Merge remote-tracking branch 'origin/master'
This commit is contained in:
Elsa
commit
59484c6121
26 changed files with 4179 additions and 1756 deletions
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@ -67,11 +67,13 @@ endif()
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option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
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option(LLAMA_BLAS "llama: use BLAS" OFF)
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set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
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option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
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option(LLAMA_CUBLAS "llama: use CUDA" OFF)
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#option(LLAMA_CUDA_CUBLAS "llama: use cuBLAS for prompt processing" OFF)
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set(LLAMA_CUDA_MMQ_Y "64" CACHE STRING "llama: y tile size for mmq CUDA kernels")
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option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF)
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set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
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set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
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option(LLAMA_CUDA_DMMV_F16 "llama: use 16 bit floats for dmmv CUDA kernels" OFF)
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option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some calculations" OFF)
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set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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option(LLAMA_METAL "llama: use Metal" OFF)
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@ -251,6 +253,10 @@ if (LLAMA_CUBLAS)
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set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
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add_compile_definitions(GGML_USE_CUBLAS)
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# if (LLAMA_CUDA_CUBLAS)
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# add_compile_definitions(GGML_CUDA_CUBLAS)
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# endif()
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add_compile_definitions(GGML_CUDA_MMQ_Y=${LLAMA_CUDA_MMQ_Y})
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if (LLAMA_CUDA_FORCE_DMMV)
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add_compile_definitions(GGML_CUDA_FORCE_DMMV)
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endif()
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@ -259,8 +265,8 @@ if (LLAMA_CUBLAS)
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if (DEFINED LLAMA_CUDA_DMMV_Y)
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add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility
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endif()
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if (LLAMA_CUDA_DMMV_F16)
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add_compile_definitions(GGML_CUDA_DMMV_F16)
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if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
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add_compile_definitions(GGML_CUDA_F16)
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endif()
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add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
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@ -271,10 +277,14 @@ if (LLAMA_CUBLAS)
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endif()
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if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
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# 52 == lowest CUDA 12 standard
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# 60 == f16 CUDA intrinsics
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# 61 == integer CUDA intrinsics
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# 70 == (assumed) compute capability at which unrolling a loop in mul_mat_q kernels is faster
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if (LLAMA_CUDA_DMMV_F16)
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set(CMAKE_CUDA_ARCHITECTURES "60;61") # needed for f16 CUDA intrinsics
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set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
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else()
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set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
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set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
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endif()
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endif()
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message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
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@ -357,6 +367,7 @@ if (LLAMA_ALL_WARNINGS)
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-Wshadow
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-Wstrict-prototypes
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-Wpointer-arith
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-Wmissing-prototypes
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)
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set(cxx_flags
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-Wall
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@ -496,6 +507,8 @@ endif()
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add_library(ggml OBJECT
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ggml.c
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ggml.h
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ggml-alloc.c
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ggml-alloc.h
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${GGML_SOURCES_CUDA}
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${GGML_SOURCES_OPENCL}
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${GGML_SOURCES_METAL}
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27
Makefile
27
Makefile
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@ -63,7 +63,8 @@ ifdef LLAMA_SERVER_VERBOSE
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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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CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \
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-Wmissing-prototypes
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CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
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# OS specific
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@ -193,7 +194,7 @@ ifdef LLAMA_CUBLAS
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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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NVCCFLAGS = --forward-unknown-to-host-compiler
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NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
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ifdef LLAMA_CUDA_NVCC
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NVCC = $(LLAMA_CUDA_NVCC)
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else
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@ -219,19 +220,30 @@ else ifdef LLAMA_CUDA_DMMV_Y
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else
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NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
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endif # LLAMA_CUDA_MMV_Y
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ifdef LLAMA_CUDA_F16
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NVCCFLAGS += -DGGML_CUDA_F16
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endif # LLAMA_CUDA_F16
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ifdef LLAMA_CUDA_DMMV_F16
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NVCCFLAGS += -DGGML_CUDA_DMMV_F16
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NVCCFLAGS += -DGGML_CUDA_F16
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endif # LLAMA_CUDA_DMMV_F16
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ifdef LLAMA_CUDA_KQUANTS_ITER
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NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
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else
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NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
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endif
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ifdef LLAMA_CUDA_MMQ_Y
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NVCCFLAGS += -DGGML_CUDA_MMQ_Y=$(LLAMA_CUDA_MMQ_Y)
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else
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NVCCFLAGS += -DGGML_CUDA_MMQ_Y=64
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endif # LLAMA_CUDA_MMQ_Y
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#ifdef LLAMA_CUDA_CUBLAS
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# NVCCFLAGS += -DGGML_CUDA_CUBLAS
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#endif # LLAMA_CUDA_CUBLAS
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ifdef LLAMA_CUDA_CCBIN
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NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
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endif
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ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
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$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
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$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) -Wno-pedantic -c $< -o $@
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endif # LLAMA_CUBLAS
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ifdef LLAMA_CLBLAST
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@ -317,7 +329,12 @@ $(info )
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ggml.o: ggml.c ggml.h ggml-cuda.h
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$(CC) $(CFLAGS) -c $< -o $@
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llama.o: llama.cpp ggml.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
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ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
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$(CC) $(CFLAGS) -c $< -o $@
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OBJS += ggml-alloc.o
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llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
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$(CXX) $(CXXFLAGS) -c $< -o $@
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|
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common.o: examples/common.cpp examples/common.h
|
||||
|
|
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20
README.md
20
README.md
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@ -77,6 +77,7 @@ as the main playground for developing new features for the [ggml](https://github
|
|||
**Supported models:**
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||||
|
||||
- [X] LLaMA 🦙
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- [x] LLaMA 2 🦙🦙
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- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
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- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
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||||
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
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||||
|
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@ -399,12 +400,16 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
|
||||
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
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|
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<!---
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| LLAMA_CUDA_CUBLAS | Boolean | false | Use cuBLAS instead of custom CUDA kernels for prompt processing. Faster for all quantization formats except for q4_0 and q8_0, especially for k-quants. Increases VRAM usage (700 MiB for 7b, 970 MiB for 13b, 1430 MiB for 33b). |
|
||||
--->
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| Option | Legal values | Default | Description |
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||||
|-------------------------|------------------------|---------|-------------|
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||||
| LLAMA_CUDA_MMQ_Y | Positive integer >= 32 | 64 | Tile size in y direction when using the custom CUDA kernels for prompt processing. Higher values can be faster depending on the amount of shared memory available. Power of 2 heavily recommended. |
|
||||
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
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||||
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
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||||
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
|
||||
| LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. |
|
||||
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
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| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
|
||||
|
||||
- #### CLBlast
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|
|
@ -650,6 +655,19 @@ python3 convert.py pygmalion-7b/ --outtype q4_1
|
|||
- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository.
|
||||
- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data.
|
||||
|
||||
### Obtaining and using the Facebook LLaMA 2 model
|
||||
|
||||
- Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data.
|
||||
- Alternatively, if you want to save time and space, you can download already converted and quantized models from [TheBloke](https://huggingface.co/TheBloke), including:
|
||||
- [LLaMA 2 7B base](https://huggingface.co/TheBloke/Llama-2-7B-GGML)
|
||||
- [LLaMA 2 13B base](https://huggingface.co/TheBloke/Llama-2-13B-GGML)
|
||||
- [LLaMA 2 70B base](https://huggingface.co/TheBloke/Llama-2-70B-GGML)
|
||||
- [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGML)
|
||||
- [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML)
|
||||
- [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGML)
|
||||
- Specify `-eps 1e-5` for best generation quality
|
||||
- Specify `-gqa 8` for 70B models to work
|
||||
|
||||
### Verifying the model files
|
||||
|
||||
Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.
|
||||
|
|
|
|||
42
convert.py
Executable file → Normal file
42
convert.py
Executable file → Normal file
|
|
@ -133,7 +133,7 @@ TENSORS_SET = set(TENSORS_LIST)
|
|||
|
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def find_n_mult(n_ff: int, n_embd: int) -> int:
|
||||
# hardcoded magic range
|
||||
for n_mult in range(256, 1, -1):
|
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for n_mult in range(8192, 1, -1):
|
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calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
|
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if calc_ff == n_ff:
|
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return n_mult
|
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|
|
@ -146,6 +146,7 @@ class Params:
|
|||
n_mult: int
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n_head: int
|
||||
n_layer: int
|
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n_kv_head: Optional[int] # This parameter is only used for Llama 2
|
||||
|
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@staticmethod
|
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def guessed(model: 'LazyModel') -> 'Params':
|
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|
|
@ -172,6 +173,7 @@ class Params:
|
|||
n_mult = 256,
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n_head = n_head,
|
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n_layer = n_layer,
|
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n_kv_head = None,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -183,6 +185,7 @@ class Params:
|
|||
n_head = config["num_attention_heads"];
|
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n_layer = config["num_hidden_layers"];
|
||||
n_ff = config["intermediate_size"];
|
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n_kv_head = config.get("num_key_value_heads")
|
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|
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n_mult = find_n_mult(n_ff, n_embd);
|
||||
|
||||
|
|
@ -192,6 +195,7 @@ class Params:
|
|||
n_mult = n_mult,
|
||||
n_head = n_head,
|
||||
n_layer = n_layer,
|
||||
n_kv_head = n_kv_head,
|
||||
)
|
||||
|
||||
# LLaMA v2 70B params.json
|
||||
|
|
@ -215,6 +219,7 @@ class Params:
|
|||
n_mult = n_mult,
|
||||
n_head = n_head,
|
||||
n_layer = n_layer,
|
||||
n_kv_head = None,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -317,7 +322,9 @@ class GGMLVocab:
|
|||
Vocab = Union[SentencePieceVocab, GGMLVocab]
|
||||
|
||||
|
||||
def permute(weights: NDArray, n_head: int) -> NDArray:
|
||||
def permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
|
||||
if n_kv_head is not None and n_head != n_kv_head:
|
||||
n_head //= n_kv_head
|
||||
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
|
||||
.swapaxes(1, 2)
|
||||
.reshape(weights.shape))
|
||||
|
|
@ -368,7 +375,7 @@ class Tensor(metaclass=ABCMeta):
|
|||
@abstractmethod
|
||||
def astype(self, data_type: DataType) -> 'Tensor': ...
|
||||
@abstractmethod
|
||||
def permute(self, n_head: int) -> 'Tensor': ...
|
||||
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'Tensor': ...
