mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 03:14:35 +00:00
Enable build with CUDA 11.0 (make) (#3132)
* CUDA 11.0 fixes * Cleaner CUDA/host flags separation Also renamed GGML_ASSUME into GGML_CUDA_ASSUME
This commit is contained in:
Vlad
GitHub
parent
b08e75baea
commit
5dbc2b3213
48
Makefile
48
Makefile
@ -95,16 +95,19 @@ CXXV := $(shell $(CXX) --version | head -n 1)
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#
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# keep standard at C11 and C++11
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MK_CPPFLAGS = -I. -Icommon
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MK_CFLAGS = -std=c11 -fPIC
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MK_CXXFLAGS = -std=c++11 -fPIC
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# -Ofast tends to produce faster code, but may not be available for some compilers.
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ifdef LLAMA_FAST
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OPT = -Ofast
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MK_CFLAGS += -Ofast
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MK_HOST_CXXFLAGS += -Ofast
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MK_CUDA_CXXFLAGS += -O3
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else
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OPT = -O3
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MK_CFLAGS += -O3
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MK_CXXFLAGS += -O3
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endif
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MK_CPPFLAGS = -I. -Icommon
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MK_CFLAGS = $(OPT) -std=c11 -fPIC
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MK_CXXFLAGS = $(OPT) -std=c++11 -fPIC
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MK_LDFLAGS =
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# clock_gettime came in POSIX.1b (1993)
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# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
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@ -232,7 +235,7 @@ ifndef RISCV
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ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
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# Use all CPU extensions that are available:
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MK_CFLAGS += -march=native -mtune=native
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MK_CXXFLAGS += -march=native -mtune=native
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MK_HOST_CXXFLAGS += -march=native -mtune=native
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# Usage AVX-only
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#MK_CFLAGS += -mfma -mf16c -mavx
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@ -372,7 +375,7 @@ 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) $(subst -Ofast,-O3,$(CXXFLAGS)) -Wno-pedantic -c $< -o $@
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$(NVCC) $(NVCCFLAGS) -Wno-pedantic -c $< -o $@
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endif # LLAMA_CUBLAS
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ifdef LLAMA_CLBLAST
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@ -440,23 +443,30 @@ k_quants.o: k_quants.c k_quants.h
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endif # LLAMA_NO_K_QUANTS
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# combine build flags with cmdline overrides
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override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
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override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
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override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS)
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override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
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override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
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override CUDA_CXXFLAGS := $(MK_CUDA_CXXFLAGS) $(CUDA_CXXFLAGS)
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override HOST_CXXFLAGS := $(MK_HOST_CXXFLAGS) $(HOST_CXXFLAGS)
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override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS)
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# save CXXFLAGS before we add host-only options
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NVCCFLAGS := $(NVCCFLAGS) $(CXXFLAGS) $(CUDA_CXXFLAGS) -Wno-pedantic -Xcompiler "$(HOST_CXXFLAGS)"
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override CXXFLAGS += $(HOST_CXXFLAGS)
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#
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# Print build information
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#
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$(info I llama.cpp build info: )
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$(info I UNAME_S: $(UNAME_S))
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$(info I UNAME_P: $(UNAME_P))
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$(info I UNAME_M: $(UNAME_M))
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$(info I CFLAGS: $(CFLAGS))
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$(info I CXXFLAGS: $(CXXFLAGS))
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$(info I LDFLAGS: $(LDFLAGS))
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$(info I CC: $(CCV))
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$(info I CXX: $(CXXV))
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$(info I UNAME_S: $(UNAME_S))
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$(info I UNAME_P: $(UNAME_P))
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$(info I UNAME_M: $(UNAME_M))
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$(info I CFLAGS: $(CFLAGS))
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$(info I CXXFLAGS: $(CXXFLAGS))
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$(info I NVCCFLAGS: $(NVCCFLAGS))
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$(info I LDFLAGS: $(LDFLAGS))
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$(info I CC: $(CCV))
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$(info I CXX: $(CXXV))
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$(info )
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#
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92
ggml-cuda.cu
92
ggml-cuda.cu
@ -61,7 +61,7 @@
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#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
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#define cudaStreamNonBlocking hipStreamNonBlocking
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#define cudaStreamSynchronize hipStreamSynchronize
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#define cudaStreamWaitEvent(stream, event) hipStreamWaitEvent(stream, event, 0)
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#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
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#define cudaStream_t hipStream_t
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#define cudaSuccess hipSuccess
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#else
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@ -190,6 +190,12 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
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} while (0)
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#endif // CUDART_VERSION >= 11
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#if CUDART_VERSION >= 11100
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#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
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#else
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#define GGML_CUDA_ASSUME(x)
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#endif // CUDART_VERSION >= 11100
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#ifdef GGML_CUDA_F16
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typedef half dfloat; // dequantize float
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typedef half2 dfloat2;
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@ -2145,10 +2151,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI4_0;
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const int kqsx = k % QI4_0;
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@ -2239,10 +2245,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI4_1;
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const int kqsx = k % QI4_1;
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@ -2331,10 +2337,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI5_0;
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const int kqsx = k % QI5_0;
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@ -2445,10 +2451,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI5_1;
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const int kqsx = k % QI5_1;
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@ -2551,10 +2557,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI8_0;
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const int kqsx = k % QI8_0;
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@ -2642,10 +2648,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI2_K;
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const int kqsx = k % QI2_K;
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@ -2763,10 +2769,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI3_K;
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const int kqsx = k % QI3_K;
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@ -2981,10 +2987,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI4_K; // == 0 if QK_K == 256
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const int kqsx = k % QI4_K; // == k if QK_K == 256
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@ -3162,10 +3168,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI5_K; // == 0 if QK_K == 256
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const int kqsx = k % QI5_K; // == k if QK_K == 256
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@ -3291,10 +3297,10 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
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__builtin_assume(i_offset >= 0);
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__builtin_assume(i_offset < nwarps);
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__builtin_assume(k >= 0);
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__builtin_assume(k < WARP_SIZE);
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GGML_CUDA_ASSUME(i_offset >= 0);
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GGML_CUDA_ASSUME(i_offset < nwarps);
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GGML_CUDA_ASSUME(k >= 0);
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GGML_CUDA_ASSUME(k < WARP_SIZE);
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const int kbx = k / QI6_K; // == 0 if QK_K == 256
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const int kqsx = k % QI6_K; // == k if QK_K == 256
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@ -6408,7 +6414,7 @@ static void ggml_cuda_op_mul_mat(
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// wait for main GPU data if necessary
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if (split && (id != g_main_device || is != 0)) {
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CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0]));
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CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0], 0));
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}
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for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
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@ -6530,7 +6536,7 @@ static void ggml_cuda_op_mul_mat(
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CUDA_CHECK(ggml_cuda_set_device(g_main_device));
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for (int64_t id = 0; id < g_device_count; ++id) {
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for (int64_t is = 0; is < is_max; ++is) {
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CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is]));
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CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0));
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}
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}
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}
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