cuda : performance optimizations (#1530)
* xor hack * block y dim * loop unrolling * Fixed cmake LLAMA_CUDA_BY option * Removed hipblas compatibility code * Define GGML_CUDA_DMMV_BLOCK_Y if not defined * Fewer iters, more ops per iter * Renamed DMMV X/Y compilation options
This commit is contained in:
Johannes Gäßler
GitHub
parent
ac7876ac20
commit
1fcdcc28b1
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@ -37,42 +37,44 @@ endif()
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#
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# general
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option(LLAMA_STATIC "llama: static link libraries" OFF)
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option(LLAMA_NATIVE "llama: enable -march=native flag" OFF)
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option(LLAMA_LTO "llama: enable link time optimization" OFF)
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option(LLAMA_STATIC "llama: static link libraries" OFF)
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option(LLAMA_NATIVE "llama: enable -march=native flag" OFF)
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option(LLAMA_LTO "llama: enable link time optimization" OFF)
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# debug
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option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" ON)
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option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
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option(LLAMA_GPROF "llama: enable gprof" OFF)
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option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" ON)
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option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
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option(LLAMA_GPROF "llama: enable gprof" OFF)
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# sanitizers
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option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF)
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option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF)
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option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
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option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF)
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option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF)
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option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
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# instruction set specific
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option(LLAMA_AVX "llama: enable AVX" ON)
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option(LLAMA_AVX2 "llama: enable AVX2" ON)
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option(LLAMA_AVX512 "llama: enable AVX512" OFF)
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option(LLAMA_AVX512_VBMI "llama: enable AVX512-VBMI" OFF)
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option(LLAMA_AVX512_VNNI "llama: enable AVX512-VNNI" OFF)
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option(LLAMA_FMA "llama: enable FMA" ON)
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option(LLAMA_AVX "llama: enable AVX" ON)
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option(LLAMA_AVX2 "llama: enable AVX2" ON)
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option(LLAMA_AVX512 "llama: enable AVX512" OFF)
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option(LLAMA_AVX512_VBMI "llama: enable AVX512-VBMI" OFF)
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option(LLAMA_AVX512_VNNI "llama: enable AVX512-VNNI" OFF)
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option(LLAMA_FMA "llama: enable FMA" ON)
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# in MSVC F16C is implied with AVX2/AVX512
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if (NOT MSVC)
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option(LLAMA_F16C "llama: enable F16C" ON)
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option(LLAMA_F16C "llama: enable F16C" ON)
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endif()
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# 3rd party libs
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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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option(LLAMA_BLAS_VENDOR "llama: BLA_VENDOR from https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" Generic)
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option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
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option(LLAMA_BLAS "llama: use BLAS" OFF)
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option(LLAMA_BLAS_VENDOR "llama: BLA_VENDOR from https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" Generic)
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option(LLAMA_CUBLAS "llama: use cuBLAS" 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_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
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option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
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option(LLAMA_BUILD_SERVER "llama: build server example" OFF)
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option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
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option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
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option(LLAMA_BUILD_SERVER "llama: build server example" OFF)
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#
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# Build info header
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@ -184,6 +186,8 @@ if (LLAMA_CUBLAS)
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set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
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add_compile_definitions(GGML_USE_CUBLAS)
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add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
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add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
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if (LLAMA_STATIC)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
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12
Makefile
12
Makefile
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@ -133,9 +133,19 @@ ifdef LLAMA_CUBLAS
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OBJS += ggml-cuda.o
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NVCC = nvcc
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NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
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ifdef LLAMA_CUDA_DMMV_X
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NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
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else
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NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
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endif # LLAMA_CUDA_DMMV_X
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ifdef LLAMA_CUDA_DMMV_Y
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NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y)
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else
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NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
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endif # LLAMA_CUDA_DMMV_Y
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ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
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$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
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endif
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endif # LLAMA_CUBLAS
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ifdef LLAMA_CLBLAST
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CFLAGS += -DGGML_USE_CLBLAST
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CXXFLAGS += -DGGML_USE_CLBLAST
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110
ggml-cuda.cu
110
ggml-cuda.cu
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@ -83,9 +83,19 @@ typedef struct {
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} block_q8_0;
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static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
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#define WARP_SIZE 32
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#define CUDA_MUL_BLOCK_SIZE 256
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#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
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#define CUDA_DMMV_BLOCK_SIZE 32 // dmmv = dequantize_mul_mat_vec
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// dmmv = dequantize_mul_mat_vec
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#ifndef GGML_CUDA_DMMV_X
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#define GGML_CUDA_DMMV_X 32
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#endif
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#ifndef GGML_CUDA_DMMV_Y
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#define GGML_CUDA_DMMV_Y 1
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#endif
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static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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@ -200,41 +210,51 @@ static __global__ void dequantize_block(const void * vx, float * y, const int k)
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dequantize_kernel(vx, ib, iqs, v0, v1);
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}
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template <int block_size, int qk, int qr, dequantize_kernel_t dequantize_kernel>
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template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
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static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y, float * dst, const int ncols) {
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const int row = blockIdx.x;
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// qk = quantized weights per x block
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// qr = number of quantized weights per data value in x block
