Merge branch 'master' of https://github.com/ggerganov/llama.cpp into jon/use-hardware-cores
This commit is contained in:
jon-chuang
commit
496d291d67
29 changed files with 2864 additions and 509 deletions
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@ -23,7 +23,7 @@ elif [[ $arg1 == '--all-in-one' || $arg1 == '-a' ]]; then
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echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
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else
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echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
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./quantize "$i" "${i/f16/q4_0}" 2
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./quantize "$i" "${i/f16/q4_0}" q4_0
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fi
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done
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else
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|
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3
.gitignore
vendored
3
.gitignore
vendored
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@ -15,6 +15,7 @@ build-em/
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build-debug/
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build-release/
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build-static/
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build-cublas/
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build-no-accel/
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build-sanitize-addr/
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build-sanitize-thread/
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@ -40,3 +41,5 @@ zig-out/
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zig-cache/
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ppl-*.txt
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examples/jeopardy/results.txt
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@ -67,6 +67,7 @@ endif()
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option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
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option(LLAMA_OPENBLAS "llama: use OpenBLAS" OFF)
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option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
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option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
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@ -168,6 +169,21 @@ if (LLAMA_CUBLAS)
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endif()
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endif()
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if (LLAMA_CLBLAST)
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find_package(CLBlast)
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if (CLBlast_FOUND)
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message(STATUS "CLBlast found")
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set(GGML_OPENCL_SOURCES ggml-opencl.c ggml-opencl.h)
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add_compile_definitions(GGML_USE_CLBLAST)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast)
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else()
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message(WARNING "CLBlast not found")
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endif()
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endif()
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if (LLAMA_ALL_WARNINGS)
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if (NOT MSVC)
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set(c_flags
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@ -307,7 +323,8 @@ endif()
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add_library(ggml OBJECT
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ggml.c
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ggml.h
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${GGML_CUDA_SOURCES})
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${GGML_CUDA_SOURCES}
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${GGML_OPENCL_SOURCES})
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target_include_directories(ggml PUBLIC .)
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target_compile_features(ggml PUBLIC c_std_11) # don't bump
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@ -320,7 +337,7 @@ endif()
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add_library(llama
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llama.cpp
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llama.h
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llama_util.h)
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llama-util.h)
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target_include_directories(llama PUBLIC .)
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target_compile_features(llama PUBLIC cxx_std_11) # don't bump
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|
|
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25
Makefile
25
Makefile
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@ -34,10 +34,15 @@ endif
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#
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# keep standard at C11 and C++11
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CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC
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CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
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CFLAGS = -I. -O3 -std=c11 -fPIC
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CXXFLAGS = -I. -I./examples -O3 -std=c++11 -fPIC
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LDFLAGS =
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ifndef LLAMA_DEBUG
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CFLAGS += -DNDEBUG
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CXXFLAGS += -DNDEBUG
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endif
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# warnings
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CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith
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CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
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@ -105,14 +110,22 @@ ifdef LLAMA_OPENBLAS
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LDFLAGS += -lopenblas
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endif
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ifdef LLAMA_CUBLAS
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CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include
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LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64
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CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
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CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
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LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
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OBJS += ggml-cuda.o
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NVCC = nvcc
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NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
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ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
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$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
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endif
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ifdef LLAMA_CLBLAST
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CFLAGS += -DGGML_USE_CLBLAST
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LDFLAGS += -lclblast -lOpenCL
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OBJS += ggml-opencl.o
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ggml-opencl.o: ggml-opencl.c ggml-opencl.h
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$(CC) $(CFLAGS) -c $< -o $@
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endif
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ifdef LLAMA_GPROF
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CFLAGS += -pg
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CXXFLAGS += -pg
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|
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@ -157,10 +170,10 @@ $(info )
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# Build library
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#
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ggml.o: ggml.c ggml.h
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ggml.o: ggml.c ggml.h ggml-cuda.h
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$(CC) $(CFLAGS) -c $< -o $@
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llama.o: llama.cpp ggml.h llama.h llama_util.h
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llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h
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$(CXX) $(CXXFLAGS) -c $< -o $@
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|
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common.o: examples/common.cpp examples/common.h
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||||
|
|
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|||
138
README.md
138
README.md
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@ -7,31 +7,25 @@
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|||
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||||
Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
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**Warnings**
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||||
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||||
- `Q4_2` and `Q4_3` are still in development. Do not expect any kind of backward compatibility until they are finalized
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||||
|
||||
**Hot topics:**
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||||
|
||||
- [Added LoRA support](https://github.com/ggerganov/llama.cpp/pull/820)
|
||||
- [Add GPU support to ggml](https://github.com/ggerganov/llama.cpp/discussions/915)
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||||
- [Roadmap Apr 2023](https://github.com/ggerganov/llama.cpp/discussions/784)
|
||||
- [Roadmap May 2023](https://github.com/ggerganov/llama.cpp/discussions/1220)
|
||||
- [New quantization methods](https://github.com/ggerganov/llama.cpp#quantization)
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|
||||
## Description
|
||||
|
||||
The main goal of llama.cpp is to run the llama model using 4-bit quantization on a MacBook.
|
||||
The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook
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||||
|
||||
- Plain C/C++ implementation without dependencies
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||||
- Apple silicon first-class citizen - optimized via ARM NEON and Accelerate framework
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||||
- AVX2 support for x86 architectures
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||||
- Mixed F16 / F32 precision
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||||
- 4-bit quantization support
|
||||
- 4-bit integer quantization support
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||||
- Runs on the CPU
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||||
|
||||
This was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022) - I have no idea if it works correctly.
|
||||
Please do not make conclusions about the models based on the results from this implementation.
|
||||
For all I know, it can be completely wrong. This project is for educational purposes.
|
||||
New features will probably be added mostly through community contributions.
|
||||
The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022).
|
||||
Since then, the project has improved significantly thanks to many contributions. This project is for educational purposes and serves
|
||||
as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library.
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||||
|
||||
**Supported platforms:**
|
||||
|
||||
|
|
@ -167,15 +161,27 @@ cd llama.cpp
|
|||
|
||||
### Build
|
||||
|
||||
Note: For Windows, CMake or Zig can be used.
|
||||
In order to build llama.cpp you have three different options.
|
||||
|
||||
1. Use `make`
|
||||
- Using `make`:
|
||||
- On Linux or MacOS:
|
||||
|
||||
```bash
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||||
make
|
||||
```
|
||||
```bash
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make
|
||||
```
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||||
|
||||
1. Use CMake
|
||||
- On Windows:
|
||||
|
||||
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
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||||
2. Extract `w64devkit` on your pc.
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||||
3. Run `w64devkit.exe`.
|
||||
4. Use the `cd` command to reach the `llama.cpp` folder.
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||||
5. From here you can run:
|
||||
```bash
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||||
make
|
||||
```
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||||
|
||||
- Using `CMake`:
|
||||
|
||||
```bash
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||||
mkdir build
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||||
|
|
@ -184,12 +190,71 @@ Note: For Windows, CMake or Zig can be used.
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|||
cmake --build . --config Release
|
||||
```
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||||
|
||||
1. Use Zig
|
||||
- Using `Zig`:
|
||||
|
||||
```bash
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||||
zig build -Drelease-fast
|
||||
```
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||||
|
||||
### BLAS Build
|
||||
|
||||
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
|
||||
|
||||
- Accelerate Framework:
|
||||
|
||||
This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
|
||||
|
||||
- OpenBLAS:
|
||||
|
||||
This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
|
||||
|
||||
- Using `make`:
|
||||
- On Linux:
|
||||
```bash
|
||||
make LLAMA_OPENBLAS=1
|
||||
```
|
||||
Note: In order to build on Arch Linux with OpenBLAS support enabled you must edit the Makefile adding at the end of the line 105: `-lcblas`
|
||||
|
||||
- On Windows:
|
||||
|
||||
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
|
||||
2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
|
||||
3. Extract `w64devkit` on your pc.
|
||||
4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
|
||||
5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
|
||||
6. Run `w64devkit.exe`.
|
||||
7. Use the `cd` command to reach the `llama.cpp` folder.
|
||||
8. From here you can run:
|
||||
|
||||
```bash
|
||||
make LLAMA_OPENBLAS=1
|
||||
```
|
||||
|
||||
- Using `CMake` on Linux:
|
||||
|
||||
```bash
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. -DLLAMA_OPENBLAS=ON
|
||||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
- cuBLAS
|
||||
|
||||
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
|
||||
- Using `make`:
|
||||
```bash
|
||||
make LLAMA_CUBLAS=1
|
||||
```
|
||||
- Using `CMake`:
|
||||
|
||||
```bash
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. -DLLAMA_CUBLAS=ON
|
||||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
### Prepare Data & Run
|
||||
|
||||
```bash
|
||||
|
|
@ -203,8 +268,8 @@ python3 -m pip install -r requirements.txt
|
|||
# convert the 7B model to ggml FP16 format
|
||||
python3 convert.py models/7B/
|
||||
|
||||
# quantize the model to 4-bits (using method 2 = q4_0)
|
||||
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2
|
||||
# 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
|
||||
|
||||
# run the inference
|
||||
./main -m ./models/7B/ggml-model-q4_0.bin -n 128
|
||||
|
|
@ -216,12 +281,29 @@ When running the larger models, make sure you have enough disk space to store al
|
|||
|
||||
As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
|
||||
|
||||
| model | original size | quantized size (4-bit) |
|
||||
|-------|---------------|------------------------|
|
||||
| 7B | 13 GB | 3.9 GB |
|
||||
| 13B | 24 GB | 7.8 GB |
|
||||
| 30B | 60 GB | 19.5 GB |
|
||||
| 65B | 120 GB | 38.5 GB |
|
||||
| Model | Original size | Quantized size (4-bit) |
|
||||
|------:|--------------:|-----------------------:|
|
||||
| 7B | 13 GB | 3.9 GB |
|
||||
| 13B | 24 GB | 7.8 GB |
|
||||
| 30B | 60 GB | 19.5 GB |
|
||||
| 65B | 120 GB | 38.5 GB |
|
||||
|
||||
### Quantization
|
||||
|
||||
Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
|
||||
|
||||
| Model | Measure | F16 | Q4_0 | Q4_1 | Q4_2 | Q5_0 | Q5_1 | Q8_0 |
|
||||
|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|-------:|
|
||||
| 7B | perplexity | 5.9565 | 6.2103 | 6.1286 | 6.1698 | 6.0139 | 5.9934 | 5.9571 |
|
||||
| 7B | file size | 13.0G | 4.0G | 4.8G | 4.0G | 4.4G | 4.8G | 7.1G |
|
||||
| 7B | ms/tok @ 4th | 128 | 56 | 61 | 84 | 91 | 95 | 75 |
|
||||
| 7B | ms/tok @ 8th | 128 | 47 | 55 | 48 | 53 | 59 | 75 |
|
||||
| 7B | bits/weight | 16.0 | 5.0 | 6.0 | 5.0 | 5.5 | 6.0 | 9.0 |
|
||||
| 13B | perplexity | 5.2455 | 5.3748 | 5.3471 | 5.3433 | 5.2768 | 5.2582 | 5.2458 |
|
||||
| 13B | file size | 25.0G | 7.6G | 9.1G | 7.6G | 8.4G | 9.1G | 14G |
|
||||
| 13B | ms/tok @ 4th | 239 | 104 | 113 | 160 | 176 | 185 | 141 |
|
||||
| 13B | ms/tok @ 8th | 240 | 85 | 99 | 97 | 108 | 117 | 147 |
|
||||
| 13B | bits/weight | 16.0 | 5.0 | 6.0 | 5.0 | 5.5 | 6.0 | 9.0 |
|
||||
|
||||
### Interactive mode
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@
|
|||
99aeb35f26b577fa2732716cca4d8b5ada39a78ea9b2dca2651fc632b5d101b6 models/7B/ggml-model-q4_0.bin
|
||||
cc061458339a3eb8bcecbf0a825e9924fb7d1a8150f63cd5d091caa99215aafe models/7B/ggml-model-q4_1.bin
|
||||
25b050337a87344da687a7f2adddc03bd99b7f6c140450e836649f3585fb6496 models/7B/ggml-model-q4_2.bin
|
||||
3429bf198ec771886cf81a574df45245f3ebf04f0ce0956b73ef5d0ab01ff48b models/7B/ggml-model-q4_3.bin
|
||||
7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265 models/7B/params.json
|
||||
745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08 models/13B/consolidated.00.pth
|
||||
d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085 models/13B/consolidated.01.pth
|
||||
|
|
@ -11,7 +10,6 @@ d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085 models/13B/con
|
|||
eecb575d325d935157761172e2bf05984dad216eb2b06777b73463cf9b818bab models/13B/ggml-model-q4_0.bin
|
||||
d9581b5b88e5622532fe897c9f9b0e67a317d22dd27a6f90fa4ab8c6d23ccdbb models/13B/ggml-model-q4_1.bin
|
||||
75a218a47df03f5f96354656329864613abcb67779412b9bc2282b28c1c3cbaa models/13B/ggml-model-q4_2.bin
|
||||
4208cdec9788ffa48dc1a17af2c36a0299f5bf3eb0e2b87889dda7fad591fca3 models/13B/ggml-model-q4_3.bin
|
||||
4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f models/13B/params.json
|
||||
e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067 models/30B/consolidated.00.pth
|
||||
4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff models/30B/consolidated.01.pth
|
||||
|
|
@ -21,7 +19,6 @@ e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067 models/30B/con
|
|||
517b9e525742c42b5478a6280a4b41ec66f46298c57aba7f0453d491682fe42d models/30B/ggml-model-q4_0.bin
|
||||
7b75ac615fa369ee593493a7e6ef87542bf0350255db928b22c5a24f6d598bcd models/30B/ggml-model-q4_1.bin
|
||||
aadbc9cf806313a55be570f62884eed289d30c313fac3b7838717e01bd553204 models/30B/ggml-model-q4_2.bin
|
||||
a6188660199dbcb8d5658abe7d89169869e50423494385830d9e6b330ea7fc33 models/30B/ggml-model-q4_3.bin
|
||||
2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb models/30B/params.json
|
||||
135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe models/65B/consolidated.00.pth
|
||||
9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde models/65B/consolidated.01.pth
|
||||
|
|
@ -35,6 +32,5 @@ d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638 models/65B/con
|
|||
01672072136f8be6ca9d7cebe5f86ed316e8b85851b9fe3de951809233cea4f2 models/65B/ggml-model-q4_0.bin
|
||||
4743a28aac3e5f32a6e838a815f51d3779de44fbbe251d745251e66c23c5950f models/65B/ggml-model-q4_1.bin
|
||||
1b6f6588d0e2ecfe6c4d849088e48e5e3083466b962daa32e3261363e21fc5e9 models/65B/ggml-model-q4_2.bin
|
||||
305e91a4608b4f627b9b8ad5b4af75187d2684254bfd76dcb9db571618ef293c models/65B/ggml-model-q4_3.bin
|
||||
999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b models/65B/params.json
|
||||
9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 models/tokenizer.model
|
||||
|
|
|
|||
|
|
@ -31,8 +31,6 @@ The transcript only includes text, it does not include markup like HTML and Mark
|
|||
|
||||
$USER_NAME: Hello, $AI_NAME!
|
||||
$AI_NAME: Hello $USER_NAME! How may I help you today?
|
||||
$USER_NAME: What time is it?
|
||||
$AI_NAME: It is $(date +%H:%M).
|
||||
$USER_NAME: What year is it?
|
||||
$AI_NAME: We are in $(date +%Y).
|
||||
$USER_NAME: Please tell me the largest city in Europe.
|
||||
|
|
@ -50,4 +48,6 @@ $AI_NAME: The arguments are stored in process.argv.
|
|||
argv[3] is the second argument passed to the script and so on.
|
||||
$USER_NAME: Name a color.
|
||||
$AI_NAME: Blue
|
||||
$USER_NAME: What time is it?
|
||||
$AI_NAME: It is $(date +%H:%M).
