if (llama_decode(ctx, batch) != 0) {
@ -1107,19 +1107,14 @@ struct server_context {
SRV_DBG("system prompt set: '%s'\n", system_prompt.c_str());
system_prompt = sys_prompt;
// release all slots
for (server_slot & slot : slots) {
slot.release();
}
// update system_tokens and KV cache as soon as all slots are idle
system_need_update = true;
return true;
}
bool process_token(completion_token_output & result, server_slot & slot) {
// remember which tokens were sampled - used for repetition penalties during sampling
const std::string token_str = llama_token_to_piece(ctx, result.tok, params.special);
const std::string token_str = common_token_to_piece(ctx, result.tok, params.special);
slot.sampled = result.tok;
// search stop word and delete it
@ -1230,7 +1225,7 @@ struct server_context {
std::vector<std::string> samplers;
samplers.reserve(slot.sparams.samplers.size());
for (const auto & sampler : slot.sparams.samplers) {
samplers.emplace_back(gpt_sampler_type_to_str(sampler));
samplers.emplace_back(common_sampler_type_to_str(sampler));
}
return json {
@ -1238,7 +1233,7 @@ struct server_context {
{"n_predict", slot.n_predict}, // Server configured n_predict
{"model", params.model_alias},
{"seed", slot.sparams.seed},
{"seed_cur", slot.smpl ? gpt_sampler_get_seed(slot.smpl) : 0},
{"seed_cur", slot.smpl ? common_sampler_get_seed(slot.smpl) : 0},
{"temperature", slot.sparams.temp},
{"dynatemp_range", slot.sparams.dynatemp_range},
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
@ -1303,7 +1298,7 @@ struct server_context {
};
if (slot.sparams.n_probs > 0) {
const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
const std::vector<llama_token> to_send_toks = common_tokenize(ctx, tkn.text_to_send, false);
const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size());
const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());
@ -1353,7 +1348,7 @@ struct server_context {
if (slot.sparams.n_probs > 0) {
std::vector<completion_token_output> probs;
if (!slot.params.stream && slot.stopped_word) {
const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
const std::vector<llama_token> stop_word_toks = common_tokenize(ctx, slot.stopping_word, false);
size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
probs = std::vector<completion_token_output>(
@ -1407,7 +1402,7 @@ struct server_context {
continue;
}
llama_embd_normalize(embd, embd_res.data(), n_embd);
common_embd_normalize(embd, embd_res.data(), n_embd);
res.data = json {
{"embedding", embd_res},
@ -1628,16 +1623,6 @@ struct server_context {
break;
}
if (task.data.contains("system_prompt")) {
std::string sys_prompt = json_value(task.data, "system_prompt", std::string());
system_prompt_set(sys_prompt);
for (server_slot & slot : slots) {
slot.n_past = 0;
slot.n_past_se = 0;
}
}
slot->reset();
slot->id_task = task.id;
@ -1851,7 +1836,7 @@ struct server_context {
} break;
case SERVER_TASK_TYPE_SET_LORA:
{
llama_lora_adapters_apply(ctx, loras);
common_lora_adapters_apply(ctx, loras);
server_task_result result;
result.id = task.id;
result.stop = true;
@ -1863,10 +1848,6 @@ struct server_context {
}
void update_slots() {
if (system_need_update) {
system_prompt_update();
}
// check if all slots are idle
{
bool all_idle = true;
@ -1879,6 +1860,10 @@ struct server_context {
}
if (all_idle) {
if (system_need_update) {
system_prompt_update();
}
SRV_INF("%s", "all slots are idle\n");
if (system_prompt.empty() && clean_kv_cache) {
kv_cache_clear();
@ -1937,7 +1922,7 @@ struct server_context {
}
// start populating the batch for this iteration
llama_batch_clear(batch);
common_batch_clear(batch);
// frist, add sampled tokens from any ongoing sequences
for (auto & slot : slots) {
@ -1951,7 +1936,7 @@ struct server_context {
// TODO: we always have to take into account the "system_tokens"
// this is not great and needs to be improved somehow
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);
common_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);
slot.n_past += 1;
@ -2108,7 +2093,7 @@ struct server_context {
GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
}
gpt_sampler_reset(slot.smpl);
common_sampler_reset(slot.smpl);
if (!slot.params.cache_prompt) {
slot.n_past_se = 0;
@ -2121,7 +2106,7 @@ struct server_context {
// push the prompt into the sampling context (do not apply grammar)
for (int i = 0; i < slot.n_past; ++i) {
gpt_sampler_accept(slot.smpl, slot.cache_tokens[i], false);
common_sampler_accept(slot.smpl, slot.cache_tokens[i], false);
}
}
}
@ -2175,7 +2160,7 @@ struct server_context {
slot.n_past_se = 0;
slot.ga_i = 0;
// TODO: is the system prompt ever in the sampling context?
gpt_sampler_reset(slot.smpl);
common_sampler_reset(slot.smpl);
}
// remove the non-common part from the cache
@ -2200,7 +2185,7 @@ struct server_context {
}
}
llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false);
common_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false);
if (slot.params.cache_prompt) {
slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
@ -2338,9 +2323,9 @@ struct server_context {
}
completion_token_output result;
const llama_token id = gpt_sampler_sample(slot.smpl, ctx, slot.i_batch - i);
const llama_token id = common_sampler_sample(slot.smpl, ctx, slot.i_batch - i);
gpt_sampler_accept(slot.smpl, id, true);
common_sampler_accept(slot.smpl, id, true);
slot.n_decoded += 1;
if (slot.n_decoded == 1) {
@ -2351,7 +2336,7 @@ struct server_context {
result.tok = id;
const auto * cur_p = gpt_sampler_get_candidates(slot.smpl);
const auto * cur_p = common_sampler_get_candidates(slot.smpl);
for (size_t i = 0; i < (size_t) slot.sparams.n_probs; ++i) {
result.probs.push_back({
@ -2415,13 +2400,13 @@ inline void signal_handler(int signal) {
int main(int argc, char ** argv) {
// own arguments required by this example
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
return 1;
}
gpt_init();
common_init();
// enabling this will output extra debug information in the HTTP responses from the server
// see format_final_response_oaicompat()
@ -2443,7 +2428,7 @@ int main(int argc, char ** argv) {
LOG_INF("system info: n_threads = %d, n_threads_batch = %d, total_threads = %d\n", params.cpuparams.n_threads, params.cpuparams_batch.n_threads, std::thread::hardware_concurrency());
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
std::unique_ptr<httplib::Server> svr;
@ -2537,20 +2522,10 @@ int main(int argc, char ** argv) {
//
auto middleware_validate_api_key = [&params, &res_error](const httplib::Request & req, httplib::Response & res) {
// TODO: should we apply API key to all endpoints, including "/health" and "/models"?
static const std::unordered_set<std::string> protected_endpoints = {
"/props",
"/completion",
"/completions",
"/v1/completions",
"/chat/completions",
"/v1/chat/completions",
"/infill",
"/tokenize",
"/detokenize",
"/embedding",
"/embeddings",
"/v1/embeddings",
static const std::unordered_set<std::string> public_endpoints = {
"/health",
"/models",
"/v1/models",
};
// If API key is not set, skip validation
@ -2558,8 +2533,8 @@ int main(int argc, char ** argv) {
return true;
}
// If path is not in protected_endpoints list, skip validation
if (protected_endpoints.find(req.path) == protected_endpoints.end()) {
// If path is public, skip validation
if (public_endpoints.find(req.path) != public_endpoints.end()) {
return true;
}
@ -2621,7 +2596,7 @@ int main(int argc, char ** argv) {
const auto handle_slots = [&](const httplib::Request & req, httplib::Response & res) {
if (!params.endpoint_slots) {
res_error(res, format_error_response("This server does not support slots endpoint. Start it without `--no-slots`", ERROR_TYPE_NOT_SUPPORTED));
res_error(res, format_error_response("This server does not support slots endpoint. Start it with `--slots`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
@ -2870,24 +2845,31 @@ int main(int argc, char ** argv) {
};
const auto handle_props = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
std::string template_key = "tokenizer.chat_template", curr_tmpl;
int32_t tlen = llama_model_meta_val_str(ctx_server.model, template_key.c_str(), nullptr, 0);
if (tlen > 0) {
std::vector<char> curr_tmpl_buf(tlen + 1, 0);
if (llama_model_meta_val_str(ctx_server.model, template_key.c_str(), curr_tmpl_buf.data(), curr_tmpl_buf.size()) == tlen) {
curr_tmpl = std::string(curr_tmpl_buf.data(), tlen);
}
}
json data = {
{ "system_prompt", ctx_server.system_prompt.c_str() },
{ "system_prompt", ctx_server.system_prompt },
{ "default_generation_settings", ctx_server.default_generation_settings_for_props },
{ "total_slots", ctx_server.params.n_parallel },
{ "chat_template", curr_tmpl.c_str() },
{ "chat_template", llama_get_chat_template(ctx_server.model) },
};
res_ok(res, data);
};
const auto handle_props_change = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
if (!ctx_server.params.endpoint_props) {
res_error(res, format_error_response("This server does not support changing global properties. Start it with `--props`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
json data = json::parse(req.body);
if (data.contains("system_prompt")) {
std::string system_prompt = data.at("system_prompt");
ctx_server.system_prompt_set(system_prompt);
}
res_ok(res, {{ "success", true }});
};
const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_cmpl_type cmpl_type, json & data, httplib::Response & res) {
if (ctx_server.params.embedding || ctx_server.params.reranking) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
@ -3033,7 +3015,7 @@ int main(int argc, char ** argv) {
if (with_pieces) {
for (const auto& token : tokens) {
std::string piece = llama_token_to_piece(ctx_server.ctx, token);
std::string piece = common_token_to_piece(ctx_server.ctx, token);
json piece_json;
// Check if the piece is valid UTF-8
@ -3266,6 +3248,12 @@ int main(int argc, char ** argv) {
svr->set_base_dir(params.public_path);
}
if (!params.api_keys.empty()) {
// for now, if API key is set, web UI is unusable
svr->Get("/", [&](const httplib::Request &, httplib::Response & res) {
return res.set_content("Web UI is disabled because API key is set.", "text/html; charset=utf-8");
});
} else {
// using embedded static files
svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
@ -3284,12 +3272,15 @@ int main(int argc, char ** argv) {
svr->Get("/index-new.html", handle_static_file(index_new_html, index_new_html_len, "text/html; charset=utf-8"));
svr->Get("/system-prompts.js", handle_static_file(system_prompts_js, system_prompts_js_len, "text/javascript; charset=utf-8"));
svr->Get("/prompt-formats.js", handle_static_file(prompt_formats_js, prompt_formats_js_len, "text/javascript; charset=utf-8"));
}
// register API routes
svr->Get ("/health", handle_health);
svr->Get ("/health", handle_health); // public endpoint (no API key check)
svr->Get ("/metrics", handle_metrics);
svr->Get ("/props", handle_props);
svr->Get ("/v1/models", handle_models);
svr->Post("/props", handle_props_change);
svr->Get ("/models", handle_models); // public endpoint (no API key check)
svr->Get ("/v1/models", handle_models); // public endpoint (no API key check)
svr->Post("/completion", handle_completions); // legacy
svr->Post("/completions", handle_completions);
svr->Post("/v1/completions", handle_completions);
@ -3367,7 +3358,7 @@ int main(int argc, char ** argv) {
}
// print sample chat example to make it clear which template is used
LOG_INF("%s: chat template, built_in: %d, chat_example: '%s'\n", __func__, params.chat_template.empty(), llama_chat_format_example(ctx_server.model, params.chat_template).c_str());
LOG_INF("%s: chat template, built_in: %d, chat_example: '%s'\n", __func__, params.chat_template.empty(), common_chat_format_example(ctx_server.model, params.chat_template).c_str());
ctx_server.queue_tasks.on_new_task(std::bind(
&server_context::process_single_task, &ctx_server, std::placeholders::_1));

