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cuda : fix jetson compile error (#4560)

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* fix old jetson compile error

* Update Makefile

* update jetson detect and cuda version detect

* update cuda marco define

* update makefile and cuda,fix some issue

* Update README.md

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update Makefile

* Update README.md

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
FantasyGmm 2023-12-22 23:11:12 +08:00 committed by GitHub
parent 6724ef1657
commit a55876955b
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4 changed files with 31 additions and 5 deletions
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22
Makefile
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@ -282,8 +282,17 @@ endif
ifneq ($(filter aarch64%,$(UNAME_M)),)
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
# Nvidia Jetson
MK_CFLAGS += -mcpu=native
MK_CXXFLAGS += -mcpu=native
JETSON_RELEASE_INFO = $(shell jetson_release)
ifdef JETSON_RELEASE_INFO
ifneq ($(filter TX2%,$(JETSON_RELEASE_INFO)),)
JETSON_EOL_MODULE_DETECT = 1
CC = aarch64-unknown-linux-gnu-gcc
cxx = aarch64-unknown-linux-gnu-g++
endif
endif
endif
ifneq ($(filter armv6%,$(UNAME_M)),)
@ -357,10 +366,13 @@ ifdef LLAMA_BLIS
endif # LLAMA_BLIS
ifdef LLAMA_CUBLAS
MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include
MK_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 -L/usr/local/cuda/targets/aarch64-linux/lib
OBJS += ggml-cuda.o
MK_NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
MK_NVCCFLAGS = -use_fast_math
ifndef JETSON_EOL_MODULE_DETECT
MK_NVCCFLAGS += --forward-unknown-to-host-compiler
endif # JETSON_EOL_MODULE_DETECT
ifdef LLAMA_DEBUG
MK_NVCCFLAGS += -lineinfo
@ -417,7 +429,11 @@ ifdef LLAMA_CUDA_CCBIN
MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
endif
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
ifdef JETSON_EOL_MODULE_DETECT
$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
else
$(NVCC) $(BASE_CXXFLAGS) $(NVCCFLAGS) -Wno-pedantic -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
endif # JETSON_EOL_MODULE_DETECT
endif # LLAMA_CUBLAS
ifdef LLAMA_CLBLAST

3
README.md
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@ -396,6 +396,9 @@ Building the program with BLAS support may lead to some performance improvements
- #### cuBLAS
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
- Using `make`:
```bash
make LLAMA_CUBLAS=1

7
ggml-cuda.cu
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@ -90,6 +90,13 @@
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>
// CUDA 10.2 does not have these macro definitions.
#ifndef CUBLAS_TF32_TENSOR_OP_MATH
#define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH
#define CUBLAS_COMPUTE_16F CUDA_R_16F
#define CUBLAS_COMPUTE_32F CUDA_R_32F
#define cublasComputeType_t cudaDataType_t
#endif
#endif // defined(GGML_USE_HIPBLAS)
#include "ggml-cuda.h"

4
ggml-quants.c
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@ -3677,7 +3677,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4);
const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums);
const ggml_int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))};
const ggml_int16x8x2_t mins16 = {{vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}};
const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])),
vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0])));
const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])),
@ -6626,7 +6626,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums);
const int8x16_t scales = vld1q_s8(scale);
const ggml_int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))};
const ggml_int16x8x2_t q6scales = {{vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}};
const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])),
vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))),