|
||||
$USER_NAME:" "$@"
|
||||
|
|
|
|||
|
|
@ -7,6 +7,8 @@
|
|||
#include <string>
|
||||
#include <iterator>
|
||||
#include <algorithm>
|
||||
#include <sstream>
|
||||
#include <iostream>
|
||||
|
||||
#if defined(__APPLE__) && defined(__MACH__)
|
||||
#include <sys/types.h>
|
||||
|
|
@ -92,6 +94,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
break;
|
||||
}
|
||||
params.prompt = argv[i];
|
||||
} else if (arg == "--session") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.path_session = argv[i];
|
||||
} else if (arg == "-f" || arg == "--file") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -139,6 +147,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
break;
|
||||
}
|
||||
params.temp = std::stof(argv[i]);
|
||||
} else if (arg == "--tfs") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.tfs_z = std::stof(argv[i]);
|
||||
} else if (arg == "--typical") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.typical_p = std::stof(argv[i]);
|
||||
} else if (arg == "--repeat_last_n") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -151,6 +171,36 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
break;
|
||||
}
|
||||
params.repeat_penalty = std::stof(argv[i]);
|
||||
} else if (arg == "--frequency_penalty") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.frequency_penalty = std::stof(argv[i]);
|
||||
} else if (arg == "--presence_penalty") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.presence_penalty = std::stof(argv[i]);
|
||||
} else if (arg == "--mirostat") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.mirostat = std::stoi(argv[i]);
|
||||
} else if (arg == "--mirostat_lr") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.mirostat_eta = std::stof(argv[i]);
|
||||
} else if (arg == "--mirostat_ent") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.mirostat_tau = std::stof(argv[i]);
|
||||
} else if (arg == "-b" || arg == "--batch_size") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -210,7 +260,28 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
} else if (arg == "--perplexity") {
|
||||
params.perplexity = true;
|
||||
} else if (arg == "--ignore-eos") {
|
||||
params.ignore_eos = true;
|
||||
params.logit_bias[llama_token_eos()] = -INFINITY;
|
||||
} else if (arg == "--no-penalize-nl") {
|
||||
params.penalize_nl = false;
|
||||
} else if (arg == "-l" || arg == "--logit-bias") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
std::stringstream ss(argv[i]);
|
||||
llama_token key;
|
||||
char sign;
|
||||
std::string value_str;
|
||||
try {
|
||||
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
|
||||
params.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
|
||||
} else {
|
||||
throw std::exception();
|
||||
}
|
||||
} catch (const std::exception &e) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
} else if (arg == "--n_parts") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -259,17 +330,32 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
||||
fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
|
||||
fprintf(stderr, " prompt to start generation with (default: empty)\n");
|
||||
fprintf(stderr, " --session FNAME file to cache model state in (may be large!) (default: none)\n");
|
||||
fprintf(stderr, " --random-prompt start with a randomized prompt.\n");
|
||||
fprintf(stderr, " --in-prefix STRING string to prefix user inputs with (default: empty)\n");
|
||||
fprintf(stderr, " -f FNAME, --file FNAME\n");
|
||||
fprintf(stderr, " prompt file to start generation.\n");
|
||||
fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict);
|
||||
fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
|
||||
fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", (double)params.top_p);
|
||||
fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
|
||||
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", (double)params.repeat_penalty);
|
||||
fprintf(stderr, " --top_k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
|
||||
fprintf(stderr, " --top_p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
|
||||
fprintf(stderr, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
|
||||
fprintf(stderr, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
|
||||
fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
|
||||
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
|
||||
fprintf(stderr, " --presence_penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
|
||||
fprintf(stderr, " --frequency_penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
|
||||
fprintf(stderr, " --mirostat N use Mirostat sampling.\n");
|
||||
fprintf(stderr, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
|
||||
fprintf(stderr, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
|
||||
fprintf(stderr, " --mirostat_lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
|
||||
fprintf(stderr, " --mirostat_ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
|
||||
fprintf(stderr, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
|
||||
fprintf(stderr, " modifies the likelihood of token appearing in the completion,\n");
|
||||
fprintf(stderr, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
|
||||
fprintf(stderr, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
|
||||
fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
|
||||
fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
|
||||
fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
|
||||
fprintf(stderr, " --no-penalize-nl do not penalize newline token\n");
|
||||
fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n");
|
||||
fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp);
|
||||
fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
|
||||
|
|
|
|||
|
|
@ -8,29 +8,40 @@
|
|||
#include <vector>
|
||||
#include <random>
|
||||
#include <thread>
|
||||
#include <unordered_map>
|
||||
|
||||
//
|
||||
// CLI argument parsing
|
||||
//
|
||||
int32_t get_num_physical_cores();
|
||||
|
||||
struct gpt_params {
|
||||
int32_t seed = -1; // RNG seed
|
||||
int32_t n_threads = get_num_physical_cores();
|
||||
int32_t n_predict = 128; // new tokens to predict
|
||||
int32_t repeat_last_n = 64; // last n tokens to penalize
|
||||
int32_t n_predict = -1; // new tokens to predict
|
||||
int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
|
||||
int32_t n_ctx = 512; // context size
|
||||
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
|
||||
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
||||
|
||||
// sampling parameters
|
||||
int32_t top_k = 40;
|
||||
float top_p = 0.95f;
|
||||
float temp = 0.80f;
|
||||
float repeat_penalty = 1.10f;
|
||||
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
|
||||
int32_t top_k = 40; // <= 0 to use vocab size
|
||||
float top_p = 0.95f; // 1.0 = disabled
|
||||
float tfs_z = 1.00f; // 1.0 = disabled
|
||||
float typical_p = 1.00f; // 1.0 = disabled
|
||||
float temp = 0.80f; // 1.0 = disabled
|
||||
float repeat_penalty = 1.10f; // 1.0 = disabled
|
||||
int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
|
||||
float frequency_penalty = 0.00f; // 0.0 = disabled
|
||||
float presence_penalty = 0.00f; // 0.0 = disabled
|
||||
int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
|
||||
float mirostat_tau = 5.00f; // target entropy
|
||||
float mirostat_eta = 0.10f; // learning rate
|
||||
|
||||
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
|
||||
std::string prompt = "";
|
||||
std::string path_session = ""; // path to file for saving/loading model eval state
|
||||
std::string input_prefix = ""; // string to prefix user inputs with
|
||||
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
|
||||
|
||||
|
|
@ -46,7 +57,7 @@ struct gpt_params {
|
|||
bool interactive_first = false; // wait for user input immediately
|
||||
|
||||
bool instruct = false; // instruction mode (used for Alpaca models)
|
||||
bool ignore_eos = false; // do not stop generating after eos
|
||||
bool penalize_nl = true; // consider newlines as a repeatable token
|
||||
bool perplexity = false; // compute perplexity over the prompt
|
||||
bool use_mmap = true; // use mmap for faster loads
|
||||
bool use_mlock = false; // use mlock to keep model in memory
|
||||
|
|
|
|||
21
examples/jeopardy/README.md
Normal file
21
examples/jeopardy/README.md
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
# llama.cpp/example/jeopardy
|
||||
|
||||
This is pretty much just a straight port of aigoopy/llm-jeopardy/ with an added graph viewer.
|
||||
|
||||
The jeopardy test can be used to compare the fact knowledge of different models and compare them to eachother. This is in contrast to some other tests, which test logical deduction, creativity, writing skills, etc.
|
||||
|
||||
|
||||
Step 1: Open jeopardy.sh and modify the following:
|
||||
```
|
||||
MODEL=(path to your model)
|
||||
MODEL_NAME=(name of your model)
|
||||
prefix=(basically, if you use vicuna it's Human: , if you use something else it might be User: , etc)
|
||||
opts=(add -instruct here if needed for your model, or anything else you want to test out)
|
||||
```
|
||||
Step 2: Run `jeopardy.sh` from the llama.cpp folder
|
||||
|
||||
Step 3: Repeat steps 1 and 2 until you have all the results you need.
|
||||
|
||||
Step 4: Run `graph.py`, and follow the instructions. At the end, it will generate your final graph.
|
||||
|
||||
Note: The Human bar is based off of the full, original 100 sample questions. If you modify the question count or questions, it will not be valid.
|
||||
56
examples/jeopardy/graph.py
Normal file
56
examples/jeopardy/graph.py
Normal file
|
|
@ -0,0 +1,56 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import sys, os
|
||||
import csv
|
||||
|
||||
labels = []
|
||||
numbers = []
|
||||
numEntries = 1
|
||||
|
||||
rows = []
|
||||
|
||||
def bar_chart(numbers, labels, pos):
|
||||
plt.bar(pos, numbers, color='blue')
|
||||
plt.xticks(ticks=pos, labels=labels)
|
||||
plt.title("Jeopardy Results by Model")
|
||||
plt.xlabel("Model")
|
||||
plt.ylabel("Questions Correct")
|
||||
plt.show()
|
||||
|
||||
def calculatecorrect():
|
||||
directory = os.fsencode("./examples/jeopardy/results/")
|
||||
csv_reader = csv.reader(open("./examples/jeopardy/qasheet.csv", 'rt'), delimiter=',')
|
||||
for row in csv_reader:
|
||||
global rows
|
||||
rows.append(row)
|
||||
for listing in os.listdir(directory):
|
||||
filename = os.fsdecode(listing)
|
||||
if filename.endswith(".txt"):
|
||||
file = open("./examples/jeopardy/results/" + filename, "rt")
|
||||
global labels
|
||||
global numEntries
|
||||
global numbers
|
||||
labels.append(filename[:-4])
|
||||
numEntries += 1
|
||||
i = 1
|
||||
totalcorrect = 0
|
||||
for line in file.readlines():
|
||||
if line.strip() != "------":
|
||||
print(line)
|
||||
else:
|
||||
print("Correct answer: " + rows[i][2] + "\n")
|
||||
i+=1
|
||||
print("Did the AI get the question right? (y/n)")
|
||||
if input() == "y":
|
||||
totalcorrect += 1
|
||||
numbers.append(totalcorrect)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
calculatecorrect()
|
||||
pos = list(range(numEntries))
|
||||
labels.append("Human")
|
||||
numbers.append(48.11)
|
||||
bar_chart(numbers, labels, pos)
|
||||
print(labels)
|
||||
print(numbers)
|
||||
30
examples/jeopardy/jeopardy.sh
Normal file
30
examples/jeopardy/jeopardy.sh
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
MODEL=./models/ggml-vicuna-13b-1.1-q4_0.bin
|
||||
MODEL_NAME=Vicuna
|
||||
|
||||
# exec options
|
||||
prefix="Human: " # Ex. Vicuna uses "Human: "
|
||||
opts="--temp 0 -n 80" # additional flags
|
||||
nl='
|
||||
'
|
||||
introduction="You will be playing a game of Jeopardy. Simply answer the question in the correct format (Ex. What is Paris, or Who is George Washington)."
|
||||
|
||||
# file options
|
||||
question_file=./examples/jeopardy/questions.txt
|
||||
touch ./examples/jeopardy/results/$MODEL_NAME.txt
|
||||
output_file=./examples/jeopardy/results/$MODEL_NAME.txt
|
||||
|
||||
counter=1
|
||||
|
||||
echo 'Running'
|
||||
while IFS= read -r question
|
||||
do
|
||||
exe_cmd="./main -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\""
|
||||
echo $counter
|
||||
echo "Current Question: $question"
|
||||
eval "$exe_cmd"
|
||||
echo -e "\n------" >> $output_file
|
||||
counter=$((counter+1))
|
||||
done < "$question_file"
|
||||
103
examples/jeopardy/qasheet.csv
Normal file
103
examples/jeopardy/qasheet.csv
Normal file
|
|
@ -0,0 +1,103 @@
|
|||
Index,Original Category,Original Correct Question,Model Prompt
|
||||
1,The Oscars,Who is John Williams?,Which actor Born in 1932 was the son of a percussionist in the CBS radio orchestra has been nominated for 53 Oscars?
|
||||
2,English Literature,What is Paradise Lost?,"What work in English Literature says: 'The mind is its own place, & in itself can make a heaven of hell, a hell of heaven. What matter where, if I be still the same'?"
|
||||
3,Writers’ Lesser-Known Works,Who is Niccolò Machiavelli?,"Known for more philosophical works, he wrote the play 'La Mandragola', in which Florentines are rewarded for immoral actions?"
|
||||
4,Exploration,What is Easter Island (Rapa Nui)?,"James Cook's account of a 1774 visit where records an object 'near 27 feet long, and upwards of 8 feet over the breast or shoulders'?"
|
||||
5,The Bill of Rights,What is the Eighth Amendment?,England's 'Bloody Assizes' & a 1685 life sentence for perjury were 2 main origins of which amendment to the U.S. Constitution?
|
||||
6,Nobel Peace Prize Winners,Who are Nelson Mandela & Desmond Tutu?,"Which nobel peace price winners each lived at times on Vilakazi St. in Soweto , so it claims to be the world's only street home to 2 Nobel Peace Prize winners?"
|
||||
7,Famous Names,Who is Walt Disney?,"In 1966, the year of who's death did he share plans for an experimental prototype community in Florida?"
|
||||
8,Geography,What is Colombia?,"Of the 13 nations through which the Equator passes, what is the only one whose coastline borders the Caribbean Sea?"
|
||||
9,Fashion History,What are rhinestones?,"Which decorative items in fashion history get their name from their origin in the port city of Strasbourg, on the border of France & Germany?"
|
||||
10,Movies of the ’80s,What is Driving Miss Daisy?,What 1980's movie is based on an off-Broadway play with just 3 characters and won the Best Picture Oscar & the actors in all 3 roles were nominated?
|
||||
11,Novelists,Who is John Grisham?,"A 2012 book review for which novelist noted subjects that 'sparked his ire': capital punishment, big tobacco & 'the plight of the unjustly convicted'?"
|
||||
12,20th Century Eponyms,What is the Maginot Line?,"A 1940 headline about what 20th Century Eponym included 'failure', 'liability when it came to offense' & 'stout hearts no match for tanks'?"
|
||||
13,City History,What is Stockholm?,"Over 700 years after its traditional 1252 founding date, what port city became associated with a psychological response?"
|
||||
14,Brand Names,What is Jacuzzi?,"The success of what brand has its roots with a hydrotherapy pump its cofounder created for his son, who had arthritis?"
|
||||
15,American Authors,Who is Washington Irving?,"In a periodical in 1807, what American Author called New York City 'Gotham, Gotham! Most enlightened of cities'?"
|
||||
16,Symbols,What is “less than”?,What symbol is a rotated V in math and a feeling of some marginalized or underrepresented people in society?
|
||||
17,Movie Theme Songs,Who is James Bond?,"Monty Norman, the composer of what character's theme, said the staccato riff conveyed sexiness, mystery & ruthlessness?"
|
||||
18,American Novelists,Who is Joseph Heller?,"What American Novelist served with an airman named Yohannan in World War II & despite what readers might think, he said he enjoyed his service?"
|
||||
19,Medieval Places,"What is Canterbury, England? (Canterbury Cathedral)","In what Medieval place did one of the participants in an 1170 event say, 'Let us away, knights; he will rise no more'?"
|
||||
20,Countries of Africa,What is Morocco?,"At one time a province of the Roman Empire, what African country kingdom is known to Arabic scholars as Al-Maghrib Al-Aqsa, 'the far west'?"
|
||||
21,Statehood,What is Wyoming?,Congress relented in 1890 after what prospective state said it would wait 100 years rather than come in without the women?
|
||||
22,1980s Movies,What is Raiders of the Lost Ark?,"A writer & producer of what movie said he wanted it to be like a Western or James Bond film, 'only it takes place in the 30s'?"
|
||||
23,Art Exhibitions,Who is Rembrandt?,In 1898 what's been called the first blockbuster art show was devoted to which artist & put on for Queen Wilhelmina's coronation?
|
||||
24,Countries of the World,What is Mongolia?,"Part of the largest contiguous land empire during the 1200s & 1300s, today what is the world's second-largest landlocked country?"
|
||||
25,Literature,What is “Howl”?,A 2006 book was titled 'The Poem That Changed America:' What 'Fifty Years Later'?
|
||||
26,Invasions,Who is William of Orange?,"Backed by 14,000 troops, who invaded England to restore, in his words, its 'religion, laws, and liberties'?"
|
||||
27,Landmarks,What is the Eiffel Tower?,"After its completion in the late 19th c., what was landmark was called 'a truly tragic street lamp' & a 'high & skinny pyramid of iron ladders'?"
|
||||
28,Geographic Name’s the Same,What is Dover?,"The busiest passenger port in the U.K., what shares its name with a capital of one of the original 13 states?"
|
||||
29,Names in the Bookstore,Who is Peter Mark Roget?,"This man made lists, perhaps to cope with depression; a set of lists he published in 1852 made whose name synonymous with a type of book?"
|
||||
30,U.S. History,Who is Dr. Samuel Mudd?,"An 1869 presidential pardon was granted to which man, due in part to a plea by the Medical Society of Harford County, Maryland?"
|
||||
31,American Literature,What is The Things They Carried?,"Letters, pocket knives, C rations & steel helmets are among the tangible items referred to in the title of what American literature modern war classic?"
|
||||
32,Nonfiction,What is The Communist Manifesto,"What nonfiction book has the line, 'The discovery of America…opened up fresh ground for the rising bourgeoisie'?"