12
examples/server/tests/features/security.feature
View file

@ -5,7 +5,7 @@ Feature: Security
Background: Server startup with an api key defined
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And a server api key llama.cpp
And a server api key THIS_IS_THE_KEY
Then the server is starting
Then the server is healthy
@ -17,8 +17,8 @@ Feature: Security
Examples: Prompts
| api_key | api_error |
| llama.cpp | no |
| llama.cpp | no |
| THIS_IS_THE_KEY | no |
| THIS_IS_THE_KEY | no |
| hackeme | raised |
| | raised |
@ -33,8 +33,8 @@ Feature: Security
Examples: Prompts
| api_key | api_error |
| llama.cpp | no |
| llama.cpp | no |
| THIS_IS_THE_KEY | no |
| THIS_IS_THE_KEY | no |
| hackme | raised |
Scenario Outline: OAI Compatibility (invalid response formats)
@ -55,7 +55,7 @@ Feature: Security
Scenario Outline: CORS Options
Given a user api key llama.cpp
Given a user api key THIS_IS_THE_KEY
When an OPTIONS request is sent from <origin>
Then CORS header <cors_header> is set to <cors_header_value>

4
examples/server/tests/features/steps/steps.py
View file

@ -1299,7 +1299,8 @@ async def wait_for_slots_status(context,
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
while True:
async with await session.get(f'{base_url}/slots', params=params) as slots_response:
headers = {'Authorization': f'Bearer {context.server_api_key}'}
async with await session.get(f'{base_url}/slots', params=params, headers=headers) as slots_response:
status_code = slots_response.status
slots = await slots_response.json()
if context.debug:
@ -1387,6 +1388,7 @@ def start_server_background(context):
context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
server_listen_addr = context.server_fqdn
server_args = [
'--slots', # requires to get slot status via /slots endpoint
'--host', server_listen_addr,
'--port', context.server_port,
]

21
examples/server/utils.hpp
View file

@ -57,7 +57,7 @@ static T json_value(const json & body, const std::string & key, const T & defaul
// Format given chat. If tmpl is empty, we take the template from model metadata
inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
std::vector<llama_chat_msg> chat;
std::vector<common_chat_msg> chat;
for (size_t i = 0; i < messages.size(); ++i) {
const auto & curr_msg = messages[i];
@ -84,12 +84,25 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
chat.push_back({role, content});
}
const auto formatted_chat = llama_chat_apply_template(model, tmpl, chat, true);
const auto formatted_chat = common_chat_apply_template(model, tmpl, chat, true);
LOG_DBG("formatted_chat: '%s'\n", formatted_chat.c_str());
return formatted_chat;
}
static std::string llama_get_chat_template(const struct llama_model * model) {
std::string template_key = "tokenizer.chat_template";
// call with NULL buffer to get the total size of the string
int32_t res = llama_model_meta_val_str(model, template_key.c_str(), NULL, 0);
if (res < 0) {
return "";
} else {
std::vector<char> model_template(res, 0);
llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
return std::string(model_template.data(), model_template.size());
}
}
//
// base64 utils (TODO: move to common in the future)
//
@ -233,7 +246,7 @@ template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret;
for (; begin != end; ++begin) {
ret += llama_token_to_piece(ctx, *begin);
ret += common_token_to_piece(ctx, *begin);
}
return ret;
@ -241,7 +254,7 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
// format incomplete utf-8 multibyte character for output
static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
std::string out = token == -1 ? "" : common_token_to_piece(ctx, token);
// if the size is 1 and first bit is 1, meaning it's a partial character
// (size > 1 meaning it's already a known token)