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const int row = blockIdx.x*blockDim.y + threadIdx.y;
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const int tid = threadIdx.x;
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const int iter_stride = 2*GGML_CUDA_DMMV_X;
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const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
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const int y_offset = qr == 1 ? 1 : qk/2;
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__shared__ float tmp[block_size]; // separate sum for each thread
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tmp[tid] = 0;
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float tmp = 0; // partial sum for thread in warp
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for (int i = 0; i < ncols/block_size; i += 2) {
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const int col = i*block_size + 2*tid;
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const int ib = (row*ncols + col)/qk; // block index
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const int iqs = (col%qk)/qr; // quant index
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for (int i = 0; i < ncols; i += iter_stride) {
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const int col = i + vals_per_iter*tid;
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const int ib = (row*ncols + col)/qk; // x block index
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const int iqs = (col%qk)/qr; // x quant index
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const int iybs = col - col%qk; // y block start index
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// dequantize
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float v0, v1;
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dequantize_kernel(vx, ib, iqs, v0, v1);
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// processing >2 values per i iter is faster for fast GPUs
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#pragma unroll
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for (int j = 0; j < vals_per_iter; j += 2) {
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// process 2 vals per j iter
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// matrix multiplication
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tmp[tid] += v0 * y[iybs + iqs + 0];
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tmp[tid] += v1 * y[iybs + iqs + y_offset];
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// dequantize
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float v0, v1;
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dequantize_kernel(vx, ib, iqs + j/qr, v0, v1);
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// for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
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// matrix multiplication
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tmp += v0 * y[iybs + iqs + j/qr + 0];
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tmp += v1 * y[iybs + iqs + j/qr + y_offset];
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// for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
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}
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}
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// sum up partial sums and write back result
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__syncthreads();
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for (int s=block_size/2; s>0; s>>=1) {
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if (tid < s) {
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tmp[tid] += tmp[tid + s];
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}
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__syncthreads();
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#pragma unroll
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for (int mask = 16; mask > 0; mask >>= 1) {
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tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
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}
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if (tid == 0) {
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dst[row] = tmp[0];
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dst[row] = tmp;
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}
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}
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@ -269,33 +289,43 @@ static void dequantize_row_q8_0_cuda(const void * vx, float * y, const int k, cu
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}
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static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
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dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK4_0, QR4_0, dequantize_q4_0>
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<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
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GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
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GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
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const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
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dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
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<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
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}
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static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
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dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK4_1, QR4_1, dequantize_q4_1>
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<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
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GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
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GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
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const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
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dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
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<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
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}
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static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
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dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK5_0, QR5_0, dequantize_q5_0>
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<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
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GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
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GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
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const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
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dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
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<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
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}
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static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
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dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK5_1, QR5_1, dequantize_q5_1>
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<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
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GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
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GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
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const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
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dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
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<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
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}
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static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
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dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK8_0, QR8_0, dequantize_q8_0>
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<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
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GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
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GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
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const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
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dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
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<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
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}
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static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
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@ -304,9 +334,11 @@ static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, c
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}
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static void convert_mul_mat_vec_f16_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
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dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, 32, 1, convert_f16>
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<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
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GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
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GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
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const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
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dequantize_mul_mat_vec<1, 1, convert_f16>
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<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
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}
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static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
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