haraldwolff/llama.cpp
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Enable build with CUDA 11.0 (make) (#3132)

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* CUDA 11.0 fixes

* Cleaner CUDA/host flags separation

Also renamed GGML_ASSUME into GGML_CUDA_ASSUME
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Vlad 2023-09-16 17:55:43 +03:00 committed by GitHub
parent b08e75baea
commit 5dbc2b3213
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GPG key ID: 4AEE18F83AFDEB23
2 changed files with 78 additions and 62 deletions
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48
Makefile
View file

@ -95,16 +95,19 @@ CXXV := $(shell $(CXX) --version | head -n 1)
#
# keep standard at C11 and C++11
MK_CPPFLAGS = -I. -Icommon
MK_CFLAGS = -std=c11 -fPIC
MK_CXXFLAGS = -std=c++11 -fPIC
# -Ofast tends to produce faster code, but may not be available for some compilers.
ifdef LLAMA_FAST
OPT = -Ofast
MK_CFLAGS += -Ofast
MK_HOST_CXXFLAGS += -Ofast
MK_CUDA_CXXFLAGS += -O3
else
OPT = -O3
MK_CFLAGS += -O3
MK_CXXFLAGS += -O3
endif
MK_CPPFLAGS = -I. -Icommon
MK_CFLAGS = $(OPT) -std=c11 -fPIC
MK_CXXFLAGS = $(OPT) -std=c++11 -fPIC
MK_LDFLAGS =
# clock_gettime came in POSIX.1b (1993)
# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
@ -232,7 +235,7 @@ ifndef RISCV
ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
# Use all CPU extensions that are available:
MK_CFLAGS += -march=native -mtune=native
MK_CXXFLAGS += -march=native -mtune=native
MK_HOST_CXXFLAGS += -march=native -mtune=native
# Usage AVX-only
#MK_CFLAGS += -mfma -mf16c -mavx
@ -372,7 +375,7 @@ ifdef LLAMA_CUDA_CCBIN
NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
endif
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(NVCC) $(NVCCFLAGS) $(subst -Ofast,-O3,$(CXXFLAGS)) -Wno-pedantic -c $< -o $@
$(NVCC) $(NVCCFLAGS) -Wno-pedantic -c $< -o $@
endif # LLAMA_CUBLAS
ifdef LLAMA_CLBLAST
@ -440,23 +443,30 @@ k_quants.o: k_quants.c k_quants.h
endif # LLAMA_NO_K_QUANTS
# combine build flags with cmdline overrides
override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS)
override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
override CUDA_CXXFLAGS := $(MK_CUDA_CXXFLAGS) $(CUDA_CXXFLAGS)
override HOST_CXXFLAGS := $(MK_HOST_CXXFLAGS) $(HOST_CXXFLAGS)
override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS)
# save CXXFLAGS before we add host-only options
NVCCFLAGS := $(NVCCFLAGS) $(CXXFLAGS) $(CUDA_CXXFLAGS) -Wno-pedantic -Xcompiler "$(HOST_CXXFLAGS)"
override CXXFLAGS += $(HOST_CXXFLAGS)
#
# Print build information
#
$(info I llama.cpp build info: )
$(info I UNAME_S: $(UNAME_S))
$(info I UNAME_P: $(UNAME_P))
$(info I UNAME_M: $(UNAME_M))
$(info I CFLAGS: $(CFLAGS))
$(info I CXXFLAGS: $(CXXFLAGS))
$(info I LDFLAGS: $(LDFLAGS))
$(info I CC: $(CCV))
$(info I CXX: $(CXXV))
$(info I UNAME_S: $(UNAME_S))
$(info I UNAME_P: $(UNAME_P))
$(info I UNAME_M: $(UNAME_M))
$(info I CFLAGS: $(CFLAGS))
$(info I CXXFLAGS: $(CXXFLAGS))
$(info I NVCCFLAGS: $(NVCCFLAGS))
$(info I LDFLAGS: $(LDFLAGS))
$(info I CC: $(CCV))
$(info I CXX: $(CXXV))
$(info )
#

92
ggml-cuda.cu
View file

@ -61,7 +61,7 @@
#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
#define cudaStreamNonBlocking hipStreamNonBlocking
#define cudaStreamSynchronize hipStreamSynchronize
#define cudaStreamWaitEvent(stream, event) hipStreamWaitEvent(stream, event, 0)
#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
#define cudaStream_t hipStream_t
#define cudaSuccess hipSuccess
#else
@ -190,6 +190,12 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
} while (0)
#endif // CUDART_VERSION >= 11
#if CUDART_VERSION >= 11100
#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
#else
#define GGML_CUDA_ASSUME(x)
#endif // CUDART_VERSION >= 11100
#ifdef GGML_CUDA_F16
typedef half dfloat; // dequantize float
typedef half2 dfloat2;
@ -2145,10 +2151,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI4_0;
const int kqsx = k % QI4_0;
@ -2239,10 +2245,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI4_1;
const int kqsx = k % QI4_1;
@ -2331,10 +2337,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI5_0;
const int kqsx = k % QI5_0;
@ -2445,10 +2451,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI5_1;
const int kqsx = k % QI5_1;
@ -2551,10 +2557,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI8_0;
const int kqsx = k % QI8_0;
@ -2642,10 +2648,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI2_K;
const int kqsx = k % QI2_K;
@ -2763,10 +2769,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI3_K;
const int kqsx = k % QI3_K;
@ -2981,10 +2987,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI4_K; // == 0 if QK_K == 256
const int kqsx = k % QI4_K; // == k if QK_K == 256
@ -3162,10 +3168,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI5_K; // == 0 if QK_K == 256
const int kqsx = k % QI5_K; // == k if QK_K == 256
@ -3291,10 +3297,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < nwarps);
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
GGML_CUDA_ASSUME(i_offset >= 0);
GGML_CUDA_ASSUME(i_offset < nwarps);
GGML_CUDA_ASSUME(k >= 0);
GGML_CUDA_ASSUME(k < WARP_SIZE);
const int kbx = k / QI6_K; // == 0 if QK_K == 256
const int kqsx = k % QI6_K; // == k if QK_K == 256
@ -6408,7 +6414,7 @@ static void ggml_cuda_op_mul_mat(
// wait for main GPU data if necessary
if (split && (id != g_main_device || is != 0)) {
CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0]));
CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0], 0));
}
for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
@ -6530,7 +6536,7 @@ static void ggml_cuda_op_mul_mat(
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
for (int64_t id = 0; id < g_device_count; ++id) {
for (int64_t is = 0; is < is_max; ++is) {
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is]));
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0));
}
}
}