222
examples/simple/simple.cpp
View file

@ -1,50 +1,112 @@
#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"
#include <cstdio>
#include <cstring>
#include <string>
#include <vector>
static void print_usage(int, char ** argv) {
LOG("\nexample usage:\n");
LOG("\n %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]);
LOG("\n");
printf("\nexample usage:\n");
printf("\n %s -m model.gguf [-n n_predict] [-ngl n_gpu_layers] [prompt]\n", argv[0]);
printf("\n");
}
int main(int argc, char ** argv) {
gpt_params params;
// path to the model gguf file
std::string model_path;
// prompt to generate text from
std::string prompt = "Hello my name is";
// number of layers to offload to the GPU
int ngl = 99;
// number of tokens to predict
int n_predict = 32;
params.prompt = "Hello my name is";
params.n_predict = 32;
// parse command line arguments
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
{
int i = 1;
for (; i < argc; i++) {
if (strcmp(argv[i], "-m") == 0) {
if (i + 1 < argc) {
model_path = argv[++i];
} else {
print_usage(argc, argv);
return 1;
}
gpt_init();
// total length of the sequence including the prompt
const int n_predict = params.n_predict;
// init LLM
llama_backend_init();
llama_numa_init(params.numa);
} else if (strcmp(argv[i], "-n") == 0) {
if (i + 1 < argc) {
try {
n_predict = std::stoi(argv[++i]);
} catch (...) {
print_usage(argc, argv);
return 1;
}
} else {
print_usage(argc, argv);
return 1;
}
} else if (strcmp(argv[i], "-ngl") == 0) {
if (i + 1 < argc) {
try {
ngl = std::stoi(argv[++i]);
} catch (...) {
print_usage(argc, argv);
return 1;
}
} else {
print_usage(argc, argv);
return 1;
}
} else {
// prompt starts here
break;
}
}
if (model_path.empty()) {
print_usage(argc, argv);
return 1;
}
if (i < argc) {
prompt = argv[i++];
for (; i < argc; i++) {
prompt += " ";
prompt += argv[i];
}
}
}
// initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = ngl;
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
llama_model * model = llama_load_model_from_file(model_path.c_str(), model_params);
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return 1;
}
// tokenize the prompt
// find the number of tokens in the prompt
const int n_prompt = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
// allocate space for the tokens and tokenize the prompt
std::vector<llama_token> prompt_tokens(n_prompt);
if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
return 1;
}
// initialize the context
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context_params ctx_params = llama_context_default_params();
// n_ctx is the context size
ctx_params.n_ctx = n_prompt + n_predict - 1;
// n_batch is the maximum number of tokens that can be processed in a single call to llama_decode
ctx_params.n_batch = n_prompt;
// enable performance counters
ctx_params.no_perf = false;
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
@ -53,117 +115,87 @@ int main(int argc, char ** argv) {
return 1;
}
// initialize the sampler
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = false;
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
const int n_ctx = llama_n_ctx(ctx);
const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size());
LOG("\n");
LOG_INF("%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, n_kv_req);
// make sure the KV cache is big enough to hold all the prompt and generated tokens
if (n_kv_req > n_ctx) {
LOG_ERR("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
LOG_ERR("%s: either reduce n_predict or increase n_ctx\n", __func__);
return 1;
}
// print the prompt token-by-token
LOG("\n");
for (auto id : tokens_list) {
LOG("%s", llama_token_to_piece(ctx, id).c_str());
}
// create a llama_batch with size 512
// we use this object to submit token data for decoding
llama_batch batch = llama_batch_init(512, 0, 1);
// evaluate the initial prompt
for (size_t i = 0; i < tokens_list.size(); i++) {
llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
}
// llama_decode will output logits only for the last token of the prompt
batch.logits[batch.n_tokens - 1] = true;
if (llama_decode(ctx, batch) != 0) {
LOG("%s: llama_decode() failed\n", __func__);
for (auto id : prompt_tokens) {
char buf[128];
int n = llama_token_to_piece(model, id, buf, sizeof(buf), 0, true);
if (n < 0) {
fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
return 1;
}
std::string s(buf, n);
printf("%s", s.c_str());
}
// prepare a batch for the prompt
llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size(), 0, 0);
// main loop
int n_cur = batch.n_tokens;
int n_decode = 0;
const auto t_main_start = ggml_time_us();
int n_decode = 0;
llama_token new_token_id;
for (int n_pos = 0; n_pos + batch.n_tokens < n_prompt + n_predict; ) {
// evaluate the current batch with the transformer model
if (llama_decode(ctx, batch)) {
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
n_pos += batch.n_tokens;
while (n_cur <= n_predict) {
// sample the next token
{
const llama_token new_token_id = llama_sampler_sample(smpl, ctx, -1);
new_token_id = llama_sampler_sample(smpl, ctx, -1);
// is it an end of generation?
if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
LOG("\n");
if (llama_token_is_eog(model, new_token_id)) {
break;
}
LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str());
char buf[128];
int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true);
if (n < 0) {
fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
return 1;
}
std::string s(buf, n);
printf("%s", s.c_str());
fflush(stdout);
// prepare the next batch
llama_batch_clear(batch);
// push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);
// prepare the next batch with the sampled token
batch = llama_batch_get_one(&new_token_id, 1, n_pos, 0);
n_decode += 1;
}
n_cur += 1;
// evaluate the current batch with the transformer model
if (llama_decode(ctx, batch)) {
LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
}
LOG("\n");
printf("\n");
const auto t_main_end = ggml_time_us();
LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
fprintf(stderr, "%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
LOG("\n");
fprintf(stderr, "\n");
llama_perf_sampler_print(smpl);
llama_perf_context_print(ctx);
fprintf(stderr, "\n");
LOG("\n");
llama_batch_free(batch);
llama_sampler_free(smpl);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

4
examples/tokenize/tokenize.cpp
View file

@ -365,7 +365,7 @@ int main(int raw_argc, char ** raw_argv) {
const bool parse_special = !no_parse_special;
std::vector<llama_token> tokens;
tokens = ::llama_tokenize(model, prompt, add_bos, parse_special);
tokens = common_tokenize(model, prompt, add_bos, parse_special);
if (printing_ids) {
printf("[");
@ -380,7 +380,7 @@ int main(int raw_argc, char ** raw_argv) {
} else {
bool invalid_utf8 = false;
printf("%6d -> '", tokens[i]);
write_utf8_cstr_to_stdout(llama_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8);
write_utf8_cstr_to_stdout(common_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8);
if (invalid_utf8) {
printf("' (utf-8 decode failure)\n");
} else {

3
ggml/include/ggml-backend.h
View file

@ -127,6 +127,8 @@ extern "C" {
bool async;
// pinned host buffer
bool host_buffer;
// creating buffers from host ptr
bool buffer_from_host_ptr;
// event synchronization
bool events;
};
@ -168,6 +170,7 @@ extern "C" {
// Functions that may be obtained using ggml_backend_reg_get_proc_address
typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(const float *);
typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t, int);
//
// Backend registry

2
ggml/include/ggml-blas.h
View file

@ -17,6 +17,8 @@ GGML_API bool ggml_backend_is_blas(ggml_backend_t backend);
// for openblas and blis, this will also set the number of threads used for blas operations
GGML_API void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads);
GGML_API ggml_backend_reg_t ggml_backend_blas_reg(void);
#ifdef __cplusplus
}

6
ggml/include/ggml-metal.h
View file

@ -43,7 +43,9 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
GGML_DEPRECATED(
GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size),
"obsoleted by the new device interface - https://github.com/ggerganov/llama.cpp/pull/9713");
GGML_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);
@ -57,6 +59,8 @@ GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int fam
// capture all command buffers committed the next time `ggml_backend_graph_compute` is called
GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend);
GGML_API ggml_backend_reg_t ggml_backend_metal_reg(void);
#ifdef __cplusplus
}
#endif

6
ggml/include/ggml-rpc.h
View file

@ -17,7 +17,11 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * en
GGML_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total);
GGML_API void start_rpc_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem);
GGML_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem);
GGML_API ggml_backend_reg_t ggml_backend_rpc_reg(void);
GGML_API ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint);
#ifdef __cplusplus
}

