vbatts/llama.cpp
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Merge remote-tracking branch 'origin/master' into prompt-array

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This commit is contained in:
Xiao-Yong Jin 2023-08-17 21:49:35 -05:00
commit a7871acced
60 changed files with 12627 additions and 5322 deletions
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3
.gitignore vendored
View file

@ -1,6 +1,7 @@
*.o
*.a
*.so
*.bin
.DS_Store
.build/
.cache/
@ -39,6 +40,7 @@ models-mnt
/perplexity
/embedding
/train-text-from-scratch
/convert-llama2c-to-ggml
/simple
/benchmark-matmult
/vdot
@ -68,6 +70,7 @@ poetry.lock
poetry.toml
# Test binaries
tests/test-grammar-parser
tests/test-double-float
tests/test-grad0
tests/test-opt

36
CMakeLists.txt
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@ -67,11 +67,12 @@ endif()
option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
option(LLAMA_BLAS "llama: use BLAS" OFF)
set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
option(LLAMA_CUBLAS "llama: use CUDA" OFF)
#option(LLAMA_CUDA_CUBLAS "llama: use cuBLAS for prompt processing" OFF)
option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF)
set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
option(LLAMA_CUDA_DMMV_F16 "llama: use 16 bit floats for dmmv CUDA kernels" OFF)
option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some calculations" OFF)
set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_METAL "llama: use Metal" OFF)
@ -251,6 +252,9 @@ if (LLAMA_CUBLAS)
set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
add_compile_definitions(GGML_USE_CUBLAS)
# if (LLAMA_CUDA_CUBLAS)
# add_compile_definitions(GGML_CUDA_CUBLAS)
# endif()
if (LLAMA_CUDA_FORCE_DMMV)
add_compile_definitions(GGML_CUDA_FORCE_DMMV)
endif()
@ -259,8 +263,8 @@ if (LLAMA_CUBLAS)
if (DEFINED LLAMA_CUDA_DMMV_Y)
add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility
endif()
if (LLAMA_CUDA_DMMV_F16)
add_compile_definitions(GGML_CUDA_DMMV_F16)
if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
add_compile_definitions(GGML_CUDA_F16)
endif()
add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
@ -271,10 +275,14 @@ if (LLAMA_CUBLAS)
endif()
if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
if (LLAMA_CUDA_DMMV_F16)
set(CMAKE_CUDA_ARCHITECTURES "60;61") # needed for f16 CUDA intrinsics
# 52 == lowest CUDA 12 standard
# 60 == f16 CUDA intrinsics
# 61 == integer CUDA intrinsics
# 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster
if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
else()
set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
endif()
endif()
message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
@ -288,7 +296,6 @@ if (LLAMA_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
find_library(METALPERFORMANCE_FRAMEWORK MetalPerformanceShaders REQUIRED)
set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
@ -305,7 +312,6 @@ if (LLAMA_METAL)
${FOUNDATION_LIBRARY}
${METAL_FRAMEWORK}
${METALKIT_FRAMEWORK}
${METALPERFORMANCE_FRAMEWORK}
)
endif()
@ -497,6 +503,8 @@ endif()
add_library(ggml OBJECT
ggml.c
ggml.h
ggml-alloc.c
ggml-alloc.h
${GGML_SOURCES_CUDA}
${GGML_SOURCES_OPENCL}
${GGML_SOURCES_METAL}
@ -561,6 +569,16 @@ install(
WORLD_READ
WORLD_EXECUTE
DESTINATION ${CMAKE_INSTALL_BINDIR})
if (LLAMA_METAL)
install(
FILES ggml-metal.metal
PERMISSIONS
OWNER_READ
OWNER_WRITE
GROUP_READ
WORLD_READ
DESTINATION ${CMAKE_INSTALL_BINDIR})
endif()
#
# programs, examples and tests

93
Makefile
View file

@ -1,8 +1,8 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server embd-input-test
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test
# Binaries only useful for tests
TEST_TARGETS = tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
default: $(BUILD_TARGETS)
@ -142,6 +142,28 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
#CXXFLAGS += -mssse3
endif
ifneq ($(filter aarch64%,$(UNAME_M)),)
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
CFLAGS += -mcpu=native
CXXFLAGS += -mcpu=native
endif
ifneq ($(filter armv6%,$(UNAME_M)),)
# Raspberry Pi 1, Zero
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
endif
ifneq ($(filter armv7%,$(UNAME_M)),)
# Raspberry Pi 2
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
endif
ifneq ($(filter armv8%,$(UNAME_M)),)
# Raspberry Pi 3, 4, Zero 2 (32-bit)
CFLAGS += -mfp16-format=ieee -mno-unaligned-access
endif
ifneq ($(filter ppc64%,$(UNAME_M)),)
POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
ifneq (,$(findstring POWER9,$(POWER9_M)))
@ -194,7 +216,7 @@ ifdef LLAMA_CUBLAS
CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
OBJS += ggml-cuda.o
NVCCFLAGS = --forward-unknown-to-host-compiler
NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
ifdef LLAMA_CUDA_NVCC
NVCC = $(LLAMA_CUDA_NVCC)
else
@ -220,19 +242,25 @@ else ifdef LLAMA_CUDA_DMMV_Y
else
NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
endif # LLAMA_CUDA_MMV_Y
ifdef LLAMA_CUDA_F16
NVCCFLAGS += -DGGML_CUDA_F16
endif # LLAMA_CUDA_F16
ifdef LLAMA_CUDA_DMMV_F16
NVCCFLAGS += -DGGML_CUDA_DMMV_F16
NVCCFLAGS += -DGGML_CUDA_F16
endif # LLAMA_CUDA_DMMV_F16
ifdef LLAMA_CUDA_KQUANTS_ITER
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
else
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
endif
#ifdef LLAMA_CUDA_CUBLAS
# NVCCFLAGS += -DGGML_CUDA_CUBLAS
#endif # LLAMA_CUDA_CUBLAS
ifdef LLAMA_CUDA_CCBIN
NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
endif
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) -Wno-pedantic -c $< -o $@
endif # LLAMA_CUBLAS
ifdef LLAMA_CLBLAST
@ -255,32 +283,10 @@ endif # LLAMA_CLBLAST
ifdef LLAMA_METAL
CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
CXXFLAGS += -DGGML_USE_METAL
LDFLAGS += -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
OBJS += ggml-metal.o
endif # LLAMA_METAL
ifneq ($(filter aarch64%,$(UNAME_M)),)
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
CFLAGS += -mcpu=native
CXXFLAGS += -mcpu=native
endif
ifneq ($(filter armv6%,$(UNAME_M)),)
# Raspberry Pi 1, Zero
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
endif
ifneq ($(filter armv7%,$(UNAME_M)),)
# Raspberry Pi 2
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
endif
ifneq ($(filter armv8%,$(UNAME_M)),)
# Raspberry Pi 3, 4, Zero 2 (32-bit)
CFLAGS += -mfp16-format=ieee -mno-unaligned-access
endif
ifdef LLAMA_METAL
ggml-metal.o: ggml-metal.m ggml-metal.h
$(CC) $(CFLAGS) -c $< -o $@
@ -318,12 +324,20 @@ $(info )
ggml.o: ggml.c ggml.h ggml-cuda.h
$(CC) $(CFLAGS) -c $< -o $@
llama.o: llama.cpp ggml.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
$(CC) $(CFLAGS) -c $< -o $@
OBJS += ggml-alloc.o
llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common.o: examples/common.cpp examples/common.h
$(CXX) $(CXXFLAGS) -c $< -o $@
console.o: examples/console.cpp examples/console.h
$(CXX) $(CXXFLAGS) -c $< -o $@
grammar-parser.o: examples/grammar-parser.cpp examples/grammar-parser.h
$(CXX) $(CXXFLAGS) -c $< -o $@
@ -331,13 +345,13 @@ libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
clean:
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h $(TEST_TARGETS)
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test build-info.h $(TEST_TARGETS)
#
# Examples
#
main: examples/main/main.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
main: examples/main/main.cpp build-info.h ggml.o llama.o common.o console.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@echo
@echo '==== Run ./main -h for help. ===='
@ -361,7 +375,7 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml.
save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o common.o $(OBJS)
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2)
$(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)
@ -374,6 +388,9 @@ embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-te
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
build-info.h: $(wildcard .git/index) scripts/build-info.sh
@sh scripts/build-info.sh > $@.tmp
@if ! cmp -s $@.tmp $@; then \
@ -395,13 +412,19 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o
vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
tests/test-double-float: tests/test-double-float.c build-info.h ggml.o llama.o common.o $(OBJS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-grad0: tests/test-grad0.c build-info.h ggml.o llama.o common.o $(OBJS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp examples/grammar-parser.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-opt: tests/test-opt.c build-info.h ggml.o llama.o common.o $(OBJS)
tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o common.o $(OBJS)

39
README.md
View file

@ -77,9 +77,10 @@ as the main playground for developing new features for the [ggml](https://github
**Supported models:**
- [X] LLaMA 🦙
- [x] LLaMA 2 🦙🦙
- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
@ -87,6 +88,7 @@ as the main playground for developing new features for the [ggml](https://github
- [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)
- [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) and its derivations (such as [baichuan-7b-sft](https://huggingface.co/hiyouga/baichuan-7b-sft))
- [X] [Aquila-7B](https://huggingface.co/BAAI/Aquila-7B) / [AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B)
**Bindings:**
@ -236,12 +238,17 @@ In order to build llama.cpp you have three different options.
cmake --build . --config Release
```
- Using `Zig`:
- Using `Zig` (version 0.11 or later):
Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C,
it's also possible to cross compile for other operating systems and architectures:
```bash
zig build -Doptimize=ReleaseFast
zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c
```
The `zig targets` command will give you valid options to use.
- Using `gmake` (FreeBSD):
1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
@ -399,12 +406,15 @@ Building the program with BLAS support may lead to some performance improvements
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
<!---
| LLAMA_CUDA_CUBLAS | Boolean | false | Use cuBLAS instead of custom CUDA kernels for prompt processing. Faster for all quantization formats except for q4_0 and q8_0, especially for k-quants. Increases VRAM usage (700 MiB for 7b, 970 MiB for 13b, 1430 MiB for 33b). |
--->
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
- #### CLBlast
@ -487,6 +497,9 @@ Building the program with BLAS support may lead to some performance improvements
# obtain the original LLaMA model weights and place them in ./models
ls ./models
65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
# [Optional] for models using BPE tokenizers
ls ./models
65B 30B 13B 7B vocab.json
# install Python dependencies
python3 -m pip install -r requirements.txt
@ -494,6 +507,9 @@ python3 -m pip install -r requirements.txt
# convert the 7B model to ggml FP16 format
python3 convert.py models/7B/
# [Optional] for models using BPE tokenizers
python convert.py models/7B/ --vocabtype bpe
# quantize the model to 4-bits (using q4_0 method)
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0
@ -650,6 +666,19 @@ python3 convert.py pygmalion-7b/ --outtype q4_1
- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository.
- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data.
### Obtaining and using the Facebook LLaMA 2 model
- Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data.
- Alternatively, if you want to save time and space, you can download already converted and quantized models from [TheBloke](https://huggingface.co/TheBloke), including:
- [LLaMA 2 7B base](https://huggingface.co/TheBloke/Llama-2-7B-GGML)
- [LLaMA 2 13B base](https://huggingface.co/TheBloke/Llama-2-13B-GGML)
- [LLaMA 2 70B base](https://huggingface.co/TheBloke/Llama-2-70B-GGML)
- [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGML)
- [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML)
- [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGML)
- Specify `-eps 1e-5` for best generation quality
- Specify `-gqa 8` for 70B models to work
### Verifying the model files
Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.

169
build.zig
View file

@ -1,68 +1,121 @@
// Compatible with Zig Version 0.11.0
const std = @import("std");
const commit_hash = @embedFile(".git/refs/heads/master");
const ArrayList = std.ArrayList;
const Compile = std.Build.Step.Compile;
const ConfigHeader = std.Build.Step.ConfigHeader;
const Mode = std.builtin.Mode;
const CrossTarget = std.zig.CrossTarget;
// Zig Version: 0.11.0-dev.3986+e05c242cd
pub fn build(b: *std.build.Builder) void {
const target = b.standardTargetOptions(.{});
const optimize = b.standardOptimizeOption(.{});
const Maker = struct {
builder: *std.build.Builder,
target: CrossTarget,
optimize: Mode,
config_header: *ConfigHeader,
enable_lto: bool,
const config_header = b.addConfigHeader(
.{ .style = .blank, .include_path = "build-info.h" },
.{
.BUILD_NUMBER = 0,
.BUILD_COMMIT = commit_hash[0 .. commit_hash.len - 1], // omit newline
},
);
include_dirs: ArrayList([]const u8),
cflags: ArrayList([]const u8),
cxxflags: ArrayList([]const u8),
objs: ArrayList(*Compile),
const lib = b.addStaticLibrary(.{
.name = "llama",
.target = target,
.optimize = optimize,
});
lib.linkLibC();
lib.linkLibCpp();
lib.addIncludePath(".");
lib.addIncludePath("./examples");
lib.addConfigHeader(config_header);
lib.addCSourceFiles(&.{"ggml.c"}, &.{"-std=c11"});
lib.addCSourceFiles(&.{"llama.cpp"}, &.{"-std=c++11"});
b.installArtifact(lib);
fn addInclude(m: *Maker, dir: []const u8) !void {
try m.include_dirs.append(dir);
}
fn addProjectInclude(m: *Maker, path: []const []const u8) !void {
try m.addInclude(try m.builder.build_root.join(m.builder.allocator, path));
}
fn addCFlag(m: *Maker, flag: []const u8) !void {
try m.cflags.append(flag);
}
fn addCxxFlag(m: *Maker, flag: []const u8) !void {
try m.cxxflags.append(flag);
}
fn addFlag(m: *Maker, flag: []const u8) !void {
try m.addCFlag(flag);
try m.addCxxFlag(flag);
}
const examples = .{
"main",
"baby-llama",
"embedding",
"metal",
"perplexity",
"quantize",
"quantize-stats",
"save-load-state",
"server",
"simple",
"train-text-from-scratch",
};
fn init(builder: *std.build.Builder) !Maker {
const commit_hash = @embedFile(".git/refs/heads/master");
const config_header = builder.addConfigHeader(
.{ .style = .blank, .include_path = "build-info.h" },
.{
.BUILD_NUMBER = 0,
.BUILD_COMMIT = commit_hash[0 .. commit_hash.len - 1], // omit newline
},
);
var m = Maker{
.builder = builder,
.target = builder.standardTargetOptions(.{}),
.optimize = builder.standardOptimizeOption(.{}),
.config_header = config_header,
.enable_lto = false,
.include_dirs = ArrayList([]const u8).init(builder.allocator),
.cflags = ArrayList([]const u8).init(builder.allocator),
.cxxflags = ArrayList([]const u8).init(builder.allocator),
.objs = ArrayList(*Compile).init(builder.allocator),
};
try m.addCFlag("-std=c11");
try m.addCxxFlag("-std=c++11");
try m.addProjectInclude(&.{});
try m.addProjectInclude(&.{"examples"});
return m;
}
inline for (examples) |example_name| {
const exe = b.addExecutable(.{
.name = example_name,
.target = target,
.optimize = optimize,
});
exe.addIncludePath(".");
exe.addIncludePath("./examples");
exe.addConfigHeader(config_header);
exe.addCSourceFiles(&.{
std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{ example_name, example_name }),
"examples/common.cpp",
}, &.{"-std=c++11"});
exe.linkLibrary(lib);
b.installArtifact(exe);
fn obj(m: *const Maker, name: []const u8, src: []const u8) *Compile {
const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize });
if (std.mem.endsWith(u8, src, ".c")) {
o.addCSourceFiles(&.{src}, m.cflags.items);
o.linkLibC();
} else {
o.addCSourceFiles(&.{src}, m.cxxflags.items);
o.linkLibCpp();
}
for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i });
o.want_lto = m.enable_lto;
return o;
}
const run_cmd = b.addRunArtifact(exe);
run_cmd.step.dependOn(b.getInstallStep());
if (b.args) |args| run_cmd.addArgs(args);
fn exe(m: *const Maker, name: []const u8, src: []const u8, deps: []const *Compile) *Compile {
const e = m.builder.addExecutable(.{ .name = name, .target = m.target, .optimize = m.optimize });
e.addCSourceFiles(&.{src}, m.cxxflags.items);
for (deps) |d| e.addObject(d);
for (m.objs.items) |o| e.addObject(o);
for (m.include_dirs.items) |i| e.addIncludePath(.{ .path = i });
e.linkLibC();
e.linkLibCpp();
e.addConfigHeader(m.config_header);
m.builder.installArtifact(e);
e.want_lto = m.enable_lto;
return e;
}
};
const run_step = b.step("run-" ++ example_name, "Run the app");
run_step.dependOn(&run_cmd.step);
pub fn build(b: *std.build.Builder) !void {
var make = try Maker.init(b);
make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false;
if (b.option(bool, "k-quants", "Enable K-quants, (default: true)") orelse true) {
try make.addFlag("-DGGML_USE_K_QUANTS");
const k_quants = make.obj("k_quants", "k_quants.c");
try make.objs.append(k_quants);
}
const ggml = make.obj("ggml", "ggml.c");
const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
const llama = make.obj("llama", "llama.cpp");
const common = make.obj("common", "examples/common.cpp");
const console = make.obj("common", "examples/console.cpp");
const grammar_parser = make.obj("grammar-parser", "examples/grammar-parser.cpp");
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, llama, common, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, llama });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, llama, common });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, llama, common });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, llama });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, llama, common, grammar_parser });
if (server.target.isWindows()) {
server.linkSystemLibrary("ws2_32");
}
}

103
convert.py Executable file → Normal file
View file

@ -133,7 +133,7 @@ TENSORS_SET = set(TENSORS_LIST)
def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range
for n_mult in range(256, 1, -1):
for n_mult in range(8192, 1, -1):
calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
if calc_ff == n_ff:
return n_mult
@ -141,11 +141,12 @@ def find_n_mult(n_ff: int, n_embd: int) -> int:
@dataclass
class Params:
n_vocab: int
n_embd: int
n_mult: int
n_head: int
n_layer: int
n_vocab: int
n_embd: int
n_mult: int
n_head: int
n_layer: int
n_kv_head: Optional[int] # This parameter is only used for Llama 2
@staticmethod
def guessed(model: 'LazyModel') -> 'Params':
@ -167,11 +168,12 @@ class Params:
n_head=n_embd // 128 # guessed
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = 256,
n_head = n_head,
n_layer = n_layer,
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = 256,
n_head = n_head,
n_layer = n_layer,
n_kv_head = None,
)
@staticmethod
@ -183,15 +185,17 @@ class Params:
n_head = config["num_attention_heads"];
n_layer = config["num_hidden_layers"];
n_ff = config["intermediate_size"];
n_kv_head = config.get("num_key_value_heads")
n_mult = find_n_mult(n_ff, n_embd);
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_head = n_head,
n_layer = n_layer,
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_head = n_head,
n_layer = n_layer,
n_kv_head = n_kv_head,
)
# LLaMA v2 70B params.json
@ -200,21 +204,22 @@ class Params:
def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
config = json.load(open(config_path))
n_vocab = config["vocab_size"];
n_embd = config["dim"];
n_head = config["n_heads"];
n_layer = config["n_layers"];
n_mult = config["multiple_of"];
n_vocab = config["vocab_size"];
n_embd = config["dim"];
n_head = config["n_heads"];
n_layer = config["n_layers"];
n_mult = config["multiple_of"];
if n_vocab == -1:
n_vocab = model["tok_embeddings.weight"].shape[0]
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_head = n_head,
n_layer = n_layer,
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_head = n_head,
n_layer = n_layer,
n_kv_head = None,
)
@staticmethod
@ -317,10 +322,12 @@ class GGMLVocab:
Vocab = Union[SentencePieceVocab, GGMLVocab]
def permute(weights: NDArray, n_head: int) -> NDArray:
def permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
.swapaxes(1, 2)
.reshape(weights.shape))
def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray:
@ -368,7 +375,7 @@ class Tensor(metaclass=ABCMeta):
@abstractmethod
def astype(self, data_type: DataType) -> 'Tensor': ...
@abstractmethod
def permute(self, n_head: int) -> 'Tensor': ...
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'Tensor': ...
