vbatts/llama.cpp
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Merge branch 'ggerganov:master' into mobile_vlm

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Changyeon Kim 2024-10-27 01:05:16 +09:00 • committed by GitHub
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137 changed files with 13361 additions and 5946 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" ]

3
.github/workflows/docker.yml vendored
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@ -43,6 +43,9 @@ jobs:
- { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" }
- { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" }
- { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
- { tag: "light-musa", dockerfile: ".devops/llama-cli-musa.Dockerfile", platforms: "linux/amd64" }
- { tag: "server-musa", dockerfile: ".devops/llama-server-musa.Dockerfile", platforms: "linux/amd64" }
- { tag: "full-musa", dockerfile: ".devops/full-musa.Dockerfile", platforms: "linux/amd64" }
# Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
#- { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
#- { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }

10
CMakeLists.txt
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@ -63,7 +63,7 @@ option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF)
option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
# utils
option(LLAMA_BUILD_COMMON "llama: build common utils library" ON)
option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE})
# extra artifacts
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
@ -88,6 +88,10 @@ if (NOT DEFINED GGML_LLAMAFILE)
set(GGML_LLAMAFILE_DEFAULT ON)
endif()
if (NOT DEFINED GGML_AMX)
set(GGML_AMX ON)
endif()
if (NOT DEFINED GGML_CUDA_GRAPHS)
set(GGML_CUDA_GRAPHS_DEFAULT ON)
endif()
@ -201,12 +205,12 @@ if (LLAMA_BUILD_COMMON)
add_subdirectory(common)
endif()
if (LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
include(CTest)
add_subdirectory(tests)
endif()
if (LLAMA_BUILD_EXAMPLES)
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
add_subdirectory(examples)
add_subdirectory(pocs)
endif()

24
Makefile
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@ -93,11 +93,6 @@ GGML_METAL := 1
DEPRECATE_WARNING := 1
endif
ifdef LLAMA_OPENMP
GGML_OPENMP := 1
DEPRECATE_WARNING := 1
endif
ifdef LLAMA_RPC
GGML_RPC := 1
DEPRECATE_WARNING := 1
@ -584,6 +579,11 @@ ifndef GGML_NO_LLAMAFILE
OBJ_GGML += ggml/src/llamafile/sgemm.o
endif
ifndef GGML_NO_AMX
MK_CPPFLAGS += -DGGML_USE_AMX
OBJ_GGML += ggml/src/ggml-amx.o ggml/src/ggml-amx/mmq.o
endif
ifdef GGML_RPC
MK_CPPFLAGS += -DGGML_USE_RPC
OBJ_GGML += ggml/src/ggml-rpc.o
@ -1087,6 +1087,19 @@ ggml/src/llamafile/sgemm.o: \
$(CXX) $(CXXFLAGS) -c $< -o $@
endif # GGML_NO_LLAMAFILE
ifndef GGML_NO_AMX
ggml/src/ggml-amx.o: \
ggml/src/ggml-amx.cpp \
ggml/include/ggml-amx.h
$(CXX) $(CXXFLAGS) -c $< -o $@
ggml/src/ggml-amx/mmq.o: \
ggml/src/ggml-amx/mmq.cpp \
ggml/src/ggml-amx/mmq.h \
ggml/include/ggml.h
$(CXX) $(CXXFLAGS) -c $< -o $@
endif
ifdef GGML_RPC
ggml/src/ggml-rpc.o: \
ggml/src/ggml-rpc.cpp \
@ -1238,6 +1251,7 @@ clean:
rm -vrf ggml/src/ggml-metal-embed.metal
rm -vrf ggml/src/ggml-cuda/*.o
rm -vrf ggml/src/ggml-cuda/template-instances/*.o
rm -vrf ggml/src/ggml-amx/*.o
rm -rvf $(BUILD_TARGETS)
rm -rvf $(TEST_TARGETS)
rm -f vulkan-shaders-gen ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders.cpp

12
README.md
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@ -29,9 +29,9 @@ variety of hardware - locally and in the cloud.
- Plain C/C++ implementation without any dependencies
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
- AVX, AVX2 and AVX512 support for x86 architectures
- AVX, AVX2, AVX512 and AMX support for x86 architectures
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA)
- Vulkan and SYCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
@ -93,6 +93,7 @@ Typically finetunes of the base models below are supported as well.
- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
- [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md))
@ -122,6 +123,7 @@ Typically finetunes of the base models below are supported as well.
- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
@ -130,6 +132,8 @@ Typically finetunes of the base models below are supported as well.
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)
- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp)
- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift)
- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
**UI:**
@ -170,6 +174,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
- [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL)
- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT)
- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL)
- [PocketPal AI - An iOS and Android App](https://github.com/a-ghorbani/pocketpal-ai) (MIT)
*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
@ -185,6 +190,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
**Games:**
- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
@ -413,7 +419,7 @@ Please refer to [Build llama.cpp locally](./docs/build.md)
| [BLAS](./docs/build.md#blas-build) | All |
| [BLIS](./docs/backend/BLIS.md) | All |
| [SYCL](./docs/backend/SYCL.md) | Intel and Nvidia GPU |
| [MUSA](./docs/build.md#musa) | Moore Threads GPU |
| [MUSA](./docs/build.md#musa) | Moore Threads MTT GPU |
| [CUDA](./docs/build.md#cuda) | Nvidia GPU |
| [hipBLAS](./docs/build.md#hipblas) | AMD GPU |
| [Vulkan](./docs/build.md#vulkan) | GPU |

2
ci/run.sh
View file

@ -53,7 +53,7 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then
exit 1
fi
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
fi
if [ ! -z ${GG_BUILD_VULKAN} ]; then

1243
common/arg.cpp
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File diff suppressed because it is too large Load diff

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);

157
common/common.cpp
View file

@ -12,6 +12,7 @@
#include <algorithm>
#include <cinttypes>
#include <climits>
#include <cmath>
#include <codecvt>
#include <cstdarg>
@ -23,10 +24,10 @@
#include <regex>
#include <sstream>
#include <string>
#include <thread>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include <thread>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
@ -362,10 +363,10 @@ 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);
@ -378,7 +379,7 @@ 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;
@ -400,17 +401,19 @@ std::string gpt_params_get_system_info(const gpt_params & params) {
// String utils
//
std::vector<std::string> string_split(std::string input, char separator) {
std::vector<std::string> parts;
size_t separator_pos = input.find(separator);
while (separator_pos != std::string::npos) {
std::string part = input.substr(0, separator_pos);
parts.emplace_back(part);
input = input.substr(separator_pos + 1);
separator_pos = input.find(separator);
}
parts.emplace_back(input);
return parts;
std::string string_format(const char * fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
std::string string_strip(const std::string & str) {
@ -493,7 +496,7 @@ 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(
@ -524,7 +527,7 @@ 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(
@ -819,16 +822,16 @@ 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);
}
@ -863,7 +866,7 @@ 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) {
@ -876,7 +879,7 @@ 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);
@ -900,7 +903,7 @@ 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());
@ -913,7 +916,7 @@ 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) {
@ -939,7 +942,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
}
if (llama_model_has_encoder(model)) {
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size()));
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = bos;
@ -948,7 +951,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
tmp.push_back(decoder_start_token_id);
}
if (llama_model_has_decoder(model)) {
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch)));
}
llama_kv_cache_clear(lctx);
llama_synchronize(lctx);
@ -961,7 +964,7 @@ 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) {
@ -970,7 +973,7 @@ 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) {
@ -1019,10 +1022,10 @@ static ggml_type kv_cache_type_from_str(const std::string & s) {
return GGML_TYPE_Q5_1;
}
throw std::runtime_error("Invalid cache type: " + s);
throw std::runtime_error("Unsupported 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;
@ -1031,7 +1034,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.n_ubatch = params.n_ubatch;
cparams.n_threads = params.cpuparams.n_threads;
cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ?
params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
cparams.logits_all = params.logits_all;
cparams.embeddings = params.embedding;
cparams.rope_scaling_type = params.rope_scaling_type;
@ -1112,7 +1115,7 @@ 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);
@ -1182,15 +1185,15 @@ 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);
@ -1326,7 +1329,7 @@ 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,
@ -1337,7 +1340,7 @@ 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;
}
@ -1390,7 +1393,7 @@ 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));
}
@ -1405,7 +1408,7 @@ 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,
@ -1425,12 +1428,12 @@ 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*/,
@ -1439,7 +1442,7 @@ 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*/,
@ -1455,11 +1458,11 @@ 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,
@ -1482,15 +1485,15 @@ 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,
@ -1509,7 +1512,7 @@ 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);
@ -1525,7 +1528,7 @@ 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);
@ -1545,15 +1548,15 @@ 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
@ -1595,42 +1598,42 @@ 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",
@ -1653,7 +1656,7 @@ 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",
@ -1705,7 +1708,7 @@ 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) {
@ -1739,7 +1742,7 @@ 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;
@ -1765,8 +1768,8 @@ 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 = {
@ -1850,11 +1853,11 @@ 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;
@ -1946,7 +1949,7 @@ 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;
@ -2003,6 +2006,10 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "dry_allowed_length: %d # default: 2\n", sparams.dry_allowed_length);
fprintf(stream, "dry_base: %.2f # default: 1.75\n", sparams.dry_base);
fprintf(stream, "dry_multiplier: %.1f # default: 0.0\n", sparams.dry_multiplier);
fprintf(stream, "dry_penalty_last_n: %d # default: -1 (0 = disable, -1 = context size)\n", sparams.dry_penalty_last_n);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
@ -2088,6 +2095,8 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability);
fprintf(stream, "xtc_threshold: %f # default: 0.1\n", sparams.xtc_threshold);
fprintf(stream, "typ_p: %f # default: 1.0\n", sparams.typ_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");

215
common/common.h
View file

@ -24,12 +24,12 @@
#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;
};
@ -39,7 +39,7 @@ extern char const * LLAMA_COMMIT;
extern char const * LLAMA_COMPILER;
extern char const * LLAMA_BUILD_TARGET;
struct llama_control_vector_load_info;
struct common_control_vector_load_info;
//
// CPU utils
@ -82,14 +82,17 @@ 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_DRY = 1,
COMMON_SAMPLER_TYPE_TOP_K = 2,
COMMON_SAMPLER_TYPE_TOP_P = 3,
COMMON_SAMPLER_TYPE_MIN_P = 4,
COMMON_SAMPLER_TYPE_TFS_Z = 5,
COMMON_SAMPLER_TYPE_TYPICAL_P = 6,
COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
COMMON_SAMPLER_TYPE_XTC = 8,
COMMON_SAMPLER_TYPE_INFILL = 9,
};
// dimensionality reduction methods, used by cvector-generator
@ -99,38 +102,49 @@ 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
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typ_p = 1.00f; // typical_p, 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = false; // consider newlines as a repeatable token
bool ignore_eos = false;
bool no_perf = false; // disable performance metrics
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float xtc_probability = 0.00f; // 0.0 = disabled
float xtc_threshold = 0.10f; // > 0.5 disables XTC
float tfs_z = 1.00f; // 1.0 = disabled
float typ_p = 1.00f; // typical_p, 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition:
float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length)
int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty
int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = false; // consider newlines as a repeatable token
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<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY
std::vector<enum common_sampler_type> samplers = {
COMMON_SAMPLER_TYPE_DRY,
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_XTC,
COMMON_SAMPLER_TYPE_TEMPERATURE,
};
std::string grammar; // optional BNF-like grammar to constrain sampling
@ -141,7 +155,7 @@ 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)
@ -183,7 +197,7 @@ 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
@ -208,9 +222,9 @@ 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
@ -268,21 +282,21 @@ struct gpt_params {
// embedding
bool embedding = false; // get only sentence embedding
int32_t embd_normalize = 2; // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
std::string embd_sep = "\n"; // separator of embendings
std::string embd_sep = "\n"; // separator of embeddings
bool reranking = false; // enable reranking support on server
// server params
int32_t port = 8080; // server listens on this network port
int32_t timeout_read = 600; // http read timeout in seconds
int32_t timeout_write = timeout_read; // http write timeout in seconds
int n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool)
int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool)
int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting
std::string hostname = "127.0.0.1";
std::string public_path = ""; // NOLINT
std::string chat_template = ""; // NOLINT
std::string system_prompt = ""; // NOLINT
bool enable_chat_template = true;
std::vector<std::string> api_keys;
@ -290,7 +304,10 @@ 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;
@ -345,20 +362,31 @@ 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]);
void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model = nullptr);
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]);
void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr);
bool set_process_priority(enum ggml_sched_priority prio);
//
// String utils
//
std::vector<std::string> string_split(std::string input, char separator);
#ifdef __GNUC__
#ifdef __MINGW32__
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
#endif
LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
std::string string_format(const char * fmt, ...);
std::string string_strip(const std::string & str);
std::string string_get_sortable_timestamp();
@ -367,6 +395,7 @@ void string_replace_all(std::string & s, const std::string & search, const std::
template<class T>
static std::vector<T> string_split(const std::string & str, char delim) {
static_assert(!std::is_same<T, std::string>::value, "Please use the specialized version for std::string");
std::vector<T> values;
std::istringstream str_stream(str);
std::string token;
@ -379,6 +408,22 @@ static std::vector<T> string_split(const std::string & str, char delim) {
return values;
}
template<>
std::vector<std::string> string_split<std::string>(const std::string & input, char separator)
{
std::vector<std::string> parts;
size_t begin_pos = 0;
size_t separator_pos = input.find(separator);
while (separator_pos != std::string::npos) {
std::string part = input.substr(begin_pos, separator_pos - begin_pos);
parts.emplace_back(part);
begin_pos = separator_pos + 1;
separator_pos = input.find(separator, begin_pos);
}
parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos));
return parts;
}
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
void string_process_escapes(std::string & input);
@ -401,29 +446,29 @@ 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,
@ -436,13 +481,13 @@ 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,
@ -450,7 +495,7 @@ 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);
@ -458,7 +503,7 @@ 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);
@ -468,31 +513,31 @@ 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);
//
@ -500,31 +545,31 @@ 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;
@ -532,7 +577,7 @@ 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
@ -551,5 +596,5 @@ 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);

2
common/json-schema-to-grammar.cpp
View file

@ -611,7 +611,7 @@ private:
}
return join_seq();
};
return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space");
return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space");
}
/*

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);

233
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;
@ -125,26 +125,28 @@ struct gpt_sampler {
}
};
std::string gpt_sampler_params::print() const {
std::string common_sampler_params::print() const {
char result[1024];
snprintf(result, sizeof(result),
"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
"\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
"\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
top_k, tfs_z, top_p, min_p, typ_p, temp,
dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
mirostat, mirostat_eta, mirostat_tau);
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),
@ -171,60 +173,63 @@ struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const st
params.penalize_nl,
params.ignore_eos));
if (params.temp > 0.0f) {
if (params.mirostat == 0) {
for (const auto & cnstr : params.samplers) {
switch (cnstr) {
case GPT_SAMPLER_TYPE_TOP_K:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k));
if (params.mirostat == 0) {
for (const auto & cnstr : params.samplers) {
switch (cnstr) {
case COMMON_SAMPLER_TYPE_DRY:
{
std::vector<const char*> c_breakers;
c_breakers.reserve(params.dry_sequence_breakers.size());
for (const auto& str : params.dry_sequence_breakers) {
c_breakers.push_back(str.c_str());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
}
break;
case GPT_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:
llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep));
break;
case GPT_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:
llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep));
break;
case GPT_SAMPLER_TYPE_TEMPERATURE:
llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
break;
default:
GGML_ASSERT(false && "unknown sampler type");
}
case COMMON_SAMPLER_TYPE_TOP_K:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k));
break;
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 COMMON_SAMPLER_TYPE_MIN_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep));
break;
case COMMON_SAMPLER_TYPE_XTC:
llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
break;
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 COMMON_SAMPLER_TYPE_TYPICAL_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep));
break;
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;
case COMMON_SAMPLER_TYPE_INFILL:
llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model));
break;
default:
GGML_ASSERT(false && "unknown sampler type");
}
llama_sampler_chain_add(result->chain, llama_sampler_init_softmax());
llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
} else if (params.mirostat == 1) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
} else if (params.mirostat == 2) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
} else {
GGML_ASSERT(false && "unknown mirostat version");
}
llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
} else if (params.mirostat == 1) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
} else if (params.mirostat == 2) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
} else {
if (params.n_probs > 0) {
// some use cases require to sample greedily, but still obtain the probabilities of the top tokens
// ref: https://github.com/ggerganov/llama.cpp/pull/9605
//
// the following will not produce exactly the same probs as applyging softmax to the full vocabulary, but
// it is much faster, since we avoid sorting all tokens and should give a good approximation
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k(params.n_probs));
llama_sampler_chain_add(result->chain, llama_sampler_init_softmax());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_greedy());
GGML_ASSERT(false && "unknown mirostat version");
}
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 +239,7 @@ 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 +249,14 @@ 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 +266,7 @@ 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 +277,7 @@ 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 +323,21 @@ 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 +348,7 @@ 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 +363,72 @@ 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_DRY: return 'd';
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';
case COMMON_SAMPLER_TYPE_XTC: return 'x';
case COMMON_SAMPLER_TYPE_INFILL: return 'i';
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_DRY: return "dry";
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";
case COMMON_SAMPLER_TYPE_XTC: return "xtc";
case COMMON_SAMPLER_TYPE_INFILL: return "infill";
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 {
{ "dry", COMMON_SAMPLER_TYPE_DRY },
{ "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 },
{ "xtc", COMMON_SAMPLER_TYPE_XTC },
{ "infill", COMMON_SAMPLER_TYPE_INFILL },
};
// 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 +448,20 @@ 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_DRY), COMMON_SAMPLER_TYPE_DRY },
{ 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 },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL },
};
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);

6
convert_hf_to_gguf.py
View file

@ -573,6 +573,9 @@ class Model:
if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
# ref: https://huggingface.co/BAAI/bge-small-en-v1.5
res = "bert-bge"
if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7":
# ref: https://huggingface.co/BAAI/bge-large-zh-v1.5
res = "bert-bge-large"
if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
# ref: https://huggingface.co/mosaicml/mpt-7b
res = "mpt"
@ -2864,6 +2867,9 @@ class Rwkv6Model(Model):
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
special_vocab.chat_template = "rwkv-world"
# hack: Add '\n\n' as the EOT token to make it chat normally
special_vocab._set_special_token("eot", 261)
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):

1
convert_hf_to_gguf_update.py
View file

@ -72,6 +72,7 @@ models = [
{"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
{"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
{"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
{"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
{"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
{"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
{"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },

3
convert_lora_to_gguf.py
View file

@ -348,6 +348,9 @@ if __name__ == '__main__':
if ".base_layer.weight" in name:
continue
logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
if ".embed_tokens.weight" in name or ".lm_head.weight" in name:
logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning")
logger.error("Hint: if you are using TRL, make sure not to call setup_chat_format()")
sys.exit(1)
if base_name in tensor_map:

109
docs/android.md
View file

@ -2,55 +2,82 @@
# Android
## Build on Android using Termux
[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required).
```
apt update && apt upgrade -y
apt install git make cmake
```
It's recommended to move your model inside the `~/` directory for best performance:
```
cd storage/downloads
mv model.gguf ~/
```
[Termux](https://termux.dev/en/) is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid.
[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
## Building the Project using Android NDK
Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
```
$ mkdir build-android
$ cd build-android
$ export NDK=<your_ndk_directory>
$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
$ make
```
Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
```
$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
$cd /data/data/com.termux/files/home/bin
$chmod +x ./*
```
Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
With Termux, you can install and run `llama.cpp` as if the environment were Linux. Once in the Termux shell:
```
$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
$ apt update && apt upgrade -y
$ apt install git cmake
```
Now, you can start chatting:
Then, follow the [build instructions](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md), specifically for CMake.
Once the binaries are built, download your model of choice (e.g., from Hugging Face). It's recommended to place it in the `~/` directory for best performance:
```
$cd /data/data/com.termux/files/home/bin
$./llama-cli -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
$ curl -L {model-url} -o ~/{model}.gguf
```
Here's a demo of an interactive session running on Pixel 5 phone:
Then, if you are not already in the repo directory, `cd` into `llama.cpp` and:
```
$ ./build/bin/llama-simple -m ~/{model}.gguf -c {context-size} -p "{your-prompt}"
```
Here, we show `llama-simple`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal.
To see what it might look like visually, here's an old demo of an interactive session running on a Pixel 5 phone:
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
## Cross-compile using Android NDK
It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.)
Once you're ready and have cloned `llama.cpp`, invoke the following in the project directory:
```
$ cmake \
-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
-DANDROID_ABI=arm64-v8a \
-DANDROID_PLATFORM=android-28 \
-DCMAKE_C_FLAGS="-march=armv8.7a" \
-DCMAKE_CXX_FLAGS="-march=armv8.7a" \
-DGGML_OPENMP=OFF \
-DGGML_LLAMAFILE=OFF \
-B build-android
```
Notes:
- While later versions of Android NDK ship with OpenMP, it must still be installed by CMake as a dependency, which is not supported at this time
- `llamafile` does not appear to support Android devices (see: https://github.com/Mozilla-Ocho/llamafile/issues/325)
The above command should configure `llama.cpp` with the most performant options for modern devices. Even if your device is not running `armv8.7a`, `llama.cpp` includes runtime checks for available CPU features it can use.
Feel free to adjust the Android ABI for your target. Once the project is configured:
```
$ cmake --build build-android --config Release -j{n}
$ cmake --install build-android --prefix {install-dir} --config Release
```
After installing, go ahead and download the model of your choice to your host system. Then:
```
$ adb shell "mkdir /data/local/tmp/llama.cpp"
$ adb push {install-dir} /data/local/tmp/llama.cpp/
$ adb push {model}.gguf /data/local/tmp/llama.cpp/
$ adb shell
```
In the `adb shell`:
```
$ cd /data/local/tmp/llama.cpp
$ LD_LIBRARY_PATH=lib ./bin/llama-simple -m {model}.gguf -c {context-size} -p "{your-prompt}"
```
That's it!
Be aware that Android will not find the library path `lib` on its own, so we must specify `LD_LIBRARY_PATH` in order to run the installed executables. Android does support `RPATH` in later API levels, so this could change in the future. Refer to the previous section for information about `context-size` (very important!) and running other `examples`.

