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Merge remote-tracking branch 'upstream/master'

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This commit is contained in:
Justin Parker 2024-02-06 14:53:57 -05:00
commit dc327c694e
10 changed files with 578 additions and 487 deletions
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4
CMakeLists.txt
View file

@ -809,9 +809,9 @@ if (LLAMA_CCACHE)
if (LLAMA_CCACHE_FOUND) if (LLAMA_CCACHE_FOUND)
set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache) set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
set(ENV{CCACHE_SLOPPINESS} time_macros) set(ENV{CCACHE_SLOPPINESS} time_macros)
message(STATUS "Using ccache") message(STATUS "ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.")
else() else()
message(STATUS "Warning: ccache not found - consider installing it or use LLAMA_CCACHE=OFF") message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF")
endif () endif ()
endif() endif()

169
Makefile
View file

@ -112,6 +112,18 @@ MK_CXXFLAGS += -O3
MK_NVCCFLAGS += -O3 MK_NVCCFLAGS += -O3
endif endif
ifndef LLAMA_NO_CCACHE
CCACHE := $(shell which ccache)
ifdef CCACHE
export CCACHE_SLOPPINESS = time_macros
$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.)
CC := $(CCACHE) $(CC)
CXX := $(CCACHE) $(CXX)
else
$(info I ccache not found. Consider installing it for faster compilation.)
endif # CCACHE
endif # LLAMA_NO_CCACHE
# clock_gettime came in POSIX.1b (1993) # clock_gettime came in POSIX.1b (1993)
# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
# posix_memalign came in POSIX.1-2001 / SUSv3 # posix_memalign came in POSIX.1-2001 / SUSv3
@ -374,9 +386,9 @@ ifdef LLAMA_DEBUG
MK_NVCCFLAGS += -lineinfo MK_NVCCFLAGS += -lineinfo
endif # LLAMA_DEBUG endif # LLAMA_DEBUG
ifdef LLAMA_CUDA_NVCC ifdef LLAMA_CUDA_NVCC
NVCC = $(LLAMA_CUDA_NVCC) NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC)
else else
NVCC = nvcc NVCC = $(CCACHE) nvcc
endif #LLAMA_CUDA_NVCC endif #LLAMA_CUDA_NVCC
ifdef CUDA_DOCKER_ARCH ifdef CUDA_DOCKER_ARCH
MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
@ -483,7 +495,7 @@ ifdef LLAMA_HIPBLAS
ROCM_PATH ?= /opt/rocm ROCM_PATH ?= /opt/rocm
GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
endif endif
HIPCC ?= $(ROCM_PATH)/bin/hipcc HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc
LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_DMMV_X ?= 32
LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_MMV_Y ?= 1
LLAMA_CUDA_KQUANTS_ITER ?= 2 LLAMA_CUDA_KQUANTS_ITER ?= 2
@ -607,97 +619,135 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS)
clean: clean:
rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
find examples pocs -type f -name "*.o" -delete
# #
# Examples # Examples
# #
# $< is the first prerequisite, i.e. the source file.
# Explicitly compile this to an object file so that it can be cached with ccache.
# The source file is then filtered out from $^ (the list of all prerequisites) and the object file is added instead.
# Helper function that replaces .c, .cpp, and .cu file endings with .o:
GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1))))
main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@echo @echo
@echo '==== Run ./main -h for help. ====' @echo '==== Run ./main -h for help. ===='
@echo @echo
infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS) quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2)
gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
$(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp)
$(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS)
baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS) export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
ifeq ($(UNAME_S),Darwin) ifeq ($(UNAME_S),Darwin)
swift: examples/batched.swift swift: examples/batched.swift
@ -705,7 +755,7 @@ swift: examples/batched.swift
endif endif
common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh
@sh scripts/build-info.sh $(CC) > $@.tmp @sh scripts/build-info.sh "$(CC)" > $@.tmp
@if ! cmp -s $@.tmp $@; then \ @if ! cmp -s $@.tmp $@; then \
mv $@.tmp $@; \ mv $@.tmp $@; \
else \ else \
@ -722,7 +772,8 @@ build-info.o: common/build-info.cpp
tests: $(TEST_TARGETS) tests: $(TEST_TARGETS)
benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
run-benchmark-matmult: benchmark-matmult run-benchmark-matmult: benchmark-matmult
./$@ ./$@
@ -730,58 +781,76 @@ run-benchmark-matmult: benchmark-matmult
.PHONY: run-benchmark-matmult swift .PHONY: run-benchmark-matmult swift
vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS) tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS) tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS) tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS) tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS) tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS) tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS) tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS) tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS) tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-c.o: tests/test-c.c llama.h tests/test-c.o: tests/test-c.c llama.h
$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@ $(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS) tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