6
ggml/include/ggml.h
View file

@ -2541,7 +2541,7 @@ extern "C" {
typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef struct {
struct ggml_type_traits {
const char * type_name;
int64_t blck_size;
int64_t blck_size_interleave; // interleave elements in blocks
@ -2557,9 +2557,9 @@ extern "C" {
int64_t ncols; // number of columns to process simultaneously
ggml_gemv_t gemv;
ggml_gemm_t gemm;
} ggml_type_traits_t;
};
GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
GGML_API const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type);
#ifdef __cplusplus
}

14
ggml/src/ggml-backend-impl.h
View file

@ -88,6 +88,7 @@ extern "C" {
void (*free)(ggml_backend_t backend);
// Will be moved to the device interface
// buffer allocation
ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
@ -112,17 +113,9 @@ extern "C" {
// IMPORTANT: these functions have been moved to the device interface and will be removed from the backend interface
// new backends should implement the device interface instead
// These functions are being moved to the device interface
// check if the backend can compute an operation
bool (*supports_op) (ggml_backend_t backend, const struct ggml_tensor * op);
// check if the backend can use tensors allocated in a buffer type
bool (*supports_buft)(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
// check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
// these should be expensive operations with large batch sizes that may benefit from running on this backend
// even if the weight has to be copied from the CPU temporarily
bool (*offload_op) (ggml_backend_t backend, const struct ggml_tensor * op);
// (optional) event synchronization
@ -184,9 +177,8 @@ extern "C" {
// check if the backend can use tensors allocated in a buffer type
bool (*supports_buft)(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft);
// check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
// these should be expensive operations with large batch sizes that may benefit from running on this backend
// even if the weight has to be copied from the CPU temporarily
// (optional) check if the backend wants to run an operation, even if the weights are allocated in an incompatible buffer
// these should be expensive operations that may benefit from running on this backend instead of the CPU backend
bool (*offload_op)(ggml_backend_dev_t dev, const struct ggml_tensor * op);
// (optional) event synchronization

53
ggml/src/ggml-backend.cpp
View file

@ -463,6 +463,7 @@ enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device) {
}
void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_dev_props * props) {
memset(props, 0, sizeof(*props));
device->iface.get_props(device, props);
}
@ -479,6 +480,10 @@ ggml_backend_buffer_type_t ggml_backend_dev_buffer_type(ggml_backend_dev_t devic
}
ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device) {
if (device->iface.get_host_buffer_type == NULL) {
return NULL;
}
return device->iface.get_host_buffer_type(device);
}
@ -495,9 +500,13 @@ bool ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buff
}
bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
if (device->iface.offload_op != NULL) {
return device->iface.offload_op(device, op);
}
return false;
}
// Backend (reg)
const char * ggml_backend_reg_name(ggml_backend_reg_t reg) {
@ -525,6 +534,18 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_BLAS
#include "ggml-blas.h"
#endif
#ifdef GGML_USE_RPC
#include "ggml-rpc.h"
#endif
struct ggml_backend_registry {
std::vector<ggml_backend_reg_t> backends;
std::vector<ggml_backend_dev_t> devices;
@ -533,10 +554,19 @@ struct ggml_backend_registry {
#ifdef GGML_USE_CUDA
register_backend(ggml_backend_cuda_reg());
#endif
#ifdef GGML_USE_METAL
register_backend(ggml_backend_metal_reg());
#endif
#ifdef GGML_USE_BLAS
register_backend(ggml_backend_blas_reg());
#endif
#ifdef GGML_USE_RPC
register_backend(ggml_backend_rpc_reg());
#endif
// TODO: sycl, vulkan, kompute, cann
register_backend(ggml_backend_cpu_reg());
// TODO: sycl, metal, vulkan, kompute, cann
}
void register_backend(ggml_backend_reg_t reg) {
@ -1118,9 +1148,10 @@ static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggm
props->type = ggml_backend_cpu_device_get_type(dev);
ggml_backend_cpu_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* async */ false,
/* host_buffer */ false,
/* events */ false,
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ true,
/* .events = */ false,
};
}
@ -1153,7 +1184,7 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
op->type != GGML_TYPE_IQ1_S &&
op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
case GGML_OP_MUL_MAT:
return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_get_type_traits(op->src[0]->type)->vec_dot_type;
case GGML_OP_ROPE_BACK:
return op->src[2] == NULL && (op->op_params[2] & 4) == 0;
case GGML_OP_IM2COL_BACK:
@ -1216,16 +1247,22 @@ static ggml_backend_dev_t ggml_backend_cpu_reg_get_device(ggml_backend_reg_t reg
};
return &ggml_backend_cpu_device;
}
static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_cpu_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
GGML_UNUSED(index);
}
static const struct ggml_backend_reg_i ggml_backend_cpu_reg_i = {
/* .get_name = */ ggml_backend_cpu_reg_get_name,
/* .get_device_count = */ ggml_backend_cpu_reg_get_device_count,
/* .get_device = */ ggml_backend_cpu_reg_get_device,
/* .get_proc_address = */ NULL,
/* .get_proc_address = */ ggml_backend_cpu_get_proc_address,
};
ggml_backend_reg_t ggml_backend_cpu_reg(void) {