|
||||
@abstractmethod
|
||||
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
|
||||
@abstractmethod
|
||||
|
|
@ -406,8 +413,8 @@ class UnquantizedTensor(Tensor):
|
|||
r = self.ndarray.shape[0] // 3
|
||||
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
|
||||
|
||||
def permute(self, n_head: int) -> 'UnquantizedTensor':
|
||||
return UnquantizedTensor(permute(self.ndarray, n_head))
|
||||
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'UnquantizedTensor':
|
||||
return UnquantizedTensor(permute(self.ndarray, n_head, n_kv_head))
|
||||
|
||||
|
||||
def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray:
|
||||
|
|
@ -455,26 +462,27 @@ class GGMLQuantizedTensor(Tensor):
|
|||
def to_ggml(self) -> 'GGMLQuantizedTensor':
|
||||
return self
|
||||
|
||||
def permute(self, n_head: int) -> 'GGMLQuantizedTensor':
|
||||
return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type)
|
||||
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'GGMLQuantizedTensor':
|
||||
return GGMLQuantizedTensor(permute(self.ndarray, n_head, n_kv_head), self.shape, self.data_type)
|
||||
|
||||
|
||||
GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor]
|
||||
|
||||
|
||||
class DeferredPermutedTensor(Tensor):
|
||||
def __init__(self, base: Tensor, n_head: int) -> None:
|
||||
def __init__(self, base: Tensor, n_head: int, n_kv_head: Optional[int] = None) -> None:
|
||||
self.base = base
|
||||
self.n_head = n_head
|
||||
self.n_kv_head = n_kv_head
|
||||
self.data_type = self.base.data_type
|
||||
|
||||
def astype(self, data_type: DataType) -> Tensor:
|
||||
return self.base.astype(data_type).permute(self.n_head)
|
||||
return self.base.astype(data_type).permute(self.n_head, self.n_kv_head)
|
||||
|
||||
def to_ggml(self) -> GGMLCompatibleTensor:
|
||||
return self.base.to_ggml().permute(self.n_head)
|
||||
return self.base.to_ggml().permute(self.n_head, self.n_kv_head)
|
||||
|
||||
def permute(self, n_head: int) -> Tensor:
|
||||
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
|
||||
raise Exception("shouldn't permute twice")
|
||||
|
||||
|
||||
|
|
@ -566,8 +574,8 @@ class GPTQForLLaMaQuantizedTensor(Tensor):
|
|||
ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False)
|
||||
return ret
|
||||
|
||||
def permute(self, n_head: int) -> Tensor:
|
||||
return DeferredPermutedTensor(self, n_head)
|
||||
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
|
||||
return DeferredPermutedTensor(self, n_head, n_kv_head)
|
||||
|
||||
def to_ggml(self) -> GGMLQuantizedTensor:
|
||||
# The output format looks like this:
|
||||
|
|
@ -698,10 +706,10 @@ def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus:
|
|||
return ModelPlus(model, paths, format, vocab)
|
||||
|
||||
|
||||
def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor:
|
||||
def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_kv_head: Optional[int] = None) -> LazyTensor:
|
||||
def load() -> Tensor:
|
||||
return lazy_tensor.load().permute(n_head)
|
||||
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
|
||||
return lazy_tensor.load().permute(n_head, n_kv_head)
|
||||
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_kv_head}) ' + lazy_tensor.description)
|
||||
|
||||
def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
|
||||
def load() -> Tensor:
|
||||
|
|
@ -726,7 +734,7 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
|
|||
for i in itertools.count():
|
||||
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
|
||||
out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
|
||||
out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head)
|
||||
out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_kv_head)
|
||||
out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
|
||||
elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
|
||||
out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)
|
||||
|
|
|
|||
|
|
@ -377,6 +377,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
}
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
|
||||
#endif // GGML_USE_CUBLAS
|
||||
} else if (arg == "--mul-mat-q" || arg == "-mmq") {
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
params.mul_mat_q = true;
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n");
|
||||
#endif // GGML_USE_CUBLAS
|
||||
} else if (arg == "--low-vram" || arg == "-lv") {
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
|
|
@ -402,8 +408,14 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
params.antiprompt.push_back(argv[i]);
|
||||
} else if (arg == "--perplexity") {
|
||||
params.perplexity = true;
|
||||
} else if (arg == "--perplexity-lines") {
|
||||
params.perplexity_lines = true;
|
||||
} else if (arg == "--hellaswag") {
|
||||
params.hellaswag = true;
|
||||
} else if (arg == "--hellaswag-tasks") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.hellaswag_tasks = std::stoi(argv[i]);
|
||||
} else if (arg == "--ignore-eos") {
|
||||
params.logit_bias[llama_token_eos()] = -INFINITY;
|
||||
} else if (arg == "--no-penalize-nl") {
|
||||
|
|
@ -559,8 +571,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
|
||||
fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp);
|
||||
fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n");
|
||||
fprintf(stdout, " --perplexity-lines compute perplexity over each line of the prompt\n");
|
||||
fprintf(stdout, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
|
||||
fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
|
||||
fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %d)\n", params.hellaswag_tasks);
|
||||
fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
|
||||
fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
|
||||
if (llama_mlock_supported()) {
|
||||
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
||||
|
|
@ -578,6 +591,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
|
||||
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n" );
|
||||
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
|
||||
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
|
||||
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
|
||||
#endif
|
||||
fprintf(stdout, " --mtest compute maximum memory usage\n");
|
||||
fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");
|
||||
|
|
@ -630,6 +646,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
|
|||
lparams.main_gpu = params.main_gpu;
|
||||
lparams.tensor_split = params.tensor_split;
|
||||
lparams.low_vram = params.low_vram;
|
||||
lparams.mul_mat_q = params.mul_mat_q;
|
||||
lparams.seed = params.seed;
|
||||
lparams.f16_kv = params.memory_f16;
|
||||
lparams.use_mmap = params.use_mmap;
|
||||
|
|
|
|||
|
|
@ -70,7 +70,11 @@ struct gpt_params {
|
|||
std::string lora_adapter = ""; // lora adapter path
|
||||
std::string lora_base = ""; // base model path for the lora adapter
|
||||
|
||||
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
|
||||
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
|
||||
|
||||
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
|
||||
bool mul_mat_q = false; // if true, use experimental mul_mat_q kernels
|
||||
bool memory_f16 = true; // use f16 instead of f32 for memory kv
|
||||
bool random_prompt = false; // do not randomize prompt if none provided
|
||||
bool use_color = false; // use color to distinguish generations and inputs
|
||||
|
|
@ -86,7 +90,6 @@ struct gpt_params {
|
|||
bool instruct = false; // instruction mode (used for Alpaca models)
|
||||
bool penalize_nl = true; // consider newlines as a repeatable token
|
||||
bool perplexity = false; // compute perplexity over the prompt
|
||||
bool perplexity_lines = false; // compute perplexity over each line of the prompt
|
||||
bool use_mmap = true; // use mmap for faster loads
|
||||
bool use_mlock = false; // use mlock to keep model in memory
|
||||
bool mem_test = false; // compute maximum memory usage
|
||||
|
|
|
|||
|
|
@ -202,9 +202,9 @@ Example usage: `--top-p 0.95`
|
|||
|
||||
- `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
|
||||
|
||||
Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. The method adjusts the logits (token probabilities) by raising them to the power of the parameter z. A higher value of z (e.g., 2.0) will further suppress less likely tokens from the tail of the distribution, while a value of 1.0 disables the effect of TFS. By setting the parameter z, you can control how much the probabilities of less likely tokens are reduced.
|
||||
Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens, and thus disables the effect of TFS.
|
||||
|
||||
Example usage: `--tfs 2.0`
|
||||
Example usage: `--tfs 0.95`
|
||||
|
||||
### Locally Typical Sampling
|
||||
|
||||
|
|
|
|||
|
|
@ -121,8 +121,23 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
|
|||
printf("\n");
|
||||
}
|
||||
|
||||
void perplexity_lines(llama_context * ctx, const gpt_params & params) {
|
||||
// Calculates perplexity over each line of the prompt
|
||||
void hellaswag_score(llama_context * ctx, const gpt_params & params) {
|
||||
// Calculates hellaswag score (acc_norm) from prompt
|
||||
//
|
||||
// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
|
||||
// All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68
|
||||
//
|
||||
// All 10042 tasks should be extracted to keep the results standardized like other implementations.
|
||||
//
|
||||
// Datafile layout:
|
||||
// ['??'] denotes json fields
|
||||
// 6 lines per task:
|
||||
// ['activity_label'] + ": " +['ctx'] - The first part of the query, the context
|
||||
// ['label'] - The index the best common sense ending aka gold ending
|
||||
// ['endings'][0] - Endings added to the first part of the query
|
||||
// ['endings'][1]
|
||||
// ['endings'][2]
|
||||
// ['endings'][3]
|
||||
|
||||
std::vector<std::string> prompt_lines;
|
||||
std::istringstream strstream(params.prompt);
|
||||
|
|
@ -132,63 +147,149 @@ void perplexity_lines(llama_context * ctx, const gpt_params & params) {
|
|||
prompt_lines.push_back(line);
|
||||
}
|
||||
|
||||
const int n_vocab = llama_n_vocab(ctx);
|
||||
|
||||
int counttotal = 0;
|
||||
size_t n_lines = prompt_lines.size();
|
||||
|
||||
double nll = 0.0;
|
||||
|
||||
fprintf(stderr, "%s: calculating perplexity over %lu lines\n", __func__, n_lines);
|
||||
|
||||
printf("\nLine\tPPL line\tPPL cumulative\n");
|
||||
|
||||
for (size_t i = 0; i < n_lines; ++i) {
|
||||
|
||||
// Tokenize and insert BOS at start
|
||||
std::vector<int> batch_embd = ::llama_tokenize(ctx, prompt_lines[i], true);
|
||||
|
||||
size_t batch_size = batch_embd.size();
|
||||
|
||||
// Stop if line is too long
|
||||
if( batch_size > (size_t)params.n_ctx ) {
|
||||
fprintf(stderr, "%s : tokens in line %lu > n_ctxl\n", __func__, i);
|
||||
if( prompt_lines.size() % 6 != 0) {
|
||||
fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__);
|
||||
return;
|
||||
}
|
||||
|
||||
if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) {
|
||||
size_t hs_task_count = prompt_lines.size()/6;
|
||||
fprintf(stderr, "%s : loaded %lu tasks from prompt.\n", __func__, hs_task_count);
|
||||
|
||||
// This is needed as usual for LLaMA models
|
||||
bool prepend_bos = true;
|
||||
|
||||
// Number of tasks to use when computing the score
|
||||
if ( params.hellaswag_tasks < hs_task_count ) {
|
||||
hs_task_count = params.hellaswag_tasks;
|
||||
}
|
||||
|
||||
// The tasks should be randomized so the score stabilizes quickly.
|
||||
bool randomize_tasks = true;
|
||||
|
||||
// The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now
|
||||
std::mt19937 rng(1);
|
||||
|
||||
// Dataholder for hellaswag tasks
|
||||
struct hs_data_t {
|
||||
std::string context;
|
||||
size_t gold_ending_idx;
|
||||
std::string ending[4];
|
||||
size_t ending_logprob_count[4];
|
||||
double ending_logprob[4];
|
||||
};
|
||||
|
||||
fprintf(stderr, "%s : selecting %lu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") );
|
||||
|
||||
// Select and read data from prompt lines
|
||||
hs_data_t *hs_data = new hs_data_t[hs_task_count];
|
||||
for (size_t i=0; i < hs_task_count; i++) {
|
||||
size_t idx = i;
|
||||
|
||||
// Select a random example of those left in the prompt
|
||||
if (randomize_tasks) {
|
||||
std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ;
|
||||
idx = dist(rng);
|
||||
}
|
||||
|
||||
hs_data[i].context = prompt_lines[idx*6];
|
||||
hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
|
||||
for (size_t j=0; j < 4; j++) {
|
||||
hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
|
||||
}
|
||||
|
||||
// Delete the selected random example from the prompt
|
||||
if (randomize_tasks) {
|
||||
prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) );
|
||||
}
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__);
|
||||
printf("\ntask\tacc_norm\n");
|
||||
|
||||
double acc = 0.0f;
|
||||
const int n_vocab = llama_n_vocab(ctx);
|
||||
|
||||
for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
|
||||
|
||||
// Tokenize the context to count tokens
|
||||
std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
|
||||
size_t context_size = context_embd.size();
|
||||
|
||||
for (size_t ending_idx=0;ending_idx<4;ending_idx++) {
|
||||
|
||||
// Tokenize the query
|
||||
std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos);
|
||||
size_t query_size = query_embd.size();
|
||||
|
||||
// Stop if query wont fit the ctx window
|
||||
if (query_size > (size_t)params.n_ctx) {
|
||||
fprintf(stderr, "%s : number of tokens in query %lu > n_ctxl\n", __func__, query_size);
|
||||
return;
|
||||
}
|
||||
|
||||
// Speedup small evaluations by evaluating atleast 32 tokens
|
||||
if (query_size < 32) {
|
||||
query_embd.resize(32);
|
||||
}
|
||||
|
||||
// Evaluate the query
|
||||
if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return;
|
||||
}
|
||||
|
||||
const auto batch_logits = llama_get_logits(ctx);
|
||||
const auto query_logits = llama_get_logits(ctx);
|
||||
std::vector<float> logits;
|
||||
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
|
||||
logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab);
|
||||
|
||||
double nllline = 0.0;
|
||||
int countline = 0;
|
||||
hs_data[task_idx].ending_logprob_count[ending_idx] = 0;
|
||||
hs_data[task_idx].ending_logprob[ending_idx] = 0.0f;
|
||||
|
||||
// Perplexity over second half of the line
|
||||
for (size_t j = batch_size/2; j < batch_size - 1; ++j) {
|
||||
// Calculate the logprobs over the ending
|
||||
for (size_t j = context_size-1; j < query_size - 1; j++) {
|
||||
// Calculate probability of next token, given the previous ones.