|
||||
33, a new version was passed 81 years later,Laws in U.S. History,What is the Civil Rights Act?,,,,,,,,,,,,,,,,,,0, 2/3
|
||||
34,Names of Myth,Who is Helen of Troy?,"Whose brothers, Castor & Pollux, saved her after Theseus stole her away as a kid; a larger force would seek her later in life?"
|
||||
35,African Countries,What is Sudan?,"Once Africa's largest country in area, what African Country dropped to third in 2011 when a portion of it declared independence?"
|
||||
36,The Ancient World,What is Alexandria?,"The ancient writer Galen said books on ships arriving to what city's port were seized, originals kept & copies returned?"
|
||||
37,Famous Names,Who is Andy Warhol?,"For a special 1970s cookbook, who provided one simple recipe–a can of Campbell's tomato soup & 2 cans of milk?"
|
||||
38,People & Places,What is Guam?,"Thought to descend from people of Southeast Asia, the Chamorro make up what U.S. territory’s largest ethnic group?"
|
||||
39,Current World Leaders,What is the Philippines?,"In office from 2022, the president of what country has taken so many foreign trips a play on his name is 'Ferdinand Magellan Jr.'?"
|
||||
40,Writers & The South,Who is Tennessee Williams?,In 1939 which writer lived on Toulouse Street in the French Quarter & chose the professional name that bonded him to the South?
|
||||
41,National Parks,What is Yellowstone?,"What National Park is named for a river indigenous people called Mi tse a-da-zi, translated by French-speaking trappers as 'Pierre Jaune'?"
|
||||
42,Sports,Who are the Harlem Globetrotters?,"In 2010 who introduced the 4-point shot, 35 feet from the basket?"
|
||||
43,The U.S. Military,What is “Top Gun”?,Losses over Asia in the 1960s led to the establishment of the program known as what at a San Diego naval base in 1969?
|
||||
44,Art & Science,What is Halley’s Comet?,"A craft that visited what was named for Giotto, based on the story that 680 years earlier, the painter depicted it as the Star of Bethlehem?"
|
||||
45,Words From World War I,What is “tank”?,"In World War I, 'Cistern' & 'reservoir' were suggested names for what secret invention, but the British preferred this less clumsy monosyllable?"
|
||||
46,European History,What is Holy Roman Emperor?,"Until 1806, some German nobles included among their honors the title of 'Elector' for their role in selecting this personage?"
|
||||
47,Theater History,Who is Peter Pan?,"In 1904, wearing a harness, actress Nina Boucicault became the first to play what character onstage?"
|
||||
48,European Cities,What is Aachen?,"Alphabetically the first German city in encyclopedias, what was also the first one taken by the Allies in World War II?"
|
||||
49,Word Origins,What is mantra?,This Sanskrit word referring to a spoken word or phrase comes from a word for 'to think'?
|
||||
50,Inventions,What is barbed wire?,1917's 'Elements of Trench Warfare' said what Old West invention was 'difficult to destroy' & 'difficult to get through'?
|
||||
51,World War II,What is Schindler’s list?,"Mimi Reinhard, who never learned to type using more than 2 fingers, produced what in World War II with 1,100 names, including hers?"
|
||||
52, their offspring was the source of this mythical object,Mythology,What is the Golden Fleece?
|
||||
53,Literature,What is Pride and Prejudice?,"Published in 2011, P.D. James' final novel, 'Death Comes to Pemberley', was a sequel to what novel from 200 years earlier?"
|
||||
54, only these 2 west of the Mississippi River border each other,U.S. State Names,What are Oregon & Nevada?
|
||||
55,Word Origins,What is passion?,"Originally relating to a story of suffering, what word now more commonly refers to strong emotion of any kind?"
|
||||
56,World Cinema,What is La Vie en Rose?,"The 2007 biopic called 'La Môme' in France, meaning 'The Kid', was released in the U.S. under what other French title?"
|
||||
57,History,What is Santa Maria?,"Returning home in 1493, Columbus stopped in the Azores at an island with what name, also something he'd lost off the Haiti coast?"
|
||||
58,Landmarks,What is a kremlin?,Pskov & Nizhny Novgorod are 2 of the cities that have a fortress called what?
|
||||
59,Foreign-Born Authors,Who is Vladimir Nabokov?,In the 1950s the New York Times said what author 'is writing about all lust' & his lecherous narrator 'is all of us'?
|
||||
60,Astronomy & Geography,What is Capricorn?,"At the winter solstice, the sun is in Sagittarius; it once appeared in what constellation, giving a geographic feature its name?"
|
||||
61,Television,What is Law & Order?,"Mike Post combined the sound of a slamming jail door, an anvil & 100 men stomping on a floor for what television series that debuted in 1990?"
|
||||
62,British Landmarks,What is the Tower of London?,"Like Sir Thomas More, 3 16th century English queens are buried at what British location?"
|
||||
63,Early American History,What are witches?,"In 1692 Increase Mather wrote, 'It were better that ten suspected' of these who 'escape, than that one innocent person … be condemned'?"
|
||||
64,Geography Mnemonics,What are Arkansas and Louisiana?,"The Geography Mnemonic Mimal, sometimes said to be the silhouette of a chef or elf, stands for Minnesota, Iowa, Missouri, and what other 2 states?"
|
||||
65,Business Milestones,What is the Ford Model T?,"What was first sold in 1908, at a price equivalent to about $27,000 today?"
|
||||
66,In The Bookstore,Who is Tom Clancy?,The name of what author dead since 2013 now appears on books written by a former U.S. marshal & a former Apache helicopter pilot?
|
||||
67,Historic Art,What is the Bayeux Tapestry?,The artwork once known in France as 'la tapisserie de la Reine Mathilde' is better known as what?
|
||||
68,Pop Stars,Who is Madonna?,In 2022 which pop star became the first woman to have a Billboard Top 10 album in 5 decades starting with the 1980s?
|
||||
69,Classic Tale Characters,Who is Scheherazade?,"In one 19th century translation, what female classic tale character 'perceived the dawn of day and ceased' speaking nearly 1,000 times?"
|
||||
70,USA,What is Jack Daniel’s?,"Ironically, though what company founded in the 1860s is Moore County, Tennessee's largest employer, Moore is a dry county?"
|
||||
71,Historic People,Who was William Bligh?,"After a 1789 event, who wrote, 'My first determination was to seek a supply of…water at Tofoa, & afterwards to sail for Tongataboo'?"
|
||||
72,The Movies,What is The Godfather?,Laurence Olivier & Ernest Borgnine were considered for the lead role & Sergio Leone to direct for what film that turned 50 in 2022?
|
||||
73,Continental Geography,What is Colombia?,"Until a 1903 secession, what country's contiguous territory spanned 2 continents?"
|
||||
74,Foreign-Born Authors,Who is Isabel Allende?,"Early in her career which foreign-born author translated romance novels into Spanish, often changing the dialogue to make the heroines smarter?"
|
||||
75,Historic Crimes,What is the Mona Lisa?,"Saying it was stolen by Napoleon, self-styled Italian patriot Vincenzo Peruggia took what in 1911?"
|
||||
76,U.S. Bodies of Water,What is Lake Mead?,"Continuing a downward trend, in July 2022 what US body of water was at 27% capacity, its lowest level since 1937 when it was first being filled?"
|
||||
77,Gods & Goddesses,Who is Aurora (or Eos)?,"Each morning which goddess began her ride in her chariot across the sky ahead of her brother Sol, or Helios?"
|
||||
78,America At War,What is the Battle of New Orleans?,"Until the Civil War, the Jan. 8 date of what American battle of dubious military importance but big morale value was a national holiday?"
|
||||
79,Children’s Books,What is The Velveteen Rabbit?,"Which children's book title character is told 'By the time you are real, most of your hair has been loved off your eyes drop out & you get shabby'?"
|
||||
80,TV Finales,What is Grace and Frankie?,"In a TV reunion over 40 years in the making, Dolly Parton appeared as an angel named Agnes in the final episode of what comedy in 2022?"
|
||||
81,American Poems,Who is Evangeline?,"In an 1847 American poem what character sees her town of Grand-Pré burned, but finally reunites with her beau for a kiss before his death?"
|
||||
82,Famous Names,Who is Banksy?,"In 2001 who published a book called 'Banging Your Head Against a Brick Wall'; in 2002, 'Existencilism'?"
|
||||
83,Children’s Lit,What is Charlotte’s Web?,The title object of what childrens book 'never looked more beautiful each strand held dozens of bright drops of early morning dew'?
|
||||
84,Classic Songs,What is “Here Comes Santa Claus”?,The shouts of excited children at a 1946 holiday parade are said to have inspired what perennial classic song favorite?
|
||||
85,Brand Names,What are Milk Duds?,"Unable to make what candies perfectly round, the confectioner embraced this flawed name for the product?"
|
||||
86,Countries of the World,What is Italy?,"What country is home to 58 UNESCO World Heritage Sites, more than any other country; the sites include a volcano & a lagoon?"
|
||||
87,Action Movies,What is Die Hard?,"What action movie's last line is 'If this is their idea of Christmas, I gotta be here for New Years'?"
|
||||
88,Presidential Facts,Who is Woodrow Wilson?,Only 3 presidents have married while in office— John Tyler was the first & which one was the last?
|
||||
89,19th Century Americans,Who is Frederick Douglass?,"Demonstrating the dignity & humanity of Black Americans, who sat for 160 known photographs, the most of any American in the 19th century?"
|
||||
90,Latin Phrases,What is “quid pro quo”?,"Originally, which Latin 3-word phrase referred to when a doctor or apothecary substituted one medicine for another?"
|
||||
91,1970s Movies,What is Monty Python and the Holy Grail?,The 1975 premiere of what movie comedy advertised free coconuts for the first thousand in the audience?
|
||||
92,Name’s The Same,What is Manhattan?,"A cocktail, an island & a WWII venture originally called 'Development of Substitute Materials' all bear what name?"
|
||||
93,U.S. Presidents,Who is Calvin Coolidge?,"Which US President was sworn in twice as President within 2 years, first by his father & then later by a former U.S. President?"
|
||||
94,Plays,What is The Tempest?,A 1609 story in which an exiled king of Bulgaria creates a sea palace with his magic may have inspired the plot of what play?
|
||||
95,Landmarks,What is the Berlin Wall?,"In 2009, during a 20th anniversary celebration, what landmark was called 'an edifice of fear. On Nov. 9, it became a place of joy'?"
|
||||
96,World Capitals,"What is Vienna, Austria?","Among what world capital's nicknames are the 'City of Classical Music' &, possibly in honor of a famous resident from 1860 to 1938, the 'City of Dreams'?"
|
||||
97,Language & Its Meanings,What is a night owl?,"Now meaning someone with nocturnal habits, what catches a sleeping dove in Shakespeare's 'Lucrece'?"
|
||||
98,Flags of Our Hemisphere,What is Brazil?,"The stars on what country's flag represent states, 26 of them; unlike the USA's, its 'federal district' gets its own 27th star?"
|
||||
99,Names in U.S. History,Who is Oliver Brown?,What father was the only man among the 13 plaintiffs in a US class-action case filed in 1951?
|
||||
100,Children’s Authors,"Who is Sarah? (from Sarah, Plain and Tall)","Reversing the story of what heroine she created, childrens author Patricia Maclachlan was born on the prairie but spent much of her life in New England?"
|
||||
,,,
|
||||
TOTALS,,,
|
||||
|
Can't render this file because it has a wrong number of fields in line 34.
|
100
examples/jeopardy/questions.txt
Normal file
100
examples/jeopardy/questions.txt
Normal file
|
|
@ -0,0 +1,100 @@
|
|||
Which man born in 1932 was the son of a percussionist in the CBS radio orchestra has been nominated for 53 Oscars?
|
||||
What work in English Literature says: 'The mind is its own place, & in itself can make a heaven of hell, a hell of heaven. What matter where, if I be still the same'?
|
||||
Known for more philosophical works, he wrote the play 'La Mandragola', in which Florentines are rewarded for immoral actions?
|
||||
James Cook's account of a 1774 visit where records an object 'near 27 feet long, and upwards of 8 feet over the breast or shoulders'?
|
||||
England's 'Bloody Assizes' & a 1685 life sentence for perjury were 2 main origins of which amendment to the U.S. Constitution?
|
||||
Which nobel peace price winners each lived at times on Vilakazi St. in Soweto , so it claims to be the world's only street home to 2 Nobel Peace Prize winners?
|
||||
In 1966, the year of who's death did he share plans for an experimental prototype community in Florida?
|
||||
Of the 13 nations through which the Equator passes, what is the only one whose coastline borders the Caribbean Sea?
|
||||
Which decorative items in fashion history get their name from their origin in the port city of Strasbourg, on the border of France & Germany?
|
||||
What 1980's movie is based on an off-Broadway play with just 3 characters and won the Best Picture Oscar & the actors in all 3 roles were nominated?
|
||||
A 2012 book review for which novelist noted subjects that 'sparked his ire': capital punishment, big tobacco & 'the plight of the unjustly convicted'?
|
||||
A 1940 headline about what 20th Century Eponym included 'failure', 'liability when it came to offense' & 'stout hearts no match for tanks'?
|
||||
Over 700 years after its traditional 1252 founding date, what port city became associated with a psychological response?
|
||||
The success of what brand has its roots with a hydrotherapy pump its cofounder created for his son, who had arthritis?
|
||||
In a periodical in 1807, what American Author called New York City 'Gotham, Gotham! Most enlightened of cities'?
|
||||
What symbol is a rotated V in math and a feeling of some marginalized or underrepresented people in society?
|
||||
Monty Norman, the composer of what character's theme, said the staccato riff conveyed sexiness, mystery & ruthlessness?
|
||||
What American Novelist served with an airman named Yohannan in World War II & despite what readers might think, he said he enjoyed his service?
|
||||
In what Medieval place did one of the participants in an 1170 event say, 'Let us away, knights; he will rise no more'?
|
||||
At one time a province of the Roman Empire, what African country kingdom is known to Arabic scholars as Al-Maghrib Al-Aqsa, 'the far west'?
|
||||
Congress relented in 1890 after what prospective state said it would wait 100 years rather than come in without the women?
|
||||
A writer & producer of what movie said he wanted it to be like a Western or James Bond film, 'only it takes place in the 30s'?
|
||||
In 1898 what's been called the first blockbuster art show was devoted to which artist & put on for Queen Wilhelmina's coronation?
|
||||
Part of the largest contiguous land empire during the 1200s & 1300s, today what is the world's second-largest landlocked country?
|
||||
A 2006 book was titled 'The Poem That Changed America:' What 'Fifty Years Later'?
|
||||
Backed by 14,000 troops, who invaded England to restore, in his words, its 'religion, laws, and liberties'?
|
||||
After its completion in the late 19th c., what was landmark was called 'a truly tragic street lamp' & a 'high & skinny pyramid of iron ladders'?
|
||||
The busiest passenger port in the U.K., what shares its name with a capital of one of the original 13 states?
|
||||
This man made lists, perhaps to cope with depression; a set of lists he published in 1852 made whose name synonymous with a type of book?
|
||||
An 1869 presidential pardon was granted to which man, due in part to a plea by the Medical Society of Harford County, Maryland?
|
||||
Letters, pocket knives, C rations & steel helmets are among the tangible items referred to in the title of what American literature modern war classic?
|
||||
What nonfiction book has the line, 'The discovery of America…opened up fresh ground for the rising bourgeoisie'?
|
||||
A radical Republican championed what 1875 act but the Supreme Court struck it down in 1883; a new version was passed 81 years later?
|
||||
Whose brothers, Castor & Pollux, saved her after Theseus stole her away as a kid; a larger force would seek her later in life?
|
||||
Once Africa's largest country in area, what African Country dropped to third in 2011 when a portion of it declared independence?
|
||||
The ancient writer Galen said books on ships arriving to what city's port were seized, originals kept & copies returned?
|
||||
For a special 1970s cookbook, who provided one simple recipe–a can of Campbell's tomato soup & 2 cans of milk?
|
||||
Thought to descend from people of Southeast Asia, the Chamorro make up what U.S. territory’s largest ethnic group?
|
||||
In office from 2022, the president of what country has taken so many foreign trips a play on his name is 'Ferdinand Magellan Jr.'?
|
||||
In 1939 which writer lived on Toulouse Street in the French Quarter & chose the professional name that bonded him to the South?
|
||||
What National Park is named for a river indigenous people called Mi tse a-da-zi, translated by French-speaking trappers as 'Pierre Jaune'?
|
||||
In 2010 who introduced the 4-point shot, 35 feet from the basket?
|
||||
Losses over Asia in the 1960s led to the establishment of the program known as what at a San Diego naval base in 1969?
