forked from wylab/llama.cpp
Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 slaren
|
20e12112fd | ||
|
slaren
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bf60f27cda |
@@ -48,23 +48,10 @@
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}
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},
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{
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"name": "arm64-apple-clang", "hidden": true,
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"architecture": { "value": "arm64", "strategy": "external" },
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"toolset": { "value": "host=x64", "strategy": "external" },
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"cacheVariables": {
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"CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-apple-clang.cmake"
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}
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},
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{ "name": "arm64-windows-llvm-debug" , "inherits": [ "base", "arm64-windows-llvm", "debug" ] },
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{ "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] },
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{ "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] },
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{ "name": "arm64-apple-clang-debug" , "inherits": [ "base", "arm64-apple-clang", "debug" ] },
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{ "name": "arm64-apple-clang-release" , "inherits": [ "base", "arm64-apple-clang", "reldbg" ] },
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{ "name": "arm64-apple-clang+static-release" , "inherits": [ "base", "arm64-apple-clang", "reldbg", "static" ] },
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{ "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] },
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{ "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg" ] },
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{ "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg", "static" ] },
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@@ -1,6 +1,7 @@
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# Define the default target now so that it is always the first target
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BUILD_TARGETS = \
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libllava.a \
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llama-baby-llama \
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llama-batched \
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llama-batched-bench \
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llama-bench \
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@@ -55,6 +56,7 @@ TEST_TARGETS = \
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tests/test-llama-grammar \
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tests/test-log \
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tests/test-model-load-cancel \
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tests/test-opt \
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tests/test-quantize-fns \
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tests/test-quantize-perf \
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tests/test-rope \
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@@ -62,7 +64,6 @@ TEST_TARGETS = \
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tests/test-tokenizer-0 \
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tests/test-tokenizer-1-bpe \
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tests/test-tokenizer-1-spm
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# tests/test-opt \
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# Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned
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LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot convert-llama2c-to-ggml \
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@@ -915,7 +916,6 @@ endif # GGML_METAL
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OBJ_GGML += \
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ggml/src/ggml.o \
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ggml/src/ggml-cpu.o \
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ggml/src/ggml-alloc.o \
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ggml/src/ggml-backend.o \
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ggml/src/ggml-quants.o \
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@@ -936,6 +936,7 @@ OBJ_COMMON = \
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common/console.o \
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common/ngram-cache.o \
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common/sampling.o \
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common/train.o \
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common/build-info.o \
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common/json-schema-to-grammar.o
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@@ -1047,12 +1048,6 @@ ggml/src/ggml.o: \
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ggml/include/ggml.h
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$(CC) $(CFLAGS) -c $< -o $@
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ggml/src/ggml-cpu.o: \
