mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 02:14:35 +00:00
CUDA: quantized KV support for FA vec (#7527)
* CUDA: quantized KV support for FA vec * try CI fix * fix commented-out kernel variants * add q8_0 q4_0 tests * fix nwarps > batch size * split fattn compile via extern templates * fix flake8 * fix metal tests * fix cmake * make generate_cu_files.py executable * add autogenerated .cu files * fix AMD * error if type_v != FP16 and not flash_attn * remove obsolete code
This commit is contained in:
Johannes Gäßler
GitHub
parent
a323ec60af
commit
9b596417af
@ -106,6 +106,7 @@ set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
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"llama: max. batch size for using peer access")
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option(LLAMA_CUDA_NO_PEER_COPY "llama: do not use peer to peer copies" OFF)
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option(LLAMA_CUDA_NO_VMM "llama: do not try to use CUDA VMM" OFF)
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option(LLAMA_CUDA_FA_ALL_QUANTS "llama: compile all quants for FlashAttention" OFF)
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option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
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option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
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@ -402,6 +403,8 @@ if (LLAMA_CUDA)
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file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu")
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list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu")
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list(APPEND GGML_SOURCES_CUDA ${SRCS})
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add_compile_definitions(GGML_USE_CUDA)
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add_compile_definitions(GGML_CUDA_USE_GRAPHS)
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@ -427,6 +430,18 @@ if (LLAMA_CUDA)
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if (LLAMA_CUDA_NO_PEER_COPY)
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add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
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endif()
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if (LLAMA_CUDA_FA_ALL_QUANTS)
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu")
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list(APPEND GGML_SOURCES_CUDA ${SRCS})
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add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS)
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else()
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu")
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list(APPEND GGML_SOURCES_CUDA ${SRCS})
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu")
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list(APPEND GGML_SOURCES_CUDA ${SRCS})
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu")
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list(APPEND GGML_SOURCES_CUDA ${SRCS})
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endif()
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if (LLAMA_STATIC)
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if (WIN32)
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@ -571,6 +586,8 @@ if (LLAMA_HIPBLAS)
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file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu")
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list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu")
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu")
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list(APPEND GGML_SOURCES_ROCM ${SRCS})
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add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA)
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@ -590,6 +607,19 @@ if (LLAMA_HIPBLAS)
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add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
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endif()
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if (LLAMA_CUDA_FA_ALL_QUANTS)
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu")
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list(APPEND GGML_SOURCES_ROCM ${SRCS})
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add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS)
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else()
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu")
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list(APPEND GGML_SOURCES_ROCM ${SRCS})
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu")
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list(APPEND GGML_SOURCES_ROCM ${SRCS})
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file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu")
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list(APPEND GGML_SOURCES_ROCM ${SRCS})
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endif()
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add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
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add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
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add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
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21
Makefile
21
Makefile
@ -421,6 +421,15 @@ ifdef LLAMA_CUBLAS
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LLAMA_CUDA := 1
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endif
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OBJS_CUDA_TEMP_INST = $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-wmma*.cu))
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ifdef LLAMA_CUDA_FA_ALL_QUANTS
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OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*.cu))
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else
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OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu))
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OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu))
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OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*f16-f16.cu))
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endif # LLAMA_CUDA_FA_ALL_QUANTS
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ifdef LLAMA_CUDA
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ifneq ('', '$(wildcard /opt/cuda)')
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CUDA_PATH ?= /opt/cuda
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@ -431,6 +440,7 @@ ifdef LLAMA_CUDA
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MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
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OBJS += ggml-cuda.o
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OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
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OBJS += $(OBJS_CUDA_TEMP_INST)
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MK_NVCCFLAGS += -use_fast_math
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ifdef LLAMA_FATAL_WARNINGS
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MK_NVCCFLAGS += -Werror all-warnings
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@ -493,7 +503,10 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
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endif # LLAMA_CUDA_NO_PEER_COPY
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ifdef LLAMA_CUDA_CCBIN
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MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
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endif
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endif # LLAMA_CUDA_CCBIN
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ifdef LLAMA_CUDA_FA_ALL_QUANTS
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MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS
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endif # LLAMA_CUDA_FA_ALL_QUANTS
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ifdef JETSON_EOL_MODULE_DETECT
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define NVCC_COMPILE
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@ -505,7 +518,7 @@ define NVCC_COMPILE
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endef # NVCC_COMPILE
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endif # JETSON_EOL_MODULE_DETECT
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ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
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ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh
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$(NVCC_COMPILE)
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ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
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@ -585,11 +598,12 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
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endif # LLAMA_CUDA_NO_PEER_COPY
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OBJS += ggml-cuda.o
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OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
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OBJS += $(OBJS_CUDA_TEMP_INST)
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ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
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$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
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ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
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ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh
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$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
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endif # LLAMA_HIPBLAS
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@ -749,6 +763,7 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS)
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clean:
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rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
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rm -vrf ggml-cuda/*.o
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rm -vrf ggml-cuda/template-instances/*.o
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find examples pocs -type f -name "*.o" -delete
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#
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@ -501,6 +501,7 @@ Building the program with BLAS support may lead to some performance improvements
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| 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. |
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| 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. |
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| LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
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| LLAMA_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. |
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- #### hipBLAS
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@ -2905,10 +2905,14 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
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#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
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return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128;
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#else
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if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) {
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if (op->src[0]->ne[0] == 128) {
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return true;
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}
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return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA;
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if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) {
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return true;
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}
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return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA &&
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op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
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#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
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default:
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return false;
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@ -1,4 +1,7 @@
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#pragma once
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#include "common.cuh"
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#include "vecdotq.cuh"
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#include <cstdint>
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@ -34,11 +37,523 @@ typedef void (* fattn_kernel_t)(
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const int nb11,
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const int nb12,
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const int nb13,
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const int nb21,
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const int nb22,
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const int nb23,
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const int ne0,