262
ggml/src/ggml-blas.cpp
View file

@ -4,6 +4,7 @@
#include <future>
#include <vector>
#include <cstring>
#if defined(GGML_USE_ACCELERATE)
# include <Accelerate/Accelerate.h>
@ -26,30 +27,6 @@ struct ggml_backend_blas_context {
#endif
};
// helper function to determine if it is better to use BLAS or not
// for large matrices, BLAS is faster
static bool ggml_backend_blas_use_blas(const struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
// TODO: find the optimal values for these
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
src1->type == GGML_TYPE_F32 &&
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
/*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
return true;
}
return false;
}
static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
@ -88,8 +65,8 @@ static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct gg
// convert src0 to float
if (type != GGML_TYPE_F32) {
ggml_type_traits_t type_traits = ggml_internal_get_type_traits(type);
ggml_to_float_t const to_float = type_traits.to_float;
const auto * type_traits = ggml_get_type_traits(type);
ggml_to_float_t const to_float = type_traits->to_float;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
@ -235,7 +212,7 @@ static void ggml_backend_blas_out_prod(ggml_backend_blas_context * ctx, struct g
// backend interface
static const char * ggml_backend_blas_name(ggml_backend_t backend) {
static const char * ggml_backend_blas_get_name(ggml_backend_t backend) {
return "BLAS";
GGML_UNUSED(backend);
@ -285,29 +262,8 @@ static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend,
GGML_UNUSED(backend);
}
static bool ggml_backend_blas_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
return (op->op == GGML_OP_MUL_MAT && ggml_backend_blas_use_blas(op)) ||
(op->op == GGML_OP_OUT_PROD && op->src[0]->type == GGML_TYPE_F32 &&
op->src[1]->type == GGML_TYPE_F32 &&
ggml_is_matrix(src0) &&
ggml_is_matrix(src1) &&
ggml_is_contiguous(src0) &&
(ggml_is_contiguous(src1) || ggml_is_transposed(src1)));
GGML_UNUSED(backend);
}
static bool ggml_backend_blas_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
return ggml_backend_buft_is_host(buft);
GGML_UNUSED(backend);
}
static struct ggml_backend_i blas_backend_i = {
/* .get_name = */ ggml_backend_blas_name,
/* .get_name = */ ggml_backend_blas_get_name,
/* .free = */ ggml_backend_blas_free,
/* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
@ -319,8 +275,8 @@ static struct ggml_backend_i blas_backend_i = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_blas_graph_compute,
/* .supports_op = */ ggml_backend_blas_supports_op,
/* .supports_buft = */ ggml_backend_blas_supports_buft,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
@ -337,7 +293,7 @@ ggml_backend_t ggml_backend_blas_init(void) {
ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_blas_guid(),
/* .interface = */ blas_backend_i,
/* .device = */ nullptr,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_blas_reg(), 0),
/* .context = */ ctx,
};
@ -364,3 +320,205 @@ void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads)
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context;
ctx->n_threads = n_threads;
}
// device interface
static const char * ggml_backend_blas_device_get_name(ggml_backend_dev_t dev) {
return "BLAS";
GGML_UNUSED(dev);
}
static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t dev) {
#if defined(GGML_USE_ACCELERATE)
return "Accelerate";
#elif defined(GGML_BLAS_USE_MKL)
return "MKL";
#elif defined(GGML_BLAS_USE_BLIS)
return "BLIS";
#elif defined(GGML_BLAS_USE_NVPL)
return "NVPL";
#elif defined(OPENBLAS_VERSION)
return "OpenBLAS";
#else
return "BLAS";
#endif
GGML_UNUSED(dev);
}
static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
// TODO
*free = 0;
*total = 0;
GGML_UNUSED(dev);
}
static enum ggml_backend_dev_type ggml_backend_blas_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_CPU;
GGML_UNUSED(dev);
}
static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_blas_device_get_name(dev);
props->description = ggml_backend_blas_device_get_description(dev);
props->type = ggml_backend_blas_device_get_type(dev);
ggml_backend_blas_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ true,
/* .events = */ false,
};
}
static ggml_backend_t ggml_backend_blas_device_init(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_blas_init();
GGML_UNUSED(dev);
GGML_UNUSED(params);
}
static ggml_backend_buffer_type_t ggml_backend_blas_device_get_buffer_type(ggml_backend_dev_t dev) {
return ggml_backend_cpu_buffer_type();
GGML_UNUSED(dev);
}
static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
return ggml_backend_cpu_buffer_from_ptr(ptr, size);
GGML_UNUSED(dev);
GGML_UNUSED(max_tensor_size);
}
static bool ggml_backend_blas_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
switch (op->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
return true;
case GGML_OP_MUL_MAT:
{
// BLAS usually is only faster for large matrices
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = op->ne[0];
const int64_t ne1 = op->ne[1];
// TODO: find the optimal value
const int64_t min_batch = 32;
return ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
src1->type == GGML_TYPE_F32 &&
(ne0 >= min_batch && ne1 >= min_batch && ne10 >= min_batch) &&
(src0->type == GGML_TYPE_F32 || ggml_get_type_traits(src0->type)->to_float != NULL);
}
case GGML_OP_OUT_PROD:
return op->src[0]->type == GGML_TYPE_F32 &&
op->src[1]->type == GGML_TYPE_F32 &&
ggml_is_matrix(src0) &&
ggml_is_matrix(src1) &&
ggml_is_contiguous(src0) &&
(ggml_is_contiguous(src1) || ggml_is_transposed(src1)) &&
(src0->type == GGML_TYPE_F32 || ggml_get_type_traits(src0->type)->to_float != NULL);
default:
return false;
}
GGML_UNUSED(dev);
}
static bool ggml_backend_blas_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
return ggml_backend_buft_is_host(buft);
GGML_UNUSED(dev);
}
static const struct ggml_backend_device_i ggml_backend_blas_device_i = {
/* .get_name = */ ggml_backend_blas_device_get_name,
/* .get_description = */ ggml_backend_blas_device_get_description,
/* .get_memory = */ ggml_backend_blas_device_get_memory,
/* .get_type = */ ggml_backend_blas_device_get_type,
/* .get_props = */ ggml_backend_blas_device_get_props,
/* .init_backend = */ ggml_backend_blas_device_init,
/* .get_buffer_type = */ ggml_backend_blas_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_ptr,
/* .supports_op = */ ggml_backend_blas_device_supports_op,
/* .supports_buft = */ ggml_backend_blas_device_supports_buft,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
// backend reg interface
static const char * ggml_backend_blas_reg_get_name(ggml_backend_reg_t reg) {
return "BLAS";
GGML_UNUSED(reg);
}
static size_t ggml_backend_blas_reg_get_device_count(ggml_backend_reg_t reg) {
return 1;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_blas_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_device ggml_backend_blas_device = {
/* .iface = */ ggml_backend_blas_device_i,
/* .reg = */ reg,
/* .context = */ nullptr,
};
return &ggml_backend_blas_device;
GGML_UNUSED(reg);
GGML_UNUSED(index);
}
static void * ggml_backend_blas_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_blas_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
GGML_UNUSED(name);
}
static const struct ggml_backend_reg_i ggml_backend_blas_reg_i = {
/* .get_name = */ ggml_backend_blas_reg_get_name,
/* .get_device_count = */ ggml_backend_blas_reg_get_device_count,
/* .get_device = */ ggml_backend_blas_reg_get_device,
/* .get_proc_address = */ ggml_backend_blas_get_proc_address,
};
ggml_backend_reg_t ggml_backend_blas_reg(void) {
static struct ggml_backend_reg ggml_backend_blas_reg = {
/* .iface = */ ggml_backend_blas_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_blas_reg;
}

7
ggml/src/ggml-cuda.cu
View file

@ -2924,9 +2924,10 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
#endif
props->caps = {
/* async */ true,
/* host_buffer */ host_buffer,
/* events */ events,
/* .async = */ true,
/* .host_buffer = */ host_buffer,
/* .buffer_from_host_ptr = */ false,
/* .events = */ events,
};
}

