vbatts/llama.cpp
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Merge 'origin/master' into hipblas

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Henri Vasserman 2023-05-01 22:45:29 +03:00
commit fcbc262eb9
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29 changed files with 1285 additions and 472 deletions
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1
.gitignore vendored
View file

@ -32,6 +32,7 @@ models/*
/vdot
/Pipfile
build-info.h
arm_neon.h
compile_commands.json

50
CMakeLists.txt
View file

@ -73,6 +73,41 @@ option(LLAMA_HIPBLAS "llama: use hipBLAS"
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
#
# Build info header
#
# Write header template to binary dir to keep source directory clean
file(WRITE "${CMAKE_BINARY_DIR}/BUILD_INFO.h.in" "\
#ifndef BUILD_INFO_H\n\
#define BUILD_INFO_H\n\
\n\
#define BUILD_NUMBER @BUILD_NUMBER@\n\
#define BUILD_COMMIT \"@BUILD_COMMIT@\"\n\
\n\
#endif // BUILD_INFO_H\n\
")
# Generate initial build-info.h
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/.git")
# Add a custom target for build-info.h
add_custom_target(BUILD_INFO ALL DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h")
# Add a custom command to rebuild build-info.h when .git/index changes
add_custom_command(
OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h"
COMMENT "Generating build details from Git"
COMMAND ${CMAKE_COMMAND} -P "${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake"
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/.git/index"
VERBATIM
)
else()
message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
endif()
#
# Compile flags
#
@ -288,9 +323,22 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES
# TODO: arm msvc?
else()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
add_compile_options(-mcpu=native)
endif()
# TODO: armv6,7,8 version specific flags
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
# Raspberry Pi 1, Zero
add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
# Raspberry Pi 2
add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
# Raspberry Pi 3, 4, Zero 2 (32-bit)
add_compile_options(-mfp16-format=ieee -mno-unaligned-access)
endif()
endif()
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$")
message(STATUS "x86 detected")

59
Makefile
View file

@ -148,19 +148,21 @@ ifdef LLAMA_PERF
CXXFLAGS += -DGGML_PERF
endif
ifneq ($(filter aarch64%,$(UNAME_M)),)
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
CFLAGS += -mcpu=native
CXXFLAGS += -mcpu=native
endif
ifneq ($(filter armv6%,$(UNAME_M)),)
# Raspberry Pi 1, 2, 3
# Raspberry Pi 1, Zero
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
endif
ifneq ($(filter armv7%,$(UNAME_M)),)
# Raspberry Pi 4
# Raspberry Pi 2
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
endif
ifneq ($(filter armv8%,$(UNAME_M)),)
# Raspberry Pi 4
# Raspberry Pi 3, 4, Zero 2 (32-bit)
CFLAGS += -mfp16-format=ieee -mno-unaligned-access
endif
@ -192,41 +194,56 @@ llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h
common.o: examples/common.cpp examples/common.h
$(CXX) $(CXXFLAGS) -c $< -o $@
clean:
rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-matmult
libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
main: examples/main/main.cpp ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
clean:
rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state build-info.h
#
# Examples
#
main: examples/main/main.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@echo
@echo '==== Run ./main -h for help. ===='
@echo
quantize: examples/quantize/quantize.cpp ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
quantize-stats: examples/quantize-stats/quantize-stats.cpp ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
perplexity: examples/perplexity/perplexity.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
embedding: examples/embedding/embedding.cpp ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
embedding: examples/embedding/embedding.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
build-info.h: $(wildcard .git/index) scripts/build-info.sh
@scripts/build-info.sh > $@.tmp
@if ! cmp -s $@.tmp $@; then \
mv $@.tmp $@; \
else \
rm $@.tmp; \
fi
#
# Tests
#
benchmark-matmult: examples/benchmark/benchmark-matmult.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
./$@
vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
.PHONY: tests
tests:
bash ./tests/run-tests.sh

3
examples/benchmark/CMakeLists.txt
View file

@ -2,3 +2,6 @@ set(TARGET benchmark)
add_executable(${TARGET} benchmark-matmult.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

4
examples/benchmark/benchmark-matmult.cpp
View file

@ -1,5 +1,6 @@
#include <locale.h>
#include "ggml.h"
#include "build-info.h"
#include <assert.h>
#include <math.h>
#include <cstring>
@ -90,9 +91,10 @@ int main(int argc, char ** argv) {
}
}
// create the ggml context
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
printf("Starting Test\n");
// create the ggml context
struct ggml_context * ctx;
//const int sizex = 4096;
//const int sizey = 11008;

49
examples/common.cpp
View file

@ -1,13 +1,18 @@
#include "common.h"
#include <cassert>
#include <iostream>
#include <cstring>
#include <fstream>
#include <string>
#include <iterator>
#include <algorithm>
#include <sstream>
#include <iostream>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
#include <sys/sysctl.h>
#endif
#if defined (_WIN32)
#include <fcntl.h>
@ -25,19 +30,43 @@ extern "C" __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int
#define CP_UTF8 65001
#endif
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
// determine sensible default number of threads.
// std::thread::hardware_concurrency may not be equal to the number of cores, or may return 0.
int32_t get_num_physical_cores() {
#ifdef __linux__
std::ifstream cpuinfo("/proc/cpuinfo");
params.n_threads = std::count(std::istream_iterator<std::string>(cpuinfo),
std::istream_iterator<std::string>(),
std::string("processor"));
#endif
if (params.n_threads == 0) {
params.n_threads = std::max(1, (int32_t) std::thread::hardware_concurrency());
std::string line;
while (std::getline(cpuinfo, line)) {
std::size_t pos = line.find("cpu cores");
if (pos != std::string::npos) {
pos = line.find(": ", pos);
if (pos != std::string::npos) {
try {
// Extract the number and return it
return static_cast<int32_t>(std::stoul(line.substr(pos + 2)));
} catch (const std::invalid_argument &) {
// Ignore if we could not parse
}
}
}
}
#elif defined(__APPLE__) && defined(__MACH__)
int32_t num_physical_cores;
size_t len = sizeof(num_physical_cores);
int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
if (result == 0) {
return num_physical_cores;
}
result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
if (result == 0) {
return num_physical_cores;
}
#elif defined(_WIN32)
//TODO: Implement
#endif
unsigned int n_threads = std::thread::hardware_concurrency();
return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
}
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false;
std::string arg;
gpt_params default_params;

5
examples/common.h
View file

@ -13,11 +13,12 @@
//
// CLI argument parsing
//
int32_t get_num_physical_cores();
struct gpt_params {
int32_t seed = -1; // RNG seed
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t n_predict = -1; // new tokens to predict
int32_t n_threads = get_num_physical_cores();
int32_t n_predict = -1; // new tokens to predict
int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)

3
examples/embedding/CMakeLists.txt
View file

@ -2,3 +2,6 @@ set(TARGET embedding)
add_executable(${TARGET} embedding.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

5
examples/embedding/embedding.cpp
View file

@ -1,5 +1,6 @@
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include <ctime>
@ -18,11 +19,13 @@ int main(int argc, char ** argv) {
"expect poor results\n", __func__, params.n_ctx);
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed <= 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {

3
examples/main/CMakeLists.txt
View file

@ -2,3 +2,6 @@ set(TARGET main)
add_executable(${TARGET} main.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

25
examples/main/main.cpp
View file

@ -5,6 +5,7 @@
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include <cassert>
#include <cinttypes>
@ -81,11 +82,13 @@ int main(int argc, char ** argv) {
"expect poor results\n", __func__, params.n_ctx);
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed <= 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
@ -161,23 +164,22 @@ int main(int argc, char ** argv) {
std::vector<llama_token> session_tokens;
if (!path_session.empty()) {
fprintf(stderr, "%s: attempting to load saved session from %s..\n", __func__, path_session.c_str());
fprintf(stderr, "%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
// REVIEW - fopen to check for existing session
// fopen to check for existing session
FILE * fp = std::fopen(path_session.c_str(), "rb");
if (fp != NULL) {
std::fclose(fp);
session_tokens.resize(params.n_ctx);
size_t n_token_count_out = 0;
const size_t n_session_bytes = llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out);
if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
return 1;
}
session_tokens.resize(n_token_count_out);
if (n_session_bytes > 0) {
fprintf(stderr, "%s: loaded %zu bytes of session data!\n", __func__, n_session_bytes);
} else {
fprintf(stderr, "%s: could not load session file, will recreate\n", __func__);
}
fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
} else {
fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
}
@ -214,7 +216,7 @@ int main(int argc, char ** argv) {
}
// number of tokens to keep when resetting context
if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size() || params.instruct) {
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct) {
params.n_keep = (int)embd_inp.size();
}
@ -329,7 +331,7 @@ int main(int argc, char ** argv) {
// insert n_left/2 tokens at the start of embd from last_n_tokens
embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
// REVIEW - stop saving session if we run out of context
// stop saving session if we run out of context
path_session = "";
//printf("\n---\n");
@ -355,6 +357,7 @@ int main(int argc, char ** argv) {
n_session_consumed++;
if (n_session_consumed >= (int) session_tokens.size()) {
++i;
break;
}
}

3
examples/perplexity/CMakeLists.txt
View file

@ -2,3 +2,6 @@ set(TARGET perplexity)
add_executable(${TARGET} perplexity.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

5
examples/perplexity/perplexity.cpp
View file

@ -1,5 +1,6 @@
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include <cmath>
#include <ctime>
@ -106,11 +107,13 @@ int main(int argc, char ** argv) {
"expect poor results\n", __func__, params.n_ctx);
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed <= 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {

3
examples/quantize-stats/quantize-stats.cpp
View file

@ -1,4 +1,5 @@
#include "ggml.h"
#include "build-info.h"
#define LLAMA_API_INTERNAL
#include "llama.h"
@ -308,6 +309,8 @@ int main(int argc, char ** argv) {
return 1;
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
// load the model
fprintf(stderr, "Loading model\n");

3
examples/quantize/CMakeLists.txt
View file

@ -2,3 +2,6 @@ set(TARGET quantize)
add_executable(${TARGET} quantize.cpp)
target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

3
examples/quantize/quantize.cpp
View file

@ -1,5 +1,6 @@
#include "ggml.h"
#include "llama.h"
#include "build-info.h"
#include <cstdio>
#include <map>
@ -50,6 +51,8 @@ int main(int argc, char ** argv) {
ftype = (enum llama_ftype)atoi(argv[3]);
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
int nthread = argc > 4 ? atoi(argv[4]) : 0;
const int64_t t_main_start_us = ggml_time_us();

3
examples/save-load-state/CMakeLists.txt
View file

@ -2,3 +2,6 @@ set(TARGET save-load-state)
add_executable(${TARGET} save-load-state.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

3
examples/save-load-state/save-load-state.cpp
View file

@ -1,5 +1,6 @@
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include <vector>
#include <cstdio>
@ -17,6 +18,8 @@ int main(int argc, char ** argv) {
return 1;
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.n_predict < 0) {
params.n_predict = 16;
}