8
docs/build.md
View file

@ -198,6 +198,8 @@ The following compilation options are also available to tweak performance:
### MUSA
This provides GPU acceleration using the MUSA cores of your Moore Threads MTT GPU. Make sure to have the MUSA SDK installed. You can download it from here: [MUSA SDK](https://developer.mthreads.com/sdk/download/musa).
- Using `make`:
```bash
make GGML_MUSA=1
@ -209,6 +211,12 @@ The following compilation options are also available to tweak performance:
cmake --build build --config Release
```
The environment variable [`MUSA_VISIBLE_DEVICES`](https://docs.mthreads.com/musa-sdk/musa-sdk-doc-online/programming_guide/Z%E9%99%84%E5%BD%95/) can be used to specify which GPU(s) will be used.
The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted.
Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet.
### hipBLAS
This provides BLAS acceleration on HIP-supported AMD GPUs.

39
docs/docker.md
View file

@ -19,8 +19,11 @@ Additionally, there the following images, similar to the above:
- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggerganov/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`)
- `ghcr.io/ggerganov/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`)
- `ghcr.io/ggerganov/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`)
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now).
## Usage
@ -84,3 +87,37 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run
docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
```
## Docker With MUSA
Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/native) properly installed on Linux, `muBLAS` should be accessible inside the container.
## Building Docker locally
```bash
docker build -t local/llama.cpp:full-musa -f .devops/full-musa.Dockerfile .
docker build -t local/llama.cpp:light-musa -f .devops/llama-cli-musa.Dockerfile .
docker build -t local/llama.cpp:server-musa -f .devops/llama-server-musa.Dockerfile .
```
You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture.
The defaults are:
- `MUSA_VERSION` set to `rc3.1.0`
The resulting images, are essentially the same as the non-MUSA images:
1. `local/llama.cpp:full-musa`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
2. `local/llama.cpp:light-musa`: This image only includes the main executable file.
3. `local/llama.cpp:server-musa`: This image only includes the server executable file.
## Usage
After building locally, Usage is similar to the non-MUSA examples, but you'll need to set `mthreads` as default Docker runtime. This can be done by executing `(cd /usr/bin/musa && sudo ./docker setup $PWD)` and verifying the changes by executing `docker info | grep mthreads` on the host machine. You will also want to use the `--n-gpu-layers` flag.
```bash
docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run -v /path/to/models:/models local/llama.cpp:light-musa -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
```

21
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());
@ -74,7 +74,6 @@ int main(int argc, char ** argv) {
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
const int ret = llama_decode(ctx, batch_view);
@ -92,7 +91,7 @@ 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 +121,11 @@ 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 +150,10 @@ 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)) {

18
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);
@ -339,7 +339,7 @@ static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
static bool get_hidden_layers(llama_context * ctx, std::vector<llama_token> & tokens) {
llama_kv_cache_clear(ctx);
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
@ -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;

24
examples/embedding/embedding.cpp
View file

@ -28,7 +28,7 @@ static std::vector<std::string> split_lines(const std::string & s, const std::st
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);
}
}
@ -74,18 +74,18 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
}
float * out = output + embd_pos * n_embd;
llama_embd_normalize(embd, out, n_embd, embd_norm);
common_embd_normalize(embd, out, n_embd, embd_norm);
}
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
return 1;
}
gpt_init();
common_init();
params.embedding = true;
// For non-causal models, batch size must be equal to ubatch size
@ -95,7 +95,7 @@ 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;
@ -122,7 +122,7 @@ 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());
}
// split the prompt into lines
@ -135,7 +135,7 @@ int main(int argc, char ** argv) {
// tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs;
for (const auto & prompt : prompts) {
auto inp = ::llama_tokenize(ctx, prompt, true, true);
auto inp = common_tokenize(ctx, prompt, true, true);
if (inp.size() > n_batch) {
LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
@ -159,7 +159,7 @@ int main(int argc, char ** argv) {
LOG_INF("%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
for (int j = 0; j < (int) inputs[i].size(); j++) {
LOG("%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str());
LOG("%6d -> '%s'\n", inputs[i][j], common_token_to_piece(ctx, inputs[i][j]).c_str());
}
LOG("\n\n");
}
@ -199,7 +199,7 @@ int main(int argc, char ** argv) {
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
s = 0;
llama_batch_clear(batch);
common_batch_clear(batch);
}
// add to batch
@ -263,7 +263,7 @@ int main(int argc, char ** argv) {
LOG("\n");
for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) {
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
LOG("%6.2f ", sim);
}
LOG("%1.10s", prompts[i].c_str());
@ -296,7 +296,7 @@ int main(int argc, char ** argv) {
for (int i = 0;;) { // at least two iteration (n_embd_count > 1)
LOG(" [");
for (int j = 0;;) { // at least two iteration (n_embd_count > 1)
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
LOG("%6.2f", sim);
j++;
if (j < n_embd_count) LOG(", "); else break;

16
examples/eval-callback/eval-callback.cpp
View file

@ -126,12 +126,12 @@ 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))) {
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
}
@ -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);

31
examples/imatrix/imatrix.cpp
View file

@ -37,13 +37,13 @@ struct Stats {
class IMatrixCollector {
public:
IMatrixCollector() = default;
void set_params(gpt_params params) { m_params = std::move(params); }
void set_params(common_params params) { m_params = std::move(params); }
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
void save_imatrix(int ncall = -1) const;
bool load_imatrix(const char * file_name);
private:
std::unordered_map<std::string, Stats> m_stats;
gpt_params m_params;
common_params m_params;
std::mutex m_mutex;
int m_last_call = 0;
std::vector<float> m_src1_data;
@ -428,7 +428,7 @@ static void process_logits(
}
}
static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
static bool compute_imatrix(llama_context * ctx, const common_params & params) {
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
const int n_ctx = llama_n_ctx(ctx);
@ -436,7 +436,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
auto tim1 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
auto tim2 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -496,6 +496,8 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
// clear the KV cache
llama_kv_cache_clear(ctx);
llama_batch batch = llama_batch_init(n_batch, 0, 1);
for (int j = 0; j < num_batches; ++j) {
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
@ -508,9 +510,14 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
// TODO: use batch.logits to save computations instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
common_batch_clear(batch);
for (int i = 0; i < batch_size; i++) {
common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
}
if (llama_decode(ctx, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
llama_batch_free(batch);
return false;
}
@ -523,6 +530,8 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
}
}
llama_batch_free(batch);
const auto t_end = std::chrono::high_resolution_clock::now();
if (i == 0) {
@ -568,17 +577,17 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.n_ctx = 512;
params.logits_all = true;
params.escape = false;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
return 1;
}
gpt_init();
common_init();
params.n_batch = std::min(params.n_batch, params.n_ctx);
@ -607,7 +616,7 @@ 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;
@ -625,7 +634,7 @@ 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());
}
if (!compute_imatrix(ctx, params)) {

82
examples/infill/infill.cpp
View file

@ -35,8 +35,8 @@
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;
@ -44,7 +44,7 @@ static std::vector<llama_token> * g_output_tokens;
static bool is_interacting = false;
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
) {
@ -95,12 +95,12 @@ 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);
}
@ -109,14 +109,14 @@ static void sigint_handler(int signo) {
#endif
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
g_params = &params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) {
return 1;
}
gpt_init();
common_init();
auto & sparams = params.sparams;
@ -166,7 +166,7 @@ int main(int argc, char ** argv) {
llama_model * model = nullptr;
llama_context * ctx = nullptr;
gpt_sampler * smpl = nullptr;
common_sampler * smpl = nullptr;
g_model = &model;
g_ctx = &ctx;
@ -174,7 +174,7 @@ 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;
@ -195,21 +195,21 @@ 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());
}
const bool add_bos = llama_add_bos_token(model);
GGML_ASSERT(!llama_add_eos_token(model));
std::vector<llama_token> embd_inp;
std::vector<llama_token> embd_end;
std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
GGML_ASSERT(llama_token_prefix(model) >= 0);
GGML_ASSERT(llama_token_suffix(model) >= 0);
GGML_ASSERT(llama_token_fim_pre(model) >= 0);
GGML_ASSERT(llama_token_fim_suf(model) >= 0);
inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model));
embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
embd_end = params.spm_infill ? inp_pfx : inp_sfx;
@ -218,7 +218,7 @@ int main(int argc, char ** argv) {
}
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
const llama_token middle_token = llama_token_middle(model);
const llama_token middle_token = llama_token_fim_mid(model);
if (middle_token >= 0) {
embd_inp.push_back(middle_token);
}
@ -257,13 +257,13 @@ 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 > 0) {
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");
}
@ -298,11 +298,11 @@ int main(int argc, char ** argv) {
LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
}
}
smpl = gpt_sampler_init(model, sparams);
smpl = common_sampler_init(model, sparams);
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);
@ -396,7 +396,7 @@ int main(int argc, char ** argv) {
LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str());
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
@ -411,9 +411,9 @@ int main(int argc, char ** argv) {
embd.clear();
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
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, true);
common_sampler_accept(smpl, id, true);
// LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str());
@ -434,7 +434,7 @@ 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], false);
common_sampler_accept(smpl, embd_inp[n_consumed], false);
++n_consumed;
if ((int) embd.size() >= params.n_batch) {
@ -446,7 +446,7 @@ int main(int argc, char ** argv) {
// display text
if (input_echo) {
for (auto id : embd) {
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());
if (embd.size() > 1) {
@ -465,10 +465,10 @@ int main(int argc, char ** argv) {
// if not currently processing queued inputs;
if ((int) embd_inp.size() <= n_consumed) {
// deal with eot token in infill mode
if ((gpt_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){
if ((common_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){
if (is_interacting && !params.interactive_first) {
// print an eot token
LOG("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str());
}
LOG("\n");
console::set_display(console::user_input);
@ -505,11 +505,11 @@ int main(int argc, char ** argv) {
}
// tokenize new prefix and suffix
std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model));
inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model));
embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
embd_end = params.spm_infill ? inp_pfx : inp_sfx;
@ -529,7 +529,7 @@ int main(int argc, char ** argv) {
is_interacting = false;
}
// deal with end of generation tokens in interactive mode
else if (llama_token_is_eog(model, gpt_sampler_last(smpl))) {
else if (llama_token_is_eog(model, common_sampler_last(smpl))) {
LOG_DBG("found EOS token\n");
if (params.interactive) {
@ -579,7 +579,7 @@ int main(int argc, char ** argv) {
const size_t original_size = embd_inp.size();
const auto line_inp = ::llama_tokenize(ctx, buffer, false);
const auto line_inp = common_tokenize(ctx, buffer, false);
LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str());
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
@ -587,7 +587,7 @@ 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);
}
n_remain -= line_inp.size();
@ -601,7 +601,7 @@ int main(int argc, char ** argv) {
if (n_past > 0) {
if (is_interacting) {
gpt_sampler_reset(smpl);
common_sampler_reset(smpl);
}
is_interacting = false;
}
@ -620,17 +620,17 @@ int main(int argc, char ** argv) {
}
}
if (!params.interactive && n_remain <= 0) {
LOG("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str());
}
LOG("\n");
gpt_perf_print(ctx, smpl);
common_perf_print(ctx, smpl);
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
llama_free(ctx);
llama_free_model(model);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
llama_backend_free();
return 0;

2
examples/json_schema_to_grammar.py
View file

@ -540,7 +540,7 @@ class SchemaConverter:
return self._add_rule(
name,
to_rule(transform()) if self._raw_pattern \
else "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space")
else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space")
def _resolve_ref(self, ref):

28
examples/llama-bench/llama-bench.cpp
View file

@ -151,7 +151,7 @@ static std::string get_gpu_info() {
int count = ggml_backend_sycl_get_device_count();
for (int i = 0; i < count; i++) {
char buf[128];
ggml_sycl_get_device_description(i, buf, sizeof(buf));
ggml_backend_sycl_get_device_description(i, buf, sizeof(buf));
id += buf;
if (i < count - 1) {
id += "/";
@ -304,9 +304,9 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str());
printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
#ifdef GGML_USE_RPC
printf(" -rpc, --rpc <rpc_servers> (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str());
#endif
if (llama_supports_rpc()) {
printf(" -rpc, --rpc <rpc_servers> (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str());
}
printf(" -sm, --split-mode <none|layer|row> (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
@ -497,14 +497,12 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = string_split<int>(argv[i], split_delim);
params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
#ifdef GGML_USE_RPC
} else if (arg == "-rpc" || arg == "--rpc") {
} else if (llama_supports_rpc() && (arg == "-rpc" || arg == "--rpc")) {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rpc_servers.push_back(argv[i]);
#endif
} else if (arg == "-sm" || arg == "--split-mode") {
if (++i >= argc) {
invalid_param = true;
@ -1430,7 +1428,7 @@ struct sql_printer : public printer {
}
};
static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) {
llama_set_n_threads(ctx, n_threads, n_threads);
const llama_model * model = llama_get_model(ctx);
@ -1446,14 +1444,14 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
for (int i = 1; i < n_tokens; i++) {
tokens[i] = std::rand() % n_vocab;
}
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens));
n_processed += n_tokens;
}
llama_synchronize(ctx);
}
static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
static void test_gen(llama_context * ctx, int n_gen, int n_threads) {
llama_set_n_threads(ctx, n_threads, n_threads);
const llama_model * model = llama_get_model(ctx);
@ -1462,7 +1460,7 @@ static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads)
llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
for (int i = 0; i < n_gen; i++) {
llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
llama_decode(ctx, llama_batch_get_one(&token, 1));
llama_synchronize(ctx);
token = std::rand() % n_vocab;
}
@ -1598,13 +1596,13 @@ int main(int argc, char ** argv) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count);
}
//test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count);
}
test_gen(ctx, 1, 0, t.n_threads);
test_gen(ctx, 1, t.n_threads);
}
for (int i = 0; i < params.reps; i++) {
@ -1616,13 +1614,13 @@ int main(int argc, char ** argv) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps);
}
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps);
}
test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
test_gen(ctx, t.n_gen, t.n_threads);
}
uint64_t t_ns = get_time_ns() - t_start;

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()

25
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);
@ -283,9 +283,6 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens,
nullptr,
nullptr,
nullptr,
0,
0,
0,
};
if (embd) {
@ -357,7 +354,7 @@ 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 +366,14 @@ 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 +416,7 @@ 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 +428,8 @@ 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);

1
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -46,7 +46,6 @@ actor LlamaContext {
let sparams = llama_sampler_chain_default_params()
self.sampling = llama_sampler_chain_init(sparams)
llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4))
llama_sampler_chain_add(self.sampling, llama_sampler_init_softmax())
llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234))
}

898
examples/llama.vim
View file

@ -1,135 +1,783 @@
" 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>
" LLM-based text completion using llama.cpp
"
" 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
" requires:
"
" - neovim or vim
" - curl
" - llama.cpp server instance
" - FIM-compatible model
"
" sample config:
"
" - Tab - accept the current suggestion
" - Shift+Tab - accept just the first line of the suggestion
" - Ctrl+F - toggle FIM completion manually
"
" make symlink or copy this file to ~/.config/nvim/autoload/llama.vim
"
" start the llama.cpp server with a FIM-compatible model. for example:
"
" $ llama-server -m {model.gguf} --port 8012 -ngl 99 -fa -dt 0.1 --ubatch-size 512 --batch-size 1024 --cache-reuse 256
"
" --batch-size [512, model max context]
"
" adjust the batch size to control how much of the provided local context will be used during the inference
" lower values will use smaller part of the context around the cursor, which will result in faster processing
"
" --ubatch-size [64, 2048]
"
" chunks the batch into smaller chunks for faster processing
" depends on the specific hardware. use llama-bench to profile and determine the best size
"
" --cache-reuse (ge:llama_config.n_predict, 1024]
"
" this should be either 0 (disabled) or strictly larger than g:llama_config.n_predict
" using non-zero value enables context reuse on the server side which dramatically improves the performance at
" large contexts. a value of 256 should be good for all cases
"
" run this once to initialise llama.vim:
"
" :call llama#init()
"
" more info: https://github.com/ggerganov/llama.cpp/pull/9787
"
" 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
" colors (adjust to your liking)
highlight llama_hl_hint guifg=#ff772f ctermfg=202
highlight llama_hl_info guifg=#77ff2f ctermfg=119
func llama#doLlamaGen()
if exists("b:job")
if job_status(b:job) == "run"
call job_stop(b:job)
return
endif
endif
" general parameters:
"
" endpoint: llama.cpp server endpoint
" n_prefix: number of lines before the cursor location to include in the local prefix
" n_suffix: number of lines after the cursor location to include in the local suffix
" n_predict: max number of tokens to predict
" t_max_prompt_ms: max alloted time for the prompt processing (TODO: not yet supported)
" t_max_predict_ms: max alloted time for the prediction
" show_info: show extra info about the inference (0 - disabled, 1 - statusline, 2 - inline)
" auto_fim: trigger FIM completion automatically on cursor movement
" max_line_suffix: do not auto-trigger FIM completion if there are more than this number of characters to the right of the cursor
"
" ring buffer of chunks, accumulated with time upon:
"
" - completion request
" - yank
" - entering a buffer
" - leaving a buffer
" - writing a file
"
" parameters for the ring-buffer with extra context:
"
" ring_n_chunks: max number of chunks to pass as extra context to the server (0 to disable)
" ring_chunk_size: max size of the chunks (in number of lines)
" note: adjust these numbers so that you don't overrun your context
" at ring_n_chunks = 64 and ring_chunk_size = 64 you need ~32k context
" ring_scope: the range around the cursor position (in number of lines) for gathering chunks after FIM
" ring_update_ms: how often to process queued chunks in normal mode
"
let s:default_config = {
\ 'endpoint': 'http://127.0.0.1:8012/infill',
\ 'n_prefix': 256,
\ 'n_suffix': 64,
\ 'n_predict': 128,
\ 't_max_prompt_ms': 500,
\ 't_max_predict_ms': 3000,
\ 'show_info': 2,
\ 'auto_fim': v:true,
\ 'max_line_suffix': 8,
\ 'ring_n_chunks': 64,
\ 'ring_chunk_size': 64,
\ 'ring_scope': 1024,
\ 'ring_update_ms': 1000,
\ }
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
let g:llama_config = get(g:, 'llama_config', s:default_config)
" 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]
function! s:get_indent(str)
let l:count = 0
for i in range(len(a:str))
if a:str[i] == "\t"
let l:count += &tabstop - 1
else
break
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"})
endfor
return l:count
endfunction
func s:tokenCountCallback(channel, msg)
let resp = json_decode(a:msg)
echo len(resp.tokens)
function! s:rand(i0, i1) abort
return a:i0 + rand() % (a:i1 - a:i0 + 1)
endfunction
function! llama#init()
if !executable('curl')
echohl WarningMsg
echo 'llama.vim requires the "curl" command to be available'
echohl None
return
endif
let s:pos_x = 0 " cursor position upon start of completion
let s:pos_y = 0
let s:line_cur = ''
let s:line_cur_prefix = ''
let s:line_cur_suffix = ''
let s:ring_chunks = [] " current set of chunks used as extra context
let s:ring_queued = [] " chunks that are queued to be sent for processing
let s:ring_n_evict = 0
let s:hint_shown = v:false
let s:pos_y_pick = -9999 " last y where we picked a chunk
let s:pos_dx = 0
let s:content = []
let s:can_accept = v:false
let s:timer_fim = -1
let s:t_fim_start = reltime() " used to measure total FIM time
let s:t_last_move = reltime() " last time the cursor moved
let s:current_job = v:null
let s:ghost_text_nvim = exists('*nvim_buf_get_mark')
let s:ghost_text_vim = has('textprop')
if s:ghost_text_vim
let s:hlgroup_hint = 'llama_hl_hint'
let s:hlgroup_info = 'llama_hl_info'
if empty(prop_type_get(s:hlgroup_hint))
call prop_type_add(s:hlgroup_hint, {'highlight': s:hlgroup_hint})
endif
if empty(prop_type_get(s:hlgroup_info))
call prop_type_add(s:hlgroup_info, {'highlight': s:hlgroup_info})
endif
endif
augroup llama
autocmd!
autocmd InsertEnter * inoremap <expr> <silent> <C-F> llama#fim_inline(v:false)
autocmd InsertLeavePre * call llama#fim_cancel()
autocmd CursorMoved * call s:on_move()
autocmd CursorMovedI * call s:on_move()
autocmd CompleteChanged * call llama#fim_cancel()
if g:llama_config.auto_fim
autocmd CursorMovedI * call llama#fim(v:true)
endif
" gather chunks upon yanking
autocmd TextYankPost * if v:event.operator ==# 'y' | call s:pick_chunk(v:event.regcontents, v:false, v:true) | endif
" gather chunks upon entering/leaving a buffer
autocmd BufEnter * call timer_start(100, {-> s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true)})
autocmd BufLeave * call s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true)
" gather chunk upon saving the file
autocmd BufWritePost * call s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true)
augroup END
silent! call llama#fim_cancel()
" init background update of the ring buffer
if g:llama_config.ring_n_chunks > 0
call s:ring_update()
endif
endfunction
" compute how similar two chunks of text are
" 0 - no similarity, 1 - high similarity
" TODO: figure out something better
function! s:chunk_sim(c0, c1)
let l:lines0 = len(a:c0)
let l:lines1 = len(a:c1)
let l:common = 0
for l:line0 in a:c0
for l:line1 in a:c1
if l:line0 == l:line1
let l:common += 1
break
endif
endfor
endfor
return 2.0 * l:common / (l:lines0 + l:lines1)
endfunction
" pick a random chunk of size g:llama_config.ring_chunk_size from the provided text and queue it for processing
"
" no_mod - do not pick chunks from buffers with pending changes