793
ggml/src/ggml-metal.m
View file

File diff suppressed because it is too large Load diff

204
ggml/src/ggml-rpc.cpp
View file

@ -25,7 +25,7 @@
# include <netdb.h>
# include <unistd.h>
#endif
#include <string.h>
#include <cstring>
#define UNUSED GGML_UNUSED
@ -630,22 +630,6 @@ static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, g
return (enum ggml_status)output[0];
}
static bool ggml_backend_rpc_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
UNUSED(backend);
UNUSED(op);
//TODO: call the remote backend and cache the results
return true;
}
static bool ggml_backend_rpc_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
if (!buft || buft->iface.get_name != ggml_backend_rpc_buffer_type_name) {
return false;
}
ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context;
ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
return buft_ctx->endpoint == rpc_ctx->endpoint;
}
static ggml_backend_i ggml_backend_rpc_interface = {
/* .get_name = */ ggml_backend_rpc_name,
/* .free = */ ggml_backend_rpc_free,
@ -659,8 +643,8 @@ static ggml_backend_i ggml_backend_rpc_interface = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_rpc_graph_compute,
/* .supports_op = */ ggml_backend_rpc_supports_op,
/* .supports_buft = */ ggml_backend_rpc_supports_buft,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
@ -691,7 +675,7 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * en
ggml_backend_buffer_type_t buft = new ggml_backend_buffer_type {
/* .iface = */ ggml_backend_rpc_buffer_type_interface,
/* .device = */ nullptr,
/* .device = */ ggml_backend_rpc_add_device(endpoint),
/* .context = */ buft_ctx
};
buft_map[endpoint] = buft;
@ -707,7 +691,7 @@ ggml_backend_t ggml_backend_rpc_init(const char * endpoint) {
ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_rpc_guid(),
/* .interface = */ ggml_backend_rpc_interface,
/* .device = */ nullptr,
/* .device = */ ggml_backend_rpc_add_device(endpoint),
/* .context = */ ctx
};
return backend;
@ -1189,7 +1173,7 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre
}
}
void start_rpc_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem) {
void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem) {
std::string host;
int port;
if (!parse_endpoint(endpoint, host, port)) {
@ -1226,3 +1210,179 @@ void start_rpc_server(ggml_backend_t backend, const char * endpoint, size_t free
WSACleanup();
#endif
}
// device interface
struct ggml_backend_rpc_device_context {
std::string endpoint;
std::string name;
};
static const char * ggml_backend_rpc_device_get_name(ggml_backend_dev_t dev) {
ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context;
return ctx->name.c_str();
}
static const char * ggml_backend_rpc_device_get_description(ggml_backend_dev_t dev) {
ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context;
return ctx->name.c_str();
}
static void ggml_backend_rpc_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context;
ggml_backend_rpc_get_device_memory(ctx->endpoint.c_str(), free, total);
UNUSED(dev);
}
static enum ggml_backend_dev_type ggml_backend_rpc_device_get_type(ggml_backend_dev_t dev) {
// TODO: obtain value from the server
return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
UNUSED(dev);
}
static void ggml_backend_rpc_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_rpc_device_get_name(dev);
props->description = ggml_backend_rpc_device_get_description(dev);
props->type = ggml_backend_rpc_device_get_type(dev);
ggml_backend_rpc_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ false,
/* .events = */ false,
};
}
static ggml_backend_t ggml_backend_rpc_device_init(ggml_backend_dev_t dev, const char * params) {
ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context;
return ggml_backend_rpc_init(ctx->endpoint.c_str());
UNUSED(params);
}
static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_backend_dev_t dev) {
ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context;
return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str());
UNUSED(dev);
}
static ggml_backend_buffer_t ggml_backend_rpc_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
return ggml_backend_cpu_buffer_from_ptr(ptr, size);
UNUSED(dev);
UNUSED(max_tensor_size);
}
static bool ggml_backend_rpc_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
UNUSED(dev);
UNUSED(op);
//TODO: call the remote backend and cache the results
return true;
}
static bool ggml_backend_rpc_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
if (!buft || buft->iface.get_name != ggml_backend_rpc_buffer_type_name) {
return false;
}
ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context;
ggml_backend_rpc_device_context * dev_ctx = (ggml_backend_rpc_device_context *)dev->context;
return buft_ctx->endpoint == dev_ctx->endpoint;
}
static const struct ggml_backend_device_i ggml_backend_rpc_device_i = {
/* .get_name = */ ggml_backend_rpc_device_get_name,
/* .get_description = */ ggml_backend_rpc_device_get_description,
/* .get_memory = */ ggml_backend_rpc_device_get_memory,
/* .get_type = */ ggml_backend_rpc_device_get_type,
/* .get_props = */ ggml_backend_rpc_device_get_props,
/* .init_backend = */ ggml_backend_rpc_device_init,
/* .get_buffer_type = */ ggml_backend_rpc_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ ggml_backend_rpc_device_buffer_from_ptr,
/* .supports_op = */ ggml_backend_rpc_device_supports_op,
/* .supports_buft = */ ggml_backend_rpc_device_supports_buft,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
// backend reg interface
static const char * ggml_backend_rpc_reg_get_name(ggml_backend_reg_t reg) {
return "RPC";
UNUSED(reg);
}
static size_t ggml_backend_rpc_reg_get_device_count(ggml_backend_reg_t reg) {
return 0;
UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_rpc_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ABORT("The RPC backend does not have enumerated devices - use ggml_backend_add_device instead");
UNUSED(reg);
UNUSED(index);
}
static void * ggml_backend_rpc_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (std::strcmp(name, "ggml_backend_rpc_add_device") == 0) {
return (void *)ggml_backend_rpc_add_device;
}
return NULL;
UNUSED(reg);
}
static const struct ggml_backend_reg_i ggml_backend_rpc_reg_i = {
/* .get_name = */ ggml_backend_rpc_reg_get_name,
/* .get_device_count = */ ggml_backend_rpc_reg_get_device_count,
/* .get_device = */ ggml_backend_rpc_reg_get_device,
/* .get_proc_address = */ ggml_backend_rpc_get_proc_address,
};
ggml_backend_reg_t ggml_backend_rpc_reg(void) {
static struct ggml_backend_reg ggml_backend_rpc_reg = {
/* .iface = */ ggml_backend_rpc_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_rpc_reg;
}
ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint) {
static std::unordered_map<std::string, ggml_backend_dev_t> dev_map;
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
if (dev_map.find(endpoint) != dev_map.end()) {
return dev_map[endpoint];
}
ggml_backend_rpc_device_context * ctx = new ggml_backend_rpc_device_context {
/* .endpoint = */ endpoint,
/* .name = */ "RPC[" + std::string(endpoint) + "]",
};
ggml_backend_dev_t dev = new ggml_backend_device {
/* .iface = */ ggml_backend_rpc_device_i,
/* .reg = */ ggml_backend_rpc_reg(),
/* .context = */ ctx,
};
dev_map[endpoint] = dev;
return dev;
}

19
ggml/src/ggml-vulkan.cpp
View file

@ -1070,11 +1070,26 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor
try {
buf->device_memory = device->device.allocateMemory({ mem_req.size, memory_type_index });
} catch (const vk::SystemError& e) {
if (buf->memory_property_flags != fallback_flags) {
// Try again with fallback flags
memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags);
buf->memory_property_flags = fallback_flags;
try {
buf->device_memory = device->device.allocateMemory({ mem_req.size, memory_type_index });
}
catch (const vk::SystemError& e) {
device->device.destroyBuffer(buf->buffer);
buf->size = 0;
throw e;
}
} else {
// Out of Host/Device memory, clean up buffer
device->device.destroyBuffer(buf->buffer);
buf->size = 0;
throw e;
}
}
buf->ptr = nullptr;
if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
@ -5272,9 +5287,9 @@ static void ggml_vk_dequantize_data(const void * from, float * to, size_t ne, gg
return;
}
ggml_type_traits_t tt = ggml_internal_get_type_traits(quant);
const auto * tt = ggml_get_type_traits(quant);
ggml_to_float_t dequant_fn = tt.to_float;
ggml_to_float_t dequant_fn = tt->to_float;
dequant_fn(from, to, ne);
}

6
ggml/src/ggml.c
View file

@ -737,7 +737,7 @@ static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float *
static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc);
static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc);
static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = {
[GGML_TYPE_I8] = {
.type_name = "i8",
.blck_size = 1,
@ -1159,9 +1159,9 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
};
// For internal test use
ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type) {
GGML_ASSERT(type < GGML_TYPE_COUNT);
return type_traits[type];
return &type_traits[type];
}
//

4
gpttype_adapter.cpp
View file

@ -168,7 +168,7 @@ static std::string FileFormatTokenizeID(int id, FileFormat file_format, bool ret
}
else if(file_format == FileFormat::GGUF_GENERIC)
{
return std::string(llama_token_to_piece(llama_ctx_v4, id, return_special));
return std::string(common_token_to_piece(llama_ctx_v4, id, return_special));
}
else
{
@ -194,7 +194,7 @@ static void TokenizeString(const std::string & str_to_tokenize, std::vector<int>
}
else
{
output_tokens = ::llama_tokenize(llama_ctx_v4, str_to_tokenize, add_bos, true);
output_tokens = ::common_tokenize(llama_ctx_v4, str_to_tokenize, add_bos, true);
if(add_bos)
{
llama_token bostoadd = llama_token_bos(&(llama_ctx_v4->model));

1
include/llama.h
View file

@ -433,6 +433,7 @@ extern "C" {
LLAMA_API bool llama_supports_mmap (void);
LLAMA_API bool llama_supports_mlock (void);
LLAMA_API bool llama_supports_gpu_offload(void);
LLAMA_API bool llama_supports_rpc (void);
LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);