|
||||
A craft that visited what was named for Giotto, based on the story that 680 years earlier, the painter depicted it as the Star of Bethlehem?
|
||||
In World War I, 'Cistern' & 'reservoir' were suggested names for what secret invention, but the British preferred this less clumsy monosyllable?
|
||||
Until 1806, some German nobles included among their honors the title of 'Elector' for their role in selecting this personage?
|
||||
In 1904, wearing a harness, actress Nina Boucicault became the first to play what character onstage?
|
||||
Alphabetically the first German city in encyclopedias, what was also the first one taken by the Allies in World War II?
|
||||
This Sanskrit word referring to a spoken word or phrase comes from a word for 'to think'?
|
||||
1917's 'Elements of Trench Warfare' said what Old West invention was 'difficult to destroy' & 'difficult to get through'?
|
||||
Mimi Reinhard, who never learned to type using more than 2 fingers, produced what in World War II with 1,100 names, including hers?
|
||||
Poseidon carried off the maiden Theophane & turned her into a ewe; their offspring was the source of what mythical object?
|
||||
Published in 2011, P.D. James' final novel, 'Death Comes to Pemberley', was a sequel to what novel from 200 years earlier?
|
||||
5 U.S. states have 6-letter names; only which 2 west of the Mississippi River border each other?
|
||||
Originally relating to a story of suffering, what word now more commonly refers to strong emotion of any kind?
|
||||
The 2007 biopic called 'La Môme' in France, meaning 'The Kid', was released in the U.S. under what other French title?
|
||||
Returning home in 1493, Columbus stopped in the Azores at an island with what name, also something he'd lost off the Haiti coast?
|
||||
Pskov & Nizhny Novgorod are 2 of the cities that have a fortress called what?
|
||||
In the 1950s the New York Times said what author 'is writing about all lust' & his lecherous narrator 'is all of us'?
|
||||
At the winter solstice, the sun is in Sagittarius; it once appeared in what constellation, giving a geographic feature its name?
|
||||
Mike Post combined the sound of a slamming jail door, an anvil & 100 men stomping on a floor for what television series that debuted in 1990?
|
||||
Like Sir Thomas More, 3 16th century English queens are buried at what British location?
|
||||
In 1692 Increase Mather wrote, 'It were better that ten suspected' of these who 'escape, than that one innocent person be condemned'?
|
||||
The Geography Mnemonic Mimal, sometimes said to be the silhouette of a chef or elf, stands for Minnesota, Iowa, Missouri, and what other 2 states?
|
||||
What was first sold in 1908, at a price equivalent to about $27,000 today?
|
||||
The name of what author dead since 2013 now appears on books written by a former U.S. marshal & a former Apache helicopter pilot?
|
||||
The artwork once known in France as 'la tapisserie de la Reine Mathilde' is better known as what?
|
||||
In 2022 which pop star became the first woman to have a Billboard Top 10 album in 5 decades starting with the 1980s?
|
||||
In one 19th century translation, what female classic tale character 'perceived the dawn of day and ceased' speaking nearly 1,000 times?
|
||||
Ironically, though what company founded in the 1860s is Moore County, Tennessee's largest employer, Moore is a dry county?
|
||||
After a 1789 event, who wrote, 'My first determination was to seek a supply of…water at Tofoa, & afterwards to sail for Tongataboo'?
|
||||
Laurence Olivier & Ernest Borgnine were considered for the lead role & Sergio Leone to direct for what film that turned 50 in 2022?
|
||||
Until a 1903 secession, what country's contiguous territory spanned 2 continents?
|
||||
Early in her career which foreign-born author translated romance novels into Spanish, often changing the dialogue to make the heroines smarter?
|
||||
Saying it was stolen by Napoleon, self-styled Italian patriot Vincenzo Peruggia took what in 1911?
|
||||
Continuing a downward trend, in July 2022 what US body of water was at 27% capacity, its lowest level since 1937 when it was first being filled?
|
||||
Each morning which goddess began her ride in her chariot across the sky ahead of her brother Sol, or Helios?
|
||||
Until the Civil War, the Jan. 8 date of what American battle of dubious military importance but big morale value was a national holiday?
|
||||
Which children's book title character is told 'By the time you are real, most of your hair has been loved off your eyes drop out & you get shabby'?
|
||||
In a TV reunion over 40 years in the making, Dolly Parton appeared as an angel named Agnes in the final episode of what comedy in 2022?
|
||||
In an 1847 American poem what character sees her town of Grand-Pré burned, but finally reunites with her beau for a kiss before his death?
|
||||
In 2001 who published a book called 'Banging Your Head Against a Brick Wall'; in 2002, 'Existencilism'?
|
||||
The title object of what childrens book 'never looked more beautiful each strand held dozens of bright drops of early morning dew'?
|
||||
The shouts of excited children at a 1946 holiday parade are said to have inspired what perennial classic song favorite?
|
||||
Unable to make what candies perfectly round, the confectioner embraced this flawed name for the product?
|
||||
What country is home to 58 UNESCO World Heritage Sites, more than any other country; the sites include a volcano & a lagoon?
|
||||
What action movie's last line is 'If this is their idea of Christmas, I gotta be here for New Years'?
|
||||
Only 3 presidents have married while in office— John Tyler was the first & which one was the last?
|
||||
Demonstrating the dignity & humanity of Black Americans, who sat for 160 known photographs, the most of any American in the 19th century?
|
||||
Originally, which Latin 3-word phrase referred to when a doctor or apothecary substituted one medicine for another?
|
||||
The 1975 premiere of what movie comedy advertised free coconuts for the first thousand in the audience?
|
||||
A cocktail, an island & a WWII venture originally called 'Development of Substitute Materials' all bear what name?
|
||||
Which US President was sworn in twice as President within 2 years, first by his father & then later by a former U.S. President?
|
||||
A 1609 story in which an exiled king of Bulgaria creates a sea palace with his magic may have inspired the plot of what play?
|
||||
In 2009, during a 20th anniversary celebration, what landmark was called 'an edifice of fear. On Nov. 9, it became a place of joy'?
|
||||
Among what world capital's nicknames are the 'City of Classical Music' &, possibly in honor of a famous resident from 1860 to 1938, the 'City of Dreams'?
|
||||
Now meaning someone with nocturnal habits, what catches a sleeping dove in Shakespeare's 'Lucrece'?
|
||||
The stars on what country's flag represent states, 26 of them; unlike the USA's, its 'federal district' gets its own 27th star?
|
||||
What father was the only man among the 13 plaintiffs in a US class-action case filed in 1951?
|
||||
Reversing the story of what heroine she created, childrens author Patricia Maclachlan was born on the prairie but spent much of her life in New England?
|
||||
|
|
@ -157,6 +157,32 @@ int main(int argc, char ** argv) {
|
|||
// Add a space in front of the first character to match OG llama tokenizer behavior
|
||||
params.prompt.insert(0, 1, ' ');
|
||||
|
||||
std::string path_session = params.path_session;
|
||||
std::vector<llama_token> session_tokens;
|
||||
|
||||
if (!path_session.empty()) {
|
||||
fprintf(stderr, "%s: attempting to load saved session from %s..\n", __func__, path_session.c_str());
|
||||
|
||||
// REVIEW - fopen to check for existing session
|
||||
FILE * fp = std::fopen(path_session.c_str(), "rb");
|
||||
if (fp != NULL) {
|
||||
std::fclose(fp);
|
||||
|
||||
session_tokens.resize(params.n_ctx);
|
||||
size_t n_token_count_out = 0;
|
||||
const size_t n_session_bytes = llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out);
|
||||
session_tokens.resize(n_token_count_out);
|
||||
|
||||
if (n_session_bytes > 0) {
|
||||
fprintf(stderr, "%s: loaded %zu bytes of session data!\n", __func__, n_session_bytes);
|
||||
} else {
|
||||
fprintf(stderr, "%s: could not load session file, will recreate\n", __func__);
|
||||
}
|
||||
} else {
|
||||
fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
|
||||
}
|
||||
}
|
||||
|
||||
// tokenize the prompt
|
||||
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
|
||||
|
||||
|
|
@ -167,6 +193,26 @@ int main(int argc, char ** argv) {
|
|||
return 1;
|
||||
}
|
||||
|
||||
// debug message about similarity of saved session, if applicable
|
||||
size_t n_matching_session_tokens = 0;
|
||||
if (session_tokens.size()) {
|
||||
for (llama_token id : session_tokens) {
|
||||
if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
|
||||
break;
|
||||
}
|
||||
n_matching_session_tokens++;
|
||||
}
|
||||
if (n_matching_session_tokens >= embd_inp.size()) {
|
||||
fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
|
||||
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
|
||||
fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
|
||||
__func__, n_matching_session_tokens, embd_inp.size());
|
||||
} else {
|
||||
fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
|
||||
__func__, n_matching_session_tokens, embd_inp.size());
|
||||
}
|
||||
}
|
||||
|
||||
// number of tokens to keep when resetting context
|
||||
if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size() || params.instruct) {
|
||||
params.n_keep = (int)embd_inp.size();
|
||||
|
|
@ -230,8 +276,8 @@ int main(int argc, char ** argv) {
|
|||
fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
|
||||
}
|
||||
}
|
||||
fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n",
|
||||
params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
|
||||
fprintf(stderr, "sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
|
||||
params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau);
|
||||
fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
|
||||
fprintf(stderr, "\n\n");
|
||||
|
||||
|
|
@ -252,9 +298,16 @@ int main(int argc, char ** argv) {
|
|||
bool is_antiprompt = false;
|
||||
bool input_noecho = false;
|
||||
|
||||
// HACK - because session saving incurs a non-negligible delay, for now skip re-saving session
|
||||
// if we loaded a session with at least 75% similarity. It's currently just used to speed up the
|
||||
// initial prompt so it doesn't need to be an exact match.
|
||||
bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < (embd_inp.size() * 3 / 4);
|
||||
|
||||
|
||||
int n_past = 0;
|
||||
int n_remain = params.n_predict;
|
||||
int n_consumed = 0;
|
||||
int n_session_consumed = 0;
|
||||
|
||||
// the first thing we will do is to output the prompt, so set color accordingly
|
||||
set_console_color(con_st, CONSOLE_COLOR_PROMPT);
|
||||
|
|
@ -276,6 +329,9 @@ int main(int argc, char ** argv) {
|
|||
// insert n_left/2 tokens at the start of embd from last_n_tokens
|
||||
embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
|
||||
|
||||
// REVIEW - stop saving session if we run out of context
|
||||
path_session = "";
|
||||
|
||||
//printf("\n---\n");
|
||||
//printf("resetting: '");
|
||||
//for (int i = 0; i < (int) embd.size(); i++) {
|
||||
|
|
@ -285,6 +341,28 @@ int main(int argc, char ** argv) {
|
|||
//printf("\n---\n");
|
||||
}
|
||||
|
||||
// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
|
||||
// REVIEW
|
||||
if (n_session_consumed < (int) session_tokens.size()) {
|
||||
size_t i = 0;
|
||||
for ( ; i < embd.size(); i++) {
|
||||
if (embd[i] != session_tokens[n_session_consumed]) {
|
||||
session_tokens.resize(n_session_consumed);
|
||||
break;
|
||||
}
|
||||
|
||||
n_past++;
|
||||
n_session_consumed++;
|
||||
|
||||
if (n_session_consumed >= (int) session_tokens.size()) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (i > 0) {
|
||||
embd.erase(embd.begin(), embd.begin() + i);
|
||||
}
|
||||
}
|
||||
|
||||
// evaluate tokens in batches
|
||||
// embd is typically prepared beforehand to fit within a batch, but not always
|
||||
for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
|
||||
|
|
@ -298,29 +376,93 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
n_past += n_eval;
|
||||
}
|
||||
|
||||
if (embd.size() > 0 && !path_session.empty()) {
|
||||
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
|
||||
n_session_consumed = session_tokens.size();
|
||||
}
|
||||
}
|
||||
|
||||
embd.clear();
|
||||
|
||||
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
|
||||
// out of user input, sample next token
|
||||
const int32_t top_k = params.top_k;
|
||||
const float top_p = params.top_p;
|
||||
const float temp = params.temp;
|
||||
const float repeat_penalty = params.repeat_penalty;
|
||||
const float temp = params.temp;
|
||||
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
|
||||
const float top_p = params.top_p;
|
||||
const float tfs_z = params.tfs_z;
|
||||
const float typical_p = params.typical_p;
|
||||
const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
|
||||
const float repeat_penalty = params.repeat_penalty;
|
||||
const float alpha_presence = params.presence_penalty;
|
||||
const float alpha_frequency = params.frequency_penalty;
|
||||
const int mirostat = params.mirostat;
|
||||
const float mirostat_tau = params.mirostat_tau;
|
||||
const float mirostat_eta = params.mirostat_eta;
|
||||
const bool penalize_nl = params.penalize_nl;
|
||||
|
||||
// optionally save the session on first sample (for faster prompt loading next time)
|
||||
if (!path_session.empty() && need_to_save_session) {
|
||||
need_to_save_session = false;
|
||||
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
|
||||
}
|
||||
|
||||
llama_token id = 0;
|
||||
|
||||
{
|
||||
auto logits = llama_get_logits(ctx);
|
||||
auto n_vocab = llama_n_vocab(ctx);
|
||||
|
||||
if (params.ignore_eos) {
|
||||
logits[llama_token_eos()] = 0;
|
||||
// Apply params.logit_bias map
|
||||
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
|
||||
logits[it->first] += it->second;
|
||||
}
|
||||
|
||||
id = llama_sample_top_p_top_k(ctx,
|
||||
last_n_tokens.data() + n_ctx - params.repeat_last_n,
|
||||
params.repeat_last_n, top_k, top_p, temp, repeat_penalty);
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
||||
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
|
||||
// Apply penalties
|
||||
float nl_logit = logits[llama_token_nl()];
|
||||
auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
|
||||
llama_sample_repetition_penalty(ctx, &candidates_p,
|
||||
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
|
||||
last_n_repeat, repeat_penalty);
|
||||
llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
|
||||
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
|
||||
last_n_repeat, alpha_frequency, alpha_presence);
|
||||
if (!penalize_nl) {
|
||||
logits[llama_token_nl()] = nl_logit;
|
||||
}
|
||||
|
||||
if (temp <= 0) {
|
||||
// Greedy sampling
|
||||
id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
} else {
|
||||
if (mirostat == 1) {
|
||||
static float mirostat_mu = 2.0f * mirostat_tau;
|
||||
const int mirostat_m = 100;
|
||||
llama_sample_temperature(ctx, &candidates_p, temp);
|
||||
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
|
||||
} else if (mirostat == 2) {
|
||||
static float mirostat_mu = 2.0f * mirostat_tau;
|
||||
llama_sample_temperature(ctx, &candidates_p, temp);
|
||||
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
|
||||
} else {
|
||||
// Temperature sampling
|
||||
llama_sample_top_k(ctx, &candidates_p, top_k);
|
||||
llama_sample_tail_free(ctx, &candidates_p, tfs_z);
|
||||
llama_sample_typical(ctx, &candidates_p, typical_p);
|
||||
llama_sample_top_p(ctx, &candidates_p, top_p);
|
||||
llama_sample_temperature(ctx, &candidates_p, temp);
|
||||
id = llama_sample_token(ctx, &candidates_p);
|
||||
}
|
||||
}
|
||||
// printf("`%d`", candidates_p.size);
|
||||
|
||||
last_n_tokens.erase(last_n_tokens.begin());
|
||||
last_n_tokens.push_back(id);
|
||||
|
|
|
|||
|
|
@ -2,8 +2,18 @@
|
|||
#include "llama.h"
|
||||
|
||||
#include <cstdio>
|
||||
#include <map>
|
||||
#include <string>
|
||||
|
||||
static const std::map<std::string, enum llama_ftype> LLAMA_FTYPE_MAP = {