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const int ne1,
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const int ne2,
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const int ne3);
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typedef half (*vec_dot_KQ_f16_t)(
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const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
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typedef float (*vec_dot_KQ_f32_t)(
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const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
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template<typename T, int D>
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static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
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const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
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#if __CUDA_ARCH__ > MIN_CC_DP4A
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const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c;
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GGML_UNUSED(Q_v);
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half sum = 0.0f;
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#pragma unroll
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for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
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const int k_KQ = k_KQ_0 + threadIdx.x;
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const int ib = k_KQ / QI8_1;
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const int iqs4 = k_KQ % QI4_0;
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const int shift = k_KQ & (QI8_1/2);
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const int v = (get_int_from_uint8(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
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const int u = Q_q8[k_KQ_0/WARP_SIZE];
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const int sumi = __dp4a(v, u, 0);
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#if FP16_AVAILABLE
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if (std::is_same<T, half>::value) {
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const half2 * Q_ds = (const half2 *) Q_ds_v;
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const half2 sum2 = __half2half2(K_q4_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE];
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sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2) /* *8/QI8_1 == 1 */);
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} else
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#endif // FP16_AVAILABLE
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{
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const float2 * Q_ds = (const float2 *) Q_ds_v;
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sum += (T) (__half2float(K_q4_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (8/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y));
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}
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}
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return sum;
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#else
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GGML_UNUSED(K_c);
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GGML_UNUSED(Q_v);
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GGML_UNUSED(Q_q8);
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GGML_UNUSED(Q_ds_v);
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NO_DEVICE_CODE;
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#endif // __CUDA_ARCH__ > MIN_CC_DP4A
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}
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template<typename T, int D>
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static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
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const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
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#if __CUDA_ARCH__ > MIN_CC_DP4A
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const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c;
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GGML_UNUSED(Q_v);
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T sum = 0.0f;
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#pragma unroll
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for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
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const int k_KQ = k_KQ_0 + threadIdx.x;
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const int ib = k_KQ / QI8_1;
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const int iqs4 = k_KQ % QI4_1;
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const int shift = k_KQ & (QI8_1/2);
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const int v = (get_int_from_uint8_aligned(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
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const int u = Q_q8[k_KQ_0/WARP_SIZE];
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const int sumi = __dp4a(v, u, 0);
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#if FP16_AVAILABLE
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if (std::is_same<T, half>::value) {
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const half2 * Q_ds = (const half2 *) Q_ds_v;
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const half2 d4d8_m4s8 = K_q4_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE];
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const half2 sumid4d8_m4s8scaled = d4d8_m4s8 * make_half2(sumi, 1.0f/QI8_1);
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sum += (T) (__low2half(sumid4d8_m4s8scaled) + __high2half(sumid4d8_m4s8scaled));
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} else
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#endif // FP16_AVAILABLE
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{
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const float2 * Q_ds = (const float2 *) Q_ds_v;
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const float sumid4d8 = __low2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi;
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const float m4s8scaled = __high2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1;
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sum += (T) (sumid4d8 + m4s8scaled);
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}
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}
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return sum;
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#else
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GGML_UNUSED(K_c);
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GGML_UNUSED(Q_v);
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GGML_UNUSED(Q_q8);
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GGML_UNUSED(Q_ds_v);
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NO_DEVICE_CODE;
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#endif // __CUDA_ARCH__ > MIN_CC_DP4A
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}
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template<typename T, int D>
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static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
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const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
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#if __CUDA_ARCH__ > MIN_CC_DP4A
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const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c;
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GGML_UNUSED(Q_v);
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T sum = 0.0f;
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#pragma unroll
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for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
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const int k_KQ = k_KQ_0 + threadIdx.x;
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const int ib = k_KQ / QI8_1;
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const int iqs4 = k_KQ % QI5_0;
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const int iqs8 = k_KQ % QI8_1;
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const int shift = k_KQ & (QI8_1/2);
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int v = (get_int_from_uint8(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
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const int vh = get_int_from_uint8(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0);
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v |= (vh << 4) & 0x00000010; // 0 -> 4
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v |= (vh << 11) & 0x00001000; // 1 -> 12
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v |= (vh << 18) & 0x00100000; // 2 -> 20
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v |= (vh << 25) & 0x10000000; // 3 -> 28
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const int u = Q_q8[k_KQ_0/WARP_SIZE];
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const int sumi = __dp4a(v, u, 0);
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#if FP16_AVAILABLE
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if (std::is_same<T, half>::value) {
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const half2 * Q_ds = (const half2 *) Q_ds_v;
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const half2 sum2 = __half2half2(K_q5_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE];
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sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2)*__float2half(2.0f)) /* *16/QI8_1 == 2 */;
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||||
} else
|
||||
#endif // FP16_AVAILABLE
|
||||
{
|
||||
const float2 * Q_ds = (const float2 *) Q_ds_v;
|
||||
|
||||
sum += (T) (__half2float(K_q5_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (16/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y));
|
||||
}
|
||||
}
|
||||
|
||||
return sum;
|
||||
#else
|
||||
GGML_UNUSED(K_c);
|
||||
GGML_UNUSED(Q_v);
|
||||
GGML_UNUSED(Q_q8);
|
||||
GGML_UNUSED(Q_ds_v);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // __CUDA_ARCH__ > MIN_CC_DP4A
|
||||
}
|
||||
|
||||
template<typename T, int D>
|
||||
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
|
||||
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
|
||||
#if __CUDA_ARCH__ > MIN_CC_DP4A
|
||||
|
||||
const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c;
|
||||
GGML_UNUSED(Q_v);
|
||||
|
||||
T sum = 0.0f;
|
||||
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
|
||||
const int k_KQ = k_KQ_0 + threadIdx.x;
|
||||
|
||||
const int ib = k_KQ / QI8_1;
|
||||
const int iqs4 = k_KQ % QI5_1;
|
||||
const int iqs8 = k_KQ % QI8_1;
|
||||
const int shift = k_KQ & (QI8_1/2);
|
||||
|
||||
int v = (get_int_from_uint8(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
|
||||
const int vh = get_int_from_uint8(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1);
|
||||
v |= (vh << 4) & 0x00000010; // 0 -> 4
|
||||
v |= (vh << 11) & 0x00001000; // 1 -> 12
|
||||
v |= (vh << 18) & 0x00100000; // 2 -> 20
|
||||
v |= (vh << 25) & 0x10000000; // 3 -> 28
|
||||
|
||||
const int u = Q_q8[k_KQ_0/WARP_SIZE];
|
||||
|
||||
const int sumi = __dp4a(v, u, 0);
|
||||
|
||||
#if FP16_AVAILABLE
|
||||
if (std::is_same<T, half>::value) {
|
||||
const half2 * Q_ds = (const half2 *) Q_ds_v;
|
||||
|
||||
const half2 d5d8_m5s8 = K_q5_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE];
|
||||
const half2 sumid5d8_m5s8scaled = d5d8_m5s8 * make_half2(sumi, 1.0f/QI8_1);
|
||||
sum += (T) (__low2half(sumid5d8_m5s8scaled) + __high2half(sumid5d8_m5s8scaled));
|
||||
} else
|
||||
#endif // FP16_AVAILABLE
|
||||
{
|
||||
const float2 * Q_ds = (const float2 *) Q_ds_v;
|
||||
|
||||
const float sumid5d8 = __low2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi;
|
||||
const float m5s8scaled = __high2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1;
|
||||
|
||||
sum += (T) (sumid5d8 + m5s8scaled);
|
||||
}
|
||||
}
|
||||
|
||||
return sum;
|
||||
#else
|
||||
GGML_UNUSED(K_c);
|
||||
GGML_UNUSED(Q_v);
|
||||
GGML_UNUSED(Q_q8);
|
||||
GGML_UNUSED(Q_ds_v);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // __CUDA_ARCH__ > MIN_CC_DP4A
|
||||
}
|
||||
|
||||
template <typename T, int D>
|
||||
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
|
||||
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
|
||||
#if __CUDA_ARCH__ > MIN_CC_DP4A
|
||||
|
||||
const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c;
|
||||
GGML_UNUSED(Q_v);
|
||||
|
||||
T sum = 0.0f;
|
||||
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
|
||||
const int k_KQ = k_KQ_0 + threadIdx.x;
|
||||
|
||||
const int ib = k_KQ / QI8_0;
|
||||
const int iqs = k_KQ % QI8_0;
|
||||
|
||||
const int v = get_int_from_int8(K_q8_0[ib].qs, iqs);
|
||||
|
||||
T Q_d;
|
||||
if (std::is_same<T, half>::value) {
|
||||
const half2 * Q_ds = (const half2 *) Q_ds_v;
|
||||
Q_d = __low2half(Q_ds[k_KQ_0/WARP_SIZE]);
|
||||
} else {
|
||||
const float2 * Q_ds = (const float2 *) Q_ds_v;
|
||||
Q_d = Q_ds[k_KQ_0/WARP_SIZE].x;
|
||||
}
|
||||
|
||||
sum += vec_dot_q8_0_q8_1_impl<T, 1>(&v, &Q_q8[k_KQ_0/WARP_SIZE], K_q8_0[ib].d, Q_d);
|
||||
}
|
||||
|
||||
return sum;
|
||||
#else
|
||||
GGML_UNUSED(K_c);
|
||||
GGML_UNUSED(Q_v);
|
||||
GGML_UNUSED(Q_q8);
|
||||
GGML_UNUSED(Q_ds_v);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // __CUDA_ARCH__ > MIN_CC_DP4A
|
||||
}
|
||||
|
||||
template <typename T, int D>
|
||||
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16(
|
||||
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds_v) {
|
||||
|
||||
const half2 * K_h2 = (const half2 *) K_c;
|
||||
GGML_UNUSED(Q_q8);
|
||||
GGML_UNUSED(Q_ds_v);
|
||||
|
||||
#if FP16_AVAILABLE
|
||||
if (std::is_same<T, half>::value) {
|
||||
const half2 * Q_h2 = (const half2 *) Q_v;
|
||||
|
||||
half2 sum2 = make_half2(0.0f, 0.0f);
|
||||
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
|
||||
const int k_KQ = k_KQ_0 + threadIdx.x;
|
||||
|
||||
const half2 K_ik = K_h2[k_KQ];
|
||||
sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE];
|
||||
}
|
||||
|
||||
return __low2half(sum2) + __high2half(sum2);
|
||||
}
|
||||
#endif // FP16_AVAILABLE
|
||||
|
||||
const float2 * Q_f2 = (const float2 *) Q_v;
|
||||
|
||||
float sum = 0.0f;
|
||||
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
|
||||
const int k_KQ = k_KQ_0 + threadIdx.x;
|
||||
|
||||
const half2 K_ik = K_h2[k_KQ];
|
||||
sum += __low2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].x;
|
||||
sum += __high2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].y;
|
||||
}
|
||||
|
||||
return sum;
|
||||
}
|
||||
|
||||
template <typename Tds>
|
||||
static __device__ __forceinline__ void quantize_q8_1_to_shared(
|
||||
const float * __restrict__ x, const float scale, int * __restrict__ yq32, void * __restrict__ yds) {
|
||||
|
||||
float vals[sizeof(int)] = {0.0f};
|
||||
#pragma unroll
|
||||
for (int l = 0; l < sizeof(int); ++l) {
|
||||
vals[l] = scale * x[4*threadIdx.x + l];
|
||||
}
|
||||
|
||||
float amax = fabsf(vals[0]);
|
||||
float sum = vals[0];
|
||||
#pragma unroll
|
||||
for (int l = 1; l < sizeof(int); ++l) {
|
||||
amax = fmaxf(amax, fabsf(vals[l]));
|
||||
sum += vals[l];
|
||||
}
|
||||
#pragma unroll
|
||||
for (int mask = QI8_1/2; mask > 0; mask >>= 1) {
|
||||
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, 32));
|
||||
sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, 32);
|
||||
}
|
||||
|
||||
const float d = amax / 127;
|
||||
int q32 = 0;
|
||||
int8_t * q8 = (int8_t *) &q32;
|
||||
|
||||
if (d != 0.0f) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < sizeof(int); ++l) {
|
||||
q8[l] = roundf(vals[l] / d);
|
||||
}
|
||||
}
|
||||
|
||||
yq32[threadIdx.x] = q32;
|
||||
if (threadIdx.x % QI8_1 == 0) {
|
||||
if (std::is_same<Tds, half2>::value) {
|
||||
((half2 *) yds)[threadIdx.x/QI8_1] = make_half2(d, sum);
|
||||
} else {
|
||||
((float2 *) yds)[threadIdx.x/QI8_1] = make_float2(d, sum);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
typedef half (*dequantize_1_f16_t)(const void *, const int64_t);
|
||||
typedef float (*dequantize_1_f32_t)(const void *, const int64_t);
|
||||
|
||||
template <typename T>
|
||||
static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__ vx, const int64_t i) {
|
||||
const block_q4_0 * x = (const block_q4_0 *) vx;
|
||||
|
||||
const int64_t ib = i / QK4_0;
|
||||
const int iqs = i % (QK4_0/2);
|
||||
const int shift = (i % QK4_0) / (QK4_0/2);
|
||||
|
||||
const T d = x[ib].d;
|
||||
const int q0 = x[ib].qs[iqs];
|
||||
const int q = ((q0 >> (4*shift)) & 0x0F) - 8;
|
||||
|
||||
#if FP16_AVAILABLE
|
||||
if (std::is_same<T, half>::value) {
|
||||
return ((half) d)*((half) q);
|
||||
}
|
||||
#endif // FP16_AVAILABLE
|
||||
|
||||
return ((float) d)*((float) q);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__ vx, const int64_t i) {
|
||||
const block_q4_1 * x = (const block_q4_1 *) vx;
|
||||
|
||||
const int64_t ib = i / QK4_1;
|
||||
const int iqs = i % (QK4_1/2);
|
||||
const int shift = (i % QK4_1) / (QK4_1/2);
|
||||
|
||||
const half2 dm = x[ib].dm;
|
||||
const int q0 = x[ib].qs[iqs];
|
||||
const int q = ((q0 >> (4*shift)) & 0x0F);
|
||||
|
||||
#if FP16_AVAILABLE
|
||||
if (std::is_same<T, half>::value) {
|
||||
return __low2half(dm)*((half) q) + __high2half(dm);
|
||||
}
|
||||
#endif // FP16_AVAILABLE
|
||||
|
||||
return __low2float(dm)*((float) q) + __high2float(dm);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ vx, const int64_t i) {
|
||||
const block_q5_0 * x = (const block_q5_0 *) vx;
|
||||
|
||||
const int64_t ib = i / QK5_0;
|
||||
const int idq = i % QK5_0;
|
||||
const int iqs = i % (QK5_0/2);
|
||||
const int shift = (i % QK5_0) / (QK5_0/2);
|
||||
|
||||
const T d = x[ib].d;
|
||||
const int ql0 = x[ib].qs[iqs];
|
||||
const int qh0 = get_int_from_uint8(x[ib].qh, 0);
|
||||
const int ql = ((ql0 >> (4*shift)) & 0x0F);
|
||||
const int qh = ((qh0 >> idq) << 4) & 0x10;
|
||||
const int q = (ql | qh) - 16;
|
||||
|
||||
#if FP16_AVAILABLE
|
||||
if (std::is_same<T, half>::value) {
|
||||
return ((half) d)*((half) q);
|
||||
}
|
||||
#endif // FP16_AVAILABLE
|
||||
|
||||
return ((float) d)*((float) q);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ vx, const int64_t i) {
|
||||
const block_q5_1 * x = (const block_q5_1 *) vx;
|
||||
|
||||
const int64_t ib = i / QK5_1;
|
||||
const int idq = i % QK5_1;
|
||||
const int iqs = i % (QK5_1/2);
|
||||
const int shift = (i % QK5_1) / (QK5_1/2);
|
||||
|
||||
const half2 dm = x[ib].dm;
|
||||
const int ql0 = x[ib].qs[iqs];
|
||||
const int qh0 = get_int_from_uint8_aligned(x[ib].qh, 0);
|
||||
const int ql = ((ql0 >> (4*shift)) & 0x0F);
|
||||
const int qh = ((qh0 >> idq) << 4) & 0x10;
|
||||
const int q = (ql | qh);
|
||||
|
||||
#if FP16_AVAILABLE
|
||||
if (std::is_same<T, half>::value) {
|
||||
return __low2half(dm)*((half) q) + __high2half(dm);
|
||||
}
|
||||
#endif // FP16_AVAILABLE
|
||||
|
||||
return __low2float(dm)*((float) q) + __high2float(dm);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__ vx, const int64_t i) {
|
||||
const block_q8_0 * x = (const block_q8_0 *) vx;
|
||||
|
||||
const int64_t ib = i / QK8_0;
|
||||
const int iqs = i % QK8_0;
|
||||
|
||||
const T d = x[ib].d;
|
||||
const int q = x[ib].qs[iqs];
|
||||
|
||||
#if FP16_AVAILABLE
|
||||
if (std::is_same<T, half>::value) {
|
||||
return ((half) d)*((half) q);
|
||||
}
|
||||
#endif // FP16_AVAILABLE
|
||||
|
||||
return ((float) d)*((float) q);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
static __device__ __forceinline__ T dequantize_1_f16(const void * __restrict__ vx, const int64_t i) {
|
||||
const half * x = (const half *) vx;
|
||||
|
||||
return x[i];
|
||||
}
|
||||
|
||||
template <int D>
|
||||
constexpr __device__ vec_dot_KQ_f16_t get_vec_dot_KQ_f16(ggml_type type_K) {
|
||||
return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<half, D> :
|
||||
type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<half, D> :
|
||||
type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<half, D> :
|
||||
type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<half, D> :
|
||||
type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<half, D> :
|
||||
type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<half, D> :
|
||||
nullptr;
|
||||
}
|
||||
|
||||
template <int D>
|
||||
constexpr __device__ vec_dot_KQ_f32_t get_vec_dot_KQ_f32(ggml_type type_K) {
|
||||
return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<float, D> :
|
||||
type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<float, D> :
|
||||
type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<float, D> :
|
||||
type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<float, D> :
|
||||
type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<float, D> :
|
||||
type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<float, D> :
|
||||
nullptr;
|
||||
}
|
||||
|
||||
constexpr __device__ dequantize_1_f16_t get_dequantize_1_f16(ggml_type type_V) {
|
||||
return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0<half> :
|
||||
type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1<half> :
|
||||
type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0<half> :
|
||||
type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1<half> :
|
||||
type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0<half> :
|
||||
type_V == GGML_TYPE_F16 ? dequantize_1_f16<half> :
|
||||
nullptr;
|
||||
}
|
||||
|
||||
constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
|
||||
return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0<float> :
|
||||
type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1<float> :
|
||||
type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0<float> :
|
||||
type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1<float> :
|
||||
type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0<float> :
|
||||
type_V == GGML_TYPE_F16 ? dequantize_1_f16<float> :
|
||||
nullptr;
|
||||
}
|
||||
|
||||
template<int D, int parallel_blocks> // D == head size
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(D, 1)
|
||||
@ -83,6 +598,27 @@ static __global__ void flash_attn_combine_results(
|
||||
dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
|
||||
}
|
||||
|
||||
static void on_no_fattn_vec_case(const int D) {
|
||||
if (D == 64) {
|
||||
fprintf(stderr, "Unsupported KV type combination for head_size 64.\n");
|
||||
fprintf(stderr, "By default only f16 KV cache is supported.\n");
|
||||
fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for V cache quantization support.\n");
|
||||
GGML_ASSERT(false);
|
||||
} else if (D == 128) {
|
||||
fprintf(stderr, "Unsupported KV type combination for head_size 128.\n");
|
||||
fprintf(stderr, "Supported combinations:\n");
|
||||
fprintf(stderr, " - K == q4_0, V == q4_0, 4.50 BPV\n");
|
||||
fprintf(stderr, " - K == q8_0, V == q8_0, 8.50 BPV\n");
|
||||
fprintf(stderr, " - K == f16, V == f16, 16.00 BPV\n");
|
||||
fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n");
|
||||
GGML_ASSERT(false);
|
||||
} else {
|
||||
fprintf(stderr, "Unsupported KV type combination for head_size 256.\n");
|
||||
fprintf(stderr, "Only f16 is supported.\n");
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
template <int D, int parallel_blocks>
|
||||
void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
@ -94,8 +630,6 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern
|
||||
ggml_tensor * KQV = dst;
|
||||
|
||||
GGML_ASSERT(Q->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(K->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(V->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(KQV->type == GGML_TYPE_F32);
|
||||
|
||||
GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
|
||||
@ -143,6 +677,7 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern
|
||||
mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0,
|
||||
Q->nb[1], Q->nb[2], Q->nb[3],
|
||||
K->nb[1], K->nb[2], K->nb[3],
|
||||
V->nb[1], V->nb[2], V->nb[3],
|
||||
KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
|
||||
);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
|
||||
@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f16(
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
|
||||
@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f32(
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
|
||||
@ -1,330 +0,0 @@
|
||||
#include "common.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
#include "fattn-vec-f16.cuh"
|
||||
|
||||
template<int D, int ncols, int parallel_blocks> // D == head size
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(D, 1)
|
||||
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_vec_ext_f16(
|
||||
const char * __restrict__ Q,
|
||||
const char * __restrict__ K,
|
||||
const char * __restrict__ V,
|
||||
const char * __restrict__ mask,
|
||||
float * __restrict__ dst,
|
||||
float2 * __restrict__ dst_meta,
|
||||
const float scale,
|
||||
const float max_bias,
|
||||
const float m0,
|
||||
const float m1,
|
||||
const uint32_t n_head_log2,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int ne03,
|
||||
const int ne10,
|
||||
const int ne11,
|
||||
const int ne12,
|
||||
const int ne13,
|
||||
const int ne31,
|
||||
const int nb31,
|
||||
const int nb01,
|
||||
const int nb02,
|
||||
const int nb03,
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#if FP16_AVAILABLE
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
|
||||
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
|
||||
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
|
||||
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
|
||||
const half * maskh = (const half *) mask + ne11*ic0;
|
||||
|
||||
const int stride_KV = nb11 / sizeof(half);
|
||||
const int stride_KV2 = nb11 / sizeof(half2);
|
||||
|
||||
const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
|
||||
const half slopeh = __float2half(slopef);
|
||||
|
||||
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
|
||||
constexpr int nwarps = D / WARP_SIZE;
|
||||
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
|
||||
__builtin_assume(tid < D);
|
||||
|
||||
__shared__ half KQ[ncols*D];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
KQ[j*D + tid] = -HALF_MAX_HALF;
|
||||
}
|
||||
half2 * KQ2 = (half2 *) KQ;
|
||||
|
||||
half kqmax[ncols];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqmax[j] = -HALF_MAX_HALF;
|
||||
}
|
||||
half kqsum[ncols] = {0.0f};
|
||||
|
||||
__shared__ half kqmax_shared[ncols][WARP_SIZE];
|
||||
__shared__ half kqsum_shared[ncols][WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
if (threadIdx.y == 0) {
|
||||
kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
|
||||
kqsum_shared[j][threadIdx.x] = 0.0f;
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// Convert Q to half2 and store in registers:
|
||||
half2 Q_h2[ncols][D/(2*WARP_SIZE)];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f);
|
||||
Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
|
||||
}
|
||||
}
|
||||
|
||||
half2 VKQ[ncols] = {{0.0f, 0.0f}};
|
||||
|
||||
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
|
||||
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
|
||||
// Calculate KQ tile and keep track of new maximum KQ values:
|
||||
|
||||
// For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
|
||||
// see https://github.com/ggerganov/llama.cpp/pull/7061 .
|
||||
// Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
|
||||
half kqmax_new = kqmax[0];
|
||||
half kqmax_new_arr[ncols];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqmax_new_arr[j] = kqmax[j];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
|
||||
const int i_KQ = i_KQ_0 + threadIdx.y;
|
||||
|
||||
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 sum2[ncols] = {{0.0f, 0.0f}};
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
|
||||
const int k_KQ = k_KQ_0 + threadIdx.x;
|
||||
|
||||
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE];
|
||||
}
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
sum2[j] = warp_reduce_sum(sum2[j]);
|
||||
half sum = __low2half(sum2[j]) + __high2half(sum2[j]);
|
||||
sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
|
||||
|
||||
if (ncols == 1) {
|
||||
kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
|
||||
} else {
|
||||
kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
|
||||
}
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
KQ[j*D + i_KQ] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
|
||||
|
||||
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
||||
if (threadIdx.x == 0) {
|
||||
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
half kqmax_new_j = kqmax_shared[j][threadIdx.x];
|
||||
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
||||
|
||||
const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
|
||||
kqmax[j] = kqmax_new_j;
|
||||
|
||||
const half val = hexp(KQ[j*D + tid] - kqmax[j]);
|
||||
kqsum[j] = kqsum[j]*KQ_max_scale + val;
|
||||
KQ[j*D + tid] = val;
|
||||
|
||||
VKQ[j] *= __half2half2(KQ_max_scale);
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < D; k0 += 2) {
|
||||
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 V_k;
|
||||
reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
|
||||
reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqsum[j] = warp_reduce_sum(kqsum[j]);
|
||||
if (threadIdx.x == 0) {
|
||||
kqsum_shared[j][threadIdx.y] = kqsum[j];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
|
||||
if (ncols > 2 && ic0 + j_VKQ >= ne01) {
|
||||
break;
|
||||
}
|
||||
|
||||
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
|
||||
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
|
||||
|
||||
half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= kqsum[j_VKQ];
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
|
||||
dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
|
||||
}
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // FP16_AVAILABLE
|
||||
}
|
||||
|
||||
void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
ggml_tensor * KQV = dst;
|
||||
ggml_tensor * Q = dst->src[0];
|
||||
|
||||
const int32_t precision = KQV->op_params[2];
|
||||
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
|
||||
|
||||
constexpr int cols_per_block = 1;
|
||||
constexpr int parallel_blocks = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64: {
|
||||
constexpr int D = 64;
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
} break;
|
||||
case 128: {
|
||||
constexpr int D = 128;
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
} break;
|
||||
case 256: {
|
||||
constexpr int D = 256;
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
template <int cols_per_block, int parallel_blocks>
|
||||
void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
switch (Q->ne[0]) {
|
||||
case 64: {
|
||||
constexpr int D = 64;
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
} break;
|
||||
case 128: {
|
||||
constexpr int D = 128;
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
} break;
|
||||
default: {
|
||||
GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * KQV = dst;
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
const int32_t precision = KQV->op_params[2];
|
||||
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
|
||||
|
||||
if (Q->ne[1] == 1) {
|
||||
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] == 2) {
|
||||
constexpr int cols_per_block = 2;
|
||||
constexpr int parallel_blocks = 4;
|
||||
launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 4) {
|
||||
constexpr int cols_per_block = 4;
|
||||
constexpr int parallel_blocks = 4;
|
||||
launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 8) {
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int parallel_blocks = 4;
|
||||
launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int parallel_blocks = 1;
|
||||
launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
}
|
||||
@ -1,5 +1,395 @@
|
||||
#include "common.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
|
||||
void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||
template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(D, 1)
|
||||
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_vec_ext_f16(
|
||||
const char * __restrict__ Q,
|
||||
const char * __restrict__ K,
|
||||
const char * __restrict__ V,
|
||||
const char * __restrict__ mask,
|
||||
float * __restrict__ dst,
|
||||
float2 * __restrict__ dst_meta,
|
||||
const float scale,
|
||||
const float max_bias,
|
||||
const float m0,
|
||||
const float m1,
|
||||
const uint32_t n_head_log2,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int ne03,
|
||||
const int ne10,
|
||||
const int ne11,
|
||||
const int ne12,
|
||||
const int ne13,
|
||||
const int ne31,
|
||||
const int nb31,
|
||||
const int nb01,
|
||||
const int nb02,
|
||||
const int nb03,
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#if FP16_AVAILABLE
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||
constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16<D>(type_K);
|
||||
constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16;
|
||||
constexpr dequantize_1_f16_t dequantize_1_v = get_dequantize_1_f16(type_V);
|
||||
|
||||
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
|
||||
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
Q += nb02* blockIdx.y + nb01*ic0;
|
||||
K += nb12*(blockIdx.y / gqa_ratio);
|
||||
V += nb22*(blockIdx.y / gqa_ratio);
|
||||
|
||||
const half * maskh = (const half *) mask + ne11*ic0;
|
||||
|
||||
const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
|
||||
const half slopeh = __float2half(slopef);
|
||||
|
||||
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
|
||||
constexpr int nwarps = D / WARP_SIZE;
|
||||
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
|
||||
__builtin_assume(tid < D);
|
||||
|
||||
__shared__ half KQ[ncols*D];
|
||||
half2 * KQ2 = (half2 *) KQ;
|
||||
|
||||
half kqmax[ncols];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqmax[j] = -HALF_MAX_HALF;
|
||||
}
|
||||
half kqsum[ncols] = {0.0f};
|
||||
|
||||
__shared__ half kqmax_shared[ncols][WARP_SIZE];
|
||||
__shared__ half kqsum_shared[ncols][WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
if (threadIdx.y == 0) {
|
||||
kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
|
||||
kqsum_shared[j][threadIdx.x] = 0.0f;
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// Convert Q to half2 (f16 K) or q8_1 (quantized K) and store in registers:
|
||||
half2 Q_h2[ncols][D/(2*WARP_SIZE)];
|
||||
int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D/(sizeof(int)*QK8_1)];
|
||||
half2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1];
|
||||
if (Q_q8_1) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
if (j0 + nwarps > ncols && j >= ncols) {
|
||||
break;
|
||||
}
|
||||
|
||||
// Reuse KQ as temporary storage for converting Q to q8_1:
|
||||
int * tmp_q_i32 = (int *) &KQ[j*D];
|
||||
half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int));
|
||||
|
||||
// Set memory to zero if out of bounds:
|
||||
if (ncols > 2 && ic0 + j >= ne01) {
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
tmp_q_i32[i] = 0;
|
||||
}
|
||||
if (threadIdx.x < D/QK8_1) {
|
||||
tmp_q_ds[threadIdx.x] = make_half2(0.0f, 0.0f);
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
const float * Q_f = (const float *) (Q + j*nb01);
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
||||
quantize_q8_1_to_shared<half2>(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
int * tmp_q_i32 = (int *) &KQ[j*D];
|
||||
half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int));
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i];
|
||||
Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
} else {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
const float2 * Q_f2_j = (const float2 *) (Q + j*nb01);
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2_j[i] : make_float2(0.0f, 0.0f);
|
||||
Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
KQ[j*D + tid] = -HALF_MAX_HALF;
|
||||
}
|
||||
|
||||
half2 VKQ[ncols] = {{0.0f, 0.0f}};
|
||||
|
||||
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
|
||||
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
|
||||
// Calculate KQ tile and keep track of new maximum KQ values:
|
||||
|
||||
// For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
|
||||
// see https://github.com/ggerganov/llama.cpp/pull/7061 .