12
otherarch/sdcpp/model.cpp
View file

@ -580,25 +580,25 @@ void convert_tensor(void* src,
if (src_type == GGML_TYPE_F16) {
ggml_fp16_to_fp32_row((ggml_fp16_t*)src, (float*)dst, n);
} else {
auto qtype = ggml_internal_get_type_traits(src_type);
if (qtype.to_float == NULL) {
auto qtype = ggml_get_type_traits(src_type);
if (qtype->to_float == NULL) {
throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available",
ggml_type_name(src_type)));
}
qtype.to_float(src, (float*)dst, n);
qtype->to_float(src, (float*)dst, n);
}
} else {
// src_type == GGML_TYPE_F16 => dst_type is quantized
// src_type is quantized => dst_type == GGML_TYPE_F16 or dst_type is quantized
auto qtype = ggml_internal_get_type_traits(src_type);
if (qtype.to_float == NULL) {
auto qtype = ggml_get_type_traits(src_type);
if (qtype->to_float == NULL) {
throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available",
ggml_type_name(src_type)));
}
std::vector<char> buf;
buf.resize(sizeof(float) * n);
char* src_data_f32 = buf.data();
qtype.to_float(src, (float*)src_data_f32, n);
qtype->to_float(src, (float*)src_data_f32, n);
if (dst_type == GGML_TYPE_F16) {
ggml_fp32_to_fp16_row((float*)src_data_f32, (ggml_fp16_t*)dst, n);
} else {

232
src/llama.cpp
View file

@ -11,10 +11,6 @@
#include "ggml-alloc.h"
#include "ggml-backend.h"
#ifdef GGML_USE_RPC
# include "ggml-rpc.h"
#endif
#ifdef GGML_USE_CUDA
# include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
@ -29,14 +25,6 @@
# include "ggml-cann.h"
#endif
#ifdef GGML_USE_BLAS
# include "ggml-blas.h"
#endif
#ifdef GGML_USE_METAL
# include "ggml-metal.h"
#endif
// TODO: replace with ggml API call
#define QK_K 256
@ -3307,12 +3295,8 @@ struct llama_context {
std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
std::vector<ggml_backend_t> backends;
#ifdef GGML_USE_METAL
ggml_backend_t backend_metal = nullptr;
#endif
#ifdef GGML_USE_BLAS
ggml_backend_t backend_blas = nullptr;
#endif
std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
ggml_backend_t backend_cpu = nullptr;
ggml_threadpool_t threadpool = nullptr;
@ -3431,13 +3415,7 @@ struct llama_lora_adapter {
static int llama_get_device_count(const llama_model & model) {
int count = (int) model.devices.size();
#if defined(GGML_USE_RPC)
count += (int) model.rpc_servers.size();
#endif
#if defined(GGML_USE_METAL)
count += 1;
#elif defined(GGML_USE_SYCL)
#if defined(GGML_USE_SYCL)
count += ggml_backend_sycl_get_device_count();
#elif defined(GGML_USE_VULKAN)
count += ggml_backend_vk_get_device_count();
@ -3489,23 +3467,12 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(const llama_mode
static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int device) {
ggml_backend_buffer_type_t buft = nullptr;
#if defined(GGML_USE_RPC)
int rpc_count = (int)model.rpc_servers.size();
if (device < rpc_count) {
const char * endpoint = model.rpc_servers[device].c_str();
return ggml_backend_rpc_buffer_type(endpoint);
}
device -= rpc_count;
#endif
if (device < (int)model.devices.size()) {
return ggml_backend_dev_buffer_type(model.devices[device]);
}
device -= (int)model.devices.size();
#if defined(GGML_USE_METAL)
buft = ggml_backend_metal_buffer_type();
#elif defined(GGML_USE_VULKAN)
#if defined(GGML_USE_VULKAN)
buft = ggml_backend_vk_buffer_type(device);
#elif defined(GGML_USE_SYCL)
buft = ggml_backend_sycl_buffer_type(device);
@ -3556,18 +3523,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_mo
}
static size_t llama_get_device_memory(const llama_model & model, int device) {
#if defined(GGML_USE_RPC)
int rpc_count = (int)model.rpc_servers.size();
if (device < rpc_count) {
size_t total;
size_t free;
const char * endpoint = model.rpc_servers[device].c_str();
ggml_backend_rpc_get_device_memory(endpoint, &free, &total);
return free;
}
device = device - rpc_count;
#endif
if (device < (int)model.devices.size()) {
ggml_backend_dev_t dev = model.devices[device];
size_t total;
@ -8970,48 +8925,40 @@ static bool llm_load_tensors(
llama_buf_map bufs;
bufs.reserve(n_max_backend_buffer);
// check if this backend device supports buffer_from_host_ptr
// when using a host buffer as the CPU bakcend buffer, use the CPU device to prioritize using buffer_from_host_ptr over the host buffer
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft == llama_default_buffer_type_cpu(model, true) ? ggml_backend_cpu_buffer_type() : buft);
bool buffer_from_host_ptr_supported = false;
if (dev) {
ggml_backend_dev_props props;
ggml_backend_dev_get_props(dev, &props);
buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
}
if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported) {
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
// only the mmap region containing the tensors in the model is mapped to the backend buffer
// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
// this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(model, true)) {
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
void * addr = nullptr;
size_t first, last;
size_t first, last; // NOLINT
ml.get_mapping_range(&first, &last, &addr, idx, ctx);
if (first >= last) {
continue;
}
ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
if (buf == nullptr) {
throw std::runtime_error("unable to allocate backend CPU buffer");
}
model.bufs.push_back(buf);
bufs.emplace(idx, buf);
}
}
#ifdef GGML_USE_METAL
else if (ml.use_mmap && use_mmap_buffer && buft == ggml_backend_metal_buffer_type()) {
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
const size_t max_size = ggml_get_max_tensor_size(ctx);
void * addr = nullptr;
size_t first, last;
ml.get_mapping_range(&first, &last, &addr, idx, ctx);
if (first >= last) {
continue;
}
ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
if (buf == nullptr) {
throw std::runtime_error("unable to allocate backend metal buffer");
throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
}
model.bufs.push_back(buf);
bufs.emplace(idx, buf);
}
}
#endif
else {
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
if (buf == nullptr) {
throw std::runtime_error("unable to allocate backend buffer");
throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
}
model.bufs.push_back(buf);
if (use_mlock && ggml_backend_buffer_is_host(buf)) {
@ -17138,17 +17085,19 @@ static void llama_graph_compute(
int n_threads,
ggml_threadpool * threadpool) {
if (lctx.backend_cpu != nullptr) {
ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
ggml_backend_cpu_set_threadpool(lctx.backend_cpu, threadpool);
ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
}
#ifdef GGML_USE_BLAS
if (lctx.backend_blas != nullptr) {
ggml_backend_blas_set_n_threads(lctx.backend_blas, n_threads);
}
#endif
ggml_backend_sched_graph_compute_async(lctx.sched, gf);
// set the number of threads for all the backends
for (const auto & set_n_threads_fn : lctx.set_n_threads_fns) {
set_n_threads_fn.second(set_n_threads_fn.first, n_threads);
}
auto err = ggml_backend_sched_graph_compute_async(lctx.sched, gf);
if (err != GGML_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, err);
}
// fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
}
@ -17964,10 +17913,9 @@ static void llama_tensor_dequantize_internal(
}
float * f32_output = (float *) output.data();
ggml_type_traits_t qtype;
const ggml_type_traits * qtype = ggml_get_type_traits(tensor->type);
if (ggml_is_quantized(tensor->type)) {
qtype = ggml_internal_get_type_traits(tensor->type);
if (qtype.to_float == NULL) {
if (qtype->to_float == NULL) {
throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
}
} else if (tensor->type != GGML_TYPE_F16 &&
@ -17981,7 +17929,7 @@ static void llama_tensor_dequantize_internal(
} else if (tensor->type == GGML_TYPE_BF16) {
ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements);
} else if (ggml_is_quantized(tensor->type)) {
qtype.to_float(tensor->data, f32_output, nelements);
qtype->to_float(tensor->data, f32_output, nelements);
} else {
GGML_ABORT("fatal error"); // unreachable
}
@ -18017,7 +17965,7 @@ static void llama_tensor_dequantize_internal(
} else if (typ == GGML_TYPE_BF16) {
ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels);
} else {
qtype.to_float(inbuf, outbuf, nels);
qtype->to_float(inbuf, outbuf, nels);
}
};
workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
@ -19111,16 +19059,21 @@ bool llama_supports_mlock(void) {
}
bool llama_supports_gpu_offload(void) {
#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC)
#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_VULKAN) || \
defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
return true;
#else
return ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU) != nullptr ||
ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU_FULL) != nullptr;
ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU_FULL) != nullptr ||
llama_supports_rpc();
#endif
}
bool llama_supports_rpc(void) {
return ggml_backend_reg_by_name("RPC") != nullptr;
}
void llama_backend_init(void) {
ggml_time_init();
@ -19195,14 +19148,51 @@ struct llama_model * llama_load_model_from_file(
model->rpc_servers.push_back(servers);
}
// add RPC devices
if (!model->rpc_servers.empty()) {
ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
if (!rpc_reg) {
LLAMA_LOG_ERROR("%s: failed to find RPC backend\n", __func__);
llama_free_model(model);
return nullptr;
}
// ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint);
using ggml_backend_rpc_add_device_t = ggml_backend_dev_t (*)(const char *);
ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
if (!ggml_backend_rpc_add_device_fn) {
LLAMA_LOG_ERROR("%s: failed to find RPC device add function\n", __func__);
llama_free_model(model);
return nullptr;
}
for (const std::string & server : model->rpc_servers) {
ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
if (dev) {
model->devices.push_back(dev);
} else {
LLAMA_LOG_ERROR("%s: failed to add RPC device for server '%s'\n", __func__, server.c_str());
llama_free_model(model);
return nullptr;
}
}
}
// create list of devices to use with this model
// currently, we use all available devices
// TODO: rework API to give user more control over device selection
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
// skip the CPU backend since it is handled separately
if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU_FULL) {
switch (ggml_backend_dev_type(dev)) {
case GGML_BACKEND_DEVICE_TYPE_CPU:
case GGML_BACKEND_DEVICE_TYPE_CPU_FULL:
// skip CPU backends since they are `handled separately
break;
case GGML_BACKEND_DEVICE_TYPE_GPU:
case GGML_BACKEND_DEVICE_TYPE_GPU_FULL:
model->devices.push_back(dev);
break;
}
}
@ -19214,7 +19204,7 @@ struct llama_model * llama_load_model_from_file(
} else if (status == -2) {
LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
}
delete model;
llama_free_model(model);
return nullptr;
}
@ -19397,34 +19387,7 @@ struct llama_context * llama_new_context_with_model(
main_gpu -= (int)model->devices.size();
}
#if defined(GGML_USE_RPC)
if (model->n_gpu_layers > 0) {
for (const auto & endpoint : model->rpc_servers) {
ggml_backend_t backend = ggml_backend_rpc_init(endpoint.c_str());
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize RPC to '%s'\n", __func__, endpoint.c_str());
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
}
if (main_gpu >= (int)model->rpc_servers.size()) {
main_gpu -= (int)model->rpc_servers.size();
}
#endif
#if defined(GGML_USE_METAL)
if (model->n_gpu_layers > 0) {
ctx->backend_metal = ggml_backend_metal_init();
if (ctx->backend_metal == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(ctx->backend_metal);
}
#elif defined(GGML_USE_VULKAN)
#if defined(GGML_USE_VULKAN)
if (model->split_mode == LLAMA_SPLIT_MODE_ROW) {
LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__);
llama_free(ctx);
@ -19507,14 +19470,19 @@ struct llama_context * llama_new_context_with_model(
}
#endif
#ifdef GGML_USE_BLAS
ctx->backend_blas = ggml_backend_blas_init();
if (ctx->backend_blas == nullptr) {
LLAMA_LOG_WARN("%s: failed to initialize BLAS backend\n", __func__);
} else {
ctx->backends.push_back(ctx->backend_blas);
// add other backends (such as BLAS)
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev));
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
}
#endif
ctx->backend_cpu = ggml_backend_cpu_init();
if (ctx->backend_cpu == nullptr) {
@ -19524,6 +19492,18 @@ struct llama_context * llama_new_context_with_model(
}
ctx->backends.push_back(ctx->backend_cpu);
// create a list of the set_n_threads functions in the backends
for (auto * backend : ctx->backends) {
ggml_backend_dev_t dev = ggml_backend_get_device(backend);
ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr;
if (reg) {
auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
if (ggml_backend_set_n_threads_fn) {
ctx->set_n_threads_fns.emplace_back(backend, ggml_backend_set_n_threads_fn);
}
}
}
if (!llama_kv_cache_init(ctx->kv_self, ctx, type_k, type_v, kv_size, cparams.offload_kqv)) {
LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
llama_free(ctx);

