haraldwolff/llama.cpp
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ROCm Port (#1087)

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* use hipblas based on cublas
* Update Makefile for the Cuda kernels
* Expand arch list and make it overrideable
* Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5)
* add hipBLAS to README
* new build arg LLAMA_CUDA_MMQ_Y
* fix half2 decomposition
* Add intrinsics polyfills for AMD
* AMD assembly optimized __dp4a
* Allow overriding CC_TURING
* use "ROCm" instead of "CUDA"
* ignore all build dirs
* Add Dockerfiles
* fix llama-bench
* fix -nommq help for non CUDA/HIP

---------

Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com>
Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com>
Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com>
Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com>
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Co-authored-by: jammm <2500920+jammm@users.noreply.github.com>
Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
This commit is contained in:
Henri Vasserman 2023-08-25 12:09:42 +03:00 committed by GitHub
parent 3f460a2b72
commit 6bbc598a63
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12 changed files with 335 additions and 59 deletions
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44
.devops/full-rocm.Dockerfile Normal file
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@ -0,0 +1,44 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG ROCM_VERSION=5.6
# Target the CUDA build image
ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
FROM ${BASE_ROCM_DEV_CONTAINER} as build
# Unless otherwise specified, we make a fat build.
# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
# This is mostly tied to rocBLAS supported archs.
ARG ROCM_DOCKER_ARCH=\
gfx803 \
gfx900 \
gfx906 \
gfx908 \
gfx90a \
gfx1010 \
gfx1030 \
gfx1100 \
gfx1101 \
gfx1102
COPY requirements.txt requirements.txt
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
# Set nvcc architecture
ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
ENV LLAMA_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
RUN make
ENTRYPOINT ["/app/.devops/tools.sh"]

44
.devops/main-rocm.Dockerfile Normal file
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@ -0,0 +1,44 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG ROCM_VERSION=5.6
# Target the CUDA build image
ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
FROM ${BASE_ROCM_DEV_CONTAINER} as build
# Unless otherwise specified, we make a fat build.
# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
# This is mostly tied to rocBLAS supported archs.
ARG ROCM_DOCKER_ARCH=\
gfx803 \
gfx900 \
gfx906 \
gfx908 \
gfx90a \
gfx1010 \
gfx1030 \
gfx1100 \
gfx1101 \
gfx1102
COPY requirements.txt requirements.txt
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
# Set nvcc architecture
ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
ENV LLAMA_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
RUN make
ENTRYPOINT [ "/app/main" ]

9
.dockerignore
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@ -5,14 +5,7 @@
.vscode/
.DS_Store
build/
build-em/
build-debug/
build-release/
build-static/
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/
build*/
models/*

15
.gitignore vendored
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@ -16,20 +16,7 @@
.vs/
.vscode/
build/
build-em/
build-debug/
build-release/
build-ci-debug/
build-ci-release/
build-static/
build-cublas/
build-opencl/
build-metal/
build-mpi/
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/
build*/
out/
tmp/

38
CMakeLists.txt
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@ -74,6 +74,7 @@ set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kern
set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some calculations" OFF)
set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_METAL "llama: use Metal" OFF)
option(LLAMA_MPI "llama: use MPI" OFF)
@ -352,6 +353,43 @@ if (LLAMA_CLBLAST)
endif()
endif()
if (LLAMA_HIPBLAS)
list(APPEND CMAKE_PREFIX_PATH /opt/rocm)
if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang")
message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang")
endif()
if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang")
message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++")
endif()
find_package(hip)
find_package(hipblas)
find_package(rocblas)
if (${hipblas_FOUND} AND ${hip_FOUND})
message(STATUS "HIP and hipBLAS found")
add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS)
add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h)
if (LLAMA_CUDA_FORCE_DMMV)
target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV)
endif()
target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
target_compile_definitions(ggml-rocm PRIVATE CC_TURING=1000000000)
set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX)
target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas)
if (LLAMA_STATIC)
message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
endif()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-rocm)
else()
message(WARNING "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm")
endif()
endif()
if (LLAMA_ALL_WARNINGS)
if (NOT MSVC)
set(c_flags