|
||||
{"q4_0", LLAMA_FTYPE_MOSTLY_Q4_0},
|
||||
{"q4_1", LLAMA_FTYPE_MOSTLY_Q4_1},
|
||||
{"q4_2", LLAMA_FTYPE_MOSTLY_Q4_2},
|
||||
{"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
|
||||
{"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
|
||||
{"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
|
||||
};
|
||||
|
||||
// usage:
|
||||
// ./quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
|
||||
//
|
||||
|
|
@ -12,11 +22,9 @@ int main(int argc, char ** argv) {
|
|||
|
||||
if (argc < 4) {
|
||||
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type [nthread]\n", argv[0]);
|
||||
fprintf(stderr, " type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
|
||||
fprintf(stderr, " type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
|
||||
fprintf(stderr, " type = %d - q4_2\n", LLAMA_FTYPE_MOSTLY_Q4_2);
|
||||
fprintf(stderr, " type = %d - q4_3\n", LLAMA_FTYPE_MOSTLY_Q4_3);
|
||||
fprintf(stderr, " type = %d - q8_0\n", LLAMA_FTYPE_MOSTLY_Q8_0);
|
||||
for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
|
||||
fprintf(stderr, " type = \"%s\" or %d\n", it->first.c_str(), it->second);
|
||||
}
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
@ -30,7 +38,18 @@ int main(int argc, char ** argv) {
|
|||
const std::string fname_inp = argv[1];
|
||||
const std::string fname_out = argv[2];
|
||||
|
||||
const enum llama_ftype ftype = (enum llama_ftype)atoi(argv[3]);
|
||||
enum llama_ftype ftype;
|
||||
if (argv[3][0] == 'q') {
|
||||
auto it = LLAMA_FTYPE_MAP.find(argv[3]);
|
||||
if (it == LLAMA_FTYPE_MAP.end()) {
|
||||
fprintf(stderr, "%s: unknown ftype '%s'\n", __func__, argv[3]);
|
||||
return 1;
|
||||
}
|
||||
ftype = it->second;
|
||||
} else {
|
||||
ftype = (enum llama_ftype)atoi(argv[3]);
|
||||
}
|
||||
|
||||
int nthread = argc > 4 ? atoi(argv[4]) : 0;
|
||||
|
||||
const int64_t t_main_start_us = ggml_time_us();
|
||||
|
|
|
|||
|
|
@ -1,13 +1,10 @@
|
|||
#include "common.h"
|
||||
#include "llama.h"
|
||||
|
||||
#include <vector>
|
||||
#include <cstdio>
|
||||
#include <chrono>
|
||||
|
||||
#include "common.h"
|
||||
#include "llama.h"
|
||||
#include "llama.cpp"
|
||||
|
||||
using namespace std;
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
gpt_params params;
|
||||
params.model = "models/llama-7B/ggml-model.bin";
|
||||
|
|
@ -20,21 +17,25 @@ int main(int argc, char ** argv) {
|
|||
return 1;
|
||||
}
|
||||
|
||||
if (params.n_predict < 0) {
|
||||
params.n_predict = 16;
|
||||
}
|
||||
|
||||
auto lparams = llama_context_default_params();
|
||||
|
||||
lparams.n_ctx = params.n_ctx;
|
||||
lparams.n_parts = params.n_parts;
|
||||
lparams.seed = params.seed;
|
||||
lparams.f16_kv = params.memory_f16;
|
||||
lparams.use_mmap = params.use_mmap;
|
||||
lparams.use_mlock = params.use_mlock;
|
||||
lparams.n_ctx = params.n_ctx;
|
||||
lparams.n_parts = params.n_parts;
|
||||
lparams.seed = params.seed;
|
||||
lparams.f16_kv = params.memory_f16;
|
||||
lparams.use_mmap = params.use_mmap;
|
||||
lparams.use_mlock = params.use_mlock;
|
||||
|
||||
auto n_past = 0;
|
||||
auto last_n_tokens_data = vector<llama_token>(params.repeat_last_n, 0);
|
||||
auto last_n_tokens_data = std::vector<llama_token>(params.repeat_last_n, 0);
|
||||
|
||||
// init
|
||||
auto ctx = llama_init_from_file(params.model.c_str(), lparams);
|
||||
auto tokens = vector<llama_token>(params.n_ctx);
|
||||
auto tokens = std::vector<llama_token>(params.n_ctx);
|
||||
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), tokens.size(), true);
|
||||
|
||||
if (n_prompt_tokens < 1) {
|
||||
|
|
@ -43,37 +44,42 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
|
||||
// evaluate prompt
|
||||
|
||||
llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past, params.n_threads);
|
||||
|
||||
last_n_tokens_data.insert(last_n_tokens_data.end(), tokens.data(), tokens.data() + n_prompt_tokens);
|
||||
n_past += n_prompt_tokens;
|
||||
|
||||
const size_t state_size = llama_get_state_size(ctx);
|
||||
uint8_t * state_mem = new uint8_t[state_size];
|
||||
|
||||
// Save state (rng, logits, embedding and kv_cache) to file
|
||||
FILE *fp_write = fopen("dump_state.bin", "wb");
|
||||
auto state_size = llama_get_state_size(ctx);
|
||||
auto state_mem = new uint8_t[state_size];
|
||||
llama_copy_state_data(ctx, state_mem); // could also copy directly to memory mapped file
|
||||
fwrite(state_mem, 1, state_size, fp_write);
|
||||
fclose(fp_write);
|
||||
{
|
||||
FILE *fp_write = fopen("dump_state.bin", "wb");
|
||||
llama_copy_state_data(ctx, state_mem); // could also copy directly to memory mapped file
|
||||
fwrite(state_mem, 1, state_size, fp_write);
|
||||
fclose(fp_write);
|
||||
}
|
||||
|
||||
// save state (last tokens)
|
||||
auto last_n_tokens_data_saved = vector<llama_token>(last_n_tokens_data);
|
||||
auto n_past_saved = n_past;
|
||||
const auto last_n_tokens_data_saved = std::vector<llama_token>(last_n_tokens_data);
|
||||
const auto n_past_saved = n_past;
|
||||
|
||||
// first run
|
||||
printf("\n%s", params.prompt.c_str());
|
||||
|
||||
for (auto i = 0; i < params.n_predict; i++) {
|
||||
auto next_token = llama_sample_top_p_top_k(
|
||||
ctx,
|
||||
&last_n_tokens_data.back() - params.repeat_last_n,
|
||||
params.repeat_last_n,
|
||||
40,
|
||||
1.0,
|
||||
1.0,
|
||||
1.1);
|
||||
auto logits = llama_get_logits(ctx);
|
||||
auto n_vocab = llama_n_vocab(ctx);
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
||||
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
|
||||
}
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
auto next_token = llama_sample_token(ctx, &candidates_p);
|
||||
auto next_token_str = llama_token_to_str(ctx, next_token);
|
||||
last_n_tokens_data.push_back(next_token);
|
||||
|
||||
printf("%s", next_token_str);
|
||||
if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) {
|
||||
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
|
||||
|
|
@ -81,24 +87,34 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
n_past += 1;
|
||||
}
|
||||
|
||||
printf("\n\n");
|
||||
|
||||
// free old model
|
||||
llama_free(ctx);
|
||||
|
||||
// load new model
|
||||
|
||||
auto ctx2 = llama_init_from_file(params.model.c_str(), lparams);
|
||||
|
||||
// Load state (rng, logits, embedding and kv_cache) from file
|
||||
FILE *fp_read = fopen("dump_state.bin", "rb");
|
||||
auto state_size2 = llama_get_state_size(ctx2);
|
||||
if (state_size != state_size2) {
|
||||
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
|
||||
{
|
||||
FILE *fp_read = fopen("dump_state.bin", "rb");
|
||||
if (state_size != llama_get_state_size(ctx2)) {
|
||||
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
const size_t ret = fread(state_mem, 1, state_size, fp_read);
|
||||
if (ret != state_size) {
|
||||
fprintf(stderr, "\n%s : failed to read state\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
llama_set_state_data(ctx2, state_mem); // could also read directly from memory mapped file
|
||||
fclose(fp_read);
|
||||
}
|
||||
fread(state_mem, 1, state_size, fp_read);
|
||||
llama_set_state_data(ctx2, state_mem); // could also read directly from memory mapped file
|
||||
fclose(fp_read);
|
||||
|
||||
delete[] state_mem;
|
||||
|
||||
// restore state (last tokens)
|
||||
last_n_tokens_data = last_n_tokens_data_saved;
|
||||
|
|
@ -106,16 +122,18 @@ int main(int argc, char ** argv) {
|
|||
|
||||
// second run
|
||||
for (auto i = 0; i < params.n_predict; i++) {
|
||||
auto next_token = llama_sample_top_p_top_k(
|
||||
ctx2,
|
||||
&last_n_tokens_data.back() - params.repeat_last_n,
|
||||
params.repeat_last_n,
|
||||
40,
|
||||
1.0,
|
||||
1.0,
|
||||
1.1);
|
||||
auto logits = llama_get_logits(ctx2);
|
||||
auto n_vocab = llama_n_vocab(ctx2);
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
||||
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
|
||||
}
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
auto next_token = llama_sample_token(ctx2, &candidates_p);
|
||||
auto next_token_str = llama_token_to_str(ctx2, next_token);
|
||||
last_n_tokens_data.push_back(next_token);
|
||||
|
||||
printf("%s", next_token_str);
|
||||
if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) {
|
||||
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
|
||||
|
|
@ -123,6 +141,8 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
n_past += 1;
|
||||
}
|
||||
|
||||
printf("\n\n");
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
153
ggml-cuda.cu
153
ggml-cuda.cu
|
|
@ -29,13 +29,22 @@ typedef struct {
|
|||
} block_q4_2;
|
||||
static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
|
||||
|
||||
#define QK4_3 16
|
||||
#define QK5_0 32
|
||||
typedef struct {
|
||||
__half d; // delta
|
||||
__half m; // min
|
||||
uint8_t qs[QK4_3 / 2]; // nibbles / quants
|
||||
} block_q4_3;
|
||||
static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding");
|
||||
__half d; // delta
|
||||
uint8_t qh[4]; // 5-th bit of quants
|
||||
uint8_t qs[QK5_0 / 2]; // nibbles / quants
|
||||
} block_q5_0;
|
||||
static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
|
||||
|
||||
#define QK5_1 32
|
||||
typedef struct {
|
||||
__half d; // delta
|
||||
__half m; // min
|
||||
uint32_t qh; // 5-th bit of quants
|
||||
uint8_t qs[QK5_1 / 2]; // nibbles / quants
|
||||
} block_q5_1;
|
||||
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
|
||||
|
||||
#define QK8_0 32
|
||||
typedef struct {
|
||||
|
|
@ -114,8 +123,37 @@ static __global__ void dequantize_block_q4_2(const void * vx, float * y) {
|
|||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_block_q4_3(const void * vx, float * y) {
|
||||
const block_q4_3 * x = (const block_q4_3 *) vx;
|
||||
static __global__ void dequantize_block_q5_0(const void * vx, float * y) {
|
||||
const block_q5_0 * x = (const block_q5_0 *) vx;
|
||||
|
||||
const int i = blockIdx.x;
|
||||
|
||||
const float d = x[i].d;
|
||||
|
||||
const uint8_t * pp = x[i].qs;
|
||||
|
||||
uint32_t qh;
|
||||
memcpy(&qh, x[i].qh, sizeof(qh));
|
||||
|
||||
for (int l = 0; l < QK5_0; l += 2) {
|
||||
const uint8_t vi = pp[l/2];
|
||||
|
||||
const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
|
||||
const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
|
||||
|
||||
const int8_t vi0 = ((vi & 0xf) | vh0);
|
||||
const int8_t vi1 = ((vi >> 4) | vh1);
|
||||
|
||||
const float v0 = (vi0 - 16)*d;
|
||||
const float v1 = (vi1 - 16)*d;
|
||||
|
||||
y[i*QK5_0 + l + 0] = v0;
|
||||
y[i*QK5_0 + l + 1] = v1;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_block_q5_1(const void * vx, float * y) {
|
||||
const block_q5_1 * x = (const block_q5_1 *) vx;
|
||||
|
||||
const int i = blockIdx.x;
|
||||
|
||||
|
|
@ -124,17 +162,22 @@ static __global__ void dequantize_block_q4_3(const void * vx, float * y) {
|
|||
|
||||
const uint8_t * pp = x[i].qs;
|
||||
|
||||
for (int l = 0; l < QK4_3; l += 2) {
|
||||
const uint32_t qh = x[i].qh;
|
||||
|
||||
for (int l = 0; l < QK5_1; l += 2) {
|
||||
const uint8_t vi = pp[l/2];
|
||||
|
||||
const int8_t vi0 = vi & 0xf;
|
||||
const int8_t vi1 = vi >> 4;
|
||||
const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
|
||||
const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
|
||||
|
||||
const int8_t vi0 = (vi & 0xf) | vh0;
|
||||
const int8_t vi1 = (vi >> 4) | vh1;
|
||||
|
||||
const float v0 = vi0*d + m;
|
||||
const float v1 = vi1*d + m;
|
||||
|
||||
y[i*QK4_3 + l + 0] = v0;
|
||||
y[i*QK4_3 + l + 1] = v1;
|
||||
y[i*QK5_1 + l + 0] = v0;
|
||||
y[i*QK5_1 + l + 1] = v1;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -169,9 +212,14 @@ void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t st
|
|||
dequantize_block_q4_2<<<nb, 1, 0, stream>>>(vx, y);
|
||||
}
|
||||
|
||||
void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
|
||||
const int nb = k / QK4_3;
|
||||
dequantize_block_q4_3<<<nb, 1, 0, stream>>>(vx, y);
|
||||
void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
|
||||
const int nb = k / QK5_0;
|
||||
dequantize_block_q5_0<<<nb, 1, 0, stream>>>(vx, y);
|
||||
}
|
||||
|
||||
void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
|
||||
const int nb = k / QK5_1;
|
||||
dequantize_block_q5_1<<<nb, 1, 0, stream>>>(vx, y);
|
||||
}
|
||||
|
||||
void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
|
||||
|
|
@ -179,6 +227,25 @@ void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t st
|
|||
dequantize_block_q8_0<<<nb, 1, 0, stream>>>(vx, y);
|
||||
}
|
||||
|
||||
dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
return dequantize_row_q4_0_cuda;
|
||||
case GGML_TYPE_Q4_1:
|
||||
return dequantize_row_q4_1_cuda;
|
||||
case GGML_TYPE_Q4_2:
|
||||
return dequantize_row_q4_2_cuda;
|
||||
case GGML_TYPE_Q5_0:
|
||||
return dequantize_row_q5_0_cuda;
|
||||
case GGML_TYPE_Q5_1:
|
||||
return dequantize_row_q5_1_cuda;
|
||||
case GGML_TYPE_Q8_0:
|
||||
return dequantize_row_q8_0_cuda;
|
||||
default:
|
||||
return nullptr;
|
||||
}
|
||||
}
|
||||
|
||||
// buffer pool for cuda
|
||||
#define MAX_CUDA_BUFFERS 16
|
||||
|
||||
|
|
@ -238,19 +305,61 @@ void ggml_cuda_pool_free(void * ptr, size_t size) {
|
|||
CUDA_CHECK(cudaFree(ptr));
|
||||
}
|
||||
|
||||
cublasHandle_t g_cublasH = NULL;
|
||||
cudaStream_t g_cudaStream = NULL;
|
||||
cublasHandle_t g_cublasH = nullptr;
|
||||
cudaStream_t g_cudaStream = nullptr;
|
||||
cudaStream_t g_cudaStream2 = nullptr;
|
||||
cudaEvent_t g_cudaEvent = nullptr;
|
||||
|
||||
void ggml_init_cublas(void) {
|
||||
if (g_cublasH == NULL) {
|
||||
void ggml_init_cublas() {
|
||||
if (g_cublasH == nullptr) {
|
||||
// create cublas handle, bind a stream
|
||||
CUBLAS_CHECK(cublasCreate(&g_cublasH));
|
||||
|
||||
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream, cudaStreamNonBlocking));
|
||||
|
||||
CUBLAS_CHECK(cublasSetStream(g_cublasH, g_cudaStream));
|
||||
|
||||
// create additional stream and event for synchronization
|
||||
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream2, cudaStreamNonBlocking));
|
||||
CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvent, cudaEventDisableTiming));
|
||||
|
||||
// configure logging to stdout
|
||||
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL));
|
||||
}
|
||||
}
|
||||
|
||||
cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
|
||||
const uint64_t ne0 = src->ne[0];
|
||||
const uint64_t ne1 = src->ne[1];
|
||||
const uint64_t nb0 = src->nb[0];
|
||||
const uint64_t nb1 = src->nb[1];
|
||||
const uint64_t nb2 = src->nb[2];
|
||||
const uint64_t nb3 = src->nb[3];
|
||||
const enum ggml_type type = src->type;
|
||||
const size_t ts = ggml_type_size(type);
|
||||
const size_t bs = ggml_blck_size(type);
|
||||
|
||||
const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
|
||||
if (nb0 == ts && nb1 == ts*ne0/bs) {
|
||||
return cudaMemcpyAsync(dst, x, ne1*nb1, cudaMemcpyHostToDevice, stream);
|
||||
} else if (nb0 == ts) {
|
||||
return cudaMemcpy2DAsync(dst, ts*ne0/bs, x, nb1, ts*ne0/bs, ne1, cudaMemcpyHostToDevice, stream);
|
||||
} else {
|
||||
for (uint64_t i1 = 0; i1 < ne1; i1++) {
|
||||
const void * rx = (const void *) ((const char *) x + i1*nb1);
|
||||
void * rd = (void *) ((char *) dst + i1*ts*ne0/bs);
|
||||
// pretend the row is a matrix with cols=1
|
||||
cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyHostToDevice, stream);
|
||||
if (r != cudaSuccess) return r;
|
||||
}
|
||||
return cudaSuccess;
|
||||
}
|
||||
}
|
||||
|
||||
void * ggml_cuda_host_malloc(size_t size) {
|
||||
void * ptr;
|
||||
CUDA_CHECK(cudaMallocHost((void **) &ptr, size));
|
||||
return ptr;
|
||||
}
|
||||
|
||||
void ggml_cuda_host_free(void * ptr) {
|
||||
CUDA_CHECK(cudaFreeHost(ptr));
|
||||
}
|
||||
|
|
|
|||
16
ggml-cuda.h
16
ggml-cuda.h
|
|
@ -1,5 +1,6 @@
|
|||
#include <cublas_v2.h>
|
||||
#include <cuda_runtime.h>
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
|
|
@ -25,18 +26,29 @@ extern "C" {
|
|||
} while (0)
|
||||
|
||||
extern cublasHandle_t g_cublasH;
|
||||
extern cudaStream_t g_cudaStream;
|
||||
extern cudaStream_t g_cudaStream;
|
||||
extern cudaStream_t g_cudaStream2;
|
||||
extern cudaEvent_t g_cudaEvent;
|
||||
|
||||
void ggml_init_cublas(void);
|
||||
void * ggml_cuda_host_malloc(size_t size);
|
||||
void ggml_cuda_host_free(void * ptr);
|
||||
|
||||
void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size);
|
||||
void ggml_cuda_pool_free(void * ptr, size_t size);
|
||||
|
||||