20
README.md
View file

@ -105,11 +105,14 @@ improved significantly thanks to many contributions. It is the main playground f
- [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
- [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) - [X] [StableLM models](https://huggingface.co/stabilityai)
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
- [x] [GPT-2](https://huggingface.co/gpt2) - [x] [GPT-2](https://huggingface.co/gpt2)
- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
- [x] [CodeShell](https://github.com/WisdomShell/codeshell) - [x] [CodeShell](https://github.com/WisdomShell/codeshell)
**Multimodal models:** **Multimodal models:**
@ -119,6 +122,7 @@ improved significantly thanks to many contributions. It is the main playground f
- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
**Bindings:** **Bindings:**
@ -732,9 +736,21 @@ Several quantization methods are supported. They differ in the resulting model d
| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | | 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) - [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
- recent k-quants improvements - recent k-quants improvements and new i-quants
- [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
- [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
- [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
- [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
- [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
- [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
- [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
- [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
- [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
- [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
- [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
- [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
- [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
- [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
### Perplexity (measuring model quality) ### Perplexity (measuring model quality)

10
convert.py
View file

@ -515,10 +515,14 @@ class HfVocab:
# Yield token text, score, and type # Yield token text, score, and type
yield token_text, self.get_token_score(token_id), self.get_token_type( yield token_text, self.get_token_score(token_id), self.get_token_type(
token_id, self.special_ids # Reuse already stored special IDs token_id, token_text, self.special_ids # Reuse already stored special IDs
) )
def get_token_type(self, token_id: int, special_ids: set[int]) -> gguf.TokenType: def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
# Special case for byte tokens
if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
return gguf.TokenType.BYTE
# Determine token type based on whether it's a special token # Determine token type based on whether it's a special token
return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
@ -530,7 +534,7 @@ class HfVocab:
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list: for text in self.added_tokens_list:
if text in self.specials: if text in self.specials:
toktype = self.get_token_type(self.specials[text], self.special_ids) toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
score = self.get_token_score(self.specials[text]) score = self.get_token_score(self.specials[text])
else: else:
toktype = gguf.TokenType.USER_DEFINED toktype = gguf.TokenType.USER_DEFINED

4
examples/server/README.md
View file

@ -137,6 +137,10 @@ node index.js
`temperature`: Adjust the randomness of the generated text (default: 0.8). `temperature`: Adjust the randomness of the generated text (default: 0.8).
`dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled).
`dynatemp_exponent`: Dynamic temperature exponent (default: 1.0).
`top_k`: Limit the next token selection to the K most probable tokens (default: 40). `top_k`: Limit the next token selection to the K most probable tokens (default: 40).
`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).

448
examples/server/completion.js.hpp
View file

@ -236,214 +236,250 @@ unsigned char completion_js[] = {
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}; };
unsigned int completion_js_len = 5346; unsigned int completion_js_len = 5782;

3
examples/server/public/completion.js
View file

@ -195,7 +195,8 @@ export const llamaComplete = async (params, controller, callback) => {
// Get the model info from the server. This is useful for getting the context window and so on. // Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async () => { export const llamaModelInfo = async () => {
if (!generation_settings) { if (!generation_settings) {
generation_settings = await fetch("/model.json").then(r => r.json()); const props = await fetch("/props").then(r => r.json());
generation_settings = props.default_generation_settings;
} }
return generation_settings; return generation_settings;
} }