24
Makefile
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@ -280,6 +280,30 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
$(CXX) $(CXXFLAGS) -c $< -o $@
endif # LLAMA_CLBLAST
ifdef LLAMA_HIPBLAS
ROCM_PATH ?= /opt/rocm
HIPCC ?= $(ROCM_PATH)/bin/hipcc
GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
LLAMA_CUDA_DMMV_X ?= 32
LLAMA_CUDA_MMV_Y ?= 1
LLAMA_CUDA_KQUANTS_ITER ?= 2
CFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
CXXFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
LDFLAGS += -lhipblas -lamdhip64 -lrocblas
HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS))
HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
HIPFLAGS += -DCC_TURING=1000000000
ifdef LLAMA_CUDA_FORCE_DMMV
HIPFLAGS += -DGGML_CUDA_FORCE_DMMV
endif # LLAMA_CUDA_FORCE_DMMV
OBJS += ggml-cuda.o
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
endif # LLAMA_HIPBLAS
ifdef LLAMA_METAL
CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
CXXFLAGS += -DGGML_USE_METAL

29
README.md
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@ -422,6 +422,35 @@ Building the program with BLAS support may lead to some performance improvements
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
- #### hipBLAS
This provide BLAS acceleation on HIP supported GPU like AMD GPU.
Make sure to have ROCm installed.
You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html).
Windows support is coming soon...
- Using `make`:
```bash
make LLAMA_HIPBLAS=1
```
- Using `CMake`:
```bash
mkdir build
cd build
CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake .. -DLLAMA_HIPBLAS=ON
cmake --build .
```
The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
If your GPU is not officialy supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3.
The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
- #### CLBlast
OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.

4
common/common.cpp
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@ -613,9 +613,11 @@ 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");
#ifdef GGML_USE_CUBLAS
fprintf(stdout, " -nommq, --no-mul-mat-q\n");
fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
fprintf(stdout, " use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n");
fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
#endif // GGML_USE_CUBLAS
#endif
fprintf(stdout, " --mtest compute maximum memory usage\n");
fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");

4
examples/llama-bench/llama-bench.cpp
View file

@ -18,9 +18,7 @@
#include "llama.h"
#include "common.h"
#include "build-info.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#endif
// utils
static uint64_t get_time_ns() {
@ -504,7 +502,7 @@ struct test {
static std::string get_backend() {
if (cuda) {
return "CUDA";
return GGML_CUDA_NAME;
}
if (opencl) {
return "OpenCL";

173
ggml-cuda.cu
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@ -6,15 +6,116 @@
#include <atomic>
#include <assert.h>
#if defined(GGML_USE_HIPBLAS)
#include <hip/hip_runtime.h>
#include <hipblas/hipblas.h>
#include <hip/hip_fp16.h>
#ifdef __HIP_PLATFORM_AMD__
// for rocblas_initialize()
#include "rocblas/rocblas.h"
#endif
#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
#define CUBLAS_OP_N HIPBLAS_OP_N
#define CUBLAS_OP_T HIPBLAS_OP_T
#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
#define CUBLAS_TF32_TENSOR_OP_MATH 0
#define CUDA_R_16F HIPBLAS_R_16F
#define CUDA_R_32F HIPBLAS_R_32F
#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
#define cublasCreate hipblasCreate
#define cublasGemmEx hipblasGemmEx
#define cublasHandle_t hipblasHandle_t
#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
#define cublasSetStream hipblasSetStream
#define cublasSgemm hipblasSgemm
#define cublasStatus_t hipblasStatus_t
#define cudaDeviceProp hipDeviceProp_t
#define cudaDeviceSynchronize hipDeviceSynchronize
#define cudaError_t hipError_t
#define cudaEventCreateWithFlags hipEventCreateWithFlags
#define cudaEventDisableTiming hipEventDisableTiming
#define cudaEventRecord hipEventRecord
#define cudaEvent_t hipEvent_t
#define cudaEventDestroy hipEventDestroy
#define cudaFree hipFree
#define cudaFreeHost hipHostFree
#define cudaGetDevice hipGetDevice
#define cudaGetDeviceCount hipGetDeviceCount
#define cudaGetDeviceProperties hipGetDeviceProperties
#define cudaGetErrorString hipGetErrorString
#define cudaGetLastError hipGetLastError
#define cudaMalloc hipMalloc
#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
#define cudaMemcpy hipMemcpy
#define cudaMemcpy2DAsync hipMemcpy2DAsync
#define cudaMemcpyAsync hipMemcpyAsync
#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
#define cudaMemcpyKind hipMemcpyKind
#define cudaMemset hipMemset
#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
#define cudaSetDevice hipSetDevice
#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
#define cudaStreamNonBlocking hipStreamNonBlocking
#define cudaStreamSynchronize hipStreamSynchronize
#define cudaStreamWaitEvent(stream, event) hipStreamWaitEvent(stream, event, 0)
#define cudaStream_t hipStream_t
#define cudaSuccess hipSuccess
#else
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>
#endif
#include "ggml-cuda.h"
#include "ggml.h"
#define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
#ifndef CC_TURING
#define CC_TURING 700
#endif
#if defined(GGML_USE_HIPBLAS)
#define __CUDA_ARCH__ 1300
typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
return reinterpret_cast<const int&>(c);
}
static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
c = __builtin_amdgcn_sdot4(a, b, c, false);
#elif defined(__gfx1100__)
c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
#elif defined(__gfx1010__) || defined(__gfx900__)
int tmp1;
int tmp2;
asm("\n \
v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
v_add3_u32 %0, %1, %2, %0 \n \
v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
v_add3_u32 %0, %1, %2, %0 \n \
"
: "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
: "v"(a), "v"(b)
);
#else
const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
#endif
return c;
}
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
@ -424,8 +525,8 @@ static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const in
static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
const block_q4_1 * x = (const block_q4_1 *) vx;
const dfloat d = x[ib].dm.x;
const dfloat m = x[ib].dm.y;
const dfloat d = __low2half(x[ib].dm);
const dfloat m = __high2half(x[ib].dm);
const int vui = x[ib].qs[iqs];
@ -467,8 +568,8 @@ static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const in
static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
const block_q5_1 * x = (const block_q5_1 *) vx;
const dfloat d = x[ib].dm.x;
const dfloat m = x[ib].dm.y;
const dfloat d = __low2half(x[ib].dm);
const dfloat m = __high2half(x[ib].dm);
uint32_t qh;
memcpy(&qh, x[ib].qh, sizeof(qh));
@ -520,8 +621,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float
const uint8_t q = x[i].qs[32*n + l];
float * y = yy + i*QK_K + 128*n;
float dall = x[i].dm.x;
float dmin = x[i].dm.y;
float dall = __low2half(x[i].dm);
float dmin = __high2half(x[i].dm);
y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
@ -531,8 +632,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float
const int il = tid%16; // 0...15
const uint8_t q = x[i].qs[il] >> (2*is);
float * y = yy + i*QK_K + 16*is + il;
float dall = x[i].dm.x;
float dmin = x[i].dm.y;
float dall = __low2half(x[i].dm);
float dmin = __high2half(x[i].dm);
y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
#endif
@ -618,8 +719,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float
float * y = yy + i*QK_K + 64*il + n*ir;
const float dall = x[i].dm.x;
const float dmin = x[i].dm.y;
const float dall = __low2half(x[i].dm);
const float dmin = __high2half(x[i].dm);
const uint8_t * q = x[i].qs + 32*il + n*ir;
@ -657,8 +758,8 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, float
float * y = yy + i*QK_K + 64*il + 2*ir;
const float dall = x[i].dm.x;
const float dmin = x[i].dm.y;
const float dall = __low2half(x[i].dm);
const float dmin = __high2half(x[i].dm);
const uint8_t * ql = x[i].qs + 32*il + 2*ir;
const uint8_t * qh = x[i].qh + 2*ir;
@ -770,8 +871,8 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
const float * y = yy + i * QK_K + y_offset;
const uint8_t * q = x[i].qs + q_offset;
const float dall = x[i].dm.x;
const float dmin = x[i].dm.y;
const float dall = __low2half(x[i].dm);
const float dmin = __high2half(x[i].dm);
const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
aux[0] = a[0] & 0x0f0f0f0f;
@ -991,8 +1092,8 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
const float * y1 = yy + i*QK_K + y_offset;
const float * y2 = y1 + 128;
const float dall = x[i].dm.x;
const float dmin = x[i].dm.y;
const float dall = __low2half(x[i].dm);
const float dmin = __high2half(x[i].dm);
const uint16_t * a = (const uint16_t *)x[i].scales;
aux[0] = a[im+0] & kmask1;
@ -1124,8 +1225,8 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
const float * y1 = yy + i*QK_K + y_offset;
const float * y2 = y1 + 128;
const float dall = x[i].dm.x;
const float dmin = x[i].dm.y;
const float dall = __low2half(x[i].dm);
const float dmin = __high2half(x[i].dm);
const uint16_t * a = (const uint16_t *)x[i].scales;
aux[0] = a[im+0] & kmask1;
@ -1348,8 +1449,8 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
return;
}
y[ib].ds.x = d;
y[ib].ds.y = sum;
reinterpret_cast<half&>(y[ib].ds.x) = d;
reinterpret_cast<half&>(y[ib].ds.y) = sum;
}
template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
@ -2346,7 +2447,7 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
}
return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, bq8_1->ds.x);
return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
}
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
@ -2432,7 +2533,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
#pragma unroll
for (int i = 0; i < QR2_K; ++ i) {
u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
d8[i] = bq8_1[bq8_offset + i].ds.x;
d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
}
return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
@ -2551,7 +2652,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
#pragma unroll
for (int i = 0; i < QR3_K; ++i) {
u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
d8[i] = bq8_1[bq8_offset + i].ds.x;
d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
}
return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
@ -2720,7 +2821,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
for (int i = 0; i < QR4_K; ++i) {
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
d8[i] = bq8i->ds.x;
d8[i] = __low2half(bq8i->ds);
const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
u[2*i+0] = q8[0];
@ -2747,8 +2848,8 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
const float dall = bq4_K->d[0];
const float dmin = bq4_K->d[1];
const float d8_1 = bq8_1[0].ds.x;
const float d8_2 = bq8_1[1].ds.x;
const float d8_1 = __low2float(bq8_1[0].ds);
const float d8_2 = __low2float(bq8_1[1].ds);
const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
@ -2901,7 +3002,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
#pragma unroll
for (int i = 0; i < QR5_K; ++i) {
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
d8[i] = bq8i->ds.x;
d8[i] = __low2float(bq8i->ds);
const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
u[2*i+0] = q8[0];
@ -2919,8 +3020,8 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
const float d = bq5_K->d;
const float d8_1 = bq8_1[0].ds.x;
const float d8_2 = bq8_1[1].ds.x;
const float d8_1 = __low2half(bq8_1[0].ds);
const float d8_2 = __low2half(bq8_1[1].ds);
const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
@ -3075,7 +3176,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
#pragma unroll
for (int i = 0; i < QR6_K; ++i) {
u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
d8[i] = bq8_1[bq8_offset + 2*i].ds.x;
d8[i] = __low2half(bq8_1[bq8_offset + 2*i].ds);
}
return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
@ -3243,7 +3344,7 @@ static __device__ __forceinline__ void mul_mat_q(
*dsi_dst = *dsi_src;
} else {
float * dfi_dst = (float *) dsi_dst;
*dfi_dst = (*dsi_src).x;
*dfi_dst = __low2half(*dsi_src);
}
}
@ -4944,10 +5045,18 @@ void ggml_init_cublas() {
static bool initialized = false;
if (!initialized) {
#ifdef __HIP_PLATFORM_AMD__
// Workaround for a rocBLAS bug when using multiple graphics cards:
// https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
rocblas_initialize();
CUDA_CHECK(cudaDeviceSynchronize());
#endif
CUDA_CHECK(cudaGetDeviceCount(&g_device_count));
GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
int64_t total_vram = 0;
fprintf(stderr, "%s: found %d CUDA devices:\n", __func__, g_device_count);
fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
for (int id = 0; id < g_device_count; ++id) {
cudaDeviceProp prop;
CUDA_CHECK(cudaGetDeviceProperties(&prop, id));

8
ggml-cuda.h
View file

@ -2,6 +2,14 @@
#include "ggml.h"
#ifdef GGML_USE_HIPBLAS
#define GGML_CUDA_NAME "ROCm"
#define GGML_CUBLAS_NAME "hipBLAS"
#else
#define GGML_CUDA_NAME "CUDA"
#define GGML_CUBLAS_NAME "cuBLAS"
#endif
#ifdef __cplusplus
extern "C" {
#endif

2
llama.cpp
View file

@ -1836,7 +1836,7 @@ static void llm_load_tensors(
(void) main_gpu;
(void) mul_mat_q;
#if defined(GGML_USE_CUBLAS)
LLAMA_LOG_INFO("%s: using CUDA for GPU acceleration\n", __func__);
LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " 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