From 99009e72f8072fa552eb02efee436be596c71cdd Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Jun 2023 22:56:18 +0300 Subject: ggml : add SOTA 2,3,4,5,6 bit k-quantizations (#1684) * Starting to add k-quantization to ggml I think it is better to have quantization separate from ggml. For now just adding the k-quants there, but it would be better to also factor out the existing ggml quantizations. * Adding Q3_K and Q8_K (de)-quantization * Q3_K now working on CUDA and AVX2/scalar CUDA is not ideal - ~50% slower than Q4_0 for single token prediction, about the same in batch mode (perplexity). CPU single token is ~55 ms (on Ryzen 7950X). * Some improvement for Q3_K on CUDA It is now ~22.5 ms/token on my GPU, so ~30% slower than Q4_0. * Some more CUDA optimizations for Q3_K Single token is now 20.5 ms/token (~20% slower than Q4_0). Perplexity is on par with Q4_0. * Adding Q4_K - scalar, AVX2, CUDA Performance is the same or perhaps very slightly better than Q4_0 on the CPU. On the GPU, single token prediction is ~10% better than Q4_0, batch mode (perplexity is about the same). * Adding Q6_K - scalar, AVX2, CUDA Performance is ~40% lower compared to Q4_K on the CPU. This is to be expected, considering that we are memory bound on the CPU and the 6-bit model is ~44% larger than the 4-bit. On the GPU, single token prediction is ~6% lower than Q4_0, batch mode (perplexity) is even closer (but still slower). * Adding Q5_K - scalar, AVX2, CUDA Performance is ~20% lower compared to Q4_K on the CPU. This is to be expected, considering that we are memory bound on the CPU and the 5-bit model is ~22% larger than the 4-bit. On the GPU, single token prediction is about the same as Q4_0 for both, single token and batch prediction. * Per convention, all QX_K quantizations use Q5_K for output.weight * Adding quantization mixes * Quantization mixes: didn't quite get what I wanted in the last commit * Q4_K dot product for ARM_NEON * Q6_K dot product for ARM_NEON * Q5_K dot product for ARM_NEON * Adding Q3_K dot for ARM_NEON It is 22% slower than Q4_K, despite the smaller model size. On x86_64, where we are memory bound, the Q3_K model is quite a bit faster than Q4_K. * A very slightly faster ARM_NEON Q3_K dot * Adding Q2_K - just CUDA for now Token prediction is pretty good - about 15.5 ms on a RTX 4080. Perplexity is about the same as Q4_K. * Adding scalar and AVX2 Q2_K dot * Adding ARM_NEON Q2_K dot About the same performance as Q4_K. * A slightly faster ARM_NEON Q2_K dot Single token prediction is now ~36 ms on M2 Max. The code is much simpler too. * Fixed bug in Q2_K CUDA dot product kernel Stranegly enough, for the few prompts I tried with the 7B model the responses looked perfectly reasonable. Only realized something is not quite right when I tried the larger models and started getting nonse back. In any case, Q2_K single token evaluation time on an RTX 4080 in a Ryzen7950X box iusing CUDA and model fully loaded on the GPU are ~15.5 ms for 7B, ~25.4 ms for 13B, and ~55.8 ms for 30B. The max number of layers that fit in VRAM for The 65B is 32. With that, we get ~330 ms per token, which is not that much faster than just running on the CPU (~470 ms per token). * Don't print zeros/NaNs when no count histogram has been collected * A 10% faster CUDA vector dot kernel for Q3_K Q3_K is now running at ~18.5 ms / token on CUDA, so the gap to Q4_0 is only 10%. It seems memory acccess pattern is more important for performance than the amount of computation the kernel does. * A slightly daster Q4_K AVX2 dot product For perplexity, where we are less memory bound, time per pass drops by ~5%. Barely measurable difference for single token prediction. * A slightly faster ARM_NEON A4_K dot product * Minor * Fix quantization error test We cannot possibly be expecting rmse < 0.002 for 2- and 3-bit quantization variants. * Fix docker build I have been sloppy with vector reinterpret casts on ARM_NEON. It seems clang is very forgiving in that regard. * Added forgotten ggml.o dependence on k_quants.h to the Makefile * Had unintentionally committed the Makefile with -Ofast enabled * ggml : rename k_quants -> ggml-quants-k, use lowercase in code --------- Co-authored-by: Iwan Kawrakow Co-authored-by: Georgi Gerganov --- Makefile | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) (limited to 'Makefile') diff --git a/Makefile b/Makefile index 1f910c3..7c9e7f7 100644 --- a/Makefile +++ b/Makefile @@ -40,8 +40,11 @@ endif # # keep standard at C11 and C++11 -CFLAGS = -I. -O3 -std=c11 -fPIC -CXXFLAGS = -I. -I./examples -O3 -std=c++11 -fPIC +# -Ofast tends to produce faster code, but may not be available for some compilers. +#OPT = -Ofast +OPT = -O3 +CFLAGS = -I. $(OPT) -std=c11 -fPIC +CXXFLAGS = -I. -I./examples $(OPT) -std=c++11 -fPIC LDFLAGS = ifdef LLAMA_DEBUG @@ -228,7 +231,10 @@ $(info ) # Build library # -ggml.o: ggml.c ggml.h ggml-cuda.h +ggml.o: ggml.c ggml.h ggml-cuda.h ggml-quants-k.h + $(CC) $(CFLAGS) -c $< -o $@ + +ggml-quants-k.o: ggml-quants-k.c ggml-quants-k.h ggml.h ggml-cuda.h $(CC) $(CFLAGS) -c $< -o $@ llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h @@ -247,25 +253,25 @@ clean: # Examples # -main: examples/main/main.cpp build-info.h ggml.o llama.o common.o $(OBJS) +main: examples/main/main.cpp build-info.h ggml.o ggml-quants-k.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) @echo @echo '==== Run ./main -h for help. ====' @echo -quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS) +quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o ggml-quants-k.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.h ggml.o llama.o $(OBJS) +quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.h ggml.o llama.o ggml-quants-k.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -perplexity: examples/perplexity/perplexity.cpp build-info.h ggml.o llama.o common.o $(OBJS) +perplexity: examples/perplexity/perplexity.cpp build-info.h ggml.o llama.o common.o ggml-quants-k.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -embedding: examples/embedding/embedding.cpp build-info.h ggml.o llama.o common.o $(OBJS) +embedding: examples/embedding/embedding.cpp build-info.h ggml.o llama.o common.o ggml-quants-k.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o $(OBJS) +save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o ggml-quants-k.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS) @@ -287,7 +293,7 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) ./$@ -vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) +vdot: pocs/vdot/vdot.cpp ggml.o ggml-quants-k.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) .PHONY: tests clean -- cgit v1.2.3