4
src/unicode-data.cpp
View file

@ -2311,7 +2311,7 @@ const std::unordered_set<uint32_t> unicode_set_whitespace = {
0x003000,
};
// list is always in ascending order, to enable binary searh
// list is always in ascending order, to enable binary search
const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_lowercase = {
{0x000041, 0x000061},
{0x000042, 0x000062},
@ -3748,7 +3748,7 @@ const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_lowercase
{0x01E921, 0x01E943},
};
// list is always in ascending order, to enable binary searh
// list is always in ascending order, to enable binary search
const std::initializer_list<std::pair<uint32_t, uint32_t>> unicode_map_uppercase = {
{0x000061, 0x000041},
{0x000062, 0x000042},

28
tests/test-arg-parser.cpp
View file

@ -10,12 +10,12 @@
#include <cassert>
int main(void) {
gpt_params params;
common_params params;
printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
for (int ex = 0; ex < LLAMA_EXAMPLE_COUNT; ex++) {
try {
auto ctx_arg = gpt_params_parser_init(params, (enum llama_example)ex);
auto ctx_arg = common_params_parser_init(params, (enum llama_example)ex);
std::unordered_set<std::string> seen_args;
std::unordered_set<std::string> seen_env_vars;
for (const auto & opt : ctx_arg.options) {
@ -58,44 +58,44 @@ int main(void) {
// missing value
argv = {"binary_name", "-m"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
// wrong value (int)
argv = {"binary_name", "-ngl", "hello"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
// wrong value (enum)
argv = {"binary_name", "-sm", "hello"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
// non-existence arg in specific example (--draft cannot be used outside llama-speculative)
argv = {"binary_name", "--draft", "123"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SERVER));
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SERVER));
printf("test-arg-parser: test valid usage\n\n");
argv = {"binary_name", "-m", "model_file.gguf"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model == "model_file.gguf");
argv = {"binary_name", "-t", "1234"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.cpuparams.n_threads == 1234);
argv = {"binary_name", "--verbose"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.verbosity > 1);
argv = {"binary_name", "-m", "abc.gguf", "--predict", "6789", "--batch-size", "9090"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model == "abc.gguf");
assert(params.n_predict == 6789);
assert(params.n_batch == 9090);
// --draft cannot be used outside llama-speculative
argv = {"binary_name", "--draft", "123"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
assert(params.n_draft == 123);
// skip this part on windows, because setenv is not supported
@ -106,12 +106,12 @@ int main(void) {
setenv("LLAMA_ARG_THREADS", "blah", true);
argv = {"binary_name"};
assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
setenv("LLAMA_ARG_THREADS", "1010", true);
argv = {"binary_name"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model == "blah.gguf");
assert(params.cpuparams.n_threads == 1010);
@ -121,7 +121,7 @@ int main(void) {
setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
setenv("LLAMA_ARG_THREADS", "1010", true);
argv = {"binary_name", "-m", "overwritten.gguf"};
assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
assert(params.model == "overwritten.gguf");
assert(params.cpuparams.n_threads == 1010);
#endif // _WIN32

4
tests/test-log.cpp
View file

@ -24,8 +24,8 @@ int main() {
}
if (rand () % 10 < 5) {
gpt_log_set_timestamps(gpt_log_main(), rand() % 2);
gpt_log_set_prefix (gpt_log_main(), rand() % 2);
common_log_set_timestamps(common_log_main(), rand() % 2);
common_log_set_prefix (common_log_main(), rand() % 2);
}
}
});