" do_evict - evict chunks that are very similar to the new one
"
function! s:pick_chunk(text, no_mod, do_evict)
" do not pick chunks from buffers with pending changes or buffers that are not files
if a:no_mod && (getbufvar(bufnr('%'), '&modified') || !buflisted(bufnr('%')) || !filereadable(expand('%')))
return
endif
" if the extra context option is disabled - do nothing
if g:llama_config.ring_n_chunks <= 0
return
endif
" don't pick very small chunks
if len(a:text) < 3
return
endif
if len(a:text) + 1 < g:llama_config.ring_chunk_size
let l:chunk = a:text
else
let l:l0 = s:rand(0, max([0, len(a:text) - g:llama_config.ring_chunk_size/2]))
let l:l1 = min([l:l0 + g:llama_config.ring_chunk_size/2, len(a:text)])
let l:chunk = a:text[l:l0:l:l1]
endif
let l:chunk_str = join(l:chunk, "\n") . "\n"
" check if this chunk is already added
let l:exist = v:false
for i in range(len(s:ring_chunks))
if s:ring_chunks[i].data == l:chunk
let l:exist = v:true
break
endif
endfor
for i in range(len(s:ring_queued))
if s:ring_queued[i].data == l:chunk
let l:exist = v:true
break
endif
endfor
if l:exist
return
endif
" evict queued chunks that are very similar to the new one
for i in range(len(s:ring_queued) - 1, 0, -1)
if s:chunk_sim(s:ring_queued[i].data, l:chunk) > 0.9
if a:do_evict
call remove(s:ring_queued, i)
let s:ring_n_evict += 1
else
return
endif
endif
endfor
" also from s:ring_chunks
for i in range(len(s:ring_chunks) - 1, 0, -1)
if s:chunk_sim(s:ring_chunks[i].data, l:chunk) > 0.9
if a:do_evict
call remove(s:ring_chunks, i)
let s:ring_n_evict += 1
else
return
endif
endif
endfor
" TODO: become parameter ?
if len(s:ring_queued) == 16
call remove(s:ring_queued, 0)
endif
call add(s:ring_queued, {'data': l:chunk, 'str': l:chunk_str, 'time': reltime(), 'filename': expand('%')})
"let &statusline = 'extra context: ' . len(s:ring_chunks) . ' / ' . len(s:ring_queued)
endfunction
" picks a queued chunk, sends it for processing and adds it to s:ring_chunks
" called every g:llama_config.ring_update_ms
function! s:ring_update()
call timer_start(g:llama_config.ring_update_ms, {-> s:ring_update()})
" update only if in normal mode or if the cursor hasn't moved for a while
if mode() !=# 'n' && reltimefloat(reltime(s:t_last_move)) < 3.0
return
endif
if len(s:ring_queued) == 0
return
endif
" move the first queued chunk to the ring buffer
if len(s:ring_chunks) == g:llama_config.ring_n_chunks
call remove(s:ring_chunks, 0)
endif
call add(s:ring_chunks, remove(s:ring_queued, 0))
"let &statusline = 'updated context: ' . len(s:ring_chunks) . ' / ' . len(s:ring_queued)
" send asynchronous job with the new extra context so that it is ready for the next FIM
let l:extra_context = []
for l:chunk in s:ring_chunks
call add(l:extra_context, {
\ 'text': l:chunk.str,
\ 'time': l:chunk.time,
\ 'filename': l:chunk.filename
\ })
endfor
" no samplers needed here
let l:request = json_encode({
\ 'input_prefix': "",
\ 'input_suffix': "",
\ 'input_extra': l:extra_context,
\ 'prompt': "",
\ 'n_predict': 1,
\ 'temperature': 0.0,
\ 'stream': v:false,
\ 'samplers': ["temperature"],
\ 'cache_prompt': v:true,
\ 't_max_prompt_ms': 1,
\ 't_max_predict_ms': 1
\ })
let l:curl_command = [
\ "curl",
\ "--silent",
\ "--no-buffer",
\ "--request", "POST",
\ "--url", g:llama_config.endpoint,
\ "--header", "Content-Type: application/json",
\ "--data", l:request
\ ]
" no callbacks because we don't need to process the response
if s:ghost_text_nvim
call jobstart(l:curl_command, {})
elseif s:ghost_text_vim
call job_start(l:curl_command, {})
endif
endfunction
" necessary for 'inoremap <expr>'
function! llama#fim_inline(is_auto) abort
call llama#fim(a:is_auto)
return ''
endfunction
" the main FIM call
" takes local context around the cursor and sends it together with the extra context to the server for completion
function! llama#fim(is_auto) abort
" we already have a suggestion for the current cursor position
if s:hint_shown && !a:is_auto
call llama#fim_cancel()
return
endif
call llama#fim_cancel()
" avoid sending repeated requests too fast
if reltimefloat(reltime(s:t_fim_start)) < 0.6
if s:timer_fim != -1
call timer_stop(s:timer_fim)
let s:timer_fim = -1
endif
let s:t_fim_start = reltime()
let s:timer_fim = timer_start(600, {-> llama#fim(v:true)})
return
endif
let s:t_fim_start = reltime()
let s:content = []
let s:can_accept = v:false
let s:pos_x = col('.') - 1
let s:pos_y = line('.')
let l:max_y = line('$')
let l:lines_prefix = getline(max([1, s:pos_y - g:llama_config.n_prefix]), s:pos_y - 1)
let l:lines_suffix = getline(s:pos_y + 1, min([l:max_y, s:pos_y + g:llama_config.n_suffix]))
let s:line_cur = getline('.')
let s:line_cur_prefix = strpart(s:line_cur, 0, s:pos_x)
let s:line_cur_suffix = strpart(s:line_cur, s:pos_x)
if a:is_auto && len(s:line_cur_suffix) > g:llama_config.max_line_suffix
return
endif
let l:prefix = ""
\ . join(l:lines_prefix, "\n")
\ . "\n"
let l:prompt = ""
\ . s:line_cur_prefix
let l:suffix = ""
\ . s:line_cur_suffix
\ . "\n"
\ . join(l:lines_suffix, "\n")
\ . "\n"
" prepare the extra context data
let l:extra_context = []
for l:chunk in s:ring_chunks
call add(l:extra_context, {
\ 'text': l:chunk.str,
\ 'time': l:chunk.time,
\ 'filename': l:chunk.filename
\ })
endfor
" the indentation of the current line
let l:indent = strlen(matchstr(s:line_cur_prefix, '^\s*'))
let l:request = json_encode({
\ 'input_prefix': l:prefix,
\ 'input_suffix': l:suffix,
\ 'input_extra': l:extra_context,
\ 'prompt': l:prompt,
\ 'n_predict': g:llama_config.n_predict,
\ 'n_indent': l:indent,
\ 'top_k': 40,
\ 'top_p': 0.99,
\ 'stream': v:false,
\ 'samplers': ["top_k", "top_p", "infill"],
\ 'cache_prompt': v:true,
\ 't_max_prompt_ms': g:llama_config.t_max_prompt_ms,
\ 't_max_predict_ms': g:llama_config.t_max_predict_ms
\ })
let l:curl_command = [
\ "curl",
\ "--silent",
\ "--no-buffer",
\ "--request", "POST",
\ "--url", g:llama_config.endpoint,
\ "--header", "Content-Type: application/json",
\ "--data", l:request
\ ]
if s:current_job != v:null
if s:ghost_text_nvim
call jobstop(s:current_job)
elseif s:ghost_text_vim
call job_stop(s:current_job)
endif
endif
" send the request asynchronously
if s:ghost_text_nvim
let s:current_job = jobstart(l:curl_command, {
\ 'on_stdout': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]),
\ 'on_exit': function('s:fim_on_exit'),
\ 'stdout_buffered': v:true
\ })
elseif s:ghost_text_vim
let s:current_job = job_start(l:curl_command, {
\ 'out_cb': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]),
\ 'exit_cb': function('s:fim_on_exit')
\ })
endif
" TODO: per-file location
let l:delta_y = abs(s:pos_y - s:pos_y_pick)
" gather some extra context nearby and process it in the background
" only gather chunks if the cursor has moved a lot
" TODO: something more clever? reranking?
if a:is_auto && l:delta_y > 32
" expand the prefix even further
call s:pick_chunk(getline(max([1, s:pos_y - g:llama_config.ring_scope]), max([1, s:pos_y - g:llama_config.n_prefix])), v:false, v:false)
" pick a suffix chunk
call s:pick_chunk(getline(min([l:max_y, s:pos_y + g:llama_config.n_suffix]), min([l:max_y, s:pos_y + g:llama_config.n_suffix + g:llama_config.ring_chunk_size])), v:false, v:false)
let s:pos_y_pick = s:pos_y
endif
endfunction
" if first_line == v:true accept only the first line of the response
function! llama#fim_accept(first_line)
" insert the suggestion at the cursor location
if s:can_accept && len(s:content) > 0
call setline(s:pos_y, s:line_cur[:(s:pos_x - 1)] . s:content[0])
if len(s:content) > 1
if !a:first_line
call append(s:pos_y, s:content[1:-1])
endif
endif
" move the cursor to the end of the accepted text
if !a:first_line && len(s:content) > 1
call cursor(s:pos_y + len(s:content) - 1, s:pos_x + s:pos_dx + 1)
else
call cursor(s:pos_y, s:pos_x + len(s:content[0]))
endif
endif
call llama#fim_cancel()
endfunction
function! llama#fim_cancel()
let s:hint_shown = v:false
" clear the virtual text
let l:bufnr = bufnr('%')
if s:ghost_text_nvim
let l:id_vt_fim = nvim_create_namespace('vt_fim')
call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1)
elseif s:ghost_text_vim
call prop_remove({'type': s:hlgroup_hint, 'all': v:true})
call prop_remove({'type': s:hlgroup_info, 'all': v:true})
endif
" remove the mappings
silent! iunmap <buffer> <Tab>
silent! iunmap <buffer> <S-Tab>
silent! iunmap <buffer> <Esc>
endfunction
function! s:on_move()
let s:t_last_move = reltime()
call llama#fim_cancel()
endfunction
" callback that processes the FIM result from the server and displays the suggestion
function! s:fim_on_stdout(pos_x, pos_y, is_auto, job_id, data, event = v:null)
if s:ghost_text_nvim
let l:raw = join(a:data, "\n")
elseif s:ghost_text_vim
let l:raw = a:data
endif
if len(l:raw) == 0
return
endif
if a:pos_x != col('.') - 1 || a:pos_y != line('.')
return
endif
" show the suggestion only in insert mode
if mode() !=# 'i'
return
endif
let s:pos_x = a:pos_x
let s:pos_y = a:pos_y
let s:can_accept = v:true
let l:has_info = v:false
if s:can_accept && v:shell_error
if !a:is_auto
call add(s:content, "<| curl error: is the server on? |>")
endif
let s:can_accept = v:false
endif
let l:n_prompt = 0
let l:t_prompt_ms = 1.0
let l:s_prompt = 0
let l:n_predict = 0
let l:t_predict_ms = 1.0
let l:s_predict = 0
" get the generated suggestion
if s:can_accept
let l:response = json_decode(l:raw)
for l:part in split(get(l:response, 'content', ''), "\n", 1)
call add(s:content, l:part)
endfor
" remove trailing new lines
while len(s:content) > 0 && s:content[-1] == ""
call remove(s:content, -1)
endwhile
let l:generation_settings = get(l:response, 'generation_settings', {})
let l:n_ctx = get(l:generation_settings, 'n_ctx', 0)
let l:n_cached = get(l:response, 'tokens_cached', 0)
let l:truncated = get(l:response, 'truncated', v:false)
" if response.timings is available
if len(get(l:response, 'timings', {})) > 0
let l:has_info = v:true
let l:timings = get(l:response, 'timings', {})
let l:n_prompt = get(l:timings, 'prompt_n', 0)
let l:t_prompt_ms = get(l:timings, 'prompt_ms', 1)
let l:s_prompt = get(l:timings, 'prompt_per_second', 0)
let l:n_predict = get(l:timings, 'predicted_n', 0)
let l:t_predict_ms = get(l:timings, 'predicted_ms', 1)
let l:s_predict = get(l:timings, 'predicted_per_second', 0)
endif
endif
if len(s:content) == 0
call add(s:content, "")
let s:can_accept = v:false
endif
if len(s:content) == 0
return
endif
" NOTE: the following is logic for discarding predictions that repeat existing text
" the code is quite ugly and there is very likely a simpler and more canonical way to implement this
"
" still, I wonder if there is some better way that avoids having to do these special hacks?
" on one hand, the LLM 'sees' the contents of the file before we start editing, so it is normal that it would
" start generating whatever we have given it via the extra context. but on the other hand, it's not very
" helpful to re-generate the same code that is already there
" truncate the suggestion if the first line is empty
if len(s:content) == 1 && s:content[0] == ""
let s:content = [""]
endif
" ... and the next lines are repeated
if len(s:content) > 1 && s:content[0] == "" && s:content[1:] == getline(s:pos_y + 1, s:pos_y + len(s:content) - 1)
let s:content = [""]
endif
" truncate the suggestion if it repeats the suffix
if len(s:content) == 1 && s:content[0] == s:line_cur_suffix
let s:content = [""]
endif
" find the first non-empty line (strip whitespace)
let l:cmp_y = s:pos_y + 1
while l:cmp_y < line('$') && getline(l:cmp_y) =~? '^\s*$'
let l:cmp_y += 1
endwhile
if (s:line_cur_prefix . s:content[0]) == getline(l:cmp_y)
" truncate the suggestion if it repeats the next line
if len(s:content) == 1
let s:content = [""]
endif
" ... or if the second line of the suggestion is the prefix of line l:cmp_y + 1
if len(s:content) == 2 && s:content[-1] == getline(l:cmp_y + 1)[:len(s:content[-1]) - 1]
let s:content = [""]
endif
" ... or if the middle chunk of lines of the suggestion is the same as [l:cmp_y + 1, l:cmp_y + len(s:content) - 1)
if len(s:content) > 2 && join(s:content[1:-1], "\n") == join(getline(l:cmp_y + 1, l:cmp_y + len(s:content) - 1), "\n")
let s:content = [""]
endif
endif
" keep only lines that have the same or larger whitespace prefix as s:line_cur_prefix
"let l:indent = strlen(matchstr(s:line_cur_prefix, '^\s*'))
"for i in range(1, len(s:content) - 1)
" if strlen(matchstr(s:content[i], '^\s*')) < l:indent
" let s:content = s:content[:i - 1]
" break
" endif
"endfor
let s:pos_dx = len(s:content[-1])
let s:content[-1] .= s:line_cur_suffix
call llama#fim_cancel()
" display virtual text with the suggestion
let l:bufnr = bufnr('%')
if s:ghost_text_nvim
let l:id_vt_fim = nvim_create_namespace('vt_fim')
endif
" construct the info message
if g:llama_config.show_info > 0 && l:has_info
let l:prefix = ' '
if l:truncated
let l:info = printf("%s | WARNING: the context is full: %d / %d, increase the server context size or reduce g:llama_config.ring_n_chunks",
\ g:llama_config.show_info == 2 ? l:prefix : 'llama.vim',
\ l:n_cached, l:n_ctx
\ )
else
let l:info = printf("%s | c: %d / %d, r: %d / %d, e: %d, q: %d / 16 | p: %d (%.2f ms, %.2f t/s) | g: %d (%.2f ms, %.2f t/s) | t: %.2f ms",
\ g:llama_config.show_info == 2 ? l:prefix : 'llama.vim',
\ l:n_cached, l:n_ctx, len(s:ring_chunks), g:llama_config.ring_n_chunks, s:ring_n_evict, len(s:ring_queued),
\ l:n_prompt, l:t_prompt_ms, l:s_prompt,
\ l:n_predict, l:t_predict_ms, l:s_predict,
\ 1000.0 * reltimefloat(reltime(s:t_fim_start))
\ )
endif
if g:llama_config.show_info == 1
" display the info in the statusline
let &statusline = l:info
let l:info = ''
endif
endif
" display the suggestion and append the info to the end of the first line
if s:ghost_text_nvim
call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, {
\ 'virt_text': [[s:content[0], 'llama_hl_hint'], [l:info, 'llama_hl_info']],
\ 'virt_text_win_col': virtcol('.') - 1
\ })
call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, 0, {
\ 'virt_lines': map(s:content[1:], {idx, val -> [[val, 'llama_hl_hint']]}),
\ 'virt_text_win_col': virtcol('.')
\ })
elseif s:ghost_text_vim
let l:new_suffix = s:content[0]
if !empty(l:new_suffix)
call prop_add(s:pos_y, s:pos_x + 1, {
\ 'type': s:hlgroup_hint,
\ 'text': l:new_suffix
\ })
endif
for line in s:content[1:]
call prop_add(s:pos_y, 0, {
\ 'type': s:hlgroup_hint,
\ 'text': line,
\ 'text_padding_left': s:get_indent(line),
\ 'text_align': 'below'
\ })
endfor
if !empty(l:info)
call prop_add(s:pos_y, 0, {
\ 'type': s:hlgroup_info,
\ 'text': l:info,
\ 'text_padding_left': col('$'),
\ 'text_wrap': 'truncate'
\ })
endif
endif
" setup accept shortcuts
inoremap <buffer> <Tab> <C-O>:call llama#fim_accept(v:false)<CR>
inoremap <buffer> <S-Tab> <C-O>:call llama#fim_accept(v:true)<CR>
let s:hint_shown = v:true
endfunction
function! s:fim_on_exit(job_id, exit_code, event = v:null)
if a:exit_code != 0
echom "Job failed with exit code: " . a:exit_code
endif
let s:current_job = v:null
endfunction

46
examples/llava/llava-cli.cpp
View file

@ -20,7 +20,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) {
LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
return false;
}
@ -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/llava.cpp
View file

@ -401,6 +401,39 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
return true;
}
struct llava_embd_batch {
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id> seq_id_0;
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
pos .resize(n_tokens);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
logits .resize(n_tokens);
seq_id_0.resize(1);
seq_id_0[0] = seq_id;
seq_ids [n_tokens] = nullptr;
batch = {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
/*logits =*/ logits.data(),
};
for (int i = 0; i < n_tokens; i++) {
batch.pos [i] = pos_0 + i;
batch.n_seq_id[i] = 1;
batch.seq_id [i] = seq_id_0.data();
batch.logits [i] = false;
}
}
};
bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
int n_embd = llama_n_embd(llama_get_model(ctx_llama));
@ -409,8 +442,9 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
if (n_eval > n_batch) {
n_eval = n_batch;
}
llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
if (llama_decode(ctx_llama, batch)) {
float * embd = image_embed->embed+i*n_embd;
llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
if (llama_decode(ctx_llama, llava_batch.batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
}
@ -432,7 +466,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
if (!image_embed_result) {
clip_image_u8_free(img);
LOG_ERR("%s: coulnd't embed the image\n", __func__);
LOG_ERR("%s: couldn't embed the image\n", __func__);
return NULL;
}

42
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;
@ -97,7 +97,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) {
LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
return false;
}
@ -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");

52
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);
@ -89,8 +89,8 @@ int main(int argc, char ** argv) {
const auto t_enc_start = ggml_time_us();
// eval the prompt
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1));
for (int s = 1; s < W + G + 1; ++s) {
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
@ -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();
}

58
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);
@ -89,8 +89,8 @@ int main(int argc, char ** argv){
const auto t_enc_start = ggml_time_us();
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1));
const auto t_enc_end = ggml_time_us();
@ -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);

41
examples/main/README.md
View file

@ -187,6 +187,30 @@ Use the `--no-penalize-nl` option to disable newline penalization when applying
Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`
### DRY Repetition Penalty
DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)).
- `--dry-multiplier N`: Set the DRY sampling multiplier (default: 0.0, 0.0 = disabled).
- `--dry-base N`: Set the DRY sampling base value (default: 1.75).
- `--dry-allowed-length N`: Set the allowed length for DRY sampling (default: 2).
- `--dry-penalty-last-n N`: Set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size).
- `--dry-sequence-breaker STRING`: Add a sequence breaker for DRY sampling. Can be used more than once to add multiple sequence breakers. Using this clears out the default breakers, which consist of: `['\n', ':', '"', '*']`. If the string `"none"` is supplied, no sequence breakers are used.
The `dry-multiplier` option controls the strength of the DRY sampling effect. A value of 0.0 disables DRY sampling, while higher values increase its influence. A typical recommended value is 0.8.
The `dry-base` option sets the base value for the exponential penalty calculation in DRY sampling. Higher values lead to more aggressive penalization of repetitions.
The `dry-allowed-length` option sets the maximum length of repeated sequences that will not be penalized. Repetitions shorter than or equal to this length are not penalized, allowing for natural repetitions of short phrases or common words.
The `dry-penalty-last-n` option controls how many recent tokens to consider when applying the DRY penalty. A value of -1 considers the entire context. Use a positive value to limit the consideration to a specific number of recent tokens.
The `dry-sequence-breaker` option adds a single sequence breaker and can be used more than once to specify multiple sequence breakers. Sequence breakers interrupt sequence matching and break the input into parts where matching can be applied.
DRY sampling provides more nuanced control over text generation, particularly for reducing long-range repetitions and maintaining global coherence.
Example usage: `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n -1 --dry-sequence-breaker "—" --dry-sequence-breaker "##"`
### Top-K Sampling
- `--top-k N`: Limit the next token selection to the K most probable tokens (default: 40).
@ -241,6 +265,19 @@ The `--mirostat-ent` option sets the Mirostat target entropy (tau), which repres
Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0`
### XTC Sampling
- `--xtc-probability N`: Sets the chance for token removal (checked once on sampler start) (default: 0.0).
- `--xtc-threshold N`: Sets a minimum probability threshold for tokens to be removed (default: 0.1).
Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of `xtc-probability` it searches for tokens with probabilities of `xtc-threshold` and above, then removes all such tokens except the least probable one.
By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models.
Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: `--sampling-seq mx --min-p 0.02 --xtc-probability 0.5`.
Example usage: `--xtc-probability 0.5 --xtc-threshold 0.1`
### Logit Bias
- `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion.
@ -284,10 +321,6 @@ These options help improve the performance and memory usage of the LLaMA models.
These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.
### Memory Float 32
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended.
### Batch Size
- `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.

142
examples/main/main.cpp
View file

@ -33,8 +33,8 @@
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;
@ -63,7 +63,7 @@ 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
) {
@ -114,12 +114,12 @@ 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);
}
@ -127,22 +127,22 @@ 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;
@ -187,9 +187,9 @@ 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;
@ -197,7 +197,7 @@ 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;
@ -246,7 +246,7 @@ 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__);
}
@ -255,7 +255,7 @@ 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");
}
@ -296,7 +296,7 @@ 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;
@ -379,13 +379,13 @@ 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");
}
@ -415,9 +415,9 @@ 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());
}
}
}
@ -430,9 +430,9 @@ 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());
}
}
}
@ -440,23 +440,23 @@ 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);
@ -521,14 +521,14 @@ 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)) {
int enc_input_size = embd_inp.size();
llama_token * enc_input_buf = embd_inp.data();
if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) {
if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size))) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
@ -569,30 +569,30 @@ int main(int argc, char ** argv) {
if (!params.ctx_shift){
LOG_DBG("\n\n%s: context full and context shift is disabled => stopping\n", __func__);
break;
} else {
if (params.n_predict == -2) {
LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
break;
}
const int n_left = n_past - params.n_keep;
const int n_discard = n_left/2;
LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard);
llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
n_past -= n_discard;
LOG_DBG("after swap: n_past = %d\n", n_past);
LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str());
LOG_DBG("clear session path\n");
path_session.clear();
}
if (params.n_predict == -2) {
LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
break;
}
const int n_left = n_past - params.n_keep;
const int n_discard = n_left/2;
LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard);
llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
n_past -= n_discard;
LOG_DBG("after swap: n_past = %d\n", n_past);
LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str());
LOG_DBG("clear session path\n");
path_session.clear();
}
} else {
// context extension via Self-Extend
@ -648,7 +648,7 @@ int main(int argc, char ** argv) {
LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str());
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
@ -679,9 +679,9 @@ 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());
@ -702,7 +702,7 @@ 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) {
@ -714,7 +714,7 @@ 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());
@ -743,7 +743,7 @@ 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.
@ -765,7 +765,7 @@ 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) {
@ -782,13 +782,13 @@ 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;
}
@ -803,8 +803,8 @@ 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) {
@ -862,9 +862,9 @@ 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());
@ -882,7 +882,7 @@ 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
@ -899,7 +899,7 @@ int main(int argc, char ** argv) {
if (n_past > 0) {
if (is_interacting) {
gpt_sampler_reset(smpl);
common_sampler_reset(smpl);
}
is_interacting = false;
}
@ -925,10 +925,10 @@ 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);

37
examples/parallel/parallel.cpp
View file

@ -54,7 +54,7 @@ static std::vector<std::string> k_prompts = {
struct client {
~client() {
if (smpl) {
gpt_sampler_free(smpl);
common_sampler_free(smpl);
}
}
@ -75,7 +75,7 @@ struct client {
std::string prompt;
std::string response;
struct gpt_sampler * smpl = nullptr;
struct common_sampler * smpl = nullptr;
};
static void print_date_time() {
@ -103,13 +103,13 @@ static std::vector<std::string> split_string(const std::string& input, char deli
int main(int argc, char ** argv) {
srand(1234);
gpt_params params;
common_params params;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
return 1;
}
gpt_init();
common_init();
// number of simultaneous "clients" to simulate
const int32_t n_clients = params.n_parallel;
@ -130,7 +130,7 @@ 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;
@ -160,11 +160,11 @@ int main(int argc, char ** argv) {
for (size_t i = 0; i < clients.size(); ++i) {
auto & client = clients[i];
client.id = i;
client.smpl = gpt_sampler_init(model, params.sparams);
client.smpl = common_sampler_init(model, params.sparams);
}
std::vector<llama_token> tokens_system;
tokens_system = ::llama_tokenize(ctx, k_system, true);
tokens_system = common_tokenize(ctx, k_system, true);
const int32_t n_tokens_system = tokens_system.size();
llama_seq_id g_seq_id = 0;
@ -189,7 +189,7 @@ int main(int argc, char ** argv) {
LOG_INF("%s: Evaluating the system prompt ...\n", __func__);
for (int32_t i = 0; i < n_tokens_system; ++i) {
llama_batch_add(batch, tokens_system[i], i, { 0 }, false);
common_batch_add(batch, tokens_system[i], i, { 0 }, false);
}
if (llama_decode(ctx, batch) != 0) {
@ -210,10 +210,10 @@ int main(int argc, char ** argv) {
while (true) {
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);
}
llama_batch_clear(batch);
common_batch_clear(batch);
// decode any currently ongoing sequences
for (auto & client : clients) {
@ -223,7 +223,7 @@ int main(int argc, char ** argv) {
client.i_batch = batch.n_tokens;
llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true);
common_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true);
client.n_decoded += 1;
}
@ -252,14 +252,14 @@ int main(int argc, char ** argv) {
client.prompt = client.input + "\nAssistant:";
client.response = "";
gpt_sampler_reset(client.smpl);
common_sampler_reset(client.smpl);
// do not prepend BOS because we have a system prompt!
std::vector<llama_token> tokens_prompt;
tokens_prompt = ::llama_tokenize(ctx, client.prompt, false);
tokens_prompt = common_tokenize(ctx, client.prompt, false);
for (size_t i = 0; i < tokens_prompt.size(); ++i) {
llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false);
common_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false);
}
// extract the logits only for the last token
@ -308,7 +308,6 @@ int main(int argc, char ** argv) {
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
const int ret = llama_decode(ctx, batch_view);
@ -340,9 +339,9 @@ int main(int argc, char ** argv) {
//printf("client %d, seq %d, token %d, pos %d, batch %d\n",
// client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);
const llama_token id = gpt_sampler_sample(client.smpl, ctx, client.i_batch - i);
const llama_token id = common_sampler_sample(client.smpl, ctx, client.i_batch - i);
gpt_sampler_accept(client.smpl, id, true);
common_sampler_accept(client.smpl, id, true);
if (client.n_decoded == 1) {
// start measuring generation time after the first token to make sure all concurrent clients
@ -350,7 +349,7 @@ int main(int argc, char ** argv) {
client.t_start_gen = ggml_time_us();
}
const std::string token_str = llama_token_to_piece(ctx, id);
const std::string token_str = common_token_to_piece(ctx, id);
client.response += token_str;
client.sampled = id;

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;

170
examples/perplexity/perplexity.cpp
View file

@ -35,7 +35,7 @@ struct results_log_softmax {
};
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 struct results_perplexity & results
) {
if (params.logdir.empty()) {
@ -169,7 +169,7 @@ static void process_logits(
break;
}
lock.unlock();
const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
const results_log_softmax results = log_softmax(n_vocab, logits + size_t(i)*n_vocab, tokens[i+1]);
const double v = -results.log_softmax;
local_nll += v;
local_nll2 += v*v;
@ -203,7 +203,7 @@ static void process_logits(std::ostream& out, int n_vocab, const float * logits,
break;
}
lock.unlock();
const double v = log_softmax(n_vocab, logits + i*n_vocab, log_probs.data() + i*nv, tokens[i+1]);
const double v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, log_probs.data() + i*nv, tokens[i+1]);
local_nll += v;
local_nll2 += v*v;
}
@ -281,7 +281,9 @@ static std::pair<double, float> log_softmax(int n_vocab, const float * logits, c
kld.sum_kld += sum;
kld.sum_kld2 += sum*sum;
++kld.count;
if (imax == imax_base) ++kld.n_same_top;
if (imax == imax_base) {
++kld.n_same_top;
}
const float p_base = expf(-nll_base);
const float p = expf(-nll);
@ -323,7 +325,7 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens
break;
}
lock.unlock();
std::pair<double, float> v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
std::pair<double, float> v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
kld_values[i] = (float)v.first;
p_diff_values[i] = v.second;
}
@ -337,7 +339,7 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens
}
}
static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
static results_perplexity perplexity_v2(llama_context * ctx, const common_params & params) {
// Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
@ -348,7 +350,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
const int n_ctx = llama_n_ctx(ctx);
@ -383,9 +385,10 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride;
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
int count = 0;
double nll = 0.0;
@ -405,14 +408,21 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
// clear the KV cache
llama_kv_cache_clear(ctx);
llama_batch batch = llama_batch_init(n_batch, 0, 1);
for (int j = 0; j < num_batches; ++j) {
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
common_batch_clear(batch);
for (int i = 0; i < batch_size; i++) {
common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
}
//LOG_DBG(" Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
// TODO: use llama_batch.logits instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
if (llama_decode(ctx, batch)) {
//LOG_ERR("%s : failed to eval\n", __func__);
llama_batch_free(batch);
return {tokens, -1, logit_history, prob_history};
}
@ -424,14 +434,16 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
const auto batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab);
if (j == 0) {
tokens[batch_start] = token_org;
}
}
llama_batch_free(batch);
const auto t_end = std::chrono::high_resolution_clock::now();
if (i == 0) {
@ -447,11 +459,10 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
//LOG_DBG("%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start);
for (int j = n_ctx - params.ppl_stride - 1; j < n_ctx - 1; ++j) {
// Calculate probability of next token, given the previous ones.
const std::vector<float> tok_logits(
logits.begin() + (j + 0) * n_vocab,
logits.begin() + (j + 1) * n_vocab);
logits.begin() + size_t(j + 0) * n_vocab,
logits.begin() + size_t(j + 1) * n_vocab);
const float prob = softmax(tok_logits)[tokens[start + j + 1]];
logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]];
@ -472,7 +483,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
return {tokens, std::exp(nll / count), logit_history, prob_history};
}
static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) {
static results_perplexity perplexity(llama_context * ctx, const common_params & params, const int32_t n_ctx) {
if (params.ppl_stride > 0) {
return perplexity_v2(ctx, params);
}
@ -500,7 +511,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
auto tim1 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
auto tim2 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -521,9 +532,10 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
const int n_chunk_max = tokens.size() / n_ctx;
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
int count = 0;
double nll = 0.0;
double nll2 = 0.0;
@ -538,7 +550,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
std::vector<float> logits;
if (num_batches > 1) {
logits.reserve((size_t)n_ctx * n_vocab);
logits.reserve(size_t(n_ctx) * n_vocab);
}
LOG_INF("%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
@ -620,7 +632,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
if (num_batches > 1 && n_outputs > 0) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + n_outputs * n_vocab);
logits.insert(logits.end(), batch_logits, batch_logits + size_t(n_outputs) * n_vocab);
}
}
@ -661,7 +673,9 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
} else {
double av = nll/count;
double av2 = nll2/count - av*av;
if (av2 > 0) av2 = sqrt(av2/(count-1));
if (av2 > 0) {
av2 = sqrt(av2/(count-1));
}
LOG("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
}
}
@ -686,10 +700,10 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
return {tokens, ppl, logit_history, prob_history};
}
static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int32_t n_batch, int32_t n_vocab) {
static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int n_batch, int n_vocab) {
int prev_outputs = 0;
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
for (int i = 0; i < (int) batch.n_tokens; i += n_batch) {
const int n_tokens = std::min<int>(n_batch, batch.n_tokens - i);
llama_batch batch_view = {
n_tokens,
@ -699,7 +713,6 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
const int ret = llama_decode(ctx, batch_view);
@ -713,7 +726,7 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
n_outputs += batch_view.logits[i] != 0;
}
memcpy(batch_logits.data() + prev_outputs*n_vocab, llama_get_logits(ctx), n_outputs*n_vocab*sizeof(float));
memcpy(batch_logits.data() + size_t(prev_outputs)*n_vocab, llama_get_logits(ctx), size_t(n_outputs)*n_vocab*sizeof(float));
prev_outputs += n_outputs;
}
@ -728,7 +741,9 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
if (eval_results.size() != eval_pairs.size()) {
eval_results.resize(eval_pairs.size());
}
if (eval_pairs.empty()) return;
if (eval_pairs.empty()) {
return;
}
size_t max_threads = std::min((eval_pairs.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK, workers.size());
@ -736,11 +751,13 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
auto compute = [&counter, &eval_pairs, &eval_results, batch_logits, n_vocab] () {
float local_logprobs[K_TOKEN_CHUNK];
while (true) {
size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed);
if (first >= eval_results.size()) break;
size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size());
const size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed);
if (first >= eval_results.size()) {
break;
}
const size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size());
for (size_t i = first; i < last; ++i) {
auto logits = batch_logits + eval_pairs[i].first * n_vocab;
const auto * logits = batch_logits + eval_pairs[i].first * n_vocab;
float max_logit = logits[0];
for (int j = 1; j < n_vocab; ++j) {
max_logit = std::max(max_logit, logits[j]);
@ -763,7 +780,7 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
}
}
static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
static void hellaswag_score(llama_context * ctx, const common_params & params) {
// Calculates hellaswag score (acc_norm) from prompt
//
// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
@ -844,7 +861,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
for (size_t j = 0; j < 4; j++) {
hs_cur.ending[j] = prompt_lines[idx*6+2+j];
hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
hs_cur.seq_tokens[j] = common_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
}
// determine the common prefix of the endings
@ -877,10 +894,11 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
double acc = 0.0f;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int max_tasks_per_batch = 32;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@ -888,7 +906,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
std::vector<float> tok_logits(n_vocab);
// TODO: this could be made smaller; it's currently the worst-case size
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
std::vector<float> eval_results;
@ -900,7 +918,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
size_t i1 = i0;
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch);
common_batch_clear(batch);
// batch as much tasks as possible into the available context
// each task has 4 unique sequence ids - one for each ending
@ -916,7 +934,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
}
for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
common_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
}
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1;
@ -926,7 +944,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
// TODO: don't evaluate the last token of each sequence
for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
common_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits;
}
}
@ -975,7 +993,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
auto & hs_cur = hs_data[i];
// get the logits of the last token of the common prefix
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*hs_cur.i_logits, n_vocab*sizeof(float));
std::memcpy(tok_logits.data(), batch_logits.data() + hs_cur.i_logits*n_vocab, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);
@ -1102,7 +1120,7 @@ static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string
* 0,Sarah was a much better surgeon than Maria so _ always got the easier cases.,Sarah,Maria,2
*
*/
static void winogrande_score(llama_context * ctx, const gpt_params & params) {
static void winogrande_score(llama_context * ctx, const common_params & params) {
constexpr int k_min_trailing_ctx = 3;
@ -1136,8 +1154,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
LOG_INF("%s : tokenizing selected tasks\n", __func__);
for (auto & task : data) {
task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, true);
task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, true);
task.seq_tokens[0] = common_tokenize(ctx, task.first + task.choices[0] + task.second, true);
task.seq_tokens[1] = common_tokenize(ctx, task.first + task.choices[1] + task.second, true);
task.common_prefix = 0;
for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
@ -1152,16 +1170,17 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
task.seq_tokens[0].size() - task.common_prefix +
task.seq_tokens[1].size() - task.common_prefix;
task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], true).size();
task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], true).size();
task.n_base1 = common_tokenize(ctx, task.first + task.choices[0], true).size();
task.n_base2 = common_tokenize(ctx, task.first + task.choices[1], true).size();
}
LOG_INF("%s : calculating winogrande score over selected tasks.\n", __func__);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int max_tasks_per_batch = 128;
const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@ -1169,7 +1188,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
std::vector<float> tok_logits(n_vocab);
// TODO: this could be made smaller; it's currently the worst-case size
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
std::vector<float> eval_results;
@ -1184,7 +1203,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
size_t i1 = i0;
size_t i_logits = 0;
llama_batch_clear(batch);
common_batch_clear(batch);
while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
int n_logits = 0;
@ -1194,7 +1213,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
}
for (size_t i = 0; i < data[i1].common_prefix; ++i) {
llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
common_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
}
batch.logits[batch.n_tokens - 1] = true;
n_logits += 1;
@ -1202,7 +1221,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
for (int s = 0; s < 2; ++s) {
// TODO: end before the last token, no need to predict past the end of the sequences
for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
common_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
n_logits += 1;
}
}
@ -1359,7 +1378,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
}
return false;
}
task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, true));
task.seq_tokens.emplace_back(::common_tokenize(ctx, task.question + " " + answer, true));
}
auto min_len = task.seq_tokens.front().size();
for (auto& seq : task.seq_tokens) {
@ -1403,7 +1422,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
// git@hf.co:datasets/Stevross/mmlu
// https://huggingface.co/datasets/truthful_qa
//
static void multiple_choice_score(llama_context * ctx, const gpt_params & params) {
static void multiple_choice_score(llama_context * ctx, const common_params & params) {
std::istringstream strstream(params.prompt);
uint32_t n_task;
@ -1509,17 +1528,18 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
LOG("\ntask\tacc_norm\n");
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int max_tasks_per_batch = 32;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
std::vector<float> tok_logits(n_vocab);
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
std::vector<float> eval_results;
@ -1536,7 +1556,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
size_t i1 = i0;
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch);
common_batch_clear(batch);
// batch as much tasks as possible into the available context
// each task has 4 unique sequence ids - one for each ending
@ -1559,7 +1579,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
for (size_t i = 0; i < cur_task.common_prefix; ++i) {
//llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
common_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
}
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1;
@ -1569,7 +1589,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
// TODO: don't evaluate the last token of each sequence
for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
common_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits;
}
}
@ -1627,7 +1647,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
//LOG("\n common_prefix: %zu\n", cur_task.common_prefix);
// get the logits of the last token of the common prefix
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*cur_task.i_logits, n_vocab*sizeof(float));
std::memcpy(tok_logits.data(), batch_logits.data() + cur_task.i_logits*n_vocab, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);
@ -1683,7 +1703,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
LOG_INF("\n");
}
static void kl_divergence(llama_context * ctx, const gpt_params & params) {
static void kl_divergence(llama_context * ctx, const common_params & params) {
if (params.logits_file.empty()) {
LOG_ERR("%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__);
return;
@ -1709,7 +1729,8 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
__func__, params.logits_file.c_str(), n_ctx, params.n_ctx);
}
int n_vocab, n_chunk;
int n_vocab;
int n_chunk;
in.read((char *)&n_vocab, sizeof(n_vocab));
in.read((char *)&n_chunk, sizeof(n_chunk));
if (in.fail()) {
@ -1720,7 +1741,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_n_vocab(llama_get_model(ctx)));
}
std::vector<llama_token> tokens(n_ctx * n_chunk);
std::vector<llama_token> tokens(size_t(n_ctx) * n_chunk);
if (in.read((char *)tokens.data(), tokens.size()*sizeof(tokens[0])).fail()) {
LOG_ERR("%s: failed reading evaluation tokens from %s\n", __func__, params.logits_file.c_str());
return;
@ -1737,7 +1758,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
std::vector<float> p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
std::vector<float> logits;
if (num_batches > 1) {
logits.reserve(n_ctx * n_vocab);
logits.reserve(size_t(n_ctx) * n_vocab);
}
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
@ -1778,6 +1799,8 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
// clear the KV cache
llama_kv_cache_clear(ctx);
llama_batch batch = llama_batch_init(n_batch, 0, 1);
for (int j = 0; j < num_batches; ++j) {
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
@ -1790,9 +1813,14 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
// TODO: use llama_batch.logits instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
common_batch_clear(batch);
for (int i = 0; i < batch_size; i++) {
common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
}
if (llama_decode(ctx, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
llama_batch_free(batch);
return;
}
@ -1801,10 +1829,12 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
if (num_batches > 1) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab);
}
}
llama_batch_free(batch);
const auto t_end = std::chrono::high_resolution_clock::now();
if (i == 0) {
@ -1822,7 +1852,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
const int first = n_ctx/2;
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
process_logits(n_vocab, all_logits + size_t(first)*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr);
p_diff_ptr += n_ctx - 1 - first;
kld_ptr += n_ctx - 1 - first;
@ -1955,17 +1985,17 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.n_ctx = 512;
params.logits_all = true;
params.escape = false;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
return 1;
}
gpt_init();
common_init();
const int32_t n_ctx = params.n_ctx;
@ -2004,7 +2034,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the model and apply lora adapter, if any
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;
@ -2023,7 +2053,7 @@ 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());
}
struct results_perplexity results;

10
examples/quantize-stats/quantize-stats.cpp
View file

@ -142,7 +142,7 @@ static bool tensor_is_contiguous(const struct ggml_tensor * tensor) {
}
static void test_roundtrip_on_chunk(
const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits_t & qfns, bool use_reference,
const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits & qfns, bool use_reference,
float * input_scratch, char * quantized_scratch, float * output_scratch, error_stats & stats
) {
if (layer->type == GGML_TYPE_F16) {
@ -166,7 +166,7 @@ static void test_roundtrip_on_chunk(
// Run quantization function for a single layer and update error stats
static void test_roundtrip_on_layer(
std::string & name, bool print_layer_stats, const ggml_type_traits_t & qfns, bool use_reference,
std::string & name, bool print_layer_stats, const ggml_type_traits & qfns, bool use_reference,
const ggml_tensor * layer, std::vector<float> & input_scratch, std::vector<char> & quantized_scratch,
std::vector<float> & output_scratch, error_stats & total_error, int max_thread = 0
) {
@ -371,8 +371,8 @@ int main(int argc, char ** argv) {
if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
continue;
}
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
if (qfns.from_float && qfns.to_float) {
const auto * qfns = ggml_get_type_traits(type);
if (qfns->from_float && qfns->to_float) {
if (params.verbose) {
printf("testing %s ...\n", ggml_type_name(type));
}
@ -393,7 +393,7 @@ int main(int argc, char ** argv) {
test_roundtrip_on_layer(
layer_name,
params.per_layer_stats,
qfns,
*qfns,
params.reference,
kv_tensor.second,
input_scratch,

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;
}

51
examples/save-load-state/save-load-state.cpp
View file

@ -6,12 +6,12 @@
#include <cstdio>
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.prompt = "The quick brown fox";
params.sparams.seed = 1234;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
@ -28,7 +28,7 @@ int main(int argc, char ** argv) {
std::string result2;
// 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;
@ -42,15 +42,21 @@ int main(int argc, char ** argv) {
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed));
// tokenize prompt
auto tokens = llama_tokenize(ctx, params.prompt, true);
auto tokens = common_tokenize(ctx, params.prompt, true);
// prepare the batch
llama_batch batch = llama_batch_init(tokens.size(), 0, 1);
for (size_t i = 0; i < tokens.size(); i++) {
common_batch_add(batch, tokens[i], i, {0}, false);
}
batch.logits[batch.n_tokens - 1] = true; // generate next token
// evaluate prompt
llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0));
n_past += tokens.size();
llama_decode(ctx, batch);
n_past += batch.n_tokens;
// save state (rng, logits, embedding and kv_cache) to file
{
@ -72,13 +78,17 @@ int main(int argc, char ** argv) {
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl, ctx, -1);
auto next_token_str = llama_token_to_piece(ctx, next_token);
auto next_token_str = common_token_to_piece(ctx, next_token);
printf("%s", next_token_str.c_str());
result0 += next_token_str;
if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
common_batch_clear(batch);
common_batch_add(batch, next_token, n_past, {0}, true);
if (llama_decode(ctx, batch)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
return 1;
@ -92,11 +102,10 @@ int main(int argc, char ** argv) {
llama_free(ctx);
// make new context
auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
auto * ctx2 = llama_new_context_with_model(model, common_context_params_to_llama(params));
llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl2, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sparams.seed));
printf("\nsecond run: %s", params.prompt.c_str());
@ -128,13 +137,17 @@ int main(int argc, char ** argv) {
// second run
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl2, ctx2, -1);
auto next_token_str = llama_token_to_piece(ctx2, next_token);
auto next_token_str = common_token_to_piece(ctx2, next_token);
printf("%s", next_token_str.c_str());
result1 += next_token_str;
if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) {
common_batch_clear(batch);
common_batch_add(batch, next_token, n_past, {0}, true);
if (llama_decode(ctx2, batch)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_batch_free(batch);
llama_free(ctx2);
llama_free_model(model);
return 1;
@ -152,11 +165,10 @@ int main(int argc, char ** argv) {
}
// make new context
auto * ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
auto * ctx3 = llama_new_context_with_model(model, common_context_params_to_llama(params));
llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl3, llama_sampler_init_softmax());
llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sparams.seed));
printf("\nsingle seq run: %s", params.prompt.c_str());
@ -216,13 +228,17 @@ int main(int argc, char ** argv) {
// third run with seq 1 instead of 0
for (auto i = 0; i < params.n_predict; i++) {
auto next_token = llama_sampler_sample(smpl3, ctx3, -1);
auto next_token_str = llama_token_to_piece(ctx3, next_token);
auto next_token_str = common_token_to_piece(ctx3, next_token);
printf("%s", next_token_str.c_str());
result2 += next_token_str;
if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) {
common_batch_clear(batch);
common_batch_add(batch, next_token, n_past, {1}, true);
if (llama_decode(ctx3, batch)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_batch_free(batch);
llama_free(ctx3);
llama_free_model(model);
return 1;
@ -236,6 +252,7 @@ int main(int argc, char ** argv) {
llama_sampler_free(smpl2);
llama_sampler_free(smpl3);
llama_batch_free(batch);
llama_free(ctx3);
llama_free_model(model);

112
examples/server/README.md
View file

@ -18,6 +18,8 @@ The project is under active development, and we are [looking for feedback and co
## Usage
<!-- Note for contributors: The list below is generated by llama-gen-docs -->
**Common params**
| Argument | Explanation |
@ -58,8 +60,6 @@ The project is under active development, and we are [looking for feedback and co
| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0)<br/>(env: LLAMA_ARG_YARN_ATTN_FACTOR) |
| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0)<br/>(env: LLAMA_ARG_YARN_BETA_SLOW) |
| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
| `-gan, --grp-attn-n N` | group-attention factor (default: 1)<br/>(env: LLAMA_ARG_GRP_ATTN_N) |
| `-gaw, --grp-attn-w N` | group-attention width (default: 512.0)<br/>(env: LLAMA_ARG_GRP_ATTN_W) |
| `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
| `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
@ -114,6 +114,11 @@ The project is under active development, and we are [looking for feedback and co
| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) |
| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) |
| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) |
| `--dry-multiplier N` | DRY sampling multiplier (default: 0.0, 0.0 = disabled) |
| `--dry-base N` | DRY sampling base value (default: 1.75) |
| `--dry-allowed-length N` | allowed length for DRY sampling (default: 2) |
| `--dry-penalty-last-n N` | DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) |
| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers (`['\n', ':', '"', '*']`) in the process; use `"none"` to not use any sequence breakers
| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) |
| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) |
| `--mirostat N` | use Mirostat sampling.<br/>Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.<br/>(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) |
@ -147,9 +152,11 @@ The project is under active development, and we are [looking for feedback and co
| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate<br/>(env: LLAMA_ARG_SSL_CERT_FILE) |
| `-to, --timeout N` | server read/write timeout in seconds (default: 600)<br/>(env: LLAMA_ARG_TIMEOUT) |
| `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
| `-spf, --system-prompt-file FNAME` | set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications |
| `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)<br/>(env: LLAMA_ARG_CACHE_REUSE) |
| `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_METRICS) |
| `--no-slots` | disables slots monitoring endpoint (default: enabled)<br/>(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) |
| `--slots` | enable slots monitoring endpoint (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_SLOTS) |
| `--props` | enable changing global properties via POST /props (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_PROPS) |
| `--no-slots` | disables slots monitoring endpoint<br/>(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) |
| `--slot-save-path PATH` | path to save slot kv cache (default: disabled) |
| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted:<br/>https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
| `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
@ -316,7 +323,18 @@ node index.js
- The prompt is a string or an array with the first element given as a string
- The model's `tokenizer.ggml.add_bos_token` metadata is `true`
- The system prompt is empty
These input shapes and data type are allowed for `prompt`:
- Single string: `"string"`
- Single sequence of tokens: `[12, 34, 56]`
- Mixed tokens and strings: `[12, 34, "string", 56, 78]`
Multiple prompts are also supported. In this case, the completion result will be an array.
- Only strings: `["string1", "string2"]`
- Strings and sequences of tokens: `["string1", [12, 34, 56]]`
- Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]`
`temperature`: Adjust the randomness of the generated text. Default: `0.8`
@ -332,6 +350,8 @@ node index.js
`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
`n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0`
`n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
@ -354,6 +374,16 @@ node index.js
`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
`dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled.
`dry_base`: Set the DRY repetition penalty base value. Default: `1.75`
`dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2`
`dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size.
`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']`
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
@ -374,14 +404,14 @@ node index.js
`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`
`t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled.
`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1`
`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false`
`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
**Response format**
@ -519,34 +549,62 @@ Requires a reranker model (such as [bge-reranker-v2-m3](https://huggingface.co/B
Takes a prefix and a suffix and returns the predicted completion as stream.
*Options:*
*Options:*
`input_prefix`: Set the prefix of the code to infill.
- `input_prefix`: Set the prefix of the code to infill.
- `input_suffix`: Set the suffix of the code to infill.
- `input_extra`: Additional context inserted before the FIM prefix.
- `prompt`: Added after the `FIM_MID` token
`input_suffix`: Set the suffix of the code to infill.
`input_extra` is array of `{"filename": string, "text": string}` objects.
It also accepts all the options of `/completion` except `stream` and `prompt`.
The endpoint also accepts all the options of `/completion`.
- **GET** `/props`: Return current server settings.
If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used:
```txt
<FIM_REP>myproject
<FIM_SEP>{chunk 0 filename}
{chunk 0 text}
<FIM_SEP>{chunk 1 filename}
{chunk 1 text}
...
<FIM_SEP>filename
<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
```
If the tokens are missing, then the extra context is simply prefixed at the start:
```txt
[input_extra]<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
```
### **GET** `/props`: Get server global properties.
This endpoint is public (no API key check). By default, it is read-only. To make POST request to change global properties, you need to start server with `--props`
**Response format**
```json
{
"assistant_name": "",
"user_name": "",
"default_generation_settings": { ... },
"total_slots": 1,
"chat_template": ""
}
```
- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
- `chat_template` - the model's original Jinja2 prompt template
### POST `/props`: Change server global properties.
To use this endpoint with POST method, you need to start server with `--props`
*Options:*
- None yet
### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API
Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used.
@ -813,28 +871,6 @@ To know the `id` of the adapter, use GET `/lora-adapters`
## More examples
### Change system prompt on runtime
To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt`. This only needs to be used once.
`prompt`: Specify a context that you want all connecting clients to respect.
`anti_prompt`: Specify the word you want to use to instruct the model to stop. This must be sent to each client through the `/props` endpoint.
`assistant_name`: The bot's name is necessary for each customer to generate the prompt. This must be sent to each client through the `/props` endpoint.
```json
{
"system_prompt": {
"prompt": "Transcript of a never ending dialog, where the User interacts with an Assistant.\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\nUser: Recommend a nice restaurant in the area.\nAssistant: I recommend the restaurant \"The Golden Duck\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\nUser: Who is Richard Feynman?\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \"Surely You're Joking, Mr. Feynman!\" and \"What Do You Care What Other People Think?\".\nUser:",
"anti_prompt": "User:",
"assistant_name": "Assistant:"
}
}
```
**NOTE**: You can do this automatically when starting the server by simply creating a .json file with these options and using the CLI option `-spf FNAME` or `--system-prompt-file FNAME`.
### Interactive mode
Check the sample in [chat.mjs](chat.mjs).

20
examples/server/public/index-new.html
View file

@ -40,9 +40,15 @@
repeat_last_n: 0, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.0, // 1.0 = disabled
penalize_nl: false, // true only useful for infinite completion
dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
dry_base: 1.75, // 0.0 = disabled
dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
dry_penalty_last_n: -1, // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
top_k: 0, // <= 0 to use vocab size
top_p: 1.0, // 1.0 = disabled
min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4
xtc_probability: 0.0, // 0 = disabled;
xtc_threshold: 0.1, // > 0.5 disables XTC;
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
@ -831,11 +837,17 @@ return html`
<fieldset class="params">
${IntField({ label: "Top-K", title: "Limits the selection of the next token to the K most probable tokens. 1 means no randomness = greedy sampling. If set to 0, it means the entire vocabulary size is considered.", max: 100, min: 0, step: 1, name: "top_k", value: params.value.top_k })}
${IntField({ label: "Penalize Last N", title: "The last n tokens that are taken into account to penalise repetitions. A value of 0 means that this function is deactivated and -1 means that the entire size of the context is taken into account.", max: 2048, min: 0, step: 16, name: "repeat_last_n", value: params.value.repeat_last_n })}
${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Presence Penalty", title: "A penalty that is applied if certain tokens appear repeatedly in the generated text. A higher value leads to fewer repetitions.", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
${FloatField({ label: "XTC probability", title: "Sets the chance for token removal (checked once on sampler start)", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
${FloatField({ label: "XTC threshold", title: "Sets a minimum probability threshold for tokens to be removed", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
${FloatField({ label: "DRY Penalty Multiplier", title: "Set the DRY repetition penalty multiplier. Default is 0.0, which disables DRY.", max: 5.0, min: 0.0, name: "dry_multiplier", step: 0.01, value: params.value.dry_multiplier })}
${FloatField({ label: "DRY Base", title: "Set the DRY repetition penalty base value. Default is 1.75", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })}
${IntField({ label: "DRY Allowed Length", title: "Tokens that extend repetition beyond this receive exponentially increasing penalty. Default is 2", max: 10, min: 1, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })}
${IntField({ label: "DRY Penalty Last N", title: "How many tokens to scan for repetitions. Default is -1, where 0 is disabled and -1 is context size", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })}
${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
</fieldset>
@ -1132,12 +1144,16 @@ document.addEventListener('DOMContentLoaded', (event) => {
const snapSettings = {
temperature: { snapValue: 1.0, snapRangeMultiplier: 6 },
min_p: { snapValue: 0.05, snapRangeMultiplier: 2 },
xtc_probability: { snapValue: 0.0, snapRangeMultiplier: 4 },
xtc_threshold: { snapValue: 0.5, snapRangeMultiplier: 4 },
top_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 },
typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
repeat_penalty: { snapValue: 1.0, snapRangeMultiplier: 4 },
presence_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 },
frequency_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 },
dry_multiplier: { snapValue: 0.0, snapRangeMultiplier: 4 },
dry_base: { snapValue: 1.75, snapRangeMultiplier: 4 },
};
// add an event listener for each slider
Object.keys(snapSettings).forEach(sliderName => {

12
examples/server/public/index.html
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@ -304,9 +304,15 @@
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.18, // 1.0 = disabled
penalize_nl: false,
dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
dry_base: 1.75, // 0.0 = disabled
dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
dry_penalty_last_n: -1, // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
xtc_probability: 0.0, // 0 = disabled;
xtc_threshold: 0.1, // > 0.5 disables XTC;
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
@ -1013,6 +1019,12 @@
${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
${FloatField({ label: "DRY Penalty Multiplier", max: 5.0, min: 0.0, name: "dry_multiplier", step: 0.01, value: params.value.dry_multiplier })}
${FloatField({ label: "DRY Base", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })}
${IntField({ label: "DRY Allowed Length", max: 10, min: 2, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })}
${IntField({ label: "DRY Penalty Last N", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })}
${FloatField({ label: "XTC probability", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
${FloatField({ label: "XTC threshold", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
</fieldset>
<hr />
<fieldset class="three">

2
examples/server/public/index.js
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2
examples/server/public/json-schema-to-grammar.mjs
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@ -529,7 +529,7 @@ export class SchemaConverter {
return joinSeq();
};
return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space")
return this._addRule(name, "\"\\\"\" (" + toRule(transform()) + ") \"\\\"\" space")
}
_notStrings(strings) {

0
examples/server/public/style.css Executable file → Normal file
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1073
examples/server/server.cpp
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4
examples/server/tests/features/ctx_shift.feature
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@ -13,6 +13,10 @@ Feature: llama.cpp server
And 32 as batch size
And 2 slots
# the prompt is 301 tokens
# the slot context is 256/2 = 128 tokens
# the prompt is truncated to keep the last 109 tokens
# 64 tokens are generated thanks to shifting the context when it gets full
Scenario: Inference with context shift
And 64 server max tokens to predict
Then the server is starting

36
examples/server/tests/features/infill.feature Normal file
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@ -0,0 +1,36 @@
@llama.cpp
@infill
Feature: llama.cpp server
# The current model is made by adding FIM tokens to the existing stories260K
# We may want to use a better model in the future, maybe something like SmolLM 360M
Background: Server startup
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K-infill.gguf from HF repo ggml-org/models
And a model file test-model-infill.gguf
And a model alias tinyllama-infill
And 42 as server seed
And 1024 as batch size
And 1024 as ubatch size
And 2048 KV cache size
And 64 max tokens to predict
And 0.0 temperature
Then the server is starting
Then the server is healthy
Scenario: Infill without input_extra
Given a prompt "Complete this"
And an infill input extra none none
And an infill input prefix "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_"
And an infill input suffix "}\n"
And an infill request with no api error
Then 64 tokens are predicted matching One|day|she|saw|big|scary|bird
Scenario: Infill with input_extra
Given a prompt "Complete this"
And an infill input extra "llama.h" "LLAMA_API int32_t llama_n_threads();\n"
And an infill input prefix "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_"
And an infill input suffix "}\n"
And an infill request with no api error
Then 64 tokens are predicted matching cuts|Jimmy|mom|came|into|the|room"

22
examples/server/tests/features/security.feature
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@ -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
@ -16,11 +16,11 @@ Feature: Security
And a completion request with <api_error> api error
Examples: Prompts
| api_key | api_error |
| llama.cpp | no |
| llama.cpp | no |
| hackeme | raised |
| | raised |
| api_key | api_error |
| THIS_IS_THE_KEY | no |
| THIS_IS_THE_KEY | no |
| hackeme | raised |
| | raised |
Scenario Outline: OAI Compatibility
Given a system prompt test
@ -32,10 +32,10 @@ Feature: Security
Given an OAI compatible chat completions request with <api_error> api error
Examples: Prompts
| api_key | api_error |
| llama.cpp | no |
| llama.cpp | no |
| hackme | raised |
| api_key | api_error |
| THIS_IS_THE_KEY | no |
| THIS_IS_THE_KEY | no |
| hackme | raised |
Scenario Outline: OAI Compatibility (invalid response formats)
Given a system prompt test
@ -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>

50
examples/server/tests/features/steps/steps.py
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@ -80,6 +80,11 @@ def step_server_config(context, server_fqdn: str, server_port: str):
context.lora_file = None
context.disable_ctx_shift = False
# infill
context.infill_input_extra = None
context.infill_input_suffix = ''
context.infill_input_prefix = ''
context.tasks_result = []
context.concurrent_tasks = []
context.prompts = []
@ -291,6 +296,28 @@ async def step_request_completion(context, api_error: Literal['raised'] | str):
assert completion == api_error_code, f"completion must be an {api_error_code} status code: {completion}"
@step('an infill request with {api_error} api error')
@async_run_until_complete
async def step_request_completion(context, api_error: Literal['raised'] | str):
if api_error != 'no':
raise ValueError(f'api_error={api_error} is not yet implemented')
payload = {
"prompt": context.prompts[0],
"input_suffix": context.infill_input_suffix,
"input_prefix": context.infill_input_prefix,
"n_predict": context.n_predict,
"seed": context.seed,
"temperature": context.temperature,
}
if context.infill_input_extra is not None:
payload['input_extra'] = context.infill_input_extra
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{context.base_url}/infill',
json=payload) as response:
assert response.status == 200
context.tasks_result = [await response.json()]
@step('{predicted_n:d} tokens are predicted matching {re_content}')
def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
context.completion = context.tasks_result.pop()
@ -539,6 +566,25 @@ def step_a_prompt_prompt(context, prompt):
context.n_prompts = len(context.prompts)
# TODO: allow this to be repeated
@step('an infill input extra {filename} {text}')
def step_infill_input_extra(context, filename, text):
if filename == 'none':
context.infill_input_extra = None
else:
context.infill_input_extra = [{'filename': filename, 'text': text}]
@step('an infill input suffix {text}')
def step_infill_input_suffix(context, text):
context.infill_input_suffix = text
@step('an infill input prefix {text}')
def step_infill_input_prefix(context, text):
context.infill_input_prefix = text
@step('{num_prompts:d} prompts {prompt} with seed {seed:d}')
def step_many_prompts(context, num_prompts, prompt, seed):
if context.seed is None:
@ -1299,7 +1345,8 @@ 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 +1434,7 @@ 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,
]

285
examples/server/utils.hpp
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@ -24,6 +24,22 @@
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
using json = nlohmann::ordered_json;
using llama_tokens = std::vector<llama_token>;
#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
enum error_type {
@ -52,12 +68,238 @@ static T json_value(const json & body, const std::string & key, const T & defaul
}
//
// chat template utils
// tokenizer and input processing utils
//
static bool json_is_array_of_numbers(const json & data) {
if (data.is_array()) {
for (const auto & e : data) {
if (!e.is_number_integer()) {
return false;
}
}
return true;
}
return false;
}
// is array having BOTH numbers & strings?
static bool json_is_array_of_mixed_numbers_strings(const json & data) {
bool seen_string = false;
bool seen_number = false;
if (data.is_array()) {
for (const auto & e : data) {
seen_string |= e.is_string();
seen_number |= e.is_number_integer();
if (seen_number && seen_string) {
return true;
}
}
}
return false;
}
/**
* this handles 2 cases:
* - only string, example: "string"
* - mixed string and tokens, example: [12, 34, "string", 56, 78]
*/
static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
// or the first element of the json_prompt array is a string.
llama_tokens prompt_tokens;
if (json_prompt.is_array()) {
bool first = true;
for (const auto & p : json_prompt) {
if (p.is_string()) {
auto s = p.template get<std::string>();
llama_tokens p;
if (first) {
p = common_tokenize(ctx, s, add_special, parse_special);
first = false;
} else {
p = common_tokenize(ctx, s, false, parse_special);
}
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
} else {
if (first) {
first = false;
}
prompt_tokens.push_back(p.template get<llama_token>());
}
}
} else {
auto s = json_prompt.template get<std::string>();
prompt_tokens = common_tokenize(ctx, s, add_special, parse_special);
}
return prompt_tokens;
}
/**
* break the input "prompt" object into multiple prompt if needed, then tokenize them
* this supports these cases:
* - "prompt": "string"
* - "prompt": [12, 34, 56]
* - "prompt": [12, 34, "string", 56, 78]
* and multiple prompts (multi-tasks):
* - "prompt": ["string1", "string2"]
* - "prompt": ["string1", [12, 34, 56]]
* - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]]
*/
static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {
std::vector<llama_tokens> result;
if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) {
// string or mixed
result.push_back(tokenize_mixed(ctx, json_prompt, add_special, parse_special));
} else if (json_is_array_of_numbers(json_prompt)) {
// array of tokens
result.push_back(json_prompt.get<llama_tokens>());
} else if (json_prompt.is_array()) {
// array of prompts
result.reserve(json_prompt.size());
for (const auto & p : json_prompt) {
if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) {
result.push_back(tokenize_mixed(ctx, p, add_special, parse_special));
} else if (json_is_array_of_numbers(p)) {
// array of tokens
result.push_back(p.get<llama_tokens>());
} else {
throw std::runtime_error("element of \"prompt\" must be a string, an list of tokens, or a list of mixed strings & tokens");
}
}
} else {
throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts");
}
return result;
}
//
// template utils
//
// format rerank task: [BOS]query[EOS][SEP]doc[EOS]
static llama_tokens format_rerank(const struct llama_model * model, const llama_tokens & query, const llama_tokens & doc) {
llama_tokens result;
result.reserve(doc.size() + query.size() + 4);
result.push_back(llama_token_bos(model));
result.insert(result.end(), query.begin(), query.end());
result.push_back(llama_token_eos(model));
result.push_back(llama_token_sep(model));
result.insert(result.end(), doc.begin(), doc.end());
result.push_back(llama_token_eos(model));
return result;
}
// format infill task
static llama_tokens format_infill(
const llama_context * ctx,
const json & input_prefix,
const json & input_suffix,
const json & input_extra,
const int n_batch,
const int n_predict,
const int n_ctx,
const bool spm_infill,
const llama_tokens & tokens_prompt
) {
// TODO: optimize this block by reducing memory allocations and movement
// use FIM repo-level pattern:
// ref: https://arxiv.org/pdf/2409.12186
//
// [FIM_REP]myproject
// [FIM_SEP]filename0
// extra chunk 0
// [FIM_SEP]filename1
// extra chunk 1
// ...
// [FIM_SEP]filename
// [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt
//
llama_tokens extra_tokens;
extra_tokens.reserve(n_ctx);
auto model = llama_get_model(ctx);
auto tokens_prefix = tokenize_mixed(ctx, input_prefix, false, false);
auto tokens_suffix = tokenize_mixed(ctx, input_suffix, false, false);
if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) {
// TODO: make project name an input
static const auto k_fim_repo = common_tokenize(ctx, "myproject\n", false, false);
extra_tokens.push_back(llama_token_fim_rep(model));
extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end());
}
for (const auto & chunk : input_extra) {
// { "text": string, "filename": string }
const std::string text = json_value(chunk, "text", std::string());
const std::string filename = json_value(chunk, "filename", std::string("tmp"));
if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
const auto k_fim_file = common_tokenize(ctx, filename + "\n", false, false);
extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model));
extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
} else {
// chunk separator in binary form to avoid confusing the AI
static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00};
static const auto k_chunk_prefix_tokens = common_tokenize(ctx, k_chunk_prefix_str, false, false);
extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end());
}
const auto chunk_tokens = common_tokenize(ctx, text, false, false);
extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end());
}
if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
// TODO: current filename
static const auto k_fim_file = common_tokenize(ctx, "filename\n", false, false);
extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model));
extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
}
// for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?)
const int n_suffix_take = std::min<int>(tokens_suffix.size(), (n_batch/4));
const int n_prefix_take = std::min<int>(tokens_prefix.size(), 3*(n_batch/4) - 3);
// fill the rest of the context with extra chunks
const int n_extra_take = std::min<int>(std::max<int>(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size());
tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take);
tokens_suffix.resize(n_suffix_take);
tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model));
tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end());
tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model));
auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix;
auto embd_end = spm_infill ? tokens_prefix : tokens_suffix;
if (llama_add_bos_token(model)) {
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
}
SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size());
// put the extra context before the FIM prefix
embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end());
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
embd_inp.push_back(llama_token_fim_mid(model));
return embd_inp;
}
// 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 +326,25 @@ 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)
//
@ -182,14 +437,14 @@ static std::string gen_chatcmplid() {
// other common utils
//
static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
static size_t longest_common_prefix(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
return i;
}
static size_t common_part(const std::string & a, const std::string & b) {
static size_t longest_common_prefix(const std::string & a, const std::string & b) {
size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
@ -216,24 +471,12 @@ static size_t find_partial_stop_string(const std::string &stop, const std::strin
return std::string::npos;
}
static bool json_is_array_of_numbers(const json & data) {
if (data.is_array()) {
for (const auto & e : data) {
if (!e.is_number()) {
return false;
}
}
return true;
}
return false;
}
// TODO: reuse llama_detokenize
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 +484,7 @@ 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)
@ -347,9 +590,9 @@ static json oaicompat_completion_params_parse(
// Handle "logprobs" field
// TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future
if (body.contains("logprobs")) {
if (json_value(body, "logprobs", false)) {
llama_params["n_probs"] = json_value(body, "top_logprobs", 20);
} else if (body.contains("top_logprobs")) {
} else if (body.contains("top_logprobs") && !body.at("top_logprobs").is_null()) {
throw std::runtime_error("top_logprobs requires logprobs to be set to true");
}

2
examples/simple/CMakeLists.txt
View file

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

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)) {
return 1;
{
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;
}
} 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];
}
}
}
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);
// 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());
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());
}
// create a llama_batch with size 512
// we use this object to submit token data for decoding
// prepare a batch for the prompt
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__);
return 1;
}
llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
// 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_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;
}

100
examples/speculative/speculative.cpp
View file

@ -26,20 +26,25 @@ struct seq_draft {
std::vector<llama_token> tokens;
std::vector<std::vector<llama_token_data>> dists;
struct gpt_sampler * smpl = nullptr;
struct common_sampler * smpl = nullptr;
};
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
// needed to get candidate probs even for temp <= 0.0
params.sparams.n_probs = 128;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
return 1;
}
gpt_init();
if (params.n_predict < -1) {
LOG_ERR("%s: --n-predict must be >= -1\n", __func__);
return 1;
}
common_init();
if (params.model_draft.empty()) {
LOG_ERR("%s: --model-draft is required\n", __func__);
@ -66,7 +71,7 @@ int main(int argc, char ** argv) {
llama_context * ctx_dft = NULL;
// load the target model
llama_init_result llama_init_tgt = llama_init_from_gpt_params(params);
common_init_result llama_init_tgt = common_init_from_params(params);
model_tgt = llama_init_tgt.model;
ctx_tgt = llama_init_tgt.context;
@ -78,7 +83,7 @@ int main(int argc, char ** argv) {
}
params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads;
llama_init_result llama_init_dft = llama_init_from_gpt_params(params);
common_init_result llama_init_dft = common_init_from_params(params);
model_dft = llama_init_dft.model;
ctx_dft = llama_init_dft.context;
@ -124,8 +129,8 @@ int main(int argc, char ** argv) {
if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
LOG_ERR("%s: draft model vocab must match target model to use speculation but ", __func__);
LOG_ERR("token %d content differs - target '%s', draft '%s'\n", i,
llama_token_to_piece(ctx_tgt, i).c_str(),
llama_token_to_piece(ctx_dft, i).c_str());
common_token_to_piece(ctx_tgt, i).c_str(),
common_token_to_piece(ctx_dft, i).c_str());
return 1;
}
}
@ -134,7 +139,7 @@ int main(int argc, char ** argv) {
// Tokenize the prompt
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx_tgt, params.prompt, true, true);
inp = common_tokenize(ctx_tgt, params.prompt, true, true);
const int max_context_size = llama_n_ctx(ctx_tgt);
const int max_tokens_list_size = max_context_size - 4;
@ -147,7 +152,7 @@ int main(int argc, char ** argv) {
LOG("\n\n");
for (auto id : inp) {
LOG("%s", llama_token_to_piece(ctx_tgt, id).c_str());
LOG("%s", common_token_to_piece(ctx_tgt, id).c_str());
}
const int n_input = inp.size();
@ -155,9 +160,9 @@ int main(int argc, char ** argv) {
const auto t_enc_start = ggml_time_us();
// eval the prompt with both models
llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input, 0, 0));
llama_decode(ctx_tgt, llama_batch_get_one( inp.data(), n_input - 1));
llama_decode(ctx_tgt, llama_batch_get_one(&inp.back(), 1));
llama_decode(ctx_dft, llama_batch_get_one( inp.data(), n_input));
const auto t_enc_end = ggml_time_us();
@ -178,20 +183,18 @@ int main(int argc, char ** argv) {
bool has_eos = false;
// target model sampling context (reuse the llama_context's sampling instance)
struct gpt_sampler * smpl = gpt_sampler_init(model_tgt, params.sparams);
struct llama_sampler * softmax = llama_sampler_init_softmax();
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams);
// draft sequence data
std::vector<seq_draft> drafts(n_seq_dft);
for (int s = 0; s < n_seq_dft; ++s) {
// allocate gpt_sampler for each draft sequence
drafts[s].smpl = gpt_sampler_init(model_dft, params.sparams);
// allocate llama_sampler for each draft sequence
drafts[s].smpl = common_sampler_init(model_dft, params.sparams);
}
llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1);
llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, n_seq_dft);
llama_batch batch_dft = llama_batch_init(llama_n_batch(ctx_dft), 0, 1);
llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, n_seq_dft);
const auto t_dec_start = ggml_time_us();
@ -229,9 +232,9 @@ int main(int argc, char ** argv) {
bool accept = false;
if (params.sparams.temp > 0) {
// stochastic verification
gpt_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
auto & dist_tgt = *gpt_sampler_get_candidates(smpl);
auto & dist_tgt = *common_sampler_get_candidates(smpl);
float p_tgt = 0.0f;
float p_dft = 0.0f;
@ -277,13 +280,13 @@ int main(int argc, char ** argv) {
s_keep = s;
accept = true;
token_id = drafts[s].tokens[i_dft];
token_str = llama_token_to_piece(ctx_tgt, token_id);
gpt_sampler_accept(smpl, token_id, true);
token_str = common_token_to_piece(ctx_tgt, token_id);
common_sampler_accept(smpl, token_id, true);
LOG_DBG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
break;
} else {
LOG_DBG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
LOG_DBG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], common_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
drafts[s].active = false;
// calculate residual probability
@ -349,19 +352,19 @@ int main(int argc, char ** argv) {
const int idx = dist(rng);
token_id = dist_tgt.data[idx].id;
gpt_sampler_accept(smpl, token_id, true);
token_str = llama_token_to_piece(ctx_tgt, token_id);
common_sampler_accept(smpl, token_id, true);
token_str = common_token_to_piece(ctx_tgt, token_id);
}
} else {
// greedy verification
// sample from the target model
LOG_DBG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
token_id = gpt_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);
token_id = common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);
gpt_sampler_accept(smpl, token_id, true);
common_sampler_accept(smpl, token_id, true);
token_str = llama_token_to_piece(ctx_tgt, token_id);
token_str = common_token_to_piece(ctx_tgt, token_id);
for (int s = 0; s < n_seq_dft; ++s) {
if (!drafts[s].active) {
@ -431,8 +434,8 @@ int main(int argc, char ** argv) {
drafts[0].dists.push_back(std::vector<llama_token_data>());
drafts[0].i_batch_tgt.push_back(0);
llama_batch_clear(batch_dft);
llama_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true);
common_batch_clear(batch_dft);
common_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true);
llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
// LOG_DBG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
@ -441,14 +444,14 @@ int main(int argc, char ** argv) {
++n_past_dft;
}
if (n_predict > params.n_predict || has_eos) {
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
break;
}
if (drafts[0].smpl) {
gpt_sampler_free(drafts[0].smpl);
common_sampler_free(drafts[0].smpl);
}
drafts[0].smpl = gpt_sampler_clone(smpl);
drafts[0].smpl = common_sampler_clone(smpl);
int n_seq_cur = 1;
int n_past_cur = n_past_dft;
@ -461,8 +464,8 @@ int main(int argc, char ** argv) {
drafts[0].drafting = true;
drafts[0].i_batch_dft = 0;
llama_batch_clear(batch_tgt);
llama_batch_add (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true);
common_batch_clear(batch_tgt);
common_batch_add (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true);
// sample n_draft tokens from the draft model using tree-based sampling
for (int i = 0; i < n_draft; ++i) {
@ -477,13 +480,13 @@ int main(int argc, char ** argv) {
continue;
}
gpt_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
const auto * cur_p = gpt_sampler_get_candidates(drafts[s].smpl);
const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl);
for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p->size); ++k) {
LOG_DBG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",
k, s, i, cur_p->data[k].id, cur_p->data[k].p, llama_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
k, s, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
}
std::vector<int> sa(1, s);
@ -518,9 +521,9 @@ int main(int argc, char ** argv) {
drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
if (drafts[n_seq_cur].smpl) {
gpt_sampler_free(drafts[n_seq_cur].smpl);
common_sampler_free(drafts[n_seq_cur].smpl);
}
drafts[n_seq_cur].smpl = gpt_sampler_clone(drafts[s].smpl);
drafts[n_seq_cur].smpl = common_sampler_clone(drafts[s].smpl);
sa.push_back(n_seq_cur);
@ -536,7 +539,7 @@ int main(int argc, char ** argv) {
const int s = sa[is];
gpt_sampler_accept(drafts[s].smpl, id, true);
common_sampler_accept(drafts[s].smpl, id, true);
drafts[s].tokens.push_back(id);
// save cur_p.data into drafts[s].dists
@ -545,12 +548,12 @@ int main(int argc, char ** argv) {
// add unique drafted tokens to the target batch
drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
llama_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true);
common_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true);
// add the token to the batch for batched decoding with the draft model
drafts[s].i_batch_dft = batch_dft.n_tokens;
llama_batch_add(batch_dft, id, n_past_cur, { s }, true);
common_batch_add(batch_dft, id, n_past_cur, { s }, true);
if (batch_tgt.n_tokens > n_draft) {
drafts[s].drafting = false;
@ -617,14 +620,13 @@ int main(int argc, char ** argv) {
LOG_INF("\n");
LOG_INF("target:\n\n");
gpt_perf_print(ctx_tgt, smpl);
common_perf_print(ctx_tgt, smpl);
gpt_sampler_free(smpl);
common_sampler_free(smpl);
for (int s = 0; s < n_seq_dft; ++s) {
gpt_sampler_free(drafts[s].smpl);
common_sampler_free(drafts[s].smpl);
}
llama_sampler_free(softmax);
llama_batch_free(batch_dft);
llama_free(ctx_tgt);

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 {

20
flake.lock generated
View file

@ -5,11 +5,11 @@
"nixpkgs-lib": "nixpkgs-lib"
},
"locked": {
"lastModified": 1726153070,
"narHash": "sha256-HO4zgY0ekfwO5bX0QH/3kJ/h4KvUDFZg8YpkNwIbg1U=",
"lastModified": 1727826117,
"narHash": "sha256-K5ZLCyfO/Zj9mPFldf3iwS6oZStJcU4tSpiXTMYaaL0=",
"owner": "hercules-ci",
"repo": "flake-parts",
"rev": "bcef6817a8b2aa20a5a6dbb19b43e63c5bf8619a",
"rev": "3d04084d54bedc3d6b8b736c70ef449225c361b1",
"type": "github"
},
"original": {
@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1727348695,
"narHash": "sha256-J+PeFKSDV+pHL7ukkfpVzCOO7mBSrrpJ3svwBFABbhI=",
"lastModified": 1729256560,
"narHash": "sha256-/uilDXvCIEs3C9l73JTACm4quuHUsIHcns1c+cHUJwA=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "1925c603f17fc89f4c8f6bf6f631a802ad85d784",
"rev": "4c2fcb090b1f3e5b47eaa7bd33913b574a11e0a0",
"type": "github"
},
"original": {
@ -36,14 +36,14 @@
},
"nixpkgs-lib": {
"locked": {
"lastModified": 1725233747,
"narHash": "sha256-Ss8QWLXdr2JCBPcYChJhz4xJm+h/xjl4G0c0XlP6a74=",
"lastModified": 1727825735,
"narHash": "sha256-0xHYkMkeLVQAMa7gvkddbPqpxph+hDzdu1XdGPJR+Os=",
"type": "tarball",
"url": "https://github.com/NixOS/nixpkgs/archive/356624c12086a18f2ea2825fed34523d60ccc4e3.tar.gz"
"url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz"
},
"original": {
"type": "tarball",
"url": "https://github.com/NixOS/nixpkgs/archive/356624c12086a18f2ea2825fed34523d60ccc4e3.tar.gz"
"url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz"
}
},
"root": {

4
ggml/CMakeLists.txt
View file

@ -99,6 +99,9 @@ option(GGML_AVX512 "ggml: enable AVX512" OFF)
option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI" OFF)
option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI" OFF)
option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16" OFF)
option(GGML_AMX_TILE "ggml: enable AMX-TILE" OFF)
option(GGML_AMX_INT8 "ggml: enable AMX-INT8" OFF)
option(GGML_AMX_BF16 "ggml: enable AMX-BF16" OFF)
option(GGML_FMA "ggml: enable FMA" ${INS_ENB})
if (NOT MSVC)
option(GGML_F16C "ggml: enable F16C" ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512
@ -158,6 +161,7 @@ set (GGML_METAL_MACOSX_VERSION_MIN "" CACHE STRING
set (GGML_METAL_STD "" CACHE STRING "ggml: metal standard version (-std flag)")
option(GGML_OPENMP "ggml: use OpenMP" ON)
option(GGML_RPC "ggml: use RPC" OFF)
option(GGML_AMX "ggml: use AMX" OFF)
option(GGML_SYCL "ggml: use SYCL" OFF)
option(GGML_SYCL_F16 "ggml: use 16 bit floats for sycl calculations" OFF)
set (GGML_SYCL_TARGET "INTEL" CACHE STRING

25
ggml/include/ggml-amx.h Normal file
View file

@ -0,0 +1,25 @@
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
// buffer_type API
GGML_API ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void);
GGML_API bool ggml_backend_is_amx(ggml_backend_t backend);
// backend API
GGML_API ggml_backend_t ggml_backend_amx_init(void);
GGML_API void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads);
GGML_API ggml_backend_reg_t ggml_backend_amx_reg(void);
#ifdef __cplusplus
}
#endif

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
}

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

@ -34,6 +34,8 @@ extern "C" {
*/
#define GGML_CANN_MAX_DEVICES 16
GGML_API ggml_backend_reg_t ggml_backend_cann_reg(void);
/**
* @brief Initializes the CANN backend for a specified device.
*

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
}

11
ggml/include/ggml-sycl.h
View file

@ -19,6 +19,8 @@ extern "C" {
// backend API
GGML_API ggml_backend_t ggml_backend_sycl_init(int device);
GGML_API bool ggml_backend_is_sycl(ggml_backend_t backend);
// devide buffer
GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device);
@ -29,14 +31,19 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const fl
GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
GGML_API void ggml_backend_sycl_print_sycl_devices(void);
GGML_API void ggml_sycl_get_gpu_list(int *id_list, int max_len);
GGML_API void ggml_sycl_get_device_description(int device, char *description, size_t description_size);
GGML_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len);
GGML_API void ggml_backend_sycl_get_device_description(int device,
char *description,
size_t description_size);
GGML_API int ggml_backend_sycl_get_device_count();
GGML_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
// SYCL doesn't support registering host memory, keep here for reference
// GGML_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size);
// GGML_API void ggml_backend_sycl_unregister_host_buffer(void * buffer);
GGML_API ggml_backend_reg_t ggml_backend_sycl_reg(void);
#ifdef __cplusplus
}
#endif

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

@ -24,6 +24,8 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);
GGML_API ggml_backend_reg_t ggml_backend_vk_reg(void);
#ifdef __cplusplus
}
#endif

7
ggml/include/ggml.h
View file

@ -2488,6 +2488,7 @@ extern "C" {
GGML_API int ggml_cpu_has_avx512_vbmi(void);
GGML_API int ggml_cpu_has_avx512_vnni(void);
GGML_API int ggml_cpu_has_avx512_bf16(void);
GGML_API int ggml_cpu_has_amx_int8 (void);
GGML_API int ggml_cpu_has_fma (void);
GGML_API int ggml_cpu_has_neon (void);
GGML_API int ggml_cpu_has_sve (void);
@ -2535,7 +2536,7 @@ 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
@ -2551,9 +2552,9 @@ 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
}

64
ggml/src/CMakeLists.txt
View file

@ -163,8 +163,8 @@ if (GGML_OPENMP)
list(APPEND GGML_EXTRA_LIBS_PRIVATE OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
if (GGML_MUSA)
list(APPEND GGML_EXTRA_INCLUDES "/usr/lib/llvm-10/include/openmp")
list(APPEND GGML_EXTRA_LIBS_PRIVATE "/usr/lib/llvm-10/lib/libomp.so")
list(APPEND GGML_EXTRA_INCLUDES "/usr/lib/llvm-14/lib/clang/14.0.0/include")
list(APPEND GGML_EXTRA_LIBS_PRIVATE "/usr/lib/llvm-14/lib/libomp.so")
endif()
else()
message(WARNING "OpenMP not found")
@ -190,22 +190,24 @@ if (GGML_BLAS)
# see https://gitlab.kitware.com/cmake/cmake/-/issues/20268
find_package(PkgConfig REQUIRED)
if (${GGML_BLAS_VENDOR} MATCHES "Generic")
pkg_check_modules(DepBLAS REQUIRED blas)
pkg_check_modules(DepBLAS blas)
elseif (${GGML_BLAS_VENDOR} MATCHES "OpenBLAS")
# As of openblas v0.3.22, the 64-bit is named openblas64.pc
pkg_check_modules(DepBLAS openblas64)
if (NOT DepBLAS_FOUND)
pkg_check_modules(DepBLAS REQUIRED openblas)
pkg_check_modules(DepBLAS openblas)
endif()
elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME")
pkg_check_modules(DepBLAS REQUIRED blis)
add_compile_definitions(GGML_BLAS_USE_BLIS)
pkg_check_modules(DepBLAS blis)
elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS")
pkg_check_modules(DepBLAS REQUIRED blas-atlas)
pkg_check_modules(DepBLAS blas-atlas)
elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS")
pkg_check_modules(DepBLAS REQUIRED flexiblas_api)
pkg_check_modules(DepBLAS flexiblas_api)
elseif (${GGML_BLAS_VENDOR} MATCHES "Intel")
add_compile_definitions(GGML_BLAS_USE_MKL)
# all Intel* libraries share the same include path
pkg_check_modules(DepBLAS REQUIRED mkl-sdl)
pkg_check_modules(DepBLAS mkl-sdl)
elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC")
# this doesn't provide pkg-config
# suggest to assign BLAS_INCLUDE_DIRS on your own
@ -265,6 +267,26 @@ if (GGML_LLAMAFILE)
set(GGML_SOURCES_LLAMAFILE llamafile/sgemm.cpp)
endif()
if (GGML_AMX)
if (CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 11.0)
else()
set(GGML_AMX OFF)
message(WARNING "AMX requires gcc version > 11.0. Turning off GGML_AMX.")
endif()
if (GGML_AMX)
message(STATUS "Using AMX")
list(APPEND GGML_CDEF_PUBLIC GGML_USE_AMX)
file(GLOB GGML_HEADERS_AMX "ggml-amx/*.h")
list(APPEND GGML_HEADERS_AMX "../include/ggml-amx.h")
file(GLOB GGML_SOURCES_AMX "ggml-amx/*.cpp")
list(APPEND GGML_SOURCES_AMX "ggml-amx.cpp")
endif()
endif()
if (GGML_CUDA)
cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES
@ -1178,6 +1200,18 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512BF16__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512BF16__>)
endif()
if (GGML_AMX_TILE)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AMX_TILE__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AMX_TILE__>)
endif()
if (GGML_AMX_INT8)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AMX_INT8__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AMX_INT8__>)
endif()
if (GGML_AMX_BF16)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AMX_BF16__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AMX_BF16__>)
endif()
elseif (GGML_AVX2)
list(APPEND ARCH_FLAGS /arch:AVX2)
elseif (GGML_AVX)
@ -1213,6 +1247,15 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW
if (GGML_AVX512_BF16)
list(APPEND ARCH_FLAGS -mavx512bf16)
endif()
if (GGML_AMX_TILE)
list(APPEND ARCH_FLAGS -mamx-tile)
endif()
if (GGML_AMX_INT8)
list(APPEND ARCH_FLAGS -mamx-int8)
endif()
if (GGML_AMX_BF16)
list(APPEND ARCH_FLAGS -mamx-bf16)
endif()
endif()
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
message(STATUS "PowerPC detected")
@ -1338,6 +1381,7 @@ add_library(ggml
${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM}
${GGML_SOURCES_BLAS} ${GGML_HEADERS_BLAS}
${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE}
${GGML_SOURCES_AMX} ${GGML_HEADERS_AMX}
${GGML_SOURCES_CANN} ${GGML_HEADERS_CANN}
ggml-aarch64.c ggml-aarch64.h
)
@ -1361,6 +1405,10 @@ if (MATH_LIBRARY)
endif()
endif()
if (CMAKE_SYSTEM_NAME MATCHES "Android")
list(APPEND GGML_EXTRA_LIBS_PRIVATE dl) # Must be linked explicitly
endif()
list(REMOVE_DUPLICATES GGML_EXTRA_LIBS_PRIVATE)
list(REMOVE_DUPLICATES GGML_EXTRA_LIBS_PUBLIC)
target_link_libraries(ggml PRIVATE ${GGML_EXTRA_LIBS_PRIVATE} PUBLIC ${GGML_EXTRA_LIBS_PUBLIC})

36
ggml/src/ggml-alloc.c
View file

@ -14,7 +14,7 @@
//#define GGML_ALLOCATOR_DEBUG
//#define AT_PRINTF(...) fprintf(stderr, __VA_ARGS__)
//#define AT_PRINTF(...) GGML_LOG_DEBUG(__VA_ARGS__)