|
||||
const std::vector<float> tok_logits(
|
||||
logits.begin() + (j + 0) * n_vocab,
|
||||
logits.begin() + (j + 1) * n_vocab);
|
||||
|
||||
const float prob = softmax(tok_logits)[batch_embd[ j + 1]];
|
||||
const float prob = softmax(tok_logits)[query_embd[ j + 1]];
|
||||
|
||||
nllline += -std::log(prob);
|
||||
++countline;
|
||||
hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);
|
||||
hs_data[task_idx].ending_logprob_count[ending_idx]++;
|
||||
}
|
||||
|
||||
nll += nllline;
|
||||
counttotal += countline;
|
||||
// Calculate the mean token logprob for acc_norm
|
||||
hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx];
|
||||
|
||||
// perplexity is e^(average negative log-likelihood)
|
||||
printf("%lu\t%.8lf\t%.8lf\n", i + 1, std::exp(nllline/countline), std::exp(nll / counttotal) );
|
||||
|
||||
// printf("task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\n",
|
||||
// task_idx,ending_idx,whole_size,context_size, hs_data[task_idx].ending_logprob_count[ending_idx], hs_data[task_idx].ending_logprob[ending_idx] );
|
||||
}
|
||||
|
||||
// Find the ending with maximum logprob
|
||||
size_t ending_logprob_max_idx = -1;
|
||||
double ending_logprob_max_val = -INFINITY;
|
||||
for (size_t j=0; j < 4; j++) {
|
||||
if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
|
||||
ending_logprob_max_idx = j;
|
||||
ending_logprob_max_val = hs_data[task_idx].ending_logprob[j];
|
||||
}
|
||||
}
|
||||
|
||||
// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
|
||||
|
||||
// If the gold ending got the maximum logprobe add one accuracy point
|
||||
if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) {
|
||||
acc += 1.0;
|
||||
}
|
||||
|
||||
// Print the accumulated accuracy mean x 100
|
||||
printf("%li\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
|
||||
fflush(stdout);
|
||||
}
|
||||
|
||||
delete [] hs_data;
|
||||
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
|
|
@ -240,8 +341,8 @@ int main(int argc, char ** argv) {
|
|||
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
|
||||
}
|
||||
|
||||
if (params.perplexity_lines) {
|
||||
perplexity_lines(ctx, params);
|
||||
if (params.hellaswag) {
|
||||
hellaswag_score(ctx, params);
|
||||
} else {
|
||||
perplexity(ctx, params);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ int main(int argc, char ** argv) {
|
|||
auto lparams = llama_context_default_params();
|
||||
|
||||
lparams.n_ctx = params.n_ctx;
|
||||
lparams.n_gqa = params.n_gqa;
|
||||
lparams.seed = params.seed;
|
||||
lparams.f16_kv = params.memory_f16;
|
||||
lparams.use_mmap = params.use_mmap;
|
||||
|
|
|
|||
26
examples/server-llama2-13B.sh
Normal file
26
examples/server-llama2-13B.sh
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
|
||||
cd "$(dirname "$0")/.." || exit
|
||||
|
||||
# Specify the model you want to use here:
|
||||
MODEL="${MODEL:-./models/llama-2-13b-chat.ggmlv3.q5_K_M.bin}"
|
||||
PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat-system.txt}
|
||||
|
||||
# Adjust to the number of CPU cores you want to use.
|
||||
N_THREAD="${N_THREAD:-12}"
|
||||
|
||||
# Note: you can also override the generation options by specifying them on the command line:
|
||||
GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 4096 --batch-size 1024}"
|
||||
|
||||
|
||||
# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
|
||||
./server $GEN_OPTIONS \
|
||||
--model "$MODEL" \
|
||||
--threads "$N_THREAD" \
|
||||
--rope-freq-scale 1.0 \
|
||||
"$@"
|
||||
|
||||
# I used this to test the model with mps, but omitted it from the general purpose. If you want to use it, just specify it on the command line.
|
||||
# -ngl 1 \
|
||||
|
|
@ -163,7 +163,7 @@ node .
|
|||
|
||||
`content`: Set the text to tokenize.
|
||||
|
||||
Note that the special `BOS` token is not added in fron of the text and also a space character is not inserted automatically as it is for `/completion`.
|
||||
Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
|
||||
|
||||
- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
|
||||
|
||||
|
|
|
|||
109
examples/server/chat-llama2.sh
Normal file
109
examples/server/chat-llama2.sh
Normal file
|
|
@ -0,0 +1,109 @@
|
|||
#!/bin/bash
|
||||
|
||||
API_URL="${API_URL:-http://127.0.0.1:8080}"
|
||||
|
||||
CHAT=(
|
||||
"Hello, Assistant."
|
||||
"Hello. How may I help you today?"
|
||||
)
|
||||
|
||||
INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
|
||||
|
||||
trim() {
|
||||
shopt -s extglob
|
||||
set -- "${1##+([[:space:]])}"
|
||||
printf "%s" "${1%%+([[:space:]])}"
|
||||
}
|
||||
|
||||
trim_trailing() {
|
||||
shopt -s extglob
|
||||
printf "%s" "${1%%+([[:space:]])}"
|
||||
}
|
||||
|
||||
format_prompt() {
|
||||
if [[ "${#CHAT[@]}" -eq 0 ]]; then
|
||||
echo -n "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>"
|
||||
else
|
||||
LAST_INDEX=$(( ${#CHAT[@]} - 1 ))
|
||||
echo -n "${CHAT[$LAST_INDEX]}\n[INST] $1 [/INST]"
|
||||
fi
|
||||
}
|
||||
|
||||
tokenize() {
|
||||
curl \
|
||||
--silent \
|
||||
--request POST \
|
||||
--url "${API_URL}/tokenize" \
|
||||
--header "Content-Type: application/json" \
|
||||
--data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
|
||||
| jq '.tokens[]'
|
||||
}
|
||||
|
||||
N_KEEP=$(tokenize "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>" | wc -l)
|
||||
|
||||
chat_completion() {
|
||||
PROMPT="$(trim_trailing "$(format_prompt "$1")")"
|
||||
DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
|
||||
prompt: .,
|
||||
temperature: 0.2,
|
||||
top_k: 40,
|
||||
top_p: 0.9,
|
||||
n_keep: $n_keep,
|
||||
n_predict: 1024,
|
||||
stop: ["[INST]"],
|
||||
stream: true
|
||||
}')"
|
||||
|
||||
# Create a temporary file to hold the Python output
|
||||
TEMPFILE=$(mktemp)
|
||||
|
||||
exec 3< <(curl \
|
||||
--silent \
|
||||
--no-buffer \
|
||||
--request POST \
|
||||
--url "${API_URL}/completion" \
|
||||
--header "Content-Type: application/json" \
|
||||
--data-raw "${DATA}")
|
||||
|
||||
python -c "
|
||||
import json
|
||||
import sys
|
||||
|
||||
answer = ''
|
||||
while True:
|
||||
line = sys.stdin.readline()
|
||||
if not line:
|
||||
break
|
||||
if line.startswith('data: '):
|
||||
json_content = line[6:].strip()
|
||||
content = json.loads(json_content)['content']
|
||||
sys.stdout.write(content)
|
||||
sys.stdout.flush()
|
||||
answer += content
|
||||
|
||||
answer = answer.rstrip('\n')
|
||||
|
||||
# Write the answer to the temporary file
|
||||
with open('$TEMPFILE', 'w') as f:
|
||||
f.write(answer)
|
||||
" <&3
|
||||
|
||||
exec 3<&-
|
||||
|
||||
# Read the answer from the temporary file
|
||||
ANSWER=$(cat $TEMPFILE)
|
||||
|
||||
# Clean up the temporary file
|
||||
rm $TEMPFILE
|
||||
|
||||
printf "\n"
|
||||
|
||||
CHAT+=("$1" "$(trim "$ANSWER")")
|
||||
}
|
||||
|
||||
while true; do
|
||||
echo -en "\033[0;32m" # Green color
|
||||
read -r -e -p "> " QUESTION
|
||||
echo -en "\033[0m" # Reset color
|
||||
chat_completion "${QUESTION}"
|
||||
done
|
||||
File diff suppressed because it is too large
Load diff
|
|
@ -3,12 +3,11 @@
|
|||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
|
||||
<meta name="color-scheme" content="light dark">
|
||||
<title>llama.cpp - chat</title>
|
||||
|
||||
<style>
|
||||
body {
|
||||
background-color: #fff;
|
||||
color: #000;
|
||||
font-family: system-ui;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -631,6 +631,9 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms,
|
|||
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
||||
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
||||
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
|
||||
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
|
||||
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
|
||||
#endif
|
||||
fprintf(stdout, " -m FNAME, --model FNAME\n");
|
||||
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
|
||||
|
|
@ -827,7 +830,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
|||
}
|
||||
}
|
||||
#else
|
||||
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.", {});
|
||||
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
else if (arg == "--low-vram" || arg == "-lv")
|
||||
|
|
@ -835,7 +838,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
|||
#ifdef GGML_USE_CUBLAS
|
||||
params.low_vram = true;
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
|
||||
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {});
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
else if (arg == "--mul-mat-q" || arg == "-mmq")
|
||||
{
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
params.mul_mat_q = true;
|
||||
#else
|
||||
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n", {});
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
else if (arg == "--main-gpu" || arg == "-mg")
|
||||
|
|
|
|||
541
ggml-alloc.c
Normal file
541
ggml-alloc.c
Normal file
|
|
@ -0,0 +1,541 @@
|
|||
#include "ggml-alloc.h"
|
||||
#include "ggml.h"
|
||||
#include <assert.h>
|
||||
#include <stdarg.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
|
||||
#define UNUSED(x) (void)(x)
|
||||
#define MAX(a, b) ((a) > (b) ? (a) : (b))
|
||||
|
||||
//#define GGML_ALLOCATOR_DEBUG
|
||||
|
||||
//#define AT_PRINTF printf
|
||||
#define AT_PRINTF(...) ((void)0)
|
||||
|
||||
struct hash_node {
|
||||
struct ggml_tensor * t;
|
||||
int n_children;
|
||||
int n_views;
|
||||
};
|
||||
|
||||
static size_t hash(void * p) {
|
||||
return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
|
||||
}
|
||||
|
||||
static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) {
|
||||
size_t h = hash(t);
|
||||
|
||||
// linear probing
|
||||
size_t i = h;
|
||||
while (hash_table[i].t != NULL) {
|
||||
if (hash_table[i].t == t) {
|
||||
return &hash_table[i];
|
||||
}
|
||||
i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
|
||||
if (i == h) {
|
||||
// hash table is full
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
hash_table[i].t = t;
|
||||
return &hash_table[i];
|
||||
}
|
||||
|
||||
// TODO: GGML_PAD ?