@abstractmethod
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
@abstractmethod
@ -406,8 +413,8 @@ class UnquantizedTensor(Tensor):
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
def permute(self, n_head: int) -> 'UnquantizedTensor':
return UnquantizedTensor(permute(self.ndarray, n_head))
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'UnquantizedTensor':
return UnquantizedTensor(permute(self.ndarray, n_head, n_kv_head))
def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray:
@ -455,26 +462,34 @@ class GGMLQuantizedTensor(Tensor):
def to_ggml(self) -> 'GGMLQuantizedTensor':
return self
def permute(self, n_head: int) -> 'GGMLQuantizedTensor':
return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type)
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'GGMLQuantizedTensor':
return GGMLQuantizedTensor(permute(self.ndarray, n_head, n_kv_head), self.shape, self.data_type)
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
def part(self, n_part: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor]
class DeferredPermutedTensor(Tensor):
def __init__(self, base: Tensor, n_head: int) -> None:
def __init__(self, base: Tensor, n_head: int, n_kv_head: Optional[int] = None) -> None:
self.base = base
self.n_head = n_head
self.n_kv_head = n_kv_head
self.data_type = self.base.data_type
def astype(self, data_type: DataType) -> Tensor:
return self.base.astype(data_type).permute(self.n_head)
return self.base.astype(data_type).permute(self.n_head, self.n_kv_head)
def to_ggml(self) -> GGMLCompatibleTensor:
return self.base.to_ggml().permute(self.n_head)
return self.base.to_ggml().permute(self.n_head, self.n_kv_head)
def permute(self, n_head: int) -> Tensor:
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
raise Exception("shouldn't permute twice")
@ -566,8 +581,8 @@ class GPTQForLLaMaQuantizedTensor(Tensor):
ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False)
return ret
def permute(self, n_head: int) -> Tensor:
return DeferredPermutedTensor(self, n_head)
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
return DeferredPermutedTensor(self, n_head, n_kv_head)
def to_ggml(self) -> GGMLQuantizedTensor:
# The output format looks like this:
@ -698,10 +713,10 @@ def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus:
return ModelPlus(model, paths, format, vocab)
def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor:
def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_kv_head: Optional[int] = None) -> LazyTensor:
def load() -> Tensor:
return lazy_tensor.load().permute(n_head)
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
return lazy_tensor.load().permute(n_head, n_kv_head)
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_kv_head}) ' + lazy_tensor.description)
def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
def load() -> Tensor:
@ -726,7 +741,7 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
for i in itertools.count():
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head)
out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_kv_head)
out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)

3
examples/CMakeLists.txt
View file

@ -13,6 +13,8 @@ set(TARGET common)
add_library(${TARGET} OBJECT
common.h
common.cpp
console.h
console.cpp
grammar-parser.h
grammar-parser.cpp
)
@ -40,6 +42,7 @@ else()
add_subdirectory(benchmark)
add_subdirectory(baby-llama)
add_subdirectory(train-text-from-scratch)
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(simple)
add_subdirectory(embd-input)
if (LLAMA_METAL)

440
examples/common.cpp
View file

@ -25,7 +25,6 @@
#else
#include <sys/ioctl.h>
#include <unistd.h>
#include <wchar.h>
#endif
#if defined(_MSC_VER)
@ -195,6 +194,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.rope_freq_scale = std::stof(argv[i]);
} else if (arg == "--rope-scale") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rope_freq_scale = 1.0f/std::stof(argv[i]);
} else if (arg == "--memory-f32") {
params.memory_f16 = false;
} else if (arg == "--top-p") {
@ -269,6 +274,21 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.cfg_negative_prompt = argv[i];
} else if (arg == "--cfg-negative-prompt-file") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
break;
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.cfg_negative_prompt));
if (params.cfg_negative_prompt.back() == '\n') {
params.cfg_negative_prompt.pop_back();
}
} else if (arg == "--cfg-scale") {
if (++i >= argc) {
invalid_param = true;
@ -329,6 +349,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.instruct = true;
} else if (arg == "--multiline-input") {
params.multiline_input = true;
} else if (arg == "--simple-io") {
params.simple_io = true;
} else if (arg == "--color") {
params.use_color = true;
} else if (arg == "--mlock") {
@ -352,7 +374,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
#ifdef GGML_USE_CUBLAS
params.main_gpu = std::stoi(argv[i]);
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
#endif
} else if (arg == "--tensor-split" || arg == "-ts") {
if (++i >= argc) {
@ -376,13 +398,19 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
}
}
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--mul-mat-q" || arg == "-mmq") {
#ifdef GGML_USE_CUBLAS
params.mul_mat_q = true;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--low-vram" || arg == "-lv") {
#ifdef GGML_USE_CUBLAS
params.low_vram = true;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--no-mmap") {
params.use_mmap = false;
@ -402,8 +430,14 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.antiprompt.push_back(argv[i]);
} else if (arg == "--perplexity") {
params.perplexity = true;
} else if (arg == "--perplexity-lines") {
params.perplexity_lines = true;
} else if (arg == "--hellaswag") {
params.hellaswag = true;
} else if (arg == "--hellaswag-tasks") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.hellaswag_tasks = std::stoi(argv[i]);
} else if (arg == "--ignore-eos") {
params.logit_bias[llama_token_eos()] = -INFINITY;
} else if (arg == "--no-penalize-nl") {
@ -524,7 +558,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
fprintf(stdout, " -f FNAME, --file FNAME\n");
fprintf(stdout, " prompt file to start generation.\n");
fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict);
fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa);
@ -548,19 +582,23 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
fprintf(stdout, " --grammar-file FNAME file to read grammar from\n");
fprintf(stdout, " --cfg-negative-prompt PROMPT \n");
fprintf(stdout, " --cfg-negative-prompt PROMPT\n");
fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n");
fprintf(stdout, " --cfg-negative-prompt-file FNAME\n");
fprintf(stdout, " negative prompt file to use for guidance. (default: empty)\n");
fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
fprintf(stdout, " --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale);
fprintf(stdout, " --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base);
fprintf(stdout, " --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale);
fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
fprintf(stdout, " --no-penalize-nl do not penalize newline token\n");
fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp);
fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n");
fprintf(stdout, " --perplexity-lines compute perplexity over each line of the prompt\n");
fprintf(stdout, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
if (llama_mlock_supported()) {
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
@ -578,10 +616,14 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n" );
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
#endif
fprintf(stdout, " --mtest compute maximum memory usage\n");
fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");
fprintf(stdout, " --verbose-prompt print prompt before generation\n");
fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stdout, " -m FNAME, --model FNAME\n");
@ -630,6 +672,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
lparams.main_gpu = params.main_gpu;
lparams.tensor_split = params.tensor_split;
lparams.low_vram = params.low_vram;
lparams.mul_mat_q = params.mul_mat_q;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;
@ -673,376 +716,3 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
return std::make_tuple(model, lctx);
}
void console_init(console_state & con_st) {
#if defined(_WIN32)
// Windows-specific console initialization
DWORD dwMode = 0;
con_st.hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
if (con_st.hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(con_st.hConsole, &dwMode)) {
con_st.hConsole = GetStdHandle(STD_ERROR_HANDLE);
if (con_st.hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(con_st.hConsole, &dwMode))) {
con_st.hConsole = NULL;
}
}
if (con_st.hConsole) {
// Enable ANSI colors on Windows 10+
if (con_st.use_color && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {
SetConsoleMode(con_st.hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING);
}
// Set console output codepage to UTF8
SetConsoleOutputCP(CP_UTF8);
}
HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE);
if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) {
// Set console input codepage to UTF16
_setmode(_fileno(stdin), _O_WTEXT);
// Turn off ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)
dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT);
SetConsoleMode(hConIn, dwMode);
}
#else
// POSIX-specific console initialization
struct termios new_termios;
tcgetattr(STDIN_FILENO, &con_st.prev_state);
new_termios = con_st.prev_state;
new_termios.c_lflag &= ~(ICANON | ECHO);
new_termios.c_cc[VMIN] = 1;
new_termios.c_cc[VTIME] = 0;
tcsetattr(STDIN_FILENO, TCSANOW, &new_termios);
con_st.tty = fopen("/dev/tty", "w+");
if (con_st.tty != nullptr) {
con_st.out = con_st.tty;
}
setlocale(LC_ALL, "");
#endif
}
void console_cleanup(console_state & con_st) {
// Reset console color
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
#if !defined(_WIN32)
if (con_st.tty != nullptr) {
con_st.out = stdout;
fclose(con_st.tty);
con_st.tty = nullptr;
}
// Restore the terminal settings on POSIX systems
tcsetattr(STDIN_FILENO, TCSANOW, &con_st.prev_state);
#endif
}
/* Keep track of current color of output, and emit ANSI code if it changes. */
void console_set_color(console_state & con_st, console_color_t color) {
if (con_st.use_color && con_st.color != color) {
fflush(stdout);
switch(color) {
case CONSOLE_COLOR_DEFAULT:
fprintf(con_st.out, ANSI_COLOR_RESET);
break;
case CONSOLE_COLOR_PROMPT:
fprintf(con_st.out, ANSI_COLOR_YELLOW);
break;
case CONSOLE_COLOR_USER_INPUT:
fprintf(con_st.out, ANSI_BOLD ANSI_COLOR_GREEN);
break;
case CONSOLE_COLOR_ERROR:
fprintf(con_st.out, ANSI_BOLD ANSI_COLOR_RED);
break;
}
con_st.color = color;
fflush(con_st.out);
}
}
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;
}
#if WCHAR_MAX == 0xFFFF
if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
wchar_t low_surrogate = getwchar();
if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate
return (static_cast<char32_t>(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000;
}
}
if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair
return 0xFFFD; // Return the replacement character U+FFFD
}
#endif
return static_cast<char32_t>(wc);
#endif
}
void pop_cursor(console_state & con_st) {
#if defined(_WIN32)
if (con_st.hConsole != NULL) {
CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
GetConsoleScreenBufferInfo(con_st.hConsole, &bufferInfo);
COORD newCursorPosition = bufferInfo.dwCursorPosition;
if (newCursorPosition.X == 0) {
newCursorPosition.X = bufferInfo.dwSize.X - 1;
newCursorPosition.Y -= 1;
} else {
newCursorPosition.X -= 1;
}
SetConsoleCursorPosition(con_st.hConsole, newCursorPosition);
return;
}
#endif
putc('\b', con_st.out);
}
int estimateWidth(char32_t codepoint) {
#if defined(_WIN32)
return 1;
#else
return wcwidth(codepoint);
#endif
}
int put_codepoint(console_state & con_st, const char* utf8_codepoint, size_t length, int expectedWidth) {
#if defined(_WIN32)
CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
if (!GetConsoleScreenBufferInfo(con_st.hConsole, &bufferInfo)) {
// go with the default
return expectedWidth;
}
COORD initialPosition = bufferInfo.dwCursorPosition;
DWORD nNumberOfChars = length;
WriteConsole(con_st.hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL);
CONSOLE_SCREEN_BUFFER_INFO newBufferInfo;
GetConsoleScreenBufferInfo(con_st.hConsole, &newBufferInfo);
// Figure out our real position if we're in the last column
if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) {
DWORD nNumberOfChars;
WriteConsole(con_st.hConsole, &" \b", 2, &nNumberOfChars, NULL);
GetConsoleScreenBufferInfo(con_st.hConsole, &newBufferInfo);
}
int width = newBufferInfo.dwCursorPosition.X - initialPosition.X;
if (width < 0) {
width += newBufferInfo.dwSize.X;
}
return width;
#else
// we can trust expectedWidth if we've got one
if (expectedWidth >= 0 || con_st.tty == nullptr) {
fwrite(utf8_codepoint, length, 1, con_st.out);
return expectedWidth;
}
fputs("\033[6n", con_st.tty); // Query cursor position
int x1, x2, y1, y2;
int results = 0;
results = fscanf(con_st.tty, "\033[%d;%dR", &y1, &x1);
fwrite(utf8_codepoint, length, 1, con_st.tty);
fputs("\033[6n", con_st.tty); // Query cursor position
results += fscanf(con_st.tty, "\033[%d;%dR", &y2, &x2);
if (results != 4) {
return expectedWidth;
}
int width = x2 - x1;
if (width < 0) {
// Calculate the width considering text wrapping
struct winsize w;
ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
width += w.ws_col;
}
return width;
#endif
}
void replace_last(console_state & con_st, char ch) {
#if defined(_WIN32)
pop_cursor(con_st);
put_codepoint(con_st, &ch, 1, 1);
#else
fprintf(con_st.out, "\b%c", ch);
#endif
}
void append_utf8(char32_t ch, std::string & out) {
if (ch <= 0x7F) {
out.push_back(static_cast<unsigned char>(ch));
} else if (ch <= 0x7FF) {
out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else if (ch <= 0xFFFF) {
out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else if (ch <= 0x10FFFF) {
out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else {
// Invalid Unicode code point
}
}
// Helper function to remove the last UTF-8 character from a string
void pop_back_utf8_char(std::string & line) {
if (line.empty()) {
return;
}
size_t pos = line.length() - 1;
// Find the start of the last UTF-8 character (checking up to 4 bytes back)
for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) {
if ((line[pos] & 0xC0) != 0x80) break; // Found the start of the character
}
line.erase(pos);
}
bool console_readline(console_state & con_st, std::string & line) {
console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
if (con_st.out != stdout) {
fflush(stdout);
}
line.clear();
std::vector<int> widths;
bool is_special_char = false;
bool end_of_stream = false;
char32_t input_char;
while (true) {
fflush(con_st.out); // Ensure all output is displayed before waiting for input
input_char = getchar32();
if (input_char == '\r' || input_char == '\n') {
break;
}
if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {
end_of_stream = true;
break;
}
if (is_special_char) {
console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
replace_last(con_st, line.back());
is_special_char = false;
}
if (input_char == '\033') { // Escape sequence
char32_t code = getchar32();
if (code == '[' || code == 0x1B) {
// Discard the rest of the escape sequence
while ((code = getchar32()) != (char32_t) WEOF) {
if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {
break;
}
}
}
} else if (input_char == 0x08 || input_char == 0x7F) { // Backspace
if (!widths.empty()) {
int count;
do {
count = widths.back();
widths.pop_back();
// Move cursor back, print space, and move cursor back again
for (int i = 0; i < count; i++) {
replace_last(con_st, ' ');
pop_cursor(con_st);
}
pop_back_utf8_char(line);
} while (count == 0 && !widths.empty());
}
} else {
int offset = line.length();
append_utf8(input_char, line);
int width = put_codepoint(con_st, line.c_str() + offset, line.length() - offset, estimateWidth(input_char));
if (width < 0) {
width = 0;
}
widths.push_back(width);
}
if (!line.empty() && (line.back() == '\\' || line.back() == '/')) {
console_set_color(con_st, CONSOLE_COLOR_PROMPT);
replace_last(con_st, line.back());
is_special_char = true;
}
}
bool has_more = con_st.multiline_input;
if (is_special_char) {
replace_last(con_st, ' ');
pop_cursor(con_st);
char last = line.back();
line.pop_back();
if (last == '\\') {
line += '\n';
fputc('\n', con_st.out);
has_more = !has_more;
} else {
// llama will just eat the single space, it won't act as a space
if (line.length() == 1 && line.back() == ' ') {
line.clear();
pop_cursor(con_st);
}
has_more = false;
}
} else {
if (end_of_stream) {
has_more = false;
} else {
line += '\n';
fputc('\n', con_st.out);
}
}
fflush(con_st.out);
return has_more;
}

52
examples/common.h
View file

@ -11,11 +11,6 @@
#include <unordered_map>
#include <tuple>
#if !defined (_WIN32)
#include <stdio.h>
#include <termios.h>
#endif
//
// CLI argument parsing
//
@ -70,7 +65,11 @@ struct gpt_params {
std::string lora_adapter = ""; // lora adapter path
std::string lora_base = ""; // base model path for the lora adapter
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
bool mul_mat_q = false; // if true, use experimental mul_mat_q kernels
bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs
@ -81,12 +80,12 @@ struct gpt_params {
bool embedding = false; // get only sentence embedding
bool interactive_first = false; // wait for user input immediately
bool multiline_input = false; // reverse the usage of `\`
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
bool instruct = false; // instruction mode (used for Alpaca models)
bool penalize_nl = true; // consider newlines as a repeatable token
bool perplexity = false; // compute perplexity over the prompt
bool perplexity_lines = false; // compute perplexity over each line of the prompt
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
bool mem_test = false; // compute maximum memory usage
@ -113,42 +112,3 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s
std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(const gpt_params & params);
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
//
// Console utils
//
#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
#define ANSI_COLOR_BLUE "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN "\x1b[36m"
#define ANSI_COLOR_RESET "\x1b[0m"
#define ANSI_BOLD "\x1b[1m"
enum console_color_t {
CONSOLE_COLOR_DEFAULT=0,
CONSOLE_COLOR_PROMPT,
CONSOLE_COLOR_USER_INPUT,
CONSOLE_COLOR_ERROR
};
struct console_state {
bool multiline_input = false;
bool use_color = false;
console_color_t color = CONSOLE_COLOR_DEFAULT;
FILE* out = stdout;
#if defined (_WIN32)
void* hConsole;
#else
FILE* tty = nullptr;
termios prev_state;
#endif
};
void console_init(console_state & con_st);
void console_cleanup(console_state & con_st);
void console_set_color(console_state & con_st, console_color_t color);
bool console_readline(console_state & con_st, std::string & line);

500
examples/console.cpp Normal file
View file

@ -0,0 +1,500 @@
#include "console.h"
#include <vector>
#include <iostream>
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <fcntl.h>
#include <io.h>
#ifndef ENABLE_VIRTUAL_TERMINAL_PROCESSING
#define ENABLE_VIRTUAL_TERMINAL_PROCESSING 0x0004
#endif
#else
#include <climits>
#include <sys/ioctl.h>
#include <unistd.h>
#include <wchar.h>
#include <stdio.h>
#include <stdlib.h>
#include <signal.h>
#include <termios.h>
#endif
#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
#define ANSI_COLOR_BLUE "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN "\x1b[36m"
#define ANSI_COLOR_RESET "\x1b[0m"
#define ANSI_BOLD "\x1b[1m"
namespace console {
//
// Console state
//
static bool advanced_display = false;
static bool simple_io = true;
static display_t current_display = reset;
static FILE* out = stdout;
#if defined (_WIN32)
static void* hConsole;
#else
static FILE* tty = nullptr;
static termios initial_state;
#endif
//
// Init and cleanup
//
void init(bool use_simple_io, bool use_advanced_display) {
advanced_display = use_advanced_display;
simple_io = use_simple_io;
#if defined(_WIN32)
// Windows-specific console initialization
DWORD dwMode = 0;
hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) {
hConsole = GetStdHandle(STD_ERROR_HANDLE);
if (hConsole != INVALID_HANDLE_VALUE && (!GetConsoleMode(hConsole, &dwMode))) {
hConsole = nullptr;
simple_io = true;
}
}
if (hConsole) {
// Check conditions combined to reduce nesting
if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING) &&
!SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {
advanced_display = false;
}
// Set console output codepage to UTF8
SetConsoleOutputCP(CP_UTF8);
}
HANDLE hConIn = GetStdHandle(STD_INPUT_HANDLE);
if (hConIn != INVALID_HANDLE_VALUE && GetConsoleMode(hConIn, &dwMode)) {
// Set console input codepage to UTF16
_setmode(_fileno(stdin), _O_WTEXT);
// Set ICANON (ENABLE_LINE_INPUT) and ECHO (ENABLE_ECHO_INPUT)
if (simple_io) {
dwMode |= ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT;
} else {
dwMode &= ~(ENABLE_LINE_INPUT | ENABLE_ECHO_INPUT);
}
if (!SetConsoleMode(hConIn, dwMode)) {
simple_io = true;
}
}
#else
// POSIX-specific console initialization
if (!simple_io) {
struct termios new_termios;
tcgetattr(STDIN_FILENO, &initial_state);
new_termios = initial_state;
new_termios.c_lflag &= ~(ICANON | ECHO);
new_termios.c_cc[VMIN] = 1;
new_termios.c_cc[VTIME] = 0;
tcsetattr(STDIN_FILENO, TCSANOW, &new_termios);
tty = fopen("/dev/tty", "w+");
if (tty != nullptr) {
out = tty;
}
}
setlocale(LC_ALL, "");
#endif
}
void cleanup() {
// Reset console display
set_display(reset);
#if !defined(_WIN32)
// Restore settings on POSIX systems
if (!simple_io) {
if (tty != nullptr) {
out = stdout;
fclose(tty);
tty = nullptr;
}
tcsetattr(STDIN_FILENO, TCSANOW, &initial_state);
}
#endif
}
//
// Display and IO
//
// Keep track of current display and only emit ANSI code if it changes
void set_display(display_t display) {
if (advanced_display && current_display != display) {
fflush(stdout);
switch(display) {
case reset:
fprintf(out, ANSI_COLOR_RESET);
break;
case prompt:
fprintf(out, ANSI_COLOR_YELLOW);
break;
case user_input:
fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN);
break;
case error:
fprintf(out, ANSI_BOLD ANSI_COLOR_RED);
}
current_display = display;
fflush(out);
}
}
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;
}
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;
}
#if WCHAR_MAX == 0xFFFF
if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
wchar_t low_surrogate = getwchar();
if ((low_surrogate >= 0xDC00) && (low_surrogate <= 0xDFFF)) { // Check if the next wchar is a low surrogate
return (static_cast<char32_t>(wc & 0x03FF) << 10) + (low_surrogate & 0x03FF) + 0x10000;
}
}
if ((wc >= 0xD800) && (wc <= 0xDFFF)) { // Invalid surrogate pair
return 0xFFFD; // Return the replacement character U+FFFD
}
#endif
return static_cast<char32_t>(wc);
#endif
}
void pop_cursor() {
#if defined(_WIN32)
if (hConsole != NULL) {
CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
GetConsoleScreenBufferInfo(hConsole, &bufferInfo);
COORD newCursorPosition = bufferInfo.dwCursorPosition;
if (newCursorPosition.X == 0) {
newCursorPosition.X = bufferInfo.dwSize.X - 1;
newCursorPosition.Y -= 1;
} else {
newCursorPosition.X -= 1;
}
SetConsoleCursorPosition(hConsole, newCursorPosition);
return;
}
#endif
putc('\b', out);
}
int estimateWidth(char32_t codepoint) {
#if defined(_WIN32)
return 1;
#else
return wcwidth(codepoint);
#endif
}
int put_codepoint(const char* utf8_codepoint, size_t length, int expectedWidth) {
#if defined(_WIN32)
CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
if (!GetConsoleScreenBufferInfo(hConsole, &bufferInfo)) {
// go with the default
return expectedWidth;
}
COORD initialPosition = bufferInfo.dwCursorPosition;
DWORD nNumberOfChars = length;
WriteConsole(hConsole, utf8_codepoint, nNumberOfChars, &nNumberOfChars, NULL);
CONSOLE_SCREEN_BUFFER_INFO newBufferInfo;
GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);
// Figure out our real position if we're in the last column
if (utf8_codepoint[0] != 0x09 && initialPosition.X == newBufferInfo.dwSize.X - 1) {
DWORD nNumberOfChars;
WriteConsole(hConsole, &" \b", 2, &nNumberOfChars, NULL);
GetConsoleScreenBufferInfo(hConsole, &newBufferInfo);
}
int width = newBufferInfo.dwCursorPosition.X - initialPosition.X;
if (width < 0) {
width += newBufferInfo.dwSize.X;
}
return width;
#else
// We can trust expectedWidth if we've got one
if (expectedWidth >= 0 || tty == nullptr) {
fwrite(utf8_codepoint, length, 1, out);
return expectedWidth;
}
fputs("\033[6n", tty); // Query cursor position
int x1;
int y1;
int x2;
int y2;
int results = 0;
results = fscanf(tty, "\033[%d;%dR", &y1, &x1);
fwrite(utf8_codepoint, length, 1, tty);
fputs("\033[6n", tty); // Query cursor position
results += fscanf(tty, "\033[%d;%dR", &y2, &x2);
if (results != 4) {
return expectedWidth;
}
int width = x2 - x1;
if (width < 0) {
// Calculate the width considering text wrapping
struct winsize w;
ioctl(STDOUT_FILENO, TIOCGWINSZ, &w);
width += w.ws_col;
}
return width;
#endif
}
void replace_last(char ch) {
#if defined(_WIN32)
pop_cursor();
put_codepoint(&ch, 1, 1);
#else
fprintf(out, "\b%c", ch);
#endif
}
void append_utf8(char32_t ch, std::string & out) {
if (ch <= 0x7F) {
out.push_back(static_cast<unsigned char>(ch));
} else if (ch <= 0x7FF) {
out.push_back(static_cast<unsigned char>(0xC0 | ((ch >> 6) & 0x1F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else if (ch <= 0xFFFF) {
out.push_back(static_cast<unsigned char>(0xE0 | ((ch >> 12) & 0x0F)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else if (ch <= 0x10FFFF) {
out.push_back(static_cast<unsigned char>(0xF0 | ((ch >> 18) & 0x07)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 12) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | ((ch >> 6) & 0x3F)));
out.push_back(static_cast<unsigned char>(0x80 | (ch & 0x3F)));
} else {
// Invalid Unicode code point
}
}
// Helper function to remove the last UTF-8 character from a string
void pop_back_utf8_char(std::string & line) {
if (line.empty()) {
return;
}
size_t pos = line.length() - 1;
// Find the start of the last UTF-8 character (checking up to 4 bytes back)
for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) {
if ((line[pos] & 0xC0) != 0x80) {
break; // Found the start of the character
}
}
line.erase(pos);
}
bool readline_advanced(std::string & line, bool multiline_input) {
if (out != stdout) {
fflush(stdout);
}
line.clear();
std::vector<int> widths;
bool is_special_char = false;
bool end_of_stream = false;
char32_t input_char;
while (true) {
fflush(out); // Ensure all output is displayed before waiting for input
input_char = getchar32();
if (input_char == '\r' || input_char == '\n') {
break;
}
if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {
end_of_stream = true;
break;
}
if (is_special_char) {
set_display(user_input);
replace_last(line.back());
is_special_char = false;
}
if (input_char == '\033') { // Escape sequence
char32_t code = getchar32();
if (code == '[' || code == 0x1B) {
// Discard the rest of the escape sequence
while ((code = getchar32()) != (char32_t) WEOF) {
if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {
break;
}
}
}
} else if (input_char == 0x08 || input_char == 0x7F) { // Backspace
if (!widths.empty()) {
int count;
do {
count = widths.back();
widths.pop_back();
// Move cursor back, print space, and move cursor back again
for (int i = 0; i < count; i++) {
replace_last(' ');
pop_cursor();
}
pop_back_utf8_char(line);
} while (count == 0 && !widths.empty());
}
} else {
int offset = line.length();
append_utf8(input_char, line);
int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char));
if (width < 0) {
width = 0;
}
widths.push_back(width);
}
if (!line.empty() && (line.back() == '\\' || line.back() == '/')) {
set_display(prompt);
replace_last(line.back());
is_special_char = true;
}
}
bool has_more = multiline_input;
if (is_special_char) {
replace_last(' ');
pop_cursor();
char last = line.back();
line.pop_back();
if (last == '\\') {
line += '\n';
fputc('\n', out);
has_more = !has_more;
} else {
// llama will just eat the single space, it won't act as a space
if (line.length() == 1 && line.back() == ' ') {
line.clear();
pop_cursor();
}
has_more = false;
}
} else {
if (end_of_stream) {
has_more = false;
} else {
line += '\n';
fputc('\n', out);
}
}
fflush(out);
return has_more;
}
bool readline_simple(std::string & line, bool multiline_input) {
#if defined(_WIN32)
std::wstring wline;
if (!std::getline(std::wcin, wline)) {
// Input stream is bad or EOF received
line.clear();
GenerateConsoleCtrlEvent(CTRL_C_EVENT, 0);
return false;
}
int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), NULL, 0, NULL, NULL);
line.resize(size_needed);
WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), &line[0], size_needed, NULL, NULL);
#else
if (!std::getline(std::cin, line)) {
// Input stream is bad or EOF received
line.clear();
return false;
}
#endif
if (!line.empty()) {
char last = line.back();
if (last == '/') { // Always return control on '/' symbol
line.pop_back();
return false;
}
if (last == '\\') { // '\\' changes the default action
line.pop_back();
multiline_input = !multiline_input;
}
}
line += '\n';
// By default, continue input if multiline_input is set
return multiline_input;
}
bool readline(std::string & line, bool multiline_input) {
set_display(user_input);
if (simple_io) {
return readline_simple(line, multiline_input);
}
return readline_advanced(line, multiline_input);
}
}

19
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// Console functions
#pragma once
#include <string>
namespace console {
enum display_t {
reset = 0,
prompt,
user_input,
error
};
void init(bool use_simple_io, bool use_advanced_display);
void cleanup();
void set_display(display_t display);
bool readline(std::string & line, bool multiline_input);
}

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set(TARGET convert-llama2c-to-ggml)
add_executable(${TARGET} convert-llama2c-to-ggml.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

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## Convert llama2.c model to ggml
This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default.
To convert the model first download the models from the [llma2.c](https://github.com/karpathy/llama2.c) repository:
`$ make -j`
After successful compilation, following usage options are available:
```
usage: ./convert-llama2c-to-ggml [options]
options:
-h, --help show this help message and exit
--copy-vocab-from-model FNAME model path from which to copy vocab (default 'models/ggml-vocab.bin')
--llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model
--llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin')
```
An example command is as follows:
`$ ./convert-llama2c-to-ggml --copy-vocab-from-model <ggml-vocab.bin> --llama2c-model <llama2.c model path> --llama2c-output-model <ggml output model path>`
Now you can use the model with command like:
`$ ./main -m <ggml output model path> -p "One day, Lily met a Shoggoth" -n 500 -c 256 -eps 1e-5`

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#include "ggml.h"
#include "llama.h"
#include <unordered_map>
#include <vector>
#include <cassert>
#include <climits>
#include <cstring>
#include <cstdarg>
#include <ctime>
#include <random>
#include <stdexcept>
#include <algorithm>
#include <string>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
//////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
typedef struct {
int dim; // transformer dimension
int hidden_dim; // for ffn layers
int n_layers; // number of layers
int n_heads; // number of query heads
int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
int vocab_size; // vocabulary size, usually 256 (byte-level)
int seq_len; // max sequence length
} Config;
typedef struct {
// token embedding table
float* token_embedding_table; // (vocab_size, dim)
// weights for rmsnorms
float* rms_att_weight; // (layer, dim) rmsnorm weights
float* rms_ffn_weight; // (layer, dim)
// weights for matmuls
float* wq; // (layer, dim, dim)
float* wk; // (layer, dim, dim)
float* wv; // (layer, dim, dim)
float* wo; // (layer, dim, dim)
// weights for ffn
float* w1; // (layer, hidden_dim, dim)
float* w2; // (layer, dim, hidden_dim)
float* w3; // (layer, hidden_dim, dim)
// final rmsnorm
float* rms_final_weight; // (dim,)
// freq_cis for RoPE relatively positional embeddings
// float* freq_cis_real; // (seq_len, dim/2)
// float* freq_cis_imag; // (seq_len, dim/2)
// (optional) classifier weights for the logits, on the last layer
//float* wcls;
} TransformerWeights;
void malloc_weights(TransformerWeights* w, Config* p) {
// we calloc instead of malloc to keep valgrind happy
w->token_embedding_table = new float[p->vocab_size * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
w->rms_att_weight = new float[p->n_layers * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
w->rms_ffn_weight = new float[p->n_layers * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
w->wq = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wk = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wv = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wo = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->w1 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
w->w2 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
w->w3 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
w->rms_final_weight = new float[p->dim]();
printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
}
int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f) {
if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
return 0;
}
void free_weights(TransformerWeights* w) {
delete w->token_embedding_table;
delete w->rms_att_weight;
delete w->rms_ffn_weight;
delete w->wq;
delete w->wk;
delete w->wv;
delete w->wo;
delete w->w1;
delete w->w2;
delete w->w3;
delete w->rms_final_weight;
}
void print_sample_weights(TransformerWeights *w){
printf("----- Quick print of first of the weight vales of all the variables\n");
printf("%f\n", w->token_embedding_table[0]);
printf("%f\n", w->rms_att_weight[0]);
printf("%f\n", w->rms_ffn_weight[0]);
printf("%f\n", w->wq[0]);
printf("%f\n", w->wk[0]);
printf("%f\n", w->wv[0]);
printf("%f\n", w->wo[0]);
printf("%f\n", w->w1[0]);
printf("%f\n", w->w2[0]);
printf("%f\n", w->w3[0]);
printf("%f\n", w->rms_att_weight[0]);
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////// ggml structs and functions required to load models, configs and save the model.
struct llama_vocab {
using id = int32_t;
using token = std::string;
struct token_score {
token tok;
float score;
};
std::unordered_map<token, id> token_to_id;
std::vector<token_score> id_to_token;
};
struct my_llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_mult = 4;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
bool operator!=(const my_llama_hparams& other) const {
return memcmp(this, &other, sizeof(my_llama_hparams));
}
};
struct my_llama_layer {
// normalization
struct ggml_tensor * attention_norm;
// attention
struct ggml_tensor * wq;
struct ggml_tensor * wk;
struct ggml_tensor * wv;
struct ggml_tensor * wo;
// normalization
struct ggml_tensor * ffn_norm;
// ff
struct ggml_tensor * w1;
struct ggml_tensor * w2;
struct ggml_tensor * w3;
};
struct my_llama_model {
struct ggml_context * ctx = NULL;
my_llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * output;
std::vector<my_llama_layer> layers;
uint32_t train_its = 0;
uint32_t train_samples = 0;
uint32_t train_tokens = 0;
};
struct train_params {
const char * fn_vocab_model;
const char * fn_llama2c_model;
const char * fn_llama2c_output_model;
const char * fn_train_data;
const char * fn_checkpoint_in;
const char * fn_checkpoint_out;
const char * fn_model_out;
uint32_t seed;
int n_ctx;
int n_embd;
int n_mult;
int n_head;
int n_layer;
int n_rotmax;
int n_threads;
int n_batch;
int n_examples;
int n_predict;
int print_info_interval;
int print_details_interval;
bool samples_start_after_nl;
bool use_adam;
bool use_flash;
bool use_scratch;
// only adam
int warmup;
int cos_decay_steps;
float cos_decay_restart;
float cos_decay_alpha;
int lbfgs_n_iter;
int adam_n_iter;
float adam_alpha;
float adam_decay;
int mem_model_gb;
int mem_compute_gb;
int mem_compute0_gb;
int mem_compute1_gb;
};
uint32_t get_n_ff(const struct my_llama_hparams* hparams) {
const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
return n_ff;
}
void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
printf("%s: n_ctx: %d\n", __func__, params->n_ctx);
printf("%s: n_embd: %d\n", __func__, params->n_embd);
printf("%s: n_mult: %d\n", __func__, params->n_mult);
printf("%s: n_head: %d\n", __func__, params->n_head);
printf("%s: n_ff: %d\n", __func__, get_n_ff(params));
printf("%s: n_layer: %d\n", __func__, params->n_layer);
printf("%s: n_rot: %d\n", __func__, params->n_rot);
}
void init_model(struct my_llama_model * model) {
const auto & hparams = model->hparams;
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab;
const uint32_t n_ff = get_n_ff(&hparams);
struct ggml_context * ctx = model->ctx;
model->train_its = 0;
model->train_samples = 0;
model->train_tokens = 0;
model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
printf("[%s:GG] Allocating [%d] x [%d] = [%d] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab);
model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
printf("[%s:GG] Allocating [%d] float space for model->norm\n",__func__,n_embd);
model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab);
// printing the per-layer allocations here so we dont print in the for loop.
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wq for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wk for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wv for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wo for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] float space for layer.ffn_norm for [%d] layers\n",__func__,n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w1 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w2 for [%d] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w3 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
ggml_set_name(model->norm, "norm.weight");
ggml_set_name(model->output, "output.weight");
model->layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[i];
std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ggml_set_name(layer.attention_norm, (layers_i + ".attention_norm.weight").c_str());
ggml_set_name(layer.wq, (layers_i + ".attention.wq.weight").c_str());
ggml_set_name(layer.wk, (layers_i + ".attention.wk.weight").c_str());
ggml_set_name(layer.wv, (layers_i + ".attention.wv.weight").c_str());
ggml_set_name(layer.wo, (layers_i + ".attention.wo.weight").c_str());
ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str());
ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str());
ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
}
}
float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
return *ptr;
}
int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
return *ptr;
}
void print_row(struct ggml_tensor * probs, int i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
printf(" %f", p);
}
printf("\n");
}
void print_matrix(struct ggml_tensor * probs) {
assert(probs->n_dims == 2);
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
printf(" %.2f", p);
}
printf("\n");
}
}
#ifdef __GNUC__
#ifdef __MINGW32__
__attribute__((format(gnu_printf, 1, 2)))
#else
__attribute__((format(printf, 1, 2)))
#endif
#endif
static std::string format(const char * fmt, ...) {
va_list ap, ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX);
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
size = 0;
} else {
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
GGML_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
GGML_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, size, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret != 1) {
throw std::runtime_error(std::string("unexpectedly reached end of file"));
}
}
std::uint32_t read_u32() {
std::uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::float_t read_f32() {
std::float_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::string read_string(std::uint32_t len) {
std::vector<char> chars(len);
read_raw(chars.data(), len);
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
if (tensor == NULL) {
file->write_u32(0);
file->write_u32(0);
file->write_u32(GGML_TYPE_F32);
file->seek((0-file->tell()) & 31, SEEK_CUR);
return;
}
const char * name = ggml_get_name(tensor);
uint32_t name_len = strlen(name);
uint32_t nd = tensor->n_dims;
uint32_t ne[4] = { (uint32_t)tensor->ne[0],
(uint32_t)tensor->ne[1],
(uint32_t)tensor->ne[2],
(uint32_t)tensor->ne[3] };
file->write_u32(nd);
file->write_u32(name_len);
file->write_u32(tensor->type);
file->write_raw(ne, sizeof(ne[0]) * nd);
file->write_raw(name, name_len);
file->seek((0-file->tell()) & 31, SEEK_CUR);
file->write_raw(tensor->data, ggml_nbytes(tensor));
}
bool is_ggml_file(const char *filename) {
llama_file file(filename, "rb");
if (file.size < 4) {
return false;
}
uint32_t magic = file.read_u32();
return magic == LLAMA_FILE_MAGIC;
}
void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
// heuristic to infer whether vocab is from ggml or from llama2.c vocabulary
if (is_ggml_file(filename)) {
struct llama_context_params llama_params = llama_context_default_params();
llama_params.vocab_only = true;
struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params);
struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
std::vector<const char *> strings;
std::vector<float> scores;
int n_vocab = llama_n_vocab(lctx);
strings.resize(n_vocab, NULL);
scores.resize(n_vocab, 0);
n_vocab = llama_get_vocab(lctx, strings.data(), scores.data(), n_vocab);
GGML_ASSERT(n_vocab == llama_n_vocab(lctx));
vocab->id_to_token.resize(n_vocab);
for (int i=0; i<n_vocab; ++i) {
std::string tok = std::string(strings[i]);
float score = scores[i];
vocab->id_to_token[i].tok = tok;
vocab->id_to_token[i].score = score;
vocab->token_to_id.emplace(tok, i);
}
llama_free(lctx);
llama_free_model(lmodel);
} else { // assume llama2.c vocabulary
printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename);
llama_file file(filename, "rb");
uint32_t n_vocab = config->vocab_size;
/* uint32_t max_token_length = */ file.read_u32(); // unused
vocab->id_to_token.resize(n_vocab);
for (uint32_t i=0; i<n_vocab; ++i) {
float_t score = file.read_f32();
uint32_t len = file.read_u32();
std::string tok = file.read_string(len);
vocab->id_to_token[i].tok = tok;
vocab->id_to_token[i].score = score;
vocab->token_to_id.emplace(tok, i);
}
}
}
void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * karpathy_weights){
int ct;
switch (gg_weights->n_dims){
case 1:
ct = 0;
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]);
*ptr = karpathy_weights[ct];
ct++;
}
break;
case 2:
ct = 0;
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]);
*ptr = karpathy_weights[ct];
ct++;
}
}
break;
case 3:
ct = 0;
for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) {
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]);
*ptr = karpathy_weights[ct];
ct++;
}
}
}
break;
}
}
void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) {
struct llama_file file(filename, "wb");
if (file.fp == NULL) {
return;
}
// write_magic
file.write_u32(LLAMA_FILE_MAGIC); // magic
file.write_u32(LLAMA_FILE_VERSION); // version
// write_hparams
file.write_u32(model->hparams.n_vocab);
file.write_u32(model->hparams.n_embd);
file.write_u32(model->hparams.n_mult);
file.write_u32(model->hparams.n_head);
file.write_u32(model->hparams.n_layer);
file.write_u32(model->hparams.n_rot);
file.write_u32(LLAMA_FTYPE_ALL_F32);
// write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
uint32_t n_vocab = model->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
const auto & token_score = vocab->id_to_token.at(i);
file.write_u32((uint32_t) token_score.tok.size());
file.write_raw(token_score.tok.data(), token_score.tok.size());
file.write_raw(&token_score.score, sizeof(token_score.score));
}
// stuff AK weights into GG weights one by one.
// w->token_embedding_table -> model->tok_embeddings
// float* -> struct ggml_tensor
stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
stuff_karpathy_weights_into_gg(model->output, w->token_embedding_table);
stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
//print_row(model->norm, 0);
// for rms-att-weight
int row_length = model->hparams.n_embd;
const auto & hparams = model->hparams;
//int n_ff = model->hparams.n_embd;
int n_ff = get_n_ff(&hparams);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i];
// 1d
stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
stuff_karpathy_weights_into_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]);
// from 3d matrix layer x dim x dim to 2d matrix dim x dim
stuff_karpathy_weights_into_gg(layer.wq , &w->wq[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wk , &w->wk[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wv , &w->wv[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wo , &w->wo[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.w1 , &w->w1[i*row_length*n_ff]);
stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]);
stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]);
}
// write tensors
write_tensor(&file, model->tok_embeddings);
write_tensor(&file, model->norm);
write_tensor(&file, model->output); // ?
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm);
write_tensor(&file, layer.wq);
write_tensor(&file, layer.wk);
write_tensor(&file, layer.wv);
write_tensor(&file, layer.wo);
write_tensor(&file, layer.ffn_norm);
write_tensor(&file, layer.w1);
write_tensor(&file, layer.w2);
write_tensor(&file, layer.w3);
}
}
struct train_params get_default_train_params() {
struct train_params params;
params.fn_vocab_model = "models/ggml-vocab.bin";
params.fn_llama2c_output_model = "ak_llama_model.bin";
params.fn_train_data = "shakespeare.txt";
params.fn_checkpoint_in = "checkpoint.bin";
params.fn_checkpoint_out = "checkpoint.bin";
params.fn_model_out = "ggml-checkpoint-f32.bin";
params.seed = -1;
params.n_ctx = 128;
params.n_embd = 256;
params.n_mult = 256;
params.n_head = 8;
params.n_layer = 16;
params.n_rotmax = 64;
params.n_threads = 6;
params.n_batch = 8;
params.n_examples = 8;
params.n_predict = 1024;
params.print_info_interval = 1;
params.print_details_interval = 2;
params.samples_start_after_nl = false;
params.use_adam = true;
params.use_flash = true;
params.use_scratch = true;
// only adam
params.warmup = 100;
params.cos_decay_steps = 1000;
params.cos_decay_restart = 1.1f;
params.cos_decay_alpha = 0.0f;
params.lbfgs_n_iter = 16;
params.adam_n_iter = 16;
params.adam_alpha = 1e-3f;
params.adam_decay = 1e-3f;
params.mem_model_gb = 2;
params.mem_compute_gb = 24;
params.mem_compute0_gb = 8;
params.mem_compute1_gb = 2;
return params;
}
void print_usage(int /*argc*/, char ** argv, const struct train_params * params) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " --copy-vocab-from-model FNAME llama2.c vocabulary or ggml model path from which to copy vocab (default '%s')\n", params->fn_vocab_model);
fprintf(stderr, " --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n");
fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
fprintf(stderr, "\n");
}
bool params_parse(int argc, char ** argv, struct train_params * params) {
bool invalid_param = false;
bool reqd_param_found = false;
std::string arg;
struct train_params default_params = get_default_train_params();
const std::string arg_prefix = "--";
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (arg == "--copy-vocab-from-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_vocab_model = argv[i];
} else if (arg == "--llama2c-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
reqd_param_found = true;
params->fn_llama2c_model = argv[i];
} else if (arg == "--llama2c-output-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_llama2c_output_model = argv[i];
} else if (arg == "-h" || arg == "--help") {
print_usage(argc, argv, &default_params);
exit(0);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
print_usage(argc, argv, &default_params);
exit(1);
}
}
if (invalid_param) {
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
print_usage(argc, argv, &default_params);
exit(1);
}
if (!reqd_param_found){
fprintf(stderr, "error: please specify a llama2.c .bin file to be converted with argument --llama2c-model\n");
print_usage(argc, argv, &default_params);
exit(1);
}
return true;
}
int main(int argc, char ** argv) {
struct train_params params = get_default_train_params();
if (!params_parse(argc, argv, &params)) {
return 1;
}
Config config;
TransformerWeights weights;
{
FILE *file = fopen(params.fn_llama2c_model, "rb");
if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
// read in the config header
if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
// read in the Transformer weights
malloc_weights(&weights, &config);
if(checkpoint_init_weights(&weights, &config, file)) { return 1; }
fclose(file);
}
struct llama_vocab vocab;
load_vocab(params.fn_vocab_model, &config, &vocab);
struct my_llama_model model;
model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = config.dim; //params.n_embd;
model.hparams.n_mult = 32;//params.n_mult;
model.hparams.n_head = config.n_heads; //params.n_head;
model.hparams.n_layer = config.n_layers; //params.n_layer;
model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
print_params(&model.hparams);
struct ggml_init_params lcparams;
lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
lcparams.mem_buffer = NULL;
lcparams.no_alloc = false;
model.ctx = ggml_init(lcparams);
init_model(&model);
save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);
ggml_free(model.ctx);
free_weights(&weights);
return 0;
}

2
examples/embd-input/embd-input-lib.cpp
View file

@ -30,7 +30,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
params.seed = uint32_t(time(NULL));
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);

2
examples/grammar-parser.cpp
View file

@ -405,7 +405,7 @@ namespace grammar_parser {
for (size_t i = 0, end = state.rules.size(); i < end; i++) {
// fprintf(file, "%zu: ", i);
// print_rule_binary(file, state.rules[i]);
print_rule(file, i, state.rules[i], symbol_id_names);
print_rule(file, uint32_t(i), state.rules[i], symbol_id_names);
// fprintf(file, "\n");
}
} catch (const std::exception & err) {

132
examples/json-schema-to-grammar.py Normal file
View file

@ -0,0 +1,132 @@
import argparse
import json
import re
import sys
# whitespace is constrained to a single space char to prevent model "running away" in
# whitespace. Also maybe improves generation quality?
SPACE_RULE = '" "?'
PRIMITIVE_RULES = {
'boolean': '("true" | "false") space',
'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space',
'string': r''' "\"" (
[^"\\] |
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
)* "\"" space ''',
'null': '"null" space',
}
INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')
GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}
class SchemaConverter:
def __init__(self, prop_order):
self._prop_order = prop_order
self._rules = {'space': SPACE_RULE}
def _format_literal(self, literal):
escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub(
lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), json.dumps(literal)
)
return f'"{escaped}"'
def _add_rule(self, name, rule):
esc_name = INVALID_RULE_CHARS_RE.sub('-', name)
if esc_name not in self._rules or self._rules[esc_name] == rule:
key = esc_name
else:
i = 0
while f'{esc_name}{i}' in self._rules:
i += 1
key = f'{esc_name}{i}'
self._rules[key] = rule
return key
def visit(self, schema, name):
schema_type = schema.get('type')
rule_name = name or 'root'
if 'oneOf' in schema or 'anyOf' in schema:
rule = ' | '.join((
self.visit(alt_schema, f'{name}{"-" if name else ""}{i}')
for i, alt_schema in enumerate(schema.get('oneOf') or schema['anyOf'])
))
return self._add_rule(rule_name, rule)
elif 'const' in schema:
return self._add_rule(rule_name, self._format_literal(schema['const']))
elif 'enum' in schema:
rule = ' | '.join((self._format_literal(v) for v in schema['enum']))
return self._add_rule(rule_name, rule)
elif schema_type == 'object' and 'properties' in schema:
# TODO: `required` keyword
prop_order = self._prop_order
prop_pairs = sorted(
schema['properties'].items(),
# sort by position in prop_order (if specified) then by key
key=lambda kv: (prop_order.get(kv[0], len(prop_order)), kv[0]),
)
rule = '"{" space'
for i, (prop_name, prop_schema) in enumerate(prop_pairs):
prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}')
if i > 0:
rule += ' "," space'
rule += fr' {self._format_literal(prop_name)} space ":" space {prop_rule_name}'
rule += ' "}" space'
return self._add_rule(rule_name, rule)
elif schema_type == 'array' and 'items' in schema:
# TODO `prefixItems` keyword
item_rule_name = self.visit(schema['items'], f'{name}{"-" if name else ""}item')
rule = f'"[" space ({item_rule_name} ("," space {item_rule_name})*)? "]" space'
return self._add_rule(rule_name, rule)
else:
assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}'
return self._add_rule(
'root' if rule_name == 'root' else schema_type,
PRIMITIVE_RULES[schema_type]
)
def format_grammar(self):
return '\n'.join((f'{name} ::= {rule}' for name, rule in self._rules.items()))
def main(args_in = None):
parser = argparse.ArgumentParser(
description='''
Generates a grammar (suitable for use in ./main) that produces JSON conforming to a
given JSON schema. Only a subset of JSON schema features are supported; more may be
added in the future.
''',
)
parser.add_argument(
'--prop-order',
default=[],
type=lambda s: s.split(','),
help='''
comma-separated property names defining the order of precedence for object properties;
properties not specified here are given lower precedence than those that are, and are
sorted alphabetically
'''
)
parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)')
args = parser.parse_args(args_in)
schema = json.load(sys.stdin if args.schema == '-' else open(args.schema))
prop_order = {name: idx for idx, name in enumerate(args.prop_order)}
converter = SchemaConverter(prop_order)
converter.visit(schema, '')
print(converter.format_grammar())
if __name__ == '__main__':
main()

132
examples/llama.vim Normal file
View file

@ -0,0 +1,132 @@
" Requires an already running llama.cpp server
" To install either copy or symlink to ~/.vim/autoload/llama.vim
" Then start with either :call llama#doLlamaGen(),
" or add a keybind to your vimrc such as
" nnoremap Z :call llama#doLlamaGen()<CR>
" Similarly, you could add an insert mode keybind with
" inoremap <C-B> <Cmd>call llama#doLlamaGen()<CR>
"
" g:llama_api_url and g:llama_overrides can be configured in your .vimrc
" let g:llama_api_url = "192.168.1.10:8080"
" llama_overrides can also be set through buffer/window scopes. For instance
" autocmd filetype python let b:llama_overrides = {"temp": 0.2}
" Could be added to your .vimrc to automatically set a lower temperature when
" editing a python script
" Additionally, an override dict can be stored at the top of a file
" !*{"stop": ["User:"]}
" Could be added to the start of your chatlog.txt to set the stopping token
" These parameter dicts are merged together from lowest to highest priority:
" server default -> g:llama_overrides -> w:llama_overrides ->
" b:llama_overrides -> in file (!*) overrides
"
" Sublists (like logit_bias and stop) are overridden, not merged
" Example override:
" !*{"logit_bias": [[13, -5], [2, false]], "temperature": 1, "top_k": 5, "top_p": 0.5, "n_predict": 256, "repeat_last_n": 256, "repeat_penalty": 1.17647}
if !exists("g:llama_api_url")
let g:llama_api_url= "127.0.0.1:8080"
endif
if !exists("g:llama_overrides")
let g:llama_overrides = {}
endif
const s:querydata = {"n_predict": 256, "stop": [ "\n" ], "stream": v:true }
const s:curlcommand = ['curl','--data-raw', "{\"prompt\":\"### System:\"}", '--silent', '--no-buffer', '--request', 'POST', '--url', g:llama_api_url .. '/completion', '--header', "Content-Type: application/json"]
let s:linedict = {}
func s:callbackHandler(bufn, channel, msg)
if len(a:msg) < 3
return
elseif a:msg[0] == "d"
let l:msg = a:msg[6:-1]
else
let l:msg = a:msg
endif
let l:decoded_msg = json_decode(l:msg)
let l:newtext = split(l:decoded_msg['content'], "\n", 1)
if len(l:newtext) > 0
call setbufline(a:bufn, s:linedict[a:bufn], getbufline(a:bufn, s:linedict[a:bufn])[0] .. newtext[0])
else
echo "nothing genned"
endif
if len(newtext) > 1
let l:failed = appendbufline(a:bufn, s:linedict[a:bufn], newtext[1:-1])
let s:linedict[a:bufn] = s:linedict[a:bufn] + len(newtext)-1
endif
if has_key(l:decoded_msg, "stop") && l:decoded_msg.stop
echo "Finished generation"
endif
endfunction
func llama#doLlamaGen()
if exists("b:job")
if job_status(b:job) == "run"
call job_stop(b:job)
return
endif
endif
let l:cbuffer = bufnr("%")
let s:linedict[l:cbuffer] = line('$')
let l:buflines = getbufline(l:cbuffer, 1, 1000)
let l:querydata = copy(s:querydata)
call extend(l:querydata, g:llama_overrides)
if exists("w:llama_overrides")
call extend(l:querydata, w:llama_overrides)
endif
if exists("b:llama_overrides")
call extend(l:querydata, b:llama_overrides)
endif
if l:buflines[0][0:1] == '!*'
let l:userdata = json_decode(l:buflines[0][2:-1])
call extend(l:querydata, l:userdata)
let l:buflines = l:buflines[1:-1]
endif
let l:querydata.prompt = join(l:buflines, "\n")
let l:curlcommand = copy(s:curlcommand)
let l:curlcommand[2] = json_encode(l:querydata)
let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])})
endfunction
" Echos the tokkenization of the provided string , or cursor to end of word
" Onus is placed on the user to include the preceding space
func llama#tokenizeWord(...)
if (a:0 > 0)
let l:input = a:1
else
exe "normal \"*ye"
let l:input = @*
endif
let l:querydata = {"content": l:input}
let l:curlcommand = copy(s:curlcommand)
let l:curlcommand[2] = json_encode(l:querydata)
let l:curlcommand[8] = g:llama_api_url .. "/tokenize"
let s:token_job = job_start(l:curlcommand, {"callback": function("s:tokenizeWordCallback", [l:input])})
endfunction
func s:tokenizeWordCallback(plaintext, channel, msg)
echo '"' .. a:plaintext ..'" - ' .. string(json_decode(a:msg).tokens)
endfunction
" Echos the token count of the entire buffer (or provided string)
" Example usage :echo llama#tokenCount()
func llama#tokenCount(...)
if (a:0 > 0)
let l:buflines = a:1
else
let l:buflines = getline(1,1000)
if l:buflines[0][0:1] == '!*'
let l:buflines = l:buflines[1:-1]
endif
let l:buflines = join(l:buflines, "\n")
endif
let l:querydata = {"content": l:buflines}
let l:curlcommand = copy(s:curlcommand)
let l:curlcommand[2] = json_encode(l:querydata)
let l:curlcommand[8] = g:llama_api_url .. "/tokenize"
let s:token_job = job_start(l:curlcommand, {"callback": "s:tokenCountCallback"})
endfunction
func s:tokenCountCallback(channel, msg)
let resp = json_decode(a:msg)
echo len(resp.tokens)
endfunction

6
examples/llm.vim
View file

@ -1,3 +1,5 @@
" Basic plugin example
function! Llm()
let url = "http://127.0.0.1:8080/completion"
@ -16,8 +18,10 @@ function! Llm()
" Extract the content field from the response
let content = json_decode(response).content
let split_newlines = split(content, '\n', 1)
" Insert the content at the cursor position
call setline(line('.'), getline('.') . content)
call setline(line('.'), [ getline('.') . split_newlines[0] ] + split_newlines[1:])
endfunction
command! Llm call Llm()

18
examples/main/README.md
View file

@ -140,6 +140,12 @@ The `--ctx-size` option allows you to set the size of the prompt context used by
- `-c N, --ctx-size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results.
### Extended Context Size
Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model.
### Keep Prompt
The `--keep` option allows users to retain the original prompt when the model runs out of context, ensuring a connection to the initial instruction or conversation topic is maintained.
@ -154,9 +160,13 @@ The following options allow you to control the text generation process and fine-
### Number of Tokens to Predict
- `-n N, --n-predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).
- `-n N, --n-predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity, -2 = until context filled)
The `--n-predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text. A value of -1 will cause text to be generated without limit.
The `--n-predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text.
A value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after `--n-keep`) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in significant pause in output.
If the pause is undesirable, a value of -2 will stop generation immediately when the context is filled.
It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `n-predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter.
@ -202,9 +212,9 @@ Example usage: `--top-p 0.95`
- `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. The method adjusts the logits (token probabilities) by raising them to the power of the parameter z. A higher value of z (e.g., 2.0) will further suppress less likely tokens from the tail of the distribution, while a value of 1.0 disables the effect of TFS. By setting the parameter z, you can control how much the probabilities of less likely tokens are reduced.
Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens, and thus disables the effect of TFS.
Example usage: `--tfs 2.0`
Example usage: `--tfs 0.95`
### Locally Typical Sampling

35
examples/main/main.cpp
View file

@ -4,6 +4,7 @@
#endif
#include "common.h"
#include "console.h"
#include "llama.h"
#include "build-info.h"
#include "grammar-parser.h"
@ -35,9 +36,7 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static console_state con_st;
static llama_context ** g_ctx;
static bool is_interacting = false;
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
@ -46,7 +45,7 @@ void sigint_handler(int signo) {
if (!is_interacting) {
is_interacting=true;
} else {
console_cleanup(con_st);
console::cleanup();
printf("\n");
llama_print_timings(*g_ctx);
_exit(130);
@ -64,10 +63,8 @@ int main(int argc, char ** argv) {
// save choice to use color for later
// (note for later: this is a slightly awkward choice)
con_st.use_color = params.use_color;
con_st.multiline_input = params.multiline_input;
console_init(con_st);
atexit([]() { console_cleanup(con_st); });
console::init(params.simple_io, params.use_color);
atexit([]() { console::cleanup(); });
if (params.perplexity) {
printf("\n************\n");
@ -373,7 +370,7 @@ int main(int argc, char ** argv) {
if (params.interactive) {
const char *control_message;
if (con_st.multiline_input) {
if (params.multiline_input) {
control_message = " - To return control to LLaMa, end your input with '\\'.\n"
" - To return control without starting a new line, end your input with '/'.\n";
} else {
@ -401,7 +398,7 @@ int main(int argc, char ** argv) {
int n_past_guidance = 0;
// the first thing we will do is to output the prompt, so set color accordingly
console_set_color(con_st, CONSOLE_COLOR_PROMPT);
console::set_display(console::prompt);
std::vector<llama_token> embd;
std::vector<llama_token> embd_guidance;
@ -422,9 +419,9 @@ int main(int argc, char ** argv) {
// Ensure the input doesn't exceed the context size by truncating embd if necessary.
if ((int)embd.size() > max_embd_size) {
auto skipped_tokens = embd.size() - max_embd_size;
console_set_color(con_st, CONSOLE_COLOR_ERROR);
console::set_display(console::error);
printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
console::set_display(console::reset);
fflush(stdout);
embd.resize(max_embd_size);
}
@ -434,8 +431,12 @@ int main(int argc, char ** argv) {
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
const int n_left = n_past - params.n_keep;
if (params.n_predict == -2) {
fprintf(stderr, "\n\n%s: context full, stopping generation\n", __func__);
break;
}
const int n_left = n_past - params.n_keep;
// always keep the first token - BOS
n_past = std::max(1, params.n_keep);
n_past_guidance = std::max(1, params.n_keep + guidance_offset);
@ -667,7 +668,7 @@ int main(int argc, char ** argv) {
}
// reset color to default if we there is no pending user input
if (input_echo && (int)embd_inp.size() == n_consumed) {
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
console::set_display(console::reset);
}
// if not currently processing queued inputs;
@ -693,7 +694,7 @@ int main(int argc, char ** argv) {
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);
console::set_display(console::user_input);
}
is_antiprompt = true;
fflush(stdout);
@ -714,7 +715,7 @@ int main(int argc, char ** argv) {
is_interacting = true;
printf("\n");
console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
console::set_display(console::user_input);
fflush(stdout);
} else if (params.instruct) {
is_interacting = true;
@ -739,12 +740,12 @@ int main(int argc, char ** argv) {
std::string line;
bool another_line = true;
do {
another_line = console_readline(con_st, line);
another_line = console::readline(line, params.multiline_input);
buffer += line;
} while (another_line);
// done taking input, reset color
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
console::set_display(console::reset);
// Add tokens to embd only if the input buffer is non-empty
// Entering a empty line lets the user pass control back

185
examples/perplexity/perplexity.cpp
View file

@ -121,8 +121,23 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
printf("\n");
}
void perplexity_lines(llama_context * ctx, const gpt_params & params) {
// Calculates perplexity over each line of the prompt
void hellaswag_score(llama_context * ctx, const gpt_params & params) {
// Calculates hellaswag score (acc_norm) from prompt
//
// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
// All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68
//
// All 10042 tasks should be extracted to keep the results standardized like other implementations.
//
// Datafile layout:
// ['??'] denotes json fields
// 6 lines per task:
// ['activity_label'] + ": " +['ctx'] - The first part of the query, the context
// ['label'] - The index the best common sense ending aka gold ending
// ['endings'][0] - Endings added to the first part of the query
// ['endings'][1]
// ['endings'][2]
// ['endings'][3]
std::vector<std::string> prompt_lines;
std::istringstream strstream(params.prompt);
@ -132,63 +147,149 @@ void perplexity_lines(llama_context * ctx, const gpt_params & params) {
prompt_lines.push_back(line);
}
if( prompt_lines.size() % 6 != 0) {
fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__);
return;
}
size_t hs_task_count = prompt_lines.size()/6;
fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
// This is needed as usual for LLaMA models
bool prepend_bos = true;
// Number of tasks to use when computing the score
if ( params.hellaswag_tasks < hs_task_count ) {
hs_task_count = params.hellaswag_tasks;
}
// The tasks should be randomized so the score stabilizes quickly.
bool randomize_tasks = true;
// The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now
std::mt19937 rng(1);
// Dataholder for hellaswag tasks
struct hs_data_t {
std::string context;
size_t gold_ending_idx;
std::string ending[4];
size_t ending_logprob_count[4];
double ending_logprob[4];
};
fprintf(stderr, "%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") );
// Select and read data from prompt lines
hs_data_t *hs_data = new hs_data_t[hs_task_count];
for (size_t i=0; i < hs_task_count; i++) {
size_t idx = i;
// Select a random example of those left in the prompt
if (randomize_tasks) {
std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ;
idx = dist(rng);
}
hs_data[i].context = prompt_lines[idx*6];
hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
for (size_t j=0; j < 4; j++) {
hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
}
// Delete the selected random example from the prompt
if (randomize_tasks) {
prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) );
}
}
fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__);
printf("\ntask\tacc_norm\n");
double acc = 0.0f;
const int n_vocab = llama_n_vocab(ctx);
int counttotal = 0;
size_t n_lines = prompt_lines.size();
for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
double nll = 0.0;
// Tokenize the context to count tokens
std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
size_t context_size = context_embd.size();
fprintf(stderr, "%s: calculating perplexity over %lu lines\n", __func__, n_lines);
for (size_t ending_idx=0;ending_idx<4;ending_idx++) {
printf("\nLine\tPPL line\tPPL cumulative\n");
// Tokenize the query
std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos);
size_t query_size = query_embd.size();
for (size_t i = 0; i < n_lines; ++i) {
// Stop if query wont fit the ctx window
if (query_size > (size_t)params.n_ctx) {
fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
return;
}
// Tokenize and insert BOS at start
std::vector<int> batch_embd = ::llama_tokenize(ctx, prompt_lines[i], true);
// Speedup small evaluations by evaluating atleast 32 tokens
if (query_size < 32) {
query_embd.resize(32);
}
size_t batch_size = batch_embd.size();
// Evaluate the query
if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}
// Stop if line is too long
if( batch_size > (size_t)params.n_ctx ) {
fprintf(stderr, "%s : tokens in line %lu > n_ctxl\n", __func__, i);
return;
const auto query_logits = llama_get_logits(ctx);
std::vector<float> logits;
logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab);
hs_data[task_idx].ending_logprob_count[ending_idx] = 0;
hs_data[task_idx].ending_logprob[ending_idx] = 0.0f;
// Calculate the logprobs over the ending
for (size_t j = context_size-1; j < query_size - 1; j++) {
// Calculate probability of next token, given the previous ones.
const std::vector<float> tok_logits(
logits.begin() + (j + 0) * n_vocab,
logits.begin() + (j + 1) * n_vocab);
const float prob = softmax(tok_logits)[query_embd[ j + 1]];
hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);
hs_data[task_idx].ending_logprob_count[ending_idx]++;
}
// Calculate the mean token logprob for acc_norm
hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx];
// printf("task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\n",
// task_idx,ending_idx,whole_size,context_size, hs_data[task_idx].ending_logprob_count[ending_idx], hs_data[task_idx].ending_logprob[ending_idx] );
}
if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
// Find the ending with maximum logprob
size_t ending_logprob_max_idx = -1;
double ending_logprob_max_val = -INFINITY;
for (size_t j=0; j < 4; j++) {
if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
ending_logprob_max_idx = j;
ending_logprob_max_val = hs_data[task_idx].ending_logprob[j];
}
}
const auto batch_logits = llama_get_logits(ctx);
std::vector<float> logits;
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
double nllline = 0.0;
int countline = 0;
// Perplexity over second half of the line
for (size_t j = batch_size/2; j < batch_size - 1; ++j) {
// Calculate probability of next token, given the previous ones.
const std::vector<float> tok_logits(
logits.begin() + (j + 0) * n_vocab,
logits.begin() + (j + 1) * n_vocab);
const float prob = softmax(tok_logits)[batch_embd[ j + 1]];
nllline += -std::log(prob);
++countline;
// If the gold ending got the maximum logprobe add one accuracy point
if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) {
acc += 1.0;
}
nll += nllline;
counttotal += countline;
// perplexity is e^(average negative log-likelihood)
printf("%lu\t%.8lf\t%.8lf\n", i + 1, std::exp(nllline/countline), std::exp(nll / counttotal) );
// Print the accumulated accuracy mean x 100
printf("%zu\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
fflush(stdout);
}
delete [] hs_data;
printf("\n");
}
@ -240,8 +341,8 @@ int main(int argc, char ** argv) {
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
if (params.perplexity_lines) {
perplexity_lines(ctx, params);
if (params.hellaswag) {
hellaswag_score(ctx, params);
} else {
perplexity(ctx, params);
}

1
examples/save-load-state/save-load-state.cpp
View file

@ -26,6 +26,7 @@ int main(int argc, char ** argv) {
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_gqa = params.n_gqa;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;

26
examples/server-llama2-13B.sh Normal file
View file

@ -0,0 +1,26 @@
#!/bin/bash
set -e
cd "$(dirname "$0")/.." || exit
# Specify the model you want to use here:
MODEL="${MODEL:-./models/llama-2-13b-chat.ggmlv3.q5_K_M.bin}"
PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat-system.txt}
# Adjust to the number of CPU cores you want to use.
N_THREAD="${N_THREAD:-12}"
# Note: you can also override the generation options by specifying them on the command line:
GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 4096 --batch-size 1024}"
# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
./server $GEN_OPTIONS \
--model "$MODEL" \
--threads "$N_THREAD" \
--rope-freq-scale 1.0 \
"$@"
# I used this to test the model with mps, but omitted it from the general purpose. If you want to use it, just specify it on the command line.
# -ngl 1 \

5
examples/server/README.md
View file

@ -16,6 +16,7 @@ Command line options:
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
- `--numa`: Attempt optimizations that help on some NUMA systems.
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
@ -151,6 +152,8 @@ node .
`mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
`grammar`: Set grammar for grammar-based sampling (default: no grammar)
`seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
`ignore_eos`: Ignore end of stream token and continue generating (default: false).
@ -163,7 +166,7 @@ node .
`content`: Set the text to tokenize.
Note that the special `BOS` token is not added in fron of the text and also a space character is not inserted automatically as it is for `/completion`.
Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.

109
examples/server/chat-llama2.sh Normal file
View file

@ -0,0 +1,109 @@
#!/bin/bash
API_URL="${API_URL:-http://127.0.0.1:8080}"
CHAT=(
"Hello, Assistant."
"Hello. How may I help you today?"
)
INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
trim() {
shopt -s extglob
set -- "${1##+([[:space:]])}"
printf "%s" "${1%%+([[:space:]])}"
}
trim_trailing() {
shopt -s extglob
printf "%s" "${1%%+([[:space:]])}"
}
format_prompt() {
if [[ "${#CHAT[@]}" -eq 0 ]]; then
echo -n "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>"
else
LAST_INDEX=$(( ${#CHAT[@]} - 1 ))
echo -n "${CHAT[$LAST_INDEX]}\n[INST] $1 [/INST]"
fi
}
tokenize() {
curl \
--silent \
--request POST \
--url "${API_URL}/tokenize" \
--header "Content-Type: application/json" \
--data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
| jq '.tokens[]'
}
N_KEEP=$(tokenize "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>" | wc -l)
chat_completion() {
PROMPT="$(trim_trailing "$(format_prompt "$1")")"
DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
prompt: .,
temperature: 0.2,
top_k: 40,
top_p: 0.9,
n_keep: $n_keep,
n_predict: 1024,
stop: ["[INST]"],
stream: true
}')"
# Create a temporary file to hold the Python output
TEMPFILE=$(mktemp)
exec 3< <(curl \
--silent \
--no-buffer \
--request POST \
--url "${API_URL}/completion" \
--header "Content-Type: application/json" \
--data-raw "${DATA}")
python -c "
import json
import sys
answer = ''
while True:
line = sys.stdin.readline()
if not line:
break
if line.startswith('data: '):
json_content = line[6:].strip()
content = json.loads(json_content)['content']
sys.stdout.write(content)
sys.stdout.flush()
answer += content
answer = answer.rstrip('\n')
# Write the answer to the temporary file
with open('$TEMPFILE', 'w') as f:
f.write(answer)
" <&3
exec 3<&-
# Read the answer from the temporary file
ANSWER=$(cat $TEMPFILE)
# Clean up the temporary file
rm $TEMPFILE
printf "\n"
CHAT+=("$1" "$(trim "$ANSWER")")
}
while true; do
echo -en "\033[0;32m" # Green color
read -r -e -p "> " QUESTION
echo -en "\033[0m" # Reset color
chat_completion "${QUESTION}"
done

30
examples/server/chat.mjs
View file

@ -1,5 +1,34 @@
import * as readline from 'node:readline'
import { stdin, stdout } from 'node:process'
import { readFileSync } from 'node:fs'
import { SchemaConverter } from './public/json-schema-to-grammar.mjs'
const args = process.argv.slice(2);
const grammarJsonSchemaFile = args.find(
(_, index) => args[index - 1] === "--grammar-json-schema"
);
const grammarFile = args.find((_, index) => args[index - 1] === "--grammar");
// Example usage: function,arguments
const grammarJsonSchemaPropOrder = args.find(
(_, index) => args[index - 1] === "--grammar-json-schema-prop-order"
);
const propOrder = grammarJsonSchemaPropOrder
? grammarJsonSchemaPropOrder
.split(",")
.reduce((acc, cur, index) => ({ ...acc, [cur]: index }), {})
: {};
let grammar = null
if (grammarJsonSchemaFile) {
const schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8'))
const converter = new SchemaConverter(propOrder)
converter.visit(schema, '')
grammar = converter.formatGrammar()
}
if (grammarFile) {
grammar = readFileSync(grammarFile, 'utf-8')
}
const API_URL = 'http://127.0.0.1:8080'
@ -48,6 +77,7 @@ async function chat_completion(question) {
n_keep: n_keep,
n_predict: 256,
stop: ["\n### Human:"], // stop completion after generating this
grammar,
stream: true,
})
})

613
examples/server/completion.js.hpp
View file

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};
unsigned int completion_js_len = 4462;
unsigned int completion_js_len = 5099;

2703
examples/server/index.html.hpp
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3397
examples/server/index.js.hpp
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311
examples/server/json-schema-to-grammar.mjs.hpp Normal file
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@ -0,0 +1,311 @@
unsigned char json_schema_to_grammar_mjs[] = {
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};
unsigned int json_schema_to_grammar_mjs_len = 3695;

53
examples/server/public/completion.js
View file

@ -43,6 +43,7 @@ export async function* llama(prompt, params = {}, config = {}) {
const decoder = new TextDecoder();
let content = "";
let leftover = ""; // Buffer for partially read lines
try {
let cont = true;
@ -53,29 +54,47 @@ export async function* llama(prompt, params = {}, config = {}) {
break;
}
// sse answers in the form multiple lines of: value\n with data always present as a key. in our case we
// mainly care about the data: key here, which we expect as json
const text = decoder.decode(result.value);
// Add any leftover data to the current chunk of data
const text = leftover + decoder.decode(result.value);
// parse all sse events and add them to result
const regex = /^(\S+):\s(.*)$/gm;
for (const match of text.matchAll(regex)) {
result[match[1]] = match[2]
// Check if the last character is a line break
const endsWithLineBreak = text.endsWith('\n');
// Split the text into lines
let lines = text.split('\n');
// If the text doesn't end with a line break, then the last line is incomplete
// Store it in leftover to be added to the next chunk of data
if (!endsWithLineBreak) {
leftover = lines.pop();
} else {
leftover = ""; // Reset leftover if we have a line break at the end
}
// since we know this is llama.cpp, let's just decode the json in data
result.data = JSON.parse(result.data);
content += result.data.content;
// Parse all sse events and add them to result
const regex = /^(\S+):\s(.*)$/gm;
for (const line of lines) {
const match = regex.exec(line);
if (match) {
result[match[1]] = match[2]
// since we know this is llama.cpp, let's just decode the json in data
if (result.data) {
result.data = JSON.parse(result.data);
content += result.data.content;
// yield
yield result;
// yield
yield result;
// if we got a stop token from server, we will break here
if (result.data.stop) {
if (result.data.generation_settings) {
generation_settings = result.data.generation_settings;
// if we got a stop token from server, we will break here
if (result.data.stop) {
if (result.data.generation_settings) {
generation_settings = result.data.generation_settings;
}
cont = false;
break;
}
}
}
break;
}
}
} catch (e) {

193
examples/server/public/index.html
View file

@ -3,12 +3,11 @@
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
<meta name="color-scheme" content="light dark">
<title>llama.cpp - chat</title>
<style>
body {
background-color: #fff;
color: #000;
font-family: system-ui;
font-size: 90%;
}
@ -142,6 +141,7 @@
} from '/index.js';
import { llama } from '/completion.js';
import { SchemaConverter } from '/json-schema-to-grammar.mjs';
const session = signal({
prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
@ -167,8 +167,139 @@
mirostat: 0, // 0/1/2
mirostat_tau: 5, // target entropy
mirostat_eta: 0.1, // learning rate
grammar: '',
})
/* START: Support for storing prompt templates and parameters in borwser LocalStorage */
const local_storage_storageKey = "llamacpp_server_local_storage";
function local_storage_setDataFromObject(tag, content) {
localStorage.setItem(local_storage_storageKey + '/' + tag, JSON.stringify(content));
}
function local_storage_setDataFromRawText(tag, content) {
localStorage.setItem(local_storage_storageKey + '/' + tag, content);
}
function local_storage_getDataAsObject(tag) {
const item = localStorage.getItem(local_storage_storageKey + '/' + tag);
if (!item) {
return null;
} else {
return JSON.parse(item);
}
}
function local_storage_getDataAsRawText(tag) {
const item = localStorage.getItem(local_storage_storageKey + '/' + tag);
if (!item) {
return null;
} else {
return item;
}
}
// create a container for user templates and settings
const savedUserTemplates = signal({})
const selectedUserTemplate = signal({ name: '', template: { session: {}, params: {} } })
// let's import locally saved templates and settings if there are any
// user templates and settings are stored in one object
// in form of { "templatename": "templatedata" } and { "settingstemplatename":"settingsdata" }
console.log('Importing saved templates')
let importedTemplates = local_storage_getDataAsObject('user_templates')
if (importedTemplates) {
// saved templates were successfuly imported.
console.log('Processing saved templates and updating default template')
//console.log(importedTemplates);
savedUserTemplates.value = importedTemplates;
//override default template
savedUserTemplates.value.default = { session: session.value, params: params.value }
local_storage_setDataFromObject('user_templates', savedUserTemplates.value)
} else {
// no saved templates detected.
console.log('Initializing LocalStorage and saving default template')
savedUserTemplates.value = { "default": { session: session.value, params: params.value } }
local_storage_setDataFromObject('user_templates', savedUserTemplates.value)
}
function userTemplateResetToDefault() {
console.log('Reseting themplate to default')
selectedUserTemplate.value.name = 'default';
selectedUserTemplate.value.data = savedUserTemplates.value['default'];
}
function userTemplateApply(t) {
session.value = t.data.session;
params.value = t.data.params;
}
function userTemplateResetToDefaultAndApply() {
userTemplateResetToDefault()
userTemplateApply(selectedUserTemplate.value)
}
function userTemplateLoadAndApplyAutosaved() {
// get autosaved last used template
let lastUsedTemplate = local_storage_getDataAsObject('user_templates_last')
if (lastUsedTemplate) {
console.log('Autosaved template found, restoring')
selectedUserTemplate.value = lastUsedTemplate
}
else {
console.log('No autosaved template found, using default template')
// no autosaved last used template was found, so load from default.
userTemplateResetToDefault()
}
console.log('Applying template')
// and update internal data from templates
userTemplateApply(selectedUserTemplate.value)
}
//console.log(savedUserTemplates.value)
//console.log(selectedUserTemplate.value)
function userTemplateAutosave() {
console.log('Template Autosave...')
if (selectedUserTemplate.value.name == 'default') {
// we don't want to save over default template, so let's create a new one
let newTemplateName = 'UserTemplate-' + Date.now().toString()
let newTemplate = { 'name': newTemplateName, 'data': { 'session': session.value, 'params': params.value } }
console.log('Saving as ' + newTemplateName)
// save in the autosave slot
local_storage_setDataFromObject('user_templates_last', newTemplate)
// and load it back and apply
userTemplateLoadAndApplyAutosaved()
} else {
local_storage_setDataFromObject('user_templates_last', { 'name': selectedUserTemplate.value.name, 'data': { 'session': session.value, 'params': params.value } })
}
}
console.log('Checking for autosaved last used template')
userTemplateLoadAndApplyAutosaved()
/* END: Support for storing prompt templates and parameters in browsers LocalStorage */
const llamaStats = signal(null)
const controller = signal(null)
@ -283,8 +414,9 @@
useEffect(() => {
// scroll to bottom (if needed)
if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) {
container.current.scrollTo(0, container.current.scrollHeight)
const parent = container.current.parentElement;
if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) {
parent.scrollTo(0, parent.scrollHeight)
}
}, [messages])
@ -304,6 +436,26 @@
const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
const grammarJsonSchemaPropOrder = signal('')
const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value
const convertJSONSchemaGrammar = () => {
try {
const schema = JSON.parse(params.value.grammar)
const converter = new SchemaConverter(
grammarJsonSchemaPropOrder.value
.split(',')
.reduce((acc, cur, i) => ({...acc, [cur.trim()]: i}), {})
)
converter.visit(schema, '')
params.value = {
...params.value,
grammar: converter.formatGrammar(),
}
} catch (e) {
alert(`Convert failed: ${e.message}`)
}
}
const FloatField = ({label, max, min, name, step, value}) => {
return html`
<div>
@ -324,8 +476,34 @@
`
};
const userTemplateReset = (e) => {
e.preventDefault();
userTemplateResetToDefaultAndApply()
}
const UserTemplateResetButton = () => {
if (selectedUserTemplate.value.name == 'default') {
return html`
<button disabled>Using default template</button>
`
}
return html`
<button onclick=${userTemplateReset}>Reset all to default</button>
`
};
useEffect(() => {
// autosave template on every change
userTemplateAutosave()
}, [session.value, params.value])
return html`
<form>
<fieldset>
<${UserTemplateResetButton}/>
</fieldset>
<fieldset>
<div>
<label for="prompt">Prompt</label>
@ -355,6 +533,13 @@
<label for="template">Chat history template</label>
<textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
</div>
<div>
<label for="template">Grammar</label>
<textarea id="grammar" name="grammar" placeholder="Use gbnf or JSON Schema+convert" value="${params.value.grammar}" rows=4 oninput=${updateParams}/>
<input type="text" name="prop-order" placeholder="order: prop1,prop2,prop3" oninput=${updateGrammarJsonSchemaPropOrder} />
<button type="button" onclick=${convertJSONSchemaGrammar}>Convert JSON Schema</button>
</div>
</fieldset>
<fieldset class="two">

2
examples/server/public/index.js
View file

File diff suppressed because one or more lines are too long

112
examples/server/public/json-schema-to-grammar.mjs Normal file
View file

@ -0,0 +1,112 @@
const SPACE_RULE = '" "?';
const PRIMITIVE_RULES = {
boolean: '("true" | "false") space',
number: '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
integer: '("-"? ([0-9] | [1-9] [0-9]*)) space',
string: ` "\\"" (
[^"\\\\] |
"\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
)* "\\"" space`,
null: '"null" space',
};
const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g;
const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g;
const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'};
export class SchemaConverter {
constructor(propOrder) {
this._propOrder = propOrder || {};
this._rules = new Map();
this._rules.set('space', SPACE_RULE);
}
_formatLiteral(literal) {
const escaped = JSON.stringify(literal).replace(
GRAMMAR_LITERAL_ESCAPE_RE,
m => GRAMMAR_LITERAL_ESCAPES[m]
);
return `"${escaped}"`;
}
_addRule(name, rule) {
let escName = name.replace(INVALID_RULE_CHARS_RE, '-');
let key = escName;
if (this._rules.has(escName)) {
if (this._rules.get(escName) === rule) {
return key;
}
let i = 0;
while (this._rules.has(`${escName}${i}`)) {
i += 1;
}
key = `${escName}${i}`;
}
this._rules.set(key, rule);
return key;
}
visit(schema, name) {
const schemaType = schema.type;
const ruleName = name || 'root';
if (schema.oneOf || schema.anyOf) {
const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) =>
this.visit(altSchema, `${name}${name ? "-" : ""}${i}`)
).join(' | ');
return this._addRule(ruleName, rule);
} else if ('const' in schema) {
return this._addRule(ruleName, this._formatLiteral(schema.const));
} else if ('enum' in schema) {
const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | ');
return this._addRule(ruleName, rule);
} else if (schemaType === 'object' && 'properties' in schema) {
// TODO: `required` keyword (from python implementation)
const propOrder = this._propOrder;
const propPairs = Object.entries(schema.properties).sort((a, b) => {
// sort by position in prop_order (if specified) then by key
const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity;
const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity;
return orderA - orderB || a[0].localeCompare(b[0]);
});
let rule = '"{" space';
propPairs.forEach(([propName, propSchema], i) => {
const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`);
if (i > 0) {
rule += ' "," space';
}
rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`;
});
rule += ' "}" space';
return this._addRule(ruleName, rule);
} else if (schemaType === 'array' && 'items' in schema) {
// TODO `prefixItems` keyword (from python implementation)
const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`);
const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`;
return this._addRule(ruleName, rule);
} else {
if (!PRIMITIVE_RULES[schemaType]) {
throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`);
}
return this._addRule(
ruleName === 'root' ? 'root' : schemaType,
PRIMITIVE_RULES[schemaType]
);
}
}
formatGrammar() {
let grammar = '';
this._rules.forEach((rule, name) => {
grammar += `${name} ::= ${rule}\n`;
});
return grammar;
}
}

100
examples/server/server.cpp
View file

@ -1,6 +1,7 @@
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include "grammar-parser.h"
#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
@ -14,6 +15,7 @@
#include "index.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"
#ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1
@ -196,6 +198,9 @@ struct llama_server_context
llama_context *ctx = nullptr;
gpt_params params;
grammar_parser::parse_state parsed_grammar;
llama_grammar *grammar = nullptr;
bool truncated = false;
bool stopped_eos = false;
bool stopped_word = false;
@ -227,6 +232,7 @@ struct llama_server_context
void rewind()
{
params.antiprompt.clear();
params.grammar.clear();
num_prompt_tokens = 0;
num_tokens_predicted = 0;
generated_text = "";
@ -238,9 +244,13 @@ struct llama_server_context
stopped_limit = false;
stopping_word = "";
multibyte_pending = 0;
n_remain = 0;
n_past = 0;
if (grammar != nullptr) {
llama_grammar_free(grammar);
grammar = nullptr;
}
}
bool loadModel(const gpt_params &params_)
@ -305,6 +315,31 @@ struct llama_server_context
return prompt_tokens;
}
bool loadGrammar()
{
if (!params.grammar.empty()) {
parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
LOG_ERROR("grammar parse error", {{"grammar", params.grammar}});
return false;
}
grammar_parser::print_grammar(stderr, parsed_grammar);
{
auto it = params.logit_bias.find(llama_token_eos());
if (it != params.logit_bias.end() && it->second == -INFINITY) {
LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {});
}
}
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
return true;
}
void loadPrompt()
{
auto prompt_tokens = tokenize(prompt, true); // always add BOS
@ -468,6 +503,10 @@ struct llama_server_context
logits[llama_token_nl()] = nl_logit;
}
if (grammar != nullptr) {
llama_sample_grammar(ctx, &candidates_p, grammar);
}
if (temp <= 0)
{
// Greedy sampling
@ -505,10 +544,15 @@ struct llama_server_context
}
}
if (grammar != nullptr) {
llama_grammar_accept_token(ctx, grammar, result.tok);
}
for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
{
result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
}
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(result.tok);
num_tokens_predicted++;
@ -671,6 +715,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
{
fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n");
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
fprintf(stdout, " -ngl N, --n-gpu-layers N\n");
fprintf(stdout, " number of layers to store in VRAM\n");
@ -679,6 +724,9 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
#endif
fprintf(stdout, " -m FNAME, --model FNAME\n");
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
@ -875,7 +923,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
}
#else
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.", {});
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--low-vram" || arg == "-lv")
@ -883,7 +931,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
#ifdef GGML_USE_CUBLAS
params.low_vram = true;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--mul-mat-q" || arg == "-mmq")
{
#ifdef GGML_USE_CUBLAS
params.mul_mat_q = true;
#else
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--main-gpu" || arg == "-mg")
@ -934,6 +990,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
{
params.use_mmap = false;
}
else if (arg == "--numa")
{
params.numa = true;
}
else if (arg == "--embedding")
{
params.embedding = true;
@ -984,6 +1044,7 @@ static json format_generation_settings(llama_server_context &llama)
{"stream", llama.stream},
{"logit_bias", llama.params.logit_bias},
{"n_probs", llama.params.n_probs},
{"grammar", llama.params.grammar},
};
}
@ -1001,7 +1062,7 @@ static json format_timings(llama_server_context &llama)
assert(timings.n_eval == llama.num_tokens_predicted);
return json{
{"prompt_n", timings.n_eval},
{"prompt_n", timings.n_p_eval},
{"prompt_ms", timings.t_p_eval_ms},
{"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval},
{"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval},
@ -1030,7 +1091,6 @@ static json format_final_response(llama_server_context &llama, const std::string
{"stopped_limit", llama.stopped_limit},
{"stopping_word", llama.stopping_word},
{"tokens_cached", llama.n_past},
{"tokens_predicted", llama.num_tokens_predicted},
{"timings", format_timings(llama)},
};
@ -1084,6 +1144,7 @@ static void parse_options_completion(const json &body, llama_server_context &lla
llama.params.penalize_nl = body.value("penalize_nl", default_params.penalize_nl);
llama.params.n_keep = body.value("n_keep", default_params.n_keep);
llama.params.seed = body.value("seed", default_params.seed);
llama.params.grammar = body.value("grammar", default_params.grammar);
llama.params.n_probs = body.value("n_probs", default_params.n_probs);
if (body.count("prompt") != 0)
@ -1214,6 +1275,12 @@ int main(int argc, char **argv)
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
return false; });
// this is only called if no index.html is found in the public --path
svr.Get("/json-schema-to-grammar.mjs", [](const Request &, Response &res)
{
res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript");
return false; });
svr.Post("/completion", [&llama](const Request &req, Response &res)
{
auto lock = llama.lock();
@ -1224,6 +1291,12 @@ int main(int argc, char **argv)
parse_options_completion(json::parse(req.body), llama);
if (!llama.loadGrammar())
{
res.status = 400;
return;
}
llama.loadPrompt();
llama.beginCompletion();
@ -1319,7 +1392,11 @@ int main(int argc, char **argv)
sink.done();
return true;
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider);
const auto on_complete = [&](bool) {
llama.mutex.unlock();
};
lock.release();
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
} });
svr.Get("/model.json", [&llama](const Request &, Response &res)
@ -1385,8 +1462,12 @@ int main(int argc, char **argv)
svr.set_error_handler([](const Request &, Response &res)
{
res.set_content("File Not Found", "text/plain");
res.status = 404; });
if (res.status == 400) {
res.set_content("Invalid request", "text/plain");
} else {
res.set_content("File Not Found", "text/plain");
res.status = 404;
} });
// set timeouts and change hostname and port
svr.set_read_timeout(sparams.read_timeout);
@ -1414,6 +1495,9 @@ int main(int argc, char **argv)
return 1;
}
if (llama.grammar != nullptr) {
llama_grammar_free(llama.grammar);
}
llama_backend_free();
return 0;

2
examples/simple/simple.cpp
View file

@ -123,7 +123,7 @@ int main(int argc, char ** argv)
// Evaluate the tokens :
//---------------------------------
if ( llama_eval( ctx , tokens_list.data() , tokens_list.size() , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
{
fprintf( stderr, "%s : failed to eval\n" , __func__ );
return 1;

2
flake.nix
View file

@ -14,8 +14,6 @@
with pkgs.darwin.apple_sdk_11_0.frameworks; [
Accelerate
MetalKit
MetalPerformanceShaders
MetalPerformanceShadersGraph
]
else if isAarch32 && isDarwin then
with pkgs.darwin.apple_sdk.frameworks; [

579
ggml-alloc.c Normal file
View file

@ -0,0 +1,579 @@
#include "ggml-alloc.h"
#include "ggml.h"
#include <assert.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define UNUSED(x) (void)(x)
#define MAX(a, b) ((a) > (b) ? (a) : (b))
//#define GGML_ALLOCATOR_DEBUG
//#define AT_PRINTF printf
#define AT_PRINTF(...) ((void)0)
struct hash_node {
struct ggml_tensor * t;
int n_children;
int n_views;
};
static size_t hash(void * p) {
return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
}
static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) {
size_t h = hash(t);
// linear probing
size_t i = h;
while (hash_table[i].t != NULL) {
if (hash_table[i].t == t) {
return &hash_table[i];
}
i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
if (i == h) {
// hash table is full
GGML_ASSERT(false);
}
}
hash_table[i].t = t;
return &hash_table[i];
}
// TODO: GGML_PAD ?
static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
assert(alignment && !(alignment & (alignment - 1))); // power of 2
size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
return offset + align;
}
struct free_block {
void * addr;
size_t size;
};
#define MAX_FREE_BLOCKS 128
struct ggml_allocr {
void * data;
size_t size;
size_t alignment;
int n_free_blocks;
struct free_block free_blocks[MAX_FREE_BLOCKS];
struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
size_t max_size;
bool measure;
int parse_seq[GGML_MAX_NODES];
bool has_parse_seq;
#ifdef GGML_ALLOCATOR_DEBUG
struct ggml_tensor * allocated_tensors[1024];
#endif
};
#ifdef GGML_ALLOCATOR_DEBUG
static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i] == NULL) {
alloc->allocated_tensors[i] = tensor;
return;
}
}
GGML_ASSERT(!"out of allocated_tensors");
}
static void remove_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i] == tensor ||
(alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
alloc->allocated_tensors[i] = NULL;
return;
}
}
printf("tried to free tensor %s not found\n", tensor->name);
GGML_ASSERT(!"tensor not found");
}
#endif
static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
return ggml_nbytes(tensor);
UNUSED(alloc);
}
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
size_t max_avail = 0;
// find the best fitting free block besides the last block
int best_fit_block = -1;
size_t best_fit_size = SIZE_MAX;
for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
struct free_block * block = &alloc->free_blocks[i];
max_avail = MAX(max_avail, block->size);
if (block->size >= size && block->size <= best_fit_size) {
best_fit_block = i;
best_fit_size = block->size;
}
}
AT_PRINTF("block %d\n", best_fit_block);
if (best_fit_block == -1) {
// the last block is our last resort
struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
if (block->size >= size) {
best_fit_block = alloc->n_free_blocks - 1;
max_avail = MAX(max_avail, block->size);
} else {
fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
__func__, size, max_avail);
GGML_ASSERT(!"not enough space in the buffer");
return;
}
}
struct free_block * block = &alloc->free_blocks[best_fit_block];
void * addr = block->addr;
block->addr = (char*)block->addr + size;
block->size -= size;
if (block->size == 0) {
// remove block if empty
alloc->n_free_blocks--;
for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
}
}
tensor->data = addr;
#ifdef GGML_ALLOCATOR_DEBUG
add_allocated_tensor(alloc, tensor);
size_t cur_max = (char*)addr - (char*)alloc->data + size;
if (cur_max > alloc->max_size) {
printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i]) {
printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0);
}
}
printf("\n");
}
#endif
alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
}
// this is a very naive implementation, but for our case the number of free blocks should be very small
static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
void * ptr = tensor->data;
if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
// the tensor was not allocated in this buffer
// this can happen because the graph allocator will try to free weights and other tensors from different buffers
// the easiest way to deal with this is just to ignore it
return;
}
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
#ifdef GGML_ALLOCATOR_DEBUG
remove_allocated_tensor(alloc, tensor);
#endif
// see if we can merge with an existing block
for (int i = 0; i < alloc->n_free_blocks; i++) {
struct free_block * block = &alloc->free_blocks[i];
// check if ptr is at the end of the block
if ((char*)block->addr + block->size == ptr) {
block->size += size;
// check if we can merge with the next block
if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) {
block->size += alloc->free_blocks[i+1].size;
alloc->n_free_blocks--;
for (int j = i+1; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
}
}
return;
}
// check if ptr is at the beginning of the block
if ((char*)ptr + size == block->addr) {
block->addr = ptr;
block->size += size;
// check if we can merge with the previous block
if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) {
alloc->free_blocks[i-1].size += block->size;
alloc->n_free_blocks--;
for (int j = i; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
}
}
return;
}
}
// otherwise, add a new block
GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
// insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
int insert_pos = 0;
while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) {
insert_pos++;
}
// shift all blocks from insert_pos onward to make room for the new block
for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
alloc->free_blocks[i] = alloc->free_blocks[i-1];
}
// insert the new block
alloc->free_blocks[insert_pos].addr = ptr;
alloc->free_blocks[insert_pos].size = size;
alloc->n_free_blocks++;
}
void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n) {
int pos = 0;
for (int i = 0; i < n; i++) {
if (list[i] != -1) {
alloc->parse_seq[pos] = list[i];
pos++;
}
}
alloc->has_parse_seq = true;
}
void ggml_allocr_reset(struct ggml_allocr * alloc) {
alloc->n_free_blocks = 1;
size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
alloc->free_blocks[0].size = alloc->size - align_offset;
}
struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
*alloc = (struct ggml_allocr){
/*.data = */ data,
/*.size = */ size,
/*.alignment = */ alignment,
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ false,
/*.parse_seq = */ {0},
/*.has_parse_seq = */ false,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0},
#endif
};
ggml_allocr_reset(alloc);
return alloc;
}
// address and size of the buffer when measuring
// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB
struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
*alloc = (struct ggml_allocr){
/*.data = */ MEASURE_BASE_ADDR,
/*.size = */ MEASURE_MAX_SIZE,
/*.alignment = */ alignment,
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ true,
/*.parse_seq = */ {0},
/*.has_parse_seq = */ false,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0},
#endif
};
ggml_allocr_reset(alloc);
return alloc;
}
void ggml_allocr_free(struct ggml_allocr * alloc) {
free(alloc);
}
bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
return alloc->measure;
}
//////////// compute graph allocator
static bool ggml_is_view(struct ggml_tensor * t) {
return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
}
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
if (a->type != b->type) {
return false;
}
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (a->ne[i] != b->ne[i]) {
return false;
}
if (a->nb[i] != b->nb[i]) {
return false;
}
}
return true;
}
static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
switch (t->op) {
case GGML_OP_PERMUTE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
case GGML_OP_VIEW:
return t->src[0];
case GGML_OP_CPY:
return t->src[1];
default:
return NULL;
}
}
static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
struct ggml_tensor * parent = t;
do {
parent = get_view_parent(parent);
} while (ggml_is_view(parent));
return parent;
}
static bool ggml_op_can_inplace(enum ggml_op op) {
switch (op) {
case GGML_OP_SCALE:
case GGML_OP_DIAG_MASK_ZERO:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_ADD:
case GGML_OP_ADD1:
case GGML_OP_ACC:
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_LOG:
case GGML_OP_UNARY:
case GGML_OP_ROPE:
case GGML_OP_RMS_NORM:
case GGML_OP_SET:
case GGML_OP_SOFT_MAX:
case GGML_OP_CONT:
return true;
default:
return false;
}
}
static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
struct hash_node * ht = alloc->hash_table;
if (node->data == NULL) {
if (ggml_is_view(node)) {
size_t offset;
switch(node->op) {
case GGML_OP_VIEW:
memcpy(&offset, node->op_params, sizeof(size_t));
node->data = (char *) node->src[0]->data + offset;
break;
case GGML_OP_PERMUTE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
node->data = node->src[0]->data;
break;
case GGML_OP_CPY:
node->data = node->src[1]->data;
break;
default:
GGML_ASSERT(!"unknown view op");
break;
}
} else {
// see if we can reuse a parent's buffer (inplace)
if (ggml_op_can_inplace(node->op)) {
for (int i = 0; i < GGML_MAX_SRC; i++) {
struct ggml_tensor * parent = node->src[i];
if (parent == NULL) {
break;
}
// if the node's data is external, then we cannot re-use it
if ((char *) parent->data < (char *) alloc->data ||
(char *) parent->data >= ((char *) alloc->data + alloc->size)) {
AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
continue;
}
struct hash_node * p_hn = hash_get(ht, parent);
if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = get_view_source(parent);
struct hash_node * view_src_hn = hash_get(ht, view_src);
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
// TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
// the parent's data that it will need later (same layout requirement). the problem is that then
// we cannot free the tensor because the original address of the allocation is lost.
// adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
// for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
node->data = parent->data;
return;
}
}
else {
AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
node->data = parent->data;
}
return;
}
}
}
ggml_allocr_alloc(alloc, node);
}
}
}
static size_t ggml_allocator_alloc_graph_tensors_n(
struct ggml_allocr * alloc,
struct ggml_cgraph ** graphs, int n_graphs,
struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
// reset hash table
struct hash_node * ht = alloc->hash_table;
memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE);
// count number of children and views
for (int g = 0; g < n_graphs; g++) {
struct ggml_cgraph * gf = graphs[g];
for (int i = 0; i < gf->n_nodes; i++) {
struct ggml_tensor * node = gf->nodes[i];
if (ggml_is_view(node)) {
struct ggml_tensor * view_src = get_view_source(node);
hash_get(ht, view_src)->n_views += 1;
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * parent = node->src[j];
if (parent == NULL) {
break;
}
hash_get(ht, parent)->n_children += 1;
}
}
}
// allocate tensors
for (int g = 0; g < n_graphs; g++) {
struct ggml_cgraph * gf = graphs[g];
AT_PRINTF("####### graph %d/%d\n", g, n_graphs);
// graph inputs are allocated first to ensure that they are not overwritten by each other
if (inputs != NULL && inputs[g] != NULL) {
for (int i = 0; inputs[g][i] != NULL; i++) {
struct ggml_tensor * input = inputs[g][i];
AT_PRINTF("input: %s\n", input->name);
allocate_node(alloc, input);
}
}
for (int ind = 0; ind < gf->n_nodes; ind++) {
int i;
if (alloc->has_parse_seq) {
i = alloc->parse_seq[ind];
} else {
i = ind;
}
struct ggml_tensor * node = gf->nodes[i];
// allocate parents (leafs)
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * parent = node->src[j];
if (parent == NULL) {
break;
}
allocate_node(alloc, parent);
}
// allocate node
allocate_node(alloc, node);
AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * parent = node->src[j];
if (parent == NULL) {
break;
}
AT_PRINTF("%s", parent->name);
if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
AT_PRINTF(", ");
}
}
AT_PRINTF("\n");
// update parents
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * parent = node->src[j];
if (parent == NULL) {
break;
}
struct hash_node * p_hn = hash_get(ht, parent);
p_hn->n_children -= 1;
//AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
if (p_hn->n_children == 0 && p_hn->n_views == 0) {
if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = get_view_source(parent);
struct hash_node * view_src_hn = hash_get(ht, view_src);
view_src_hn->n_views -= 1;
AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src->n_children, view_src->n_views);
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
ggml_allocator_free_tensor(alloc, view_src);
}
}
else {
if (parent->data != node->data) {
ggml_allocator_free_tensor(alloc, parent);
}
}
}
}
AT_PRINTF("\n");
}
// free graph outputs here that wouldn't be freed otherwise because they have no children
if (outputs != NULL && outputs[g] != NULL) {
for (int i = 0; outputs[g][i] != NULL; i++) {
struct ggml_tensor * output = outputs[g][i];
AT_PRINTF("output: %s\n", output->name);
ggml_allocator_free_tensor(alloc, output);
}
}
}
return alloc->max_size;
}
size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
}

26
ggml-alloc.h Normal file
View file

@ -0,0 +1,26 @@
#pragma once
#include "ggml.h"
#ifdef __cplusplus
extern "C" {
#endif
GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
// tell the allocator to parse nodes following the order described in the list
// you should call this if your graph are optimized to execute out-of-order
GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n);
GGML_API void ggml_allocr_free(struct ggml_allocr * alloc);
GGML_API bool ggml_allocr_is_measure(struct ggml_allocr * alloc);
GGML_API void ggml_allocr_reset(struct ggml_allocr * alloc);
GGML_API void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
#ifdef __cplusplus
}
#endif

3078
ggml-cuda.cu
View file

File diff suppressed because it is too large Load diff

1
ggml-cuda.h
View file

@ -27,6 +27,7 @@ void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
void ggml_cuda_set_main_device(int main_device);
void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
void ggml_cuda_set_scratch_size(size_t scratch_size);
void ggml_cuda_free_scratch(void);
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);

9
ggml-metal.h
View file

@ -63,10 +63,13 @@ void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor *
// try to find operations that can be run concurrently in the graph
// you should run it again if the topology of your graph changes
void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
// if the graph has been optimized for concurrently dispatch
bool ggml_metal_if_optimized(struct ggml_metal_context * ctx);
// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
// output the concur_list for ggml_alloc
int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
// same as ggml_graph_compute but uses Metal
// creates gf->n_threads command buffers in parallel

285
ggml-metal.m
View file

@ -5,7 +5,11 @@
#import <Foundation/Foundation.h>
#import <Metal/Metal.h>
#import <MetalPerformanceShaders/MetalPerformanceShaders.h>
#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#ifdef GGML_METAL_NDEBUG
#define metal_printf(...)
@ -15,6 +19,8 @@
#define UNUSED(x) (void)(x)
#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
struct ggml_metal_buffer {
const char * name;
@ -36,7 +42,7 @@ struct ggml_metal_context {
int n_buffers;
struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
int concur_list[GGML_MAX_NODES];
int concur_list[GGML_MAX_CONCUR];
int concur_list_len;
// custom kernels
@ -72,6 +78,14 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(mul_mat_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q6_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_DECL_KERNEL(rope);
GGML_METAL_DECL_KERNEL(alibi_f32);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
@ -103,13 +117,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->n_buffers = 0;
ctx->concur_list_len = 0;
// determine if we can use MPS
if (MPSSupportsMTLDevice(ctx->device)) {
fprintf(stderr, "%s: using MPS\n", __func__);
} else {
fprintf(stderr, "%s: not using MPS\n", __func__);
GGML_ASSERT(false && "MPS not supported");
}
#if 0
// compile from source string and show compile log
@ -119,7 +126,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error];
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
exit(1);
return NULL;
}
}
#else
@ -137,7 +144,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error];
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
exit(1);
return NULL;
}
#ifdef GGML_QKK_64
@ -149,17 +156,22 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
#endif
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
exit(1);
return NULL;
}
}
#endif
// load kernels
{
NSError * error = nil;
#define GGML_METAL_ADD_KERNEL(name) \
ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:nil]; \
fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name);
ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name); \
if (error) { \
fprintf(stderr, "%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
return NULL; \
}
GGML_METAL_ADD_KERNEL(add);
GGML_METAL_ADD_KERNEL(add_row);
@ -189,6 +201,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(mul_mat_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q6_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_ADD_KERNEL(rope);
GGML_METAL_ADD_KERNEL(alibi_f32);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
@ -221,11 +241,12 @@ void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
ctx->n_cb = n_cb;
}
bool ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
if (ctx->concur_list_len) {
return true;
}
return false;
int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
return ctx->concur_list_len;
}
int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
return ctx->concur_list;
}
// finds the Metal buffer that contains the tensor data on the GPU device
@ -368,17 +389,17 @@ void ggml_metal_get_tensor(
void ggml_metal_graph_find_concurrency(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
struct ggml_cgraph * gf, bool check_mem) {
int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
int nodes_unused[GGML_MAX_NODES];
int nodes_unused[GGML_MAX_CONCUR];
for (int i = 0; i < GGML_MAX_NODES; i++) {ctx->concur_list[i] = 0;}
for (int i = 0; i < gf->n_nodes; i++) {nodes_unused[i] = 1;}
for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; }
for (int i = 0; i < gf->n_nodes; i++) { nodes_unused[i] = 1; }
ctx->concur_list_len = 0;
int n_left = gf->n_nodes;
int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
int n_left = gf->n_nodes;
int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
while (n_left > 0) {
// number of nodes at a layer (that can be issued concurrently)
@ -386,28 +407,40 @@ void ggml_metal_graph_find_concurrency(
for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) {
if (nodes_unused[i]) {
// if the requirements for gf->nodes[i] are satisfied
int exe_flag=1;
int exe_flag = 1;
// scan all srcs
for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) {
struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind];
if (src_cur) {
// if is leaf nodes it's satisfied.
if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {continue;}
// TODO: ggml_is_leaf()
if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {
continue;
}
// otherwise this src should be the output from previous nodes.
int is_found = 0;
// scan 2*search_depth back because we inserted barrier.
for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
if (gf->nodes[ctx->concur_list[j]] == src_cur) {is_found = 1; break;}
//for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) {
if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) {
is_found = 1;
break;
}
}
if (is_found == 0) {
exe_flag = 0;
break;
}
if (is_found == 0) {exe_flag = 0; break;}
}
}
if (exe_flag) {
if (exe_flag && check_mem) {
// check if nodes[i]'s data will be overwritten by a node before nodes[i].
// if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
int64_t data_start = (int64_t) gf->nodes[i]->data;
int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]);
int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]);
for (int j = n_start; j < i; j++) {
if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \
&& gf->nodes[j]->op != GGML_OP_VIEW \
@ -416,9 +449,9 @@ void ggml_metal_graph_find_concurrency(
if (((int64_t)gf->nodes[j]->data) >= data_start + length || \
((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) {
continue;
} else {
exe_flag = 0;
}
exe_flag = 0;
}
}
}
@ -435,11 +468,13 @@ void ggml_metal_graph_find_concurrency(
ctx->concur_list[level_pos + concurrency] = -1;
ctx->concur_list_len++;
// jump all sorted nodes at nodes_bak
while (!nodes_unused[n_start]) {n_start++;}
while (!nodes_unused[n_start]) {
n_start++;
}
level_pos += concurrency + 1;
}
if (ctx->concur_list_len > GGML_MAX_NODES) {
if (ctx->concur_list_len > GGML_MAX_CONCUR) {
fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__);
}
}
@ -453,7 +488,7 @@ void ggml_metal_graph_compute(
// else fallback to serial dispatch
MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_NODES;
const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR;
const int n_nodes = has_concur ? ctx->concur_list_len : gf->n_nodes;
edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial;
@ -485,7 +520,7 @@ void ggml_metal_graph_compute(
id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
id<MTLComputeCommandEncoder> encoder = nil;
id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
const int node_start = (cb_idx + 0) * n_nodes_per_cb;
const int node_end = (cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb;
@ -494,10 +529,6 @@ void ggml_metal_graph_compute(
const int i = has_concur ? ctx->concur_list[ind] : ind;
if (i == -1) {
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
continue;
}
[encoder memoryBarrierWithScope:MTLBarrierScopeBuffers];
continue;
}
@ -571,10 +602,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_ADD:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
if (ggml_nelements(src1) == ne10) {
// src1 is a row
[encoder setComputePipelineState:ctx->pipeline_add_row];
@ -592,10 +619,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_MUL:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
if (ggml_nelements(src1) == ne10) {
// src1 is a row
[encoder setComputePipelineState:ctx->pipeline_mul_row];
@ -613,10 +636,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_SCALE:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const float scale = *(const float *) src1->data;
[encoder setComputePipelineState:ctx->pipeline_scale];
@ -632,10 +651,6 @@ void ggml_metal_graph_compute(
switch (ggml_get_unary_op(gf->nodes[i])) {
case GGML_UNARY_OP_SILU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
[encoder setComputePipelineState:ctx->pipeline_silu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
@ -646,10 +661,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_UNARY_OP_RELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
[encoder setComputePipelineState:ctx->pipeline_relu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
@ -660,10 +671,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_UNARY_OP_GELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
[encoder setComputePipelineState:ctx->pipeline_gelu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
@ -680,10 +687,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_SOFT_MAX:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const int nth = 32;
[encoder setComputePipelineState:ctx->pipeline_soft_max];
@ -698,10 +701,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_DIAG_MASK_INF:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const int n_past = ((int32_t *)(dst->op_params))[0];
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
@ -718,53 +717,44 @@ void ggml_metal_graph_compute(
// TODO: needs to be updated after PR: https://github.com/ggerganov/ggml/pull/224
GGML_ASSERT(ne00 == ne10);
GGML_ASSERT(ne02 == ne12);
// GGML_ASSERT(ne02 == ne12); // Should be checked on individual data types until broadcast is implemented everywhere
uint gqa = ne12/ne02;
GGML_ASSERT(ne03 == ne13);
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
(src0t == GGML_TYPE_F32 || src0t == GGML_TYPE_F16) && ne11 > 1) {
if (encoder != nil) {
[encoder endEncoding];
encoder = nil;
src1t == GGML_TYPE_F32 &&
[ctx->device supportsFamily:MTLGPUFamilyApple7] &&
ne00%32 == 0 &&
ne11 > 1) {
switch (src0->type) {
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break;
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break;
case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break;
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break;
default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:8];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:9];
[encoder setBytes:&gqa length:sizeof(gqa) atIndex:10];
[encoder setThreadgroupMemoryLength:8192 atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
}
MPSDataType src0dt = src0t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16;
MPSDataType src1dt = src1t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16;
// for F32 x F32 we use MPS
MPSMatrixDescriptor * desc0 = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne01 columns:ne00 rowBytes:src0->nb[1] dataType:src0dt];
MPSMatrixDescriptor * desc1 = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne11 columns:ne10 rowBytes:src1->nb[1] dataType:src1dt];
MPSMatrixDescriptor * desc = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne1 columns:ne0 rowBytes:dst->nb[1] dataType:MPSDataTypeFloat32];
MPSMatrixMultiplication * mul = [[MPSMatrixMultiplication alloc]
initWithDevice:ctx->device transposeLeft:false transposeRight:true
resultRows:ne11 resultColumns:ne01 interiorColumns:ne00 alpha:1.0 beta:0.0];
// we need to do ne02 multiplications
// TODO: is there a way to do this in parallel - currently very slow ..
// TODO: might be possible to offload part of the computation to ANE using Accelerate's CBLAS
for (int64_t i02 = 0; i02 < ne02; ++i02) {
size_t offs_src0_cur = offs_src0 + i02*nb02;
size_t offs_src1_cur = offs_src1 + i02*nb12;
size_t offs_dst_cur = offs_dst + i02*nb2;
MPSMatrix * mat_src0 = [[MPSMatrix alloc] initWithBuffer:id_src0 offset:offs_src0_cur descriptor:desc0];
MPSMatrix * mat_src1 = [[MPSMatrix alloc] initWithBuffer:id_src1 offset:offs_src1_cur descriptor:desc1];
MPSMatrix * mat_dst = [[MPSMatrix alloc] initWithBuffer:id_dst offset:offs_dst_cur descriptor:desc ];
[mul encodeToCommandBuffer:command_buffer leftMatrix:mat_src1 rightMatrix:mat_src0 resultMatrix:mat_dst];
}
} else {
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
else {
int nth0 = 32;
int nth1 = 1;
@ -772,8 +762,6 @@ void ggml_metal_graph_compute(
switch (src0t) {
case GGML_TYPE_F16:
{
GGML_ASSERT(ne02 == ne12);
nth0 = 64;
nth1 = 1;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
@ -853,33 +841,36 @@ void ggml_metal_graph_compute(
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:5];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:6];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:7];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:8];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:9];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16];
[encoder setBytes:&gqa length:sizeof(gqa) atIndex:17];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q3_K) {
#ifdef GGML_QKK_64
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#else
[encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#endif
}
else if (src0t == GGML_TYPE_Q5_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q6_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -888,10 +879,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_GET_ROWS:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
switch (src0->type) {
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
@ -917,10 +904,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_RMS_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
@ -940,10 +923,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const float eps = 1e-5f;
const int nth = 256;
@ -962,10 +941,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_ALIBI:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
GGML_ASSERT((src0t == GGML_TYPE_F32));
const int n_past = ((int32_t *) dst->op_params)[0]; UNUSED(n_past);
@ -1005,10 +980,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_ROPE:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
@ -1049,10 +1020,6 @@ void ggml_metal_graph_compute(
case GGML_OP_CPY:
case GGML_OP_CONT:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const int nth = 32;
switch (src0t) {

974
ggml-metal.metal
View file

File diff suppressed because it is too large Load diff

619
ggml.c
View file

@ -195,8 +195,8 @@ typedef void * thread_ret_t;
#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr)
#else
inline static void* ggml_aligned_malloc(size_t size) {
void* aligned_memory = NULL;
inline static void * ggml_aligned_malloc(size_t size) {
void * aligned_memory = NULL;
#ifdef GGML_USE_METAL
int result = posix_memalign(&aligned_memory, getpagesize(), size);
#else
@ -3811,7 +3811,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK",
};
static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59");
static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -3883,7 +3883,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)",
};
static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59");
static_assert(GGML_OP_COUNT == 62, "GGML_OP_COUNT != 62");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -4110,7 +4110,7 @@ size_t ggml_nbytes(const struct ggml_tensor * tensor) {
//
// is enough, but just in case, adding the second part
return MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]);
return GGML_PAD(MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]), GGML_MEM_ALIGN);
}
size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
@ -4253,7 +4253,7 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
static inline bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
bool ggml_are_same_shape(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
@ -4557,10 +4557,12 @@ static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml
static struct ggml_tensor * ggml_new_tensor_impl(
struct ggml_context * ctx,
enum ggml_type type,
int n_dims,
const int64_t* ne,
void* data) {
enum ggml_type type,
int n_dims,
const int64_t * ne,
void * data) {
assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);
size_t data_size = 0;
@ -4600,7 +4602,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
/*.ne =*/ { 1, 1, 1, 1 },
/*.nb =*/ { 0, 0, 0, 0 },
/*.op =*/ GGML_OP_NONE,
/*.op_params =*/ {0},
/*.op_params =*/ { 0 },
/*.is_param =*/ false,
/*.grad =*/ NULL,
/*.src =*/ { NULL },
@ -4632,6 +4634,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
}
static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings
assert(params_size <= GGML_MAX_OP_PARAMS);
memcpy(tensor->op_params, params, params_size);
}
@ -4648,22 +4651,22 @@ static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int3
struct ggml_tensor * ggml_new_tensor(
struct ggml_context * ctx,
enum ggml_type type,
int n_dims,
const int64_t * ne) {
enum ggml_type type,
int n_dims,
const int64_t * ne) {
return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL);
}
struct ggml_tensor * ggml_new_tensor_1d(
struct ggml_context * ctx,
enum ggml_type type,
enum ggml_type type,
int64_t ne0) {
return ggml_new_tensor(ctx, type, 1, &ne0);
}
struct ggml_tensor * ggml_new_tensor_2d(
struct ggml_context * ctx,
enum ggml_type type,
enum ggml_type type,
int64_t ne0,
int64_t ne1) {
const int64_t ne[2] = { ne0, ne1 };
@ -4672,7 +4675,7 @@ struct ggml_tensor * ggml_new_tensor_2d(
struct ggml_tensor * ggml_new_tensor_3d(
struct ggml_context * ctx,
enum ggml_type type,
enum ggml_type type,
int64_t ne0,
int64_t ne1,
int64_t ne2) {
@ -6238,6 +6241,27 @@ struct ggml_tensor * ggml_reshape_4d(
// ggml_view_1d
static struct ggml_tensor * ggml_view_tensor_offset(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_dims,
const int64_t * ne,
size_t offset) {
// don't calculate an offset from an unallocated tensor
void * data = NULL;
if (a->data != NULL) {
data = (char *) a->data + offset;
}
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, data);
ggml_format_name(result, "%s (view)", a->name);
ggml_set_op_params(result, &offset, sizeof(offset));
return result;
}
struct ggml_tensor * ggml_view_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -6250,10 +6274,7 @@ struct ggml_tensor * ggml_view_1d(
is_node = true;
}
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset);
ggml_format_name(result, "%s (view)", a->name);
ggml_set_op_params(result, &offset, sizeof(offset));
struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 1, &ne0, offset);
result->op = GGML_OP_VIEW;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6280,10 +6301,7 @@ struct ggml_tensor * ggml_view_2d(
const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset);
ggml_format_name(result, "%s (view)", a->name);
ggml_set_op_params(result, &offset, sizeof(offset));
struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 2, ne, offset);
result->nb[1] = nb1;
result->nb[2] = result->nb[1]*ne1;
@ -6316,10 +6334,7 @@ struct ggml_tensor * ggml_view_3d(
const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset);
ggml_format_name(result, "%s (view)", a->name);
ggml_set_op_params(result, &offset, sizeof(offset));
struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 3, ne, offset);
result->nb[1] = nb1;
result->nb[2] = nb2;
@ -6354,10 +6369,7 @@ struct ggml_tensor * ggml_view_4d(
const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, ne3 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, (char *) a->data + offset);
ggml_format_name(result, "%s (view)", a->name);
ggml_set_op_params(result, &offset, sizeof(offset));
struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 4, ne, offset);
result->nb[1] = nb1;
result->nb[2] = nb2;
@ -6428,7 +6440,7 @@ struct ggml_tensor * ggml_permute(
result->src[0] = a;
int32_t params[] = { axis0, axis1, axis2, axis3 };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
return result;
}
@ -6554,7 +6566,7 @@ static struct ggml_tensor * ggml_diag_mask_inf_impl(
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[] = { n_past, inplace ? 1 : 0 };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_DIAG_MASK_INF;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6594,7 +6606,7 @@ static struct ggml_tensor * ggml_diag_mask_zero_impl(
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[] = { n_past, inplace ? 1 : 0 };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_DIAG_MASK_ZERO;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6710,9 +6722,9 @@ static struct ggml_tensor * ggml_rope_impl(
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[6] = { n_past, n_dims, mode, n_ctx };
memcpy(params + 4, &freq_base, sizeof(float));
memcpy(params + 4, &freq_base, sizeof(float));
memcpy(params + 5, &freq_scale, sizeof(float));
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_ROPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6741,6 +6753,18 @@ struct ggml_tensor * ggml_rope_inplace(
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, true);
}
struct ggml_tensor * ggml_rope_custom(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx,
float freq_base,
float freq_scale) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, false);
}
struct ggml_tensor * ggml_rope_custom_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -6774,7 +6798,7 @@ struct ggml_tensor * ggml_rope_back(
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
int32_t params[] = { n_past, n_dims, mode, n_ctx };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_ROPE_BACK;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6805,7 +6829,7 @@ struct ggml_tensor * ggml_alibi(
int32_t op_params[3] = { n_past, n_head };
memcpy(op_params + 2, &bias_max, sizeof(float));
ggml_set_op_params(result, &op_params, sizeof(op_params));
ggml_set_op_params(result, op_params, sizeof(op_params));
result->op = GGML_OP_ALIBI;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6832,7 +6856,7 @@ struct ggml_tensor * ggml_clamp(
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
float params[] = { min, max };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_CLAMP;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6867,10 +6891,10 @@ GGML_API struct ggml_tensor * ggml_conv_1d(
ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
a->ne[2], 1, 1,
};
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
int32_t params[] = { s0, p0, d0 };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_CONV_1D;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6882,10 +6906,10 @@ GGML_API struct ggml_tensor * ggml_conv_1d(
// ggml_conv_2d
struct ggml_tensor* ggml_conv_2d(
struct ggml_context* ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * ggml_conv_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int s1,
int p0,
@ -6906,10 +6930,10 @@ struct ggml_tensor* ggml_conv_2d(
ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1),
a->ne[3], b->ne[3],
};
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
int32_t params[] = { s0, s1, p0, p1, d0, d1 };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_CONV_2D;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6922,7 +6946,7 @@ struct ggml_tensor* ggml_conv_2d(
// ggml_conv_1d_ph
struct ggml_tensor* ggml_conv_1d_ph(
struct ggml_tensor * ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@ -6940,7 +6964,7 @@ static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) {
// ggml_pool_1d
struct ggml_tensor* ggml_pool_1d(
struct ggml_tensor * ggml_pool_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_op_pool op,
@ -6959,10 +6983,10 @@ struct ggml_tensor* ggml_pool_1d(
ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
a->ne[1],
};
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
int32_t params[] = { op, k0, s0, p0 };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_POOL_1D;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6973,7 +6997,7 @@ struct ggml_tensor* ggml_pool_1d(
// ggml_pool_2d
struct ggml_tensor* ggml_pool_2d(
struct ggml_tensor * ggml_pool_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_op_pool op,
@ -6996,10 +7020,10 @@ struct ggml_tensor* ggml_pool_2d(
ggml_calc_pool_output_size(a->ne[1], k1, s1, p1),
a->ne[2],
};
struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_POOL_2D;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -7167,7 +7191,7 @@ struct ggml_tensor * ggml_win_part(
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
int32_t params[] = { npx, npy, w };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_WIN_PART;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -7197,7 +7221,7 @@ struct ggml_tensor * ggml_win_unpart(
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
int32_t params[] = { w };
ggml_set_op_params(result, &params, sizeof(params));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_WIN_UNPART;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -7326,7 +7350,7 @@ struct ggml_tensor * ggml_map_binary_inplace_f32(
return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
}
// ggml_map_custom1
// ggml_map_custom1_f32
static struct ggml_tensor * ggml_map_custom1_impl_f32(
struct ggml_context * ctx,
@ -7343,7 +7367,7 @@ static struct ggml_tensor * ggml_map_custom1_impl_f32(
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
result->op = GGML_OP_MAP_CUSTOM1;
result->op = GGML_OP_MAP_CUSTOM1_F32;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
@ -7364,7 +7388,7 @@ struct ggml_tensor * ggml_map_custom1_inplace_f32(
return ggml_map_custom1_impl_f32(ctx, a, fun, true);
}
// ggml_map_custom2
// ggml_map_custom2_f32
static struct ggml_tensor * ggml_map_custom2_impl_f32(
struct ggml_context * ctx,
@ -7382,7 +7406,7 @@ static struct ggml_tensor * ggml_map_custom2_impl_f32(
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
result->op = GGML_OP_MAP_CUSTOM2;
result->op = GGML_OP_MAP_CUSTOM2_F32;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = b;
@ -7406,7 +7430,7 @@ struct ggml_tensor * ggml_map_custom2_inplace_f32(
return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
}
// ggml_map_custom3
// ggml_map_custom3_f32
static struct ggml_tensor * ggml_map_custom3_impl_f32(
struct ggml_context * ctx,
@ -7425,7 +7449,7 @@ static struct ggml_tensor * ggml_map_custom3_impl_f32(
ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
result->op = GGML_OP_MAP_CUSTOM3;
result->op = GGML_OP_MAP_CUSTOM3_F32;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = b;
@ -7452,6 +7476,190 @@ struct ggml_tensor * ggml_map_custom3_inplace_f32(
return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
}
// ggml_map_custom1
struct ggml_map_custom1_op_params {
ggml_custom1_op_t fun;
int n_tasks;
void * userdata;
};
static struct ggml_tensor * ggml_map_custom1_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_t fun,
int n_tasks,
void * userdata,
bool inplace) {
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
bool is_node = false;
if (!inplace && a->grad) {
is_node = true;
}
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
struct ggml_map_custom1_op_params params = {
/*.fun =*/ fun,
/*.n_tasks =*/ n_tasks,
/*.userdata =*/ userdata
};
ggml_set_op_params(result, (const void *) &params, sizeof(params));
result->op = GGML_OP_MAP_CUSTOM1;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_map_custom1(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_t fun,
int n_tasks,
void * userdata) {
return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false);
}
struct ggml_tensor * ggml_map_custom1_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_t fun,
int n_tasks,
void * userdata) {
return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true);
}
// ggml_map_custom2
struct ggml_map_custom2_op_params {
ggml_custom2_op_t fun;
int n_tasks;
void * userdata;
};
static struct ggml_tensor * ggml_map_custom2_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_t fun,
int n_tasks,
void * userdata,
bool inplace) {
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
bool is_node = false;
if (!inplace && (a->grad || b->grad)) {
is_node = true;
}
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
struct ggml_map_custom2_op_params params = {
/*.fun =*/ fun,
/*.n_tasks =*/ n_tasks,
/*.userdata =*/ userdata
};
ggml_set_op_params(result, (const void *) &params, sizeof(params));
result->op = GGML_OP_MAP_CUSTOM2;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = b;
return result;
}
struct ggml_tensor * ggml_map_custom2(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_t fun,
int n_tasks,
void * userdata) {
return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false);
}
struct ggml_tensor * ggml_map_custom2_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_t fun,
int n_tasks,
void * userdata) {
return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true);
}
// ggml_map_custom3
struct ggml_map_custom3_op_params {
ggml_custom3_op_t fun;
int n_tasks;
void * userdata;
};
static struct ggml_tensor * ggml_map_custom3_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_t fun,
int n_tasks,
void * userdata,
bool inplace) {
GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0);
bool is_node = false;
if (!inplace && (a->grad || b->grad || c->grad)) {
is_node = true;
}
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
struct ggml_map_custom3_op_params params = {
/*.fun =*/ fun,
/*.n_tasks =*/ n_tasks,
/*.userdata =*/ userdata
};
ggml_set_op_params(result, (const void *) &params, sizeof(params));
result->op = GGML_OP_MAP_CUSTOM3;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = b;
result->src[2] = c;
return result;
}
struct ggml_tensor * ggml_map_custom3(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_t fun,
int n_tasks,
void * userdata) {
return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false);
}
struct ggml_tensor * ggml_map_custom3_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_t fun,
int n_tasks,
void * userdata) {
return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
}
// ggml_cross_entropy_loss
struct ggml_tensor * ggml_cross_entropy_loss(
@ -9260,8 +9468,8 @@ static void ggml_compute_forward_sum_rows_f32(
for (int64_t i3 = 0; i3 < ne03; i3++) {
for (int64_t i2 = 0; i2 < ne02; i2++) {
for (int64_t i1 = 0; i1 < ne01; i1++) {
float* src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03);
float* dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3);
float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03);
float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3);
float row_sum = 0;
ggml_vec_sum_f32(ne00, &row_sum, src_row);
dst_row[0] = row_sum;
@ -10523,72 +10731,96 @@ static void ggml_compute_forward_mul_mat(
return;
}
// parallelize by src0 rows
const int64_t dr = (ne01 + nth - 1)/nth;
const int64_t ir10 = dr*ith;
const int64_t ir11 = MIN(ir10 + dr, ne01);
// src1 rows
const int64_t nr1 = ne11*ne12*ne13;
const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
for (int64_t ir1 = 0; ir1 < nr1; ++ir1) {
const int64_t i13 = (ir1/(ne12*ne11));
const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11;
const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11);
const int64_t nr0 = ne01; // src0 rows
const int64_t nr1 = ne11*ne12*ne13; // src1 rows
const int64_t ir0 = (ir1/ne11)%(ne02*ne03);
const int64_t i03 = (ir0/(ne02));
// Hack for "Falcon multi-query-attention key stutter" / alternative to ggml_repeat2.
// See https://github.com/ggerganov/llama.cpp/issues/1602#issuecomment-1606087470:
// GG: this is likely the correct way to broadcast, though need some more thought
// therefore leaving the comments to remind us for now
const int64_t i02 = (i12 / (ne12 / ne02));
// Original from PR/224 (and also essential/correct for non-broadcast matmuls in Falcon)
// const int64_t i02 = (ir0 - i03*ne02);
//printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
const int64_t i1 = i11;
const int64_t i2 = i12;
const int64_t i3 = i13;
// distribute the thread work across the inner or outer loop based on which one is larger
const char * src0_row = (const char *) src0->data + ( 0 + i02*nb02 + i03*nb03 );
const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
// the original src1 data pointer, so we should index using the indices directly
// TODO: this is a bit of a hack, we should probably have a better way to handle this
const char * src1_col = (const char *) wdata +
(src1_cont || src1->type != vec_dot_type
? (i11 + i12*ne11 + i13*ne12*ne11)*row_size
: (i11*nb11 + i12*nb12 + i13*nb13));
const int64_t ith0 = ith % nth0;
const int64_t ith1 = ith / nth0;
float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
const int64_t dr0 = (nr0 + nth0 - 1)/nth0;
const int64_t dr1 = (nr1 + nth1 - 1)/nth1;
for (int64_t ir = ir10; ir < ir11; ++ir) {
vec_dot(ne00, &dst_col[ir], src0_row + ir*nb01, src1_col);
}
const int64_t ir010 = dr0*ith0;
const int64_t ir011 = MIN(ir010 + dr0, nr0);
const int64_t ir110 = dr1*ith1;
const int64_t ir111 = MIN(ir110 + dr1, nr1);
//printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
// threads with no work simply yield (not sure if it helps)
if (ir010 >= ir011 || ir110 >= ir111) {
sched_yield();
return;
}
//int64_t t1 = ggml_time_us();
//static int64_t acc = 0;
//acc += t1 - t0;
//if (t1 - t0 > 10) {
// printf("\n");
// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
assert(ne12 % ne02 == 0);
assert(ne13 % ne03 == 0);
// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
//}
// broadcast factors
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
// block-tiling attempt
const int64_t blck_0 = 16;
const int64_t blck_1 = 16;
// attempt to reduce false-sharing (does not seem to make a difference)
float tmp[16];
for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
const int64_t i13 = (ir1/(ne12*ne11));
const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11;
const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11);
// broadcast src0 into src1
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
const int64_t i1 = i11;
const int64_t i2 = i12;
const int64_t i3 = i13;
const char * src0_row = (const char *) src0->data + (0 + i02*nb02 + i03*nb03);
// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
// the original src1 data pointer, so we should index using the indices directly
// TODO: this is a bit of a hack, we should probably have a better way to handle this
const char * src1_col = (const char *) wdata +
(src1_cont || src1->type != vec_dot_type
? (i11 + i12*ne11 + i13*ne12*ne11)*row_size
: (i11*nb11 + i12*nb12 + i13*nb13));
float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
//for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
// vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
//}
for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
}
memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
}
}
}
}
// ggml_compute_forward_out_prod
static void ggml_compute_forward_out_prod_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
@ -12871,7 +13103,7 @@ static void ggml_compute_forward_pool_1d(
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
const int32_t* opts = (const int32_t*)dst->op_params;
const int32_t * opts = (const int32_t *)dst->op_params;
enum ggml_op_pool op = opts[0];
const int k0 = opts[1];
const int s0 = opts[2];
@ -14204,24 +14436,6 @@ static void ggml_compute_forward_map_custom1_f32(
fun(dst, a);
}
static void ggml_compute_forward_map_custom1(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
struct ggml_tensor * dst,
const ggml_custom1_op_f32_t fun) {
switch (a->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_custom1_f32(params, a, dst, fun);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_map_custom2
static void ggml_compute_forward_map_custom2_f32(
@ -14240,24 +14454,6 @@ static void ggml_compute_forward_map_custom2_f32(
}
static void ggml_compute_forward_map_custom2(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
const struct ggml_tensor * b,
struct ggml_tensor * dst,
const ggml_custom2_op_f32_t fun) {
switch (a->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_custom2_f32(params, a, b, dst, fun);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_map_custom3
static void ggml_compute_forward_map_custom3_f32(
@ -14276,24 +14472,52 @@ static void ggml_compute_forward_map_custom3_f32(
fun(dst, a, b, c);
}
// ggml_compute_forward_map_custom1
static void ggml_compute_forward_map_custom1(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params;
p->fun(dst, a, params->ith, params->nth, p->userdata);
}
// ggml_compute_forward_map_custom2
static void ggml_compute_forward_map_custom2(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
const struct ggml_tensor * b,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params;
p->fun(dst, a, b, params->ith, params->nth, p->userdata);
}
// ggml_compute_forward_map_custom3
static void ggml_compute_forward_map_custom3(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
const struct ggml_tensor * b,
const struct ggml_tensor * c,
struct ggml_tensor * dst,
const ggml_custom3_op_f32_t fun) {
switch (a->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_custom3_f32(params, a, b, c, dst, fun);
} break;
default:
{
GGML_ASSERT(false);
} break;
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params;
p->fun(dst, a, b, c, params->ith, params->nth, p->userdata);
}
// ggml_compute_forward_cross_entropy_loss
@ -14815,25 +15039,40 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM1:
case GGML_OP_MAP_CUSTOM1_F32:
{
ggml_custom1_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_custom1(params, tensor->src[0], tensor, fun);
ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM2_F32:
{
ggml_custom2_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM3_F32:
{
ggml_custom3_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM1:
{
ggml_compute_forward_map_custom1(params, tensor->src[0], tensor);
}
break;
case GGML_OP_MAP_CUSTOM2:
{
ggml_custom2_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor, fun);
ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor);
}
break;
case GGML_OP_MAP_CUSTOM3:
{
ggml_custom3_op_f32_t fun;
memcpy(&fun, tensor->op_params, sizeof(fun));
ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
}
break;
case GGML_OP_CROSS_ENTROPY_LOSS:
@ -15641,6 +15880,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
} break;
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
case GGML_OP_MAP_CUSTOM1_F32:
case GGML_OP_MAP_CUSTOM2_F32:
case GGML_OP_MAP_CUSTOM3_F32:
case GGML_OP_MAP_CUSTOM1:
case GGML_OP_MAP_CUSTOM2:
case GGML_OP_MAP_CUSTOM3:
@ -16426,12 +16668,39 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
case GGML_OP_WIN_UNPART:
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
case GGML_OP_MAP_CUSTOM1:
case GGML_OP_MAP_CUSTOM2:
case GGML_OP_MAP_CUSTOM3:
case GGML_OP_MAP_CUSTOM1_F32:
case GGML_OP_MAP_CUSTOM2_F32:
case GGML_OP_MAP_CUSTOM3_F32:
{
n_tasks = 1;
} break;
case GGML_OP_MAP_CUSTOM1:
{
struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params;
if (p->n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p->n_tasks, n_threads);
}
} break;
case GGML_OP_MAP_CUSTOM2:
{
struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params;
if (p->n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p->n_tasks, n_threads);
}
} break;
case GGML_OP_MAP_CUSTOM3:
{
struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params;
if (p->n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p->n_tasks, n_threads);
}
} break;
case GGML_OP_CROSS_ENTROPY_LOSS:
{
n_tasks = n_threads;

158
ggml.h
View file

@ -183,6 +183,15 @@
# define GGML_API
#endif
// TODO: support for clang
#ifdef __GNUC__
# define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
#elif defined(_MSC_VER)
# define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
#else
# define GGML_DEPRECATED(func, hint) func
#endif
#include <stdint.h>
#include <stddef.h>
#include <stdbool.h>
@ -374,6 +383,10 @@ extern "C" {
GGML_OP_MAP_UNARY,
GGML_OP_MAP_BINARY,
GGML_OP_MAP_CUSTOM1_F32,
GGML_OP_MAP_CUSTOM2_F32,
GGML_OP_MAP_CUSTOM3_F32,
GGML_OP_MAP_CUSTOM1,
GGML_OP_MAP_CUSTOM2,
GGML_OP_MAP_CUSTOM3,
@ -570,6 +583,8 @@ extern "C" {
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
// use this to compute the memory overhead of a tensor
GGML_API size_t ggml_tensor_overhead(void);
@ -1170,7 +1185,18 @@ extern "C" {
int mode,
int n_ctx);
// custom RoPE, in-place, returns view(a)
// custom RoPE
GGML_API struct ggml_tensor * ggml_rope_custom(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx,
float freq_base,
float freq_scale);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1229,7 +1255,7 @@ extern "C" {
// conv_1d with padding = half
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
GGML_API struct ggml_tensor* ggml_conv_1d_ph(
GGML_API struct ggml_tensor * ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@ -1242,7 +1268,7 @@ extern "C" {
GGML_OP_POOL_COUNT,
};
GGML_API struct ggml_tensor* ggml_pool_1d(
GGML_API struct ggml_tensor * ggml_pool_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_op_pool op,
@ -1250,7 +1276,7 @@ extern "C" {
int s0, // stride
int p0); // padding
GGML_API struct ggml_tensor* ggml_pool_2d(
GGML_API struct ggml_tensor * ggml_pool_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_op_pool op,
@ -1304,15 +1330,6 @@ extern "C" {
int h0,
int w);
// custom operators
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
GGML_API struct ggml_tensor * ggml_unary(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1323,63 +1340,138 @@ extern "C" {
struct ggml_tensor * a,
enum ggml_unary_op op);
GGML_API struct ggml_tensor * ggml_map_unary_f32(
// custom operators
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun);
ggml_unary_op_f32_t fun),
"use ggml_map_custom1 instead");
GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun);
ggml_unary_op_f32_t fun),
"use ggml_map_custom1_inplace instead");
GGML_API struct ggml_tensor * ggml_map_binary_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun);
ggml_binary_op_f32_t fun),
"use ggml_map_custom2 instead");
GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun);
ggml_binary_op_f32_t fun),
"use ggml_map_custom2_inplace instead");
GGML_API struct ggml_tensor * ggml_map_custom1_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun);
ggml_custom1_op_f32_t fun),
"use ggml_map_custom1 instead");
GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun);
ggml_custom1_op_f32_t fun),
"use ggml_map_custom1_inplace instead");
GGML_API struct ggml_tensor * ggml_map_custom2_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun);
ggml_custom2_op_f32_t fun),
"use ggml_map_custom2 instead");
GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun);
ggml_custom2_op_f32_t fun),
"use ggml_map_custom2_inplace instead");
GGML_API struct ggml_tensor * ggml_map_custom3_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun);
ggml_custom3_op_f32_t fun),
"use ggml_map_custom3 instead");
GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun);
ggml_custom3_op_f32_t fun),
"use ggml_map_custom3_inplace instead");
// custom operators v2
typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
#define GGML_N_TASKS_MAX -1
GGML_API struct ggml_tensor * ggml_map_custom1(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_t fun,
int n_tasks,
void * userdata);
GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_t fun,
int n_tasks,
void * userdata);
GGML_API struct ggml_tensor * ggml_map_custom2(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_t fun,
int n_tasks,
void * userdata);
GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_t fun,
int n_tasks,
void * userdata);
GGML_API struct ggml_tensor * ggml_map_custom3(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_t fun,
int n_tasks,
void * userdata);
GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_t fun,
int n_tasks,
void * userdata);
// loss function

16
grammars/json.gbnf
View file

@ -1,29 +1,25 @@
# Grammar for subset of JSON - doesn't support full string or number syntax
root ::= object
value ::= object | array | string | number | boolean | "null"
root ::= object
value ::= object | array | string | number | ("true" | "false" | "null") ws
object ::=
"{" ws (
string ":" ws value
("," ws string ":" ws value)*
)? "}"
)? "}" ws
array ::=
"[" ws (
value
("," ws value)*
)? "]"
)? "]" ws
string ::=
string ::=
"\"" (
[^"\\] |
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
)* "\"" ws
# Only plain integers currently
number ::= "-"? [0-9]+ ws
boolean ::= ("true" | "false") ws
number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
# Optional space: by convention, applied in this grammar after literal chars when allowed
ws ::= ([ \t\n] ws)?

62
k_quants.c
View file

@ -39,6 +39,8 @@
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
//
// 2-6 bit quantization in super-blocks
//
@ -1353,7 +1355,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i all_scales = _mm256_cvtepi8_epi16(scales8);
const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
const __m256i scales[2] = {_mm256_set_m128i(l_scales, l_scales), _mm256_set_m128i(h_scales, h_scales)};
const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
__m256i sumi = _mm256_setzero_si256();
@ -1421,7 +1423,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8]));
// sumf += -dmin * summs in 32bits*8
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(_mm256_set_m128i(summs_1, summs_0))), acc);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc);
const __m128i scales_0 = _mm_cvtepi8_epi16(scales16);
const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16));
@ -1493,7 +1495,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
}
// sumf += dall * isum - dmin * summs in 32bits
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc);
}
@ -1644,8 +1646,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
summs += dmin * smin;
const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
const __m256i q2_0 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 2), q2bits), m3);
const __m256i q2_1 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3);
const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
@ -1709,10 +1711,10 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0));
const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1));
const __m256i p_0 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
const __m256i p_1 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
const __m256i p_2 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
const __m256i p_3 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc);
@ -1917,7 +1919,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i all_scales = _mm256_cvtepi8_epi16(scales128);
const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
const __m256i scales[2] = {_mm256_set_m128i(l_scales, l_scales), _mm256_set_m128i(h_scales, h_scales)};
const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
// high bit
const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask);
@ -2128,7 +2130,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
}
// multiply with block scale and accumulate
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
}
@ -2303,13 +2305,13 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
aux16[0] = a & 0x0f0f;
aux16[1] = (a >> 4) & 0x0f0f;
const __m256i scale_0 = _mm256_set_m128i(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
const __m256i scale_1 = _mm256_set_m128i(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
memcpy(&aux64, x[i].hmask, 8);
const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
__m256i q3h_0 = _mm256_set_m128i(_mm_srli_epi16(haux, 2), haux);
__m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux);
__m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4);
q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2);
q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2);
@ -2318,7 +2320,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
// prepare low and high bits
const __m256i q3aux = _mm256_set_m128i(_mm_srli_epi16(q3bits, 2), q3bits);
const __m256i q3aux = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits);
const __m256i q3l_0 = _mm256_and_si256(q3aux, m3);
const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3);
@ -2429,7 +2431,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
p16_0 = _mm_add_epi32(p16_0, p16_2);
p16_1 = _mm_add_epi32(p16_1, p16_3);
__m256i p16 = _mm256_set_m128i(p16_1, p16_0);
__m256i p16 = MM256_SET_M128I(p16_1, p16_0);
// multiply with block scale and accumulate
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc);
@ -2620,7 +2622,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m);
const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0);
const __m256i scales = _mm256_set_m128i(sc128, sc128);
const __m256i scales = MM256_SET_M128I(sc128, sc128);
__m256i sumi = _mm256_setzero_si256();
@ -2727,7 +2729,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
}
__m256 vd = _mm256_set1_ps(d);
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
}
@ -2968,11 +2970,11 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0);
const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(_mm256_set_m128i(p32_1, p32_0))), acc);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc);
const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2);
const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(_mm256_set_m128i(p32_3, p32_2))), acc);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc);
}
@ -3160,7 +3162,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
summs += dmin * _mm_extract_epi32(hsum, 0);
const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0);
const __m256i scales = _mm256_set_m128i(sc128, sc128);
const __m256i scales = MM256_SET_M128I(sc128, sc128);
const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh);
__m256i hmask = mone;
@ -3299,7 +3301,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
}
__m256 vd = _mm256_set1_ps(d);
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
}
@ -3462,13 +3464,13 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
const __m256i scale_l = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
const __m256i scale_h = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
int64_t aux64;
memcpy(&aux64, x[i].qh, 8);
const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64);
const __m256i haux256 = _mm256_set_m128i(_mm_srli_epi16(haux128, 2), haux128);
const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128);
const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4);
const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4);
@ -3543,7 +3545,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2));
const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3));
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_set_m128i(dot_1, dot_0))), acc);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc);
}
@ -3925,7 +3927,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
}
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
}
@ -4083,8 +4085,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1);
@ -4177,7 +4179,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3));
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(_mm256_set_m128i(sumi_1, sumi_0))), acc);
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc);
}
*s = hsum_float_8(acc);

73
llama-util.h
View file

@ -149,6 +149,46 @@ struct llama_file {
}
};
// llama_context_data
struct llama_data_context {
virtual void write(const void * src, size_t size) = 0;
virtual size_t get_size_written() = 0;
virtual ~llama_data_context() = default;
};
struct llama_data_buffer_context : llama_data_context {
uint8_t* ptr;
size_t size_written = 0;
llama_data_buffer_context(uint8_t * p) : ptr(p) {}
void write(const void * src, size_t size) override {
memcpy(ptr, src, size);
ptr += size;
size_written += size;
}
size_t get_size_written() override {
return size_written;
}
};
struct llama_data_file_context : llama_data_context {
llama_file* file;
size_t size_written = 0;
llama_data_file_context(llama_file * f) : file(f) {}
void write(const void * src, size_t size) override {
file->write_raw(src, size);
size_written += size;
}
size_t get_size_written() override {
return size_written;
}
};
#if defined(_WIN32)
static std::string llama_format_win_err(DWORD err) {
LPSTR buf;
@ -179,7 +219,7 @@ struct llama_mmap {
// prefetch/readahead impairs performance on NUMA systems
if (numa) { prefetch = 0; }
#ifdef __linux__
if (prefetch) { flags |= MAP_POPULATE; }
if (prefetch >= file->size) { flags |= MAP_POPULATE; }
#endif
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
if (addr == MAP_FAILED) {
@ -231,20 +271,29 @@ struct llama_mmap {
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
}
#if _WIN32_WINNT >= _WIN32_WINNT_WIN8
if (prefetch) {
// Advise the kernel to preload the mapped memory
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
range.NumberOfBytes = (SIZE_T)size;
if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
// The PrefetchVirtualMemory API is only present on Windows 8 and above, so we
// will dynamically load it using GetProcAddress.
BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
HMODULE hKernel32;
// This call is guaranteed to succeed.
hKernel32 = GetModuleHandleW(L"kernel32.dll");
// This call may fail if on a pre-Win8 system.
pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));
if (pPrefetchVirtualMemory) {
// Advise the kernel to preload the mapped memory.
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
range.NumberOfBytes = (SIZE_T)size;
if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
}
#else
#pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
#endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
}
~llama_mmap() {

839
llama.cpp
View file

File diff suppressed because it is too large Load diff

20
llama.h
View file

@ -86,7 +86,20 @@ extern "C" {
typedef void (*llama_progress_callback)(float progress, void *ctx);
struct llama_context_params {
enum llama_log_level {
LLAMA_LOG_LEVEL_ERROR = 2,
LLAMA_LOG_LEVEL_WARN = 3,
LLAMA_LOG_LEVEL_INFO = 4
};
// Signature for logging events
// Note that text includes the new line character at the end for most events.
// If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
// if it exists.
// It might not exist for progress report where '.' is output repeatedly.
typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
int32_t n_batch; // prompt processing batch size
@ -108,6 +121,7 @@ extern "C" {
// Keep the booleans together to avoid misalignment during copy-by-value.
bool low_vram; // if true, reduce VRAM usage at the cost of performance
bool mul_mat_q; // if true, use experimental mul_mat_q kernels
bool f16_kv; // use fp16 for KV cache
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool vocab_only; // only load the vocabulary, no weights
@ -194,6 +208,10 @@ extern "C" {
int32_t n_eval;
};
// Set callback for all future logging events.
// If this is not called, or NULL is supplied, everything is output on stderr.
LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
LLAMA_API int llama_max_devices();
LLAMA_API struct llama_context_params llama_context_default_params();

3
scripts/get-wikitext-2.sh Normal file
View file

@ -0,0 +1,3 @@
#!/bin/bash
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip

4
scripts/sync-ggml.sh
View file

@ -10,5 +10,5 @@ cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m
cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
cp -rpv ../ggml/tests/test-opt.c ./tests/test-opt.c
cp -rpv ../ggml/tests/test-grad0.c ./tests/test-grad0.c
cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp
cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp

8
tests/CMakeLists.txt
View file

@ -6,10 +6,12 @@ function(llama_add_test source)
add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
endfunction()
# llama_add_test(test-double-float.c) # SLOW
# llama_add_test(test-double-float.cpp) # SLOW
llama_add_test(test-quantize-fns.cpp)
llama_add_test(test-quantize-perf.cpp)
llama_add_test(test-sampling.cpp)
llama_add_test(test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)
llama_add_test(test-grad0.c) # SLOW
# llama_add_test(test-opt.c) # SLOW
llama_add_test(test-grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp)
llama_add_test(test-llama-grammar.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/common.cpp)
llama_add_test(test-grad0.cpp) # SLOW
# llama_add_test(test-opt.cpp) # SLOW

12
tests/test-double-float.c → tests/test-double-float.cpp
View file

@ -3,10 +3,11 @@
// This is done by checking all finite (non-NaN, non-infinite) floats.
#undef NDEBUG
#include <assert.h>
#include <cassert>
#include <immintrin.h>
#include <math.h>
#include <stdint.h>
#include <cmath>
#include <cstdint>
#include <cstring>
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wdouble-promotion"
@ -32,8 +33,9 @@ inline static float silu_float(float x) {
int main(void) {
uint32_t x = UINT32_MAX;
do {
float f = *(float *)&x;
assert(!isfinite(f) || (round_orig(f) == round_float(f)));
float f;
memcpy(&f, &x, sizeof(x));
assert(!std::isfinite(f) || (round_orig(f) == round_float(f)));
} while (x--);
#ifdef __F16C__

32
tests/test-grad0.c → tests/test-grad0.cpp
View file

@ -1,10 +1,10 @@
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
#include "ggml.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cassert>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
@ -47,16 +47,16 @@
#define GGML_PRINT(...) printf(__VA_ARGS__)
float frand(void) {
static float frand(void) {
return (float)rand()/(float)RAND_MAX;
}
int irand(int n) {
static int irand(int n) {
if (n == 0) return 0;
return rand()%n;
}
void get_random_dims(int64_t * dims, int ndims) {
static void get_random_dims(int64_t * dims, int ndims) {
dims[0] = dims[1] = dims[2] = dims[3] = 1;
for (int i = 0; i < ndims; i++) {
@ -64,7 +64,7 @@ void get_random_dims(int64_t * dims, int ndims) {
}
}
struct ggml_tensor * get_random_tensor_f32(
static struct ggml_tensor * get_random_tensor_f32(
struct ggml_context * ctx0,
int ndims,
int64_t ne[],
@ -112,7 +112,7 @@ struct ggml_tensor * get_random_tensor_f32(
return result;
}
struct ggml_tensor * get_random_tensor_f16(
static struct ggml_tensor * get_random_tensor_f16(
struct ggml_context * ctx0,
int ndims,
int64_t ne[],
@ -160,7 +160,7 @@ struct ggml_tensor * get_random_tensor_f16(
return result;
}
struct ggml_tensor * get_random_tensor_i32(
static struct ggml_tensor * get_random_tensor_i32(
struct ggml_context * ctx0,
int ndims,
int64_t ne[],
@ -208,7 +208,7 @@ struct ggml_tensor * get_random_tensor_i32(
return result;
}
void print_elements(const char* label, const struct ggml_tensor * t) {
static void print_elements(const char* label, const struct ggml_tensor * t) {
if (!t) {
printf("%s: %s = null\n", __func__, label);
return;
@ -228,7 +228,7 @@ void print_elements(const char* label, const struct ggml_tensor * t) {
}
bool check_gradient(
static bool check_gradient(
const char * op_name,
struct ggml_context * ctx0,
struct ggml_tensor * x[],
@ -310,7 +310,7 @@ bool check_gradient(
}
// TODO: clean-up this ..
bool check_mat_mul(
static bool check_mat_mul(
const struct ggml_tensor * y,
const struct ggml_tensor * x0,
const struct ggml_tensor * x1) {
@ -373,9 +373,9 @@ bool check_mat_mul(
int main(int argc, const char ** argv) {
struct ggml_init_params params = {
.mem_size = 128*1024*1024,
.mem_buffer = NULL,
.no_alloc = false,
/* .mem_size = */ 128*1024*1024,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
int64_t ne[4];

249
tests/test-grammar-parser.cpp Normal file
View file

@ -0,0 +1,249 @@
#ifdef NDEBUG
#undef NDEBUG
#endif
#include "llama.h"
#include "examples/grammar-parser.cpp"
#include <cassert>
int main()
{
grammar_parser::parse_state parsed_grammar;
const char *grammar_bytes = R"""(root ::= (expr "=" term "\n")+
expr ::= term ([-+*/] term)*
term ::= [0-9]+)""";
parsed_grammar = grammar_parser::parse(grammar_bytes);
std::vector<std::pair<std::string, uint32_t>> expected = {
{"expr", 2},
{"expr_5", 5},
{"expr_6", 6},
{"root", 0},
{"root_1", 1},
{"root_4", 4},
{"term", 3},
{"term_7", 7},
};
uint32_t index = 0;
for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it)
{
std::string key = it->first;
uint32_t value = it->second;
std::pair<std::string, uint32_t> expected_pair = expected[index];
// pretty print error message before asserting
if (expected_pair.first != key || expected_pair.second != value)
{
fprintf(stderr, "expected_pair: %s, %d\n", expected_pair.first.c_str(), expected_pair.second);
fprintf(stderr, "actual_pair: %s, %d\n", key.c_str(), value);
fprintf(stderr, "expected_pair != actual_pair\n");
}
assert(expected_pair.first == key && expected_pair.second == value);
index++;
}
std::vector<llama_grammar_element> expected_rules = {
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 2},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 10},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_RULE_REF, 6},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 1},
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_RULE_REF, 1},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_CHAR, 45},
{LLAMA_GRETYPE_CHAR_ALT, 43},
{LLAMA_GRETYPE_CHAR_ALT, 42},
{LLAMA_GRETYPE_CHAR_ALT, 47},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_RULE_REF, 6},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_END, 0},
};
index = 0;
for (auto rule : parsed_grammar.rules)
{
// compare rule to expected rule
for (uint32_t i = 0; i < rule.size(); i++)
{
llama_grammar_element element = rule[i];
llama_grammar_element expected_element = expected_rules[index];
// pretty print error message before asserting
if (expected_element.type != element.type || expected_element.value != element.value)
{
fprintf(stderr, "index: %d\n", index);
fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value);
fprintf(stderr, "actual_element: %d, %d\n", element.type, element.value);
fprintf(stderr, "expected_element != actual_element\n");
}
assert(expected_element.type == element.type && expected_element.value == element.value);
index++;
}
}
const char *longer_grammar_bytes = R"""(
root ::= (expr "=" ws term "\n")+
expr ::= term ([-+*/] term)*
term ::= ident | num | "(" ws expr ")" ws
ident ::= [a-z] [a-z0-9_]* ws
num ::= [0-9]+ ws
ws ::= [ \t\n]*
)""";
parsed_grammar = grammar_parser::parse(longer_grammar_bytes);
expected = {
{"expr", 2},
{"expr_6", 6},
{"expr_7", 7},
{"ident", 8},
{"ident_10", 10},
{"num", 9},
{"num_11", 11},
{"root", 0},
{"root_1", 1},
{"root_5", 5},
{"term", 4},
{"ws", 3},
{"ws_12", 12},
};
index = 0;
for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it)
{
std::string key = it->first;
uint32_t value = it->second;
std::pair<std::string, uint32_t> expected_pair = expected[index];
// pretty print error message before asserting
if (expected_pair.first != key || expected_pair.second != value)
{
fprintf(stderr, "expected_pair: %s, %d\n", expected_pair.first.c_str(), expected_pair.second);
fprintf(stderr, "actual_pair: %s, %d\n", key.c_str(), value);
fprintf(stderr, "expected_pair != actual_pair\n");
}
assert(expected_pair.first == key && expected_pair.second == value);
index++;
}
expected_rules = {
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 2},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_CHAR, 10},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 12},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 8},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_RULE_REF, 9},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_CHAR, 40},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_RULE_REF, 2},
{LLAMA_GRETYPE_CHAR, 41},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 1},
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_RULE_REF, 1},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_CHAR, 45},
{LLAMA_GRETYPE_CHAR_ALT, 43},
{LLAMA_GRETYPE_CHAR_ALT, 42},
{LLAMA_GRETYPE_CHAR_ALT, 47},
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 6},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_CHAR, 97},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
{LLAMA_GRETYPE_RULE_REF, 10},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 11},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_CHAR, 97},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
{LLAMA_GRETYPE_CHAR_ALT, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_CHAR_ALT, 95},
{LLAMA_GRETYPE_RULE_REF, 10},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_RULE_REF, 11},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_CHAR, 32},
{LLAMA_GRETYPE_CHAR_ALT, 9},
{LLAMA_GRETYPE_CHAR_ALT, 10},
{LLAMA_GRETYPE_RULE_REF, 12},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
};
index = 0;
for (auto rule : parsed_grammar.rules)
{
// compare rule to expected rule
for (uint32_t i = 0; i < rule.size(); i++)
{
llama_grammar_element element = rule[i];
llama_grammar_element expected_element = expected_rules[index];
// pretty print error message before asserting
if (expected_element.type != element.type || expected_element.value != element.value)
{
fprintf(stderr, "index: %d\n", index);
fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value);
fprintf(stderr, "actual_element: %d, %d\n", element.type, element.value);
fprintf(stderr, "expected_element != actual_element\n");
}
assert(expected_element.type == element.type && expected_element.value == element.value);
index++;
}
}
return 0;
}

403
tests/test-llama-grammar.cpp Normal file
View file

@ -0,0 +1,403 @@
#ifdef NDEBUG
#undef NDEBUG
#endif
#include "llama.cpp"
#include "examples/common.cpp"
#include "examples/grammar-parser.cpp"
#include <cassert>
int main()
{
grammar_parser::parse_state parsed_grammar;
std::vector<std::pair<std::string, uint32_t>> expected = {
{"expr", 2},
{"expr_6", 6},
{"expr_7", 7},
{"ident", 8},
{"ident_10", 10},
{"num", 9},
{"num_11", 11},
{"root", 0},
{"root_1", 1},
{"root_5", 5},
{"term", 4},
{"ws", 3},
{"ws_12", 12},
};
std::vector<std::vector<llama_grammar_element>> expected_rules = {
{{LLAMA_GRETYPE_RULE_REF, 5}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_RULE_REF, 2},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_CHAR, 10},
{LLAMA_GRETYPE_END, 0},
},
{{LLAMA_GRETYPE_RULE_REF, 4}, {LLAMA_GRETYPE_RULE_REF, 7}, {LLAMA_GRETYPE_END, 0}},
{{LLAMA_GRETYPE_RULE_REF, 12}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_RULE_REF, 8},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_RULE_REF, 9},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_CHAR, 40},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_RULE_REF, 2},
{LLAMA_GRETYPE_CHAR, 41},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_END, 0},
},
{{LLAMA_GRETYPE_RULE_REF, 1}, {LLAMA_GRETYPE_RULE_REF, 5}, {LLAMA_GRETYPE_ALT, 0}, {LLAMA_GRETYPE_RULE_REF, 1}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_CHAR, 45},
{LLAMA_GRETYPE_CHAR_ALT, 43},
{LLAMA_GRETYPE_CHAR_ALT, 42},
{LLAMA_GRETYPE_CHAR_ALT, 47},
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_END, 0},
},
{{LLAMA_GRETYPE_RULE_REF, 6}, {LLAMA_GRETYPE_RULE_REF, 7}, {LLAMA_GRETYPE_ALT, 0}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_CHAR, 97},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
{LLAMA_GRETYPE_RULE_REF, 10},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_END, 0},
},
{{LLAMA_GRETYPE_RULE_REF, 11}, {LLAMA_GRETYPE_RULE_REF, 3}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_CHAR, 97},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
{LLAMA_GRETYPE_CHAR_ALT, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_CHAR_ALT, 95},
{LLAMA_GRETYPE_RULE_REF, 10},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
},
{
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_RULE_REF, 11},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_END, 0},
},
{
{LLAMA_GRETYPE_CHAR, 32},
{LLAMA_GRETYPE_CHAR_ALT, 9},
{LLAMA_GRETYPE_CHAR_ALT, 10},
{LLAMA_GRETYPE_RULE_REF, 12},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
},
};
for (auto pair : expected)
{
parsed_grammar.symbol_ids[pair.first] = pair.second;
}
for (auto rule : expected_rules)
{
parsed_grammar.rules.push_back({});
for (auto element : rule)
{
parsed_grammar.rules.back().push_back(element);
}
}
llama_grammar *grammar = NULL;
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
std::vector<std::vector<llama_grammar_element>> expected_stacks = {
{
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_CHAR, 97},
},
{
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 48},
},
{
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 48},
},
{
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_CHAR, 40},
},
{
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_CHAR, 97},
},
{
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 48},
},
{
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 48},
},
{
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_CHAR, 40},
}};
auto index = 0;
for (auto stack : grammar->stacks)
{
// compare stack to expected_stack
for (uint32_t i = 0; i < stack.size(); i++)
{
auto element = stack[i];
auto expected_element = expected_stacks[index][i];
// pretty print error message before asserting
if (expected_element.type != element->type || expected_element.value != element->value)
{
fprintf(stderr, "index: %d\n", index);
fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value);
fprintf(stderr, "actual_element: %d, %d\n", element->type, element->value);
fprintf(stderr, "expected_element != actual_element\n");
}
assert(expected_element.type == element->type && expected_element.value == element->value);
}
index++;
}
std::vector<std::vector<const llama_grammar_element *>> next_stacks;
std::vector<llama_grammar_candidate> next_candidates;
next_candidates.resize(24);
for (size_t i = 0; i < 24; ++i)
{
uint32_t *cp = new uint32_t[2]; // dynamically allocate memory for code_point
cp[0] = 37 + i;
cp[1] = 0;
next_candidates[i] = {i, cp, {}};
}
std::vector<std::vector<std::pair<uint32_t, uint16_t>>> expected_reject = {
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{11, 48},
{12, 49},
{13, 50},
{14, 51},
{15, 52},
{16, 53},
{17, 54},
{18, 55},
{19, 56},
{20, 57},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{11, 48},
{12, 49},
{13, 50},
{14, 51},
{15, 52},
{16, 53},
{17, 54},
{18, 55},
{19, 56},
{20, 57},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{11, 48},
{12, 49},
{13, 50},
{14, 51},
{15, 52},
{16, 53},
{17, 54},
{18, 55},
{19, 56},
{20, 57},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{11, 48},
{12, 49},
{13, 50},
{14, 51},
{15, 52},
{16, 53},
{17, 54},
{18, 55},
{19, 56},
{20, 57},
{21, 58},
{22, 59},
{23, 60},
},
};
std::vector<llama_grammar_candidate> rejects = llama_grammar_reject_candidates_for_stack(grammar->rules, grammar->stacks[0], next_candidates);
std::vector<std::vector<llama_grammar_candidate>> all_rejects;
for (std::size_t count = 0; count < grammar->stacks.size(); ++count)
{
rejects = llama_grammar_reject_candidates_for_stack(grammar->rules, grammar->stacks[count], next_candidates);
all_rejects.push_back(rejects);
}
index = 0;
for (auto rej : all_rejects)
{
for (uint32_t i = 0; i < rej.size(); i++)
{
auto element = rej[i];
auto expected_element = expected_reject[index][i];
assert(element.index == expected_element.first && *element.code_points == expected_element.second);
}
index++;
}
for (auto &candidate : next_candidates)
{
delete[] candidate.code_points;
candidate.code_points = nullptr;
}
delete grammar;
return 0;
}

15
tests/test-opt.c → tests/test-opt.cpp
View file

@ -1,9 +1,9 @@
#include "ggml.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cassert>
#define MAX_NARGS 2
@ -119,10 +119,11 @@ void set_element(struct ggml_tensor * t, int idx, float value) {
int main(void) {
struct ggml_init_params params = {
.mem_size = 1024*1024*1024,
.mem_buffer = NULL,
.no_alloc = false,
/* .mem_size = */ 1024*1024*1024,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
struct ggml_context * ctx = ggml_init(params);
int64_t ne1[4] = {4, 128, 1, 1};