#define AT_PRINTF(...)
@ -89,7 +89,7 @@ void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tenso
size = GGML_PAD(size, talloc->alignment);
if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) {
fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
__func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset);
GGML_ABORT("not enough space in the buffer");
}
@ -172,7 +172,7 @@ static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t siz
best_fit_block = alloc->n_free_blocks - 1;
} else {
// this should never happen
fprintf(stderr, "%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
__func__, size, max_avail);
GGML_ABORT("not enough space in the buffer");
}
@ -209,16 +209,16 @@ static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t siz
}
}
}
fprintf(stderr, "max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
GGML_LOG_DEBUG("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i].tensor) {
fprintf(stderr, "%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
GGML_LOG_DEBUG("%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
alloc->allocated_tensors[i].offset,
alloc->allocated_tensors[i].offset + ggml_nbytes(alloc->allocated_tensors[i].tensor),
ggml_nbytes(alloc->allocated_tensors[i].tensor) / 1024.0 / 1024.0);
}
}
fprintf(stderr, "\n");
GGML_LOG_DEBUG("\n");
}
#endif
@ -348,7 +348,6 @@ struct tensor_alloc {
};
struct leaf_alloc {
int buffer_id;
struct tensor_alloc leaf;
};
@ -740,7 +739,6 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
if (leaf->view_src || leaf->data) {
galloc->leaf_allocs[i].leaf.buffer_id = -1;
galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
@ -768,13 +766,13 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
// even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
if (new_size > cur_size || galloc->buffers[i] == NULL) {
#ifndef NDEBUG
fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif
ggml_backend_buffer_free(galloc->buffers[i]);
galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size);
if (galloc->buffers[i] == NULL) {
fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
return false;
}
ggml_backend_buffer_set_usage(galloc->buffers[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
@ -825,14 +823,14 @@ static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_t
static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph * graph) {
if (galloc->n_nodes != graph->n_nodes) {
#ifndef NDEBUG
fprintf(stderr, "%s: graph has different number of nodes\n", __func__);
GGML_LOG_DEBUG("%s: graph has different number of nodes\n", __func__);
#endif
return true;
}
if (galloc->n_leafs != graph->n_leafs) {
#ifndef NDEBUG
fprintf(stderr, "%s: graph has different number of leafs\n", __func__);
GGML_LOG_DEBUG("%s: graph has different number of leafs\n", __func__);
#endif
return true;
}
@ -843,7 +841,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph
if (!ggml_gallocr_node_needs_realloc(galloc, node, &node_alloc->dst)) {
#ifndef NDEBUG
fprintf(stderr, "%s: node %s is not valid\n", __func__, node->name);
GGML_LOG_DEBUG("%s: node %s is not valid\n", __func__, node->name);
#endif
return true;
}
@ -855,7 +853,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph
}
if (!ggml_gallocr_node_needs_realloc(galloc, src, &node_alloc->src[j])) {
#ifndef NDEBUG
fprintf(stderr, "%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name);
GGML_LOG_DEBUG("%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name);
#endif
return true;
}
@ -869,14 +867,14 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
if (ggml_gallocr_needs_realloc(galloc, graph)) {
if (galloc->n_buffers == 1) {
#ifndef NDEBUG
fprintf(stderr, "%s: reallocating buffers automatically\n", __func__);
GGML_LOG_DEBUG("%s: reallocating buffers automatically\n", __func__);
#endif
if (!ggml_gallocr_reserve(galloc, graph)) {
return false;
}
} else {
#ifndef NDEBUG
fprintf(stderr, "%s: cannot reallocate multi buffer graph automatically, call reserve\n", __func__);
GGML_LOG_DEBUG("%s: cannot reallocate multi buffer graph automatically, call reserve\n", __func__);
#endif
return false;
}
@ -940,7 +938,7 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size);
if (buffer == NULL) {
#ifndef NDEBUG
fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
GGML_LOG_DEBUG("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
#endif
for (size_t i = 0; i < *n_buffers; i++) {
ggml_backend_buffer_free((*buffers)[i]);
@ -990,7 +988,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
}
if (this_size > max_size) {
fprintf(stderr, "%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
GGML_LOG_ERROR("%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
__func__, t->name,
ggml_backend_buft_name(buft),
this_size, max_size);
@ -1022,7 +1020,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
if (n_buffers == 0) {
#ifndef NDEBUG
fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__);
GGML_LOG_DEBUG("%s: all tensors in the context are already allocated\n", __func__);
#endif
return NULL;
}

453
ggml/src/ggml-amx.cpp Normal file
View file

@ -0,0 +1,453 @@
#include "ggml-amx.h"
#include "ggml-amx/common.h"
#include "ggml-amx/mmq.h"
#include "ggml-backend-impl.h"
#include "ggml-impl.h"
#if defined(__gnu_linux__)
#include <sys/syscall.h>
#include <unistd.h>
#endif
#include <cstdlib>
#include <cstring>
#include <memory>
#if defined(__AMX_INT8__)
// AMX buffer interface
static const char * ggml_backend_amx_buffer_get_name(ggml_backend_buffer_t buffer) {
return "AMX";
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
}
static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) {
return (void *)(buffer->context);
}
static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
memset((char *)tensor->data + offset, value, size);
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
if (qtype_has_amx_kernels(tensor->type)) {
ggml_backend_amx_convert_weight(tensor, data, offset, size);
} else {
memcpy((char *)tensor->data + offset, data, size);
}
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(!qtype_has_amx_kernels(tensor->type));
memcpy(data, (const char *)tensor->data + offset, size);
GGML_UNUSED(buffer);
}
static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
if (ggml_backend_buffer_is_host(src->buffer)) {
if (qtype_has_amx_kernels(src->type)) {
ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_backend_amx_get_alloc_size(dst));
} else {
memcpy(dst->data, src->data, ggml_nbytes(src));
}
return true;
}
return false;
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
memset(buffer->context, value, buffer->size);
}
static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = {
/* .get_name = */ ggml_backend_amx_buffer_get_name,
/* .free_buffer = */ ggml_backend_amx_buffer_free_buffer,
/* .get_base = */ ggml_backend_amx_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
/* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_amx_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_amx_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor,
/* .clear = */ ggml_backend_amx_buffer_clear,
/* .reset = */ NULL,
};
static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return "AMX";
GGML_UNUSED(buft);
}
static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
void * data = aligned_alloc(TENSOR_ALIGNMENT, size);
if (data == NULL) {
fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
return NULL;
}
return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size);
}
static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
return TENSOR_ALIGNMENT;
GGML_UNUSED(buft);
}
static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) {
return ggml_backend_amx_get_alloc_size(tensor);
GGML_UNUSED(buft);
}
static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
return false;
GGML_UNUSED(buft);
}
ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() {
static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = {
/* .iface = */ {
/* .get_name = */ ggml_backend_amx_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment,
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
/* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size,
/* .is_host = */ ggml_backend_amx_buffer_type_is_host,
},
/* .device = */ NULL,
/* .context = */ NULL,
};
return &ggml_backend_buffer_type_amx;
}
// backend interface
static const char * ggml_backend_amx_name(ggml_backend_t backend) {
return "AMX";
GGML_UNUSED(backend);
}
static void ggml_backend_amx_free(ggml_backend_t backend) {
ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context;
delete ctx;
delete backend;
}
static ggml_backend_buffer_type_t ggml_backend_amx_get_default_buffer_type(ggml_backend_t backend) {
return ggml_backend_amx_buffer_type();
GGML_UNUSED(backend);
}
static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context;
for (int i = 0; i < cgraph->n_nodes; i++) {
struct ggml_tensor * node = cgraph->nodes[i];
switch (node->op) {
case GGML_OP_MUL_MAT:
ggml_backend_amx_mul_mat(ctx, node);
break;
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
break;
default:
fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node));
GGML_ASSERT(false);
}
}
return GGML_STATUS_SUCCESS;
GGML_UNUSED(backend);
}
static struct ggml_backend_i ggml_backend_amx_i = {
/* .get_name = */ ggml_backend_amx_name,
/* .free = */ ggml_backend_amx_free,
/* .get_default_buffer_type = */ ggml_backend_amx_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
/* .synchronize = */ NULL,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_amx_graph_compute,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
static ggml_guid_t ggml_backend_amx_guid() {
static ggml_guid guid = { 0x13, 0xb8, 0xa4, 0xc4, 0xba, 0xfe, 0x51, 0x67, 0x87, 0x44, 0x55, 0x15, 0xb2, 0x35, 0x62, 0x3e };
return &guid;
}
#define ARCH_GET_XCOMP_PERM 0x1022
#define ARCH_REQ_XCOMP_PERM 0x1023
#define XFEATURE_XTILECFG 17
#define XFEATURE_XTILEDATA 18
static bool ggml_amx_init() {
#if defined(__gnu_linux__)
if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) {
fprintf(stderr, "AMX is not ready to be used!\n");
return false;
}
return true;
#elif defined(_WIN32)
return true;
#endif
}
ggml_backend_t ggml_backend_amx_init() {
// invoke a Linux system call to request access to AMX features
ggml_amx_init();
// backend context
ggml_backend_amx_context * ctx = new ggml_backend_amx_context;
// ggml amx backend
ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_amx_guid(),
/* .interface = */ ggml_backend_amx_i,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0),
/* .context = */ ctx,
};
return backend;
}
bool ggml_backend_is_amx(ggml_backend_t backend) {
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_amx_guid());
}
void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) {
GGML_ASSERT(ggml_backend_is_amx(backend_amx));
ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend_amx->context;
ctx->n_threads = n_threads;
}
// device interface
static const char * ggml_backend_amx_device_get_name(ggml_backend_dev_t dev) {
return "AMX";
GGML_UNUSED(dev);
}
static const char * ggml_backend_amx_device_get_description(ggml_backend_dev_t dev) {
return "Intel Advanced Matrix Extensions";
GGML_UNUSED(dev);
}
static void ggml_backend_amx_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_amx_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_CPU;
GGML_UNUSED(dev);
}
static void ggml_backend_amx_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_amx_device_get_name(dev);
props->description = ggml_backend_amx_device_get_description(dev);
props->type = ggml_backend_amx_device_get_type(dev);
ggml_backend_amx_device_get_memory(dev, &props->memory_free, &props->memory_total);
// `buffer_from_host_ptr` is intended to be used in mmap, when memory layout unchanged
props->caps = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ false,
/* .events = */ false,
};
}
static ggml_backend_t ggml_backend_amx_device_init(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_amx_init();
GGML_UNUSED(dev);
GGML_UNUSED(params);
}
static ggml_backend_buffer_type_t ggml_backend_amx_device_get_buffer_type(ggml_backend_dev_t dev) {
return ggml_backend_amx_buffer_type();
GGML_UNUSED(dev);
}
static bool ggml_backend_amx_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
// handle only 2d gemm for now
auto is_contiguous_2d = [](const struct ggml_tensor * t) {
return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 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: {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const enum ggml_type type = src0->type;
const int64_t ne0 = op->ne[0];
bool is_training = src0->grad || src1->grad;
// amx kernels enables for Q4_0, Q4_1, Q8_0, F16
// Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256
bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16);
bool can_use_amx =
is_contiguous_2d(src0) && // src0 must be contiguous
is_contiguous_2d(src1) && // src1 must be contiguous
!is_training && // inference only
src1->type == GGML_TYPE_F32 && // src1 must be float32
has_amx_kernels && // with amx kernel impls
ne0 % (TILE_N * 2) == 0; // out_features is 32x
return can_use_amx;
}
default:
return false;
}
GGML_UNUSED(dev);
}
static bool ggml_backend_amx_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name;
GGML_UNUSED(dev);
}
static const struct ggml_backend_device_i ggml_backend_amx_device_i = {
/* .get_name = */ ggml_backend_amx_device_get_name,
/* .get_description = */ ggml_backend_amx_device_get_description,
/* .get_memory = */ ggml_backend_amx_device_get_memory,
/* .get_type = */ ggml_backend_amx_device_get_type,
/* .get_props = */ ggml_backend_amx_device_get_props,
/* .init_backend = */ ggml_backend_amx_device_init,
/* .get_buffer_type = */ ggml_backend_amx_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ NULL,
/* .supports_op = */ ggml_backend_amx_device_supports_op,
/* .supports_buft = */ ggml_backend_amx_device_supports_buft,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
// backend reg interface
static const char * ggml_backend_amx_reg_get_name(ggml_backend_reg_t reg) {
return "AMX";
GGML_UNUSED(reg);
}
static size_t ggml_backend_amx_reg_get_device_count(ggml_backend_reg_t reg) {
return 1;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_amx_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_device ggml_backend_amx_device = {
/* .iface = */ ggml_backend_amx_device_i,
/* .reg = */ reg,
/* .context = */ nullptr,
};
return &ggml_backend_amx_device;
GGML_UNUSED(reg);
GGML_UNUSED(index);
}
static void * ggml_backend_amx_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_amx_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
GGML_UNUSED(name);
}
static const struct ggml_backend_reg_i ggml_backend_amx_reg_i = {
/* .get_name = */ ggml_backend_amx_reg_get_name,
/* .get_device_count = */ ggml_backend_amx_reg_get_device_count,
/* .get_device = */ ggml_backend_amx_reg_get_device,
/* .get_proc_address = */ ggml_backend_amx_get_proc_address,
};
ggml_backend_reg_t ggml_backend_amx_reg(void) {
static struct ggml_backend_reg ggml_backend_amx_reg = {
/* .iface = */ ggml_backend_amx_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_amx_reg;
}
#else // if defined(__AMX_INT8__)
ggml_backend_t ggml_backend_amx_init(void) {
fprintf(stderr, "GGML is not compiled with AMX support!\n");
return ggml_backend_t{};
}
void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) {
fprintf(stderr, "GGML is not compiled with AMX support!\n");
GGML_UNUSED(backend_amx);
GGML_UNUSED(n_threads);
}
#endif

93
ggml/src/ggml-amx/common.h Normal file
View file

@ -0,0 +1,93 @@
#pragma once
#include "ggml.h"
#include "ggml-cpu-impl.h" // <immintrin.h>
#include <algorithm>
#include <memory>
#include <type_traits>
#if defined(_OPENMP)
#include <omp.h>
#endif
#define TILE_M 16
#define TILE_N 16
#define TILE_K 32
#define VNNI_BLK 4
#define AMX_BLK_SIZE 32
#define TMM0 0
#define TMM1 1
#define TMM2 2
#define TMM3 3
#define TMM4 4
#define TMM5 5
#define TMM6 6
#define TMM7 7
// parallel routines
template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
inline T div_up(T x, T y) { return (x + y - 1) / y; }
template <typename T>
inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) {
#if 0
// onednn partition pattern
T& n_my = n_end;
if (nth <= 1 || n == 0) {
n_start = 0;
n_my = n;
} else {
T n1 = div_up(n, nth);
T n2 = n1 - 1;
T T1 = n - n2 * nth;
n_my = ith < T1 ? n1 : n2;
n_start = ith <= T1 ? ith*n1 : T1 * n1 + (ith - T1) * n2;
}
n_end += n_start;
#else
// pytorch aten partition pattern
T n_my = div_up(n, nth);
n_start = ith * n_my;
n_end = std::min(n_start + n_my, n);
#endif
}
template <typename func_t>
inline void parallel_for(int nth, int n, const func_t& f) {
#if defined(_OPENMP)
#pragma omp parallel num_threads(nth)
{
//int nth = omp_get_num_threads();
int ith = omp_get_thread_num();
int tbegin, tend;
balance211(n, nth, ith, tbegin, tend);
f(tbegin, tend);
}
#else
f(0, n);
GGML_UNUSED(nth);
#endif
}
// quantized types that have AMX support
inline bool qtype_has_amx_kernels(const enum ggml_type type) {
// TODO: fix padding for vnni format
return (type == GGML_TYPE_Q4_0) ||
(type == GGML_TYPE_Q4_1);
//(type == GGML_TYPE_Q8_0) ||
//(type == GGML_TYPE_Q4_K) ||
//(type == GGML_TYPE_Q5_K) ||
//(type == GGML_TYPE_Q6_K) ||
//(type == GGML_TYPE_IQ4_XS);
}
// ggml backend context
struct ggml_backend_amx_context {
int n_threads = GGML_DEFAULT_N_THREADS;
std::unique_ptr<char[]> work_data;
size_t work_size = 0;
};

2509
ggml/src/ggml-amx/mmq.cpp Normal file
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File diff suppressed because it is too large Load diff

17
ggml/src/ggml-amx/mmq.h Normal file
View file

@ -0,0 +1,17 @@
#pragma once
#include "common.h"
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif
size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor);
void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst);
#ifdef __cplusplus
}
#endif

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

136
ggml/src/ggml-backend.cpp
View file

@ -329,7 +329,6 @@ bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type
if (backend->device) {
return ggml_backend_dev_supports_buft(backend->device, buft);
}
return backend->iface.supports_buft(backend, buft);
}
@ -379,7 +378,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
} else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
#ifndef NDEBUG
fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
GGML_LOG_DEBUG("%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
#endif
size_t nbytes = ggml_nbytes(src);
void * data = malloc(nbytes);
@ -463,6 +462,7 @@ 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 +479,10 @@ 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);
}
@ -495,7 +499,11 @@ 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) {
return device->iface.offload_op(device, op);
if (device->iface.offload_op != NULL) {
return device->iface.offload_op(device, op);
}
return false;
}
// Backend (reg)
@ -525,6 +533,38 @@ 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_SYCL
#include "ggml-sycl.h"
#endif
#ifdef GGML_USE_VULKAN
#include "ggml-vulkan.h"
#endif
#ifdef GGML_USE_BLAS
#include "ggml-blas.h"
#endif
#ifdef GGML_USE_RPC
#include "ggml-rpc.h"
#endif
#ifndef __AMX_INT8__
#undef GGML_USE_AMX
#endif
#ifdef GGML_USE_AMX
# include "ggml-amx.h"
#endif
#ifdef GGML_USE_CANN
#include "ggml-cann.h"
#endif
struct ggml_backend_registry {
std::vector<ggml_backend_reg_t> backends;
std::vector<ggml_backend_dev_t> devices;
@ -533,15 +573,36 @@ 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_SYCL
register_backend(ggml_backend_sycl_reg());
#endif
#ifdef GGML_USE_VULKAN
register_backend(ggml_backend_vk_reg());
#endif
#ifdef GGML_USE_BLAS
register_backend(ggml_backend_blas_reg());
#endif
#ifdef GGML_USE_RPC
register_backend(ggml_backend_rpc_reg());
#endif
#ifdef GGML_USE_AMX
register_backend(ggml_backend_amx_reg());
#endif
#ifdef GGML_USE_CANN
register_backend(ggml_backend_cann_reg());
#endif
// TODO: kompute
register_backend(ggml_backend_cpu_reg());
// TODO: sycl, metal, vulkan, kompute, cann
}
void register_backend(ggml_backend_reg_t reg) {
#ifndef NDEBUG
fprintf(stderr, "%s: registered backend %s (%zu devices)\n",
GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n",
__func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg));
#endif
backends.push_back(reg);
@ -552,7 +613,7 @@ struct ggml_backend_registry {
void register_device(ggml_backend_dev_t device) {
#ifndef NDEBUG
fprintf(stderr, "%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
#endif
devices.push_back(device);
}
@ -652,8 +713,6 @@ ggml_backend_t ggml_backend_init_best(void) {
// backend CPU
static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment
static const char * ggml_backend_cpu_buffer_get_name(ggml_backend_buffer_t buffer) {
return "CPU";
@ -672,7 +731,7 @@ static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
}
static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
ggml_aligned_free(buffer->context, buffer->size);
}
static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
@ -740,14 +799,19 @@ static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_ty
}
static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned
void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h)
auto alloc_size = size;
if (alloc_size == 0) {
alloc_size = 1;
}
void * data = ggml_aligned_malloc(alloc_size);
if (data == NULL) {
fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, alloc_size);
return NULL;
}
return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size);
return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, alloc_size);
}
static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
@ -806,7 +870,7 @@ static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_
void * ptr;
int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
if (result != 0) {
fprintf(stderr, "failed to allocate HBM buffer of size %zu\n", size);
GGML_LOG_ERROR("failed to allocate HBM buffer of size %zu\n", size);
return NULL;
}
@ -1118,9 +1182,10 @@ 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 +1218,7 @@ 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 +1281,22 @@ 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) {
@ -1422,7 +1493,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co
}
#ifndef NDEBUG
fprintf(stderr, "%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n",
GGML_LOG_DEBUG("%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n",
__func__, ggml_op_desc(tensor), ggml_backend_buffer_name(buffer), tensor->name);
#endif
@ -1511,13 +1582,13 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
for (int i = 0; i < graph->n_nodes; i++) {
if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id];
fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
GGML_LOG_DEBUG("\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
sched->splits[cur_split].n_inputs);
for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
GGML_LOG_DEBUG("[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
}
fprintf(stderr, "\n");
GGML_LOG_DEBUG("\n");
cur_split++;
}
struct ggml_tensor * node = graph->nodes[i];
@ -1525,7 +1596,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
continue;
}
ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
@ -1533,10 +1604,10 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
continue;
}
ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
GGML_LOG_DEBUG(" %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
}
fprintf(stderr, "\n");
GGML_LOG_DEBUG("\n");
}
}
@ -2050,11 +2121,11 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
// the re-allocation may cause the split inputs to be moved to a different address
ggml_backend_sched_synchronize(sched);
#ifndef NDEBUG
fprintf(stderr, "%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
GGML_LOG_DEBUG("%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
#endif
ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
fprintf(stderr, "%s: failed to allocate graph\n", __func__);
GGML_LOG_ERROR("%s: failed to allocate graph\n", __func__);
return false;
}
}
@ -2197,6 +2268,7 @@ ggml_backend_sched_t ggml_backend_sched_new(
sched->backends[b] = backends[b];
sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
GGML_ASSERT(ggml_backend_supports_buft(backends[b], sched->bufts[b]));
if (sched->n_copies > 1) {
for (int c = 0; c < sched->n_copies; c++) {
sched->events[b][c] = ggml_backend_event_new(backends[b]->device);
@ -2448,7 +2520,7 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s
struct ggml_context * ctx_unallocated = ggml_init(params);
if (ctx_allocated == NULL || ctx_unallocated == NULL) {
fprintf(stderr, "failed to allocate context for graph copy\n");
GGML_LOG_ERROR("%s: failed to allocate context for graph copy\n", __func__);
ggml_hash_set_free(&hash_set);
free(node_copies);
free(node_init);
@ -2471,7 +2543,7 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s
// allocate nodes
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend);
if (buffer == NULL) {
fprintf(stderr, "failed to allocate buffer for graph copy\n");
GGML_LOG_ERROR("%s: failed to allocate buffer for graph copy\n", __func__);
ggml_hash_set_free(&hash_set);
free(node_copies);
free(node_init);

270
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,
@ -337,18 +293,18 @@ 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,
};
#if !defined(NDEBUG) && defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
#if defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
if (openblas_get_parallel() != OPENBLAS_OPENMP) {
fprintf(stderr, "%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
}
#endif
#if !defined(NDEBUG) && defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP)
fprintf(stderr, "%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__);
#if defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP)
GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__);
#endif
return backend;
@ -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;
}

361
ggml/src/ggml-cann.cpp
View file

@ -39,6 +39,8 @@
#include "ggml-common.h"
#define GGML_CANN_NAME "CANN"
/**
* @brief Handles CANN errors by printing an error message and aborting.
*
@ -851,13 +853,6 @@ static void ggml_backend_cann_buffer_set_tensor(
void *transform_buffer = malloc(size);
ggml_backend_cann_transform(tensor, data, transform_buffer);
#ifndef NDEBUG
void *check_buffer = malloc(size);
ggml_backend_cann_transform_back(tensor, transform_buffer,
check_buffer);
GGML_ASSERT(memcmp(data, check_buffer, size) == 0);
free(check_buffer);
#endif
ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size,
transform_buffer, size,
ACL_MEMCPY_HOST_TO_DEVICE));
@ -969,7 +964,7 @@ static void ggml_backend_cann_buffer_clear(
* This structure defines function pointers to operations that can be performed
* on a CANN buffer within the backend.
*/
static ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
/* .get_name = */ ggml_backend_cann_buffer_get_name,
/* .free_buffer = */ ggml_backend_cann_buffer_free_buffer,
/* .get_base = */ ggml_backend_cann_buffer_get_base,
@ -1105,19 +1100,25 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size(
GGML_UNUSED(buft);
}
static bool ggml_backend_cann_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
return false;
GGML_UNUSED(buft);
}
/**
* @brief Interface for managing CANN buffer types in the GGML backend.
*
* Provides function pointers for allocating, querying properties, and managing
* memory for CANN buffer types in the GGML backend.
*/
static ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = {
static const ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = {
/* .get_name = */ ggml_backend_cann_buffer_type_name,
/* .alloc_buffer = */ ggml_backend_cann_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_cann_buffer_type_get_alignment,
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
/* .get_alloc_size = */ ggml_backend_cann_buffer_type_get_alloc_size,
/* .is_host = */ NULL,
/* .is_host = */ ggml_backend_cann_buffer_type_is_host,
};
/**
@ -1148,6 +1149,7 @@ ggml_backend_cann_buffer_type(int32_t device) {
for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) {
ggml_backend_cann_buffer_types[i] = {
/* .iface = */ ggml_backend_cann_buffer_type_interface,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device),
/* .context = */
new ggml_backend_cann_buffer_type_context{
i, "CANN" + std::to_string(i)},
@ -1263,7 +1265,7 @@ ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() {
/* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
/* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host,
},
/* .device = */ nullptr,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), 0),
/* .context = */ nullptr,
};
@ -1510,13 +1512,6 @@ static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend,
void *transform_buffer = malloc(size);
ggml_backend_cann_transform(tensor, data, transform_buffer);
#ifndef NDEBUG
void *check_buffer = malloc(size);
ggml_backend_cann_transform_back(tensor, transform_buffer,
check_buffer);
GGML_ASSERT(memcmp(data, check_buffer, size));
free(check_buffer);
#endif
ACL_CHECK(aclrtMemcpyAsync(
(char *)tensor->data + offset, size, transform_buffer, size,
ACL_MEMCPY_HOST_TO_DEVICE, cann_ctx->stream()));
@ -1691,7 +1686,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(
* @return bool Returns true if the operation is supported by the backend,
* otherwise false.
*/
static bool ggml_backend_cann_supports_op(ggml_backend_t backend,
static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
const ggml_tensor* op) {
switch (op->op) {
case GGML_OP_UNARY:
@ -1782,7 +1777,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_t backend,
return false;
}
GGML_UNUSED(backend);
GGML_UNUSED(dev);
}
/**
@ -1800,31 +1795,6 @@ static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
}
/**
* @brief Checks if the CANN backend supports a specific backend buffer type.
*
* This function determines whether the CANN backend supports the given backend
* buffer type by comparing the device context of the backend and buffer type.
* It returns true if the devices are same between the backend context and
* buffer type context.
*
* @param backend Pointer to the CANN backend.
* @param buft Pointer to the backend buffer type to check.
* @return bool Returns true if the CANN backend supports the buffer type,
* otherwise false.
*/
static bool ggml_backend_cann_supports_buft(
ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
if (ggml_backend_buft_is_cann(buft)) {
ggml_backend_cann_context * cann_ctx =
(ggml_backend_cann_context *)backend->context;
ggml_backend_cann_buffer_type_context * buft_ctx =
(ggml_backend_cann_buffer_type_context *)buft->context;
return buft_ctx->device == cann_ctx->device;
}
return false;
}
/**
* @brief Determines if a tensor operation should be offloaded to the CANN
* backend.
@ -1839,54 +1809,14 @@ static bool ggml_backend_cann_supports_buft(
* @return bool Returns true if the operation should be offloaded, otherwise
* false.
*/
static bool ggml_backend_cann_offload_op(ggml_backend_t backend,
static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev,
const ggml_tensor* op) {
const int min_batch_size = 32;
GGML_UNUSED(backend);
GGML_UNUSED(dev);
return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS;
}
/**
* @brief Creates a new event for the CANN backend.
*
* This function initializes a new event for the CANN backend by setting the
* device and creating an ACL runtime event. The created event is then wrapped
* in a ggml_backend_event structure and returned.
*
* @param backend Pointer to the CANN backend.
* @return ggml_backend_event_t Returns a pointer to the new event structure.
*/
static ggml_backend_event_t ggml_backend_cann_event_new(
ggml_backend_t backend) {
ggml_backend_cann_context* cann_ctx =
(ggml_backend_cann_context*)backend->context;
ggml_cann_set_device(cann_ctx->device);
aclrtEvent event;
ACL_CHECK(aclrtCreateEvent(&event));
return new ggml_backend_event{
/* .backend = */ backend,
/* .context = */ event,
};
}
/**
* @brief Frees a CANN backend event.
*
* This function destroys the ACL runtime event associated with the given CANN
* backend event and then deletes the event structure itself.
*
* @param event Pointer to the event structure to be freed.
*/
static void ggml_backend_cann_event_free(ggml_backend_event_t event) {
ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context));
delete event;
}
/**
* @brief Records an event on the CANN backend stream.
*
@ -1895,10 +1825,9 @@ static void ggml_backend_cann_event_free(ggml_backend_event_t event) {
*
* @param event Pointer to the event structure to be recorded.
*/
static void ggml_backend_cann_event_record(ggml_backend_event_t event) {
static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_event_t event) {
ggml_backend_cann_context* cann_ctx =
(ggml_backend_cann_context*)event->backend->context;
(ggml_backend_cann_context*)backend->context;
ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream()));
}
@ -1916,8 +1845,7 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend,
ggml_backend_event_t event) {
ggml_backend_cann_context* cann_ctx =
(ggml_backend_cann_context*)backend->context;
if (ggml_backend_is_cann(event->backend)) {
if (ggml_backend_is_cann(backend)) {
ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(),
(aclrtEvent)event->context));
} else {
@ -1925,17 +1853,6 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend,
}
}
/**
* @brief Synchronizes the given event on the CANN backend.
*
* This function waits for the specified event to complete on the ACL runtime.
*
* @param event Pointer to the event structure to be synchronized.
*/
static void ggml_backend_cann_event_synchronize(ggml_backend_event_t event) {
ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context));
}
/**
* @brief Structure defining the interface for the CANN backend.
*
@ -1943,7 +1860,7 @@ static void ggml_backend_cann_event_synchronize(ggml_backend_event_t event) {
* supported by the CANN backend, including name retrieval, memory
* management, tensor operations, synchronization, and event handling.
*/
static ggml_backend_i ggml_backend_cann_interface = {
static const ggml_backend_i ggml_backend_cann_interface = {
/* .get_name = */ ggml_backend_cann_name,
/* .free = */ ggml_backend_cann_free,
/* .get_default_buffer_type = */ ggml_backend_cann_get_default_buffer_type,
@ -1956,9 +1873,9 @@ static ggml_backend_i ggml_backend_cann_interface = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_cann_graph_compute,
/* .supports_op = */ ggml_backend_cann_supports_op,
/* .supports_buft = */ ggml_backend_cann_supports_buft,
/* .offload_op = */ ggml_backend_cann_offload_op,
/* .supports_op = */ NULL, // moved to device
/* .supports_buft = */ NULL, // moved to device
/* .offload_op = */ NULL, // moved to device
/* .event_record = */ ggml_backend_cann_event_record,
/* .event_wait = */ ggml_backend_cann_event_wait,
};
@ -1977,6 +1894,234 @@ static ggml_guid_t ggml_backend_cann_guid() {
return &guid;
}
// backend device
struct ggml_backend_cann_device_context {
int device;
std::string name;
std::string description;
};
static const char * ggml_backend_cann_device_get_name(ggml_backend_dev_t dev) {
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
return ctx->name.c_str();
}
static const char* ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) {
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
return ctx->description.c_str();
}
static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
ggml_backend_cann_get_device_memory(ctx->device, free, total);
}
static enum ggml_backend_dev_type ggml_backend_cann_device_get_type(ggml_backend_dev_t dev) {
GGML_UNUSED(dev);
return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
}
static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
props->name = ggml_backend_cann_device_get_name(dev);
props->description = ggml_backend_cann_device_get_description(dev);
props->type = ggml_backend_cann_device_get_type(dev);
ggml_backend_cann_device_get_memory(dev, &props->memory_free, &props->memory_total);
bool host_buffer = getenv("GGML_CANN_NO_PINNED") == nullptr;
props->caps = {
/* .async = */ false,
/* .host_buffer = */ host_buffer,
/* .buffer_from_host_ptr = */ false,
/* .events = */ true,
};
}
static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, const char * params) {
GGML_UNUSED(params);
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
return ggml_backend_cann_init(ctx->device);
}
/**
* @brief Checks if the CANN backend supports a specific backend buffer type.
*
* This function determines whether the CANN backend supports the given backend
* buffer type by comparing the device context of the backend and buffer type.
* It returns true if the devices are same between the backend context and
* buffer type context.
*
* @param backend Pointer to the CANN backend.
* @param buft Pointer to the backend buffer type to check.
* @return bool Returns true if the CANN backend supports the buffer type,
* otherwise false.
*/
static bool ggml_backend_cann_supports_buft(
ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
if (ggml_backend_buft_is_cann(buft)) {
ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context;
ggml_backend_cann_buffer_type_context * buft_ctx =
(ggml_backend_cann_buffer_type_context *)buft->context;
return buft_ctx->device == dev_ctx->device;
}
return false;
}
static ggml_backend_buffer_type_t ggml_backend_cann_device_get_buffer_type(ggml_backend_dev_t dev) {
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
return ggml_backend_cann_buffer_type(ctx->device);
}
static ggml_backend_buffer_type_t ggml_backend_cann_device_get_host_buffer_type(ggml_backend_dev_t dev) {
GGML_UNUSED(dev);
return ggml_backend_cann_host_buffer_type();
}
/**
* @brief Creates a new event for the CANN backend device.
*
* This function initializes a new event for the CANN backend by setting the
* device and creating an ACL runtime event. The created event is then wrapped
* in a ggml_backend_event structure and returned.
*
* @param backend Pointer to the CANN backend.
* @return ggml_backend_event_t Returns a pointer to the new event structure.
*/
static ggml_backend_event_t ggml_backend_cann_device_event_new(
ggml_backend_dev_t dev) {
ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context;
ggml_cann_set_device(dev_ctx->device);
aclrtEvent event;
ACL_CHECK(aclrtCreateEvent(&event));
return new ggml_backend_event{
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), dev_ctx->device),
/* .context = */ event,
};
}
/**
* @brief Frees a CANN backend event.
*
* This function destroys the ACL runtime event associated with the given CANN
* backend event and then deletes the event structure itself.
*
* @param event Pointer to the event structure to be freed.
*/
static void ggml_backend_cann_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) {
ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context));
delete event;
GGML_UNUSED(dev);
}
/**
* @brief Synchronizes the given event on the CANN backend.
*
* This function waits for the specified event to complete on the ACL runtime.
*
* @param event Pointer to the event structure to be synchronized.
*/
static void ggml_backend_cann_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) {
ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context));
GGML_UNUSED(dev);
}
static const ggml_backend_device_i ggml_backend_cann_device_interface = {
/* .get_name = */ ggml_backend_cann_device_get_name,
/* .get_description = */ ggml_backend_cann_device_get_description,
/* .get_memory = */ ggml_backend_cann_device_get_memory,
/* .get_type = */ ggml_backend_cann_device_get_type,
/* .get_props = */ ggml_backend_cann_device_get_props,
/* .init_backend = */ ggml_backend_cann_device_init, // called for every card
/* .get_buffer_type = */ ggml_backend_cann_device_get_buffer_type,
/* .get_host_buffer_type = */ ggml_backend_cann_device_get_host_buffer_type,
/* .buffer_from_host_ptr = */ NULL, // not supported for CANN
/* .supports_op = */ ggml_backend_cann_supports_op,
/* .supports_buft = */ ggml_backend_cann_supports_buft,
/* .offload_op = */ ggml_backend_cann_offload_op,
/* .event_new = */ ggml_backend_cann_device_event_new,
/* .event_free = */ ggml_backend_cann_device_event_free,
/* .event_synchronize = */ ggml_backend_cann_device_event_synchronize,
};
// backend reg
struct ggml_backend_cann_reg_context {
std::vector<ggml_backend_dev_t> devices;
};
static const char * ggml_backend_cann_reg_get_name(ggml_backend_reg_t reg) {
GGML_UNUSED(reg);
return GGML_CANN_NAME;
}
static size_t ggml_backend_cann_reg_get_device_count(ggml_backend_reg_t reg) {
ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context;
return ctx->devices.size();
}
static ggml_backend_dev_t ggml_backend_cann_reg_get_device(ggml_backend_reg_t reg, size_t index) {
ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context;
GGML_ASSERT(index < ctx->devices.size());
return ctx->devices[index];
}
static void * ggml_backend_cann_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) {
GGML_UNUSED(reg);
GGML_UNUSED(name);
// reserved for future use
return nullptr;
}
static const ggml_backend_reg_i ggml_backend_cann_reg_interface = {
/* .get_name = */ ggml_backend_cann_reg_get_name,
/* .get_device_count = */ ggml_backend_cann_reg_get_device_count,
/* .get_device_get = */ ggml_backend_cann_reg_get_device,
/* .get_proc_address = */ ggml_backend_cann_reg_get_proc_address,
};
// backend registry, called only once for cann backend
ggml_backend_reg_t ggml_backend_cann_reg() {
static ggml_backend_reg reg;
static bool initialized = false;
{
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
if (!initialized) {
aclInit(nullptr);
ggml_backend_cann_reg_context * ctx = new ggml_backend_cann_reg_context;
for (int i = 0; i < ggml_cann_info().device_count; i++) {
ggml_backend_cann_device_context* dev_ctx = new ggml_backend_cann_device_context();
dev_ctx->description = aclrtGetSocName();
dev_ctx->device = i;
dev_ctx->name = GGML_CANN_NAME + std::to_string(i);
ggml_cann_set_device(i);
ggml_backend_dev_t dev = new ggml_backend_device {
/* .interface = */ ggml_backend_cann_device_interface,
/* .reg = */ &reg,
/* .context = */ dev_ctx
};
ctx->devices.push_back(dev);
}
reg = ggml_backend_reg {
/* .interface = */ ggml_backend_cann_reg_interface,
/* .context = */ ctx
};
}
initialized = true;
}
return &reg;
}
ggml_backend_t ggml_backend_cann_init(int32_t device) {
aclInit(nullptr);
if (device < 0 || device >= ggml_backend_cann_get_device_count()) {
@ -1993,7 +2138,7 @@ ggml_backend_t ggml_backend_cann_init(int32_t device) {
ggml_backend_t cann_backend =
new ggml_backend{/* .guid = */ ggml_backend_cann_guid(),
/* .interface = */ ggml_backend_cann_interface,
/* .device = */ nullptr,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device),
/* .context = */ ctx};
return cann_backend;

49
ggml/src/ggml-cuda.cu
View file

@ -291,7 +291,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
return;
}
}
GGML_LOG_WARN(GGML_CUDA_NAME " buffer pool full, increase MAX_CUDA_BUFFERS\n");
GGML_LOG_DEBUG(GGML_CUDA_NAME " buffer pool full, increase MAX_CUDA_BUFFERS\n");
ggml_cuda_set_device(device);
CUDA_CHECK(cudaFree(ptr));
pool_size -= size;
@ -980,7 +980,7 @@ static void * ggml_cuda_host_malloc(size_t size) {
if (err != cudaSuccess) {
// clear the error
cudaGetLastError();
GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
GGML_LOG_DEBUG("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
size / 1024.0 / 1024.0, cudaGetErrorString(err));
return nullptr;
}
@ -1151,8 +1151,8 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) {
GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer));
char * src_ptr = (char *) src->data;
char * dst_ptr = (char *) dst;
const char * src_ptr = (const char *) src->data;
char * dst_ptr = (char *) dst;
const int64_t ne0 = src->ne[0];
const int64_t nb0 = src->nb[0];
@ -1162,7 +1162,7 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
const enum ggml_type type = src->type;
const int64_t ts = ggml_type_size(type);
const int64_t bs = ggml_blck_size(type);
int64_t i1_diff = i1_high - i1_low;
const int64_t i1_diff = i1_high - i1_low;
const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
if (nb0 == ts && nb1 == ts*ne0/bs) {
@ -1479,13 +1479,18 @@ static void ggml_cuda_op_mul_mat(
if (src0_is_contiguous) {
dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data;
} else {
dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0));
// If src0 is not contiguous it will be copied to a temporary buffer.
// This buffer needs to be cleared entirely because multiple regions will function as padding.
const size_t nbytes_data = ggml_nbytes(src0);
const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), nbytes_data + nbytes_padding);
CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd, 0, nbytes_data + nbytes_padding, stream));
}
// If src0 is on a temporary compute buffers (partial offloading) there may be some padding that needs to be cleared:
// If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared:
if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) {
const int64_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00);
const int64_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
const size_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00);
const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream));
}
@ -2406,7 +2411,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
#ifndef NDEBUG
GGML_LOG_WARN("%s: backend and buffer devices do not match\n", __func__);
GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__);
#endif
return false;
}
@ -2524,7 +2529,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
#ifndef NDEBUG
GGML_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
#endif
}
}
@ -2575,14 +2580,14 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (node->src[0] && node->src[0]->buffer && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
#ifndef NDEBUG
GGML_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__);
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to split buffer\n", __func__);
#endif
}
if (node->op == GGML_OP_MUL_MAT_ID) {
use_cuda_graph = false; // This node type is not supported by CUDA graph capture
#ifndef NDEBUG
GGML_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
#endif
}
@ -2591,7 +2596,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
// Changes in batch size or context size can cause changes to the grid size of some kernels.
use_cuda_graph = false;
#ifndef NDEBUG
GGML_LOG_WARN("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
#endif
}
@ -2603,7 +2608,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (!ptr) {
use_cuda_graph = false;
#ifndef NDEBUG
GGML_LOG_WARN("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
#endif
} else {
if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
@ -2627,7 +2632,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
#ifndef NDEBUG
GGML_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
#endif
}
}
@ -2685,7 +2690,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
use_cuda_graph = false;
cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
#ifndef NDEBUG
GGML_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
#endif
} else {
graph_evaluated_or_captured = true; // CUDA graph has been captured
@ -2854,7 +2859,7 @@ bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) {
// clear the error
cudaGetLastError();
GGML_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
GGML_LOG_DEBUG("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
size / 1024.0 / 1024.0, cudaGetErrorString(err));
return false;
}
@ -2920,9 +2925,10 @@ 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,
};
}
@ -3140,7 +3146,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_ROPE:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_IM2COL:
return op->src[0]->type == GGML_TYPE_F16;
case GGML_OP_POOL_2D:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:

2
ggml/src/ggml-cuda/cpy.cuh
View file

@ -1,6 +1,6 @@
#include "common.cuh"
#define CUDA_CPY_BLOCK_SIZE 32
#define CUDA_CPY_BLOCK_SIZE 64
void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1);

34
ggml/src/ggml-cuda/dmmv.cu
View file

@ -416,10 +416,11 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx,
static __device__ void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
const half * x = (const half *) vx;
// load 2 halfs into register in a single instruction
const half2 x_reg = *((half2 *) &(x[ib + iqs]));
// automatic half -> float type cast if dfloat == float
v.x = x[ib + iqs + 0];
v.y = x[ib + iqs + 1];
v.x = __low2float(x_reg);
v.y = __high2float(x_reg);
}
static constexpr __device__ dequantize_kernel_t get_dequantize_kernel(ggml_type type) {
@ -476,13 +477,28 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
// matrix multiplication
// for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
#ifdef GGML_CUDA_F16
tmp += __hmul2(v, {
y[iybs + iqs + j/qr + 0],
y[iybs + iqs + j/qr + y_offset]
});
if ( y_offset == 1 ) {
// load 2 dfloats into register in a single instruction
const dfloat2 y_reg = *((dfloat2 *) &(y[iybs + iqs + j/qr]));
tmp += __hmul2(v, y_reg);
}
else {
tmp += __hmul2(v, {
y[iybs + iqs + j/qr + 0],
y[iybs + iqs + j/qr + y_offset]
});
}
#else
tmp += v.x * y[iybs + iqs + j/qr + 0];
tmp += v.y * y[iybs + iqs + j/qr + y_offset];
if ( y_offset == 1 ) {
// load 2 dfloats into register in a single instruction
const dfloat2 y_reg = *((dfloat2 *) &(y[iybs + iqs + j/qr]));
tmp += v.x * y_reg.x;
tmp += v.y * y_reg.y;
}
else {
tmp += v.x * y[iybs + iqs + j/qr + 0];
tmp += v.y * y[iybs + iqs + j/qr + y_offset];
}
#endif // GGML_CUDA_F16
}
}

6
ggml/src/ggml-cuda/im2col.cu
View file

@ -91,9 +91,9 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int64_t OH = is_2D ? dst->ne[2] : 1;
const int64_t OW = dst->ne[1];
const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
const int64_t batch = src1->ne[3];
const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32
const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
const int64_t batch = src1->ne[is_2D ? 3 : 2];
const size_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
if(dst->type == GGML_TYPE_F16) {
im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);

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