|
||||
static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
|
||||
assert(alignment && !(alignment & (alignment - 1))); // power of 2
|
||||
size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
|
||||
return offset + align;
|
||||
}
|
||||
|
||||
struct free_block {
|
||||
void * addr;
|
||||
size_t size;
|
||||
};
|
||||
|
||||
#define MAX_FREE_BLOCKS 128
|
||||
|
||||
struct ggml_allocr {
|
||||
void * data;
|
||||
size_t size;
|
||||
size_t alignment;
|
||||
int n_free_blocks;
|
||||
struct free_block free_blocks[MAX_FREE_BLOCKS];
|
||||
struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
|
||||
size_t max_size;
|
||||
bool measure;
|
||||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
struct ggml_tensor * allocated_tensors[1024];
|
||||
#endif
|
||||
};
|
||||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
|
||||
for (int i = 0; i < 1024; i++) {
|
||||
if (alloc->allocated_tensors[i] == NULL) {
|
||||
alloc->allocated_tensors[i] = tensor;
|
||||
return;
|
||||
}
|
||||
}
|
||||
GGML_ASSERT(!"out of allocated_tensors");
|
||||
}
|
||||
static void remove_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
|
||||
for (int i = 0; i < 1024; i++) {
|
||||
if (alloc->allocated_tensors[i] == tensor ||
|
||||
(alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
|
||||
alloc->allocated_tensors[i] = NULL;
|
||||
return;
|
||||
}
|
||||
}
|
||||
printf("tried to free tensor %s not found\n", tensor->name);
|
||||
GGML_ASSERT(!"tensor not found");
|
||||
}
|
||||
#endif
|
||||
|
||||
|
||||
static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
return ggml_nbytes(tensor);
|
||||
|
||||
UNUSED(alloc);
|
||||
}
|
||||
|
||||
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
|
||||
size = aligned_offset(NULL, size, alloc->alignment);
|
||||
|
||||
AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
|
||||
|
||||
size_t max_avail = 0;
|
||||
|
||||
// find the best fitting free block
|
||||
int best_fit_block = -1;
|
||||
size_t best_fit_size = SIZE_MAX;
|
||||
for (int i = 0; i < alloc->n_free_blocks; i++) {
|
||||
struct free_block * block = &alloc->free_blocks[i];
|
||||
max_avail = MAX(max_avail, block->size);
|
||||
if (block->size >= size && block->size <= best_fit_size) {
|
||||
best_fit_block = i;
|
||||
best_fit_size = block->size;
|
||||
}
|
||||
}
|
||||
|
||||
AT_PRINTF("block %d\n", best_fit_block);
|
||||
|
||||
if (best_fit_block == -1) {
|
||||
fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
|
||||
__func__, size, max_avail);
|
||||
GGML_ASSERT(!"not enough space in the buffer");
|
||||
return;
|
||||
}
|
||||
struct free_block * block = &alloc->free_blocks[best_fit_block];
|
||||
void * addr = block->addr;
|
||||
block->addr = (char*)block->addr + size;
|
||||
block->size -= size;
|
||||
if (block->size == 0) {
|
||||
// remove block if empty
|
||||
alloc->n_free_blocks--;
|
||||
for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
|
||||
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
||||
}
|
||||
}
|
||||
|
||||
tensor->data = addr;
|
||||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
add_allocated_tensor(alloc, tensor);
|
||||
size_t cur_max = (char*)addr - (char*)alloc->data + size;
|
||||
if (cur_max > alloc->max_size) {
|
||||
printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
|
||||
for (int i = 0; i < 1024; i++) {
|
||||
if (alloc->allocated_tensors[i]) {
|
||||
printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0);
|
||||
}
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
#endif
|
||||
|
||||
alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
|
||||
}
|
||||
|
||||
// this is a very naive implementation, but for our case the number of free blocks should be very small
|
||||
static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
void * ptr = tensor->data;
|
||||
|
||||
if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
|
||||
// the tensor was not allocated in this buffer
|
||||
// this can happen because the graph allocator will try to free weights and other tensors from different buffers
|
||||
// the easiest way to deal with this is just to ignore it
|
||||
return;
|
||||
}
|
||||
|
||||
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
|
||||
size = aligned_offset(NULL, size, alloc->alignment);
|
||||
AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
|
||||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
remove_allocated_tensor(alloc, tensor);
|
||||
#endif
|
||||
|
||||
// see if we can merge with an existing block
|
||||
for (int i = 0; i < alloc->n_free_blocks; i++) {
|
||||
struct free_block * block = &alloc->free_blocks[i];
|
||||
// check if ptr is at the end of the block
|
||||
if ((char*)block->addr + block->size == ptr) {
|
||||
block->size += size;
|
||||
// check if we can merge with the next block
|
||||
if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) {
|
||||
block->size += alloc->free_blocks[i+1].size;
|
||||
alloc->n_free_blocks--;
|
||||
for (int j = i+1; j < alloc->n_free_blocks; j++) {
|
||||
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
// check if ptr is at the beginning of the block
|
||||
if ((char*)ptr + size == block->addr) {
|
||||
block->addr = ptr;
|
||||
block->size += size;
|
||||
// check if we can merge with the previous block
|
||||
if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) {
|
||||
alloc->free_blocks[i-1].size += block->size;
|
||||
alloc->n_free_blocks--;
|
||||
for (int j = i; j < alloc->n_free_blocks; j++) {
|
||||
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
}
|
||||
// otherwise, add a new block
|
||||
GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
|
||||
// insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
|
||||
int insert_pos = 0;
|
||||
while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) {
|
||||
insert_pos++;
|
||||
}
|
||||
// shift all blocks from insert_pos onward to make room for the new block
|
||||
for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
|
||||
alloc->free_blocks[i] = alloc->free_blocks[i-1];
|
||||
}
|
||||
// insert the new block
|
||||
alloc->free_blocks[insert_pos].addr = ptr;
|
||||
alloc->free_blocks[insert_pos].size = size;
|
||||
alloc->n_free_blocks++;
|
||||
}
|
||||
|
||||
void ggml_allocr_reset(struct ggml_allocr * alloc) {
|
||||
alloc->n_free_blocks = 1;
|
||||
size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
|
||||
alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
|
||||
alloc->free_blocks[0].size = alloc->size - align_offset;
|
||||
}
|
||||
|
||||
struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
|
||||
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
|
||||
|
||||
*alloc = (struct ggml_allocr){
|
||||
/*.data = */ data,
|
||||
/*.size = */ size,
|
||||
/*.alignment = */ alignment,
|
||||
/*.n_free_blocks = */ 0,
|
||||
/*.free_blocks = */ {{0}},
|
||||
/*.hash_table = */ {{0}},
|
||||
/*.max_size = */ 0,
|
||||
/*.measure = */ false,
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
/*.allocated_tensors = */ = {0},
|
||||
#endif
|
||||
};
|
||||
|
||||
ggml_allocr_reset(alloc);
|
||||
|
||||
return alloc;
|
||||
}
|
||||
|
||||
// address and size of the buffer when measuring
|
||||
// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
|
||||
static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
|
||||
static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB
|
||||
|
||||
struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
|
||||
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
|
||||
|
||||
*alloc = (struct ggml_allocr){
|
||||
/*.data = */ MEASURE_BASE_ADDR,
|
||||
/*.size = */ MEASURE_MAX_SIZE,
|
||||
/*.alignment = */ alignment,
|
||||
/*.n_free_blocks = */ 0,
|
||||
/*.free_blocks = */ {{0}},
|
||||
/*.hash_table = */ {{0}},
|
||||
/*.max_size = */ 0,
|
||||
/*.measure = */ true,
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
/*.allocated_tensors = */ = {0},
|
||||
#endif
|
||||
};
|
||||
|
||||
ggml_allocr_reset(alloc);
|
||||
|
||||
return alloc;
|
||||
}
|
||||
|
||||
void ggml_allocr_free(struct ggml_allocr * alloc) {
|
||||
free(alloc);
|
||||
}
|
||||
|
||||
bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
|
||||
return alloc->measure;
|
||||
}
|
||||
|
||||
//////////// compute graph allocator
|
||||
|
||||
static bool ggml_is_view(struct ggml_tensor * t) {
|
||||
return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
|
||||
t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
|
||||
}
|
||||
|
||||
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
|
||||
if (a->type != b->type) {
|
||||
return false;
|
||||
}
|
||||
for (int i = 0; i < GGML_MAX_DIMS; i++) {
|
||||
if (a->ne[i] != b->ne[i]) {
|
||||
return false;
|
||||
}
|
||||
if (a->nb[i] != b->nb[i]) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
|
||||
switch (t->op) {
|
||||
case GGML_OP_PERMUTE:
|
||||
case GGML_OP_RESHAPE:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
case GGML_OP_VIEW:
|
||||
return t->src[0];
|
||||
case GGML_OP_CPY:
|
||||
return t->src[1];
|
||||
default:
|
||||
return NULL;
|
||||
}
|
||||
}
|
||||
|
||||
static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
|
||||
struct ggml_tensor * parent = t;
|
||||
do {
|
||||
parent = get_view_parent(parent);
|
||||
} while (ggml_is_view(parent));
|
||||
return parent;
|
||||
}
|
||||
|
||||
static bool ggml_op_can_inplace(enum ggml_op op) {
|
||||
switch (op) {
|
||||
case GGML_OP_SCALE:
|
||||
case GGML_OP_DIAG_MASK_ZERO:
|
||||
case GGML_OP_DIAG_MASK_INF:
|
||||
case GGML_OP_ADD:
|
||||
case GGML_OP_ADD1:
|
||||
case GGML_OP_ACC:
|
||||
case GGML_OP_SUB:
|
||||
case GGML_OP_MUL:
|
||||
case GGML_OP_DIV:
|
||||
case GGML_OP_SQR:
|
||||
case GGML_OP_SQRT:
|
||||
case GGML_OP_LOG:
|
||||
case GGML_OP_UNARY:
|
||||
case GGML_OP_ROPE:
|
||||
case GGML_OP_RMS_NORM:
|
||||
case GGML_OP_SET:
|
||||
case GGML_OP_SOFT_MAX:
|
||||
case GGML_OP_CONT:
|
||||
return true;
|
||||
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
|
||||
struct hash_node * ht = alloc->hash_table;
|
||||
if (node->data == NULL) {
|
||||
if (ggml_is_view(node)) {
|
||||
size_t offset;
|
||||
switch(node->op) {
|
||||
case GGML_OP_VIEW:
|
||||
memcpy(&offset, node->op_params, sizeof(size_t));
|
||||
node->data = (char *) node->src[0]->data + offset;
|
||||
break;
|
||||
case GGML_OP_PERMUTE:
|
||||
case GGML_OP_RESHAPE:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
node->data = node->src[0]->data;
|
||||
break;
|
||||
case GGML_OP_CPY:
|
||||
node->data = node->src[1]->data;
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(!"unknown view op");
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
// see if we can reuse a parent's buffer (inplace)
|
||||
if (ggml_op_can_inplace(node->op)) {
|
||||
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
||||
struct ggml_tensor * parent = node->src[i];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
struct hash_node * p_hn = hash_get(ht, parent);
|
||||
if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
|
||||
if (ggml_is_view(parent)) {
|
||||
struct ggml_tensor * view_src = get_view_source(parent);
|
||||
struct hash_node * view_src_hn = hash_get(ht, view_src);
|
||||
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
|
||||
// TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
|
||||
// the parent's data that it will need later (same layout requirement). the problem is that then
|
||||
// we cannot free the tensor because the original address of the allocation is lost.