void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
|
||||
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
|
||||
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream);
|
||||
void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t stream);
|
||||
void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
|
||||
void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
|
||||
void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
|
||||
|
||||
cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream);
|
||||
|
||||
typedef void (*dequantize_row_q_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
|
||||
dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(enum ggml_type type);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
63
ggml-opencl-dequant.cl
Normal file
63
ggml-opencl-dequant.cl
Normal file
|
|
@ -0,0 +1,63 @@
|
|||
#define MULTILINE_QUOTE(...) #__VA_ARGS__
|
||||
const char * clblast_dequant = MULTILINE_QUOTE(
|
||||
|
||||
struct block_q4_0
|
||||
{
|
||||
float d;
|
||||
uchar qs[16];
|
||||
};
|
||||
|
||||
__kernel void dequantize_row_q4_0(__global struct block_q4_0* blocks, __global float* result) {
|
||||
const uint i = get_global_id(0) / 32;
|
||||
const uint l = get_local_id(0);
|
||||
|
||||
const float d = blocks[i].d;
|
||||
|
||||
const uchar vi = blocks[i].qs[l];
|
||||
|
||||
const uint index = i*32 + l*2;
|
||||
result[index + 0] = ((vi & 0xf) - 8)*d;
|
||||
result[index + 1] = ((vi >> 4) - 8)*d;
|
||||
}
|
||||
|
||||
struct block_q4_1
|
||||
{
|
||||
float d;
|
||||
float m;
|
||||
uchar qs[16];
|
||||
};
|
||||
|
||||
__kernel void dequantize_row_q4_1(__global struct block_q4_1* blocks, __global float* result) {
|
||||
const uint i = get_global_id(0) / 32;
|
||||
const uint l = get_local_id(0);
|
||||
|
||||
const float d = blocks[i].d;
|
||||
const float m = blocks[i].m;
|
||||
|
||||
const uchar vi = blocks[i].qs[l];
|
||||
|
||||
const uint index = i*32 + l*2;
|
||||
result[index + 0] = (vi & 0xf) * d + m;
|
||||
result[index + 1] = (vi >> 4) * d + m;
|
||||
}
|
||||
|
||||
struct block_q4_2
|
||||
{
|
||||
ushort d;
|
||||
uchar qs[8];
|
||||
};
|
||||
|
||||
__kernel void dequantize_row_q4_2(__global struct block_q4_2* blocks, __global float* result) {
|
||||
const uint i = get_global_id(0) / 16;
|
||||
const uint l = get_local_id(0);
|
||||
|
||||
const float d = vload_half(0, (__global half*) &blocks[i].d);;
|
||||
|
||||
const uchar vi = blocks[i].qs[l];
|
||||
|
||||
const uint index = i*16 + l*2;
|
||||
result[index + 0] = ((vi & 0xf) - 8)*d;
|
||||
result[index + 1] = ((vi >> 4) - 8)*d;
|
||||
}
|
||||
|
||||
);
|
||||
208
ggml-opencl.c
Normal file
208
ggml-opencl.c
Normal file
|
|
@ -0,0 +1,208 @@
|
|||
#include "ggml-opencl.h"
|
||||
|
||||
#define CL_TARGET_OPENCL_VERSION 110
|
||||
#include <clblast_c.h>
|
||||
|
||||
#include <stdio.h>
|
||||
#include <string.h>
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#include "ggml-opencl-dequant.cl"
|
||||
|
||||
#define CL_CHECK(err, name) \
|
||||
do { \
|
||||
cl_int err_ = (err); \
|
||||
if (err_ != CL_SUCCESS) { \
|
||||
fprintf(stderr, "OpenCL %s error %d at %s:%d\n", name, err_, __FILE__, __LINE__); \
|
||||
exit(1); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
static cl_platform_id platform;
|
||||
static cl_device_id device;
|
||||
static cl_context context;
|
||||
static cl_command_queue queue;
|
||||
static cl_program program;
|
||||
static cl_kernel kernel_q4_0, kernel_q4_1, kernel_q4_2;
|
||||
static cl_mem cl_buffer_a, cl_buffer_qb, cl_buffer_b, cl_buffer_c;
|
||||
static size_t cl_size_a = 0, cl_size_qb = 0, cl_size_b = 0, cl_size_c = 0;
|
||||
|
||||
static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
|
||||
cl_program p;
|
||||
char *program_log;
|
||||
size_t program_size, log_size;
|
||||
int err;
|
||||
|
||||
program_size = strlen(program_buffer);
|
||||
|
||||
p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
|
||||
if(err < 0) {
|
||||
fprintf(stderr, "OpenCL error creating program");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
err = clBuildProgram(p, 0, NULL, NULL, NULL, NULL);
|
||||
if(err < 0) {
|
||||
|
||||
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
|
||||
program_log = (char*) malloc(log_size + 1);
|
||||
program_log[log_size] = '\0';
|
||||
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
|
||||
printf("%s\n", program_log);
|
||||
free(program_log);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
return p;
|
||||
}
|
||||
|
||||
void ggml_cl_init(void) {
|
||||
cl_int err = 0;
|
||||
char * GGML_CLBLAST_PLATFORM = getenv("GGML_CLBLAST_PLATFORM");
|
||||
char * GGML_CLBLAST_DEVICE = getenv("GGML_CLBLAST_DEVICE");
|
||||
int plat_num = (GGML_CLBLAST_PLATFORM == NULL ? 0 : atoi(GGML_CLBLAST_PLATFORM));
|
||||
int dev_num = (GGML_CLBLAST_DEVICE == NULL ? 0 : atoi(GGML_CLBLAST_DEVICE));
|
||||
printf("\nInitializing CLBlast (First Run)...");
|
||||
printf("\nAttempting to use: Platform=%d, Device=%d (If invalid, program will crash)\n",plat_num,dev_num);
|
||||
cl_uint num_platforms;
|
||||
clGetPlatformIDs(0, NULL, &num_platforms);
|
||||
cl_platform_id* platforms = (cl_platform_id*)malloc(num_platforms*sizeof(cl_platform_id));
|
||||
clGetPlatformIDs(num_platforms, platforms, NULL);
|
||||
platform = platforms[plat_num];
|
||||
char platform_buffer[1024];
|
||||
clGetPlatformInfo(platform, CL_PLATFORM_NAME, sizeof(platform_buffer), &platform_buffer, NULL);
|
||||
cl_uint num_devices;
|
||||
clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, 0, NULL, &num_devices);
|
||||
cl_device_id* devices = (cl_device_id*)malloc(num_devices*sizeof(cl_device_id));
|
||||
clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, num_devices, devices, NULL);
|
||||
device = devices[dev_num];
|
||||
char device_buffer[1024];
|
||||
clGetDeviceInfo(device, CL_DEVICE_NAME, sizeof(device_buffer), &device_buffer, NULL);
|
||||
printf("Using Platform: %s Device: %s\n", platform_buffer, device_buffer);
|
||||
context = clCreateContext(NULL, 1, &device, NULL, NULL, &err);
|
||||
CL_CHECK(err, "clCreateContext");
|
||||
queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err);
|
||||
CL_CHECK(err, "clCreateCommandQueue");
|
||||
|
||||
free(platforms);
|
||||
free(devices);
|
||||
|
||||
program = build_program_from_source(context, device, clblast_dequant);
|
||||
|
||||
// Prepare dequantize kernels
|
||||
kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err);
|
||||
CL_CHECK(err, "clCreateKernel");
|
||||
kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &err);
|
||||
CL_CHECK(err, "clCreateKernel");
|
||||
kernel_q4_2 = clCreateKernel(program, "dequantize_row_q4_2", &err);
|
||||
CL_CHECK(err, "clCreateKernel");
|
||||
}
|
||||
|
||||
static void ggml_cl_malloc(size_t req_size, size_t* cur_size, cl_mem_flags flags, cl_mem* buf) {
|
||||
if (req_size <= *cur_size) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Reallocate buffer with enough space
|
||||
if (*cur_size > 0) {
|
||||
clReleaseMemObject(*buf);
|
||||
}
|
||||
cl_int err;
|
||||
*buf = clCreateBuffer(context, flags, req_size, NULL, &err);
|
||||
*cur_size = req_size;
|
||||
CL_CHECK(err, "clCreateBuffer");
|
||||
}
|
||||
|
||||
void ggml_cl_sgemm_wrapper(
|
||||
const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b,
|
||||
const int m, const int n, const int k,
|
||||
const float alpha, const void *host_a, const int lda,
|
||||
const float *host_b, const int ldb, const float beta,
|
||||
float *host_c, const int ldc, const int btype) {
|
||||
cl_int err = 0;
|
||||
|
||||
cl_kernel kernel;
|
||||
size_t global = n * k, local, size_qb;
|
||||
bool dequant;
|
||||
|
||||
switch (btype) {
|
||||
case GGML_TYPE_F32:
|
||||
dequant = false;
|
||||
break;
|
||||
case GGML_TYPE_Q4_0:
|
||||
dequant = true;
|
||||
kernel = kernel_q4_0;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(float) + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q4_1:
|
||||
dequant = true;
|
||||
kernel = kernel_q4_1;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(float) * 2 + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q4_2:
|
||||
dequant = true;
|
||||
kernel = kernel_q4_2;
|
||||
local = 8;
|
||||
size_qb = global * (sizeof(short) + local) / 16;
|
||||
break;
|
||||
default:
|
||||
fprintf(stderr, "Error: Unsupported OpenCL btype %d\n", btype);
|
||||
abort();
|
||||
}
|
||||
|
||||
const size_t size_a = m * k * sizeof(float);
|
||||
const size_t size_b = n * k * sizeof(float);
|
||||
const size_t size_c = m * n * sizeof(float);
|
||||
|
||||
// Prepare buffers
|
||||
ggml_cl_malloc(size_a, &cl_size_a, CL_MEM_READ_ONLY, &cl_buffer_a);
|
||||
if (dequant) {
|
||||
ggml_cl_malloc(size_qb, &cl_size_qb, CL_MEM_READ_ONLY, &cl_buffer_qb);
|
||||
}
|
||||
ggml_cl_malloc(size_b, &cl_size_b, CL_MEM_READ_WRITE, &cl_buffer_b);
|
||||
ggml_cl_malloc(size_c, &cl_size_c, CL_MEM_WRITE_ONLY, &cl_buffer_c);
|
||||
|
||||
cl_event ev_a, ev_qb, ev_b;
|
||||
|
||||
if (dequant) {
|
||||
err = clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb);
|
||||
err |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b);
|
||||
CL_CHECK(err, "clSetKernelArg");
|
||||
clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb);
|
||||
} else {
|
||||
clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b);
|
||||
}
|
||||
|
||||
clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a);
|
||||
if (dequant) {
|
||||
err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b);
|
||||
CL_CHECK(err, "clEnqueueNDRangeKernel");
|
||||
clReleaseEvent(ev_qb);
|
||||
}
|
||||
clWaitForEvents(1, &ev_a);
|
||||
clWaitForEvents(1, &ev_b);
|
||||
clReleaseEvent(ev_a);
|
||||
clReleaseEvent(ev_b);
|
||||
|
||||
cl_event ev_sgemm;
|
||||
CLBlastSgemm((CLBlastLayout)order,
|
||||
(CLBlastTranspose)trans_a, (CLBlastTranspose)trans_b,
|
||||
m, n, k,
|
||||
alpha,
|
||||
cl_buffer_a, 0, lda,
|
||||
cl_buffer_b, 0, ldb,
|
||||
beta,
|
||||
cl_buffer_c, 0, ldc,
|
||||
&queue, &ev_sgemm);
|
||||
|
||||
cl_event ev_c;
|
||||
clEnqueueReadBuffer(queue, cl_buffer_c, CL_TRUE, 0, size_c, host_c, 1, &ev_sgemm, &ev_c);
|
||||
|
||||
// Wait for completion
|
||||
clWaitForEvents(1, &ev_c);
|
||||
clReleaseEvent(ev_sgemm);
|
||||
clReleaseEvent(ev_c);
|
||||
}
|
||||
24
ggml-opencl.h
Normal file
24
ggml-opencl.h
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
#pragma once
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
void ggml_cl_init(void);
|
||||
|
||||
enum ggml_blas_order {
|
||||
GGML_BLAS_ORDER_ROW_MAJOR = 101,
|
||||
GGML_BLAS_ORDER_COLUMN_MAJOR = 102,
|
||||
};
|
||||
|
||||
enum ggml_blas_op {
|
||||
GGML_BLAS_OP_N = 111,
|
||||
GGML_BLAS_OP_T = 112,
|
||||
GGML_BLAS_OP_C = 113,
|
||||
};
|
||||
|
||||
void ggml_cl_sgemm_wrapper(const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b, const int m, const int n, const int k, const float alpha, const void *host_a, const int lda, const float *host_b, const int ldb, const float beta, float *host_c, const int ldc, const int btype);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
27
ggml.h
27
ggml.h
|
|
@ -221,9 +221,11 @@ extern "C" {
|
|||
GGML_TYPE_Q4_0 = 2,
|
||||
GGML_TYPE_Q4_1 = 3,
|
||||
GGML_TYPE_Q4_2 = 4,
|
||||
GGML_TYPE_Q4_3 = 5,
|
||||
GGML_TYPE_Q8_0 = 6,
|
||||
GGML_TYPE_Q8_1 = 7,
|
||||
// GGML_TYPE_Q4_3 (5) support has been removed
|
||||
GGML_TYPE_Q5_0 = 6,
|
||||
GGML_TYPE_Q5_1 = 7,
|
||||
GGML_TYPE_Q8_0 = 8,
|
||||
GGML_TYPE_Q8_1 = 9,
|
||||
GGML_TYPE_I8,
|
||||
GGML_TYPE_I16,
|
||||
GGML_TYPE_I32,
|
||||
|
|
@ -267,6 +269,7 @@ extern "C" {
|
|||
GGML_OP_DIAG_MASK_INF,
|
||||
GGML_OP_SOFT_MAX,
|
||||
GGML_OP_ROPE,
|
||||
GGML_OP_ALIBI,
|
||||
GGML_OP_CONV_1D_1S,
|
||||
GGML_OP_CONV_1D_2S,
|
||||
|
||||
|
|
@ -660,6 +663,14 @@ extern "C" {
|
|||
int n_dims,
|
||||
int mode);
|
||||
|
||||
// alibi position embedding
|
||||
// in-place, returns view(a)
|
||||
struct ggml_tensor * ggml_alibi(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_head);
|
||||
|
||||
// padding = 1
|
||||
// TODO: we don't support extra parameters for now
|
||||
// that's why we are hard-coding the stride, padding, and dilation
|
||||
|
|
@ -690,8 +701,8 @@ extern "C" {
|
|||
struct ggml_tensor * c1);
|
||||
|
||||
// Mapping operations
|
||||
GGML_API typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
|
||||
GGML_API typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
|
||||
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 *);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_unary_f32(
|
||||
struct ggml_context * ctx,
|
||||
|
|
@ -832,7 +843,8 @@ extern "C" {
|
|||
GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
|
||||
|
|
@ -854,10 +866,11 @@ extern "C" {
|
|||
GGML_API int ggml_cpu_has_wasm_simd (void);
|
||||
GGML_API int ggml_cpu_has_blas (void);
|
||||
GGML_API int ggml_cpu_has_cublas (void);
|
||||
GGML_API int ggml_cpu_has_clblast (void);
|
||||
GGML_API int ggml_cpu_has_gpublas (void);
|
||||
GGML_API int ggml_cpu_has_sse3 (void);
|
||||
GGML_API int ggml_cpu_has_vsx (void);
|
||||
|
||||
|
||||
//
|
||||
// Internal types and functions exposed for tests and benchmarks
|
||||
//
|
||||
|
|
|
|||
25
llama_util.h → llama-util.h
Executable file → Normal file
25
llama_util.h → llama-util.h
Executable file → Normal file
|
|
@ -405,4 +405,29 @@ struct llama_buffer {
|
|||
delete[] addr;
|
||||
}
|
||||
};
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#include "ggml-cuda.h"
|
||||
struct llama_ctx_buffer {
|
||||
uint8_t * addr = NULL;
|
||||
size_t size = 0;
|
||||
|
||||
void resize(size_t size) {
|
||||
if (addr) {
|
||||
ggml_cuda_host_free(addr);
|
||||
}
|
||||
addr = (uint8_t *) ggml_cuda_host_malloc(size);
|
||||
this->size = size;
|
||||
}
|
||||
|
||||
~llama_ctx_buffer() {
|
||||
if (addr) {
|
||||
ggml_cuda_host_free(addr);
|
||||
}
|
||||
}
|
||||
};
|
||||
#else
|
||||
typedef llama_buffer llama_ctx_buffer;
|
||||
#endif
|
||||
|
||||
#endif
|
||||
578
llama.cpp
578
llama.cpp
|
|
@ -5,7 +5,7 @@
|
|||
#include <cstdio>
|
||||
#endif
|
||||
|
||||
#include "llama_util.h"
|
||||
#include "llama-util.h"
|
||||
#include "llama.h"
|
||||
|
||||
#include "ggml.h"
|
||||
|
|
@ -28,11 +28,11 @@
|
|||
#include <atomic>
|
||||
#include <mutex>
|
||||
#include <sstream>
|
||||
#include <numeric>
|
||||
|
||||
#define LLAMA_USE_SCRATCH
|
||||
#define LLAMA_MAX_SCRATCH_BUFFERS 16
|
||||
|
||||
|
||||
// available llama models
|
||||
enum e_model {
|
||||
MODEL_UNKNOWN,
|
||||
|
|
@ -136,7 +136,7 @@ struct llama_kv_cache {
|
|||
|
||||
struct ggml_context * ctx = NULL;
|
||||
|
||||
llama_buffer buf;
|
||||
llama_ctx_buffer buf;
|
||||
|
||||
int n; // number of tokens currently in the cache
|
||||
|
||||
|
|
@ -167,7 +167,7 @@ struct llama_model {
|
|||
struct llama_kv_cache kv_self;
|
||||
|
||||
// the model memory buffer
|
||||
llama_buffer buf;
|
||||
llama_ctx_buffer buf;
|
||||
|
||||
// model memory mapped file
|
||||
std::unique_ptr<llama_mmap> mapping;
|
||||
|
|
@ -228,8 +228,8 @@ struct llama_context {
|
|||
|
||||
// memory buffers used to evaluate the model
|
||||
// TODO: move in llama_state
|
||||
llama_buffer buf_compute;
|
||||
llama_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
|
||||
llama_ctx_buffer buf_compute;
|
||||
llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
|
||||
|
||||
int buf_last = 0;
|
||||
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
|
||||
|
|
@ -483,7 +483,8 @@ struct llama_file_loader {
|
|||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q4_2:
|
||||
case GGML_TYPE_Q4_3:
|
||||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
break;
|
||||
default: {
|
||||
|
|
@ -558,7 +559,8 @@ struct llama_file_saver {
|
|||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q4_2:
|
||||
case GGML_TYPE_Q4_3:
|
||||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
break;
|
||||
default: LLAMA_ASSERT(false);
|
||||
|
|
@ -778,7 +780,7 @@ static bool kv_cache_init(
|
|||
const int n_embd = hparams.n_embd;
|
||||
const int n_layer = hparams.n_layer;
|
||||
|
||||
const int64_t n_mem = (int64_t)n_layer*n_ctx;
|
||||
const int64_t n_mem = n_layer*n_ctx;
|
||||
const int64_t n_elements = n_embd*n_mem;
|
||||
|
||||
cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
|
||||
|
|
@ -849,7 +851,8 @@ static const char *llama_ftype_name(enum llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
|
||||