429
ggml-cuda.cu
View file

@ -1,15 +1,44 @@
#include <cstddef>
#include <cstdint>
#include <stdint.h>
#include <stdio.h>
#include <atomic>
#if defined(GGML_USE_HIPBLAS)
#include "hip/hip_runtime.h"
#include "hipblas/hipblas.h"
#include "hip/hip_fp16.h"
#else
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>
#endif
#include <atomic>
#include "ggml-cuda.h"
typedef uint16_t ggml_fp16_t;
static_assert(sizeof(__half) == sizeof(ggml_fp16_t), "wrong fp16 size");
#include "ggml-cuda.h"
#include "ggml.h"
static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
#define CUDA_CHECK(err) \
do { \
cudaError_t err_ = (err); \
if (err_ != cudaSuccess) { \
fprintf(stderr, "CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
cudaGetErrorString(err_)); \
exit(1); \
} \
} while (0)
#define CUBLAS_CHECK(err) \
do { \
cublasStatus_t err_ = (err); \
if (err_ != CUBLAS_STATUS_SUCCESS) { \
fprintf(stderr, "cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
typedef void (*to_fp32_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
#define QK4_0 32
typedef struct {
@ -28,14 +57,14 @@ static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 b
#define QK4_2 16
typedef struct {
__half d; // delta
half d; // delta
uint8_t qs[QK4_2 / 2]; // nibbles / quants
} block_q4_2;
static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
#define QK5_0 32
typedef struct {
__half d; // delta
half d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
@ -43,9 +72,9 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5
#define QK5_1 32
typedef struct {
__half d; // delta
__half m; // min
uint32_t qh; // 5-th bit of quants
half d; // delta
half m; // min
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_1 / 2]; // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
@ -166,7 +195,8 @@ static __global__ void dequantize_block_q5_1(const void * vx, float * y) {
const uint8_t * pp = x[i].qs;
const uint32_t qh = x[i].qh;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int l = 0; l < QK5_1; l += 2) {
const uint8_t vi = pp[l/2];
@ -201,37 +231,50 @@ static __global__ void dequantize_block_q8_0(const void * vx, float * y) {
}
}
void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_0;
dequantize_block_q4_0<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_1;
dequantize_block_q4_1<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_2;
dequantize_block_q4_2<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_0;
dequantize_block_q5_0<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_1;
dequantize_block_q5_1<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK8_0;
dequantize_block_q8_0<<<nb, 1, 0, stream>>>(vx, y);
}
dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(ggml_type type) {
// TODO: optimize
static __global__ void convert_fp16_to_fp32(const void * vx, float * y) {
const half * x = (const half *) vx;
const int i = blockIdx.x;
y[i] = __half2float(x[i]);
}
static void convert_fp16_to_fp32_cuda(const void * x, float * y, int k, cudaStream_t stream) {
convert_fp16_to_fp32<<<k, 1, 0, stream>>>(x, y);
}
static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
return dequantize_row_q4_0_cuda;
@ -245,6 +288,8 @@ dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(ggml_type type) {
return dequantize_row_q5_1_cuda;
case GGML_TYPE_Q8_0:
return dequantize_row_q8_0_cuda;
case GGML_TYPE_F16:
return convert_fp16_to_fp32_cuda;
default:
return nullptr;
}
@ -275,7 +320,7 @@ struct cuda_buffer {
static cuda_buffer g_cuda_buffer_pool[MAX_CUDA_BUFFERS];
static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
scoped_spin_lock lock(g_cuda_pool_lock);
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
@ -294,7 +339,7 @@ void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
return ptr;
}
void ggml_cuda_pool_free(void * ptr, size_t size) {
static void ggml_cuda_pool_free(void * ptr, size_t size) {
scoped_spin_lock lock(g_cuda_pool_lock);
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
@ -309,28 +354,55 @@ void ggml_cuda_pool_free(void * ptr, size_t size) {
CUDA_CHECK(cudaFree(ptr));
}
cublasHandle_t g_cublasH = nullptr;
cudaStream_t g_cudaStream = nullptr;
cudaStream_t g_cudaStream2 = nullptr;
cudaEvent_t g_cudaEvent = nullptr;
#define GGML_CUDA_MAX_STREAMS 8
#define GGML_CUDA_MAX_EVENTS 64
static cublasHandle_t g_cublasH = nullptr;
static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_STREAMS] = { nullptr };
static cudaStream_t g_cudaStreams2[GGML_CUDA_MAX_STREAMS] = { nullptr };
static cudaEvent_t g_cudaEvents[GGML_CUDA_MAX_EVENTS] = { nullptr };
void ggml_init_cublas() {
if (g_cublasH == nullptr) {
// create cublas handle, bind a stream
CUBLAS_CHECK(cublasCreate(&g_cublasH));
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream, cudaStreamNonBlocking));
CUBLAS_CHECK(cublasSetStream(g_cublasH, g_cudaStream));
// create streams
for (int i = 0; i < GGML_CUDA_MAX_STREAMS; ++i) {
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[i], cudaStreamNonBlocking));
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams2[i], cudaStreamNonBlocking));
}
// create events
for (int i = 0; i < GGML_CUDA_MAX_EVENTS; ++i) {
CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvents[i], cudaEventDisableTiming));
}
// create additional stream and event for synchronization
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream2, cudaStreamNonBlocking));
CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvent, cudaEventDisableTiming));
// create cublas handle
CUBLAS_CHECK(cublasCreate(&g_cublasH));
CUBLAS_CHECK(cublasSetMathMode(g_cublasH, CUBLAS_TF32_TENSOR_OP_MATH));
// configure logging to stdout
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL));
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
}
}
cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
void * ggml_cuda_host_malloc(size_t size) {
if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
return nullptr;
}
void * ptr = nullptr;
cudaError_t err = cudaMallocHost((void **) &ptr, size);
if (err != cudaSuccess) {
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
size/1024.0/1024.0, cudaGetErrorString(err));
return nullptr;
}
return ptr;
}
void ggml_cuda_host_free(void * ptr) {
CUDA_CHECK(cudaFreeHost(ptr));
}
static cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
const uint64_t ne0 = src->ne[0];
const uint64_t ne1 = src->ne[1];
const uint64_t nb0 = src->nb[0];
@ -358,12 +430,293 @@ cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src,
}
}
void * ggml_cuda_host_malloc(size_t size) {
void * ptr;
CUDA_CHECK(cudaMallocHost((void **) &ptr, size));
return ptr;
static void ggml_cuda_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
size_t x_size, y_size, d_size;
float * d_X = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * x_ne, &x_size);
float * d_Y = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * y_ne, &y_size);
float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];
float * c_X = d_X + i * x_ne;
float * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
// copy data to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X, src0, i03, i02, cudaStream));
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, ne00,
c_Y, ne10,
&beta, c_D, ne01));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
}
void ggml_cuda_host_free(void * ptr) {
CUDA_CHECK(cudaFreeHost(ptr));
static void ggml_cuda_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t /* wsize */) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
const int nb12 = src1->nb[2];
const int nb13 = src1->nb[3];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
size_t x_size, y_size, d_size;
half * d_X = (half *) ggml_cuda_pool_malloc(n_mm * sizeof(half) * x_ne, &x_size);
half * d_Y = (half *) ggml_cuda_pool_malloc(n_mm * sizeof(half) * y_ne, &y_size);
float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
bool src1_cont_rows = nb10 == sizeof(float);
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];
half * c_X = d_X + i * x_ne;
half * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
// copy src0 to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X, src0, i03, i02, cudaStream));
// convert src1 to fp16
// TODO: use multiple threads
ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata + (ne11 * ne10) * (i03 * ne02 + i02);
char * src1i = (char *) src1->data + i03*nb13 + i02*nb12;
if (src1_cont_rows) {
if (src1_cont_cols) {
ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
}
else {
for (int64_t i01 = 0; i01 < ne11; i01++) {
ggml_fp32_to_fp16_row((float *) (src1i + i01*nb11), tmp + i01*ne10, ne10);
}
}
}
else {
for (int64_t i01 = 0; i01 < ne11; i01++) {
for (int64_t i00 = 0; i00 < ne10; i00++) {
// very slow due to no inlining
tmp[i01*ne10 + i00] = ggml_fp32_to_fp16(*(float *) (src1i + i01*nb11 + i00*nb10));
}
}
}
// copy src1 to device
CUDA_CHECK(cudaMemcpyAsync(c_Y, tmp, sizeof(half) * y_ne, cudaMemcpyHostToDevice, cudaStream));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, CUDA_R_16F, ne00,
c_Y, CUDA_R_16F, ne10,
&beta, c_D, CUDA_R_32F, ne01,
CUBLAS_COMPUTE_32F_FAST_16F,
CUBLAS_GEMM_DEFAULT));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
}
static void ggml_cuda_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const ggml_type type = src0->type;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
const size_t q_sz = ggml_type_size(type) * x_ne / ggml_blck_size(type);
size_t x_size, y_size, d_size, q_size;
float * d_X = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * x_ne, &x_size);
float * d_Y = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * y_ne, &y_size);
float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
char * d_Q = (char *) ggml_cuda_pool_malloc(n_mm * q_sz, &q_size);
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(type);
GGML_ASSERT(to_fp32_cuda != nullptr);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];
cudaStream_t cudaStream2 = g_cudaStreams2[i % GGML_CUDA_MAX_STREAMS];
cudaEvent_t cudaEvent = g_cudaEvents[i % GGML_CUDA_MAX_EVENTS];
float * c_X = d_X + i * x_ne;
float * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
char * c_Q = d_Q + i * q_sz;
// copy src0 and convert to fp32 on device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Q, src0, i03, i02, cudaStream2));
to_fp32_cuda(c_Q, c_X, x_ne, cudaStream2);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));
// copy src1 to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));
// wait for conversion
CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, ne00,
c_Y, ne10,
&beta, c_D, ne01));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
ggml_cuda_pool_free(d_Q, q_size);
}
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
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 ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
src1->type == GGML_TYPE_F32 &&
dst->type == GGML_TYPE_F32 &&
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
return true;
}
return false;
}
bool ggml_cuda_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) {
size_t src0_sz = ggml_nbytes(src0);
size_t src1_sz = ggml_nbytes(src1);
// mul_mat_q: src0 is converted to fp32 on device
size_t mul_mat_q_transfer = src0_sz + src1_sz;
// mul_mat_f16: src1 is converted to fp16 on cpu
size_t mul_mat_f16_transfer = src0_sz + sizeof(half) * ggml_nelements(src1);
// choose the smaller one to transfer to the device
// TODO: this is not always the best choice due to the overhead of converting to fp16
return mul_mat_f16_transfer < mul_mat_q_transfer;
}
void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) {
GGML_ASSERT(ggml_cuda_can_mul_mat(src0, src1, dst));
if (src0->type == GGML_TYPE_F32) {
ggml_cuda_mul_mat_f32(src0, src1, dst);
}
else if (src0->type == GGML_TYPE_F16) {
if (ggml_cuda_mul_mat_use_f16(src0, src1, dst)) {
ggml_cuda_mul_mat_f16(src0, src1, dst, wdata, wsize);
}
else {
ggml_cuda_mul_mat_q_f32(src0, src1, dst);
}
}
else if (ggml_is_quantized(src0->type)) {
ggml_cuda_mul_mat_q_f32(src0, src1, dst);
}
else {
GGML_ASSERT(false);
}
}
size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
if (ggml_cuda_mul_mat_use_f16(src0, src1, dst)) {
return ggml_nelements(src1) * sizeof(ggml_fp16_t);
}
else {
return 0;
}
}

46
ggml-cuda.h
View file

@ -2,6 +2,7 @@
#include "hipblas/hipblas.h"
#include "hip/hip_runtime.h"
#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
#define CUBLAS_OP_N HIPBLAS_OP_N
#define CUBLAS_OP_T HIPBLAS_OP_T
@ -49,49 +50,16 @@
extern "C" {
#endif
#define CUDA_CHECK(err) \
do { \
cudaError_t err_ = (err); \
if (err_ != cudaSuccess) { \
fprintf(stderr, "CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
cudaGetErrorString(err_)); \
exit(1); \
} \
} while (0)
#define CUBLAS_CHECK(err) \
do { \
cublasStatus_t err_ = (err); \
if (err_ != CUBLAS_STATUS_SUCCESS) { \
fprintf(stderr, "cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
extern cublasHandle_t g_cublasH;
extern cudaStream_t g_cudaStream;
extern cudaStream_t g_cudaStream2;
extern cudaEvent_t g_cudaEvent;
void ggml_init_cublas(void);
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
// TODO: export these with GGML_API
void * ggml_cuda_host_malloc(size_t size);
void ggml_cuda_host_free(void * ptr);
void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size);
void ggml_cuda_pool_free(void * ptr, size_t size);
void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream);
typedef void (*dequantize_row_q_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(enum ggml_type type);
#ifdef __cplusplus
}
#endif

63
ggml-opencl-dequant.cl
View file

@ -1,63 +0,0 @@
#define MULTILINE_QUOTE(...) #__VA_ARGS__
const char * clblast_dequant = MULTILINE_QUOTE(
struct block_q4_0
{
float d;
uchar qs[16];
};
__kernel void dequantize_row_q4_0(__global struct block_q4_0* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const uchar vi = blocks[i].qs[l];
const uint index = i*32 + l*2;
result[index + 0] = ((vi & 0xf) - 8)*d;
result[index + 1] = ((vi >> 4) - 8)*d;
}
struct block_q4_1
{
float d;
float m;
uchar qs[16];
};
__kernel void dequantize_row_q4_1(__global struct block_q4_1* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const float m = blocks[i].m;
const uchar vi = blocks[i].qs[l];
const uint index = i*32 + l*2;
result[index + 0] = (vi & 0xf) * d + m;
result[index + 1] = (vi >> 4) * d + m;
}
struct block_q4_2
{
ushort d;
uchar qs[8];
};
__kernel void dequantize_row_q4_2(__global struct block_q4_2* blocks, __global float* result) {
const uint i = get_global_id(0) / 16;
const uint l = get_local_id(0);
const float d = vload_half(0, (__global half*) &blocks[i].d);;
const uchar vi = blocks[i].qs[l];
const uint index = i*16 + l*2;
result[index + 0] = ((vi & 0xf) - 8)*d;
result[index + 1] = ((vi >> 4) - 8)*d;
}
);

220
ggml-opencl.c
View file

@ -3,12 +3,141 @@
#define CL_TARGET_OPENCL_VERSION 110
#include <clblast_c.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "ggml.h"
#include "ggml-opencl-dequant.cl"
#define MULTILINE_QUOTE(...) #__VA_ARGS__
const char * clblast_dequant = MULTILINE_QUOTE(
struct block_q4_0
{
float d;
uchar qs[16];
};
__kernel void dequantize_row_q4_0(__global struct block_q4_0* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const uchar vi = blocks[i].qs[l];
const uint index = i*32 + l*2;
result[index + 0] = ((vi & 0xf) - 8)*d;
result[index + 1] = ((vi >> 4) - 8)*d;
}
struct block_q4_1
{
float d;
float m;
uchar qs[16];
};
__kernel void dequantize_row_q4_1(__global struct block_q4_1* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const float m = blocks[i].m;
const uchar vi = blocks[i].qs[l];
const uint index = i*32 + l*2;
result[index + 0] = (vi & 0xf) * d + m;
result[index + 1] = (vi >> 4) * d + m;
}
struct block_q4_2
{
ushort d;
uchar qs[8];
};
__kernel void dequantize_row_q4_2(__global struct block_q4_2* blocks, __global float* result) {
const uint i = get_global_id(0) / 16;
const uint l = get_local_id(0);
const float d = vload_half(0, (__global half*) &blocks[i].d);
const uchar vi = blocks[i].qs[l];
const uint index = i*16 + l*2;
result[index + 0] = ((vi & 0xf) - 8)*d;
result[index + 1] = ((vi >> 4) - 8)*d;
}
struct block_q5_0
{
float d;
uint qh;
uchar qs[16];
};
__kernel void dequantize_row_q5_0(__global struct block_q5_0* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const uchar vi = blocks[i].qs[l];
const uint l2 = l * 2;
const uchar vh0 = ((blocks[i].qh & (1 << (l2 + 0))) >> (l2 + 0)) << 4;
const uchar vh1 = ((blocks[i].qh & (1 << (l2 + 1))) >> (l2 + 1)) << 4;
const uint index = i*32 + l2;
result[index + 0] = (((vi & 0xf) | vh0) - 16)*d;
result[index + 1] = (((vi >> 4) | vh1) - 16)*d;
}
struct block_q5_1
{
ushort d;
ushort m;
uint qh;
uchar qs[16];
};
__kernel void dequantize_row_q5_1(__global struct block_q5_1* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = vload_half(0, (__global half*) &blocks[i].d);
const float m = vload_half(0, (__global half*) &blocks[i].m);
const uchar vi = blocks[i].qs[l];
const uint l2 = l * 2;
const uchar vh0 = ((blocks[i].qh & (1 << (l2 + 0))) >> (l2 + 0)) << 4;
const uchar vh1 = ((blocks[i].qh & (1 << (l2 + 1))) >> (l2 + 1)) << 4;
const uint index = i*32 + l2;
result[index + 0] = ((vi & 0xf) | vh0)*d + m;
result[index + 1] = ((vi >> 4) | vh1)*d + m;
}
struct block_q8_0
{
float d;
char qs[32];
};
__kernel void dequantize_row_q8_0(__global struct block_q8_0* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
result[i*32 + l] = blocks[i].qs[l] * blocks[i].d;
}
);
#define CL_CHECK(err, name) \
do { \
@ -19,12 +148,26 @@
} \
} while (0)
#define QK5_0 32
typedef struct {
ggml_fp16_t d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
typedef struct {
float d; // delta
uint32_t qh; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} cl_block_q5_0;
static cl_platform_id platform;
static cl_device_id device;
static cl_context context;
static cl_command_queue queue;
static cl_program program;
static cl_kernel kernel_q4_0, kernel_q4_1, kernel_q4_2;
static cl_kernel kernel_q4_0, kernel_q4_1, kernel_q4_2, kernel_q5_0, kernel_q5_1, kernel_q8_0;
static cl_mem cl_buffer_a, cl_buffer_qb, cl_buffer_b, cl_buffer_c;
static size_t cl_size_a = 0, cl_size_qb = 0, cl_size_b = 0, cl_size_c = 0;
@ -97,6 +240,12 @@ void ggml_cl_init(void) {
CL_CHECK(err, "clCreateKernel");
kernel_q4_2 = clCreateKernel(program, "dequantize_row_q4_2", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q5_0 = clCreateKernel(program, "dequantize_row_q5_0", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q5_1 = clCreateKernel(program, "dequantize_row_q5_1", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q8_0 = clCreateKernel(program, "dequantize_row_q8_0", &err);
CL_CHECK(err, "clCreateKernel");
}
static void ggml_cl_malloc(size_t req_size, size_t* cur_size, cl_mem_flags flags, cl_mem* buf) {
@ -125,6 +274,7 @@ void ggml_cl_sgemm_wrapper(
cl_kernel kernel;
size_t global = n * k, local, size_qb;
bool dequant;
cl_block_q5_0* cl_host_b;
switch (btype) {
case GGML_TYPE_F32:
@ -146,7 +296,36 @@ void ggml_cl_sgemm_wrapper(
dequant = true;
kernel = kernel_q4_2;
local = 8;
size_qb = global * (sizeof(short) + local) / 16;
size_qb = global * (sizeof(ggml_fp16_t) + local) / 16;
break;
case GGML_TYPE_Q5_0:
dequant = true;
kernel = kernel_q5_0;
local = 16;
// For some reason OpenCL seems to be incapable of working with structs of size 22.
// 20 and 24 bytes are fine. Workaround to do the fp16 to fp32 step on CPU...
// TODO Find the reason, fix and remove workaround.
const block_q5_0* b = (const block_q5_0*) host_b;
cl_host_b = (cl_block_q5_0*) malloc(sizeof(cl_block_q5_0) * global / 32);
for (size_t i = 0; i < global / 32; i++) {
cl_host_b[i].d = ggml_fp16_to_fp32(b[i].d);
memcpy(&cl_host_b[i].qh, b[i].qh, sizeof(uint32_t));
memcpy(&cl_host_b[i].qs, b[i].qs, QK5_0 / 2);
}
host_b = (const float*) cl_host_b;
size_qb = global * (sizeof(float) + sizeof(uint32_t) + local) / 32;
break;
case GGML_TYPE_Q5_1:
dequant = true;
kernel = kernel_q5_1;
local = 16;
size_qb = global * (sizeof(ggml_fp16_t) * 2 + sizeof(uint32_t) + local) / 32;
break;
case GGML_TYPE_Q8_0:
dequant = true;
kernel = kernel_q8_0;
local = 32;
size_qb = global * (sizeof(float) + local) / 32;
break;
default:
fprintf(stderr, "Error: Unsupported OpenCL btype %d\n", btype);
@ -171,12 +350,15 @@ void ggml_cl_sgemm_wrapper(
err = clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb);
err |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b);
CL_CHECK(err, "clSetKernelArg");
clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb);
err = clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb);
CL_CHECK(err, "clEnqueueWriteBuffer qb");
} else {
clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b);
err = clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b);
CL_CHECK(err, "clEnqueueWriteBuffer b");
}
clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a);
err = clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a);
CL_CHECK(err, "clEnqueueWriteBuffer a");
if (dequant) {
err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b);
CL_CHECK(err, "clEnqueueNDRangeKernel");
@ -188,15 +370,20 @@ void ggml_cl_sgemm_wrapper(
clReleaseEvent(ev_b);
cl_event ev_sgemm;
CLBlastSgemm((CLBlastLayout)order,
(CLBlastTranspose)trans_a, (CLBlastTranspose)trans_b,
m, n, k,
alpha,
cl_buffer_a, 0, lda,
cl_buffer_b, 0, ldb,
beta,
cl_buffer_c, 0, ldc,
&queue, &ev_sgemm);
CLBlastStatusCode status = CLBlastSgemm((CLBlastLayout)order,
(CLBlastTranspose)trans_a, (CLBlastTranspose)trans_b,
m, n, k,
alpha,
cl_buffer_a, 0, lda,
cl_buffer_b, 0, ldb,
beta,
cl_buffer_c, 0, ldc,
&queue, &ev_sgemm);
if (status != CLBlastSuccess) {
fprintf(stderr, "Error: CLBlast SGEMM %d\n", status);
abort();
}
cl_event ev_c;
clEnqueueReadBuffer(queue, cl_buffer_c, CL_TRUE, 0, size_c, host_c, 1, &ev_sgemm, &ev_c);
@ -205,4 +392,7 @@ void ggml_cl_sgemm_wrapper(
clWaitForEvents(1, &ev_c);
clReleaseEvent(ev_sgemm);
clReleaseEvent(ev_c);
if (btype == GGML_TYPE_Q5_0) {
free((void*) cl_host_b);
}
}

440
ggml.c
View file

@ -135,14 +135,6 @@ inline static void* ggml_aligned_malloc(size_t size) {
#define UNUSED(x) (void)(x)
#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
#if defined(GGML_USE_ACCELERATE)
#include <Accelerate/Accelerate.h>
#elif defined(GGML_USE_OPENBLAS)
@ -330,7 +322,7 @@ static ggml_fp16_t table_exp_f16[1 << 16];
// precomputed f32 table for f16 (256 KB)
static float table_f32_f16[1 << 16];
#if defined(__ARM_NEON)
#if defined(__ARM_NEON) || defined(__wasm_simd128__)
#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s
#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s)
@ -370,6 +362,32 @@ ggml_fp16_t ggml_fp32_to_fp16(float x) {
return GGML_FP32_TO_FP16(x);
}
void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n) {
for (size_t i = 0; i < n; i++) {
y[i] = GGML_FP16_TO_FP32(x[i]);
}
}
void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) {
size_t i = 0;
#if defined(__F16C__)
for (; i + 7 < n; i += 8) {
__m256 x_vec = _mm256_loadu_ps(x + i);
__m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
_mm_storeu_si128((__m128i *)(y + i), y_vec);
}
for(; i + 3 < n; i += 4) {
__m128 x_vec = _mm_loadu_ps(x + i);
__m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
_mm_storel_epi64((__m128i *)(y + i), y_vec);
}
#endif
for (; i < n; i++) {
y[i] = GGML_FP32_TO_FP16(x[i]);
}
}
//
// timing
//
@ -1087,7 +1105,7 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int
const v128_t v = wasm_f32x4_mul(srcv[l], wasm_f32x4_splat(id));
const v128_t vf = wasm_f32x4_add(v, wasm_f32x4_splat(8.5f));
const v128_t vi = wasm_i32x4_trunc_sat_f32x4(vf);
const v128_t vc = wasm_i32x4_min_u(vi, wasm_i32x4_splat(15));
const v128_t vc = wasm_i32x4_min(vi, wasm_i32x4_splat(15));
y[i].qs[2*l + 0] = wasm_i32x4_extract_lane(vc, 0) | (wasm_i32x4_extract_lane(vc, 1) << 4);
y[i].qs[2*l + 1] = wasm_i32x4_extract_lane(vc, 2) | (wasm_i32x4_extract_lane(vc, 3) << 4);
@ -1911,8 +1929,8 @@ static void dequantize_row_q5_0(const void * restrict vx, float * restrict y, in
const uint8_t vi = pp[l/2];
// extract the 5-th bit from qh
const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const uint8_t vh0 = ((qh & (1u << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1u << (l + 1))) >> (l + 1)) << 4;
const int8_t vi0 = (vi & 0x0F) | vh0;
const int8_t vi1 = (vi >> 4) | vh1;
@ -1948,8 +1966,8 @@ static void dequantize_row_q5_1(const void * restrict vx, float * restrict y, in
const uint8_t vi = pp[l/2];
// extract the 5-th bit from qh
const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const uint8_t vh0 = ((qh & (1u << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1u << (l + 1))) >> (l + 1)) << 4;
const uint8_t vi0 = (vi & 0x0F) | vh0;
const uint8_t vi1 = (vi >> 4) | vh1;
@ -3180,6 +3198,72 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
}
*s = vaddvq_f32(sumv);
#elif defined(__wasm_simd128__)
v128_t sumv = wasm_f32x4_splat(0.0f);
uint64_t tmp[4];
for (int i = 0; i < nb; ++i) {
const block_q5_0 * restrict x0 = &x[i];
const block_q8_0 * restrict y0 = &y[i];
const v128_t m4b = wasm_i8x16_splat(0x0F);
const v128_t s16b = wasm_i8x16_splat(0x10);
// extract the 5th bit
uint32_t qh;
memcpy(&qh, x0->qh, sizeof(qh));
tmp[0] = table_b2b_u[(qh >> 0) & 0xFF];
tmp[1] = table_b2b_u[(qh >> 8) & 0xFF];
tmp[2] = table_b2b_u[(qh >> 16) & 0xFF];
tmp[3] = table_b2b_u[(qh >> 24) ];
const v128_t qhl = wasm_v128_load(tmp + 0);
const v128_t qhh = wasm_v128_load(tmp + 2);
const v128_t v0 = wasm_v128_load(x0->qs);
// 4-bit -> 8-bit
const v128_t v0l = wasm_v128_and (v0, m4b);
const v128_t v0h = wasm_u8x16_shr(v0, 4);
// interleave
const v128_t v0lz = wasm_v8x16_shuffle(v0l, v0h, 0, 16, 1, 17, 2, 18, 3, 19, 4, 20, 5, 21, 6, 22, 7, 23);
const v128_t v0hz = wasm_v8x16_shuffle(v0l, v0h, 8, 24, 9, 25, 10, 26, 11, 27, 12, 28, 13, 29, 14, 30, 15, 31);
// add high bit and sub 16
const v128_t v0lf = wasm_i8x16_sub(wasm_v128_or(v0lz, qhl), s16b);
const v128_t v0hf = wasm_i8x16_sub(wasm_v128_or(v0hz, qhh), s16b);
// load y
const v128_t v1l = wasm_v128_load(y0->qs);
const v128_t v1h = wasm_v128_load(y0->qs + 16);
// int8x16 -> int16x8
const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf);
const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf);
const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);
const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l);
const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
const float x0d = GGML_FP16_TO_FP32(x0->d);
// dot product
sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(
wasm_i32x4_add(
wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), wasm_f32x4_splat(x0d*y0->d)));
}
*s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
#elif defined(__AVX2__)
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
@ -3220,8 +3304,8 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
for (int j = 0; j < QK8_0/2; j++) {
const uint8_t v0 = x0[j];
const int x0_0h = ((qh & (1 << (2*j + 0))) >> (2*j + 0)) << 4;
const int x1_0h = ((qh & (1 << (2*j + 1))) >> (2*j + 1)) << 4;
const int x0_0h = ((qh & (1u << (2*j + 0))) >> (2*j + 0)) << 4;
const int x1_0h = ((qh & (1u << (2*j + 1))) >> (2*j + 1)) << 4;
const int x0_0 = ((v0 & 0x0F) | x0_0h) - 16;
const int x1_0 = ((v0 >> 4) | x1_0h) - 16;
@ -3311,6 +3395,77 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
}
*s = vaddvq_f32(sumv) + summs;
#elif defined(__wasm_simd128__)
v128_t sumv = wasm_f32x4_splat(0.0f);
float summs = 0.0f;
uint64_t tmp[4];
for (int i = 0; i < nb; ++i) {
const block_q5_1 * restrict x0 = &x[i];
const block_q8_1 * restrict y0 = &y[i];
summs += GGML_FP16_TO_FP32(x0->m) * (y0->s0 + y0->s1);
const v128_t m4b = wasm_i8x16_splat(0x0F);
// extract the 5th bit
uint32_t qh;
memcpy(&qh, x0->qh, sizeof(qh));
tmp[0] = table_b2b_u[(qh >> 0) & 0xFF];
tmp[1] = table_b2b_u[(qh >> 8) & 0xFF];
tmp[2] = table_b2b_u[(qh >> 16) & 0xFF];
tmp[3] = table_b2b_u[(qh >> 24) ];
const v128_t qhl = wasm_v128_load(tmp + 0);
const v128_t qhh = wasm_v128_load(tmp + 2);
const v128_t v0 = wasm_v128_load(x0->qs);
// 4-bit -> 8-bit
const v128_t v0l = wasm_v128_and (v0, m4b);
const v128_t v0h = wasm_u8x16_shr(v0, 4);
static bool x = true;
// interleave
const v128_t v0lz = wasm_v8x16_shuffle(v0l, v0h, 0, 16, 1, 17, 2, 18, 3, 19, 4, 20, 5, 21, 6, 22, 7, 23);
const v128_t v0hz = wasm_v8x16_shuffle(v0l, v0h, 8, 24, 9, 25, 10, 26, 11, 27, 12, 28, 13, 29, 14, 30, 15, 31);
// add high bit
const v128_t v0lf = wasm_v128_or(v0lz, qhl);
const v128_t v0hf = wasm_v128_or(v0hz, qhh);
// load y
const v128_t v1l = wasm_v128_load(y0->qs);
const v128_t v1h = wasm_v128_load(y0->qs + 16);
// int8x16 -> int16x8
const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf);
const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf);
const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);
const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l);
const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
const float x0d = GGML_FP16_TO_FP32(x0->d);
// dot product
sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(
wasm_i32x4_add(
wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), wasm_f32x4_splat(x0d*y0->d)));
}
*s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
#elif defined(__AVX2__)
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
@ -3354,8 +3509,8 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
for (int j = 0; j < QK8_1/2; j++) {
const uint8_t v0 = x0[j];
const int x0_0h = ((qh & (1 << (2*j + 0))) >> (2*j + 0)) << 4;
const int x1_0h = ((qh & (1 << (2*j + 1))) >> (2*j + 1)) << 4;
const int x0_0h = ((qh & (1u << (2*j + 0))) >> (2*j + 0)) << 4;
const int x1_0h = ((qh & (1u << (2*j + 1))) >> (2*j + 1)) << 4;
const int x0_0 = (v0 & 0x0F) | x0_0h;
const int x1_0 = (v0 >> 4) | x1_0h;
@ -4057,6 +4212,27 @@ bool ggml_is_quantized(enum ggml_type type) {
return GGML_IS_QUANTIZED[type];
}
enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
enum ggml_type wtype = GGML_TYPE_COUNT;
switch (ftype) {
case GGML_FTYPE_ALL_F32: wtype = GGML_TYPE_F32; break;
case GGML_FTYPE_MOSTLY_F16: wtype = GGML_TYPE_F16; break;
case GGML_FTYPE_MOSTLY_Q4_0: wtype = GGML_TYPE_Q4_0; break;
case GGML_FTYPE_MOSTLY_Q4_1: wtype = GGML_TYPE_Q4_1; break;
case GGML_FTYPE_MOSTLY_Q4_2: wtype = GGML_TYPE_Q4_2; break;
case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break;
case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break;
case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break;
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
}
GGML_ASSERT(wtype != GGML_TYPE_COUNT);
return wtype;
}
static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) {
return tensor->nb[0] > tensor->nb[1];
}
@ -4167,12 +4343,11 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
}
// initialize cuBLAS
#if defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_CUBLAS)
ggml_init_cublas();
#elif defined(GGML_USE_CLBLAST)
#elif defined(GGML_USE_CLBLAST)
ggml_cl_init();
#endif
#endif
is_first_call = false;
}
@ -4253,7 +4428,7 @@ void ggml_free(struct ggml_context * ctx) {
}
size_t ggml_used_mem(const struct ggml_context * ctx) {
return ctx->objects_end->offs + ctx->objects_end->size;
return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size;
}
size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) {
@ -7943,7 +8118,7 @@ static void ggml_compute_forward_rms_norm(
// ggml_compute_forward_mul_mat
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
// helper function to determine if it is better to use BLAS or not
// for large matrices, BLAS is faster
static bool ggml_compute_forward_mul_mat_use_blas(
@ -7959,12 +8134,9 @@ static bool ggml_compute_forward_mul_mat_use_blas(
const int64_t ne1 = dst->ne[1];
// TODO: find the optimal values for these
if (
#if !defined(GGML_USE_CUBLAS)
ggml_is_contiguous(src0) &&
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
#endif
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) {
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
/*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
return true;
@ -7972,7 +8144,6 @@ static bool ggml_compute_forward_mul_mat_use_blas(
return false;
}
#endif
static void ggml_compute_forward_mul_mat_f32(
@ -7988,7 +8159,7 @@ static void ggml_compute_forward_mul_mat_f32(
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
const int64_t ne10 = src1->ne[0];
#endif
const int64_t ne11 = src1->ne[1];
@ -8045,7 +8216,16 @@ static void ggml_compute_forward_mul_mat_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}
return;
}
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
if (params->ith != 0) {
return;
@ -8059,43 +8239,13 @@ static void ggml_compute_forward_mul_mat_f32(
return;
}
#if defined(GGML_USE_CUBLAS)
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
size_t x_size, y_size, d_size;
float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
#endif
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
#if !defined(GGML_USE_CUBLAS)
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
#endif
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
#if defined(GGML_USE_CUBLAS)
// copy data to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_X, src0, i03, i02, g_cudaStream));
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Y, src1, i03, i02, g_cudaStream));
// compute
CUBLAS_CHECK(
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, d_X, ne00,
d_Y, ne10,
&beta, d_D, ne01));
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#elif defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST)
// zT = y * xT
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
ne11, ne01, ne10,
@ -8112,12 +8262,6 @@ static void ggml_compute_forward_mul_mat_f32(
#endif
}
}
#if defined(GGML_USE_CUBLAS)
CUDA_CHECK(cudaStreamSynchronize(g_cudaStream));
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
#endif
//printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
return;
@ -8247,7 +8391,16 @@ static void ggml_compute_forward_mul_mat_f16_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}
return;
}
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
GGML_ASSERT(nb10 == sizeof(float));
@ -8263,37 +8416,8 @@ static void ggml_compute_forward_mul_mat_f16_f32(
return;
}
#if defined(GGML_USE_CUBLAS)
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
size_t x_size, y_size, d_size;
ggml_fp16_t * d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
ggml_fp16_t * d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
float * d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
#endif
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
#if defined(GGML_USE_CUBLAS)
// copy src0 while converting src1
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_X, src0, i03, i02, g_cudaStream));
// with cuBlAS, instead of converting src0 to fp32, we convert src1 to fp16
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + (ne11 * ne10) * (i03 * ne02 + i02);
{
size_t id = 0;
for (int64_t i01 = 0; i01 < ne11; ++i01) {
for (int64_t i00 = 0; i00 < ne10; ++i00) {
wdata[id++] = GGML_FP32_TO_FP16(*(float *) ((char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11 + i00*nb10));
}
}
assert(id*sizeof(ggml_fp16_t) <= params->wsize);
}
#else
float * const wdata = params->wdata;
{
size_t id = 0;
@ -8305,28 +8429,8 @@ static void ggml_compute_forward_mul_mat_f16_f32(
assert(id*sizeof(float) <= params->wsize);
}
#endif
#if defined(GGML_USE_CUBLAS)
const ggml_fp16_t * y = (ggml_fp16_t *) wdata;
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// copy data to device
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, g_cudaStream));
// compute
CUBLAS_CHECK(
cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, d_X, CUDA_R_16F, ne00,
d_Y, CUDA_R_16F, ne10,
&beta, d_D, CUDA_R_32F, ne01,
CUBLAS_COMPUTE_32F,
CUBLAS_GEMM_DEFAULT));
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#elif defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST)
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
@ -8355,12 +8459,6 @@ static void ggml_compute_forward_mul_mat_f16_f32(
}
}
#if defined(GGML_USE_CUBLAS)
CUDA_CHECK(cudaStreamSynchronize(g_cudaStream));
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
#endif
/*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/
return;
@ -8513,7 +8611,16 @@ static void ggml_compute_forward_mul_mat_q_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}
return;
}
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
if (params->ith != 0) {
return;
@ -8527,25 +8634,8 @@ static void ggml_compute_forward_mul_mat_q_f32(
return;
}
#if defined(GGML_USE_CUBLAS)
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
size_t x_size, y_size, d_size, q_size;
float * d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
float * d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
float * d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
void * d_Q = ggml_cuda_pool_malloc(GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], &q_size);
const dequantize_row_q_cuda_t dequantize_row_q_cuda = ggml_get_dequantize_row_q_cuda(type);
GGML_ASSERT(dequantize_row_q_cuda != NULL);
#else
float * const wdata = params->wdata;
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
#endif
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
@ -8553,14 +8643,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
#if defined(GGML_USE_CUBLAS)
// copy and dequantize on device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, src0, i03, i02, g_cudaStream2));
dequantize_row_q_cuda(d_Q, d_X, x_ne, g_cudaStream2);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaEventRecord(g_cudaEvent, g_cudaStream2));
#elif defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST)
const void* x = (char *) src0->data + i03*nb03 + i02*nb02;
#else
{
@ -8576,24 +8659,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
const float * x = wdata;
#endif
#if defined(GGML_USE_CUBLAS)
// copy data to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Y, src1, i03, i02, g_cudaStream));
// wait for dequantization
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStream, g_cudaEvent, 0));
// compute
CUBLAS_CHECK(
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, d_X, ne00,
d_Y, ne10,
&beta, d_D, ne01));
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#elif defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST)
// zT = y * xT
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
ne11, ne01, ne10,
@ -8611,13 +8677,6 @@ static void ggml_compute_forward_mul_mat_q_f32(
}
}
#if defined(GGML_USE_CUBLAS)
CUDA_CHECK(cudaStreamSynchronize(g_cudaStream));
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
ggml_cuda_pool_free(d_Q, q_size);
#endif
//printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
return;
@ -11601,18 +11660,21 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
size_t cur = 0;
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) {
node->n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
cur = ggml_cuda_mul_mat_get_wsize(node->src0, node->src1, node);
}
else
#endif
if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
#if defined(GGML_USE_CUBLAS)
// with cuBLAS, we need memory for the full 3D / 4D data of src1
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
#else
// here we need memory just for single 2D matrix from src0
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
#endif
} else {
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
}
@ -11621,13 +11683,13 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
#endif
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
cur = 0;
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
}
#endif
} else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
@ -12899,8 +12961,8 @@ size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t *
memcpy(&qh, &y[i].qh, sizeof(qh));
for (int l = 0; l < QK5_0; l += 2) {
const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const uint8_t vh0 = ((qh & (1u << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1u << (l + 1))) >> (l + 1)) << 4;
// cast to 16 bins
const uint8_t vi0 = ((y[i].qs[l/2] & 0x0F) | vh0) / 2;
@ -12929,8 +12991,8 @@ size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t *
memcpy(&qh, &y[i].qh, sizeof(qh));
for (int l = 0; l < QK5_1; l += 2) {
const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const uint8_t vh0 = ((qh & (1u << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1u << (l + 1))) >> (l + 1)) << 4;
// cast to 16 bins
const uint8_t vi0 = ((y[i].qs[l/2] & 0x0F) | vh0) / 2;