|
||||
// adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
|
||||
// for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
|
||||
AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
|
||||
node->data = parent->data;
|
||||
return;
|
||||
}
|
||||
}
|
||||
else {
|
||||
AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
|
||||
node->data = parent->data;
|
||||
}
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
ggml_allocr_alloc(alloc, node);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static size_t ggml_allocator_alloc_graph_tensors_n(
|
||||
struct ggml_allocr * alloc,
|
||||
struct ggml_cgraph ** graphs, int n_graphs,
|
||||
struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
|
||||
|
||||
// reset hash table
|
||||
struct hash_node * ht = alloc->hash_table;
|
||||
memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE);
|
||||
|
||||
// count number of children and views
|
||||
for (int g = 0; g < n_graphs; g++) {
|
||||
struct ggml_cgraph * gf = graphs[g];
|
||||
for (int i = 0; i < gf->n_nodes; i++) {
|
||||
struct ggml_tensor * node = gf->nodes[i];
|
||||
|
||||
if (ggml_is_view(node)) {
|
||||
struct ggml_tensor * view_src = get_view_source(node);
|
||||
hash_get(ht, view_src)->n_views += 1;
|
||||
}
|
||||
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * parent = node->src[j];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
hash_get(ht, parent)->n_children += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// allocate tensors
|
||||
for (int g = 0; g < n_graphs; g++) {
|
||||
struct ggml_cgraph * gf = graphs[g];
|
||||
AT_PRINTF("####### graph %d/%d\n", g, n_graphs);
|
||||
// graph inputs are allocated first to ensure that they are not overwritten by each other
|
||||
if (inputs != NULL && inputs[g] != NULL) {
|
||||
for (int i = 0; inputs[g][i] != NULL; i++) {
|
||||
struct ggml_tensor * input = inputs[g][i];
|
||||
AT_PRINTF("input: %s\n", input->name);
|
||||
allocate_node(alloc, input);
|
||||
}
|
||||
}
|
||||
for (int i = 0; i < gf->n_nodes; i++) {
|
||||
struct ggml_tensor * node = gf->nodes[i];
|
||||
|
||||
// allocate parents (leafs)
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * parent = node->src[j];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
allocate_node(alloc, parent);
|
||||
}
|
||||
|
||||
// allocate node
|
||||
allocate_node(alloc, node);
|
||||
|
||||
AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * parent = node->src[j];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
AT_PRINTF("%s", parent->name);
|
||||
if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
|
||||
AT_PRINTF(", ");
|
||||
}
|
||||
}
|
||||
AT_PRINTF("\n");
|
||||
|
||||
// update parents
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * parent = node->src[j];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
struct hash_node * p_hn = hash_get(ht, parent);
|
||||
p_hn->n_children -= 1;
|
||||
|
||||
//AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
|
||||
|
||||
if (p_hn->n_children == 0 && p_hn->n_views == 0) {
|
||||
if (ggml_is_view(parent)) {
|
||||
struct ggml_tensor * view_src = get_view_source(parent);
|
||||
struct hash_node * view_src_hn = hash_get(ht, view_src);
|
||||
view_src_hn->n_views -= 1;
|
||||
AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src->n_children, view_src->n_views);
|
||||
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
|
||||
ggml_allocator_free_tensor(alloc, view_src);
|
||||
}
|
||||
}
|
||||
else {
|
||||
if (parent->data != node->data) {
|
||||
ggml_allocator_free_tensor(alloc, parent);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
AT_PRINTF("\n");
|
||||
}
|
||||
// free graph outputs here that wouldn't be freed otherwise because they have no children
|
||||
if (outputs != NULL && outputs[g] != NULL) {
|
||||
for (int i = 0; outputs[g][i] != NULL; i++) {
|
||||
struct ggml_tensor * output = outputs[g][i];
|
||||
AT_PRINTF("output: %s\n", output->name);
|
||||
ggml_allocator_free_tensor(alloc, output);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return alloc->max_size;
|
||||
}
|
||||
|
||||
size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
|
||||
return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
|
||||
}
|
||||
22
ggml-alloc.h
Normal file
22
ggml-alloc.h
Normal file
|
|
@ -0,0 +1,22 @@
|
|||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
|
||||
GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
|
||||
GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
|
||||
|
||||
GGML_API void ggml_allocr_free(struct ggml_allocr * alloc);
|
||||
GGML_API bool ggml_allocr_is_measure(struct ggml_allocr * alloc);
|
||||
GGML_API void ggml_allocr_reset(struct ggml_allocr * alloc);
|
||||
GGML_API void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
|
||||
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
1980
ggml-cuda.cu
1980
ggml-cuda.cu
File diff suppressed because it is too large
Load diff
|
|
@ -27,6 +27,7 @@ void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
|
|||
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_set_main_device(int main_device);
|
||||
void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
|
||||
void ggml_cuda_set_scratch_size(size_t scratch_size);
|
||||
void ggml_cuda_free_scratch(void);
|
||||
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
|
|
|
|||
33
ggml-metal.m
33
ggml-metal.m
|
|
@ -718,7 +718,8 @@ void ggml_metal_graph_compute(
|
|||
// TODO: needs to be updated after PR: https://github.com/ggerganov/ggml/pull/224
|
||||
|
||||
GGML_ASSERT(ne00 == ne10);
|
||||
GGML_ASSERT(ne02 == ne12);
|
||||
// GGML_ASSERT(ne02 == ne12); // Should be checked on individual data types until broadcast is implemented everywhere
|
||||
GGML_ASSERT(ne03 == ne13);
|
||||
|
||||
if (ggml_is_contiguous(src0) &&
|
||||
ggml_is_contiguous(src1) &&
|
||||
|
|
@ -746,11 +747,11 @@ void ggml_metal_graph_compute(
|
|||
initWithDevice:ctx->device transposeLeft:false transposeRight:true
|
||||
resultRows:ne11 resultColumns:ne01 interiorColumns:ne00 alpha:1.0 beta:0.0];
|
||||
|
||||
// we need to do ne02 multiplications
|
||||
// we need to do ne12 multiplications
|
||||
// TODO: is there a way to do this in parallel - currently very slow ..
|
||||
// TODO: might be possible to offload part of the computation to ANE using Accelerate's CBLAS
|
||||
for (int64_t i02 = 0; i02 < ne02; ++i02) {
|
||||
size_t offs_src0_cur = offs_src0 + i02*nb02;
|
||||
for (int64_t i02 = 0; i02 < ne12; ++i02) {
|
||||
size_t offs_src0_cur = offs_src0 + i02/(ne12/ne02)*nb02; // gqa not used for now
|
||||
size_t offs_src1_cur = offs_src1 + i02*nb12;
|
||||
size_t offs_dst_cur = offs_dst + i02*nb2;
|
||||
|
||||
|
|
@ -772,8 +773,6 @@ void ggml_metal_graph_compute(
|
|||
switch (src0t) {
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
GGML_ASSERT(ne02 == ne12);
|
||||
|
||||
nth0 = 64;
|
||||
nth1 = 1;
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
|
||||
|
|
@ -853,16 +852,18 @@ void ggml_metal_graph_compute(
|
|||
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
|
||||
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
|
||||
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
|
||||
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:5];
|
||||
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:6];
|
||||
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:7];
|
||||
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:8];
|
||||
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:9];
|
||||
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10];
|
||||
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11];
|
||||
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12];
|
||||
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
|
||||
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14];
|
||||
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
|
||||
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
|
||||
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
|
||||
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
|
||||
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
|
||||
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
|
||||
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11];
|
||||
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12];
|
||||
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13];
|
||||
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
|
||||
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15];
|
||||
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16];
|
||||
|
||||
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
|
||||
src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
|
||||
|
|
|
|||
|
|
@ -509,11 +509,13 @@ kernel void kernel_mul_mat_f16_f32(
|
|||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant int64_t & ne12,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
|
|
@ -529,7 +531,7 @@ kernel void kernel_mul_mat_f16_f32(
|
|||
const int64_t r1 = tgpig.y;
|
||||
const int64_t im = tgpig.z;
|
||||
|
||||
device const half * x = (device const half *) (src0 + r0*nb01 + im*nb02);
|
||||
device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
|
||||
device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
|
||||
|
||||
sum[tpitg.x] = 0.0f;
|
||||
|
|
@ -552,6 +554,7 @@ kernel void kernel_mul_mat_f16_f32(
|
|||
}
|
||||
}
|
||||
|
||||
|
||||
kernel void kernel_alibi_f32(
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
|
|
|
|||
199
ggml.c
199
ggml.c
|
|
@ -4071,8 +4071,8 @@ bool ggml_is_numa(void) {
|
|||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
void ggml_print_object(const struct ggml_object * obj) {
|
||||
GGML_PRINT(" - ggml_object: offset = %zu, size = %zu, next = %p\n",
|
||||
obj->offs, obj->size, (const void *) obj->next);
|
||||
GGML_PRINT(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n",
|
||||
obj->type, obj->offs, obj->size, (const void *) obj->next);
|
||||
}
|
||||
|
||||
void ggml_print_objects(const struct ggml_context * ctx) {
|
||||
|
|
@ -4212,7 +4212,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
|||
}
|
||||
|
||||
size_t ggml_tensor_overhead(void) {
|
||||
return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE + 16;
|
||||
return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE;
|
||||
}
|
||||
|
||||
bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
|
|
@ -4383,7 +4383,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
|
|||
return NULL;
|
||||
}
|
||||
|
||||
const size_t mem_size = (params.mem_size + GGML_MEM_ALIGN - 1) & ~(GGML_MEM_ALIGN - 1);
|
||||
const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN);
|
||||
|
||||
*ctx = (struct ggml_context) {
|
||||
/*.mem_size =*/ mem_size,
|
||||
|
|
@ -4472,6 +4472,7 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
|
|||
struct ggml_object * obj = ctx->objects_begin;
|
||||
|
||||
while (obj != NULL) {
|
||||
if (obj->type == GGML_OBJECT_TENSOR) {
|
||||
struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs);
|
||||
|
||||
const size_t size = ggml_nbytes(tensor);
|
||||
|
|
@ -4479,6 +4480,7 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
|
|||
if (max_size < size) {
|
||||
max_size = size;
|
||||
}
|
||||
}
|
||||
|
||||
obj = obj->next;
|
||||
}
|
||||
|
|
@ -4509,12 +4511,7 @@ static void ggml_scratch_load(struct ggml_context * ctx) {
|
|||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
static struct ggml_tensor * ggml_new_tensor_impl(
|
||||
struct ggml_context * ctx,
|
||||
enum ggml_type type,
|
||||
int n_dims,
|
||||
const int64_t* ne,
|
||||
void* data) {
|
||||
static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) {
|
||||
// always insert objects at the end of the context's memory pool
|
||||
struct ggml_object * obj_cur = ctx->objects_end;
|
||||
|
||||
|
|
@ -4522,26 +4519,15 @@ static struct ggml_tensor * ggml_new_tensor_impl(
|
|||
const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size;
|
||||
const size_t cur_end = cur_offs + cur_size;
|
||||
|
||||
size_t size_needed = 0;
|
||||
|
||||
if (data == NULL && !ctx->no_alloc) {
|
||||
size_needed += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]);
|
||||
for (int i = 1; i < n_dims; i++) {
|
||||
size_needed *= ne[i];
|
||||
}
|
||||
// align to GGML_MEM_ALIGN
|
||||
size_needed = ((size_needed + GGML_MEM_ALIGN - 1)/GGML_MEM_ALIGN)*GGML_MEM_ALIGN;
|
||||
}
|
||||
size_t size_needed = GGML_PAD(size, GGML_MEM_ALIGN);
|
||||
|
||||
char * const mem_buffer = ctx->mem_buffer;
|
||||
struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);
|
||||
|
||||
if (ctx->scratch.data == NULL || data != NULL) {
|
||||
size_needed += GGML_TENSOR_SIZE;
|
||||
|
||||
if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
|
||||
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
|
||||
__func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size);
|
||||
__func__, cur_end + size_needed, ctx->mem_size);
|
||||
assert(false);
|
||||
return NULL;
|
||||
}
|
||||
|
|
@ -4550,34 +4536,10 @@ static struct ggml_tensor * ggml_new_tensor_impl(
|
|||
.offs = cur_end + GGML_OBJECT_SIZE,
|
||||
.size = size_needed,
|
||||
.next = NULL,
|
||||
};
|
||||
} else {
|
||||
if (ctx->scratch.offs + size_needed > ctx->scratch.size) {
|
||||
GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
|
||||
__func__, ctx->scratch.offs + size_needed, ctx->scratch.size);
|
||||
assert(false);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
if (cur_end + GGML_TENSOR_SIZE + GGML_OBJECT_SIZE > ctx->mem_size) {
|
||||
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
|
||||
__func__, cur_end + GGML_TENSOR_SIZE + GGML_OBJECT_SIZE, ctx->mem_size);
|
||||
assert(false);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
data = (char * const) ctx->scratch.data + ctx->scratch.offs;
|
||||
|
||||
*obj_new = (struct ggml_object) {
|
||||
.offs = cur_end + GGML_OBJECT_SIZE,
|
||||
.size = GGML_TENSOR_SIZE,
|
||||
.next = NULL,
|
||||
.type = type,
|
||||
};
|
||||
|
||||
//printf("scratch offs = %zu, size_needed = %zu\n", ctx->scratch.offs, size_needed);
|
||||
|
||||
ctx->scratch.offs += size_needed;
|
||||
}
|
||||
ggml_assert_aligned(mem_buffer + obj_new->offs);
|
||||
|
||||
if (obj_cur != NULL) {
|
||||
obj_cur->next = obj_new;
|
||||
|
|
@ -4590,9 +4552,48 @@ static struct ggml_tensor * ggml_new_tensor_impl(
|
|||
|
||||
//printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size);
|
||||
|
||||
struct ggml_tensor * const result = (struct ggml_tensor *)(mem_buffer + obj_new->offs);
|
||||
return obj_new;
|
||||
}
|
||||
|
||||
ggml_assert_aligned(result);
|
||||
static struct ggml_tensor * ggml_new_tensor_impl(
|
||||
struct ggml_context * ctx,
|
||||
enum ggml_type type,
|
||||
int n_dims,
|
||||
const int64_t * ne,
|
||||
void * data) {
|
||||
|
||||
assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS);
|
||||
|
||||
size_t data_size = 0;
|
||||
|
||||
if (data == NULL && !ctx->no_alloc) {
|
||||
data_size += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]);
|
||||
for (int i = 1; i < n_dims; i++) {
|
||||
data_size *= ne[i];
|
||||
}
|
||||
}
|
||||
|
||||
if (ctx->scratch.data != NULL && data == NULL) {
|
||||
// allocate tensor data in the scratch buffer
|
||||
if (ctx->scratch.offs + data_size > ctx->scratch.size) {
|
||||
GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
|
||||
__func__, ctx->scratch.offs + data_size, ctx->scratch.size);
|
||||
assert(false);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
data = (char * const) ctx->scratch.data + ctx->scratch.offs;
|
||||
|
||||
ctx->scratch.offs += data_size;
|
||||
|
||||
data_size = 0;
|
||||
}
|
||||
|
||||
struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + data_size);
|
||||
|
||||
// TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here
|
||||
|
||||
struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs);
|
||||
|
||||
*result = (struct ggml_tensor) {
|
||||
/*.type =*/ type,
|
||||
|
|
@ -4983,11 +4984,6 @@ enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
|
|||
return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0);
|
||||
}
|
||||
|
||||
static void ggml_set_unary_op(struct ggml_tensor * tensor, enum ggml_unary_op op) {
|
||||
GGML_ASSERT(tensor->op = GGML_OP_UNARY);
|
||||
ggml_set_op_params_i32(tensor, 0, (int32_t) op);
|
||||
}
|
||||
|
||||
const char * ggml_get_name(const struct ggml_tensor * tensor) {
|
||||
return tensor->name;
|
||||
}
|
||||
|
|
@ -5026,10 +5022,12 @@ struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * nam
|
|||
char * const mem_buffer = ctx->mem_buffer;
|
||||
|
||||
while (obj != NULL) {
|
||||
if (obj->type == GGML_OBJECT_TENSOR) {
|
||||
struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs);
|
||||
if (strcmp(cur->name, name) == 0) {
|
||||
return cur;
|
||||
}
|
||||
}
|
||||
|
||||
obj = obj->next;
|
||||
}
|
||||
|
|
@ -6242,6 +6240,27 @@ struct ggml_tensor * ggml_reshape_4d(
|
|||
|
||||
// ggml_view_1d
|
||||
|
||||
static struct ggml_tensor * ggml_view_tensor_offset(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_dims,
|
||||
const int64_t * ne,
|
||||
size_t offset) {
|
||||
// don't calculate an offset from an unallocated tensor
|
||||
void * data = NULL;
|
||||
if (a->data != NULL) {
|
||||
data = (char *) a->data + offset;
|
||||
}
|
||||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, data);
|
||||
|
||||
ggml_format_name(result, "%s (view)", a->name);
|
||||
|
||||
ggml_set_op_params(result, &offset, sizeof(offset));
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_view_1d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
|
|
@ -6254,10 +6273,7 @@ struct ggml_tensor * ggml_view_1d(
|
|||
is_node = true;
|
||||
}
|
||||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset);
|
||||
ggml_format_name(result, "%s (view)", a->name);
|
||||
|
||||
ggml_set_op_params(result, &offset, sizeof(offset));
|
||||
struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 1, &ne0, offset);
|
||||
|
||||
result->op = GGML_OP_VIEW;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
|
|
@ -6284,10 +6300,7 @@ struct ggml_tensor * ggml_view_2d(
|
|||
|
||||
const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 };
|
||||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset);
|
||||
ggml_format_name(result, "%s (view)", a->name);
|
||||
|
||||
ggml_set_op_params(result, &offset, sizeof(offset));
|
||||
struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 2, ne, offset);
|
||||
|
||||
result->nb[1] = nb1;
|
||||
result->nb[2] = result->nb[1]*ne1;
|
||||
|
|
@ -6320,10 +6333,7 @@ struct ggml_tensor * ggml_view_3d(
|
|||
|
||||
const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 };
|
||||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset);
|
||||
ggml_format_name(result, "%s (view)", a->name);
|
||||
|
||||
ggml_set_op_params(result, &offset, sizeof(offset));
|
||||
struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 3, ne, offset);
|
||||
|
||||
result->nb[1] = nb1;
|
||||
result->nb[2] = nb2;
|
||||
|
|
@ -6358,10 +6368,7 @@ struct ggml_tensor * ggml_view_4d(
|
|||
|
||||
const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, ne3 };
|
||||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, (char *) a->data + offset);
|
||||
ggml_format_name(result, "%s (view)", a->name);
|
||||
|
||||
ggml_set_op_params(result, &offset, sizeof(offset));
|
||||
struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 4, ne, offset);
|
||||
|
||||
result->nb[1] = nb1;
|
||||
result->nb[2] = nb2;
|
||||
|
|
@ -6745,6 +6752,18 @@ struct ggml_tensor * ggml_rope_inplace(
|
|||
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, true);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_rope_custom(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_dims,
|
||||
int mode,
|
||||
int n_ctx,
|
||||
float freq_base,
|
||||
float freq_scale) {
|
||||
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, false);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_rope_custom_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
|
|
@ -7225,7 +7244,7 @@ static struct ggml_tensor * ggml_unary_impl(
|
|||
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
ggml_set_unary_op(result, op);
|
||||
ggml_set_op_params_i32(result, 0, (int32_t) op);
|
||||
|
||||
result->op = GGML_OP_UNARY;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
|
|
@ -15829,6 +15848,35 @@ struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cg
|
|||
return result;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) {
|
||||
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, GGML_GRAPH_SIZE);
|
||||
struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs);
|
||||
|
||||
*cgraph = (struct ggml_cgraph) {
|
||||
/*.n_nodes =*/ 0,
|
||||
/*.n_leafs =*/ 0,
|
||||
/*.nodes =*/ { NULL },
|
||||
/*.grads =*/ { NULL },
|
||||
/*.leafs =*/ { NULL },
|
||||
/*.hash_table =*/ { NULL },
|
||||
/*.perf_runs =*/ 0,
|
||||
/*.perf_cycles =*/ 0,
|
||||
/*.perf_time_us =*/ 0,
|
||||
};
|
||||
|
||||
return cgraph;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor) {
|
||||
struct ggml_cgraph * cgraph = ggml_new_graph(ctx);
|
||||
ggml_build_forward_impl(cgraph, tensor, false);
|
||||
return cgraph;
|
||||
}
|
||||
|
||||
size_t ggml_graph_overhead(void) {
|
||||
return GGML_OBJECT_SIZE + GGML_PAD(GGML_GRAPH_SIZE, GGML_MEM_ALIGN);
|
||||
}
|
||||
|
||||
//
|
||||
// thread data
|
||||
//
|
||||
|
|
@ -16544,10 +16592,9 @@ void ggml_graph_reset(struct ggml_cgraph * cgraph) {
|
|||
void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
|
||||
struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads);
|
||||
|
||||
struct ggml_tensor * buf = ggml_new_tensor_1d(ctx, GGML_TYPE_I8, cplan.work_size);
|
||||
GGML_ASSERT(buf);
|
||||
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
|
||||
|
||||
cplan.work_data = buf->data;
|
||||
cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
|
||||
|
||||
ggml_graph_compute(cgraph, &cplan);
|
||||
}
|
||||
|
|
|
|||
34
ggml.h
34
ggml.h
|
|
@ -208,6 +208,7 @@
|
|||
|
||||
#define GGML_UNUSED(x) (void)(x)
|
||||
|
||||
#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
|
||||
|
||||
#define GGML_ASSERT(x) \
|
||||
do { \
|
||||
|
|
@ -396,6 +397,12 @@ extern "C" {
|
|||
GGML_UNARY_OP_SILU,
|
||||
};
|
||||
|
||||
enum ggml_object_type {
|
||||
GGML_OBJECT_TENSOR,
|
||||
GGML_OBJECT_GRAPH,
|
||||
GGML_OBJECT_WORK_BUFFER
|
||||
};
|
||||
|
||||
// ggml object
|
||||
struct ggml_object {
|
||||
size_t offs;
|
||||
|
|
@ -403,7 +410,9 @@ extern "C" {
|
|||
|
||||
struct ggml_object * next;
|
||||
|
||||
char padding[8];
|
||||
enum ggml_object_type type;
|
||||
|
||||
char padding[4];
|
||||
};
|
||||
|
||||
static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
|
||||
|
|
@ -424,7 +433,7 @@ extern "C" {
|
|||
enum ggml_op op;
|
||||
|
||||
// op params - allocated as int32_t for alignment
|
||||
int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(uint32_t)];
|
||||
int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
|
||||
|
||||
bool is_param;
|
||||
|
||||
|
|
@ -485,6 +494,8 @@ extern "C" {
|
|||
int64_t perf_time_us;
|
||||
};
|
||||
|
||||
static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
|
||||
|
||||
// scratch buffer
|
||||
struct ggml_scratch {
|
||||
size_t offs;
|
||||
|
|
@ -1159,7 +1170,18 @@ extern "C" {
|
|||
int mode,
|
||||
int n_ctx);
|
||||
|
||||
// custom RoPE, in-place, returns view(a)
|
||||
// custom RoPE
|
||||
GGML_API struct ggml_tensor * ggml_rope_custom(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_dims,
|
||||
int mode,
|
||||
int n_ctx,
|
||||
float freq_base,
|
||||
float freq_scale);
|
||||
|
||||
// in-place, returns view(a)
|
||||
GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
|
|
@ -1391,11 +1413,17 @@ extern "C" {
|
|||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * tensor);
|
||||
|
||||
|
||||
GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
|
||||
|
||||
// graph allocation in a context
|
||||
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx);
|
||||
GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_graph_overhead(void);
|
||||
|
||||
// ggml_graph_plan() has to be called before ggml_graph_compute()
|
||||
// when plan.work_size > 0, caller must allocate memory for plan.work_data
|
||||
GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
|
||||
|
|
|
|||
62
k_quants.c
62
k_quants.c
|
|
@ -39,6 +39,8 @@
|
|||
#define MIN(a, b) ((a) < (b) ? (a) : (b))
|
||||
#define MAX(a, b) ((a) > (b) ? (a) : (b))
|
||||
|
||||
#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
|
||||
|
||||
//
|
||||
// 2-6 bit quantization in super-blocks
|
||||
//
|
||||
|
|
@ -1353,7 +1355,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const __m256i all_scales = _mm256_cvtepi8_epi16(scales8);
|
||||
const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
|
||||
const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
|
||||
const __m256i scales[2] = {_mm256_set_m128i(l_scales, l_scales), _mm256_set_m128i(h_scales, h_scales)};
|
||||
const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
|
||||
|
||||
__m256i sumi = _mm256_setzero_si256();
|
||||
|
||||
|
|
@ -1421,7 +1423,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8]));
|
||||
|
||||
// sumf += -dmin * summs in 32bits*8
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(_mm256_set_m128i(summs_1, summs_0))), acc);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc);
|
||||
|
||||
const __m128i scales_0 = _mm_cvtepi8_epi16(scales16);
|
||||
const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16));
|
||||
|
|
@ -1493,7 +1495,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
}
|
||||
|
||||
// sumf += dall * isum - dmin * summs in 32bits
|
||||
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
|
||||
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc);
|
||||
}
|
||||
|
||||
|
|
@ -1644,8 +1646,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
summs += dmin * smin;
|
||||
|
||||
const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2);
|
||||
const __m256i q2_0 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 2), q2bits), m3);
|
||||
const __m256i q2_1 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
|
||||
const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3);
|
||||
const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3);
|
||||
|
||||
const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0));
|
||||
const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32));
|
||||
|
|
@ -1709,10 +1711,10 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0));
|
||||
const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1));
|
||||
|
||||
const __m256i p_0 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
|
||||
const __m256i p_1 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
|
||||
const __m256i p_2 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
|
||||
const __m256i p_3 = _mm256_set_m128i(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
|
||||
const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0));
|
||||
const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1));
|
||||
const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2));
|
||||
const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3));
|
||||
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc);
|
||||
|
|
@ -1917,7 +1919,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const __m256i all_scales = _mm256_cvtepi8_epi16(scales128);
|
||||
const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0);
|
||||
const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1);
|
||||
const __m256i scales[2] = {_mm256_set_m128i(l_scales, l_scales), _mm256_set_m128i(h_scales, h_scales)};
|
||||
const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)};
|
||||
|
||||
// high bit
|
||||
const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask);
|
||||
|
|
@ -2128,7 +2130,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
}
|
||||
|
||||
// multiply with block scale and accumulate
|
||||
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
|
||||
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
|
||||
|
||||
}
|
||||
|
|
@ -2303,13 +2305,13 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
aux16[0] = a & 0x0f0f;
|
||||
aux16[1] = (a >> 4) & 0x0f0f;
|
||||
|
||||
const __m256i scale_0 = _mm256_set_m128i(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
|
||||
const __m256i scale_1 = _mm256_set_m128i(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
|
||||
const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8));
|
||||
const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8));
|
||||
|
||||
memcpy(&aux64, x[i].hmask, 8);
|
||||
|
||||
const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0);
|
||||
__m256i q3h_0 = _mm256_set_m128i(_mm_srli_epi16(haux, 2), haux);
|
||||
__m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux);
|
||||
__m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4);
|
||||
q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2);
|
||||
q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2);
|
||||
|
|
@ -2318,7 +2320,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3);
|
||||
|
||||
// prepare low and high bits
|
||||
const __m256i q3aux = _mm256_set_m128i(_mm_srli_epi16(q3bits, 2), q3bits);
|
||||
const __m256i q3aux = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits);
|
||||
const __m256i q3l_0 = _mm256_and_si256(q3aux, m3);
|
||||
const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3);
|
||||
|
||||
|
|
@ -2429,7 +2431,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
p16_0 = _mm_add_epi32(p16_0, p16_2);
|
||||
p16_1 = _mm_add_epi32(p16_1, p16_3);
|
||||
__m256i p16 = _mm256_set_m128i(p16_1, p16_0);
|
||||
__m256i p16 = MM256_SET_M128I(p16_1, p16_0);
|
||||
|
||||
// multiply with block scale and accumulate
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc);
|
||||
|
|
@ -2620,7 +2622,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m);
|
||||
|
||||
const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0);
|
||||
const __m256i scales = _mm256_set_m128i(sc128, sc128);
|
||||
const __m256i scales = MM256_SET_M128I(sc128, sc128);
|
||||
|
||||
__m256i sumi = _mm256_setzero_si256();
|
||||
|
||||
|
|
@ -2727,7 +2729,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
}
|
||||
|
||||
__m256 vd = _mm256_set1_ps(d);
|
||||
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
|
||||
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
|
||||
|
||||
}
|
||||
|
|
@ -2968,11 +2970,11 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0);
|
||||
const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(_mm256_set_m128i(p32_1, p32_0))), acc);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc);
|
||||
|
||||
const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2);
|
||||
const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(_mm256_set_m128i(p32_3, p32_2))), acc);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc);
|
||||
|
||||
}
|
||||
|
||||
|
|
@ -3160,7 +3162,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
summs += dmin * _mm_extract_epi32(hsum, 0);
|
||||
|
||||
const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0);
|
||||
const __m256i scales = _mm256_set_m128i(sc128, sc128);
|
||||
const __m256i scales = MM256_SET_M128I(sc128, sc128);
|
||||
|
||||
const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh);
|
||||
__m256i hmask = mone;
|
||||
|
|
@ -3299,7 +3301,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
}
|
||||
|
||||
__m256 vd = _mm256_set1_ps(d);
|
||||
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
|
||||
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc);
|
||||
|
||||
}
|
||||
|
|
@ -3462,13 +3464,13 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
|
||||
|
||||
const __m256i scale_l = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
|
||||
const __m256i scale_h = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
|
||||
const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0]));
|
||||
const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2]));
|
||||
|
||||
int64_t aux64;
|
||||
memcpy(&aux64, x[i].qh, 8);
|
||||
const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64);
|
||||
const __m256i haux256 = _mm256_set_m128i(_mm_srli_epi16(haux128, 2), haux128);
|
||||
const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128);
|
||||
|
||||
const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4);
|
||||
const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4);
|
||||
|
|
@ -3543,7 +3545,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2));
|
||||
const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3));
|
||||
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_set_m128i(dot_1, dot_0))), acc);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc);
|
||||
|
||||
}
|
||||
|
||||
|
|
@ -3925,7 +3927,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
|
||||
}
|
||||
|
||||
__m256i sumi = _mm256_set_m128i(sumi_1, sumi_0);
|
||||
__m256i sumi = MM256_SET_M128I(sumi_1, sumi_0);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc);
|
||||
}
|
||||
|
||||
|
|
@ -4083,8 +4085,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4);
|
||||
const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh);
|
||||
|
||||
const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
|
||||
const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
|
||||
const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4);
|
||||
const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4);
|
||||
|
||||
const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0);
|
||||
const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1);
|
||||
|
|
@ -4177,7 +4179,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
|
|||
sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2));
|
||||
sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3));
|
||||
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(_mm256_set_m128i(sumi_1, sumi_0))), acc);
|
||||
acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc);
|
||||
}
|
||||
|
||||
*s = hsum_float_8(acc);
|
||||
|
|
|
|||
298
llama.cpp
298
llama.cpp
|
|
@ -56,8 +56,14 @@
|
|||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
#if !defined(GGML_USE_CUBLAS) && !defined(GGML_USE_METAL)
|
||||
#include "ggml-alloc.h"
|
||||
#define LLAMA_USE_ALLOCATOR
|
||||
#else
|
||||
#define LLAMA_USE_SCRATCH
|
||||
#define LLAMA_MAX_SCRATCH_BUFFERS 16
|
||||
#endif
|
||||
|
||||
|
||||
// available llama models
|
||||
enum e_model {
|
||||
|
|
@ -327,13 +333,22 @@ struct llama_model {
|
|||
|
||||
struct llama_context {
|
||||
llama_context(const llama_model & model) : model(model), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
|
||||
#ifdef GGML_USE_METAL
|
||||
~llama_context() {
|
||||
if (model_owner) {
|
||||
delete &model;
|
||||
}
|
||||
#ifdef GGML_USE_METAL
|
||||
if (ctx_metal) {
|
||||
ggml_metal_free(ctx_metal);
|
||||
}
|
||||
#endif
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
if (alloc) {
|
||||
ggml_allocr_free(alloc);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
std::mt19937 rng;
|
||||
|
||||
bool has_evaluated_once = false;
|
||||
|
|
@ -371,7 +386,17 @@ struct llama_context {
|
|||
// memory buffers used to evaluate the model
|
||||
// TODO: move in llama_state
|
||||
llama_ctx_buffer buf_compute;
|
||||
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
llama_ctx_buffer buf_alloc;
|
||||
ggml_allocr * alloc = NULL;
|
||||
#endif
|
||||
|
||||
#ifdef LLAMA_USE_SCRATCH
|
||||
llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
|
||||
int buf_last = 0;
|
||||
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
|
||||
#endif
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
ggml_metal_context * ctx_metal = NULL;
|
||||
|
|
@ -381,9 +406,6 @@ struct llama_context {
|
|||
ggml_mpi_context * ctx_mpi = NULL;
|
||||
#endif
|
||||
|
||||
int buf_last = 0;
|
||||
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
|
||||
|
||||
void use_buf(struct ggml_context * ctx, int i) {
|
||||
#if defined(LLAMA_USE_SCRATCH)
|
||||
size_t last_size = 0;
|
||||
|
|
@ -879,6 +901,7 @@ struct llama_context_params llama_context_default_params() {
|
|||
/*.progress_callback =*/ nullptr,
|
||||
/*.progress_callback_user_data =*/ nullptr,
|
||||
/*.low_vram =*/ false,
|
||||
/*.mul_mat_q =*/ false,
|
||||
/*.f16_kv =*/ true,
|
||||
/*.logits_all =*/ false,
|
||||
/*.vocab_only =*/ false,
|
||||
|
|
@ -1006,6 +1029,7 @@ static void llama_model_load_internal(
|
|||
int n_gpu_layers,
|
||||
int main_gpu,
|
||||
const float * tensor_split,
|
||||
const bool mul_mat_q,
|
||||
float rope_freq_base,
|
||||
float rope_freq_scale,
|
||||
bool low_vram,
|
||||
|
|
@ -1134,9 +1158,11 @@ static void llama_model_load_internal(
|
|||
}
|
||||
|
||||
(void) main_gpu;
|
||||
(void) mul_mat_q;
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
|
||||
ggml_cuda_set_main_device(main_gpu);
|
||||
ggml_cuda_set_mul_mat_q(mul_mat_q);
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
||||
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
|
|
@ -1230,12 +1256,16 @@ static void llama_model_load_internal(
|
|||
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
|
||||
|
||||
// this is the total memory required to run the inference
|
||||
const size_t mem_required =
|
||||
size_t mem_required =
|
||||
ctx_size +
|
||||
mmapped_size - vram_weights + // weights in VRAM not in memory
|
||||
mmapped_size - vram_weights; // weights in VRAM not in memory
|
||||
|
||||
#ifndef LLAMA_USE_ALLOCATOR
|
||||
mem_required +=
|
||||
MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
|
||||
MEM_REQ_SCRATCH1().at(model.type) +
|
||||
MEM_REQ_EVAL().at(model.type);
|
||||
#endif
|
||||
|
||||
// this is the memory required by one llama_state
|
||||
const size_t mem_required_state =
|
||||
|
|
@ -1341,6 +1371,7 @@ static bool llama_model_load(
|
|||
int n_gpu_layers,
|
||||
int main_gpu,
|
||||
const float * tensor_split,
|
||||
const bool mul_mat_q,
|
||||
float rope_freq_base,
|
||||
float rope_freq_scale,
|
||||
bool low_vram,
|
||||
|
|
@ -1351,7 +1382,8 @@ static bool llama_model_load(
|
|||
llama_progress_callback progress_callback,
|
||||
void *progress_callback_user_data) {
|
||||
try {
|
||||
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type,
|
||||
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers,
|
||||
main_gpu, tensor_split, mul_mat_q, rope_freq_base, rope_freq_scale, low_vram, memory_type,
|
||||
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
|
||||
return true;
|
||||
} catch (const std::exception & err) {
|
||||
|
|
@ -1360,32 +1392,15 @@ static bool llama_model_load(
|
|||
}
|
||||
}
|
||||
|
||||
// evaluate the transformer
|
||||
//
|
||||
// - lctx: llama context
|
||||
// - tokens: new batch of tokens to process
|
||||
// - embd embeddings input
|
||||
// - n_tokens number of tokens
|
||||
// - n_past: the context size so far
|
||||
// - n_threads: number of threads to use
|
||||
//
|
||||
static bool llama_eval_internal(
|
||||
static struct ggml_cgraph * llama_build_graph(
|
||||
llama_context & lctx,
|
||||
const llama_token * tokens,
|
||||
const float * embd,
|
||||
int n_tokens,
|
||||
int n_past,
|
||||
int n_threads,
|
||||
const char * cgraph_fname) {
|
||||
int n_past) {
|
||||
|
||||
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
|
||||
|
||||
#ifdef GGML_USE_MPI
|
||||
ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
|
||||
#endif
|
||||
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
||||
const int N = n_tokens;
|
||||
|
||||
const auto & model = lctx.model;
|
||||
|
|
@ -1401,10 +1416,8 @@ static bool llama_eval_internal(
|
|||
const int64_t n_head = hparams.n_head;
|
||||
const int64_t n_head_kv = hparams.n_head_kv;
|
||||
const int64_t n_embd_head = hparams.n_embd_head();
|
||||
const int64_t n_vocab = hparams.n_vocab;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
||||
|
||||
|
||||
LLAMA_ASSERT(n_embd_head == hparams.n_rot);
|
||||
|
||||
const float freq_base = hparams.rope_freq_base;
|
||||
|
|
@ -1416,26 +1429,35 @@ static bool llama_eval_internal(
|
|||
auto & mem_per_token = lctx.mem_per_token;
|
||||
auto & buf_compute = lctx.buf_compute;
|
||||
|
||||
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ buf_compute.size,
|
||||
/*.mem_buffer =*/ buf_compute.addr,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
params.no_alloc = true;
|
||||
#endif
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
|
||||
ggml_cgraph gf = {};
|
||||
|
||||
// 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
|
||||
n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
|
||||
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
if (tokens) {
|
||||
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
|
||||
}
|
||||
#else
|
||||
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
|
||||
#endif
|
||||
ggml_set_name(inp_tokens, "inp_tokens");
|
||||
|
||||
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
||||
|
|
@ -1445,8 +1467,16 @@ static bool llama_eval_internal(
|
|||
#endif
|
||||
|
||||
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
|
||||
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
ggml_allocr_alloc(lctx.alloc, inpL);
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
|
||||
}
|
||||
#else
|
||||
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
|
||||
#endif
|
||||
}
|
||||
|
||||
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||
(void) i_gpu_start;
|
||||
|
|
@ -1472,6 +1502,17 @@ static bool llama_eval_internal(
|
|||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
|
||||
}
|
||||
#else
|
||||
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
|
||||
#endif
|
||||
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_format_name(inpL, "layer_inp_%d", il);
|
||||
|
||||
|
|
@ -1541,8 +1582,8 @@ static bool llama_eval_internal(
|
|||
ggml_set_name(v, "v");
|
||||
|
||||
// important: storing RoPE-ed version of K in the KV cache!
|
||||
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
||||
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
||||
}
|
||||
|
||||
struct ggml_tensor * Q =
|
||||
|
|
@ -1567,9 +1608,6 @@ static bool llama_eval_internal(
|
|||
ggml_set_name(KQ, "KQ");
|
||||
|
||||
// KQ_scaled = KQ / sqrt(n_embd_head)
|
||||
struct ggml_tensor * KQ_scale = ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head));
|
||||
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
||||
|
||||
// KQ_scaled shape [n_past + N, N, n_head, 1]
|
||||
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
|
||||
offload_func_kq(KQ_scaled);
|
||||
|
|
@ -1685,9 +1723,6 @@ static bool llama_eval_internal(
|
|||
|
||||
lctx.use_buf(ctx0, 0);
|
||||
|
||||
// used at the end to optionally extract the embeddings
|
||||
struct ggml_tensor * embeddings = NULL;
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
|
||||
|
|
@ -1698,8 +1733,6 @@ static bool llama_eval_internal(
|
|||
cur = ggml_mul(ctx0, cur, model.norm);
|
||||
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
||||
ggml_set_name(cur, "result_norm");
|
||||
|
||||
embeddings = cur;
|
||||
}
|
||||
|
||||
// lm_head
|
||||
|
|
@ -1711,23 +1744,103 @@ static bool llama_eval_internal(
|
|||
// logits -> probs
|
||||
//cur = ggml_soft_max_inplace(ctx0, cur);
|
||||
|
||||
// run the computation
|
||||
ggml_build_forward_expand(&gf, cur);
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
// fprintf(stderr, "graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf.n_nodes, gf.n_leafs);
|
||||
if (mem_per_token == 0) {
|
||||
mem_per_token = ggml_used_mem(ctx0)/N;
|
||||
}
|
||||
|
||||
#if 0
|
||||
printf("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
|
||||
ggml_used_mem(ctx0)/1024.0/1024.0,
|
||||
lctx.get_buf_max_mem(0)/1024.0/1024.0,
|
||||
lctx.get_buf_max_mem(1)/1024.0/1024.0,
|
||||
lctx.work_buffer.size()/1024.0/1024.0,
|
||||
n_past, N);
|
||||
#endif
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
// evaluate the transformer
|
||||
//
|
||||
// - lctx: llama context
|
||||
// - tokens: new batch of tokens to process
|
||||
// - embd embeddings input
|
||||
// - n_tokens number of tokens
|
||||
// - n_past: the context size so far
|
||||
// - n_threads: number of threads to use
|
||||
//
|
||||
static bool llama_eval_internal(
|
||||
llama_context & lctx,
|
||||
const llama_token * tokens,
|
||||
const float * embd,
|
||||
int n_tokens,
|
||||
int n_past,
|
||||
int n_threads,
|
||||
const char * cgraph_fname) {
|
||||
|
||||
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
|
||||
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
||||
#ifdef GGML_USE_MPI
|
||||
ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
|
||||
#endif
|
||||
|
||||
const int N = n_tokens;
|
||||
|
||||
const auto & model = lctx.model;
|
||||
const auto & hparams = model.hparams;
|
||||
|
||||
const auto & kv_self = lctx.kv_self;
|
||||
|
||||
LLAMA_ASSERT(!!kv_self.ctx);
|
||||
|
||||
const int64_t n_embd = hparams.n_embd;
|
||||
const int64_t n_vocab = hparams.n_vocab;
|
||||
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
ggml_allocr_reset(lctx.alloc);
|
||||
#endif
|
||||
|
||||
ggml_cgraph * gf = llama_build_graph(lctx, tokens, embd, n_tokens, n_past);
|
||||
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
ggml_allocr_alloc_graph(lctx.alloc, gf);
|
||||
#endif
|
||||
|
||||
// fprintf(stderr, "graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
|
||||
|
||||
// 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
|
||||
n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
|
||||
|
||||
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
|
||||
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
|
||||
|
||||
LLAMA_ASSERT(strcmp(res->name, "result_output") == 0);
|
||||
LLAMA_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
|
||||
|
||||
#if GGML_USE_MPI
|
||||
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, &gf, n_layer);
|
||||
const int64_t n_layer = hparams.n_layer;
|
||||
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
|
||||
#endif
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
if (lctx.ctx_metal && N == 1) {
|
||||
if (!ggml_metal_if_optimized(lctx.ctx_metal)) {
|
||||
ggml_metal_graph_find_concurrency(lctx.ctx_metal,&gf);
|
||||
}
|
||||
// TODO: disabled until #2413 is resolved
|
||||
//if (!ggml_metal_if_optimized(lctx.ctx_metal)) {
|
||||
// ggml_metal_graph_find_concurrency(lctx.ctx_metal, gf);
|
||||
//}
|
||||
ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
|
||||
ggml_metal_graph_compute(lctx.ctx_metal, &gf);
|
||||
ggml_metal_get_tensor (lctx.ctx_metal, cur);
|
||||
ggml_metal_graph_compute(lctx.ctx_metal, gf);
|
||||
ggml_metal_get_tensor (lctx.ctx_metal, res);
|
||||
if (!lctx.embedding.empty()) {
|
||||
ggml_metal_get_tensor(lctx.ctx_metal, embeddings);
|
||||
}
|
||||
} else {
|
||||
// IMPORTANT:
|
||||
// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
|
||||
|
|
@ -1745,34 +1858,32 @@ static bool llama_eval_internal(
|
|||
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
|
||||
}
|
||||
|
||||
ggml_graph_compute_helper(lctx.work_buffer, &gf, n_threads);
|
||||
ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
|
||||
}
|
||||
#else
|
||||
ggml_graph_compute_helper(lctx.work_buffer, &gf, n_threads);
|
||||
ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
|
||||
#endif
|
||||
|
||||
#if GGML_USE_MPI
|
||||
ggml_mpi_graph_compute_post(lctx.ctx_mpi, &gf, n_layer);
|
||||
ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
|
||||
#endif
|
||||
|
||||
// update kv token count
|
||||
lctx.kv_self.n = n_past + N;
|
||||
|
||||
struct ggml_tensor * res = gf.nodes[gf.n_nodes - 1];
|
||||
|
||||
if (cgraph_fname) {
|
||||
ggml_graph_export(&gf, cgraph_fname);
|
||||
ggml_graph_export(gf, cgraph_fname);
|
||||
}
|
||||
|
||||
#ifdef GGML_PERF
|
||||
// print timing information per ggml operation (for debugging purposes)
|
||||
// requires GGML_PERF to be defined
|
||||
ggml_graph_print(&gf);
|
||||
ggml_graph_print(gf);
|
||||
#endif
|
||||
|
||||
// plot the computation graph in dot format (for debugging purposes)
|
||||
//if (n_past%100 == 0) {
|
||||
// ggml_graph_dump_dot(&gf, NULL, "llama.dot");
|
||||
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
|
||||
//}
|
||||
|
||||
// extract logits
|
||||
|
|
@ -1797,21 +1908,6 @@ static bool llama_eval_internal(
|
|||
memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
|
||||
}
|
||||
|
||||
if (mem_per_token == 0) {
|
||||
mem_per_token = ggml_used_mem(ctx0)/N;
|
||||
}
|
||||
|
||||
#if 0
|
||||
printf("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
|
||||
ggml_used_mem(ctx0)/1024.0/1024.0,
|
||||
lctx.get_buf_max_mem(0)/1024.0/1024.0,
|
||||
lctx.get_buf_max_mem(1)/1024.0/1024.0,
|
||||
lctx.work_buffer.size()/1024.0/1024.0,
|
||||
n_past, N);
|
||||
#endif
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
// measure the performance only for the single-token evals
|
||||
if (N == 1) {
|
||||
lctx.t_eval_us += ggml_time_us() - t_start_us;
|
||||
|
|
@ -1923,7 +2019,9 @@ struct llama_tokenizer {
|
|||
if (token == vocab_.token_to_id.end()) {
|
||||
// output any symbols that did not form tokens as bytes.
|
||||
for (int j = 0; j < (int) symbol.n; ++j) {
|
||||
llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
|
||||
// NOTE: old version, before #2420 - not sure what are the implications of this
|
||||
//llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
|
||||
llama_vocab::id token_id = vocab_.token_to_id.at(std::string(1, symbol.text[j]));
|
||||
output.push_back(token_id);
|
||||
}
|
||||
} else {
|
||||
|
|
@ -3100,7 +3198,7 @@ struct llama_model * llama_load_model_from_file(
|
|||
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
||||
|
||||
if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gqa, params.rms_norm_eps, params.n_gpu_layers,
|
||||
params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
|
||||
params.main_gpu, params.tensor_split, params.mul_mat_q, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
|
||||
memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
|
||||
params.progress_callback_user_data)) {
|
||||
delete model;
|
||||
|
|
@ -3177,10 +3275,47 @@ struct llama_context * llama_new_context_with_model(
|
|||
ctx->embedding.resize(hparams.n_embd);
|
||||
}
|
||||
|
||||
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type));
|
||||
#ifdef LLAMA_USE_ALLOCATOR
|
||||
{
|
||||
static const size_t tensor_alignment = 32;
|
||||
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
|
||||
ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
|
||||
|
||||
// create measure allocator
|
||||
ctx->alloc = ggml_allocr_new_measure(tensor_alignment);
|
||||
|
||||
// build worst-case graph
|
||||
int n_tokens = std::min((int)hparams.n_ctx, params.n_batch);
|
||||
int n_past = hparams.n_ctx - n_tokens;
|
||||
llama_token token = llama_token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
|
||||
ggml_cgraph * gf = llama_build_graph(*ctx, &token, NULL, n_tokens, n_past);
|
||||
|
||||
// measure memory requirements for the graph
|
||||
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
|
||||
|
||||
fprintf(stderr, "%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
|
||||
|
||||
// debug - for comparison with scratch buffer
|
||||
//size_t prev_req =
|
||||
// MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) +
|
||||
// MEM_REQ_SCRATCH1().at(ctx->model.type) +
|
||||
// MEM_REQ_EVAL().at(ctx->model.type);
|
||||
//fprintf(stderr, "%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
|
||||
|
||||
// recreate allocator with exact memory requirements
|
||||
ggml_allocr_free(ctx->alloc);
|
||||
|
||||
ctx->buf_alloc.resize(alloc_size);
|
||||
ctx->alloc = ggml_allocr_new(ctx->buf_alloc.addr, ctx->buf_alloc.size, tensor_alignment);
|
||||
}
|
||||
#else
|
||||
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type) + ggml_graph_overhead());
|
||||
#endif
|
||||
|
||||
#ifdef LLAMA_USE_SCRATCH
|
||||
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type));
|
||||
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
|
||||
#endif
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
|
|
@ -3250,9 +3385,6 @@ struct llama_context * llama_init_from_file(
|
|||
}
|
||||
|
||||
void llama_free(struct llama_context * ctx) {
|
||||
if (ctx->model_owner) {
|
||||
delete &ctx->model;
|
||||
}
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
|
|
@ -3662,7 +3794,7 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
|
|||
const auto & kv_self = ctx->kv_self;
|
||||
const auto & hparams = ctx->model.hparams;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_embd = hparams.n_embd;
|
||||
const int n_embd = hparams.n_embd_gqa();
|
||||
const int n_ctx = hparams.n_ctx;
|
||||
|
||||
const size_t kv_size = kv_self.buf.size;
|
||||
|
|
@ -3765,7 +3897,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
|
|||
const auto & kv_self = ctx->kv_self;
|
||||
const auto & hparams = ctx->model.hparams;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_embd = hparams.n_embd;
|
||||
const int n_embd = hparams.n_embd_gqa();
|
||||
const int n_ctx = hparams.n_ctx;
|
||||
|
||||
size_t kv_size;
|
||||
|
|
|
|||
1
llama.h
1
llama.h
|
|
@ -108,6 +108,7 @@ extern "C" {
|
|||
|
||||
// Keep the booleans together to avoid misalignment during copy-by-value.
|
||||
bool low_vram; // if true, reduce VRAM usage at the cost of performance
|
||||
bool mul_mat_q; // if true, use experimental mul_mat_q kernels
|
||||
bool f16_kv; // use fp16 for KV cache
|
||||
bool logits_all; // the llama_eval() call computes all logits, not just the last one
|
||||
bool vocab_only; // only load the vocabulary, no weights
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue