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Merge 'origin/master' into hipblas

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Henri Vasserman 2023-05-20 18:29:31 +03:00
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2
.github/workflows/build.yml vendored
View file

@ -165,7 +165,7 @@ jobs:
- build: 'clblast'
defines: '-DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"'
- build: 'openblas'
defines: '-DLLAMA_OPENBLAS=ON -DBLAS_LIBRARIES="/LIBPATH:$env:RUNNER_TEMP/openblas/lib" -DOPENBLAS_INC="$env:RUNNER_TEMP/openblas/include"'
defines: '-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include"'
steps:
- name: Clone

67
BLIS.md Normal file
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@ -0,0 +1,67 @@
BLIS Installation Manual
------------------------
BLIS is a portable software framework for high-performance BLAS-like dense linear algebra libraries. It has received awards and recognition, including the 2023 James H. Wilkinson Prize for Numerical Software and the 2020 SIAM Activity Group on Supercomputing Best Paper Prize. BLIS provides a new BLAS-like API and a compatibility layer for traditional BLAS routine calls. It offers features such as object-based API, typed API, BLAS and CBLAS compatibility layers.
Project URL: https://github.com/flame/blis
### Prepare:
Compile BLIS:
```bash
git clone https://github.com/flame/blis
cd blis
./configure --enable-cblas -t openmp,pthreads auto
# will install to /usr/local/ by default.
make -j
```
Install BLIS:
```bash
sudo make install
```
We recommend using openmp since it's easier to modify the cores been used.
### llama.cpp compilation
Makefile:
```bash
make LLAMA_BLIS=1 -j
# make LLAMA_BLIS=1 benchmark-matmult
```
CMake:
```bash
mkdir build
cd build
cmake -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=FLAME ..
make -j
```
### llama.cpp execution
According to the BLIS documentation, we could set the following
environment variables to modify the behavior of openmp:
```
export GOMP_GPU_AFFINITY="0-19"
export BLIS_NUM_THREADS=14
```
And then run the binaries as normal.
### Intel specific issue
Some might get the error message saying that `libimf.so` cannot be found.
Please follow this [stackoverflow page](https://stackoverflow.com/questions/70687930/intel-oneapi-2022-libimf-so-no-such-file-or-directory-during-openmpi-compila).
### Reference:
1. https://github.com/flame/blis#getting-started
2. https://github.com/flame/blis/blob/master/docs/Multithreading.md

37
CMakeLists.txt
View file

@ -65,7 +65,8 @@ endif()
# 3rd party libs
option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
option(LLAMA_OPENBLAS "llama: use OpenBLAS" OFF)
option(LLAMA_BLAS "llama: use BLAS" OFF)
option(LLAMA_BLAS_VENDOR "llama: BLA_VENDOR from https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" Generic)
option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
@ -146,36 +147,28 @@ if (APPLE AND LLAMA_ACCELERATE)
endif()
endif()
if (LLAMA_OPENBLAS)
if (LLAMA_BLAS)
if (LLAMA_STATIC)
set(BLA_STATIC ON)
endif()
set(BLA_VENDOR OpenBLAS)
if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22)
set(BLA_SIZEOF_INTEGER 8)
endif()
set(BLA_VENDOR ${LLAMA_BLAS_VENDOR})
find_package(BLAS)
if (BLAS_FOUND)
message(STATUS "OpenBLAS found")
message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}")
add_compile_options(${BLAS_LINKER_FLAGS})
add_compile_definitions(GGML_USE_OPENBLAS)
add_link_options(${BLAS_LIBRARIES})
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} openblas)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES})
# find header file
set(OPENBLAS_INCLUDE_SEARCH_PATHS
/usr/include
/usr/include/openblas
/usr/include/openblas-base
/usr/local/include
/usr/local/include/openblas
/usr/local/include/openblas-base
/opt/OpenBLAS/include
$ENV{OpenBLAS_HOME}
$ENV{OpenBLAS_HOME}/include
)
find_path(OPENBLAS_INC NAMES cblas.h PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS})
add_compile_options(-I${OPENBLAS_INC})
message("${BLAS_LIBRARIES} ${BLAS_INCLUDE_DIRS}")
include_directories(${BLAS_INCLUDE_DIRS})
else()
message(WARNING "OpenBLAS not found")
message(WARNING "BLAS not found, please refer to "
"https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
" to set correct LLAMA_BLAS_VENDOR")
endif()
endif()

4
Makefile
View file

@ -122,6 +122,10 @@ ifdef LLAMA_OPENBLAS
LDFLAGS += -lopenblas
endif
endif
ifdef LLAMA_BLIS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis
LDFLAGS += -lblis -L/usr/local/lib
endif
ifdef LLAMA_CUBLAS
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include

41
README.md
View file

@ -9,6 +9,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
**Hot topics:**
- Quantization formats `Q4` and `Q8` have changed again (19 May) - [(info)](https://github.com/ggerganov/llama.cpp/pull/1508)
- Quantization formats `Q4` and `Q5` have changed - requantize any old models [(info)](https://github.com/ggerganov/llama.cpp/pull/1405)
- [Roadmap May 2023](https://github.com/ggerganov/llama.cpp/discussions/1220)
@ -55,7 +56,7 @@ The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quant
- Mixed F16 / F32 precision
- 4-bit, 5-bit and 8-bit integer quantization support
- Runs on the CPU
- OpenBLAS support
- Supports OpenBLAS/Apple BLAS/ARM Performance Lib/ATLAS/BLIS/Intel MKL/NVHPC/ACML/SCSL/SGIMATH and [more](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) in BLAS
- cuBLAS and CLBlast support
The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022).
@ -80,6 +81,7 @@ as the main playground for developing new features for the [ggml](https://github
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
- [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
- [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)
- [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
**Bindings:**
@ -272,10 +274,25 @@ Building the program with BLAS support may lead to some performance improvements
```bash
mkdir build
cd build
cmake .. -DLLAMA_OPENBLAS=ON
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake --build . --config Release
```
- BLIS
Check [BLIS.md](BLIS.md) for more information.
- Intel MKL
By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. You may also specify it by:
```bash
mkdir build
cd build
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake --build . -config Release
```
- cuBLAS
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
@ -333,16 +350,16 @@ Several quantization methods are supported. They differ in the resulting model d
| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 |
|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
| 7B | perplexity | 5.9066 | 6.1565 | 6.0910 | 5.9862 | 5.9481 | 5.9069 |
| 7B | file size | 13.0G | 4.0G | 4.8G | 4.4G | 4.8G | 7.1G |
| 7B | ms/tok @ 4th | 128 | 50 | 54 | 75 | 83 | 75 |
| 7B | ms/tok @ 8th | 123 | 44 | 52 | 53 | 58 | 72 |
| 7B | bits/weight | 16.0 | 5.0 | 6.0 | 5.5 | 6.0 | 9.0 |
| 13B | perplexity | 5.2543 | 5.3860 | 5.3607 | 5.2856 | 5.2706 | 5.2548 |
| 13B | file size | 25.0G | 7.6G | 9.1G | 8.4G | 9.1G | 14G |
| 13B | ms/tok @ 4th | 239 | 93 | 101 | 150 | 164 | 141 |
| 13B | ms/tok @ 8th | 240 | 81 | 96 | 96 | 104 | 136 |
| 13B | bits/weight | 16.0 | 5.0 | 6.0 | 5.5 | 6.0 | 9.0 |
| 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G |
| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 |
| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 |
| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 |
| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G |
| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 |
| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 |
| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
### Perplexity (measuring model quality)

3
convert.py
View file

@ -121,7 +121,6 @@ def make_tensors_list() -> List[str]:
f'layers.{i}.feed_forward.w1.weight',
f'layers.{i}.feed_forward.w2.weight',
f'layers.{i}.feed_forward.w3.weight',
f'layers.{i}.atttention_norm.weight',
f'layers.{i}.ffn_norm.weight',
]
return ret
@ -1055,7 +1054,7 @@ def load_some_model(path: Path) -> ModelPlus:
files = list(path.glob("model-00001-of-*.safetensors"))
if not files:
# Try the PyTorch patterns too, with lower priority
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt"]
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin" ]
files = [file for glob in globs for file in path.glob(glob)]
if not files:
# Try GGML too, but with lower priority, since if both a non-GGML

8
examples/benchmark/benchmark-matmult.cpp
View file

@ -1,6 +1,7 @@
#include <locale.h>
#include "ggml.h"
#include "build-info.h"
#include <locale.h>
#include <assert.h>
#include <math.h>
#include <cstring>
@ -211,6 +212,7 @@ int main(int argc, char ** argv) {
printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n");
printf("=====================================================================================\n");
double gflops_sum = 0;
for (int i=0;i<benchmark_params.n_iterations ;i++) {
long long int start = ggml_time_us();
@ -219,6 +221,7 @@ int main(int argc, char ** argv) {
long long int stop = ggml_time_us();
long long int usec = stop-start;
double gflops = (double)(flops_per_matrix)/usec/1000.0;
gflops_sum += gflops;
printf("%9i;%8i;%6i;%6i;%6i;%15lli;%18lli;%10.2f\n",
i,
gf31.n_threads,
@ -248,4 +251,7 @@ int main(int argc, char ** argv) {
// Running a different graph computation to make sure we override the CPU cache lines
ggml_graph_compute(ctx, &gf32);
}
printf("\n");
printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations));
printf("=====================================================================================\n");
}

151
examples/chat-persistent.sh Executable file
View file

@ -0,0 +1,151 @@
#!/bin/bash
set -euo pipefail
cd "$(dirname "$0")/.." || exit
if [[ -z "${PROMPT_CACHE_FILE+x}" || -z "${CHAT_SAVE_DIR+x}" ]]; then
echo >&2 "error: PROMPT_CACHE_FILE and CHAT_SAVE_DIR must be provided"
exit 1
fi
MODEL="${MODEL:-./models/13B/ggml-model-q4_0.bin}"
PROMPT_TEMPLATE="${PROMPT_TEMPLATE:-./prompts/chat.txt}"
USER_NAME="${USER_NAME:-User}"
AI_NAME="${AI_NAME:-ChatLLaMa}"
DATE_TIME="$(date +%H:%M)"
DATE_YEAR="$(date +%Y)"
LOG="${CHAT_SAVE_DIR}/main.log"
LOG_BG="${CHAT_SAVE_DIR}/main-bg.log"
CUR_PROMPT_FILE="${CHAT_SAVE_DIR}/current-prompt.txt"
CUR_PROMPT_CACHE="${CHAT_SAVE_DIR}/current-cache.bin"
NEXT_PROMPT_FILE="${CHAT_SAVE_DIR}/next-prompt.txt"
NEXT_PROMPT_CACHE="${CHAT_SAVE_DIR}/next-cache.bin"
SESSION_SIZE_MSG_PATTERN='main: session file matches \d+ / \d+'
SAMPLE_TIME_MSG_PATTERN='sample time =\s+\d+.\d+ ms /\s+\d+'
SED_DELETE_MESSAGES="/^(${USER_NAME}:|${AI_NAME}:|\\.\\.\\.)/,\$d"
CTX_SIZE=2048
CTX_ROTATE_POINT=$((CTX_SIZE * 3 / 5)) # REVIEW
OPTS=(--model "$MODEL" --ctx_size "$CTX_SIZE" --repeat_last_n 256 "$@")
# An unbuffered `tail -c+N`
skip_bytes() {
LANG=C IFS= read -r -n "$1" -d '' c
while LANG=C IFS= read -r -n 1 -d '' c; do
printf '%s' "$c"
done
}
mkdir -p "$CHAT_SAVE_DIR"
echo >"$LOG"
trap "tail -n100 ${LOG}" EXIT
if [[ ! -e "$CUR_PROMPT_FILE" ]]; then
sed -e "s/\[\[USER_NAME\]\]/${USER_NAME}/g" \
-e "s/\[\[AI_NAME\]\]/${AI_NAME}/g" \
-e "s/\[\[DATE_TIME\]\]/${DATE_TIME}/g" \
-e "s/\[\[DATE_YEAR\]\]/${DATE_YEAR}/g" \
"$PROMPT_TEMPLATE" >"$CUR_PROMPT_FILE"
fi
if [[ ! -e "$NEXT_PROMPT_FILE" ]]; then
sed -r "$SED_DELETE_MESSAGES" "$CUR_PROMPT_FILE" >"$NEXT_PROMPT_FILE"
fi
if [[ "$(tail -c4 "$NEXT_PROMPT_FILE")" != "..." ]]; then
echo '...' >>"$NEXT_PROMPT_FILE"
fi
if [[ ! -e "$PROMPT_CACHE_FILE" ]]; then
echo 'Prompt cache does not exist, building...'
# Default batch_size to 8 here for better user feedback during initial prompt processing
./main 2>>"$LOG" \
--batch_size 8 \
"${OPTS[@]}" \
--prompt-cache "$PROMPT_CACHE_FILE" \
--file "$CUR_PROMPT_FILE" \
--n_predict 1
echo
echo 'Done!'
fi
if [[ ! -e "$CUR_PROMPT_CACHE" ]]; then
cp "$PROMPT_CACHE_FILE" "$CUR_PROMPT_CACHE"
fi
if [[ ! -e "$NEXT_PROMPT_CACHE" ]]; then
cp "$PROMPT_CACHE_FILE" "$NEXT_PROMPT_CACHE"
fi
printf '%s ' "$(< "$CUR_PROMPT_FILE")"
n_tokens=0
while read -e line; do
# Limit generation to remaining context, with a buffer and estimating 2 chars/token for input
n_predict=$((CTX_SIZE - n_tokens - ${#line} / 2 - 32))
# Swap prompts when we're about to run out of context
if ((n_predict <= 0)); then
wait # for background main (below) to finish with next prompt
mv "$NEXT_PROMPT_FILE" "$CUR_PROMPT_FILE"
mv "$NEXT_PROMPT_CACHE" "$CUR_PROMPT_CACHE"
sed -r "$SED_DELETE_MESSAGES" "$CUR_PROMPT_FILE" >"$NEXT_PROMPT_FILE"
echo '...' >>"$NEXT_PROMPT_FILE"
cp "$PROMPT_CACHE_FILE" "$NEXT_PROMPT_CACHE"
n_tokens=0
n_predict=$((CTX_SIZE / 2))
fi
echo " ${line}" >>"$CUR_PROMPT_FILE"
if ((n_tokens > CTX_ROTATE_POINT)); then
echo " ${line}" >>"$NEXT_PROMPT_FILE"
fi
n_prompt_len_pre=$(($(wc -c <"$CUR_PROMPT_FILE")))
printf '%s: ' "$AI_NAME" >>"$CUR_PROMPT_FILE"
./main 2>>"$LOG" "${OPTS[@]}" \
--prompt-cache "$CUR_PROMPT_CACHE" \
--prompt-cache-all \
--file "$CUR_PROMPT_FILE" \
--reverse-prompt "${USER_NAME}:" \
--n_predict "$n_predict" |
skip_bytes 1 | # skip BOS token added by ./main
tee "$CUR_PROMPT_FILE.tmp" | # save prompt + generation to tmp file
skip_bytes "$n_prompt_len_pre" # print generation
mv "$CUR_PROMPT_FILE.tmp" "$CUR_PROMPT_FILE"
# if we hit n_predict instead of reverse-prompt, we need to add the prompt
if [[ "$(tail -n1 "$CUR_PROMPT_FILE")" != "${USER_NAME}:" ]]; then
printf '\n%s:' "$USER_NAME"
printf '\n%s:' "$USER_NAME" >> "$CUR_PROMPT_FILE"
fi
printf ' '
# HACK get num tokens from debug message
# TODO get both messages in one go
if ! session_size_msg="$(tail -n30 "$LOG" | grep -oE "$SESSION_SIZE_MSG_PATTERN")" ||
! sample_time_msg="$( tail -n10 "$LOG" | grep -oE "$SAMPLE_TIME_MSG_PATTERN")"; then
echo >&2 "Couldn't get number of tokens from ./main output!"
exit 1
fi
n_tokens=$(($(cut -d/ -f2 <<<"$session_size_msg") + $(cut -d/ -f2 <<<"$sample_time_msg")))
if ((n_tokens > CTX_ROTATE_POINT)); then
tail -c+$((n_prompt_len_pre + 1)) "$CUR_PROMPT_FILE" >>"$NEXT_PROMPT_FILE"
fi
# Update cache for next prompt in background, ideally during user input
./main >>"$LOG_BG" 2>&1 "${OPTS[@]}" \
--prompt-cache "$NEXT_PROMPT_CACHE" \
--file "$NEXT_PROMPT_FILE" \
--n_predict 1 &
done

50
examples/common.cpp
View file

@ -321,12 +321,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
invalid_param = true;
break;
}
} else if (arg == "--n-parts") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_parts = std::stoi(argv[i]);
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, default_params);
exit(0);
@ -357,7 +351,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
}
if (params.prompt_cache_all &&
(params.interactive || params.interactive_first ||
params.instruct || params.antiprompt.size())) {
params.instruct)) {
fprintf(stderr, "error: --prompt-cache-all not supported in interactive mode yet\n");
gpt_print_usage(argc, argv, default_params);
exit(1);
@ -379,8 +373,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
fprintf(stderr, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
fprintf(stderr, " run in interactive mode and poll user input upon seeing PROMPT (can be\n");
fprintf(stderr, " specified more than once for multiple prompts).\n");
fprintf(stderr, " halt generation at PROMPT, return control in interactive mode\n");
fprintf(stderr, " (can be specified more than once for multiple prompts).\n");
fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
@ -418,7 +412,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stderr, " --no-penalize-nl do not penalize newline token\n");
fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value\n");
fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp);
fprintf(stderr, " --n-parts N number of model parts (default: -1 = determine from dimensions)\n");
fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " --perplexity compute perplexity over the prompt\n");
fprintf(stderr, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
@ -473,7 +466,6 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.n_gpu_layers = params.n_gpu_layers;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
@ -586,6 +578,37 @@ void console_set_color(console_state & con_st, console_color_t color) {
}
char32_t getchar32() {
#if defined(_WIN32)
HANDLE hConsole = GetStdHandle(STD_INPUT_HANDLE);
wchar_t high_surrogate = 0;
while (true) {
INPUT_RECORD record;
DWORD count;
if (!ReadConsoleInputW(hConsole, &record, 1, &count) || count == 0) {
return WEOF;
}
if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) {
wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar;
if (wc == 0) {
continue;
}
if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
high_surrogate = wc;
continue;
} else if ((wc >= 0xDC00) && (wc <= 0xDFFF)) { // Check if wc is a low surrogate
if (high_surrogate != 0) { // Check if we have a high surrogate
return ((high_surrogate - 0xD800) << 10) + (wc - 0xDC00) + 0x10000;
}
}
high_surrogate = 0; // Reset the high surrogate
return static_cast<char32_t>(wc);
}
}
#else
wchar_t wc = getwchar();
if (static_cast<wint_t>(wc) == WEOF) {
return WEOF;
@ -604,6 +627,7 @@ char32_t getchar32() {
#endif
return static_cast<char32_t>(wc);
#endif
}
void pop_cursor(console_state & con_st) {
@ -757,7 +781,7 @@ bool console_readline(console_state & con_st, std::string & line) {
break;
}
if (input_char == WEOF || input_char == 0x04 /* Ctrl+D*/) {
if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {
end_of_stream = true;
break;
}
@ -772,7 +796,7 @@ bool console_readline(console_state & con_st, std::string & line) {
char32_t code = getchar32();
if (code == '[' || code == 0x1B) {
// Discard the rest of the escape sequence
while ((code = getchar32()) != WEOF) {
while ((code = getchar32()) != (char32_t) WEOF) {
if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {
break;
}

7
examples/common.h
View file

@ -24,7 +24,6 @@ struct gpt_params {
int32_t seed = -1; // RNG seed
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)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
@ -45,15 +44,15 @@ struct gpt_params {
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
std::string prompt = "";
std::string model = "models/7B/ggml-model.bin"; // model path
std::string prompt = "";
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
std::string input_prefix = ""; // string to prefix user inputs with
std::string input_suffix = ""; // string to suffix user inputs with
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
std::string lora_adapter = ""; // lora adapter path
std::string lora_base = ""; // base model path for the lora adapter
std::string lora_base = ""; // base model path for the lora adapter
bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided

3
examples/embedding/embedding.cpp
View file

@ -6,7 +6,6 @@
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
@ -32,6 +31,8 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend();
llama_context * ctx;
// load the model

32
examples/main/main.cpp
View file

@ -50,7 +50,6 @@ void sigint_handler(int signo) {
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
@ -97,8 +96,7 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
// params.prompt = R"(// this function checks if the number n is prime
//bool is_prime(int n) {)";
llama_init_backend();
llama_context * ctx;
g_ctx = &ctx;
@ -209,8 +207,8 @@ int main(int argc, char ** argv) {
params.antiprompt.push_back("### Instruction:\n\n");
}
// enable interactive mode if reverse prompt or interactive start is specified
if (params.antiprompt.size() != 0 || params.interactive_first) {
// enable interactive mode if interactive start is specified
if (params.interactive_first) {
params.interactive = true;
}
@ -242,7 +240,7 @@ int main(int argc, char ** argv) {
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
#elif defined (_WIN32)
auto console_ctrl_handler = [](DWORD ctrl_type) -> BOOL {
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(static_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
@ -306,7 +304,7 @@ int main(int argc, char ** argv) {
std::vector<llama_token> embd;
while (n_remain != 0 || params.interactive) {
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict
if (embd.size() > 0) {
// infinite text generation via context swapping
@ -504,9 +502,8 @@ int main(int argc, char ** argv) {
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
}
// in interactive mode, and not currently processing queued inputs;
// check if we should prompt the user for more
if (params.interactive && (int) embd_inp.size() <= n_consumed) {
// if not currently processing queued inputs;
if ((int) embd_inp.size() <= n_consumed) {
// check for reverse prompt
if (params.antiprompt.size()) {
@ -517,10 +514,21 @@ int main(int argc, char ** argv) {
is_antiprompt = false;
// Check if each of the reverse prompts appears at the end of the output.
// If we're not running interactively, the reverse prompt might be tokenized with some following characters
// so we'll compensate for that by widening the search window a bit.
for (std::string & antiprompt : params.antiprompt) {
if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
is_interacting = true;
size_t extra_padding = params.interactive ? 0 : 2;
size_t search_start_pos = last_output.length() > static_cast<size_t>(antiprompt.length() + extra_padding)
? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
: 0;
if (last_output.find(antiprompt.c_str(), search_start_pos) != std::string::npos) {
if (params.interactive) {
is_interacting = true;
console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
}
is_antiprompt = true;
fflush(stdout);
break;
}
}

3
examples/perplexity/perplexity.cpp
View file

@ -116,7 +116,6 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
params.n_batch = 512;
if (gpt_params_parse(argc, argv, params) == false) {
@ -144,6 +143,8 @@ int main(int argc, char ** argv) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend();
llama_context * ctx;
// load the model and apply lora adapter, if any

1
examples/quantize-stats/quantize-stats.cpp
View file

@ -321,7 +321,6 @@ int main(int argc, char ** argv) {
auto lparams = llama_context_default_params();
lparams.n_ctx = 256;
lparams.n_parts = 1;
lparams.seed = 1;
lparams.f16_kv = false;
lparams.use_mlock = false;

21
examples/quantize/quantize.cpp
View file

@ -1,7 +1,7 @@
#include "ggml.h"
#include "llama.h"
#include "build-info.h"
#include "llama.h"
#include <cstdio>
#include <map>
#include <string>
@ -42,8 +42,6 @@ bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::st
// ./quantize models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
//
int main(int argc, char ** argv) {
ggml_time_init();
if (argc < 3) {
fprintf(stderr, "usage: %s model-f32.bin [model-quant.bin] type [nthreads]\n", argv[0]);
for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
@ -52,12 +50,7 @@ int main(int argc, char ** argv) {
return 1;
}
// needed to initialize f16 tables
{
struct ggml_init_params params = { 0, NULL, false };
struct ggml_context * ctx = ggml_init(params);
ggml_free(ctx);
}
llama_init_backend();
// parse command line arguments
const std::string fname_inp = argv[1];
@ -116,25 +109,25 @@ int main(int argc, char ** argv) {
}
fprintf(stderr, "\n");
const int64_t t_main_start_us = ggml_time_us();
const int64_t t_main_start_us = llama_time_us();
int64_t t_quantize_us = 0;
// load the model
{
const int64_t t_start_us = ggml_time_us();
const int64_t t_start_us = llama_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype, nthread)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}
t_quantize_us = ggml_time_us() - t_start_us;
t_quantize_us = llama_time_us() - t_start_us;
}
// report timing
{
const int64_t t_main_end_us = ggml_time_us();
const int64_t t_main_end_us = llama_time_us();
printf("\n");
printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);

2
examples/save-load-state/save-load-state.cpp
View file

@ -8,7 +8,6 @@
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
params.seed = 42;
params.n_threads = 4;
params.repeat_last_n = 64;
@ -27,7 +26,6 @@ int main(int argc, char ** argv) {
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;

138
ggml-cuda.cu
View file

@ -36,6 +36,7 @@
#define cudaGetLastError hipGetLastError
#define cudaMalloc hipMalloc
#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
#define cudaMemcpy hipMemcpy
#define cudaMemcpy2DAsync hipMemcpy2DAsync
#define cudaMemcpyAsync hipMemcpyAsync
#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
@ -87,19 +88,19 @@ typedef void (*dequantize_mul_mat_vec_cuda_t)(const void * vx, const float * y,
#define QK4_0 32
#define QR4_0 2
typedef struct {
float d; // delta
half d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding");
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
#define QR4_1 2
typedef struct {
float d; // delta
float m; // min
half d; // delta
half m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK5_0 32
#define QR5_0 2
@ -123,14 +124,24 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) +
#define QK8_0 32
#define QR8_0 1
typedef struct {
float d; // delta
half d; // delta
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
#define CUDA_MUL_BLOCK_SIZE 256
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
#define CUDA_DMMV_BLOCK_SIZE 32 // dmmv = dequantize_mul_mat_vec
static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= kx) {
return;
}
dst[i] = x[i] * y[i%ky];
}
static __device__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){
const block_q4_0 * x = (const block_q4_0 *) vx;
@ -273,6 +284,11 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y,
}
}
static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
}
static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
@ -512,6 +528,67 @@ static cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor
}
}
static void ggml_cuda_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src1->backend == GGML_BACKEND_CUDA);
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[2];
const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
size_t x_size, d_size;
float * d_X = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &x_size); // src0
float * d_Y = (float *) src1->data; // src1 is already on device, broadcasted.
float * d_D = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &d_size); // dst
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
const int i0 = i03*ne02 + i02;
float * c_X2 = d_X + i0*ne01*ne00;
float * c_D2 = d_D + i0*ne01*ne00;
cudaStream_t cudaStream = g_cudaStreams[i0 % GGML_CUDA_MAX_STREAMS];
cudaStream_t cudaStream2 = g_cudaStreams2[i0 % GGML_CUDA_MAX_STREAMS];
cudaEvent_t cudaEvent = g_cudaEvents[i0 % GGML_CUDA_MAX_EVENTS];
// copy src0 to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X2, src0, i03, i02, cudaStream2));
CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));
// wait for data
CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));
for (int64_t i01 = 0; i01 < ne01; i01++) {
const int64_t i13 = i03%ne13;
const int64_t i12 = i02%ne12;
const int64_t i11 = i01%ne11;
const int i1 = i13*ne12*ne11 + i12*ne11 + i11;
float * c_X1 = c_X2 + i01*ne00;
float * c_Y = d_Y + i1*ne10;
float * c_D1 = c_D2 + i01*ne00;
// compute
mul_f32_cuda(c_X1, c_Y, c_D1, ne00, ne10, cudaStream);
CUDA_CHECK(cudaGetLastError());
}
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D2, sizeof(float)*ne00*ne01, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_D, d_size);
}
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];
@ -769,6 +846,11 @@ static void ggml_cuda_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor
ggml_cuda_pool_free(d_Q, q_size);
}
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
ggml_cuda_mul_f32(src0, src1, dst);
}
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];
@ -842,14 +924,48 @@ void ggml_cuda_transform_tensor(ggml_tensor * tensor) {
const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);
size_t q_size;
char * d_Q = (char *) ggml_cuda_pool_malloc(q_sz, &q_size);
char * dst = (char *) ggml_cuda_pool_malloc(q_sz, &q_size);
cudaStream_t cudaStream2 = g_cudaStreams2[0];
// copy tensor to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, tensor, 0, 0, cudaStream2));
CUDA_CHECK(cudaDeviceSynchronize());
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = 0; i2 < ne2; i2++) {
int i = i3*ne2 + i2;
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(dst + i*ne0*ne1, tensor, i3, i2, cudaStream2));
}
}
tensor->data = d_Q;
tensor->data = dst;
tensor->backend = GGML_BACKEND_CUDA;
}
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
FILE * fp = fopen(fname, "rb");
const size_t size = ggml_nbytes(tensor);
void * buf;
CUDA_CHECK(cudaMalloc(&buf, size));
void * buf_host = malloc(size);
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, SEEK_SET);
#else
int ret = fseek(fp, (long) offset, SEEK_SET);
#endif
GGML_ASSERT(ret == 0); // same
size_t ret2 = fread(buf_host, size, 1, fp);
if (ret2 != 1) {
fprintf(stderr, "unexpectedly reached end of file");
exit(1);
}
cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
tensor->data = buf;
free(buf_host);
fclose(fp);
}

2
ggml-cuda.h
View file

@ -6,6 +6,7 @@ extern "C" {
void ggml_init_cublas(void);
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
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);
@ -15,6 +16,7 @@ void * ggml_cuda_host_malloc(size_t size);
void ggml_cuda_host_free(void * ptr);
void ggml_cuda_transform_tensor(struct ggml_tensor * tensor);
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensors, size_t offset);
#ifdef __cplusplus
}

375
ggml-opencl.c
View file

@ -10,87 +10,77 @@
#include "ggml.h"
#define MULTILINE_QUOTE(...) #__VA_ARGS__
const char * clblast_dequant = MULTILINE_QUOTE(
static const char * program_source = MULTILINE_QUOTE(
typedef char int8_t;
typedef uchar uint8_t;
typedef int int32_t;
typedef uint uint32_t;
constant uint QK4_0 = 32;
struct block_q4_0
struct __attribute__ ((packed)) block_q4_0
{
float d;
uint8_t qs[QK4_0 / 2];
half d;
uint8_t qs[16]; /* QK4_0 / 2 */
};
constant uint QK4_1 = 32;
struct block_q4_1
struct __attribute__ ((packed)) block_q4_1
{
float d;
float m;
uint8_t qs[QK4_1 / 2];
half d;
half m;
uint8_t qs[16]; /* QK4_1 / 2 */
};
constant uint QK5_0 = 32;
struct __attribute__ ((packed)) block_q5_0
{
half d;
uint32_t qh;
uint8_t qs[QK5_0 / 2];
uint8_t qs[16]; /* QK5_0 / 2 */
};
constant uint QK5_1 = 32;
struct block_q5_1
struct __attribute__ ((packed)) block_q5_1
{
half d;
half m;
uint32_t qh;
uint8_t qs[QK5_1 / 2];
uint8_t qs[16]; /* QK5_1 / 2 */
};
constant uint QK8_0 = 32;
struct block_q8_0
struct __attribute__ ((packed)) block_q8_0
{
float d;
uint8_t qs[QK8_0];
half d;
int8_t qs[32]; /* QK8_0 */
};
__kernel void dequantize_row_q4_0(__global struct block_q4_0* x, __global float* y) {
constant uint qk = QK4_0;
const uint i = get_global_id(0) / qk;
const uint i = get_global_id(0) / 32; /* QK4_0 */
const uint j = get_local_id(0);
const float d = x[i].d;
const float d = vload_half(0, (__global half*) &x[i].d);
const int x0 = (x[i].qs[j] & 0xf) - 8;
const int x1 = (x[i].qs[j] >> 4) - 8;
y[i*qk + j + 0 ] = x0*d;
y[i*qk + j + qk/2] = x1*d;
y[i*32 + j + 0 ] = x0*d;
y[i*32 + j + 16] = x1*d;
}
__kernel void dequantize_row_q4_1(__global struct block_q4_1* x, __global float* y) {
constant uint qk = QK4_1;
const uint i = get_global_id(0) / qk;
const uint i = get_global_id(0) / 32; /* QK4_1 */
const uint j = get_local_id(0);
const float d = x[i].d;
const float m = x[i].m;
const float d = vload_half(0, (__global half*) &x[i].d);
const float m = vload_half(0, (__global half*) &x[i].m);
const int x0 = (x[i].qs[j] & 0xf);
const int x1 = (x[i].qs[j] >> 4);
y[i*qk + j + 0 ] = x0*d + m;
y[i*qk + j + qk/2] = x1*d + m;
y[i*32 + j + 0 ] = x0*d + m;
y[i*32 + j + 16] = x1*d + m;
}
__kernel void dequantize_row_q5_0(__global struct block_q5_0* x, __global float* y) {
constant uint qk = QK5_0;
const uint i = get_global_id(0) / qk;
const uint i = get_global_id(0) / 32; /* QK5_0 */
const uint j = get_local_id(0);
const float d = vload_half(0, (__global half*) &x[i].d);
@ -103,14 +93,12 @@ __kernel void dequantize_row_q5_0(__global struct block_q5_0* x, __global float*
const int32_t x0 = ((x[i].qs[j] & 0xf) | xh_0) - 16;
const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16;
y[i*qk + j + 0 ] = x0*d;
y[i*qk + j + qk/2] = x1*d;
y[i*32 + j + 0 ] = x0*d;
y[i*32 + j + 16] = x1*d;
}
__kernel void dequantize_row_q5_1(__global struct block_q5_1* x, __global float* y) {
constant uint qk = QK5_1;
const uint i = get_global_id(0) / qk;
const uint i = get_global_id(0) / 32; /* QK5_1 */
const uint j = get_local_id(0);
const float d = vload_half(0, (__global half*) &x[i].d);
@ -124,28 +112,38 @@ __kernel void dequantize_row_q5_1(__global struct block_q5_1* x, __global float*
const int x0 = (x[i].qs[j] & 0xf) | xh_0;
const int x1 = (x[i].qs[j] >> 4) | xh_1;
y[i*qk + j + 0 ] = x0*d + m;
y[i*qk + j + qk/2] = x1*d + m;
y[i*32 + j + 0 ] = x0*d + m;
y[i*32 + j + 16] = x1*d + m;
}
__kernel void dequantize_row_q8_0(__global struct block_q8_0* x, __global float* y) {
constant uint qk = QK8_0;
const uint i = get_global_id(0) / qk;
const uint i = get_global_id(0) / 32; /* QK8_0 */
const uint j = get_local_id(0);
const float d = x[i].d;
y[i*qk + j] = x[i].qs[j]*d;
const float d = vload_half(0, (__global half*) &x[i].d);
y[i*32 + j] = x[i].qs[j]*d;
}
);
#define CL_CHECK(err, name) \
do { \
cl_int err_ = (err); \
if (err_ != CL_SUCCESS) { \
fprintf(stderr, "OpenCL %s error %d at %s:%d\n", name, err_, __FILE__, __LINE__); \
exit(1); \
} \
#define CL_CHECK(err) \
do { \
cl_int err_ = (err); \
if (err_ != CL_SUCCESS) { \
fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
#err, err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
#define CLBLAST_CHECK(err) \
do { \
CLBlastStatusCode err_ = (err); \
if (err_ != CLBlastSuccess) { \
fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
#err, err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
static cl_platform_id platform;
@ -188,48 +186,174 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co
void ggml_cl_init(void) {
cl_int err = 0;
char * GGML_CLBLAST_PLATFORM = getenv("GGML_CLBLAST_PLATFORM");
char * GGML_CLBLAST_DEVICE = getenv("GGML_CLBLAST_DEVICE");
int plat_num = (GGML_CLBLAST_PLATFORM == NULL ? 0 : atoi(GGML_CLBLAST_PLATFORM));
int dev_num = (GGML_CLBLAST_DEVICE == NULL ? 0 : atoi(GGML_CLBLAST_DEVICE));
printf("\nInitializing CLBlast (First Run)...");
printf("\nAttempting to use: Platform=%d, Device=%d (If invalid, program will crash)\n",plat_num,dev_num);
cl_uint num_platforms;
clGetPlatformIDs(0, NULL, &num_platforms);
cl_platform_id* platforms = (cl_platform_id*)malloc(num_platforms*sizeof(cl_platform_id));
clGetPlatformIDs(num_platforms, platforms, NULL);
platform = platforms[plat_num];
char platform_buffer[1024];
clGetPlatformInfo(platform, CL_PLATFORM_NAME, sizeof(platform_buffer), &platform_buffer, NULL);
cl_uint num_devices;
clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, 0, NULL, &num_devices);
cl_device_id* devices = (cl_device_id*)malloc(num_devices*sizeof(cl_device_id));
clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, num_devices, devices, NULL);
device = devices[dev_num];
char device_buffer[1024];
clGetDeviceInfo(device, CL_DEVICE_NAME, sizeof(device_buffer), &device_buffer, NULL);
printf("Using Platform: %s Device: %s\n", platform_buffer, device_buffer);
context = clCreateContext(NULL, 1, &device, NULL, NULL, &err);
CL_CHECK(err, "clCreateContext");
queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err);
CL_CHECK(err, "clCreateCommandQueue");
free(platforms);
free(devices);
struct cl_device;
struct cl_platform {
cl_platform_id id;
unsigned number;
char name[128];
char vendor[128];
struct cl_device * devices;
unsigned n_devices;
struct cl_device * default_device;
};
program = build_program_from_source(context, device, clblast_dequant);
struct cl_device {
struct cl_platform * platform;
cl_device_id id;
unsigned number;
cl_device_type type;
char name[128];
};
enum { NPLAT = 16, NDEV = 16 };
struct cl_platform platforms[NPLAT];
unsigned n_platforms = 0;
struct cl_device devices[NDEV];
unsigned n_devices = 0;
struct cl_device * default_device = NULL;
platform = NULL;
device = NULL;
cl_platform_id platform_ids[NPLAT];
CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms));
for (unsigned i = 0; i < n_platforms; i++) {
struct cl_platform * p = &platforms[i];
p->number = i;
p->id = platform_ids[i];
CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));
cl_device_id device_ids[NDEV];
cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
p->n_devices = 0;
} else {
CL_CHECK(clGetDeviceIDsError);
}
p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
p->default_device = NULL;
for (unsigned j = 0; j < p->n_devices; j++) {
struct cl_device * d = &devices[n_devices];
d->number = n_devices++;
d->id = device_ids[j];
d->platform = p;
CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));
if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
p->default_device = d;
}
}
if (default_device == NULL && p->default_device != NULL) {
default_device = p->default_device;
}
}
if (n_devices == 0) {
fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n");
exit(1);
}
char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
char * user_device_string = getenv("GGML_OPENCL_DEVICE");
int user_platform_number = -1;
int user_device_number = -1;
unsigned n;
if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
user_platform_number = (int)n;
}
if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
user_device_number = (int)n;
}
struct cl_device * selected_devices = devices;
unsigned n_selected_devices = n_devices;
if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
for (unsigned i = 0; i < n_platforms; i++) {
struct cl_platform * p = &platforms[i];
if (strstr(p->name, user_platform_string) != NULL ||
strstr(p->vendor, user_platform_string) != NULL) {
user_platform_number = (int)i;
break;
}
}
if (user_platform_number == -1) {
fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
exit(1);
}
}
if (user_platform_number != -1) {
struct cl_platform * p = &platforms[user_platform_number];
selected_devices = p->devices;
n_selected_devices = p->n_devices;
default_device = p->default_device;
if (n_selected_devices == 0) {
fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
exit(1);
}
}
if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
for (unsigned i = 0; i < n_selected_devices; i++) {
struct cl_device * d = &selected_devices[i];
if (strstr(d->name, user_device_string) != NULL) {
user_device_number = d->number;
break;
}
}
if (user_device_number == -1) {
fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string);
exit(1);
}
}
if (user_device_number != -1) {
selected_devices = &devices[user_device_number];
n_selected_devices = 1;
default_device = &selected_devices[0];
}
GGML_ASSERT(n_selected_devices > 0);
if (default_device == NULL) {
default_device = &selected_devices[0];
}
fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name);
if (default_device->type != CL_DEVICE_TYPE_GPU) {
fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
}
platform = default_device->platform->id;
device = default_device->id;
cl_context_properties properties[] = {
(intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
};
CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));
CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
(err != CL_INVALID_PROPERTY && err != CL_INVALID_VALUE ? err :
(queue = clCreateCommandQueue(context, device, 0, &err), err)
)));
program = build_program_from_source(context, device, program_source);
// Prepare dequantize kernels
kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &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");
CL_CHECK((kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err), err));
CL_CHECK((kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &err), err));
CL_CHECK((kernel_q5_0 = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
CL_CHECK((kernel_q5_1 = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
CL_CHECK((kernel_q8_0 = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
}
static void ggml_cl_malloc(size_t req_size, size_t* cur_size, cl_mem_flags flags, cl_mem* buf) {
@ -242,9 +366,8 @@ static void ggml_cl_malloc(size_t req_size, size_t* cur_size, cl_mem_flags flags
clReleaseMemObject(*buf);
}
cl_int err;
*buf = clCreateBuffer(context, flags, req_size, NULL, &err);
CL_CHECK((*buf = clCreateBuffer(context, flags, req_size, NULL, &err), err));
*cur_size = req_size;
CL_CHECK(err, "clCreateBuffer");
}
void ggml_cl_sgemm_wrapper(
@ -253,7 +376,6 @@ void ggml_cl_sgemm_wrapper(
const float alpha, const void *host_a, const int lda,
const float *host_b, const int ldb, const float beta,
float *host_c, const int ldc, const int btype) {
cl_int err = 0;
cl_kernel kernel;
size_t global = n * k, local, size_qb;
@ -267,13 +389,13 @@ void ggml_cl_sgemm_wrapper(
dequant = true;
kernel = kernel_q4_0;
local = 16;
size_qb = global * (sizeof(float) + local) / 32;
size_qb = global * (sizeof(ggml_fp16_t) + local) / 32;
break;
case GGML_TYPE_Q4_1:
dequant = true;
kernel = kernel_q4_1;
local = 16;
size_qb = global * (sizeof(float) * 2 + local) / 32;
size_qb = global * (sizeof(ggml_fp16_t) * 2 + local) / 32;
break;
case GGML_TYPE_Q5_0:
dequant = true;
@ -291,7 +413,7 @@ void ggml_cl_sgemm_wrapper(
dequant = true;
kernel = kernel_q8_0;
local = 32;
size_qb = global * (sizeof(float) + local) / 32;
size_qb = global * (sizeof(ggml_fp16_t) + local) / 32;
break;
default:
fprintf(stderr, "Error: Unsupported OpenCL btype %d\n", btype);
@ -313,49 +435,40 @@ void ggml_cl_sgemm_wrapper(
cl_event ev_a, ev_qb, ev_b;
if (dequant) {
err = clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb);
err |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b);
CL_CHECK(err, "clSetKernelArg");
err = clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb);
CL_CHECK(err, "clEnqueueWriteBuffer qb");
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b));
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb));
} else {
err = clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b);
CL_CHECK(err, "clEnqueueWriteBuffer b");
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b));
}
err = clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a);
CL_CHECK(err, "clEnqueueWriteBuffer a");
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a));
if (dequant) {
err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b);
CL_CHECK(err, "clEnqueueNDRangeKernel");
clReleaseEvent(ev_qb);
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b));
CL_CHECK(clReleaseEvent(ev_qb));
}
clWaitForEvents(1, &ev_a);
clWaitForEvents(1, &ev_b);
clReleaseEvent(ev_a);
clReleaseEvent(ev_b);
CL_CHECK(clWaitForEvents(1, &ev_a));
CL_CHECK(clWaitForEvents(1, &ev_b));
CL_CHECK(clReleaseEvent(ev_a));
CL_CHECK(clReleaseEvent(ev_b));
cl_event 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();
}
CLBLAST_CHECK(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));
cl_event ev_c;
clEnqueueReadBuffer(queue, cl_buffer_c, CL_TRUE, 0, size_c, host_c, 1, &ev_sgemm, &ev_c);
CL_CHECK(clEnqueueReadBuffer(queue, cl_buffer_c, CL_TRUE, 0, size_c, host_c, 1, &ev_sgemm, &ev_c));
// Wait for completion
clWaitForEvents(1, &ev_c);
clReleaseEvent(ev_sgemm);
clReleaseEvent(ev_c);
CL_CHECK(clWaitForEvents(1, &ev_c));
CL_CHECK(clReleaseEvent(ev_sgemm));
CL_CHECK(clReleaseEvent(ev_c));
}

445
ggml.c
View file

@ -512,7 +512,7 @@ static inline int hsum_i32_4(const __m128i a) {
return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
}
#if __AVX2__ || __AVX512F__
#if defined(__AVX2__) || defined(__AVX512F__)
// spread 32 bits to 32 bytes { 0x00, 0xFF }
static inline __m256i bytes_from_bits_32(const uint8_t * x) {
uint32_t x32;
@ -543,12 +543,7 @@ static inline __m256 sum_i16_pairs_float(const __m256i x) {
return _mm256_cvtepi32_ps(summed_pairs);
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
// Get absolute values of x vectors
const __m256i ax = _mm256_sign_epi8(x, x);
// Sign the values of the y vectors
const __m256i sy = _mm256_sign_epi8(y, x);
static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
#if __AVXVNNI__
const __m256i zero = _mm256_setzero_si256();
const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
@ -560,6 +555,21 @@ static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
#endif
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
#if __AVXVNNIINT8__
const __m256i zero = _mm256_setzero_si256();
const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y);
return _mm256_cvtepi32_ps(summed_pairs);
#else
// Get absolute values of x vectors
const __m256i ax = _mm256_sign_epi8(x, x);
// Sign the values of the y vectors
const __m256i sy = _mm256_sign_epi8(y, x);
return mul_sum_us8_pairs_float(ax, sy);
#endif
}
static inline __m128i packNibbles( __m256i bytes )
{
// Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
@ -619,6 +629,17 @@ static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) {
return _mm256_cvtepi32_ps(summed_pairs);
}
static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
const __m128i axl = _mm256_castsi256_si128(ax);
const __m128i axh = _mm256_extractf128_si256(ax, 1);
const __m128i syl = _mm256_castsi256_si128(sy);
const __m128i syh = _mm256_extractf128_si256(sy, 1);
// Perform multiplication and create 16-bit values
const __m128i dotl = _mm_maddubs_epi16(axl, syl);
const __m128i doth = _mm_maddubs_epi16(axh, syh);
return sum_i16_pairs_float(doth, dotl);
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
const __m128i xl = _mm256_castsi256_si128(x);
@ -667,7 +688,7 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
#endif // __AVX__ || __AVX2__ || __AVX512F__
#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
#if __ARM_NEON
#if defined(__ARM_NEON)
#if !defined(__aarch64__)
@ -748,18 +769,18 @@ int32x4_t vcvtnq_s32_f32(float32x4_t v) {
#define QK4_0 32
typedef struct {
float d; // delta
ggml_fp16_t d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding");
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
typedef struct {
float d; // delta
float m; // min
ggml_fp16_t d; // delta
ggml_fp16_t m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == 2 * sizeof(float) + QK4_1 / 2, "wrong q4_1 block size/padding");
static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK5_0 32
typedef struct {
@ -780,16 +801,16 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) +
#define QK8_0 32
typedef struct {
float d; // delta
int8_t qs[QK8_0]; // quants
ggml_fp16_t d; // delta
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
#define QK8_1 32
typedef struct {
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_1]; // quants
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_1]; // quants
} block_q8_1;
static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
@ -816,7 +837,7 @@ static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * r
const float d = max / -8;
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].d = GGML_FP32_TO_FP16(d);
for (int j = 0; j < qk/2; ++j) {
const float x0 = x[i*qk + 0 + j]*id;
@ -856,8 +877,8 @@ static void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * r
const float d = (max - min) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].m = min;
y[i].d = GGML_FP32_TO_FP16(d);
y[i].m = GGML_FP32_TO_FP16(min);
for (int j = 0; j < qk/2; ++j) {
const float x0 = (x[i*qk + 0 + j] - min)*id;
@ -988,7 +1009,7 @@ static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * r
const float d = amax / ((1 << 7) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].d = GGML_FP32_TO_FP16(d);
for (int j = 0; j < QK8_0; ++j) {
const float x0 = x[i*QK8_0 + j]*id;
@ -1023,7 +1044,7 @@ static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int
const float d = amax / ((1 << 7) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = d;
y[i].d = GGML_FP32_TO_FP16(d);
for (int j = 0; j < 8; j++) {
const float32x4_t v = vmulq_n_f32(srcv[j], id);
@ -1058,7 +1079,7 @@ static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int
// Quantize these floats
const float d = maxScalar / 127.f;
y[i].d = d;
y[i].d = GGML_FP32_TO_FP16(d);
const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
const __m256 mul = _mm256_set1_ps( id );
@ -1157,7 +1178,7 @@ static void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * r
sum += y[i].qs[QK8_1/2 + j];
}
y[i].s = d * sum;
y[i].s = sum*d;
}
}
@ -1309,7 +1330,7 @@ static void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict
const int nb = k / qk;
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
const float d = GGML_FP16_TO_FP32(x[i].d);
for (int j = 0; j < qk/2; ++j) {
const int x0 = (x[i].qs[j] & 0x0F) - 8;
@ -1329,8 +1350,8 @@ static void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict
const int nb = k / qk;
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
const float m = x[i].m;
const float d = GGML_FP16_TO_FP32(x[i].d);
const float m = GGML_FP16_TO_FP32(x[i].m);
for (int j = 0; j < qk/2; ++j) {
const int x0 = (x[i].qs[j] & 0x0F);
@ -1405,7 +1426,7 @@ static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, in
const block_q8_0 * restrict x = vx;
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
const float d = GGML_FP16_TO_FP32(x[i].d);
for (int j = 0; j < qk; ++j) {
y[i*qk + j] = x[i].qs[j]*d;
@ -1669,8 +1690,9 @@ quantize_fns_t ggml_internal_get_quantize_fn(size_t i) {
static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
float tmp[8];
for (int i = 0; i < 8; i++)
for (int i = 0; i < 8; i++) {
tmp[i] = GGML_FP16_TO_FP32(x[i]);
}
return _mm256_loadu_ps(tmp);
}
@ -2090,8 +2112,8 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
const uint8x16_t m4b = vdupq_n_u8(0x0F);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t m4b = vdupq_n_u8(0x0F);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t v0_0 = vld1q_u8(x0->qs);
const uint8x16_t v0_1 = vld1q_u8(x1->qs);
@ -2119,8 +2141,8 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
@ -2137,8 +2159,8 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#endif
}
@ -2150,7 +2172,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; ++i) {
/* Compute combined scale for the block */
const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
__m256i bx = bytes_from_nibbles_32(x[i].qs);
@ -2174,7 +2196,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; ++i) {
// Compute combined scale for the block
const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
const __m128i lowMask = _mm_set1_epi8(0xF);
const __m128i off = _mm_set1_epi8(8);
@ -2216,7 +2238,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
_mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 0 and 1
const __m128 d_0_1 = _mm_mul_ps( _mm_set1_ps( x[0].d ), _mm_set1_ps( y[0].d ) );
const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) );
const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
@ -2234,7 +2256,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
_mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 2 and 3
const __m128 d_2_3 = _mm_mul_ps( _mm_set1_ps( x[1].d ), _mm_set1_ps( y[1].d ) );
const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) );
const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
@ -2267,7 +2289,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
_mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 0 and 1
const __m128 d_0_1 = _mm_mul_ps( _mm_set1_ps( x[i].d ), _mm_set1_ps( y[i].d ) );
const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
@ -2285,7 +2307,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
_mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 2 and 3
const __m128 d_2_3 = _mm_mul_ps( _mm_set1_ps( x[i + 1].d ), _mm_set1_ps( y[i + 1].d ) );
const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) );
const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
@ -2333,7 +2355,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
}
sumf += (x[i].d*y[i].d)*sumi;
sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
}
*s = sumf;
@ -2363,7 +2385,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const block_q8_1 * restrict y0 = &y[i + 0];
const block_q8_1 * restrict y1 = &y[i + 1];
summs += x0->m * y0->s + x1->m * y1->s;
summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s;
const uint8x16_t m4b = vdupq_n_u8(0x0F);
@ -2387,8 +2409,8 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
@ -2405,8 +2427,8 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
#endif
}
@ -2419,13 +2441,13 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; ++i) {
const float * d0 = &x[i].d;
const float * d1 = &y[i].d;
const float d0 = GGML_FP16_TO_FP32(x[i].d);
const float d1 = y[i].d;
summs += x[i].m * y[i].s;
summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
const __m256 d0v = _mm256_broadcast_ss( d0 );
const __m256 d1v = _mm256_broadcast_ss( d1 );
const __m256 d0v = _mm256_set1_ps( d0 );
const __m256 d1v = _mm256_set1_ps( d1 );
// Compute combined scales
const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
@ -2434,7 +2456,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const __m256i bx = bytes_from_nibbles_32(x[i].qs);
const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
const __m256 xy = mul_sum_i8_pairs_float(bx, by);
const __m256 xy = mul_sum_us8_pairs_float(bx, by);
// Accumulate d0*d1*x*y
#if defined(__AVX2__)
@ -2459,7 +2481,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
}
sumf += (x[i].d*y[i].d)*sumi + x[i].m*y[i].s;
sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
}
*s = sumf;
@ -2535,16 +2557,13 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
const int8x16_t v1_1l = vld1q_s8(y1->qs);
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
const float x0d = GGML_FP16_TO_FP32(x0->d);
const float x1d = GGML_FP16_TO_FP32(x1->d);
#if defined(__ARM_FEATURE_DOTPROD)
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), x0d*y0->d);
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), x1d*y1->d);
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
@ -2561,8 +2580,8 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#endif
}
@ -2637,7 +2656,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; i++) {
/* Compute combined scale for the block */
const __m256 d = _mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)), _mm256_broadcast_ss(&y[i].d));
const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
__m256i bx = bytes_from_nibbles_32(x[i].qs);
__m256i bxhi = bytes_from_bits_32(x[i].qh);
@ -2661,7 +2680,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; i++) {
/* Compute combined scale for the block */
const __m256 d = _mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)), _mm256_broadcast_ss(&y[i].d));
const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
__m256i bx = bytes_from_nibbles_32(x[i].qs);
const __m256i bxhi = bytes_from_bits_32(x[i].qh);
@ -2704,7 +2723,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
}
sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi;
sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
}
*s = sumf;
@ -2786,16 +2805,13 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
const int8x16_t v1_1l = vld1q_s8(y1->qs);
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
const float x0d = GGML_FP16_TO_FP32(x0->d);
const float x1d = GGML_FP16_TO_FP32(x1->d);
#if defined(__ARM_FEATURE_DOTPROD)
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), x0d*y0->d);
vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), x1d*y1->d);
vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
@ -2812,8 +2828,8 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), x1d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
#endif
}
@ -2873,15 +2889,14 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
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)));
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(GGML_FP16_TO_FP32(x0->d) * y0->d));
}
*s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
@ -2903,10 +2918,10 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
bx = _mm256_or_si256(bx, bxhi);
const __m256 dy = _mm256_broadcast_ss(&y[i].d);
const __m256 dy = _mm256_set1_ps(y[i].d);
const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256 q = mul_sum_i8_pairs_float(bx, by);
const __m256 q = mul_sum_us8_pairs_float(bx, by);
acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
}
@ -2937,10 +2952,10 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
bxh = _mm_or_si128(bxh, bxhih);
bx = _mm256_set_m128i(bxh, bxl);
const __m256 dy = _mm256_broadcast_ss(&y[i].d);
const __m256 dy = _mm256_set1_ps(y[i].d);
const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256 q = mul_sum_i8_pairs_float(bx, by);
const __m256 q = mul_sum_us8_pairs_float(bx, by);
acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
}
@ -3007,11 +3022,11 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
#if defined(__ARM_FEATURE_DOTPROD)
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), x0->d*y0->d);
vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), x1->d*y1->d);
vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#else
const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0));
@ -3029,8 +3044,8 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), x1->d*y1->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
#endif
}
@ -3042,7 +3057,7 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
// Main loop
for (int i = 0; i < nb; ++i) {
// Compute combined scale for the block
const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
__m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs);
__m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
@ -3068,7 +3083,7 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
sumi += x[i].qs[j]*y[i].qs[j];
}
sumf += (x[i].d*y[i].d)*sumi;
sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
}
*s = sumf;
@ -3457,6 +3472,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"ROPE",
"ROPE_BACK",
"ALIBI",
"CLAMP",
"CONV_1D_1S",
"CONV_1D_2S",
@ -3467,7 +3483,8 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"MAP_BINARY",
};
static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -3517,6 +3534,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"rope(x)",
"rope_back(x)",
"alibi(x)",
"clamp(x)",
"conv_1d_1s(x)",
"conv_1d_2s(x)",
@ -3527,7 +3545,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"f(x,y)",
};
static_assert(GGML_OP_COUNT == 50, "GGML_OP_COUNT != 50");
static_assert(GGML_OP_COUNT == 51, "GGML_OP_COUNT != 51");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@ -3761,6 +3779,12 @@ static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct g
(t1->ne[3]%t0->ne[3] == 0);
}
static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1);
}
static inline int ggml_up32(int n) {
return (n + 31) & ~31;
}
@ -4643,11 +4667,15 @@ struct ggml_tensor * ggml_mul_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
GGML_ASSERT(ggml_are_same_shape(a, b));
// TODO: support less-strict constraint
// GGML_ASSERT(ggml_can_repeat(b, a));
GGML_ASSERT(ggml_can_repeat_rows(b, a));
bool is_node = false;
if (!inplace && (a->grad || b->grad)) {
// TODO: support backward pass for broadcasting
GGML_ASSERT(ggml_are_same_shape(a, b));
is_node = true;
}
@ -6189,7 +6217,8 @@ struct ggml_tensor * ggml_alibi(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_head) {
int n_head,
float bias_max) {
GGML_ASSERT(n_past >= 0);
bool is_node = false;
@ -6208,6 +6237,8 @@ struct ggml_tensor * ggml_alibi(
((int32_t *) b->data)[0] = n_past;
((int32_t *) b->data)[1] = n_head;
GGML_ASSERT(sizeof(float) == sizeof(int32_t));
(((float *) b->data)[2]) = bias_max;
ggml_scratch_load(ctx);
@ -6219,6 +6250,40 @@ struct ggml_tensor * ggml_alibi(
return result;
}
// ggml_clamp
struct ggml_tensor * ggml_clamp(
struct ggml_context * ctx,
struct ggml_tensor * a,
float min,
float max) {
bool is_node = false;
if (a->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
// TODO: when implement backward, fix this:
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
ggml_scratch_save(ctx);
struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3);
((float *) b->data)[0] = min;
((float *) b->data)[1] = max;
ggml_scratch_load(ctx);
result->op = GGML_OP_CLAMP;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
return result;
}
// ggml_conv_1d_1s
struct ggml_tensor * ggml_conv_1d_1s(
@ -7945,7 +8010,7 @@ static void ggml_compute_forward_mul_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
@ -7953,10 +8018,25 @@ static void ggml_compute_forward_mul_f32(
const int ith = params->ith;
const int nth = params->nth;
const int nr = ggml_nrows(src0);
const int64_t ne0 = src0->ne[0];
const int64_t ne1 = src0->ne[1];
const int64_t ne2 = src0->ne[2];
#ifdef GGML_USE_CUBLAS
if (src1->backend == GGML_BACKEND_CUDA) {
if (ith == 0) {
ggml_cuda_mul(src0, src1, dst);
}
return;
}
#endif
const int64_t nr = ggml_nrows(src0);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
const size_t nb00 = src0->nb[0];
const size_t nb01 = src0->nb[1];
@ -7975,44 +8055,51 @@ static void ggml_compute_forward_mul_f32(
GGML_ASSERT( nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(ne00 == ne10);
if (nb10 == sizeof(float)) {
for (int ir = ith; ir < nr; ir += nth) {
// src0, src1 and dst are same shape => same indices
const int i3 = ir/(ne2*ne1);
const int i2 = (ir - i3*ne2*ne1)/ne1;
const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
for (int64_t ir = ith; ir < nr; ir += nth) {
// src0 and dst are same shape => same indices
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
#ifdef GGML_USE_ACCELERATE
UNUSED(ggml_vec_mul_f32);
vDSP_vmul(
(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1,
ne0);
vDSP_vmul( src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
#else
ggml_vec_mul_f32(ne0,
(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ),
(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
ggml_vec_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
#endif
// }
// }
}
} else {
// src1 is not contiguous
for (int ir = ith; ir < nr; ir += nth) {
// src0, src1 and dst are same shape => same indices
const int i3 = ir/(ne2*ne1);
const int i2 = (ir - i3*ne2*ne1)/ne1;
const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
for (int64_t ir = ith; ir < nr; ir += nth) {
// src0 and dst are same shape => same indices
// src1 is broadcastable across src0 and dst in i1, i2, i3
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
for (int i0 = 0; i0 < ne0; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
for (int64_t i0 = 0; i0 < ne00; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr);
}
@ -10506,6 +10593,7 @@ static void ggml_compute_forward_diag_mask_f32(
const int n_past = ((int32_t *) src1->data)[0];
const bool inplace = (bool)((int32_t *) src1->data)[1];
assert(n_past >= 0);
if (!inplace && (params->type == GGML_TASK_INIT)) {
@ -10676,14 +10764,15 @@ static void ggml_compute_forward_alibi_f32(
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 2);
assert(ggml_nelements(src1) == 3);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
const float max_bias = ((float *) src1->data)[2];
assert(n_past >= 0);
@ -10706,8 +10795,8 @@ static void ggml_compute_forward_alibi_f32(
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor);
const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor);
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
for (int i = 0; i < ne0; i++) {
for (int j = 0; j < ne1; j++) {
@ -10725,13 +10814,13 @@ static void ggml_compute_forward_alibi_f32(
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
}
pdst[0] = i * m_k + src[0];
pdst[0] = (i-ne0+1) * m_k + src[0];
}
}
}
}
static void ggml_compute_forward_alibi_f16(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
@ -10739,14 +10828,15 @@ static void ggml_compute_forward_alibi_f16(
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 2);
assert(ggml_nelements(src1) == 3);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
const int n_past = ((int32_t *) src1->data)[0];
const int n_head = ((int32_t *) src1->data)[1];
const float max_bias = ((float *) src1->data)[2];
assert(n_past >= 0);
@ -10769,8 +10859,8 @@ static void ggml_compute_forward_alibi_f16(
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -8.0f / n_heads_log2_floor);
const float m1 = powf(2.0f, -4.0f / n_heads_log2_floor);
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
for (int i = 0; i < ne0; i++) {
for (int j = 0; j < ne1; j++) {
@ -10789,7 +10879,7 @@ static void ggml_compute_forward_alibi_f16(
}
// we return F32
pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
pdst[0] = (i-ne0+1) * m_k + GGML_FP16_TO_FP32(src[0]);
}
}
}
@ -10825,6 +10915,77 @@ static void ggml_compute_forward_alibi(
}
}
// ggml_compute_forward_clamp
static void ggml_compute_forward_clamp_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(src1->type == GGML_TYPE_I32);
assert(ggml_nelements(src1) == 2);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int min = ((float *) src1->data)[0];
const int max = ((float *) src1->data)[1];
const int ith = params->ith;
const int nth = params->nth;
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
const size_t nb00 = src0->nb[0];
const size_t nb01 = src0->nb[1];
const size_t nb0 = dst->nb[0];
const size_t nb1 = dst->nb[1];
GGML_ASSERT( nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
for (int j = ith; j < n; j += nth) {
float * dst_ptr = (float *) ((char *) dst->data + j*nb1);
float * src0_ptr = (float *) ((char *) src0->data + j*nb01);
for (int i = 0; i < nc; i++) {
dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min);
}
}
}
static void ggml_compute_forward_clamp(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_clamp_f32(params, src0, src1, dst);
} break;
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q8_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_rope
static void ggml_compute_forward_rope_f32(
@ -12806,6 +12967,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_alibi(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_CLAMP:
{
ggml_compute_forward_clamp(params, tensor->src0, tensor->src1, tensor);
} break;
case GGML_OP_CONV_1D_1S:
{
ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor);
@ -13113,6 +13278,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_CLAMP:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_SILU:
{
// necessary for llama
@ -13992,6 +14161,10 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
{
node->n_tasks = 1; //TODO
} break;
case GGML_OP_CLAMP:
{
node->n_tasks = 1; //TODO
} break;
case GGML_OP_CONV_1D_1S:
case GGML_OP_CONV_1D_2S:
{

16
ggml.h
View file

@ -190,7 +190,7 @@
#define GGML_FILE_MAGIC 0x67676d6c // "ggml"
#define GGML_FILE_VERSION 1
#define GGML_QNT_VERSION 1 // bump this on quantization format changes
#define GGML_QNT_VERSION 2 // bump this on quantization format changes
#define GGML_QNT_VERSION_FACTOR 1000 // do not change this
#define GGML_MAX_DIMS 4
@ -313,6 +313,7 @@ extern "C" {
GGML_OP_ROPE,
GGML_OP_ROPE_BACK,
GGML_OP_ALIBI,
GGML_OP_CLAMP,
GGML_OP_CONV_1D_1S,
GGML_OP_CONV_1D_2S,
@ -849,7 +850,7 @@ extern "C" {
int n_past);
// in-place, returns view(a)
GGML_API struct ggml_tensor * gml_diag_mask_zero_inplace(
GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past);
@ -897,7 +898,16 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_head);
int n_head,
float bias_max);
// clamp
// in-place, returns view(a)
struct ggml_tensor * ggml_clamp(
struct ggml_context * ctx,
struct ggml_tensor * a,
float min,
float max);
// padding = 1
// TODO: we don't support extra parameters for now

46
llama-util.h
View file

@ -101,12 +101,12 @@ struct llama_file {
LLAMA_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t size) {
if (size == 0) {
void read_raw(void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, size, 1, fp);
std::size_t ret = std::fread(ptr, len, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
@ -127,12 +127,12 @@ struct llama_file {
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
void write_raw(const void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
size_t ret = std::fwrite(ptr, len, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
@ -172,7 +172,7 @@ struct llama_mmap {
#ifdef _POSIX_MAPPED_FILES
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, bool prefetch = true) {
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */) {
size = file->size;
int fd = fileno(file->fp);
int flags = MAP_SHARED;
@ -184,9 +184,9 @@ struct llama_mmap {
throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
}
if (prefetch) {
if (prefetch > 0) {
// Advise the kernel to preload the mapped memory
if (madvise(addr, file->size, MADV_WILLNEED)) {
if (madvise(addr, std::min(file->size, prefetch), MADV_WILLNEED)) {
fprintf(stderr, "warning: madvise(.., MADV_WILLNEED) failed: %s\n",
strerror(errno));
}
@ -267,9 +267,9 @@ struct llama_mlock {
}
}
void init(void * addr) {
LLAMA_ASSERT(this->addr == NULL && this->size == 0);
this->addr = addr;
void init(void * ptr) {
LLAMA_ASSERT(addr == NULL && size == 0);
addr = ptr;
}
void grow_to(size_t target_size) {
@ -340,14 +340,14 @@ struct llama_mlock {
return (size_t) si.dwPageSize;
}
bool raw_lock(void * addr, size_t size) {
bool raw_lock(void * ptr, size_t len) {
for (int tries = 1; ; tries++) {
if (VirtualLock(addr, size)) {
if (VirtualLock(ptr, len)) {
return true;
}
if (tries == 2) {
fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
size, this->size, llama_format_win_err(GetLastError()).c_str());
len, size, llama_format_win_err(GetLastError()).c_str());
return false;
}
@ -363,7 +363,7 @@ struct llama_mlock {
// is equal to the number of pages in its minimum working set minus
// a small overhead."
// Hopefully a megabyte is enough overhead:
size_t increment = size + 1048576;
size_t increment = len + 1048576;
// The minimum must be <= the maximum, so we need to increase both:
min_ws_size += increment;
max_ws_size += increment;
@ -375,8 +375,8 @@ struct llama_mlock {
}
}
void raw_unlock(void * addr, size_t size) {
if (!VirtualUnlock(addr, size)) {
void raw_unlock(void * ptr, size_t len) {
if (!VirtualUnlock(ptr, len)) {
fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
@ -388,12 +388,12 @@ struct llama_mlock {
return (size_t) 65536;
}
bool raw_lock(const void * addr, size_t size) {
bool raw_lock(const void * addr, size_t len) {
fprintf(stderr, "warning: mlock not supported on this system\n");
return false;
}
void raw_unlock(const void * addr, size_t size) {}
void raw_unlock(const void * addr, size_t len) {}
#endif
};
@ -404,10 +404,10 @@ struct llama_buffer {
llama_buffer() = default;
void resize(size_t size) {
void resize(size_t len) {
delete[] addr;
addr = new uint8_t[size];
this->size = size;
addr = new uint8_t[len];
size = len;
}
~llama_buffer() {

282
llama.cpp
View file

@ -1,6 +1,7 @@
// Defines fileno on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#include <cstddef>
#include <cstdint>
#include <cstdio>
#endif
@ -45,6 +46,7 @@ enum e_model {
MODEL_65B,
};
static const size_t MB = 1024*1024;
// computed for n_ctx == 2048
@ -110,7 +112,7 @@ struct llama_hparams {
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
bool operator!=(const llama_hparams & other) const {
return memcmp(this, &other, sizeof(llama_hparams));
return static_cast<bool>(memcmp(this, &other, sizeof(llama_hparams)));
}
};
@ -406,6 +408,7 @@ enum llama_file_version {
LLAMA_FILE_VERSION_GGMF_V1, // added version field and scores in vocab
LLAMA_FILE_VERSION_GGJT_V1, // added padding
LLAMA_FILE_VERSION_GGJT_V2, // changed quantization format
LLAMA_FILE_VERSION_GGJT_V3, // changed Q4 and Q8 quantization format
};
struct llama_file_loader {
@ -424,24 +427,30 @@ struct llama_file_loader {
}
void read_magic() {
uint32_t magic = file.read_u32();
uint32_t version = 0;
if (magic != 'ggml') {
version = file.read_u32();
}
if (magic == 'ggml' && version == 0) {
if (magic == LLAMA_FILE_MAGIC_GGML) {
file_version = LLAMA_FILE_VERSION_GGML;
} else if (magic == 'ggmf' && version == 1) {
file_version = LLAMA_FILE_VERSION_GGMF_V1;
} else if (magic == 'ggjt' && version == 1) {
file_version = LLAMA_FILE_VERSION_GGJT_V1;
} else if (magic == 'ggjt' && version == 2) {
file_version = LLAMA_FILE_VERSION_GGJT_V2;
} else {
throw format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
magic, version);
return;
}
uint32_t version = file.read_u32();
switch (magic) {
case LLAMA_FILE_MAGIC_GGMF:
switch (version) {
case 1: file_version = LLAMA_FILE_VERSION_GGMF_V1; return;
}
break;
case LLAMA_FILE_MAGIC_GGJT:
switch (version) {
case 1: file_version = LLAMA_FILE_VERSION_GGJT_V1; return;
case 2: file_version = LLAMA_FILE_VERSION_GGJT_V2; return;
case 3: file_version = LLAMA_FILE_VERSION_GGJT_V3; return;
}
}
throw format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
magic, version);
}
void read_hparams() {
hparams.n_vocab = file.read_u32();
@ -499,7 +508,7 @@ struct llama_file_loader {
if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) {
// skip to the next multiple of 32 bytes
file.seek(-file.tell() & 31, SEEK_CUR);
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
}
shard.file_idx = file_idx;
shard.file_off = file.tell();
@ -574,7 +583,7 @@ struct llama_file_saver {
file.write_u32(new_type);
file.write_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * tensor.ne.size());
file.write_raw(tensor.name.data(), tensor.name.size());
file.seek(-file.tell() & 31, SEEK_CUR);
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
LLAMA_ASSERT(new_size == llama_calc_tensor_size(tensor.ne, new_type));
file.write_raw(new_data, new_size);
}
@ -641,7 +650,7 @@ struct llama_model_loader {
}
}
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne) {
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
auto it = tensors_map.name_to_idx.find(name);
if (it == tensors_map.name_to_idx.end()) {
throw format("llama.cpp: tensor '%s' is missing from model", name.c_str());
@ -652,10 +661,10 @@ struct llama_model_loader {
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str());
}
return get_tensor_for(lt);
return get_tensor_for(lt, backend);
}
struct ggml_tensor * get_tensor_for(llama_load_tensor & lt) {
struct ggml_tensor * get_tensor_for(llama_load_tensor & lt, ggml_backend backend) {
struct ggml_tensor * tensor;
if (lt.ne.size() == 2) {
tensor = ggml_new_tensor_2d(ggml_ctx, lt.type, lt.ne.at(0), lt.ne.at(1));
@ -665,6 +674,7 @@ struct llama_model_loader {
}
ggml_set_name(tensor, lt.name.c_str());
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
tensor->backend = backend;
lt.ggml_tensor = tensor;
num_ggml_tensors_created++;
return tensor;
@ -678,12 +688,16 @@ struct llama_model_loader {
void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
size_t data_size = 0;
size_t prefetch_size = 0;
for (const llama_load_tensor & lt : tensors_map.tensors) {
data_size += lt.size;
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
prefetch_size += lt.size;
}
}
if (use_mmap) {
mapping.reset(new llama_mmap(&file_loaders.at(0)->file));
mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size));
if (!lmlock) {
// Don't call the callback since the actual loading will be lazy
// and we can't measure it.
@ -696,6 +710,9 @@ struct llama_model_loader {
size_t done_size = 0;
for (llama_load_tensor & lt : tensors_map.tensors) {
if (lt.ggml_tensor->backend != GGML_BACKEND_CPU) {
continue;
}
if (progress_callback) {
progress_callback((float) done_size / data_size, progress_callback_user_data);
}
@ -708,9 +725,6 @@ struct llama_model_loader {
lmlock->grow_to(done_size);
}
}
if (progress_callback) {
progress_callback(1.0f, progress_callback_user_data);
}
}
void load_data_for(llama_load_tensor & lt) {
@ -812,10 +826,9 @@ static bool kv_cache_init(
struct llama_context_params llama_context_default_params() {
struct llama_context_params result = {
/*.n_ctx =*/ 512,
/*.n_parts =*/ -1,
/*.gpu_layers =*/ 0,
/*.seed =*/ -1,
/*.f16_kv =*/ false,
/*.f16_kv =*/ true,
/*.logits_all =*/ false,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
@ -836,6 +849,21 @@ bool llama_mlock_supported() {
return llama_mlock::SUPPORTED;
}
void llama_init_backend() {
ggml_time_init();
// needed to initialize f16 tables
{
struct ggml_init_params params = { 0, NULL, false };
struct ggml_context * ctx = ggml_init(params);
ggml_free(ctx);
}
}
int64_t llama_time_us() {
return ggml_time_us();
}
//
// model loading
//
@ -845,7 +873,8 @@ static const char *llama_file_version_name(llama_file_version version) {
case LLAMA_FILE_VERSION_GGML: return "'ggml' (old version with low tokenizer quality and no mmap support)";
case LLAMA_FILE_VERSION_GGMF_V1: return "ggmf v1 (old version with no mmap support)";
case LLAMA_FILE_VERSION_GGJT_V1: return "ggjt v1 (pre #1405)";
case LLAMA_FILE_VERSION_GGJT_V2: return "ggjt v2 (latest)";
case LLAMA_FILE_VERSION_GGJT_V2: return "ggjt v2 (pre #1508)";
case LLAMA_FILE_VERSION_GGJT_V3: return "ggjt v3 (latest)";
}
return "unknown";
@ -925,11 +954,19 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
}
if (file_version != LLAMA_FILE_VERSION_GGJT_V2) {
if (file_version < LLAMA_FILE_VERSION_GGJT_V2) {
if (hparams.ftype != LLAMA_FTYPE_ALL_F32 &&
hparams.ftype != LLAMA_FTYPE_MOSTLY_F16 &&
hparams.ftype != LLAMA_FTYPE_MOSTLY_Q8_0) {
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1305)");
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)");
}
}
if (file_version < LLAMA_FILE_VERSION_GGJT_V3) {
if (hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 ||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)");
}
}
@ -942,27 +979,7 @@ static void llama_model_load_internal(
size_t ctx_size;
size_t mmapped_size;
ml->calc_sizes(&ctx_size, &mmapped_size);
fprintf(stderr, "%s: ggml ctx size = %6.2f KB\n", __func__, ctx_size/1024.0);
// print memory requirements
{
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
// this is the total memory required to run the inference
const size_t mem_required =
ctx_size +
mmapped_size +
MEM_REQ_SCRATCH0().at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL().at(model.type);
// this is the memory required by one llama_state
const size_t mem_required_state =
scale*MEM_REQ_KV_SELF().at(model.type);
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
}
fprintf(stderr, "%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
// create the ggml context
{
@ -984,7 +1001,14 @@ static void llama_model_load_internal(
}
}
#ifdef GGML_USE_CUBLAS
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CUDA
#else
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
#endif
// prepare memory for the weights
size_t vram_total = 0;
{
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
@ -992,33 +1016,87 @@ static void llama_model_load_internal(
ml->ggml_ctx = ctx;
model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab});
model.norm = ml->get_tensor("norm.weight", {n_embd});
model.output = ml->get_tensor("output.weight", {n_embd, n_vocab});
model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU);
model.norm = ml->get_tensor("norm.weight", {n_embd}, GGML_BACKEND_CPU);
// "output" tensor
{
ggml_backend backend_output;
if (n_gpu_layers > int(n_layer)) { // NOLINT
backend_output = LLAMA_BACKEND_OFFLOAD;
} else {
backend_output = GGML_BACKEND_CPU;
}
model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}, backend_output);
}
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
auto & layer = model.layers[i];
std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd});
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd});
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd});
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd});
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd});
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend);
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}, backend);
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}, backend);
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend);
layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd});
layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}, backend);
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff});
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd});
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff});
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend);
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend);
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend);
if (backend == GGML_BACKEND_CUDA) {
vram_total +=
ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm) +
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
}
}
}
ml->done_getting_tensors();
// print memory requirements
{
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
// this is the total memory required to run the inference
const size_t mem_required =
ctx_size +
mmapped_size - vram_total + // weights in VRAM not in memory
MEM_REQ_SCRATCH0().at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL().at(model.type);
// this is the memory required by one llama_state
const size_t mem_required_state =
scale*MEM_REQ_KV_SELF().at(model.type);
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
#ifdef GGML_USE_CUBLAS
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
fprintf(stderr, "%s: [cublas] offloading %d layers to GPU\n", __func__, n_gpu);
if (n_gpu_layers > (int) hparams.n_layer) {
fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__);
}
fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
#else
(void) n_gpu_layers;
#endif
}
// populate `tensors_by_name`
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
model.tensors_by_name.emplace_back(lt.name, lt.ggml_tensor);
@ -1026,36 +1104,34 @@ static void llama_model_load_internal(
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
model.mapping = std::move(ml->mapping);
#ifdef GGML_USE_CUBLAS
{
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
fprintf(stderr, "%s: [cublas] offloading %d layers to GPU\n", __func__, n_gpu);
size_t vram_total = 0;
for (int i = 0; i < n_gpu; ++i) {
const auto & layer = model.layers[i];
ggml_cuda_transform_tensor(layer.wq); vram_total += ggml_nbytes(layer.wq);
ggml_cuda_transform_tensor(layer.wk); vram_total += ggml_nbytes(layer.wk);
ggml_cuda_transform_tensor(layer.wv); vram_total += ggml_nbytes(layer.wv);
ggml_cuda_transform_tensor(layer.wo); vram_total += ggml_nbytes(layer.wo);
ggml_cuda_transform_tensor(layer.w1); vram_total += ggml_nbytes(layer.w1);
ggml_cuda_transform_tensor(layer.w2); vram_total += ggml_nbytes(layer.w2);
ggml_cuda_transform_tensor(layer.w3); vram_total += ggml_nbytes(layer.w3);
size_t done_size = 0;
size_t data_size = 0;
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
data_size += lt.size;
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
done_size += lt.size;
}
}
if (n_gpu_layers > (int) hparams.n_layer) {
fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__);
ggml_cuda_transform_tensor(model.output); vram_total += ggml_nbytes(model.output);
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
if (lt.ggml_tensor->backend != GGML_BACKEND_CUDA) {
continue;
}
if (progress_callback) {
progress_callback((float) done_size / data_size, progress_callback_user_data);
}
ggml_cuda_load_data(fname.c_str(), lt.ggml_tensor, lt.shards.at(0).file_off);
done_size += lt.size;
}
fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}
#else
(void) n_gpu_layers;
#endif
#endif // GGML_USE_CUBLAS
if (progress_callback) {
progress_callback(1.0f, progress_callback_user_data);
}
model.mapping = std::move(ml->mapping);
// loading time will be recalculate after the first eval, so
// we take page faults deferred by mmap() into consideration
@ -1154,10 +1230,8 @@ static bool llama_eval_internal(
{
cur = ggml_rms_norm(ctx0, inpL);
// cur = attention_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
cur);
// cur = cur*attention_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm);
}
// self-attention
@ -1264,10 +1338,8 @@ static bool llama_eval_internal(
{
cur = ggml_rms_norm(ctx0, inpFF);
// cur = ffn_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].ffn_norm, cur),
cur);
// cur = cur*ffn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
}
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
@ -1304,10 +1376,8 @@ static bool llama_eval_internal(
inpL = ggml_rms_norm(ctx0, inpL);
// inpL = norm*inpL
inpL = ggml_mul(ctx0,
ggml_repeat(ctx0, model.norm, inpL),
inpL);
// inpL = inpL*norm(broadcasted)
inpL = ggml_mul(ctx0, inpL, model.norm);
embeddings = inpL;
}
@ -2131,7 +2201,7 @@ struct llama_context * llama_init_from_file(
unsigned * cur_percentage_p = (unsigned *) ctx;
unsigned percentage = (unsigned) (100 * progress);
while (percentage > *cur_percentage_p) {
++*cur_percentage_p;
*cur_percentage_p = percentage;
fprintf(stderr, ".");
fflush(stderr);
if (percentage >= 100) {
@ -2224,7 +2294,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != 'ggla') {
if (magic != LLAMA_FILE_MAGIC_GGLA) {
fprintf(stderr, "%s: bad file magic\n", __func__);
return 1;
}
@ -2288,7 +2358,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
// maybe this should in llama_model_loader
if (model_loader->use_mmap) {
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ false));
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ 0));
}
}
@ -2381,7 +2451,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
}
size_t idx = model_loader->tensors_map.name_to_idx[base_name];
llama_load_tensor & lt = model_loader->tensors_map.tensors[idx];
base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] });
base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
lt.data = (uint8_t *) lt.ggml_tensor->data;
model_loader->load_data_for(lt);
lt.ggml_tensor->data = lt.data;
@ -2607,8 +2677,8 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
}
// Sets the state reading from the specified source address
size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
const uint8_t * inp = src;
size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
uint8_t * inp = src;
// set rng
{

48
llama.h
View file

@ -19,10 +19,16 @@
# define LLAMA_API
#endif
#define LLAMA_FILE_VERSION 2
#define LLAMA_FILE_MAGIC 'ggjt'
#define LLAMA_FILE_MAGIC_UNVERSIONED 'ggml'
#define LLAMA_SESSION_MAGIC 'ggsn'
#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
#define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
#define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml'
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_FILE_VERSION 3
#define LLAMA_FILE_MAGIC LLAMA_FILE_MAGIC_GGJT
#define LLAMA_FILE_MAGIC_UNVERSIONED LLAMA_FILE_MAGIC_GGML
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 1
#ifdef __cplusplus
@ -40,9 +46,9 @@ extern "C" {
typedef int llama_token;
typedef struct llama_token_data {
llama_token id; // token id
float logit; // log-odds of the token
float p; // probability of the token
llama_token id; // token id
float logit; // log-odds of the token
float p; // probability of the token
} llama_token_data;
typedef struct llama_token_data_array {
@ -55,7 +61,6 @@ extern "C" {
struct llama_context_params {
int n_ctx; // text context
int n_parts; // -1 for default
int n_gpu_layers; // number of layers to store in VRAM
int seed; // RNG seed, -1 for random
@ -74,16 +79,16 @@ extern "C" {
// model file types
enum llama_ftype {
LLAMA_FTYPE_ALL_F32 = 0,
LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
LLAMA_FTYPE_ALL_F32 = 0,
LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
// LLAMA_FTYPE_MOSTLY_Q4_3 (6) support has been removed
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
// LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
};
LLAMA_API struct llama_context_params llama_context_default_params();
@ -91,6 +96,13 @@ extern "C" {
LLAMA_API bool llama_mmap_supported();
LLAMA_API bool llama_mlock_supported();
// TODO: not great API - very likely to change
// Initialize the llama + ggml backend
// Call once at the start of the program
LLAMA_API void llama_init_backend();
LLAMA_API int64_t llama_time_us();
// Various functions for loading a ggml llama model.
// Allocate (almost) all memory needed for the model.
// Return NULL on failure
@ -139,7 +151,7 @@ extern "C" {
// Set the state reading from the specified address
// Returns the number of bytes read
LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src);
LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
// Save/load session file
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);

9
tests/test-sampling.cpp
View file

@ -1,6 +1,10 @@
#include "llama.h"
#include "ggml.h"
#include <cassert>
#include "llama.h"
#ifdef NDEBUG
#undef NDEBUG
#endif
#include <cmath>
#include <numeric>
#include <cassert>
@ -8,7 +12,6 @@
#include <vector>
#include <algorithm>
void dump(const llama_token_data_array * candidates) {
for (size_t i = 0; i < candidates->size; i++) {
printf("%d: %f (%f)\n", candidates->data[i].id, candidates->data[i].p, candidates->data[i].logit);