|
||||
// Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
|
||||
half kqmax_new = kqmax[0];
|
||||
half kqmax_new_arr[ncols];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqmax_new_arr[j] = kqmax[j];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
|
||||
const int i_KQ = i_KQ_0 + threadIdx.y;
|
||||
|
||||
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
|
||||
break;
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]);
|
||||
sum = warp_reduce_sum(sum);
|
||||
sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
|
||||
|
||||
if (ncols == 1) {
|
||||
kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
|
||||
} else {
|
||||
kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
|
||||
}
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
KQ[j*D + i_KQ] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
|
||||
|
||||
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
||||
if (threadIdx.x == 0) {
|
||||
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
half kqmax_new_j = kqmax_shared[j][threadIdx.x];
|
||||
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
||||
|
||||
const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
|
||||
kqmax[j] = kqmax_new_j;
|
||||
|
||||
const half val = hexp(KQ[j*D + tid] - kqmax[j]);
|
||||
kqsum[j] = kqsum[j]*KQ_max_scale + val;
|
||||
KQ[j*D + tid] = val;
|
||||
|
||||
VKQ[j] *= __half2half2(KQ_max_scale);
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < D; k0 += 2) {
|
||||
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 V_k;
|
||||
reinterpret_cast<half&>(V_k.x) = dequantize_1_v(V + (k_VKQ_0 + k0 + 0)*nb21, tid);
|
||||
reinterpret_cast<half&>(V_k.y) = dequantize_1_v(V + (k_VKQ_0 + k0 + 1)*nb21, tid);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqsum[j] = warp_reduce_sum(kqsum[j]);
|
||||
if (threadIdx.x == 0) {
|
||||
kqsum_shared[j][threadIdx.y] = kqsum[j];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
|
||||
if (ncols > 2 && ic0 + j_VKQ >= ne01) {
|
||||
break;
|
||||
}
|
||||
|
||||
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
|
||||
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
|
||||
|
||||
half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= kqsum[j_VKQ];
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
|
||||
dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
|
||||
}
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // FP16_AVAILABLE
|
||||
}
|
||||
|
||||
template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
|
||||
void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
}
|
||||
|
||||
template <int D, ggml_type type_K, ggml_type type_V>
|
||||
void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
ggml_tensor * KQV = dst;
|
||||
ggml_tensor * Q = dst->src[0];
|
||||
ggml_tensor * K = dst->src[1];
|
||||
ggml_tensor * V = dst->src[2];
|
||||
|
||||
const int32_t precision = KQV->op_params[2];
|
||||
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
|
||||
|
||||
GGML_ASSERT(K->type == type_K);
|
||||
GGML_ASSERT(V->type == type_V);
|
||||
|
||||
if (Q->ne[1] == 1) {
|
||||
constexpr int cols_per_block = 1;
|
||||
constexpr int parallel_blocks = 4;
|
||||
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] == 2) {
|
||||
constexpr int cols_per_block = 2;
|
||||
constexpr int parallel_blocks = 4;
|
||||
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 4) {
|
||||
constexpr int cols_per_block = 4;
|
||||
constexpr int parallel_blocks = 4;
|
||||
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 8) {
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int parallel_blocks = 4;
|
||||
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int parallel_blocks = 1;
|
||||
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
}
|
||||
|
||||
#define DECL_FATTN_VEC_F16_CASE(D, type_K, type_V) \
|
||||
template void ggml_cuda_flash_attn_ext_vec_f16_case \
|
||||
<D, type_K, type_V>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
|
||||
|
||||
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
||||
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
||||
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
||||
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
||||
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
||||
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
|
||||
extern DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
|
||||
@ -1,279 +0,0 @@
|
||||
#include "common.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
#include "fattn-vec-f32.cuh"
|
||||
|
||||
template<int D, int ncols, int parallel_blocks> // D == head size
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(D, 1)
|
||||
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_vec_ext_f32(
|
||||
const char * __restrict__ Q,
|
||||
const char * __restrict__ K,
|
||||
const char * __restrict__ V,
|
||||
const char * __restrict__ mask,
|
||||
float * __restrict__ dst,
|
||||
float2 * __restrict__ dst_meta,
|
||||
const float scale,
|
||||
const float max_bias,
|
||||
const float m0,
|
||||
const float m1,
|
||||
const uint32_t n_head_log2,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int ne03,
|
||||
const int ne10,
|
||||
const int ne11,
|
||||
const int ne12,
|
||||
const int ne13,
|
||||
const int ne31,
|
||||
const int nb31,
|
||||
const int nb01,
|
||||
const int nb02,
|
||||
const int nb03,
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
|
||||
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
|
||||
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
|
||||
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
|
||||
const half * maskh = (const half *) mask + ne11*ic0;
|
||||
|
||||
const int stride_KV = nb11 / sizeof(half);
|
||||
const int stride_KV2 = nb11 / sizeof(half2);
|
||||
|
||||
const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
|
||||
|
||||
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
|
||||
constexpr int nwarps = D / WARP_SIZE;
|
||||
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
|
||||
__builtin_assume(tid < D);
|
||||
|
||||
__shared__ float KQ[ncols*D];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
KQ[j*D + tid] = -FLT_MAX/2.0f;
|
||||
}
|
||||
|
||||
float kqmax[ncols];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqmax[j] = -FLT_MAX/2.0f;
|
||||
}
|
||||
float kqsum[ncols] = {0.0f};
|
||||
|
||||
__shared__ float kqmax_shared[ncols][WARP_SIZE];
|
||||
__shared__ float kqsum_shared[ncols][WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
if (threadIdx.y == 0) {
|
||||
kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f;
|
||||
kqsum_shared[j][threadIdx.x] = 0.0f;
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// Convert Q to half2 and store in registers:
|
||||
float2 Q_h2[ncols][D/(2*WARP_SIZE)];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
Q_h2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f);
|
||||
Q_h2[j][i0/WARP_SIZE].x *= scale;
|
||||
Q_h2[j][i0/WARP_SIZE].y *= scale;
|
||||
}
|
||||
}
|
||||
|
||||
float VKQ[ncols] = {0.0f};
|
||||
|
||||
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
|
||||
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
|
||||
// Calculate KQ tile and keep track of new maximum KQ values:
|
||||
|
||||
float kqmax_new_arr[ncols];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqmax_new_arr[j] = kqmax[j];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
|
||||
const int i_KQ = i_KQ_0 + threadIdx.y;
|
||||
|
||||
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
|
||||
break;
|
||||
}
|
||||
|
||||
float sum[ncols] = {0.0f};
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
|
||||
const int k_KQ = k_KQ_0 + threadIdx.x;
|
||||
|
||||
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
sum[j] += __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x;
|
||||
sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y;
|
||||
}
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
sum[j] = warp_reduce_sum(sum[j]);
|
||||
sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
|
||||
|
||||
kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]);
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
KQ[j*D + i_KQ] = sum[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
float kqmax_new_j = kqmax_new_arr[j];
|
||||
|
||||
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
||||
if (threadIdx.x == 0) {
|
||||
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
float kqmax_new_j = kqmax_shared[j][threadIdx.x];
|
||||
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
||||
|
||||
const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
|
||||
kqmax[j] = kqmax_new_j;
|
||||
|
||||
const float val = expf(KQ[j*D + tid] - kqmax[j]);
|
||||
kqsum[j] = kqsum[j]*KQ_max_scale + val;
|
||||
KQ[j*D + tid] = val;
|
||||
|
||||
VKQ[j] *= KQ_max_scale;
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int k = 0; k < D; ++k) {
|
||||
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) {
|
||||
break;
|
||||
}
|
||||
|
||||
const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
VKQ[j] += V_ki*KQ[j*D + k];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqsum[j] = warp_reduce_sum(kqsum[j]);
|
||||
if (threadIdx.x == 0) {
|
||||
kqsum_shared[j][threadIdx.y] = kqsum[j];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
|
||||
if (ncols > 2 && ic0 + j_VKQ >= ne01) {
|
||||
break;
|
||||
}
|
||||
|
||||
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
|
||||
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
|
||||
|
||||
float dst_val = VKQ[j_VKQ];
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= kqsum[j_VKQ];
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
|
||||
dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
|
||||
}
|
||||
}
|
||||
|
||||
template <int cols_per_block, int parallel_blocks>
|
||||
void launch_fattn_vec_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
switch (Q->ne[0]) {
|
||||
case 64: {
|
||||
constexpr int D = 64;
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
} break;
|
||||
case 128: {
|
||||
constexpr int D = 128;
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
} break;
|
||||
default: {
|
||||
GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
if (Q->ne[1] == 1) {
|
||||
constexpr int cols_per_block = 1;
|
||||
constexpr int parallel_blocks = 4;
|
||||
launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] == 2) {
|
||||
constexpr int cols_per_block = 2;
|
||||
constexpr int parallel_blocks = 4;
|
||||
launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 4) {
|
||||
constexpr int cols_per_block = 4;
|
||||
constexpr int parallel_blocks = 4;
|
||||
launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 8) {
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int parallel_blocks = 4;
|
||||
launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int parallel_blocks = 1;
|
||||
launch_fattn_vec_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
|
||||
}
|
||||
@ -1,3 +1,376 @@
|
||||
#include "common.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
|
||||
void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||
template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(D, 1)
|
||||
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_vec_ext_f32(
|
||||
const char * __restrict__ Q,
|
||||
const char * __restrict__ K,
|
||||
const char * __restrict__ V,
|
||||
const char * __restrict__ mask,
|
||||
float * __restrict__ dst,
|
||||
float2 * __restrict__ dst_meta,
|
||||
const float scale,
|
||||
const float max_bias,
|
||||
const float m0,
|
||||
const float m1,
|
||||
const uint32_t n_head_log2,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int ne03,
|
||||
const int ne10,
|
||||
const int ne11,
|
||||
const int ne12,
|
||||
const int ne13,
|
||||
const int ne31,
|
||||
const int nb31,
|
||||
const int nb01,
|
||||
const int nb02,
|
||||
const int nb03,
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
constexpr vec_dot_KQ_f32_t vec_dot_KQ = get_vec_dot_KQ_f32<D>(type_K);
|
||||
constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16;
|
||||
constexpr dequantize_1_f32_t dequantize_1_v = get_dequantize_1_f32(type_V);
|
||||
|
||||
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
|
||||
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
Q += nb02* blockIdx.y + nb01*ic0;
|
||||
K += nb12*(blockIdx.y / gqa_ratio);
|
||||
V += nb22*(blockIdx.y / gqa_ratio); // K and V have same shape
|
||||
const half * maskh = (const half *) mask + ne11*ic0;
|
||||
|
||||
const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
|
||||
|
||||
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
|
||||
constexpr int nwarps = D / WARP_SIZE;
|
||||
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
|
||||
__builtin_assume(tid < D);
|
||||
|
||||
__shared__ float KQ[ncols*D];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
KQ[j*D + tid] = -FLT_MAX/2.0f;
|
||||
}
|
||||
|
||||
float kqmax[ncols];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqmax[j] = -FLT_MAX/2.0f;
|
||||
}
|
||||
float kqsum[ncols] = {0.0f};
|
||||
|
||||
__shared__ float kqmax_shared[ncols][WARP_SIZE];
|
||||
__shared__ float kqsum_shared[ncols][WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
if (threadIdx.y == 0) {
|
||||
kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f;
|
||||
kqsum_shared[j][threadIdx.x] = 0.0f;
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers:
|
||||
float2 Q_f2[ncols][D/(2*WARP_SIZE)];
|
||||
int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D >= D/(sizeof(int)*QK8_1)];
|
||||
float2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1];
|
||||
if (Q_q8_1) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
if (j0 + nwarps > ncols && j >= ncols) {
|
||||
break;
|
||||
}
|
||||
|
||||
// Reuse KQ as temporary storage for converting Q to q8_1:
|
||||
int * tmp_q_i32 = (int *) &KQ[j*D];
|
||||
float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int));
|
||||
|
||||
// Set memory to zero if out of bounds:
|
||||
if (ncols > 2 && ic0 + j >= ne01) {
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
tmp_q_i32[i] = 0;
|
||||
}
|
||||
if (threadIdx.x < D/QK8_1) {
|
||||
tmp_q_ds[threadIdx.x] = make_float2(0.0f, 0.0f);
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
const float * Q_f = (const float *) (Q + j*nb01);
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
||||
quantize_q8_1_to_shared<float2>(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
int * tmp_q_i32 = (int *) &KQ[j*D];
|
||||
float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int));
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i];
|
||||
Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
} else {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
const float2 * Q_f2_j = (const float2 *) (Q + j*nb01);
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
Q_f2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2_j[i] : make_float2(0.0f, 0.0f);
|
||||
Q_f2[j][i0/WARP_SIZE].x *= scale;
|
||||
Q_f2[j][i0/WARP_SIZE].y *= scale;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
float VKQ[ncols] = {0.0f};
|
||||
|
||||
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
|
||||
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
|
||||
// Calculate KQ tile and keep track of new maximum KQ values:
|
||||
|
||||
float kqmax_new_arr[ncols];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqmax_new_arr[j] = kqmax[j];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
|
||||
const int i_KQ = i_KQ_0 + threadIdx.y;
|
||||
|
||||
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
|
||||
break;
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
float sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_f2[j], Q_i32[j], Q_ds[j]);
|
||||
sum = warp_reduce_sum(sum);
|
||||
sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
|
||||
|
||||
kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum);
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
KQ[j*D + i_KQ] = sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
float kqmax_new_j = kqmax_new_arr[j];
|
||||
|
||||
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
||||
if (threadIdx.x == 0) {
|
||||
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
float kqmax_new_j = kqmax_shared[j][threadIdx.x];
|
||||
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
||||
|
||||
const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
|
||||
kqmax[j] = kqmax_new_j;
|
||||
|
||||
const float val = expf(KQ[j*D + tid] - kqmax[j]);
|
||||
kqsum[j] = kqsum[j]*KQ_max_scale + val;
|
||||
KQ[j*D + tid] = val;
|
||||
|
||||
VKQ[j] *= KQ_max_scale;
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int k = 0; k < D; ++k) {
|
||||
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) {
|
||||
break;
|
||||
}
|
||||
|
||||
const float V_ki = dequantize_1_v(V + (k_VKQ_0 + k)*nb21, tid);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
VKQ[j] += V_ki*KQ[j*D + k];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
kqsum[j] = warp_reduce_sum(kqsum[j]);
|
||||
if (threadIdx.x == 0) {
|
||||
kqsum_shared[j][threadIdx.y] = kqsum[j];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
|
||||
if (ncols > 2 && ic0 + j_VKQ >= ne01) {
|
||||
break;
|
||||
}
|
||||
|
||||
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
|
||||
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
|
||||
|
||||
float dst_val = VKQ[j_VKQ];
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= kqsum[j_VKQ];
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
|
||||
dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
|
||||
}
|
||||
}
|
||||
|
||||
template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
|
||||
void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
constexpr int nwarps = D/WARP_SIZE;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
}
|
||||
|
||||
template <int D, ggml_type type_K, ggml_type type_V>
|
||||
void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
ggml_tensor * KQV = dst;
|
||||
ggml_tensor * Q = dst->src[0];
|
||||
ggml_tensor * K = dst->src[1];
|
||||
ggml_tensor * V = dst->src[2];
|
||||
|
||||
const int32_t precision = KQV->op_params[2];
|
||||
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
|
||||
|
||||
GGML_ASSERT(K->type == type_K);
|
||||
GGML_ASSERT(V->type == type_V);
|
||||
|
||||
if (Q->ne[1] == 1) {
|
||||
constexpr int cols_per_block = 1;
|
||||
constexpr int parallel_blocks = 4;
|
||||
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] == 2) {
|
||||
constexpr int cols_per_block = 2;
|
||||
constexpr int parallel_blocks = 4;
|
||||
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 4) {
|
||||
constexpr int cols_per_block = 4;
|
||||
constexpr int parallel_blocks = 4;
|
||||
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 8) {
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int parallel_blocks = 4;
|
||||
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int parallel_blocks = 1;
|
||||
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
||||
}
|
||||
|
||||
#define DECL_FATTN_VEC_F32_CASE(D, type_K, type_V) \
|
||||
template void ggml_cuda_flash_attn_ext_vec_f32_case \
|
||||
<D, type_K, type_V>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
|
||||
|
||||
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
||||
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
||||
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
||||
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
||||
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
||||
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
|
||||
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
|
||||
extern DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
|
||||
490
ggml-cuda/fattn-wmma-f16.cuh
Normal file
490
ggml-cuda/fattn-wmma-f16.cuh
Normal file
@ -0,0 +1,490 @@
|
||||
#include "common.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
|
||||
#if FP16_MMA_AVAILABLE
|
||||
#include <mma.h>
|
||||
#endif
|
||||
|
||||
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
|
||||
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(nwarps*WARP_SIZE, 1)
|
||||
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_ext_f16(
|
||||
const char * __restrict__ Q,
|
||||
const char * __restrict__ K,
|
||||
const char * __restrict__ V,
|
||||
const char * __restrict__ mask,
|
||||
float * __restrict__ dst,
|
||||
float2 * __restrict__ dst_meta,
|
||||
const float scale,
|
||||
const float max_bias,
|
||||
const float m0,
|
||||
const float m1,
|
||||
const uint32_t n_head_log2,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int ne03,
|
||||
const int ne10,
|
||||
const int ne11,
|
||||
const int ne12,
|
||||
const int ne13,
|
||||
const int ne31,
|
||||
const int nb31,
|
||||
const int nb01,
|
||||
const int nb02,
|
||||
const int nb03,
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#if FP16_MMA_AVAILABLE
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
|
||||
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
||||
|
||||
static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
|
||||
static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
|
||||
constexpr int frag_m = ncols == 8 ? 32 : 16;
|
||||
constexpr int frag_n = ncols == 8 ? 8 : 16;
|
||||
static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t> frag_c_KQ;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half> frag_c_VKQ;
|
||||
|
||||
constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel.