return "mostly Q4_1, some F16";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_2: return "mostly Q4_2";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_3: return "mostly Q4_3";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
|
||||
default: return "unknown, may not work";
|
||||
}
|
||||
|
|
@ -1079,7 +1082,7 @@ static bool llama_eval_internal(
|
|||
// for big prompts, if BLAS is enabled, it is better to use only one thread
|
||||
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
|
||||
ggml_cgraph gf = {};
|
||||
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_cublas() ? 1 : n_threads;
|
||||
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
|
||||
|
||||
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||
memcpy(embd->data, tokens, N*ggml_element_size(embd));
|
||||
|
|
@ -1472,109 +1475,402 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
|
|||
// sampling
|
||||
//
|
||||
|
||||
static void sample_top_k(std::vector<std::pair<float, llama_vocab::id>> & logits_id, int top_k) {
|
||||
// find the top k tokens
|
||||
std::partial_sort(
|
||||
logits_id.begin(),
|
||||
logits_id.begin() + top_k, logits_id.end(),
|
||||
[](const std::pair<float, llama_vocab::id> & a, const std::pair<float, llama_vocab::id> & b) {
|
||||
return a.first > b.first;
|
||||
});
|
||||
void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
|
||||
assert(candidates->size > 0);
|
||||
|
||||
logits_id.resize(top_k);
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
// Sort the logits in descending order
|
||||
if (!candidates->sorted) {
|
||||
std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
|
||||
return a.logit > b.logit;
|
||||
});
|
||||
candidates->sorted = true;
|
||||
}
|
||||
|
||||
float max_l = candidates->data[0].logit;
|
||||
float cum_sum = 0.0f;
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
float p = expf(candidates->data[i].logit - max_l);
|
||||
candidates->data[i].p = p;
|
||||
cum_sum += p;
|
||||
}
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
candidates->data[i].p /= cum_sum;
|
||||
}
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
}
|
||||
|
||||
static llama_vocab::id llama_sample_top_p_top_k(
|
||||
llama_context & lctx,
|
||||
const std::vector<llama_vocab::id> & last_n_tokens,
|
||||
int top_k,
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty) {
|
||||
auto & rng = lctx.rng;
|
||||
void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
const int n_logits = lctx.model.hparams.n_vocab;
|
||||
k = std::max(k, (int) min_keep);
|
||||
k = std::min(k, (int) candidates->size);
|
||||
|
||||
const auto & logits = lctx.logits;
|
||||
const auto * plogits = logits.data() + logits.size() - n_logits;
|
||||
|
||||
if (temp <= 0) {
|
||||
// select the token with the highest logit directly
|
||||
float max_logit = plogits[0];
|
||||
llama_vocab::id max_id = 0;
|
||||
|
||||
for (int i = 1; i < n_logits; ++i) {
|
||||
if (plogits[i] > max_logit) {
|
||||
max_logit = plogits[i];
|
||||
max_id = i;
|
||||
}
|
||||
// Sort scores in descending order
|
||||
if (!candidates->sorted) {
|
||||
auto comp = [](const llama_token_data & a, const llama_token_data & b) {
|
||||
return a.logit > b.logit;
|
||||
};
|
||||
if (k == (int) candidates->size) {
|
||||
std::sort(candidates->data, candidates->data + candidates->size, comp);
|
||||
} else {
|
||||
std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
|
||||
}
|
||||
return max_id;
|
||||
candidates->sorted = true;
|
||||
}
|
||||
candidates->size = k;
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
|
||||
if (p >= 1.0f) {
|
||||
return;
|
||||
}
|
||||
|
||||
std::vector<std::pair<float, llama_vocab::id>> logits_id;
|
||||
logits_id.reserve(n_logits);
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
{
|
||||
const float scale = 1.0f/temp;
|
||||
for (int i = 0; i < n_logits; ++i) {
|
||||
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
|
||||
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
|
||||
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
|
||||
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
|
||||
if (plogits[i] < 0.0f) {
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
|
||||
} else {
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
|
||||
}
|
||||
} else {
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale, i));
|
||||
}
|
||||
llama_sample_softmax(ctx, candidates);
|
||||
|
||||
// Compute the cumulative probabilities
|
||||
float cum_sum = 0.0f;
|
||||
size_t last_idx = candidates->size;
|
||||
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
cum_sum += candidates->data[i].p;
|
||||
|
||||
// Check if the running sum is greater than p or if we have kept at least min_keep tokens
|
||||
if (cum_sum > p && i >= min_keep) {
|
||||
last_idx = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
sample_top_k(logits_id, top_k > 0 ? std::min(top_k, n_logits) : n_logits);
|
||||
// Resize the output vector to keep only the top-p tokens
|
||||
candidates->size = last_idx;
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
|
||||
if (z >= 1.0f || candidates->size <= 2) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
llama_sample_softmax(nullptr, candidates);
|
||||
|
||||
// Compute the first and second derivatives
|
||||
std::vector<float> first_derivatives(candidates->size - 1);
|
||||
std::vector<float> second_derivatives(candidates->size - 2);
|
||||
|
||||
for (size_t i = 0; i < first_derivatives.size(); ++i) {
|
||||
first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
|
||||
}
|
||||
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
||||
second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
|
||||
}
|
||||
|
||||
// Calculate absolute value of second derivatives
|
||||
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
||||
second_derivatives[i] = abs(second_derivatives[i]);
|
||||
}
|
||||
|
||||
// Normalize the second derivatives
|
||||
float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
|
||||
for (float & value : second_derivatives) {
|
||||
value /= second_derivatives_sum;
|
||||
}
|
||||
|
||||
float cum_sum = 0.0f;
|
||||
size_t last_idx = candidates->size;
|
||||
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
||||
cum_sum += second_derivatives[i];
|
||||
|
||||
// Check if the running sum is greater than z or if we have kept at least min_keep tokens
|
||||
if (cum_sum > z && i >= min_keep) {
|
||||
last_idx = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// Resize the output vector to keep only the tokens above the tail location
|
||||
candidates->size = last_idx;
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
|
||||
// Reference implementation:
|
||||
// https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
|
||||
if (p >= 1.0f) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
// Compute the softmax of logits and calculate entropy
|
||||
llama_sample_softmax(nullptr, candidates);
|
||||
|
||||
float entropy = 0.0f;
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
entropy += -candidates->data[i].p * logf(candidates->data[i].p);
|
||||
}
|
||||
|
||||
// Compute the absolute difference between negative log probability and entropy for each candidate
|
||||
std::vector<float> shifted_scores;
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
|
||||
shifted_scores.push_back(shifted_score);
|
||||
}
|
||||
|
||||
// Sort tokens based on the shifted_scores and their corresponding indices
|
||||
std::vector<size_t> indices(candidates->size);
|
||||
std::iota(indices.begin(), indices.end(), 0);
|
||||
|
||||
std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
|
||||
return shifted_scores[a] < shifted_scores[b];
|
||||
});
|
||||
|
||||
// Compute the cumulative probabilities
|
||||
float cum_sum = 0.0f;
|
||||
size_t last_idx = indices.size();
|
||||
|
||||
for (size_t i = 0; i < indices.size(); ++i) {
|
||||
size_t idx = indices[i];
|
||||
cum_sum += candidates->data[idx].p;
|
||||
|
||||
// Check if the running sum is greater than typical or if we have kept at least min_keep tokens
|
||||
if (cum_sum > p && i >= min_keep - 1) {
|
||||
last_idx = i + 1;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// Resize the output vector to keep only the locally typical tokens
|
||||
std::vector<llama_token_data> new_candidates;
|
||||
for (size_t i = 0; i < last_idx; ++i) {
|
||||
size_t idx = indices[i];
|
||||
new_candidates.push_back(candidates->data[idx]);
|
||||
}
|
||||
|
||||
// Replace the data in candidates with the new_candidates data
|
||||
std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
|
||||
candidates->size = new_candidates.size();
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
for (size_t i = 0; i < candidates_p->size; ++i) {
|
||||
candidates_p->data[i].logit /= temp;
|
||||
}
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens, size_t last_tokens_size, float penalty) {
|
||||
if (last_tokens_size == 0 || penalty == 1.0f) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
auto token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
|
||||
if (token_iter == last_tokens + last_tokens_size) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
|
||||
// This is common fix for this problem, which is to multiply by the penalty instead of dividing.
|
||||
if (candidates->data[i].logit <= 0) {
|
||||
candidates->data[i].logit *= penalty;
|
||||
} else {
|
||||
candidates->data[i].logit /= penalty;
|
||||
}
|
||||
}
|
||||
|
||||
candidates->sorted = false;
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens_p, size_t last_tokens_size, float alpha_frequency, float alpha_presence) {
|
||||
if (last_tokens_size == 0 || (alpha_frequency == 0.0f && alpha_presence == 0.0f)) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
// Create a frequency map to count occurrences of each token in last_tokens
|
||||
std::unordered_map<llama_token, int> token_count;
|
||||
for (size_t i = 0; i < last_tokens_size; ++i) {
|
||||
token_count[last_tokens_p[i]]++;
|
||||
}
|
||||
|
||||
// Apply frequency and presence penalties to the candidates
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
auto token_iter = token_count.find(candidates->data[i].id);
|
||||
if (token_iter == token_count.end()) {
|
||||
continue;
|
||||
}
|
||||
|
||||
int count = token_iter->second;
|
||||
candidates->data[i].logit -= float(count) * alpha_frequency + float(count > 0) * alpha_presence;
|
||||
}
|
||||
|
||||
candidates->sorted = false;
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
|
||||
assert(ctx);
|
||||
auto N = float(llama_n_vocab(ctx));
|
||||
int64_t t_start_sample_us;
|
||||
t_start_sample_us = ggml_time_us();
|
||||
|
||||
llama_sample_softmax(nullptr, candidates);
|
||||
|
||||
// Estimate s_hat using the most probable m tokens
|
||||
float s_hat = 0.0;
|
||||
float sum_ti_bi = 0.0;
|
||||
float sum_ti_sq = 0.0;
|
||||
for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
|
||||
float t_i = logf(float(i + 2) / float(i + 1));
|
||||
float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
|
||||
sum_ti_bi += t_i * b_i;
|
||||
sum_ti_sq += t_i * t_i;
|
||||
}
|
||||
s_hat = sum_ti_bi / sum_ti_sq;
|
||||
|
||||
// Compute k from the estimated s_hat and target surprise value
|
||||
float epsilon_hat = s_hat - 1;
|
||||
float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
|
||||
|
||||
// Sample the next word X using top-k sampling
|
||||
llama_sample_top_k(nullptr, candidates, int(k));
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
llama_token X = llama_sample_token(ctx, candidates);
|
||||
t_start_sample_us = ggml_time_us();
|
||||
|
||||
// Compute error as the difference between observed surprise and target surprise value
|
||||
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
||||
return candidate.id == X;
|
||||
}));
|
||||
float observed_surprise = -log2f(candidates->data[X_idx].p);
|
||||
float e = observed_surprise - tau;
|
||||
|
||||
// Update mu using the learning rate and error
|
||||
*mu = *mu - eta * e;
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
ctx->n_sample++;
|
||||
}
|
||||
return X;
|
||||
}
|
||||
|
||||
llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
|
||||
assert(ctx);
|
||||
int64_t t_start_sample_us;
|
||||
t_start_sample_us = ggml_time_us();
|
||||
|
||||
llama_sample_softmax(ctx, candidates);
|
||||
|
||||
// Truncate the words with surprise values greater than mu
|
||||
candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
||||
return -log2f(candidate.p) > *mu;
|
||||
}));
|
||||
|
||||
// Normalize the probabilities of the remaining words
|
||||
llama_sample_softmax(ctx, candidates);
|
||||
|
||||
// Sample the next word X from the remaining words
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
llama_token X = llama_sample_token(ctx, candidates);
|
||||
t_start_sample_us = ggml_time_us();
|
||||
|
||||
// Compute error as the difference between observed surprise and target surprise value
|
||||
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
||||
return candidate.id == X;
|
||||
}));
|
||||
float observed_surprise = -log2f(candidates->data[X_idx].p);
|
||||
float e = observed_surprise - tau;
|
||||
|
||||
// Update mu using the learning rate and error
|
||||
*mu = *mu - eta * e;
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
return X;
|
||||
}
|
||||
|
||||
llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
// Find max element
|
||||
auto max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
|
||||
return a.logit < b.logit;
|
||||
});
|
||||
|
||||
llama_token result = max_iter->id;
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
ctx->n_sample++;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
|
||||
assert(ctx);
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
llama_sample_softmax(nullptr, candidates);
|
||||
|
||||
// compute probs for the top k tokens
|
||||
std::vector<float> probs;
|
||||
probs.reserve(logits_id.size());
|
||||
|
||||
float maxl = logits_id[0].first;
|
||||
double sum = 0.0;
|
||||
for (const auto & kv : logits_id) {
|
||||
const float p = expf(kv.first - maxl);
|
||||
probs.push_back(p);
|
||||
sum += p;
|
||||
probs.reserve(candidates->size);
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
probs.push_back(candidates->data[i].p);
|
||||
}
|
||||
|
||||
// normalize the probs
|
||||
for (auto & p : probs) {
|
||||
p /= sum;
|
||||
}
|
||||
|
||||
if (top_p < 1.0) {
|
||||
double cumsum = 0.0;
|
||||
for (int i = 0; i < (int) probs.size(); i++) {
|
||||
cumsum += probs[i];
|
||||
if (cumsum >= top_p) {
|
||||
probs.resize(i + 1);
|
||||
logits_id.resize(i + 1);
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//printf("\n");
|
||||
//for (int i = 0; i < (int) 10; i++) {
|
||||
// printf("%d: '%s' %f\n", i, lctx.vocab.id_to_token.at(logits_id[i].second).tok.c_str(), probs[i]);
|
||||
//}
|
||||
//printf("\n\n");
|
||||
//exit(0);
|
||||
|
||||
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
||||
auto & rng = ctx->rng;
|
||||
int idx = dist(rng);
|
||||
|
||||
return logits_id[idx].second;
|
||||
llama_token result = candidates->data[idx].id;
|
||||
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
ctx->n_sample++;
|
||||
return result;
|
||||
}
|
||||
|
||||
//
|
||||
|
|
@ -1587,7 +1883,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_2: quantized_type = GGML_TYPE_Q4_2; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_3: quantized_type = GGML_TYPE_Q4_3; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
|
||||
default: throw format("invalid output file type %d\n", ftype);
|
||||
};
|
||||
|
|
@ -2082,6 +2379,13 @@ int llama_get_kv_cache_token_count(struct llama_context * ctx) {
|
|||
|
||||
#define LLAMA_MAX_RNG_STATE 64*1024
|
||||
|
||||
void llama_set_rng_seed(struct llama_context * ctx, int seed) {
|
||||
if (seed <= 0) {
|
||||
seed = time(NULL);
|
||||
}
|
||||
ctx->rng.seed(seed);
|
||||
}
|
||||
|
||||
// Returns the size of the state
|
||||
size_t llama_get_state_size(struct llama_context * ctx) {
|
||||
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
|
||||
|
|
@ -2337,33 +2641,8 @@ llama_token llama_token_eos() {
|
|||
return 2;
|
||||
}
|
||||
|
||||
llama_token llama_sample_top_p_top_k(
|
||||
llama_context * ctx,
|
||||
const llama_token * last_n_tokens_data,
|
||||
int last_n_tokens_size,
|
||||
int top_k,
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
llama_token result = 0;
|
||||
|
||||
// TODO: avoid this ...
|
||||
const auto last_n_tokens = std::vector<llama_token>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size);
|
||||
|
||||
result = llama_sample_top_p_top_k(
|
||||
*ctx,
|
||||
last_n_tokens,
|
||||
top_k,
|
||||
top_p,
|
||||
temp,
|
||||
repeat_penalty);
|
||||
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
ctx->n_sample++;
|
||||
|
||||
return result;
|
||||
llama_token llama_token_nl() {
|
||||
return 13;
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -2416,3 +2695,56 @@ std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_te
|
|||
return ctx->model.tensors_by_name;
|
||||
}
|
||||
|
||||
size_t llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
|
||||
// TODO leverage mmap
|
||||
llama_file file(path_session, "rb");
|
||||
const uint32_t magic = file.read_u32();
|
||||
const uint32_t version = file.read_u32();
|
||||
|
||||
if (!(magic == 'ggsn' && version == 0)) {
|
||||
fprintf(stderr, "%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
|
||||
return 0;
|
||||
}
|
||||
|
||||
llama_hparams session_hparams;
|
||||
file.read_raw(&session_hparams, sizeof(llama_hparams));
|
||||
|
||||
// REVIEW
|
||||
if (session_hparams != ctx->model.hparams) {
|
||||
fprintf(stderr, "%s : model hparams didn't match from session file!\n", __func__);
|
||||
return 0;
|
||||
}
|
||||
|
||||
const uint32_t n_token_count = file.read_u32();
|
||||
LLAMA_ASSERT(n_token_capacity >= n_token_count);
|
||||
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
|
||||
*n_token_count_out = n_token_count;
|
||||
|
||||
const size_t n_state_size = file.size - file.tell();
|
||||
const size_t n_orig_state_size = llama_get_state_size(ctx);
|
||||
if (n_state_size != n_orig_state_size) {
|
||||
fprintf(stderr, "%s : failed to validate state size\n", __func__);
|
||||
}
|
||||
std::unique_ptr<uint8_t[]> state_data(new uint8_t[n_state_size]);
|
||||
file.read_raw(state_data.get(), n_state_size);
|
||||
return llama_set_state_data(ctx, state_data.get());
|
||||
}
|
||||
|
||||
size_t llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
|
||||
// TODO save temp & swap
|
||||
llama_file file(path_session, "wb");
|
||||
|
||||
const size_t n_state_size = llama_get_state_size(ctx);
|
||||
std::unique_ptr<uint8_t[]> state_data(new uint8_t[n_state_size]);
|
||||
llama_copy_state_data(ctx, state_data.get());
|
||||
|
||||
file.write_u32('ggsn'); // magic
|
||||
file.write_u32(0); // version
|
||||
file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
|
||||
|
||||
file.write_u32((uint32_t) n_token_count); // REVIEW
|
||||
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
|
||||
|
||||
file.write_raw(state_data.get(), n_state_size);
|
||||
return n_state_size; // REVIEW
|
||||
}
|
||||
|
|
|
|||
75
llama.h
75
llama.h
|
|
@ -39,12 +39,16 @@ extern "C" {
|
|||
|
||||
typedef struct llama_token_data {
|
||||
llama_token id; // token id
|
||||
|
||||
float logit; // log-odds of the token
|
||||
float p; // probability of the token
|
||||
float plog; // log probability of the token
|
||||
|
||||
} llama_token_data;
|
||||
|
||||
typedef struct llama_token_data_array {
|
||||
llama_token_data * data;
|
||||
size_t size;
|
||||
bool sorted;
|
||||
} llama_token_data_array;
|
||||
|
||||
typedef void (*llama_progress_callback)(float progress, void *ctx);
|
||||
|
||||
struct llama_context_params {
|
||||
|
|
@ -73,8 +77,10 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
|
||||
LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // except 1d tensors
|
||||
// LLAMA_FTYPE_MOSTLY_Q4_3 (6) support has been removed
|
||||
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
|
||||
};
|
||||
|
||||
LLAMA_API struct llama_context_params llama_context_default_params();
|
||||
|
|
@ -116,6 +122,9 @@ extern "C" {
|
|||
// Returns the number of tokens in the KV cache
|
||||
LLAMA_API int llama_get_kv_cache_token_count(struct llama_context * ctx);
|
||||
|
||||
// Sets the current rng seed.
|
||||
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
|
||||
|
||||
// Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
|
||||
LLAMA_API size_t llama_get_state_size(struct llama_context * ctx);
|
||||
|
||||
|
|
@ -128,6 +137,10 @@ extern "C" {
|
|||
// Returns the number of bytes read
|
||||
LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src);
|
||||
|
||||
// Save/load session file
|
||||
LLAMA_API size_t llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
|
||||
LLAMA_API size_t llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
|
||||
|
||||
// Run the llama inference to obtain the logits and probabilities for the next token.
|
||||
// tokens + n_tokens is the provided batch of new tokens to process
|
||||
// n_past is the number of tokens to use from previous eval calls
|
||||
|
|
@ -172,16 +185,52 @@ extern "C" {
|
|||
// Special tokens
|
||||
LLAMA_API llama_token llama_token_bos();
|
||||
LLAMA_API llama_token llama_token_eos();
|
||||
LLAMA_API llama_token llama_token_nl();
|
||||
|
||||
// TODO: improve the last_n_tokens interface ?
|
||||
LLAMA_API llama_token llama_sample_top_p_top_k(
|
||||
struct llama_context * ctx,
|
||||
const llama_token * last_n_tokens_data,
|
||||
int last_n_tokens_size,
|
||||
int top_k,
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty);
|
||||
// Sampling functions
|
||||
|
||||
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||||
LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens, size_t last_tokens_size, float penalty);
|
||||
|
||||
/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
|
||||
LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
|
||||
|
||||
/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
|
||||
LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
|
||||
/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||||
LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep = 1);
|
||||
|
||||
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||||
LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
|
||||
|
||||
/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
|
||||
LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep = 1);
|
||||
|
||||
/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
|
||||
LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
|
||||
LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
|
||||
|
||||
/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||||
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||||
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||||
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||||
/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
|
||||
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||||
LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
|
||||
|
||||
/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||||
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||||
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||||
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||||
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||||
LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
|
||||
|
||||
/// @details Selects the token with the highest probability.
|
||||
LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
|
||||
/// @details Randomly selects a token from the candidates based on their probabilities.
|
||||
LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
|
||||
// Performance information
|
||||
LLAMA_API void llama_print_timings(struct llama_context * ctx);
|
||||
|
|
|
|||
|
|
@ -8,4 +8,5 @@ endfunction()
|
|||
# llama_add_test(test-double-float.c) # 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)
|
||||
|
|
|
|||
199
tests/test-sampling.cpp
Normal file
199
tests/test-sampling.cpp
Normal file
|
|
@ -0,0 +1,199 @@
|
|||
#include "llama.h"
|
||||
#include "ggml.h"
|
||||
#include <cassert>
|
||||
#include <cmath>
|
||||
#include <numeric>
|
||||
#include <cassert>
|
||||
#include <iostream>
|
||||
#include <vector>
|
||||
#include <algorithm>
|
||||
|
||||
|
||||
void dump(const llama_token_data_array * candidates) {
|
||||
for (size_t i = 0; i < candidates->size; i++) {
|
||||
printf("%d: %f (%f)\n", candidates->data[i].id, candidates->data[i].p, candidates->data[i].logit);
|
||||
}
|
||||
}
|
||||
|
||||
#define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0)
|
||||
|
||||
|
||||
void test_top_k(const std::vector<float> & probs,
|
||||
const std::vector<float> & expected_probs,
|
||||
int k) {
|
||||
size_t n_vocab = probs.size();
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
|
||||
float logit = log(probs[token_id]);
|
||||
candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
llama_sample_softmax(nullptr, &candidates_p);
|
||||
DUMP(&candidates_p);
|
||||
llama_sample_top_k(nullptr, &candidates_p, k);
|
||||
DUMP(&candidates_p);
|
||||
|
||||
assert(candidates_p.size == expected_probs.size());
|
||||
for (size_t i = 0; i < candidates_p.size; i++) {
|
||||
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-5);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void test_top_p(const std::vector<float> & probs,
|
||||
const std::vector<float> & expected_probs,
|
||||
float p) {
|
||||
|
||||
size_t n_vocab = probs.size();
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
|
||||
float logit = log(probs[token_id]);
|
||||
candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
llama_sample_softmax(nullptr, &candidates_p);
|
||||
DUMP(&candidates_p);
|
||||
llama_sample_top_p(nullptr, &candidates_p, p);
|
||||
DUMP(&candidates_p);
|
||||
|
||||
assert(candidates_p.size == expected_probs.size());
|
||||
for (size_t i = 0; i < candidates_p.size; i++) {
|
||||
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void test_tfs(const std::vector<float> & probs,
|
||||
const std::vector<float> & expected_probs,
|
||||
float z) {
|
||||
size_t n_vocab = probs.size();
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
|
||||
float logit = log(probs[token_id]);
|
||||
candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
DUMP(&candidates_p);
|
||||
llama_sample_tail_free(nullptr, &candidates_p, z);
|
||||
DUMP(&candidates_p);
|
||||
|
||||
assert(candidates_p.size == expected_probs.size());
|
||||
for (size_t i = 0; i < candidates_p.size; i++) {
|
||||
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void test_typical(const std::vector<float> & probs,
|
||||
const std::vector<float> & expected_probs,
|
||||
float p) {
|
||||
size_t n_vocab = probs.size();
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
|
||||
float logit = log(probs[token_id]);
|
||||
candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
DUMP(&candidates_p);
|
||||
llama_sample_typical(nullptr, &candidates_p, p);
|
||||
DUMP(&candidates_p);
|
||||
|
||||
assert(candidates_p.size == expected_probs.size());
|
||||
for (size_t i = 0; i < candidates_p.size; i++) {
|
||||
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void test_repetition_penalty(
|
||||
const std::vector<float> & probs,
|
||||
const std::vector<llama_token> & last_tokens,
|
||||
const std::vector<float> & expected_probs,
|
||||
float penalty) {
|
||||
assert(probs.size() == expected_probs.size());
|
||||
|
||||
size_t n_vocab = probs.size();
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
|
||||
float logit = log(probs[token_id]);
|
||||
candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
llama_sample_softmax(nullptr, &candidates_p);
|
||||
DUMP(&candidates_p);
|
||||
llama_sample_repetition_penalty(nullptr, &candidates_p, (llama_token *)last_tokens.data(), last_tokens.size(), penalty);
|
||||
llama_sample_softmax(nullptr, &candidates_p);
|
||||
DUMP(&candidates_p);
|
||||
|
||||
assert(candidates_p.size == expected_probs.size());
|
||||
for (size_t i = 0; i < candidates_p.size; i++) {
|
||||
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-6);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void test_frequency_presence_penalty(
|
||||
const std::vector<float> & probs,
|
||||
const std::vector<llama_token> & last_tokens,
|
||||
const std::vector<float> & expected_probs,
|
||||
float alpha_frequency, float alpha_presence) {
|
||||
assert(probs.size() == expected_probs.size());
|
||||
|
||||
size_t n_vocab = probs.size();
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
|
||||
float logit = log(probs[token_id]);
|
||||
candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
llama_sample_softmax(nullptr, &candidates_p);
|
||||
// DUMP(&candidates_p);
|
||||
llama_sample_frequency_and_presence_penalties(nullptr, &candidates_p, (llama_token *)last_tokens.data(), last_tokens.size(), alpha_frequency, alpha_presence);
|
||||
llama_sample_softmax(nullptr, &candidates_p);
|
||||
// DUMP(&candidates_p);
|
||||
|
||||
assert(candidates_p.size == expected_probs.size());
|
||||
for (size_t i = 0; i < candidates_p.size; i++) {
|
||||
assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
|
||||
}
|
||||
}
|
||||
|
||||
int main(void) {
|
||||
ggml_time_init();
|
||||
|
||||
test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4}, 1);
|
||||
test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2}, 3);
|
||||
|
||||
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4}, 0);
|
||||
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3}, 0.7);
|
||||
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2, 0.1}, 1);
|
||||
|
||||
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3}, 0.25);
|
||||
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.75);
|
||||
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.99);
|
||||
|
||||
test_typical({0.97, 0.01, 0.01, 0.01}, {0.97}, 0.5);
|
||||
test_typical({0.4, 0.2, 0.2, 0.2}, {0.2, 0.2, 0.2}, 0.5);
|
||||
|
||||
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.25, 0.25, 0.25, 0.25, 0}, 50.0);
|
||||
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.5, 0.5, 0, 0, 0}, 50.0);
|
||||
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.5, 0.5, 0, 0, 0}, 50.0);
|
||||
|
||||
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.249997, 0.249997, 0.249997, 0.249997, 0.000011}, 5.0, 5.0);
|
||||
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.499966, 0.499966, 0.000023, 0.000023, 0.000023}, 5.0, 5.0);
|
||||
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.499977, 0.499977, 0.000023, 0.000023, 0.000000}, 5.0, 5.0);
|
||||
|
||||
printf("OK\n");
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue