diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index b6e21b4ec..84faad37a 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -197,6 +197,8 @@ jobs: strategy: matrix: include: + - build: 'noavx' + defines: '-DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF' - build: 'avx2' defines: '-DLLAMA_BUILD_SERVER=ON' - build: 'avx' diff --git a/Makefile b/Makefile index 1ea3c4562..fb7c27cd9 100644 --- a/Makefile +++ b/Makefile @@ -235,13 +235,15 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h endif # LLAMA_CUBLAS ifdef LLAMA_CLBLAST - CFLAGS += -DGGML_USE_CLBLAST - CXXFLAGS += -DGGML_USE_CLBLAST + + CFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) + CXXFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) + # Mac provides OpenCL as a framework ifeq ($(UNAME_S),Darwin) LDFLAGS += -lclblast -framework OpenCL else - LDFLAGS += -lclblast -lOpenCL + LDFLAGS += $(shell pkg-config --libs clblast OpenCL) endif OBJS += ggml-opencl.o @@ -321,6 +323,9 @@ llama.o: llama.cpp ggml.h ggml-cuda.h ggml-metal.h llama.h llama-util.h common.o: examples/common.cpp examples/common.h $(CXX) $(CXXFLAGS) -c $< -o $@ +grammar-parser.o: examples/grammar-parser.cpp examples/grammar-parser.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) @@ -331,7 +336,7 @@ 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 llama.o common.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) @echo @echo '==== Run ./main -h for help. ====' @@ -355,7 +360,7 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml. save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.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) +server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) $(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS) diff --git a/README.md b/README.md index f45e4bf08..a0e0ea2e0 100644 --- a/README.md +++ b/README.md @@ -242,6 +242,23 @@ In order to build llama.cpp you have three different options. zig build -Doptimize=ReleaseFast ``` +- Using `gmake` (FreeBSD): + + 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics) + 2. Add your user to **video** group + 3. Install compilation dependencies. + + ```bash + sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \ + opencl clblast openblas + + gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 + ``` + + **Notes:** With this packages you can build llama.cpp with OPENBLAS and + CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read + the instructions for use and activate this options in this document below. + ### Metal Build Using Metal allows the computation to be executed on the GPU for Apple devices: @@ -384,7 +401,7 @@ Building the program with BLAS support may lead to some performance improvements | Option | Legal values | Default | Description | |-------------------------|------------------------|---------|-------------| - | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. | + | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | | LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. | diff --git a/ci/README.md b/ci/README.md index 6c74c8138..65cfe63eb 100644 --- a/ci/README.md +++ b/ci/README.md @@ -16,5 +16,10 @@ It is a good practice, before publishing changes to execute the full CI locally ```bash mkdir tmp + +# CPU-only build bash ./ci/run.sh ./tmp/results ./tmp/mnt + +# with CUDA support +GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt ``` diff --git a/ci/run.sh b/ci/run.sh index 87166ba1a..8dc394964 100644 --- a/ci/run.sh +++ b/ci/run.sh @@ -1,4 +1,15 @@ #/bin/bash +# +# sample usage: +# +# mkdir tmp +# +# # CPU-only build +# bash ./ci/run.sh ./tmp/results ./tmp/mnt +# +# # with CUDA support +# GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# if [ -z "$2" ]; then echo "usage: $0 " @@ -101,7 +112,7 @@ function gg_run_ctest_release { (time cmake -DCMAKE_BUILD_TYPE=Release .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - if [ -z $GG_BUILD_LOW_PERF ]; then + if [ -z ${GG_BUILD_LOW_PERF} ]; then (time ctest --output-on-failure ) 2>&1 | tee -a $OUT/${ci}-ctest.log else (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log @@ -154,6 +165,7 @@ function gg_run_open_llama_3b_v2 { model_q4_1="${path_models}/ggml-model-q4_1.bin" model_q5_0="${path_models}/ggml-model-q5_0.bin" model_q5_1="${path_models}/ggml-model-q5_1.bin" + model_q2_k="${path_models}/ggml-model-q2_k.bin" model_q3_k="${path_models}/ggml-model-q3_k.bin" model_q4_k="${path_models}/ggml-model-q4_k.bin" model_q5_k="${path_models}/ggml-model-q5_k.bin" @@ -166,21 +178,23 @@ function gg_run_open_llama_3b_v2 { ./bin/quantize ${model_f16} ${model_q4_1} q4_1 ./bin/quantize ${model_f16} ${model_q5_0} q5_0 ./bin/quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/quantize ${model_f16} ${model_q2_k} q2_k ./bin/quantize ${model_f16} ${model_q3_k} q3_k ./bin/quantize ${model_f16} ${model_q4_k} q4_k ./bin/quantize ${model_f16} ${model_q5_k} q5_k ./bin/quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/main --model ${model_f16} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log @@ -188,6 +202,7 @@ function gg_run_open_llama_3b_v2 { (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log @@ -212,6 +227,7 @@ function gg_run_open_llama_3b_v2 { check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log @@ -232,6 +248,133 @@ function gg_sum_open_llama_3b_v2 { gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" + gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" +} + +# open_llama_7b_v2 +# requires: GG_BUILD_CUDA + +function gg_run_open_llama_7b_v2 { + cd ${SRC} + + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin + gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json + + gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + + path_models="../models-mnt/open-llama/7B-v2" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert.py ${path_models} + + model_f16="${path_models}/ggml-model-f16.bin" + model_q8_0="${path_models}/ggml-model-q8_0.bin" + model_q4_0="${path_models}/ggml-model-q4_0.bin" + model_q4_1="${path_models}/ggml-model-q4_1.bin" + model_q5_0="${path_models}/ggml-model-q5_0.bin" + model_q5_1="${path_models}/ggml-model-q5_1.bin" + model_q2_k="${path_models}/ggml-model-q2_k.bin" + model_q3_k="${path_models}/ggml-model-q3_k.bin" + model_q4_k="${path_models}/ggml-model-q4_k.bin" + model_q5_k="${path_models}/ggml-model-q5_k.bin" + model_q6_k="${path_models}/ggml-model-q6_k.bin" + + wiki_test="${path_wiki}/wiki.test.raw" + + ./bin/quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/quantize ${model_f16} ${model_q2_k} q2_k + ./bin/quantize ${model_f16} ${model_q3_k} q3_k + ./bin/quantize ${model_f16} ${model_q4_k} q4_k + ./bin/quantize ${model_f16} ${model_q5_k} q5_k + ./bin/quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/main --model ${model_f16} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/main --model ${model_q8_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/main --model ${model_q4_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/main --model ${model_q4_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/main --model ${model_q5_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/main --model ${model_q5_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/main --model ${model_q2_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/main --model ${model_q3_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/main --model ${model_q4_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/main --model ${model_q5_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/main --model ${model_q6_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + function check_ppl { + qnt="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + set +e +} + +function gg_sum_open_llama_7b_v2 { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'OpenLLaMA 7B-v2:\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" + gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" @@ -240,7 +383,7 @@ function gg_sum_open_llama_3b_v2 { ## main -if [ -z $GG_BUILD_LOW_PERF ]; then +if [ -z ${GG_BUILD_LOW_PERF} ]; then rm -rf ${SRC}/models-mnt mnt_models=${MNT}/models @@ -252,11 +395,15 @@ fi ret=0 -#test $ret -eq 0 && gg_run ctest_debug -#test $ret -eq 0 && gg_run ctest_release +test $ret -eq 0 && gg_run ctest_debug +test $ret -eq 0 && gg_run ctest_release -if [ -z $GG_BUILD_LOW_PERF ]; then - test $ret -eq 0 && gg_run open_llama_3b_v2 +if [ -z ${GG_BUILD_LOW_PERF} ]; then + if [ -z ${GG_BUILD_CUDA} ]; then + test $ret -eq 0 && gg_run open_llama_3b_v2 + else + test $ret -eq 0 && gg_run open_llama_7b_v2 + fi fi exit $ret diff --git a/convert.py b/convert.py index 13e983d2e..dbd9366b9 100755 --- a/convert.py +++ b/convert.py @@ -142,9 +142,9 @@ def find_n_mult(n_ff: int, n_embd: int) -> int: @dataclass class Params: n_vocab: int - n_embd: int - n_mult: int - n_head: int + n_embd: int + n_mult: int + n_head: int n_layer: int @staticmethod @@ -167,11 +167,11 @@ class Params: n_head=n_embd // 128 # guessed return Params( - n_vocab=n_vocab, - n_embd=n_embd, - n_mult=256, - n_head=n_head, - n_layer=n_layer, + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = 256, + n_head = n_head, + n_layer = n_layer, ) @staticmethod @@ -179,28 +179,53 @@ class Params: config = json.load(open(config_path)) n_vocab = config["vocab_size"]; - n_embd = config["hidden_size"]; - n_head = config["num_attention_heads"]; + n_embd = config["hidden_size"]; + n_head = config["num_attention_heads"]; n_layer = config["num_hidden_layers"]; - n_ff = config["intermediate_size"]; + n_ff = config["intermediate_size"]; n_mult = find_n_mult(n_ff, n_embd); return Params( - n_vocab=n_vocab, - n_embd=n_embd, - n_mult=n_mult, - n_head=n_head, - n_layer=n_layer, + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = n_mult, + n_head = n_head, + n_layer = n_layer, + ) + + # LLaMA v2 70B params.json + # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1 + @staticmethod + def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params': + config = json.load(open(config_path)) + + n_vocab = config["vocab_size"]; + n_embd = config["dim"]; + n_head = config["n_heads"]; + n_layer = config["n_layers"]; + n_mult = config["multiple_of"]; + + if n_vocab == -1: + n_vocab = model["tok_embeddings.weight"].shape[0] + + return Params( + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = n_mult, + n_head = n_head, + n_layer = n_layer, ) @staticmethod def load(model_plus: 'ModelPlus') -> 'Params': + hf_config_path = model_plus.paths[0].parent / "config.json" orig_config_path = model_plus.paths[0].parent / "params.json" - hf_transformer_config_path = model_plus.paths[0].parent / "config.json" - if hf_transformer_config_path.exists(): - params = Params.loadHFTransformerJson(model_plus.model, hf_transformer_config_path) + if hf_config_path.exists(): + params = Params.loadHFTransformerJson(model_plus.model, hf_config_path) + elif orig_config_path.exists(): + params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path) else: params = Params.guessed(model_plus.model) @@ -209,14 +234,21 @@ class Params: class SentencePieceVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: - self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path], vocabtype: Optional[str]) -> None: + self.vocabtype = vocabtype + if self.vocabtype == "bpe": + self.sentencepiece_tokenizer = json.loads(open(str(fname_tokenizer)).read()) + else: + self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) added_tokens: Dict[str, int] if fname_added_tokens is not None: added_tokens = json.load(open(fname_added_tokens)) else: added_tokens = {} - vocab_size: int = self.sentencepiece_tokenizer.vocab_size() + if self.vocabtype == "bpe": + vocab_size: int = len(self.sentencepiece_tokenizer) + else: + vocab_size: int = self.sentencepiece_tokenizer.vocab_size() expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) actual_ids = sorted(added_tokens.values()) if expected_ids != actual_ids: @@ -230,11 +262,20 @@ class SentencePieceVocab: def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float]]: tokenizer = self.sentencepiece_tokenizer - for i in range(tokenizer.vocab_size()): - piece = tokenizer.id_to_piece(i) - text: bytes = piece.encode("utf-8") - score: float = tokenizer.get_score(i) + if self.vocabtype == "bpe": + from transformers.models.gpt2 import tokenization_gpt2 + byte_encoder = tokenization_gpt2.bytes_to_unicode() + byte_decoder = {v: k for k, v in byte_encoder.items()} + for i, item in enumerate(tokenizer): + text: bytes + text = b''.join([x.to_bytes(1, byteorder='big') for x in [byte_decoder[y] for y in item]]) + score: float = -i yield text, score + else: + for i in range(tokenizer.vocab_size()): + piece = tokenizer.id_to_piece(i) + text: bytes = piece.encode("utf-8") + score: float = tokenizer.get_score(i) def added_tokens(self) -> Iterable[Tuple[bytes, float]]: for text in self.added_tokens_list: @@ -1025,8 +1066,7 @@ class OutputFile: @staticmethod def write_vocab_only(fname_out: Path, vocab: Vocab) -> None: of = OutputFile(fname_out) - params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, - n_head=1, n_layer=0) + params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, n_head=1, n_layer=0) of = OutputFile(fname_out) of.write_file_header(params, file_type=GGMLFileType.AllF32) of.write_vocab(vocab) @@ -1161,14 +1201,18 @@ def filter_and_sort_tensors(model: LazyModel) -> LazyModel: return {name: model[name] for name in TENSORS_LIST if name in model} -def load_vocab(path: Path) -> SentencePieceVocab: +def load_vocab(path: Path, vocabtype: Optional[str]) -> SentencePieceVocab: + print(f"vocabtype: {vocabtype}") # Be extra-friendly and accept either a file or a directory. Also, if it's # a directory, it might be the model directory, and tokenizer.model might # be in the parent of that. if path.is_dir(): - path2 = path / "tokenizer.model" + vocab_file = "tokenizer.model" + if vocabtype == 'bpe': + vocab_file = "vocab.json" + path2 = path / vocab_file # Use `.parent` instead of /.. to handle the symlink case better. - path3 = path.parent / "tokenizer.model" + path3 = path.parent / vocab_file if path2.exists(): path = path2 elif path3.exists(): @@ -1179,7 +1223,8 @@ def load_vocab(path: Path) -> SentencePieceVocab: "if it's in another directory, pass the directory as --vocab-dir") added_tokens_path = path.parent / "added_tokens.json" print(f"Loading vocab file {path}") - return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None) + return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None, + vocabtype) def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path: @@ -1217,6 +1262,7 @@ def main(args_in: Optional[List[str]] = None) -> None: parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") + parser.add_argument("--vocabtype", default='spm', choices=["spm", "bpe"], help="vocab format (default: spm)") args = parser.parse_args(args_in) vocab: Vocab @@ -1224,7 +1270,7 @@ def main(args_in: Optional[List[str]] = None) -> None: model_plus = lazy_load_file(args.model) do_dump_model(model_plus) elif args.vocab_only: - vocab = load_vocab(args.vocab_dir or args.model) + vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype) assert args.outfile, "need --outfile if using --vocab-only" outfile = args.outfile OutputFile.write_vocab_only(outfile, vocab) @@ -1238,7 +1284,7 @@ def main(args_in: Optional[List[str]] = None) -> None: vocab = model_plus.vocab else: vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent - vocab = load_vocab(vocab_dir) + vocab = load_vocab(vocab_dir, args.vocabtype) params = Params.load(model_plus) model = model_plus.model model = do_necessary_conversions(model, params) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 161960bb8..4b1f1cf44 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -13,6 +13,8 @@ set(TARGET common) add_library(${TARGET} OBJECT common.h common.cpp + grammar-parser.h + grammar-parser.cpp ) if (BUILD_SHARED_LIBS) diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index 4965881ec..6fa55b319 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -8,6 +8,12 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +#ifdef LLAMA_DEFAULT_RMS_EPS +static const float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; +#else +static const float rms_norm_eps = 5e-6f; +#endif + float frand() { return (float)rand()/(float)RAND_MAX; } @@ -562,7 +568,7 @@ struct ggml_tensor * forward( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // cur = attention_norm*cur cur = ggml_mul(ctx0, @@ -685,7 +691,7 @@ struct ggml_tensor * forward( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); // cur = ffn_norm*cur // cur shape [n_embd,N,1,1] @@ -729,7 +735,7 @@ struct ggml_tensor * forward( { // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // inpL = norm*inpL // inpL shape [n_embd,N,1,1] @@ -817,7 +823,7 @@ struct ggml_tensor * forward_batch( // norm { // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = attention_norm*cur @@ -981,7 +987,7 @@ struct ggml_tensor * forward_batch( // norm { // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); assert_shape_2d(cur, n_embd, N*n_batch); // cur = ffn_norm*cur @@ -1034,7 +1040,7 @@ struct ggml_tensor * forward_batch( { // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); assert_shape_2d(inpL, n_embd, N*n_batch); // inpL = norm*inpL @@ -1104,7 +1110,7 @@ struct ggml_tensor * forward_lora( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL); + cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // cur = attention_norm*cur cur = ggml_mul(ctx0, @@ -1251,7 +1257,7 @@ struct ggml_tensor * forward_lora( // norm { // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF); + cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); // cur = ffn_norm*cur // cur shape [n_embd,N,1,1] @@ -1295,7 +1301,7 @@ struct ggml_tensor * forward_lora( { // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL); + inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); // inpL = norm*inpL // inpL shape [n_embd,N,1,1] diff --git a/examples/common.cpp b/examples/common.cpp index ada0e3e60..9f1c0d904 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -117,6 +117,9 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_threads = std::stoi(argv[i]); + if (params.n_threads <= 0) { + params.n_threads = std::thread::hardware_concurrency(); + } } else if (arg == "-p" || arg == "--prompt") { if (++i >= argc) { invalid_param = true; @@ -168,6 +171,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_ctx = std::stoi(argv[i]); + } else if (arg == "-gqa" || arg == "--gqa") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_gqa = std::stoi(argv[i]); + } else if (arg == "-eps" || arg == "--rms-norm-eps") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rms_norm_eps = std::stof(argv[i]); } else if (arg == "--rope-freq-base") { if (++i >= argc) { invalid_param = true; @@ -387,6 +402,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { params.antiprompt.push_back(argv[i]); } else if (arg == "--perplexity") { params.perplexity = true; + } else if (arg == "--perplexity-lines") { + params.perplexity_lines = true; } else if (arg == "--ignore-eos") { params.logit_bias[llama_token_eos()] = -INFINITY; } else if (arg == "--no-penalize-nl") { @@ -415,6 +432,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { exit(0); } else if (arg == "--random-prompt") { params.random_prompt = true; + } else if (arg == "--in-prefix-bos") { + params.input_prefix_bos = true; } else if (arg == "--in-prefix") { if (++i >= argc) { invalid_param = true; @@ -427,6 +446,28 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.input_suffix = argv[i]; + } else if (arg == "--grammar") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.grammar = argv[i]; + } else if (arg == "--grammar-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + break; + } + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(params.grammar) + ); } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); gpt_print_usage(argc, argv, default_params); @@ -456,90 +497,96 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { } void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { - fprintf(stderr, "usage: %s [options]\n", argv[0]); - fprintf(stderr, "\n"); - fprintf(stderr, "options:\n"); - fprintf(stderr, " -h, --help show this help message and exit\n"); - fprintf(stderr, " -i, --interactive run in interactive mode\n"); - fprintf(stderr, " --interactive-first run in interactive mode and wait for input right away\n"); - fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n"); - fprintf(stderr, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); - fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n"); - fprintf(stderr, " halt generation at PROMPT, return control in interactive mode\n"); - fprintf(stderr, " (can be specified more than once for multiple prompts).\n"); - fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n"); - fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); - fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - fprintf(stderr, " -p PROMPT, --prompt PROMPT\n"); - fprintf(stderr, " prompt to start generation with (default: empty)\n"); - fprintf(stderr, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - fprintf(stderr, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); - fprintf(stderr, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); - fprintf(stderr, " not supported with --interactive or other interactive options\n"); - fprintf(stderr, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); - fprintf(stderr, " --random-prompt start with a randomized prompt.\n"); - fprintf(stderr, " --in-prefix STRING string to prefix user inputs with (default: empty)\n"); - fprintf(stderr, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); - fprintf(stderr, " -f FNAME, --file FNAME\n"); - fprintf(stderr, " prompt file to start generation.\n"); - fprintf(stderr, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict); - fprintf(stderr, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); - fprintf(stderr, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); - fprintf(stderr, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); - fprintf(stderr, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p); - fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n); - fprintf(stderr, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty); - fprintf(stderr, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty); - fprintf(stderr, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty); - fprintf(stderr, " --mirostat N use Mirostat sampling.\n"); - fprintf(stderr, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); - fprintf(stderr, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat); - fprintf(stderr, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta); - fprintf(stderr, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau); - fprintf(stderr, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); - fprintf(stderr, " modifies the likelihood of token appearing in the completion,\n"); - fprintf(stderr, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); - fprintf(stderr, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); - fprintf(stderr, " --cfg-negative-prompt PROMPT \n"); - fprintf(stderr, " negative prompt to use for guidance. (default: empty)\n"); - fprintf(stderr, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); - fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - fprintf(stderr, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base); - fprintf(stderr, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale); - fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); - fprintf(stderr, " --no-penalize-nl do not penalize newline token\n"); - fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); - fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n"); - fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp); - fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - fprintf(stderr, " --perplexity compute perplexity over the prompt\n"); - fprintf(stderr, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); - fprintf(stderr, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); + fprintf(stdout, "usage: %s [options]\n", argv[0]); + fprintf(stdout, "\n"); + fprintf(stdout, "options:\n"); + fprintf(stdout, " -h, --help show this help message and exit\n"); + fprintf(stdout, " -i, --interactive run in interactive mode\n"); + fprintf(stdout, " --interactive-first run in interactive mode and wait for input right away\n"); + fprintf(stdout, " -ins, --instruct run in instruction mode (use with Alpaca models)\n"); + fprintf(stdout, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); + fprintf(stdout, " -r PROMPT, --reverse-prompt PROMPT\n"); + fprintf(stdout, " halt generation at PROMPT, return control in interactive mode\n"); + fprintf(stdout, " (can be specified more than once for multiple prompts).\n"); + fprintf(stdout, " --color colorise output to distinguish prompt and user input from generations\n"); + fprintf(stdout, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); + fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + fprintf(stdout, " -p PROMPT, --prompt PROMPT\n"); + fprintf(stdout, " prompt to start generation with (default: empty)\n"); + fprintf(stdout, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); + fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); + fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); + fprintf(stdout, " not supported with --interactive or other interactive options\n"); + fprintf(stdout, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); + fprintf(stdout, " --random-prompt start with a randomized prompt.\n"); + fprintf(stdout, " --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); + fprintf(stdout, " --in-prefix STRING string to prefix user inputs with (default: empty)\n"); + fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); + fprintf(stdout, " -f FNAME, --file FNAME\n"); + fprintf(stdout, " prompt file to start generation.\n"); + fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict); + fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa); + fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps); + fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); + fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); + fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); + fprintf(stdout, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p); + fprintf(stdout, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n); + fprintf(stdout, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty); + fprintf(stdout, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty); + fprintf(stdout, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty); + fprintf(stdout, " --mirostat N use Mirostat sampling.\n"); + fprintf(stdout, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); + fprintf(stdout, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat); + fprintf(stdout, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta); + fprintf(stdout, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau); + fprintf(stdout, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); + fprintf(stdout, " modifies the likelihood of token appearing in the completion,\n"); + fprintf(stdout, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); + fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); + fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); + fprintf(stdout, " --grammar-file FNAME file to read grammar from\n"); + fprintf(stdout, " --cfg-negative-prompt PROMPT \n"); + fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n"); + fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); + fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base); + fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale); + fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); + fprintf(stdout, " --no-penalize-nl do not penalize newline token\n"); + fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); + fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n"); + fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp); + fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n"); + fprintf(stdout, " --perplexity-lines compute perplexity over each line of the prompt\n"); + fprintf(stdout, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); if (llama_mlock_supported()) { - fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); + fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { - fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - fprintf(stderr, " --numa attempt optimizations that help on some NUMA systems\n"); - fprintf(stderr, " if run without this previously, it is recommended to drop the system page cache before using this\n"); - fprintf(stderr, " see https://github.com/ggerganov/llama.cpp/issues/1437\n"); + fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n"); + fprintf(stdout, " if run without this previously, it is recommended to drop the system page cache before using this\n"); + fprintf(stdout, " see https://github.com/ggerganov/llama.cpp/issues/1437\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, " -ngl N, --n-gpu-layers N\n"); - fprintf(stderr, " number of layers to store in VRAM\n"); - fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n"); - fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" ); - fprintf(stderr, " -lv, --low-vram don't allocate VRAM scratch buffer\n" ); + fprintf(stdout, " -ngl N, --n-gpu-layers N\n"); + fprintf(stdout, " number of layers to store in VRAM\n"); + fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n"); + fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); + fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" ); + fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n" ); #endif - fprintf(stderr, " --mtest compute maximum memory usage\n"); - fprintf(stderr, " --export export the computation graph to 'llama.ggml'\n"); - fprintf(stderr, " --verbose-prompt print prompt before generation\n"); - fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - fprintf(stderr, " -m FNAME, --model FNAME\n"); - fprintf(stderr, " model path (default: %s)\n", params.model.c_str()); - fprintf(stderr, "\n"); + fprintf(stdout, " --mtest compute maximum memory usage\n"); + fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n"); + fprintf(stdout, " --verbose-prompt print prompt before generation\n"); + fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); + fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); + fprintf(stdout, " -m FNAME, --model FNAME\n"); + fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); + fprintf(stdout, "\n"); } std::string gpt_random_prompt(std::mt19937 & rng) { @@ -575,18 +622,20 @@ std::vector llama_tokenize(struct llama_context * ctx, const std::s struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) { auto lparams = llama_context_default_params(); - lparams.n_ctx = params.n_ctx; - lparams.n_batch = params.n_batch; - lparams.n_gpu_layers = params.n_gpu_layers; - lparams.main_gpu = params.main_gpu; - lparams.tensor_split = params.tensor_split; - lparams.low_vram = params.low_vram; - lparams.seed = params.seed; - lparams.f16_kv = params.memory_f16; - lparams.use_mmap = params.use_mmap; - lparams.use_mlock = params.use_mlock; - lparams.logits_all = params.perplexity; - lparams.embedding = params.embedding; + lparams.n_ctx = params.n_ctx; + lparams.n_batch = params.n_batch; + lparams.n_gqa = params.n_gqa; + lparams.rms_norm_eps = params.rms_norm_eps; + lparams.n_gpu_layers = params.n_gpu_layers; + lparams.main_gpu = params.main_gpu; + lparams.tensor_split = params.tensor_split; + lparams.low_vram = params.low_vram; + lparams.seed = params.seed; + lparams.f16_kv = params.memory_f16; + lparams.use_mmap = params.use_mmap; + lparams.use_mlock = params.use_mlock; + lparams.logits_all = params.perplexity; + lparams.embedding = params.embedding; lparams.rope_freq_base = params.rope_freq_base; lparams.rope_freq_scale = params.rope_freq_scale; diff --git a/examples/common.h b/examples/common.h index 2fd5d38a8..75f622c77 100644 --- a/examples/common.h +++ b/examples/common.h @@ -23,17 +23,19 @@ int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = -1; // RNG seed int32_t n_threads = get_num_physical_cores(); - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_gpu_layers = 0; // number of layers to store in VRAM - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t n_predict = -1; // new tokens to predict + int32_t n_ctx = 512; // context size + int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_gqa = 1; // grouped-query attention factor (TODO: move to hparams) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_gpu_layers = 0; // number of layers to store in VRAM + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; // rms norm epsilon float rope_freq_base = 10000.0f; // RoPE base frequency float rope_freq_scale = 1.0f; // RoPE frequency scaling factor @@ -48,7 +50,7 @@ struct gpt_params { int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) float frequency_penalty = 0.00f; // 0.0 = disabled float presence_penalty = 0.00f; // 0.0 = disabled - int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate @@ -63,6 +65,7 @@ struct gpt_params { std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state std::string input_prefix = ""; // string to prefix user inputs with std::string input_suffix = ""; // string to suffix user inputs with + std::string grammar = ""; // optional BNF-like grammar to constrain sampling std::vector antiprompt; // string upon seeing which more user input is prompted std::string lora_adapter = ""; // lora adapter path @@ -80,9 +83,11 @@ struct gpt_params { bool interactive_first = false; // wait for user input immediately bool multiline_input = false; // reverse the usage of `\` + bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool instruct = false; // instruction mode (used for Alpaca models) bool penalize_nl = true; // consider newlines as a repeatable token bool perplexity = false; // compute perplexity over the prompt + bool perplexity_lines = false; // compute perplexity over each line of the prompt bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory bool mem_test = false; // compute maximum memory usage diff --git a/examples/grammar-parser.cpp b/examples/grammar-parser.cpp new file mode 100644 index 000000000..019d5e1bf --- /dev/null +++ b/examples/grammar-parser.cpp @@ -0,0 +1,423 @@ +#include "grammar-parser.h" +#include +#include +#include +#include +#include +#include + +namespace grammar_parser { + // NOTE: assumes valid utf8 (but checks for overrun) + // copied from llama.cpp + std::pair decode_utf8(const char * src) { + static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; + uint8_t first_byte = static_cast(*src); + uint8_t highbits = first_byte >> 4; + int len = lookup[highbits]; + uint8_t mask = (1 << (8 - len)) - 1; + uint32_t value = first_byte & mask; + const char * end = src + len; // may overrun! + const char * pos = src + 1; + for ( ; pos < end && *pos; pos++) { + value = (value << 6) + (static_cast(*pos) & 0x3F); + } + return std::make_pair(value, pos); + } + + uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) { + uint32_t next_id = static_cast(state.symbol_ids.size()); + auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id)); + return result.first->second; + } + + uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) { + uint32_t next_id = static_cast(state.symbol_ids.size()); + state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id; + return next_id; + } + + void add_rule( + parse_state & state, + uint32_t rule_id, + const std::vector & rule) { + if (state.rules.size() <= rule_id) { + state.rules.resize(rule_id + 1); + } + state.rules[rule_id] = rule; + } + + bool is_word_char(char c) { + return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9'); + } + + std::pair parse_hex(const char * src, int size) { + const char * pos = src; + const char * end = src + size; + uint32_t value = 0; + for ( ; pos < end && *pos; pos++) { + value <<= 4; + char c = *pos; + if ('a' <= c && c <= 'f') { + value += c - 'a' + 10; + } else if ('A' <= c && c <= 'F') { + value += c - 'A' + 10; + } else if ('0' <= c && c <= '9') { + value += c - '0'; + } else { + break; + } + } + if (pos != end) { + throw std::runtime_error("expecting " + std::to_string(size) + " hex chars at " + src); + } + return std::make_pair(value, pos); + } + + const char * parse_space(const char * src, bool newline_ok) { + const char * pos = src; + while (*pos == ' ' || *pos == '\t' || *pos == '#' || + (newline_ok && (*pos == '\r' || *pos == '\n'))) { + if (*pos == '#') { + while (*pos && *pos != '\r' && *pos != '\n') { + pos++; + } + } else { + pos++; + } + } + return pos; + } + + const char * parse_name(const char * src) { + const char * pos = src; + while (is_word_char(*pos)) { + pos++; + } + if (pos == src) { + throw std::runtime_error(std::string("expecting name at ") + src); + } + return pos; + } + + std::pair parse_char(const char * src) { + if (*src == '\\') { + switch (src[1]) { + case 'x': return parse_hex(src + 2, 2); + case 'u': return parse_hex(src + 2, 4); + case 'U': return parse_hex(src + 2, 8); + case 't': return std::make_pair('\t', src + 2); + case 'r': return std::make_pair('\r', src + 2); + case 'n': return std::make_pair('\n', src + 2); + case '\\': + case '"': + case '[': + case ']': + return std::make_pair(src[1], src + 2); + default: + throw std::runtime_error(std::string("unknown escape at ") + src); + } + } else if (*src) { + return decode_utf8(src); + } + throw std::runtime_error("unexpected end of input"); + } + + const char * parse_alternates( + parse_state & state, + const char * src, + const std::string & rule_name, + uint32_t rule_id, + bool is_nested); + + const char * parse_sequence( + parse_state & state, + const char * src, + const std::string & rule_name, + std::vector & out_elements, + bool is_nested) { + size_t last_sym_start = out_elements.size(); + const char * pos = src; + while (*pos) { + if (*pos == '"') { // literal string + pos++; + last_sym_start = out_elements.size(); + while (*pos != '"') { + auto char_pair = parse_char(pos); + pos = char_pair.second; + out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first}); + } + pos = parse_space(pos + 1, is_nested); + } else if (*pos == '[') { // char range(s) + pos++; + enum llama_gretype start_type = LLAMA_GRETYPE_CHAR; + if (*pos == '^') { + pos++; + start_type = LLAMA_GRETYPE_CHAR_NOT; + } + last_sym_start = out_elements.size(); + while (*pos != ']') { + auto char_pair = parse_char(pos); + pos = char_pair.second; + enum llama_gretype type = last_sym_start < out_elements.size() + ? LLAMA_GRETYPE_CHAR_ALT + : start_type; + + out_elements.push_back({type, char_pair.first}); + if (pos[0] == '-' && pos[1] != ']') { + auto endchar_pair = parse_char(pos + 1); + pos = endchar_pair.second; + out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first}); + } + } + pos = parse_space(pos + 1, is_nested); + } else if (is_word_char(*pos)) { // rule reference + const char * name_end = parse_name(pos); + uint32_t ref_rule_id = get_symbol_id(state, pos, name_end - pos); + pos = parse_space(name_end, is_nested); + last_sym_start = out_elements.size(); + out_elements.push_back({LLAMA_GRETYPE_RULE_REF, ref_rule_id}); + } else if (*pos == '(') { // grouping + // parse nested alternates into synthesized rule + pos = parse_space(pos + 1, true); + uint32_t sub_rule_id = generate_symbol_id(state, rule_name); + pos = parse_alternates(state, pos, rule_name, sub_rule_id, true); + last_sym_start = out_elements.size(); + // output reference to synthesized rule + out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); + if (*pos != ')') { + throw std::runtime_error(std::string("expecting ')' at ") + pos); + } + pos = parse_space(pos + 1, is_nested); + } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator + if (last_sym_start == out_elements.size()) { + throw std::runtime_error(std::string("expecting preceeding item to */+/? at ") + pos); + } + + // apply transformation to previous symbol (last_sym_start to end) according to + // rewrite rules: + // S* --> S' ::= S S' | + // S+ --> S' ::= S S' | S + // S? --> S' ::= S | + uint32_t sub_rule_id = generate_symbol_id(state, rule_name); + std::vector sub_rule; + // add preceding symbol to generated rule + sub_rule.insert( + sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end()); + if (*pos == '*' || *pos == '+') { + // cause generated rule to recurse + sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); + } + // mark start of alternate def + sub_rule.push_back({LLAMA_GRETYPE_ALT, 0}); + if (*pos == '+') { + // add preceding symbol as alternate only for '+' (otherwise empty) + sub_rule.insert( + sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end()); + } + sub_rule.push_back({LLAMA_GRETYPE_END, 0}); + add_rule(state, sub_rule_id, sub_rule); + + // in original rule, replace previous symbol with reference to generated rule + out_elements.resize(last_sym_start); + out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); + + pos = parse_space(pos + 1, is_nested); + } else { + break; + } + } + return pos; + } + + const char * parse_alternates( + parse_state & state, + const char * src, + const std::string & rule_name, + uint32_t rule_id, + bool is_nested) { + std::vector rule; + const char * pos = parse_sequence(state, src, rule_name, rule, is_nested); + while (*pos == '|') { + rule.push_back({LLAMA_GRETYPE_ALT, 0}); + pos = parse_space(pos + 1, true); + pos = parse_sequence(state, pos, rule_name, rule, is_nested); + } + rule.push_back({LLAMA_GRETYPE_END, 0}); + add_rule(state, rule_id, rule); + return pos; + } + + const char * parse_rule(parse_state & state, const char * src) { + const char * name_end = parse_name(src); + const char * pos = parse_space(name_end, false); + size_t name_len = name_end - src; + uint32_t rule_id = get_symbol_id(state, src, name_len); + const std::string name(src, name_len); + + if (!(pos[0] == ':' && pos[1] == ':' && pos[2] == '=')) { + throw std::runtime_error(std::string("expecting ::= at ") + pos); + } + pos = parse_space(pos + 3, true); + + pos = parse_alternates(state, pos, name, rule_id, false); + + if (*pos == '\r') { + pos += pos[1] == '\n' ? 2 : 1; + } else if (*pos == '\n') { + pos++; + } else if (*pos) { + throw std::runtime_error(std::string("expecting newline or end at ") + pos); + } + return parse_space(pos, true); + } + + parse_state parse(const char * src) { + try { + parse_state state; + const char * pos = parse_space(src, true); + while (*pos) { + pos = parse_rule(state, pos); + } + return state; + } catch (const std::exception & err) { + fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what()); + return parse_state(); + } + } + + void print_grammar_char(FILE * file, uint32_t c) { + if (0x20 <= c && c <= 0x7f) { + fprintf(file, "%c", static_cast(c)); + } else { + // cop out of encoding UTF-8 + fprintf(file, "", c); + } + } + + bool is_char_element(llama_grammar_element elem) { + switch (elem.type) { + case LLAMA_GRETYPE_CHAR: return true; + case LLAMA_GRETYPE_CHAR_NOT: return true; + case LLAMA_GRETYPE_CHAR_ALT: return true; + case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true; + default: return false; + } + } + + void print_rule_binary(FILE * file, const std::vector & rule) { + for (auto elem : rule) { + switch (elem.type) { + case LLAMA_GRETYPE_END: fprintf(file, "END"); break; + case LLAMA_GRETYPE_ALT: fprintf(file, "ALT"); break; + case LLAMA_GRETYPE_RULE_REF: fprintf(file, "RULE_REF"); break; + case LLAMA_GRETYPE_CHAR: fprintf(file, "CHAR"); break; + case LLAMA_GRETYPE_CHAR_NOT: fprintf(file, "CHAR_NOT"); break; + case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break; + case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break; + } + switch (elem.type) { + case LLAMA_GRETYPE_END: + case LLAMA_GRETYPE_ALT: + case LLAMA_GRETYPE_RULE_REF: + fprintf(file, "(%u) ", elem.value); + break; + case LLAMA_GRETYPE_CHAR: + case LLAMA_GRETYPE_CHAR_NOT: + case LLAMA_GRETYPE_CHAR_RNG_UPPER: + case LLAMA_GRETYPE_CHAR_ALT: + fprintf(file, "(\""); + print_grammar_char(file, elem.value); + fprintf(file, "\") "); + break; + } + } + fprintf(file, "\n"); + } + + void print_rule( + FILE * file, + uint32_t rule_id, + const std::vector & rule, + const std::map & symbol_id_names) { + if (rule.empty() || rule.back().type != LLAMA_GRETYPE_END) { + throw std::runtime_error( + "malformed rule, does not end with LLAMA_GRETYPE_END: " + std::to_string(rule_id)); + } + fprintf(file, "%s ::= ", symbol_id_names.at(rule_id).c_str()); + for (size_t i = 0, end = rule.size() - 1; i < end; i++) { + llama_grammar_element elem = rule[i]; + switch (elem.type) { + case LLAMA_GRETYPE_END: + throw std::runtime_error( + "unexpected end of rule: " + std::to_string(rule_id) + "," + + std::to_string(i)); + case LLAMA_GRETYPE_ALT: + fprintf(file, "| "); + break; + case LLAMA_GRETYPE_RULE_REF: + fprintf(file, "%s ", symbol_id_names.at(elem.value).c_str()); + break; + case LLAMA_GRETYPE_CHAR: + fprintf(file, "["); + print_grammar_char(file, elem.value); + break; + case LLAMA_GRETYPE_CHAR_NOT: + fprintf(file, "[^"); + print_grammar_char(file, elem.value); + break; + case LLAMA_GRETYPE_CHAR_RNG_UPPER: + if (i == 0 || !is_char_element(rule[i - 1])) { + throw std::runtime_error( + "LLAMA_GRETYPE_CHAR_RNG_UPPER without preceding char: " + + std::to_string(rule_id) + "," + std::to_string(i)); + } + fprintf(file, "-"); + print_grammar_char(file, elem.value); + break; + case LLAMA_GRETYPE_CHAR_ALT: + if (i == 0 || !is_char_element(rule[i - 1])) { + throw std::runtime_error( + "LLAMA_GRETYPE_CHAR_ALT without preceding char: " + + std::to_string(rule_id) + "," + std::to_string(i)); + } + print_grammar_char(file, elem.value); + break; + } + if (is_char_element(elem)) { + switch (rule[i + 1].type) { + case LLAMA_GRETYPE_CHAR_ALT: + case LLAMA_GRETYPE_CHAR_RNG_UPPER: + break; + default: + fprintf(file, "] "); + } + } + } + fprintf(file, "\n"); + } + + void print_grammar(FILE * file, const parse_state & state) { + try { + std::map symbol_id_names; + for (auto kv : state.symbol_ids) { + symbol_id_names[kv.second] = kv.first; + } + for (size_t i = 0, end = state.rules.size(); i < end; i++) { + // fprintf(file, "%zu: ", i); + // print_rule_binary(file, state.rules[i]); + print_rule(file, i, state.rules[i], symbol_id_names); + // fprintf(file, "\n"); + } + } catch (const std::exception & err) { + fprintf(stderr, "\n%s: error printing grammar: %s\n", __func__, err.what()); + } + } + + std::vector parse_state::c_rules() { + std::vector ret; + for (const auto & rule : rules) { + ret.push_back(rule.data()); + } + return ret; + } +} diff --git a/examples/grammar-parser.h b/examples/grammar-parser.h new file mode 100644 index 000000000..9037d7272 --- /dev/null +++ b/examples/grammar-parser.h @@ -0,0 +1,29 @@ +// Implements a parser for an extended Backus-Naur form (BNF), producing the +// binary context-free grammar format specified by llama.h. Supports character +// ranges, grouping, and repetition operators. As an example, a grammar for +// arithmetic might look like: +// +// root ::= expr +// expr ::= term ([-+*/] term)* +// term ::= num | "(" space expr ")" space +// num ::= [0-9]+ space +// space ::= [ \t\n]* + +#pragma once +#include "llama.h" +#include +#include +#include +#include + +namespace grammar_parser { + struct parse_state { + std::map symbol_ids; + std::vector> rules; + + std::vector c_rules(); + }; + + parse_state parse(const char * src); + void print_grammar(FILE * file, const parse_state & state); +} diff --git a/examples/llm.vim b/examples/llm.vim new file mode 100644 index 000000000..efecad0cd --- /dev/null +++ b/examples/llm.vim @@ -0,0 +1,23 @@ +function! Llm() + + let url = "http://127.0.0.1:8080/completion" + + " Get the content of the current buffer + let buffer_content = join(getline(1, '$'), "\n") + + " Create the JSON payload + let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":10,"stream": v:false} + let json_payload.prompt = buffer_content + + " Define the curl command + let curl_command = 'curl -k -s -X POST -H "Content-Type: application/json" -d @- ' . url + let response = system(curl_command, json_encode(json_payload)) + + " Extract the content field from the response + let content = json_decode(response).content + + " Insert the content at the cursor position + call setline(line('.'), getline('.') . content) +endfunction + +command! Llm call Llm() diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 4b4d0397f..4aa25e87e 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -6,6 +6,7 @@ #include "common.h" #include "llama.h" #include "build-info.h" +#include "grammar-parser.h" #include #include @@ -93,8 +94,8 @@ int main(int argc, char ** argv) { } if (params.n_ctx > 2048) { - fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified);" - " you are on your own\n", __func__, params.n_ctx); + // TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048 + fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx); } else if (params.n_ctx < 8) { fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; @@ -139,17 +140,14 @@ int main(int argc, char ** argv) { params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } - // determine the maximum memory usage needed to do inference for the given n_batch and n_predict parameters + // determine the maximum memory usage needed to do inference for the given n_batch and n_ctx parameters // uncomment the "used_mem" line in llama.cpp to see the results if (params.mem_test) { { - const std::vector tmp(params.n_batch, llama_token_bos()); - llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); - } + fprintf(stderr, "%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx); - { - const std::vector tmp = { 0, }; - llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads); + const std::vector tmp(params.n_batch, llama_token_bos()); + llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads); } llama_print_timings(ctx); @@ -323,6 +321,10 @@ int main(int argc, char ** argv) { } } + if (params.input_prefix_bos) { + fprintf(stderr, "Input prefix with BOS\n"); + } + if (!params.input_prefix.empty()) { fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str()); } @@ -336,6 +338,31 @@ int main(int argc, char ** argv) { fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); fprintf(stderr, "\n\n"); + grammar_parser::parse_state parsed_grammar; + llama_grammar * grammar = NULL; + if (!params.grammar.empty()) { + parsed_grammar = grammar_parser::parse(params.grammar.c_str()); + // will be empty (default) if there are parse errors + if (parsed_grammar.rules.empty()) { + return 1; + } + fprintf(stderr, "%s: grammar:\n", __func__); + grammar_parser::print_grammar(stderr, parsed_grammar); + fprintf(stderr, "\n"); + + { + auto it = params.logit_bias.find(llama_token_eos()); + if (it != params.logit_bias.end() && it->second == -INFINITY) { + fprintf(stderr, + "%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__); + } + } + + std::vector grammar_rules(parsed_grammar.c_rules()); + grammar = llama_grammar_init( + grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + } + // TODO: replace with ring-buffer std::vector last_n_tokens(n_ctx); std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); @@ -569,6 +596,10 @@ int main(int argc, char ** argv) { logits[llama_token_nl()] = nl_logit; } + if (grammar != NULL) { + llama_sample_grammar(ctx, &candidates_p, grammar); + } + if (temp <= 0) { // Greedy sampling id = llama_sample_token_greedy(ctx, &candidates_p); @@ -594,20 +625,14 @@ int main(int argc, char ** argv) { } // printf("`%d`", candidates_p.size); + if (grammar != NULL) { + llama_grammar_accept_token(ctx, grammar, id); + } + last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(id); } - // replace end of text token with newline token when in interactive mode - if (id == llama_token_eos() && params.interactive && !params.instruct) { - id = llama_token_newline.front(); - if (params.antiprompt.size() != 0) { - // tokenize and inject first reverse prompt - const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false); - embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); - } - } - // add it to the context embd.push_back(id); @@ -673,11 +698,34 @@ int main(int argc, char ** argv) { } } + // deal with end of text token in interactive mode + if (last_n_tokens.back() == llama_token_eos()) { + if (params.interactive) { + if (params.antiprompt.size() != 0) { + // tokenize and inject first reverse prompt + const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false); + embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); + is_antiprompt = true; + } + + is_interacting = true; + printf("\n"); + console_set_color(con_st, CONSOLE_COLOR_USER_INPUT); + fflush(stdout); + } else if (params.instruct) { + is_interacting = true; + } + } + if (n_past > 0 && is_interacting) { if (params.instruct) { printf("\n> "); } + if (params.input_prefix_bos) { + embd_inp.push_back(llama_token_bos()); + } + std::string buffer; if (!params.input_prefix.empty()) { buffer += params.input_prefix; @@ -724,18 +772,26 @@ int main(int argc, char ** argv) { } if (n_past > 0) { + if (is_interacting) { + // reset grammar state if we're restarting generation + if (grammar != NULL) { + llama_grammar_free(grammar); + + std::vector grammar_rules( + parsed_grammar.c_rules()); + grammar = llama_grammar_init( + grammar_rules.data(), grammar_rules.size(), + parsed_grammar.symbol_ids.at("root")); + } + } is_interacting = false; } } // end of text token - if (!embd.empty() && embd.back() == llama_token_eos()) { - if (params.instruct) { - is_interacting = true; - } else { - fprintf(stderr, " [end of text]\n"); - break; - } + if (!embd.empty() && embd.back() == llama_token_eos() && !(params.instruct || params.interactive)) { + fprintf(stderr, " [end of text]\n"); + break; } // In interactive mode, respect the maximum number of tokens and drop back to user input when reached. @@ -755,6 +811,9 @@ int main(int argc, char ** argv) { llama_free(ctx); llama_free_model(model); + if (grammar != NULL) { + llama_grammar_free(grammar); + } llama_backend_free(); return 0; diff --git a/examples/make-ggml.py b/examples/make-ggml.py new file mode 100644 index 000000000..f63d9fc22 --- /dev/null +++ b/examples/make-ggml.py @@ -0,0 +1,92 @@ +""" +This script converts Hugging Face llama models to GGML and quantizes them. + +Usage: +python make-ggml.py --model {model_dir_or_hf_repo_name} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)] + +Arguments: +- --model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub. +- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used. +- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used. +- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'. +- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created. + +Quant types: +- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M +- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L +- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M +- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M +- Q2_K: smallest, extreme quality loss - not recommended +- Q3_K: alias for Q3_K_M +- Q3_K_S: very small, very high quality loss +- Q3_K_M: very small, very high quality loss +- Q3_K_L: small, substantial quality loss +- Q4_K: alias for Q4_K_M +- Q4_K_S: small, significant quality loss +- Q4_K_M: medium, balanced quality - recommended +- Q5_K: alias for Q5_K_M +- Q5_K_S: large, low quality loss - recommended +- Q5_K_M: large, very low quality loss - recommended +- Q6_K: very large, extremely low quality loss +- Q8_0: very large, extremely low quality loss - not recommended +- F16: extremely large, virtually no quality loss - not recommended +- F32: absolutely huge, lossless - not recommended +""" +import subprocess +subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True) + +import argparse +import os +from huggingface_hub import snapshot_download + +def main(model, outname, outdir, quants, keep_fp16): + ggml_version = "v3" + + if not os.path.isdir(model): + print(f"Model not found at {model}. Downloading...") + try: + if outname is None: + outname = model.split('/')[-1] + model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache') + except Exception as e: + raise Exception(f"Could not download the model: {e}") + + if outdir is None: + outdir = f'../models/{outname}' + + if not os.path.isfile(f"{model}/config.json"): + raise Exception(f"Could not find config.json in {model}") + + os.makedirs(outdir, exist_ok=True) + + print("Building llama.cpp") + subprocess.run(f"cd .. && make quantize", shell=True, check=True) + + fp16 = f"{outdir}/{outname}.ggml{ggml_version}.fp16.bin" + + print(f"Making unquantised GGML at {fp16}") + if not os.path.isfile(fp16): + subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True) + else: + print(f"Unquantised GGML already exists at: {fp16}") + + print("Making quants") + for type in quants: + outfile = f"{outdir}/{outname}.ggml{ggml_version}.{type}.bin" + print(f"Making {type} : {outfile}") + subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True) + + if not keep_fp16: + os.remove(fp16) + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Convert/Quantize HF to GGML. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.') + parser.add_argument('--model', required=True, help='Downloaded model dir or Hugging Face model repo name') + parser.add_argument('--outname', default=None, help='Output model(s) name') + parser.add_argument('--outdir', default=None, help='Output directory') + parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types') + parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False) + + args = parser.parse_args() + + main(args.model, args.outname, args.outdir, args.quants, args.keep_fp16) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index bfad99939..d23b7e7f0 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -4,6 +4,7 @@ #include #include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -120,6 +121,77 @@ void perplexity(llama_context * ctx, const gpt_params & params) { printf("\n"); } +void perplexity_lines(llama_context * ctx, const gpt_params & params) { + // Calculates perplexity over each line of the prompt + + std::vector prompt_lines; + std::istringstream strstream(params.prompt); + std::string line; + + while (std::getline(strstream,line,'\n')) { + prompt_lines.push_back(line); + } + + const int n_vocab = llama_n_vocab(ctx); + + int counttotal = 0; + size_t n_lines = prompt_lines.size(); + + double nll = 0.0; + + fprintf(stderr, "%s: calculating perplexity over %lu lines\n", __func__, n_lines); + + printf("\nLine\tPPL line\tPPL cumulative\n"); + + for (size_t i = 0; i < n_lines; ++i) { + + // Tokenize and insert BOS at start + std::vector batch_embd = ::llama_tokenize(ctx, prompt_lines[i], true); + + size_t batch_size = batch_embd.size(); + + // Stop if line is too long + if( batch_size > (size_t)params.n_ctx ) { + fprintf(stderr, "%s : tokens in line %lu > n_ctxl\n", __func__, i); + return; + } + + if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return; + } + + const auto batch_logits = llama_get_logits(ctx); + std::vector logits; + logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); + + double nllline = 0.0; + int countline = 0; + + // Perplexity over second half of the line + for (size_t j = batch_size/2; j < batch_size - 1; ++j) { + // Calculate probability of next token, given the previous ones. + const std::vector tok_logits( + logits.begin() + (j + 0) * n_vocab, + logits.begin() + (j + 1) * n_vocab); + + const float prob = softmax(tok_logits)[batch_embd[ j + 1]]; + + nllline += -std::log(prob); + ++countline; + } + + nll += nllline; + counttotal += countline; + + // perplexity is e^(average negative log-likelihood) + printf("%lu\t%.8lf\t%.8lf\n", i + 1, std::exp(nllline/countline), std::exp(nll / counttotal) ); + fflush(stdout); + } + + printf("\n"); +} + int main(int argc, char ** argv) { gpt_params params; @@ -168,7 +240,11 @@ int main(int argc, char ** argv) { params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } - perplexity(ctx, params); + if (params.perplexity_lines) { + perplexity_lines(ctx, params); + } else { + perplexity(ctx, params); + } llama_print_timings(ctx); llama_free(ctx); diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 42707fad9..1b21d4d55 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -111,103 +111,176 @@ unsigned char index_html[] = { 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x78, 0x74, 0x61, - 0x72, 0x65, 0x61, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x70, 0x61, 0x64, 0x64, 0x69, 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1; @@ -125,10 +156,17 @@ const params = signal({ n_predict: 400, temperature: 0.7, - repeat_last_n: 256, - repeat_penalty: 1.18, - top_k: 40, - top_p: 0.5, + repeat_last_n: 256, // 0 = disable penalty, -1 = context size + repeat_penalty: 1.18, // 1.0 = disabled + top_k: 40, // <= 0 to use vocab size + top_p: 0.5, // 1.0 = disabled + tfs_z: 1.0, // 1.0 = disabled + typical_p: 1.0, // 1.0 = disabled + presence_penalty: 0.0, // 0.0 = disabled + frequency_penalty: 0.0, // 0.0 = disabled + mirostat: 0, // 0/1/2 + mirostat_tau: 5, // target entropy + mirostat_eta: 0.1, // learning rate }) const llamaStats = signal(null) @@ -264,6 +302,27 @@ const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value } const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value } const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) } + const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) } + + const FloatField = ({label, max, min, name, step, value}) => { + return html` +
+ + + ${value} +
+ ` + }; + + const IntField = ({label, max, min, name, value}) => { + return html` +
+ + + ${value} +
+ ` + }; return html`
@@ -272,7 +331,9 @@