|
||||
constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
|
||||
static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
|
||||
|
||||
// Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
|
||||
constexpr int D_padded = D + 8;
|
||||
constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
|
||||
constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0);
|
||||
const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio));
|
||||
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
|
||||
const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0;
|
||||
const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2);
|
||||
|
||||
const int stride_Q = nb01 / sizeof(float);
|
||||
const int stride_KV = nb11 / sizeof(half);
|
||||
|
||||
const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
|
||||
const half slopeh = __float2half(slopef);
|
||||
const half2 slope2 = make_half2(slopef, slopef);
|
||||
|
||||
frag_b Q_b[D/16][ncols/frag_n];
|
||||
|
||||
// A single buffer for temporarily holding tiles of KQ and VKQ parts:
|
||||
constexpr int mem_KQ = ncols*kqs_padded*kqar;
|
||||
constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
|
||||
__shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
|
||||
float * KQ_f = (float *) KQ;
|
||||
half2 * KQ2 = (half2 *) KQ;
|
||||
|
||||
float KQ_rowsum_f[ncols/nwarps] = {0.0f};
|
||||
float KQ_max_f[ncols/nwarps];
|
||||
float KQ_max_scale_f[ncols/nwarps] = {0.0f};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_f[j] = -FLT_MAX/2.0f;
|
||||
}
|
||||
|
||||
half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
half2 KQ_max_h2[ncols/nwarps];
|
||||
half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
|
||||
}
|
||||
|
||||
__shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
|
||||
half2 * VKQ2 = (half2 *) VKQ;
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
|
||||
}
|
||||
}
|
||||
|
||||
// Convert Q to half and apply scale, temporarily store in KQ:
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Load Q into tensor core fragments/registers since it will be used frequently:
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += 16) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Iterate over ne11 == previous tokens:
|
||||
for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
|
||||
// Calculate tile of KQ:
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
|
||||
frag_c_KQ KQ_c[ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
|
||||
}
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
|
||||
frag_a_K K_a;
|
||||
nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
|
||||
}
|
||||
}
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Calculate softmax for each KQ column using the current max. value.
|
||||
// The divisor is stored in KQ_rowsum and will be applied at the end.
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
|
||||
}
|
||||
|
||||
float KQ_max_new = KQ_max_f[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
|
||||
KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = warp_reduce_max(KQ_max_new);
|
||||
|
||||
const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_f[j0/nwarps] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_max_scale_f[j0/nwarps] = 0.0f;
|
||||
}
|
||||
KQ_max_f[j0/nwarps] = KQ_max_new;
|
||||
|
||||
float KQ_rowsum_add = 0.0f;
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
|
||||
KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
|
||||
}
|
||||
KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
|
||||
KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
|
||||
} else {
|
||||
half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
|
||||
}
|
||||
|
||||
half2 KQ_max_new = KQ_max_h2[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
|
||||
KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
|
||||
const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
|
||||
KQ_max_h2[j0/nwarps] = KQ_max_new;
|
||||
|
||||
half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
|
||||
KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
|
||||
KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
|
||||
KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
nvcuda::wmma::load_matrix_sync(
|
||||
KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
|
||||
KQ + j0*(kqar*kqs_padded) + k,
|
||||
kqar*kqs_padded);
|
||||
}
|
||||
}
|
||||
|
||||
frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
|
||||
frag_a_V v_a;
|
||||
nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync(
|
||||
KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
|
||||
VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
|
||||
D_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
half2 VKQ_scale;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
|
||||
} else {
|
||||
VKQ_scale = KQ_max_scale_h2[j0/nwarps];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 VKQ_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int l = 0; l < VKQ_ratio; ++l) {
|
||||
VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j_VKQ = j0 + threadIdx.y;
|
||||
if (ic0 + j_VKQ >= ne01) {
|
||||
return;
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
|
||||
float KQ_rowsum_j;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
|
||||
} else {
|
||||
KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
float dst_val = VKQ[j_VKQ*D_padded + i];
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= KQ_rowsum_j;
|
||||
}
|
||||
dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks == 1 || threadIdx.x != 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
float2 dst_meta_val;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
dst_meta_val.x = KQ_max_f[j0/nwarps];
|
||||
} else {
|
||||
dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
|
||||
}
|
||||
dst_meta_val.y = KQ_rowsum_j;
|
||||
dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
|
||||
}
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // FP16_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
constexpr int get_max_power_of_2(int x) {
|
||||
return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
|
||||
}
|
||||
|
||||
static_assert(get_max_power_of_2(1) == 1, "Test failed.");
|
||||
static_assert(get_max_power_of_2(2) == 2, "Test failed.");
|
||||
static_assert(get_max_power_of_2(4) == 4, "Test failed.");
|
||||
static_assert(get_max_power_of_2(6) == 2, "Test failed.");
|
||||
|
||||
// Number of VKQ rows calculated in parallel:
|
||||
constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
|
||||
return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
|
||||
}
|
||||
|
||||
static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed.");
|
||||
|
||||
template <int D, int cols_per_block, typename KQ_acc_t>
|
||||
void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
constexpr int nwarps = 4;
|
||||
|
||||
constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16;
|
||||
const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
|
||||
const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
|
||||
|
||||
if (4*blocks_num_pb1 < 2*nsm) {
|
||||
constexpr int parallel_blocks = 4;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
return;
|
||||
}
|
||||
if (2*blocks_num_pb1 < 2*nsm) {
|
||||
constexpr int parallel_blocks = 2;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
return;
|
||||
}
|
||||
constexpr int parallel_blocks = 1;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
}
|
||||
|
||||
#define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t) \
|
||||
template void ggml_cuda_flash_attn_ext_wmma_f16_case \
|
||||
<D, cols_per_block, KQ_acc_t>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 80, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(112, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 32, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 80, 32, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 32, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(112, 32, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 32, float);
|
||||
// extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 8, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 8, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 8, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(256, 8, half);
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 80, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(112, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 80, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(112, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
|
||||
@ -4,468 +4,313 @@
|
||||
#include "fattn-tile-f32.cuh"
|
||||
#include "fattn-vec-f16.cuh"
|
||||
#include "fattn-vec-f32.cuh"
|
||||
#include "fattn-wmma-f16.cuh"
|
||||
#include "fattn.cuh"
|
||||
|
||||
#include <cstdint>
|
||||
|
||||
#if FP16_MMA_AVAILABLE
|
||||
#include <mma.h>
|
||||
#endif
|
||||
static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * KQV = dst;
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
|
||||
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(nwarps*WARP_SIZE, 1)
|
||||
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_ext_f16(
|
||||
const char * __restrict__ Q,
|
||||
const char * __restrict__ K,
|
||||
const char * __restrict__ V,
|
||||
const char * __restrict__ mask,
|
||||
float * __restrict__ dst,
|
||||
float2 * __restrict__ dst_meta,
|
||||
const float scale,
|
||||
const float max_bias,
|
||||
const float m0,
|
||||
const float m1,
|
||||
const uint32_t n_head_log2,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int ne03,
|
||||
const int ne10,
|
||||
const int ne11,
|
||||
const int ne12,
|
||||
const int ne13,
|
||||
const int ne31,
|
||||
const int nb31,
|
||||
const int nb01,
|
||||
const int nb02,
|
||||
const int nb03,
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#if FP16_MMA_AVAILABLE
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
const int32_t precision = KQV->op_params[2];
|
||||
|
||||
const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
|
||||
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
||||
|
||||
static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
|
||||
static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
|
||||
constexpr int frag_m = ncols == 8 ? 32 : 16;
|
||||
constexpr int frag_n = ncols == 8 ? 8 : 16;
|
||||
static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t> frag_c_KQ;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half> frag_c_VKQ;
|
||||
|
||||
constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel.
|
||||
constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
|
||||
static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
|
||||
|
||||
// Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
|
||||
constexpr int D_padded = D + 8;
|
||||
constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
|
||||
constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0);
|
||||
const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio));
|
||||
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
|
||||
const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0;
|
||||
const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2);
|
||||
|
||||
const int stride_Q = nb01 / sizeof(float);
|
||||
const int stride_KV = nb11 / sizeof(half);
|
||||
|
||||
const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
|
||||
const half slopeh = __float2half(slopef);
|
||||
const half2 slope2 = make_half2(slopef, slopef);
|
||||
|
||||
frag_b Q_b[D/16][ncols/frag_n];
|
||||
|
||||
// A single buffer for temporarily holding tiles of KQ and VKQ parts:
|
||||
constexpr int mem_KQ = ncols*kqs_padded*kqar;
|
||||
constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
|
||||
__shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
|
||||
float * KQ_f = (float *) KQ;
|
||||
half2 * KQ2 = (half2 *) KQ;
|
||||
|
||||
float KQ_rowsum_f[ncols/nwarps] = {0.0f};
|
||||
float KQ_max_f[ncols/nwarps];
|
||||
float KQ_max_scale_f[ncols/nwarps] = {0.0f};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_f[j] = -FLT_MAX/2.0f;
|
||||
}
|
||||
|
||||
half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
half2 KQ_max_h2[ncols/nwarps];
|
||||
half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
|
||||
}
|
||||
|
||||
__shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
|
||||
half2 * VKQ2 = (half2 *) VKQ;
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
|
||||
}
|
||||
}
|
||||
|
||||
// Convert Q to half and apply scale, temporarily store in KQ:
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Load Q into tensor core fragments/registers since it will be used frequently:
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += 16) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Iterate over ne11 == previous tokens:
|
||||
for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
|
||||
// Calculate tile of KQ:
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
|
||||
frag_c_KQ KQ_c[ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
|
||||
}
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
|
||||
frag_a_K K_a;
|
||||
nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
|
||||
}
|
||||
}
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Calculate softmax for each KQ column using the current max. value.
|
||||
// The divisor is stored in KQ_rowsum and will be applied at the end.