28
ggml.h
View file

@ -197,6 +197,14 @@
#define GGML_MAX_OPT 4
#define GGML_DEFAULT_N_THREADS 4
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
#ifdef __cplusplus
extern "C" {
#endif
@ -212,6 +220,9 @@ extern "C" {
GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x);
GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
struct ggml_object;
struct ggml_context;
@ -232,6 +243,20 @@ extern "C" {
GGML_TYPE_COUNT,
};
// model file types
enum ggml_ftype {
GGML_FTYPE_UNKNOWN = -1,
GGML_FTYPE_ALL_F32 = 0,
GGML_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
GGML_FTYPE_MOSTLY_Q4_2 = 5, // except 1d tensors
GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
};
// available tensor operations:
enum ggml_op {
GGML_OP_NONE = 0,
@ -385,6 +410,9 @@ extern "C" {
GGML_API bool ggml_is_quantized(enum ggml_type type);
// TODO: temporary until model loading of ggml examples is refactored
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
// main
GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);

54
llama-util.h
View file

@ -243,7 +243,8 @@ struct llama_mmap {
#else
static constexpr bool SUPPORTED = false;
llama_mmap(struct llama_file *) {
llama_mmap(struct llama_file *, bool prefetch = true) {
(void)prefetch;
throw std::string("mmap not supported");
}
#endif
@ -382,8 +383,13 @@ struct llama_mlock {
#else
static constexpr bool SUPPORTED = false;
void raw_lock(const void * addr, size_t size) {
size_t lock_granularity() {
return (size_t) 65536;
}
bool raw_lock(const void * addr, size_t size) {
fprintf(stderr, "warning: mlock not supported on this system\n");
return false;
}
void raw_unlock(const void * addr, size_t size) {}
@ -395,6 +401,8 @@ struct llama_buffer {
uint8_t * addr = NULL;
size_t size = 0;
llama_buffer() = default;
void resize(size_t size) {
delete[] addr;
addr = new uint8_t[size];
@ -404,27 +412,59 @@ struct llama_buffer {
~llama_buffer() {
delete[] addr;
}
// disable copy and move
llama_buffer(const llama_buffer&) = delete;
llama_buffer(llama_buffer&&) = delete;
llama_buffer& operator=(const llama_buffer&) = delete;
llama_buffer& operator=(llama_buffer&&) = delete;
};
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
struct llama_ctx_buffer {
uint8_t * addr = NULL;
bool is_cuda;
size_t size = 0;
llama_ctx_buffer() = default;
void resize(size_t size) {
if (addr) {
ggml_cuda_host_free(addr);
}
free();
addr = (uint8_t *) ggml_cuda_host_malloc(size);
if (addr) {
is_cuda = true;
}
else {
// fall back to pageable memory
addr = new uint8_t[size];
is_cuda = false;
}
this->size = size;
}
~llama_ctx_buffer() {
void free() {
if (addr) {
ggml_cuda_host_free(addr);
if (is_cuda) {
ggml_cuda_host_free(addr);
}
else {
delete[] addr;
}
}
addr = NULL;
}
~llama_ctx_buffer() {
free();
}
// disable copy and move
llama_ctx_buffer(const llama_ctx_buffer&) = delete;
llama_ctx_buffer(llama_ctx_buffer&&) = delete;
llama_ctx_buffer& operator=(const llama_ctx_buffer&) = delete;
llama_ctx_buffer& operator=(llama_ctx_buffer&&) = delete;
};
#else
typedef llama_buffer llama_ctx_buffer;

148
llama.cpp
View file

@ -727,8 +727,7 @@ struct llama_model_loader {
LLAMA_ASSERT(offset == lt.size);
} else if (lt.split_type == SPLIT_BY_COLUMNS) {
// Let's load the data into temporary buffers to ensure the OS performs large loads.
std::vector<llama_buffer> tmp_bufs;
tmp_bufs.resize(lt.shards.size());
std::vector<llama_buffer> tmp_bufs(lt.shards.size());
for (size_t i = 0; i < lt.shards.size(); i++) {
llama_load_tensor_shard & shard = lt.shards.at(i);
llama_file & file = file_loaders.at(shard.file_idx)->file;
@ -2373,7 +2372,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
}
}
int llama_get_kv_cache_token_count(struct llama_context * ctx) {
int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
return ctx->model.kv_self.n;
}
@ -2387,7 +2386,7 @@ void llama_set_rng_seed(struct llama_context * ctx, int seed) {
}
// Returns the size of the state
size_t llama_get_state_size(struct llama_context * ctx) {
size_t llama_get_state_size(const struct llama_context * ctx) {
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
// for reference, std::mt19937(1337) serializes to 6701 bytes.
const size_t s_rng_size = sizeof(size_t);
@ -2567,6 +2566,85 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
return nread;
}
bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
llama_file file(path_session, "rb");
// sanity checks
{
const uint32_t magic = file.read_u32();
const uint32_t version = file.read_u32();
if (!(magic == LLAMA_SESSION_MAGIC && version == LLAMA_SESSION_VERSION)) {
fprintf(stderr, "%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
return false;
}
llama_hparams session_hparams;
file.read_raw(&session_hparams, sizeof(llama_hparams));
if (session_hparams != ctx->model.hparams) {
fprintf(stderr, "%s : model hparams didn't match from session file!\n", __func__);
return false;
}
}
// load the prompt
{
const uint32_t n_token_count = file.read_u32();
if (n_token_count > n_token_capacity) {
fprintf(stderr, "%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
return false;
}
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
*n_token_count_out = n_token_count;
}
// restore the context state
{
const size_t n_state_size_cur = file.size - file.tell();
const size_t n_state_size_exp = llama_get_state_size(ctx);
if (n_state_size_cur != n_state_size_exp) {
fprintf(stderr, "%s : the state size in session file didn't match! expected %zu, got %zu\n", __func__, n_state_size_exp, n_state_size_cur);
return false;
}
std::vector<uint8_t> state_data(n_state_size_cur);
file.read_raw(state_data.data(), n_state_size_cur);
llama_set_state_data(ctx, state_data.data());
}
return true;
}
bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
llama_file file(path_session, "wb");
file.write_u32(LLAMA_SESSION_MAGIC);
file.write_u32(LLAMA_SESSION_VERSION);
file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
// save the prompt
file.write_u32((uint32_t) n_token_count);
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
// save the context state
{
const size_t n_state_size = llama_get_state_size(ctx);
std::vector<uint8_t> state_data(n_state_size);
llama_copy_state_data(ctx, state_data.data());
file.write_raw(state_data.data(), n_state_size);
}
return true;
}
int llama_eval(
struct llama_context * ctx,
const llama_token * tokens,
@ -2605,15 +2683,15 @@ int llama_tokenize(
return res.size();
}
int llama_n_vocab(struct llama_context * ctx) {
int llama_n_vocab(const struct llama_context * ctx) {
return ctx->vocab.id_to_token.size();
}
int llama_n_ctx(struct llama_context * ctx) {
int llama_n_ctx(const struct llama_context * ctx) {
return ctx->model.hparams.n_ctx;
}
int llama_n_embd(struct llama_context * ctx) {
int llama_n_embd(const struct llama_context * ctx) {
return ctx->model.hparams.n_embd;
}
@ -2625,7 +2703,7 @@ float * llama_get_embeddings(struct llama_context * ctx) {
return ctx->embedding.data();
}
const char * llama_token_to_str(struct llama_context * ctx, llama_token token) {
const char * llama_token_to_str(const struct llama_context * ctx, llama_token token) {
if (token >= llama_n_vocab(ctx)) {
return nullptr;
}
@ -2694,57 +2772,3 @@ const char * llama_print_system_info(void) {
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
return ctx->model.tensors_by_name;
}
size_t llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
// TODO leverage mmap
llama_file file(path_session, "rb");
const uint32_t magic = file.read_u32();
const uint32_t version = file.read_u32();
if (!(magic == 'ggsn' && version == 0)) {
fprintf(stderr, "%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
return 0;
}
llama_hparams session_hparams;
file.read_raw(&session_hparams, sizeof(llama_hparams));
// REVIEW
if (session_hparams != ctx->model.hparams) {
fprintf(stderr, "%s : model hparams didn't match from session file!\n", __func__);
return 0;
}
const uint32_t n_token_count = file.read_u32();
LLAMA_ASSERT(n_token_capacity >= n_token_count);
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
*n_token_count_out = n_token_count;
const size_t n_state_size = file.size - file.tell();
const size_t n_orig_state_size = llama_get_state_size(ctx);
if (n_state_size != n_orig_state_size) {
fprintf(stderr, "%s : failed to validate state size\n", __func__);
}
std::unique_ptr<uint8_t[]> state_data(new uint8_t[n_state_size]);
file.read_raw(state_data.get(), n_state_size);
return llama_set_state_data(ctx, state_data.get());
}
size_t llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
// TODO save temp & swap
llama_file file(path_session, "wb");
const size_t n_state_size = llama_get_state_size(ctx);
std::unique_ptr<uint8_t[]> state_data(new uint8_t[n_state_size]);
llama_copy_state_data(ctx, state_data.get());
file.write_u32('ggsn'); // magic
file.write_u32(0); // version
file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
file.write_u32((uint32_t) n_token_count); // REVIEW
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
file.write_raw(state_data.get(), n_state_size);
return n_state_size; // REVIEW
}

24
llama.h
View file

@ -19,9 +19,11 @@
# define LLAMA_API
#endif
#define LLAMA_FILE_VERSION 1
#define LLAMA_FILE_MAGIC 0x67676a74 // 'ggjt' in hex
#define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
#define LLAMA_FILE_VERSION 1
#define LLAMA_FILE_MAGIC 'ggjt'
#define LLAMA_FILE_MAGIC_UNVERSIONED 'ggml'
#define LLAMA_SESSION_MAGIC 'ggsn'
#define LLAMA_SESSION_VERSION 0
#ifdef __cplusplus
extern "C" {
@ -120,13 +122,13 @@ extern "C" {
int n_threads);
// Returns the number of tokens in the KV cache
LLAMA_API int llama_get_kv_cache_token_count(struct llama_context * ctx);
LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
// Sets the current rng seed.
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
// Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
LLAMA_API size_t llama_get_state_size(struct llama_context * ctx);
LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
// Copies the state to the specified destination address.
// Destination needs to have allocated enough memory.
@ -138,8 +140,8 @@ extern "C" {
LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src);
// Save/load session file
LLAMA_API size_t llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
LLAMA_API size_t llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
// Run the llama inference to obtain the logits and probabilities for the next token.
// tokens + n_tokens is the provided batch of new tokens to process
@ -164,9 +166,9 @@ extern "C" {
int n_max_tokens,
bool add_bos);
LLAMA_API int llama_n_vocab(struct llama_context * ctx);
LLAMA_API int llama_n_ctx (struct llama_context * ctx);
LLAMA_API int llama_n_embd (struct llama_context * ctx);
LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
LLAMA_API int llama_n_embd (const struct llama_context * ctx);
// Token logits obtained from the last call to llama_eval()
// The logits for the last token are stored in the last row
@ -180,7 +182,7 @@ extern "C" {
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
// Token Id -> String. Uses the vocabulary in the provided context
LLAMA_API const char * llama_token_to_str(struct llama_context * ctx, llama_token token);
LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
// Special tokens
LLAMA_API llama_token llama_token_bos();

53
scripts/build-info.cmake Normal file
View file

@ -0,0 +1,53 @@
set(TEMPLATE_FILE "${CMAKE_BINARY_DIR}/BUILD_INFO.h.in")
set(HEADER_FILE "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h")
set(BUILD_NUMBER 0)
set(BUILD_COMMIT "unknown")
# Look for git
find_package(Git)
if(NOT Git_FOUND)
execute_process(
COMMAND which git
OUTPUT_VARIABLE GIT_EXECUTABLE
OUTPUT_STRIP_TRAILING_WHITESPACE
)
if(NOT GIT_EXECUTABLE STREQUAL "")
set(Git_FOUND TRUE)
message(STATUS "Found Git using 'which': ${GIT_EXECUTABLE}")
else()
message(WARNING "Git not found using 'find_package' or 'which'. Build info will not be accurate. Consider installing Git or ensuring it is in the PATH.")
endif()
endif()
# Get the commit count and hash
if(Git_FOUND)
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE HEAD
OUTPUT_STRIP_TRAILING_WHITESPACE
RESULT_VARIABLE GIT_HEAD_RESULT
)
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE COUNT
OUTPUT_STRIP_TRAILING_WHITESPACE
RESULT_VARIABLE GIT_COUNT_RESULT
)
if(GIT_HEAD_RESULT EQUAL 0 AND GIT_COUNT_RESULT EQUAL 0)
set(BUILD_COMMIT ${HEAD})
set(BUILD_NUMBER ${COUNT})
endif()
endif()
# Only write the header if it's changed to prevent unnecessary recompilation
if(EXISTS ${HEADER_FILE})
file(STRINGS ${HEADER_FILE} CONTENTS REGEX "BUILD_COMMIT \"([^\"]*)\"")
list(GET CONTENTS 0 EXISTING)
if(NOT EXISTING STREQUAL "#define BUILD_COMMIT \"${BUILD_COMMIT}\"")
configure_file(${TEMPLATE_FILE} ${HEADER_FILE})
endif()
else()
configure_file(${TEMPLATE_FILE} ${HEADER_FILE})
endif()

22
scripts/build-info.sh Executable file
View file

@ -0,0 +1,22 @@
#!/bin/sh
BUILD_NUMBER="0"
BUILD_COMMIT="unknown"
REV_LIST=$(git rev-list --count HEAD)
if [ $? -eq 0 ]; then
BUILD_NUMBER=$REV_LIST
fi
REV_PARSE=$(git rev-parse --short HEAD)
if [ $? -eq 0 ]; then
BUILD_COMMIT=$REV_PARSE
fi
echo "#ifndef BUILD_INFO_H"
echo "#define BUILD_INFO_H"
echo ""
echo "#define BUILD_NUMBER $BUILD_NUMBER"
echo "#define BUILD_COMMIT \"$BUILD_COMMIT\""
echo ""
echo "#endif // BUILD_INFO_H"