92
examples/server/server.cpp
View file

@ -432,6 +432,7 @@ struct llama_server_context
} }
default_generation_settings_for_props = get_formated_generation(slots.front()); default_generation_settings_for_props = get_formated_generation(slots.front());
default_generation_settings_for_props["num_slots"] = params.n_parallel;
default_generation_settings_for_props["seed"] = -1; default_generation_settings_for_props["seed"] = -1;
batch = llama_batch_init(n_ctx, 0, params.n_parallel); batch = llama_batch_init(n_ctx, 0, params.n_parallel);
@ -524,27 +525,29 @@ struct llama_server_context
slot->oaicompat_model = ""; slot->oaicompat_model = "";
} }
slot->params.stream = json_value(data, "stream", false); slot->params.stream = json_value(data, "stream", false);
slot->params.cache_prompt = json_value(data, "cache_prompt", false); slot->params.cache_prompt = json_value(data, "cache_prompt", false);
slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot->params.seed = json_value(data, "seed", default_params.seed); slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); slot->params.seed = json_value(data, "seed", default_params.seed);
slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
// infill // infill
if (data.count("input_prefix") != 0) if (data.count("input_prefix") != 0)
@ -987,11 +990,6 @@ struct llama_server_context
queue_results.send(res); queue_results.send(res);
} }
json get_model_props()
{
return get_formated_generation(slots[0]);
}
json get_formated_generation(llama_client_slot &slot) json get_formated_generation(llama_client_slot &slot)
{ {
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
@ -1002,6 +1000,8 @@ struct llama_server_context
{"model", params.model_alias}, {"model", params.model_alias},
{"seed", slot.params.seed}, {"seed", slot.params.seed},
{"temperature", slot.sparams.temp}, {"temperature", slot.sparams.temp},
{"dynatemp_range", slot.sparams.dynatemp_range},
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
{"top_k", slot.sparams.top_k}, {"top_k", slot.sparams.top_k},
{"top_p", slot.sparams.top_p}, {"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p}, {"min_p", slot.sparams.min_p},
@ -1163,13 +1163,30 @@ struct llama_server_context
task.multitask_id = multitask_id; task.multitask_id = multitask_id;
// when a completion task's prompt array is not a singleton, we split it into multiple requests // when a completion task's prompt array is not a singleton, we split it into multiple requests
if (task.data.count("prompt") && task.data.at("prompt").size() > 1)
{
split_multiprompt_task(task_id, task);
}
// otherwise, it's a single-prompt task, we actually queue it // otherwise, it's a single-prompt task, we actually queue it
queue_tasks.post(task); // if there's numbers in the prompt array it will be treated as an array of tokens
if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
bool numbers = false;
for (const auto& e : task.data.at("prompt")) {
if (e.is_number()) {
numbers = true;
break;
}
}
// NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
// it will completely stall the server. I don't know where the bug for this is.
//
// if there are numbers, it needs to be treated like a single prompt,
// queue_tasks handles a mix of strings and numbers just fine.
if (numbers) {
queue_tasks.post(task);
} else {
split_multiprompt_task(task_id, task);
}
} else {
queue_tasks.post(task);
}
} }
// for multiple images processing // for multiple images processing
@ -1251,7 +1268,10 @@ struct llama_server_context
void split_multiprompt_task(int multitask_id, task_server& multiprompt_task) void split_multiprompt_task(int multitask_id, task_server& multiprompt_task)
{ {
int prompt_count = multiprompt_task.data.at("prompt").size(); int prompt_count = multiprompt_task.data.at("prompt").size();
assert(prompt_count > 1); if (prompt_count <= 1) {
send_error(multiprompt_task, "error while handling multiple prompts");
return;
}
// generate all the ID for subtask // generate all the ID for subtask
std::vector<int> subtask_ids(prompt_count); std::vector<int> subtask_ids(prompt_count);
@ -2871,12 +2891,6 @@ int main(int argc, char **argv)
} }
}); });
svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
{
const json data = llama.get_model_props();
return res.set_content(data.dump(), "application/json; charset=utf-8");
});
svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
{ return res.set_content("", "application/json; charset=utf-8"); }); { return res.set_content("", "application/json; charset=utf-8"); });

240
ggml-cuda.cu
View file

@ -5310,41 +5310,50 @@ template <bool need_check> static __global__ void
#endif // __CUDA_ARCH__ >= CC_VOLTA #endif // __CUDA_ARCH__ >= CC_VOLTA
} }
template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda> template <int ncols_y_template, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) { static __global__ void mul_mat_vec_q(
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par) {
const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par;
const int row = blockIdx.x*blockDim.y + threadIdx.y; const int row = blockIdx.x*blockDim.y + threadIdx.y;
if (row >= nrows) { if (row >= nrows_x) {
return; return;
} }
const int blocks_per_row = ncols / qk; const int blocks_per_row_x = ncols_x / qk;
const int blocks_per_col_y = nrows_y / QK8_1;
const int blocks_per_warp = vdr * WARP_SIZE / qi; const int blocks_per_warp = vdr * WARP_SIZE / qi;
// partial sum for each thread // partial sum for each thread
float tmp = 0.0f; float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f};
const block_q_t * x = (const block_q_t *) vx; const block_q_t * x = (const block_q_t *) vx;
const block_q8_1 * y = (const block_q8_1 *) vy; const block_q8_1 * y = (const block_q8_1 *) vy;
for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) { for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) {
const int ibx = row*blocks_per_row + i; // x block index const int ibx = row*blocks_per_row_x + i; // x block index
const int iby = i * (qk/QK8_1); // y block index that aligns with ibx const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs); #pragma unroll
for (int j = 0; j < ncols_y; ++j) {
tmp[j] += vec_dot_q_cuda(&x[ibx], &y[j*blocks_per_col_y + iby], iqs);
}
} }
// sum up partial sums and write back result // sum up partial sums and write back result
#pragma unroll #pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) { for (int j = 0; j < ncols_y; ++j) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); tmp[j] = warp_reduce_sum(tmp[j]);
}
if (threadIdx.x == 0) { if (threadIdx.x == 0) {
dst[row] = tmp; dst[j*nrows_x + row] = tmp[j];
}
} }
} }
@ -6816,121 +6825,56 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows); <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
} }
static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot>
GGML_ASSERT(ncols % QK4_0 == 0); static void mul_mat_vec_q_cuda(
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const void * vx, const void * vy, float * dst,
const dim3 block_nums(block_num_y, 1, 1); const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) {
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols_x % qk == 0);
GGML_ASSERT(ncols % QK4_1 == 0); GGML_ASSERT(ncols_y <= 4);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
GGML_ASSERT(ncols % QK5_0 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1); const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1> switch (ncols_y) {
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows); case 1:
} mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { break;
GGML_ASSERT(ncols % QK5_1 == 0); case 2:
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot>
const dim3 block_nums(block_num_y, 1, 1); <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); break;
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1> case 3:
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows); mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot>
} <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
break;
static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { case 4:
GGML_ASSERT(ncols % QK8_0 == 0); mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
const dim3 block_nums(block_num_y, 1, 1); break;
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); // case 5:
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1> // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows); // <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
} // break;
// case 6:
static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
GGML_ASSERT(ncols % QK_K == 0); // <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; // break;
const dim3 block_nums(block_num_y, 1, 1); // case 7:
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1> // <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows); // break;
} // case 8:
// mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { // <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
GGML_ASSERT(ncols % QK_K == 0); // break;
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; default:
const dim3 block_nums(block_num_y, 1, 1); GGML_ASSERT(false);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot>
mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1> // <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows); break;
} }
static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
} }
static void ggml_mul_mat_q4_0_q8_1_cuda( static void ggml_mul_mat_q4_0_q8_1_cuda(
@ -8578,50 +8522,61 @@ static void ggml_cuda_op_mul_mat_vec_q(
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
const int64_t src1_padded_row_size, cudaStream_t stream) { const int64_t src1_padded_row_size, cudaStream_t stream) {
GGML_ASSERT(ggml_nrows(src1) == 1);
const int64_t ne00 = src0->ne[0]; const int64_t ne00 = src0->ne[0];
const int64_t row_diff = row_high - row_low; const int64_t row_diff = row_high - row_low;
switch (src0->type) { switch (src0->type) {
case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_0:
mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q4_1: case GGML_TYPE_Q4_1:
mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_0:
mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q5_1: case GGML_TYPE_Q5_1:
mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q8_0: case GGML_TYPE_Q8_0:
mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q2_K: case GGML_TYPE_Q2_K:
mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q3_K: case GGML_TYPE_Q3_K:
mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q4_K: case GGML_TYPE_Q4_K:
mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q5_K: case GGML_TYPE_Q5_K:
mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_Q6_K: case GGML_TYPE_Q6_K:
mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XXS:
mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break; break;
default: default:
GGML_ASSERT(false); GGML_ASSERT(false);
@ -9945,17 +9900,18 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
#ifdef GGML_CUDA_FORCE_DMMV #ifdef GGML_CUDA_FORCE_DMMV
const bool use_mul_mat_vec_q = false; const bool use_mul_mat_vec_q = false;
#else #else
const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1; const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type);
#endif // GGML_CUDA_FORCE_DMMV #endif // GGML_CUDA_FORCE_DMMV
if (use_mul_mat_vec_q) { if (use_mul_mat_vec_q) {
// NOTE: this kernel does not support ggml_nrows(src1) > 1
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
} else { } else {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
} }
} else { } else {
if (use_mul_mat_q) { if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
} else if (use_mul_mat_q) {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
} else { } else {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);

75
ggml-quants.c
View file

@ -2381,19 +2381,20 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
uint8_t L[QK_K]; uint8_t L[QK_K];
uint8_t Laux[32]; uint8_t Laux[32];
uint8_t Ls[QK_K/32];
uint8_t Lm[QK_K/32];
float weights[32]; float weights[32];
float mins[QK_K/32]; float sw[QK_K/32];
float scales[QK_K/32]; float mins[QK_K/32];
float scales[QK_K/32];
for (int i = 0; i < nb; i++) { for (int i = 0; i < nb; i++) {
float sum_x2 = 0; float sum_x2 = 0;
for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
float sigma2 = sum_x2/QK_K; float sigma2 = 2*sum_x2/QK_K;
float av_x = sqrtf(sigma2); float av_x = sqrtf(sigma2);
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/32; ++j) { for (int j = 0; j < QK_K/32; ++j) {
if (quant_weights) { if (quant_weights) {
const float * qw = quant_weights + QK_K*i + 32*j; const float * qw = quant_weights + QK_K*i + 32*j;
@ -2401,25 +2402,17 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
} else { } else {
for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
} }
float sumw = 0;
for (int l = 0; l < 32; ++l) sumw += weights[l];
sw[j] = sumw;
scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
//scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;
}
float min = mins[j];
if (min > max_min) {
max_min = min;
}
} }
float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
float inv_min = max_min > 0 ? 63.f/max_min : 0.f; float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw);
for (int j = 0; j < QK_K/32; ++j) { for (int j = 0; j < QK_K/32; ++j) {
uint8_t ls = nearest_int(inv_scale*scales[j]); uint8_t ls = Ls[j];
uint8_t lm = nearest_int(inv_min*mins[j]); uint8_t lm = Lm[j];
ls = MIN(63, ls);
lm = MIN(63, lm);
if (j < 4) { if (j < 4) {
y[i].scales[j] = ls; y[i].scales[j] = ls;
y[i].scales[j+4] = lm; y[i].scales[j+4] = lm;
@ -2429,8 +2422,8 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
y[i].scales[j-0] |= ((lm >> 4) << 6); y[i].scales[j-0] |= ((lm >> 4) << 6);
} }
} }
y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); y[i].d = GGML_FP32_TO_FP16(d_block);
y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); y[i].dmin = GGML_FP32_TO_FP16(m_block);
uint8_t sc, m; uint8_t sc, m;
for (int j = 0; j < QK_K/32; ++j) { for (int j = 0; j < QK_K/32; ++j) {
@ -2688,20 +2681,21 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
const int nb = n_per_row / QK_K; const int nb = n_per_row / QK_K;
uint8_t L[QK_K]; uint8_t L[QK_K];
float mins[QK_K/32];
float scales[QK_K/32];
float weights[32];
uint8_t Laux[32]; uint8_t Laux[32];
uint8_t Ls[QK_K/32];
uint8_t Lm[QK_K/32];
float mins[QK_K/32];
float scales[QK_K/32];
float sw[QK_K/32];
float weights[32];
for (int i = 0; i < nb; i++) { for (int i = 0; i < nb; i++) {
float sum_x2 = 0; float sum_x2 = 0;
for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
float sigma2 = sum_x2/QK_K; float sigma2 = 2*sum_x2/QK_K;
float av_x = sqrtf(sigma2); float av_x = sqrtf(sigma2);
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/32; ++j) { for (int j = 0; j < QK_K/32; ++j) {
if (quant_weights) { if (quant_weights) {
const float * qw = quant_weights + QK_K*i + 32*j; const float * qw = quant_weights + QK_K*i + 32*j;
@ -2709,22 +2703,19 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
} else { } else {
for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
} }
float sumw = 0;
for (int l = 0; l < 32; ++l) sumw += weights[l];
sw[j] = sumw;
scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;
}
float min = mins[j];
if (min > max_min) {
max_min = min;
}
} }
float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
float inv_min = max_min > 0 ? 63.f/max_min : 0.f; float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw);
for (int j = 0; j < QK_K/32; ++j) { for (int j = 0; j < QK_K/32; ++j) {
uint8_t ls = nearest_int(inv_scale*scales[j]); uint8_t ls = Ls[j];
uint8_t lm = nearest_int(inv_min*mins[j]); uint8_t lm = Lm[j];
ls = MIN(63, ls); ls = MIN(63, ls);
lm = MIN(63, lm); lm = MIN(63, lm);
if (j < 4) { if (j < 4) {
@ -2736,8 +2727,8 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
y[i].scales[j-0] |= ((lm >> 4) << 6); y[i].scales[j-0] |= ((lm >> 4) << 6);
} }
} }
y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); y[i].d = GGML_FP32_TO_FP16(d_block);
y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); y[i].dmin = GGML_FP32_TO_FP16(m_block);
uint8_t sc, m; uint8_t sc, m;
for (int j = 0; j < QK_K/32; ++j) { for (int j = 0; j < QK_K/32; ++j) {