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
|
||||
}
|
||||
|
||||
float KQ_max_new = KQ_max_f[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
|
||||
KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = warp_reduce_max(KQ_max_new);
|
||||
|
||||
const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_f[j0/nwarps] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_max_scale_f[j0/nwarps] = 0.0f;
|
||||
}
|
||||
KQ_max_f[j0/nwarps] = KQ_max_new;
|
||||
|
||||
float KQ_rowsum_add = 0.0f;
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
|
||||
KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
|
||||
}
|
||||
KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
|
||||
KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
|
||||
} else {
|
||||
half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
|
||||
}
|
||||
|
||||
half2 KQ_max_new = KQ_max_h2[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
|
||||
KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
|
||||
const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
|
||||
KQ_max_h2[j0/nwarps] = KQ_max_new;
|
||||
|
||||
half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
|
||||
KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
|
||||
KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
|
||||
KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
nvcuda::wmma::load_matrix_sync(
|
||||
KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
|
||||
KQ + j0*(kqar*kqs_padded) + k,
|
||||
kqar*kqs_padded);
|
||||
}
|
||||
}
|
||||
|
||||
frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
|
||||
frag_a_V v_a;
|
||||
nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync(
|
||||
KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
|
||||
VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
|
||||
D_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
half2 VKQ_scale;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
|
||||
} else {
|
||||
VKQ_scale = KQ_max_scale_h2[j0/nwarps];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
if (precision != GGML_PREC_DEFAULT) {
|
||||
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
|
||||
constexpr int cols_per_block = 16;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
constexpr int cols_per_block = 32;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
// case 256:
|
||||
// ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
|
||||
// break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
|
||||
half2 VKQ_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int l = 0; l < VKQ_ratio; ++l) {
|
||||
VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
return;
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j_VKQ = j0 + threadIdx.y;
|
||||
if (ic0 + j_VKQ >= ne01) {
|
||||
return;
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
|
||||
float KQ_rowsum_j;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
|
||||
} else {
|
||||
KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
|
||||
constexpr int cols_per_block = 8;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
float dst_val = VKQ[j_VKQ*D_padded + i];
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= KQ_rowsum_j;
|
||||
}
|
||||
dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks == 1 || threadIdx.x != 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
float2 dst_meta_val;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
dst_meta_val.x = KQ_max_f[j0/nwarps];
|
||||
} else {
|
||||
dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
|
||||
}
|
||||
dst_meta_val.y = KQ_rowsum_j;
|
||||
dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 32) {
|
||||
constexpr int cols_per_block = 16;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 32;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
}
|
||||
#define FATTN_VEC_F16_CASE(D, type_K, type_V) \
|
||||
if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \
|
||||
ggml_cuda_flash_attn_ext_vec_f16_case<D, type_K, type_V>(ctx, dst); \
|
||||
return; \
|
||||
} \
|
||||
|
||||
static void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
ggml_tensor * Q = dst->src[1];
|
||||
ggml_tensor * K = dst->src[1];
|
||||
ggml_tensor * V = dst->src[2];
|
||||
|
||||
#ifdef GGML_CUDA_FA_ALL_QUANTS
|
||||
FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16 )
|
||||
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0)
|
||||
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1)
|
||||
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0)
|
||||
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1)
|
||||
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0)
|
||||
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
|
||||
FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // FP16_MMA_AVAILABLE
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
|
||||
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
|
||||
|
||||
FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
#endif // GGML_CUDA_FA_ALL_QUANTS
|
||||
|
||||
on_no_fattn_vec_case(Q->ne[0]);
|
||||
}
|
||||
|
||||
constexpr int get_max_power_of_2(int x) {
|
||||
return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
|
||||
}
|
||||
#define FATTN_VEC_F32_CASE(D, type_K, type_V) \
|
||||
if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \
|
||||
ggml_cuda_flash_attn_ext_vec_f32_case<D, type_K, type_V>(ctx, dst); \
|
||||
return; \
|
||||
} \
|
||||
|
||||
static_assert(get_max_power_of_2(1) == 1, "Test failed.");
|
||||
static_assert(get_max_power_of_2(2) == 2, "Test failed.");
|
||||
static_assert(get_max_power_of_2(4) == 4, "Test failed.");
|
||||
static_assert(get_max_power_of_2(6) == 2, "Test failed.");
|
||||
static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
ggml_tensor * Q = dst->src[1];
|
||||
ggml_tensor * K = dst->src[1];
|
||||
ggml_tensor * V = dst->src[2];
|
||||
|
||||
// Number of VKQ rows calculated in parallel:
|
||||
constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
|
||||
return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
|
||||
}
|
||||
#ifdef GGML_CUDA_FA_ALL_QUANTS
|
||||
FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
|
||||
static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed.");
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0)
|
||||
|
||||
template <int D, int cols_per_block, int nwarps, typename KQ_acc_t>
|
||||
void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1)
|
||||
|
||||
constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16;
|
||||
const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
|
||||
const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0)
|
||||
|
||||
if (4*blocks_num_pb1 < 2*nsm) {
|
||||
constexpr int parallel_blocks = 4;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
return;
|
||||
}
|
||||
if (2*blocks_num_pb1 < 2*nsm) {
|
||||
constexpr int parallel_blocks = 2;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
return;
|
||||
}
|
||||
constexpr int parallel_blocks = 1;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1)
|
||||
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0)
|
||||
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
|
||||
FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
#else
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
|
||||
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
|
||||
|
||||
FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16)
|
||||
#endif // GGML_CUDA_FA_ALL_QUANTS
|
||||
|
||||
on_no_fattn_vec_case(Q->ne[0]);
|
||||
}
|
||||
|
||||
void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * KQV = dst;
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
const ggml_tensor * K = dst->src[1];
|
||||
const ggml_tensor * V = dst->src[2];
|
||||
|
||||
ggml_cuda_set_device(ctx.device);
|
||||
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
|
||||
const int32_t precision = KQV->op_params[2];
|
||||
|
||||
const bool quantized_KV = ggml_is_quantized(K->type) || ggml_is_quantized(V->type);
|
||||
|
||||
// On AMD the tile kernels perform poorly, use the vec kernel instead:
|
||||
if (cc >= CC_OFFSET_AMD) {
|
||||
if (precision == GGML_PREC_DEFAULT) {
|
||||
ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst);
|
||||
if (cc >= CC_OFFSET_AMD || quantized_KV) {
|
||||
if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
|
||||
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
|
||||
} else {
|
||||
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
|
||||
}
|
||||
@ -483,156 +328,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
|
||||
|
||||
if (!fp16_mma_available(cc)) {
|
||||
if (Q->ne[1] <= 8) {
|
||||
ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst);
|
||||
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
|
||||
} else {
|
||||
ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (precision != GGML_PREC_DEFAULT) {
|
||||
if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) {
|
||||
if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
|
||||
if (precision == GGML_PREC_DEFAULT) {
|
||||
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
|
||||
return;
|
||||
} else if(Q->ne[0] <= 128) {
|
||||
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
|
||||
constexpr int cols_per_block = 16;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
constexpr int cols_per_block = 32;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst);
|
||||
break;
|
||||
// case 256:
|
||||
// launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst);
|
||||
// break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
|
||||
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 32) {
|
||||
constexpr int cols_per_block = 16;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 32;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return;
|
||||
ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
|
||||
}
|
||||
|
||||
@ -386,7 +386,7 @@ static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
|
||||
u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
|
||||
}
|
||||
|
||||
return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
|
||||
return vec_dot_q8_0_q8_1_impl<float, QR5_0*VDR_Q5_0_Q8_1_MMQ>
|
||||
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
|
||||
}
|
||||
|
||||
@ -547,7 +547,7 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
|
||||
const float * x_dmf = (const float *) x_dm;
|
||||
const float * y_df = (const float *) y_ds;
|
||||
|
||||
return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
|
||||
return vec_dot_q8_0_q8_1_impl<float, VDR_Q8_0_Q8_1_MMQ>
|
||||
(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
|
||||
y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
|
||||
}
|
||||
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f16.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
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|
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DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
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|
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DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
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|
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DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
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|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
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|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
||||
@ -0,0 +1,5 @@
|
||||
// This file has been autogenerated by generate-variants.py, do not edit manually.
|
||||
|
||||
#include "../fattn-vec-f32.cuh"
|
||||
|
||||
DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user