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ggml/src/ggml-cpu.c \
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ggml/include/ggml.h \
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ggml/src/ggml-common.h
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$(CC) $(CFLAGS) -c $< -o $@
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ggml/src/ggml-alloc.o: \
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ggml/src/ggml-alloc.c \
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ggml/include/ggml.h \
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@@ -1218,6 +1213,11 @@ common/json-schema-to-grammar.o: \
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common/json-schema-to-grammar.h
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$(CXX) $(CXXFLAGS) -c $< -o $@
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common/train.o: \
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common/train.cpp \
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common/train.h
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$(CXX) $(CXXFLAGS) -c $< -o $@
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common/ngram-cache.o: \
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common/ngram-cache.cpp \
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common/ngram-cache.h
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@@ -1390,6 +1390,11 @@ llama-bench: examples/llama-bench/llama-bench.cpp \
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
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llama-baby-llama: examples/baby-llama/baby-llama.cpp \
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$(OBJ_ALL)
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
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llama-export-lora: examples/export-lora/export-lora.cpp \
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$(OBJ_ALL)
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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@@ -10,7 +10,6 @@ var sources = [
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"src/unicode.cpp",
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"src/unicode-data.cpp",
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"ggml/src/ggml.c",
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"ggml/src/ggml-cpu.c",
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"ggml/src/ggml-alloc.c",
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"ggml/src/ggml-backend.cpp",
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"ggml/src/ggml-quants.c",
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@@ -326,36 +326,36 @@ function gg_run_open_llama_7b_v2 {
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./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
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./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
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(time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
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(time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
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(time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
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(time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
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(time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
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(time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
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(time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
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(time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
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(time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
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(time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
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(time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
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||||
(time ./bin/llama-cli --model ${model_f16} -t 1 -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
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(time ./bin/llama-cli --model ${model_q8_0} -t 1 -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
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||||
(time ./bin/llama-cli --model ${model_q4_0} -t 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_0.log
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(time ./bin/llama-cli --model ${model_q4_1} -t 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
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(time ./bin/llama-cli --model ${model_q5_0} -t 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_0.log
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(time ./bin/llama-cli --model ${model_q5_1} -t 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
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(time ./bin/llama-cli --model ${model_q2_k} -t 1 -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
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||||
(time ./bin/llama-cli --model ${model_q3_k} -t 1 -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
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||||
(time ./bin/llama-cli --model ${model_q4_k} -t 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_k.log
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||||
(time ./bin/llama-cli --model ${model_q5_k} -t 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_k.log
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||||
(time ./bin/llama-cli --model ${model_q6_k} -t 1 -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
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(time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
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(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
|
||||
(time ./bin/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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
|
||||
|
||||
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
|
||||
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
|
||||
|
||||
(time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
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(time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
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(time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
|
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function check_ppl {
|
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qnt="$1"
|
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@@ -460,34 +460,34 @@ function gg_run_pythia_1_4b {
|
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./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
|
||||
./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
|
||||
|
||||
(time ./bin/llama-cli --model ${model_f16} -ngl 99 -c 0 -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/llama-cli --model ${model_q8_0} -ngl 99 -c 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/llama-cli --model ${model_q4_0} -ngl 99 -c 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/llama-cli --model ${model_q4_1} -ngl 99 -c 0 -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/llama-cli --model ${model_q5_0} -ngl 99 -c 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/llama-cli --model ${model_q5_1} -ngl 99 -c 0 -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/llama-cli --model ${model_q2_k} -ngl 99 -c 0 -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/llama-cli --model ${model_q3_k} -ngl 99 -c 0 -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/llama-cli --model ${model_q4_k} -ngl 99 -c 0 -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/llama-cli --model ${model_q5_k} -ngl 99 -c 0 -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/llama-cli --model ${model_q6_k} -ngl 99 -c 0 -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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
|
||||
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
|
||||
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
|
||||
|
||||
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
|
||||
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
|
||||
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
|
||||
function check_ppl {
|
||||
qnt="$1"
|
||||
@@ -591,36 +591,36 @@ function gg_run_pythia_2_8b {
|
||||
./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
|
||||
./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
|
||||
|
||||
(time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -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/llama-cli --model ${model_f16} -t 1 -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/llama-cli --model ${model_q8_0} -t 1 -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/llama-cli --model ${model_q4_0} -t 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_0.log
|
||||
(time ./bin/llama-cli --model ${model_q4_1} -t 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/llama-cli --model ${model_q5_0} -t 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_0.log
|
||||
(time ./bin/llama-cli --model ${model_q5_1} -t 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/llama-cli --model ${model_q2_k} -t 1 -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/llama-cli --model ${model_q3_k} -t 1 -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/llama-cli --model ${model_q4_k} -t 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_k.log
|
||||
(time ./bin/llama-cli --model ${model_q5_k} -t 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_k.log
|
||||
(time ./bin/llama-cli --model ${model_q6_k} -t 1 -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/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
|
||||
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
|
||||
(time ./bin/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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/llama-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
|
||||
|
||||
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
|
||||
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
|
||||
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
|
||||
function check_ppl {
|
||||
qnt="$1"
|
||||
@@ -706,8 +706,8 @@ function gg_run_embd_bge_small {
|
||||
|
||||
./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
|
||||
|
||||
(time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
|
||||
(time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
(time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
|
||||
(time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
|
||||
set +e
|
||||
}
|
||||
@@ -752,7 +752,7 @@ function gg_run_rerank_tiny {
|
||||
model_f16="${path_models}/ggml-model-f16.gguf"
|
||||
|
||||
# for this model, the SEP token is "</s>"
|
||||
(time ./bin/llama-embedding --model ${model_f16} -p "what is panda?</s></s>hi\nwhat is panda?</s></s>it's a bear\nwhat is panda?</s></s>The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
|
||||
(time ./bin/llama-embedding --model ${model_f16} -p "what is panda?</s></s>hi\nwhat is panda?</s></s>it's a bear\nwhat is panda?</s></s>The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
|
||||
|
||||
# sample output
|
||||
# rerank score 0: 0.029
|
||||
|
||||
@@ -1,16 +0,0 @@
|
||||
set( CMAKE_SYSTEM_NAME Darwin )
|
||||
set( CMAKE_SYSTEM_PROCESSOR arm64 )
|
||||
|
||||
set( target arm64-apple-darwin-macho )
|
||||
|
||||
set( CMAKE_C_COMPILER clang )
|
||||
set( CMAKE_CXX_COMPILER clang++ )
|
||||
|
||||
set( CMAKE_C_COMPILER_TARGET ${target} )
|
||||
set( CMAKE_CXX_COMPILER_TARGET ${target} )
|
||||
|
||||
set( arch_c_flags "-march=armv8.4-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
|
||||
set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function" )
|
||||
|
||||
set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )
|
||||
set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )
|
||||
@@ -66,6 +66,8 @@ add_library(${TARGET} STATIC
|
||||
ngram-cache.h
|
||||
sampling.cpp
|
||||
sampling.h
|
||||
train.cpp
|
||||
train.h
|
||||
)
|
||||
|
||||
if (BUILD_SHARED_LIBS)
|
||||
|
||||
@@ -1951,8 +1951,6 @@ void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const cha
|
||||
|
||||
void yaml_dump_non_result_info(FILE * stream, const common_params & params, const llama_context * lctx,
|
||||
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
|
||||
ggml_cpu_init(); // some ARM features are detected at runtime
|
||||
|
||||
const auto & sparams = params.sparams;
|
||||
|
||||
fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT);
|
||||
|
||||
+1
-1
@@ -155,7 +155,7 @@ struct common_sampler_params {
|
||||
|
||||
struct common_params {
|
||||
int32_t n_predict = -1; // new tokens to predict
|
||||
int32_t n_ctx = 4096; // context size
|
||||
int32_t n_ctx = 0; // context size
|
||||
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
|
||||
int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
|
||||
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
||||
|
||||
+1515
File diff suppressed because it is too large
Load Diff
+233
@@ -0,0 +1,233 @@
|
||||
// Various helper functions and utilities for training
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <string>
|
||||
#include <random>
|
||||
#include <vector>
|
||||
|
||||
#include "ggml.h"
|
||||
#include "llama.h"
|
||||
|
||||
#define LLAMA_TRAIN_MAX_NODES 16384
|
||||
|
||||
typedef std::string mt19937_state;
|
||||
|
||||
struct train_state {
|
||||
struct ggml_opt_context * opt;
|
||||
|
||||
uint64_t train_its;
|
||||
uint64_t train_samples;
|
||||
uint64_t train_tokens;
|
||||
uint64_t train_epochs;
|
||||
|
||||
size_t shuffle_samples_hash; // fn, sample_count, *zip(sample_begins, sample_sizes)
|
||||
mt19937_state shuffle_rng_state_current;
|
||||
mt19937_state shuffle_rng_state_next;
|
||||
size_t shuffle_sample_count;
|
||||
size_t shuffle_next_sample;
|
||||
};
|
||||
|
||||
struct train_params_common {
|
||||
const char * fn_train_data;
|
||||
const char * fn_checkpoint_in;
|
||||
const char * fn_checkpoint_out;
|
||||
const char * pattern_fn_it;
|
||||
const char * fn_latest;
|
||||
|
||||
bool print_usage;
|
||||
|
||||
int save_every;
|
||||
|
||||
uint32_t seed;
|
||||
|
||||
int n_ctx;
|
||||
int n_threads;
|
||||
int n_batch;
|
||||
int n_gradient_accumulation;
|
||||
int n_epochs;
|
||||
int n_gpu_layers;
|
||||
|
||||
bool custom_n_ctx;
|
||||
|
||||
bool use_flash;
|
||||
bool use_checkpointing;
|
||||
|
||||
std::string sample_start;
|
||||
bool include_sample_start;
|
||||
bool escape;
|
||||
bool overlapping_samples;
|
||||
bool fill_with_next_samples;
|
||||
bool separate_with_eos;
|
||||
bool separate_with_bos;
|
||||
bool sample_random_offsets;
|
||||
|
||||
bool force_reshuffle;
|
||||
|
||||
int warmup;
|
||||
int cos_decay_steps;
|
||||
float cos_decay_restart;
|
||||
float cos_decay_min;
|
||||
bool enable_restart;
|
||||
|
||||
int opt_past;
|
||||
float opt_delta;
|
||||
int opt_max_no_improvement;
|
||||
|
||||
int adam_n_iter;
|
||||
float adam_alpha;
|
||||
float adam_min_alpha;
|
||||
float adam_decay;
|
||||
int adam_decay_min_ndim;
|
||||
float adam_beta1;
|
||||
float adam_beta2;
|
||||
float adam_gclip;
|
||||
float adam_eps_f;
|
||||
};
|
||||
|
||||
typedef void (*save_train_files_callback)(void * data, struct train_state * train);
|
||||
|
||||
struct train_opt_callback_data {
|
||||
struct train_params_common * params;
|
||||
struct train_state * train;
|
||||
save_train_files_callback save_cb;
|
||||
void * save_data;
|
||||
struct llama_context * lctx;
|
||||
int last_save_iter;
|
||||
llama_token * tokens_data;
|
||||
size_t tokens_size;
|
||||
size_t * samples_begin;
|
||||
size_t * samples_size;
|
||||
size_t * shuffled_samples_offs;
|
||||
size_t * shuffled_samples_begin;
|
||||
size_t * shuffled_samples_size;
|
||||
size_t samples_count;
|
||||
struct ggml_tensor * tokens_input;
|
||||
struct ggml_tensor * target_probs;
|
||||
int first_iter;
|
||||
int first_epoch;
|
||||
int iter_at_last_epoch;
|
||||
int64_t last_time;
|
||||
double millis_per_iter;
|
||||
};
|
||||
|
||||
struct train_state * init_train_state();
|
||||
void free_train_state(struct train_state * state);
|
||||
|
||||
struct train_params_common get_default_train_params_common();
|
||||
void print_common_train_usage(int /*argc*/, char ** argv, const struct train_params_common * params);
|
||||
|
||||
bool consume_common_train_arg(int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param);
|
||||
void finish_processing_train_args(struct train_params_common * params);
|
||||
|
||||
struct random_normal_distribution;
|
||||
struct random_uniform_distribution;
|
||||
|
||||
struct random_normal_distribution * init_random_normal_distribution (int seed, float mean, float std, float min, float max);
|
||||
struct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max);
|
||||
|
||||
void free_random_normal_distribution (struct random_normal_distribution * rnd);
|
||||
void free_random_uniform_distribution(struct random_uniform_distribution * rnd);
|
||||
|
||||
struct ggml_tensor * randomize_tensor_normal (struct ggml_tensor * tensor, struct random_normal_distribution * rnd);
|
||||
struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd);
|
||||
|
||||
// generate random float in interval [0,1)
|
||||
float frand();
|
||||
float frand_normal (struct random_normal_distribution * rnd);
|
||||
float frand_uniform(struct random_uniform_distribution * rnd);
|
||||
|
||||
int clamp (const int v, const int min, const int max);
|
||||
float fclamp(const float v, const float min, const float max);
|
||||
|
||||
void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0);
|
||||
void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1);
|
||||
void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2);
|
||||
void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3);
|
||||
|
||||
size_t tokenize_file(
|
||||
struct llama_context * lctx,
|
||||
const char * filename,
|
||||
const std::string & sample_start,
|
||||
bool include_sample_start,
|
||||
bool overlapping_samples,
|
||||
unsigned context_length,
|
||||
std::vector<llama_token> & out_tokens,
|
||||
std::vector<size_t> & out_samples_begin,
|
||||
std::vector<size_t> & out_samples_size);
|
||||
|
||||
int64_t get_example_targets_batch(
|
||||
struct llama_context * lctx,
|
||||
struct ggml_tensor * tokens_input,
|
||||
struct ggml_tensor * target_probs,
|
||||
int64_t example_id,
|
||||
const size_t * samples_offs,
|
||||
const size_t * samples_begin,
|
||||
const size_t * samples_size,
|
||||
size_t samples_count,
|
||||
const llama_token * train_data,
|
||||
size_t n_train_data,
|
||||
bool separate_with_eos,
|
||||
bool separate_with_bos,
|
||||
bool fill_with_next_samples,
|
||||
bool sample_random_offsets);
|
||||
|
||||
|
||||
void mt19937_set_state(std::mt19937& rng, const mt19937_state& rng_state);
|
||||
mt19937_state mt19937_get_state(const std::mt19937& rng);
|
||||
mt19937_state mt19937_seed_to_state(unsigned seed);
|
||||
|
||||
mt19937_state shuffle_samples(
|
||||
const mt19937_state & rng_state,
|
||||
size_t * shuffled_offs,
|
||||
size_t * shuffled_begins,
|
||||
size_t * shuffled_sizes,
|
||||
const size_t * begins,
|
||||
const size_t * sizes,
|
||||
size_t count);
|
||||
|
||||
size_t hash_combine(size_t h1, size_t h2);
|
||||
|
||||
size_t compute_samples_hash(
|
||||
const char* fn,
|
||||
const size_t* samples_begin,
|
||||
const size_t* samples_size,
|
||||
size_t sample_count);
|
||||
|
||||
|
||||
std::string replace_str(const char * s, const char * needle, const char * replacement);
|
||||
|
||||
void print_duration(double milliseconds);
|
||||
|
||||
float cosine_decay(
|
||||
int64_t step,
|
||||
int64_t decay_steps,
|
||||
float minimum);
|
||||
|
||||
float cosine_decay_restart(
|
||||
int64_t step,
|
||||
int64_t decay_steps,
|
||||
float minimum,
|
||||
float restart_step_mult);
|
||||
|
||||
float learning_schedule(
|
||||
int64_t step,
|
||||
int64_t warmup_steps,
|
||||
int64_t decay_steps,
|
||||
float learning_rate,
|
||||
float overall_minimum,
|
||||
float cos_decay_minimum,
|
||||
float cos_decay_restart_step_mult,
|
||||
bool enable_restart);
|
||||
|
||||
void copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name);
|
||||
|
||||
void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt);
|
||||
void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt);
|
||||
|
||||
bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train);
|
||||
void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train);
|
||||
|
||||
std::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration);
|
||||
|
||||
void train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel);
|
||||
+13
-14
@@ -72,8 +72,7 @@ class Model:
|
||||
def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False,
|
||||
use_temp_file: bool = False, eager: bool = False,
|
||||
metadata_override: Path | None = None, model_name: str | None = None,
|
||||
split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
|
||||
small_first_shard: bool = False, hparams: dict[str, Any] | None = None):
|
||||
split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
|
||||
if type(self) is Model:
|
||||
raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
|
||||
|
||||
@@ -88,7 +87,7 @@ class Model:
|
||||
self.is_safetensors = len(self.part_names) > 0
|
||||
if not self.is_safetensors:
|
||||
self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
|
||||
self.hparams = Model.load_hparams(self.dir_model) if hparams is None else hparams
|
||||
self.hparams = Model.load_hparams(self.dir_model)
|
||||
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
|
||||
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
|
||||
self.tensor_names = None
|
||||
@@ -1542,17 +1541,6 @@ class LlamaModel(Model):
|
||||
special_vocab._set_special_token("eot", 32010)
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
|
||||
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
|
||||
if tokenizer_config_file.is_file():
|
||||
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
|
||||
tokenizer_config_json = json.load(f)
|
||||
if "add_prefix_space" in tokenizer_config_json:
|
||||
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
|
||||
|
||||
# Apply to granite small models only
|
||||
if self.hparams.get("vocab_size", 32000) == 49152:
|
||||
self.gguf_writer.add_add_bos_token(False)
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
super().set_gguf_parameters()
|
||||
hparams = self.hparams
|
||||
@@ -1569,6 +1557,17 @@ class LlamaModel(Model):
|
||||
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
|
||||
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
|
||||
|
||||
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
|
||||
if tokenizer_config_file.is_file():
|
||||
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
|
||||
tokenizer_config_json = json.load(f)
|
||||
if "add_prefix_space" in tokenizer_config_json:
|
||||
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
|
||||
|
||||
# Apply to granite small models only
|
||||
if self.hparams.get("vocab_size", 32000) == 49152:
|
||||
self.gguf_writer.add_add_bos_token(False)
|
||||
|
||||
@staticmethod
|
||||
def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
|
||||
if n_head_kv is not None and n_head != n_head_kv:
|
||||
|
||||
+10
-37
@@ -12,7 +12,6 @@ import json
|
||||
from math import prod
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast
|
||||
from transformers import AutoConfig
|
||||
|
||||
import torch
|
||||
|
||||
@@ -257,8 +256,8 @@ def parse_args() -> argparse.Namespace:
|
||||
help="only print out what will be done, without writing any new files",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--base", type=Path,
|
||||
help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config",
|
||||
"--base", type=Path, required=True,
|
||||
help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required",
|
||||
)
|
||||
parser.add_argument(
|
||||
"lora_path", type=Path,
|
||||
@@ -268,12 +267,6 @@ def parse_args() -> argparse.Namespace:
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def load_hparams_from_hf(hf_model_id: str) -> dict[str, Any]:
|
||||
# normally, adapter does not come with base model config, we need to load it from AutoConfig
|
||||
config = AutoConfig.from_pretrained(hf_model_id)
|
||||
return config.to_dict()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_args()
|
||||
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
|
||||
@@ -288,7 +281,7 @@ if __name__ == '__main__':
|
||||
|
||||
ftype = ftype_map[args.outtype]
|
||||
|
||||
dir_base_model: Path | None = args.base
|
||||
dir_base_model: Path = args.base
|
||||
dir_lora: Path = args.lora_path
|
||||
lora_config = dir_lora / "adapter_config.json"
|
||||
input_model = dir_lora / "adapter_model.safetensors"
|
||||
@@ -308,29 +301,9 @@ if __name__ == '__main__':
|
||||
input_model = os.path.join(dir_lora, "adapter_model.bin")
|
||||
lora_model = torch.load(input_model, map_location="cpu", weights_only=True)
|
||||
|
||||
# load LoRA config
|
||||
with open(lora_config, "r") as f:
|
||||
lparams: dict[str, Any] = json.load(f)
|
||||
|
||||
# load base model
|
||||
if dir_base_model is None:
|
||||
if "base_model_name_or_path" in lparams:
|
||||
model_id = lparams["base_model_name_or_path"]
|
||||
logger.info(f"Loading base model from Hugging Face: {model_id}")
|
||||
try:
|
||||
hparams = load_hparams_from_hf(model_id)
|
||||
except OSError as e:
|
||||
logger.error(f"Failed to load base model config: {e}")
|
||||
logger.error("Please try downloading the base model and add its path to --base")
|
||||
sys.exit(1)
|
||||
else:
|
||||
logger.error("'base_model_name_or_path' is not found in adapter_config.json")
|
||||
logger.error("Base model config is required. Please download the base model and add its path to --base")
|
||||
sys.exit(1)
|
||||
else:
|
||||
logger.info(f"Loading base model: {dir_base_model.name}")
|
||||
hparams = Model.load_hparams(dir_base_model)
|
||||
|
||||
logger.info(f"Loading base model: {dir_base_model.name}")
|
||||
hparams = Model.load_hparams(dir_base_model)
|
||||
with torch.inference_mode():
|
||||
try:
|
||||
model_class = Model.from_model_architecture(hparams["architectures"][0])
|
||||
@@ -350,15 +323,13 @@ if __name__ == '__main__':
|
||||
self.dir_model_card = dir_lora_model
|
||||
self.lora_alpha = float(lora_alpha)
|
||||
|
||||
def set_vocab(self):
|
||||
pass
|
||||
|
||||
def set_type(self):
|
||||
self.gguf_writer.add_type(gguf.GGUFType.ADAPTER)
|
||||
self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
|
||||
super().set_gguf_parameters()
|
||||
|
||||
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
|
||||
# Never add extra tensors (e.g. rope_freqs) for LoRA adapters
|
||||
@@ -379,7 +350,7 @@ if __name__ == '__main__':
|
||||
logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
|
||||
if ".embed_tokens.weight" in name or ".lm_head.weight" in name:
|
||||
logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning")
|
||||
logger.error("Please refer to https://github.com/ggerganov/llama.cpp/pull/9948")
|
||||
logger.error("Hint: if you are using TRL, make sure not to call setup_chat_format()")
|
||||
sys.exit(1)
|
||||
|
||||
if base_name in tensor_map:
|
||||
@@ -413,6 +384,9 @@ if __name__ == '__main__':
|
||||
yield (dest_name + ".lora_a", lora_a)
|
||||
yield (dest_name + ".lora_b", lora_b)
|
||||
|
||||
with open(lora_config, "r") as f:
|
||||
lparams: dict[str, Any] = json.load(f)
|
||||
|
||||
alpha: float = lparams["lora_alpha"]
|
||||
|
||||
model_instance = LoraModel(
|
||||
@@ -425,7 +399,6 @@ if __name__ == '__main__':
|
||||
dry_run=args.dry_run,
|
||||
dir_lora_model=dir_lora,
|
||||
lora_alpha=alpha,
|
||||
hparams=hparams,
|
||||
)
|
||||
|
||||
logger.info("Exporting model...")
|
||||
|
||||
@@ -13,6 +13,7 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR})
|
||||
if (EMSCRIPTEN)
|
||||
else()
|
||||
add_subdirectory(cvector-generator)
|
||||
add_subdirectory(baby-llama)
|
||||
add_subdirectory(batched-bench)
|
||||
add_subdirectory(batched)
|
||||
add_subdirectory(convert-llama2c-to-ggml)
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
set(TARGET llama-baby-llama)
|
||||
add_executable(${TARGET} baby-llama.cpp)
|
||||
install(TARGETS ${TARGET} RUNTIME)
|
||||
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
|
||||
target_compile_features(${TARGET} PRIVATE cxx_std_11)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -4,7 +4,6 @@
|
||||
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
|
||||
#include "clip.h"
|
||||
#include "ggml.h"
|
||||
#include "ggml-cpu.h"
|
||||
#include "ggml-alloc.h"
|
||||
#include "ggml-backend.h"
|
||||
|
||||
|
||||
@@ -1,5 +1,3 @@
|
||||
#include "ggml-cpu.h"
|
||||
|
||||
#ifdef GGML_USE_CUDA
|
||||
#include "ggml-cuda.h"
|
||||
#endif
|
||||
|
||||
+17
-15
@@ -247,7 +247,6 @@ struct server_slot {
|
||||
if (is_processing()) {
|
||||
SLT_INF(*this, "stop processing: n_past = %d, truncated = %d\n", n_past, truncated);
|
||||
|
||||
t_last_used = ggml_time_us();
|
||||
t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
|
||||
state = SLOT_STATE_IDLE;
|
||||
callback_on_release(id);
|
||||
@@ -731,7 +730,7 @@ struct server_context {
|
||||
|
||||
// find the slot that has at least n% prompt similarity
|
||||
if (ret == nullptr && slot_prompt_similarity != 0.0f) {
|
||||
int lcs_len = 0;
|
||||
int max_lcs_len = 0;
|
||||
float similarity = 0;
|
||||
|
||||
for (server_slot & slot : slots) {
|
||||
@@ -746,21 +745,20 @@ struct server_context {
|
||||
}
|
||||
|
||||
// length of the Longest Common Subsequence between the current slot's prompt and the input prompt
|
||||
int cur_lcs_len = longest_common_subsequence(slot.cache_tokens, task.prompt_tokens);
|
||||
int lcs_len = longest_common_subsequence(slot.cache_tokens, task.prompt_tokens);
|
||||
|
||||
// fraction of the common subsequence length compared to the current slot's prompt length
|
||||
float cur_similarity = static_cast<float>(cur_lcs_len) / static_cast<int>(slot.cache_tokens.size());
|
||||
similarity = static_cast<float>(lcs_len) / static_cast<int>(slot.cache_tokens.size());
|
||||
|
||||
// select the current slot if the criteria match
|
||||
if (cur_lcs_len > lcs_len && cur_similarity > slot_prompt_similarity) {
|
||||
lcs_len = cur_lcs_len;
|
||||
similarity = cur_similarity;
|
||||
if (lcs_len > max_lcs_len && similarity > slot_prompt_similarity) {
|
||||
max_lcs_len = lcs_len;
|
||||
ret = &slot;
|
||||
}
|
||||
}
|
||||
|
||||
if (ret != nullptr) {
|
||||
SLT_DBG(*ret, "selected slot by lcs similarity, lcs_len = %d, similarity = %f\n", lcs_len, similarity);
|
||||
SLT_DBG(*ret, "selected slot by lcs similarity, max_lcs_len = %d, similarity = %f\n", max_lcs_len, similarity);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -2705,8 +2703,8 @@ int main(int argc, char ** argv) {
|
||||
};
|
||||
|
||||
const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_inf_type inf_type, json & data, httplib::Response & res) {
|
||||
if (ctx_server.params.embedding) {
|
||||
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
|
||||
if (ctx_server.params.embedding || ctx_server.params.reranking) {
|
||||
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -2811,8 +2809,8 @@ int main(int argc, char ** argv) {
|
||||
|
||||
// TODO: maybe merge this function with "handle_completions_generic"
|
||||
const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &res_ok, verbose](const httplib::Request & req, httplib::Response & res) {
|
||||
if (ctx_server.params.embedding) {
|
||||
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
|
||||
if (ctx_server.params.embedding || ctx_server.params.reranking) {
|
||||
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -2937,6 +2935,11 @@ int main(int argc, char ** argv) {
|
||||
};
|
||||
|
||||
const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
|
||||
// TODO: somehow clean up this checks in the future
|
||||
if (!ctx_server.params.embedding || ctx_server.params.reranking) {
|
||||
res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings` and without `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
|
||||
return;
|
||||
}
|
||||
const json body = json::parse(req.body);
|
||||
bool is_openai = false;
|
||||
|
||||
@@ -2988,11 +2991,10 @@ int main(int argc, char ** argv) {
|
||||
};
|
||||
|
||||
const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
|
||||
if (!ctx_server.params.reranking || ctx_server.params.embedding) {
|
||||
res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED));
|
||||
if (!ctx_server.params.reranking) {
|
||||
res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
|
||||
return;
|
||||
}
|
||||
|
||||
const json body = json::parse(req.body);
|
||||
|
||||
// TODO: implement
|
||||
|
||||
@@ -453,20 +453,20 @@ static size_t longest_common_subsequence(const llama_tokens & a, const llama_tok
|
||||
}
|
||||
|
||||
// get the lengths of the input sequences
|
||||
size_t a_len = a.size();
|
||||
size_t b_len = b.size();
|
||||
int a_len = a.size();
|
||||
int b_len = b.size();
|
||||
|
||||
// initialize the maximum length of the longest common subsequence (LCS)
|
||||
size_t max_length = 0;
|
||||
int max_length = 0;
|
||||
|
||||
// use two rows instead of a 2D matrix to optimize space
|
||||
std::vector<size_t> prev_row(b_len + 1, 0);
|
||||
std::vector<size_t> curr_row(b_len + 1, 0);
|
||||
std::vector<int> prev_row(b_len + 1, 0);
|
||||
std::vector<int> curr_row(b_len + 1, 0);
|
||||
|
||||
// iterate through the elements of a
|
||||
for (size_t i = 1; i <= a_len; i++) {
|
||||
for (int i = 1; i <= a_len; i++) {
|
||||
// iterate through the elements of b
|
||||
for (size_t j = 1; j <= b_len; j++) {
|
||||
for (int j = 1; j <= b_len; j++) {
|
||||
// if elements at the current positions match
|
||||
if (a[i - 1] == b[j - 1]) {
|
||||
// if it's the first element of either sequences, set LCS length to 1
|
||||
|
||||
@@ -96,7 +96,7 @@ int main(int argc, char ** argv) {
|
||||
// tokenize the prompt
|
||||
const int n_prompt_tokens = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
|
||||
std::vector<llama_token> prompt_tokens(n_prompt_tokens);
|
||||
if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), llama_get_kv_cache_used_cells(ctx) == 0, true) < 0) {
|
||||
if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
|
||||
GGML_ABORT("failed to tokenize the prompt\n");
|
||||
}
|
||||
|
||||
|
||||
Generated
+10
-10
@@ -5,11 +5,11 @@
|
||||
"nixpkgs-lib": "nixpkgs-lib"
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1730504689,
|
||||
"narHash": "sha256-hgmguH29K2fvs9szpq2r3pz2/8cJd2LPS+b4tfNFCwE=",
|
||||
"lastModified": 1727826117,
|
||||
"narHash": "sha256-K5ZLCyfO/Zj9mPFldf3iwS6oZStJcU4tSpiXTMYaaL0=",
|
||||
"owner": "hercules-ci",
|
||||
"repo": "flake-parts",
|
||||
"rev": "506278e768c2a08bec68eb62932193e341f55c90",
|
||||
"rev": "3d04084d54bedc3d6b8b736c70ef449225c361b1",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
@@ -20,11 +20,11 @@
|
||||
},
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1730200266,
|
||||
"narHash": "sha256-l253w0XMT8nWHGXuXqyiIC/bMvh1VRszGXgdpQlfhvU=",
|
||||
"lastModified": 1729665710,
|
||||
"narHash": "sha256-AlcmCXJZPIlO5dmFzV3V2XF6x/OpNWUV8Y/FMPGd8Z4=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "807e9154dcb16384b1b765ebe9cd2bba2ac287fd",
|
||||
"rev": "2768c7d042a37de65bb1b5b3268fc987e534c49d",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
@@ -36,14 +36,14 @@
|
||||
},
|
||||
"nixpkgs-lib": {
|
||||
"locked": {
|
||||
"lastModified": 1730504152,
|
||||
"narHash": "sha256-lXvH/vOfb4aGYyvFmZK/HlsNsr/0CVWlwYvo2rxJk3s=",
|
||||
"lastModified": 1727825735,
|
||||
"narHash": "sha256-0xHYkMkeLVQAMa7gvkddbPqpxph+hDzdu1XdGPJR+Os=",
|
||||
"type": "tarball",
|
||||
"url": "https://github.com/NixOS/nixpkgs/archive/cc2f28000298e1269cea6612cd06ec9979dd5d7f.tar.gz"
|
||||
"url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz"
|
||||
},
|
||||
"original": {
|
||||
"type": "tarball",
|
||||
"url": "https://github.com/NixOS/nixpkgs/archive/cc2f28000298e1269cea6612cd06ec9979dd5d7f.tar.gz"
|
||||
"url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz"
|
||||
}
|
||||
},
|
||||
"root": {
|
||||
|
||||
@@ -305,10 +305,27 @@ extern "C" {
|
||||
GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
|
||||
GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
|
||||
|
||||
// CPU buffer types are always available
|
||||
//
|
||||
// CPU backend
|
||||
//
|
||||
|
||||
GGML_API ggml_backend_t ggml_backend_cpu_init(void);
|
||||
|
||||
GGML_API bool ggml_backend_is_cpu (ggml_backend_t backend);
|
||||
GGML_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads);
|
||||
GGML_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
|
||||
GGML_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
|
||||
|
||||
// Create a backend buffer from an existing pointer
|
||||
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
|
||||
|
||||
GGML_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
|
||||
|
||||
#ifdef GGML_USE_CPU_HBM
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
|
||||
#endif
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
@@ -1,150 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
#include "ggml-backend.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
// Scheduling priorities
|
||||
enum ggml_sched_priority {
|
||||
GGML_SCHED_PRIO_NORMAL,
|
||||
GGML_SCHED_PRIO_MEDIUM,
|
||||
GGML_SCHED_PRIO_HIGH,
|
||||
GGML_SCHED_PRIO_REALTIME
|
||||
};
|
||||
|
||||
// Threadpool params
|
||||
// Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults
|
||||
struct ggml_threadpool_params {
|
||||
bool cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings)
|
||||
int n_threads; // number of threads
|
||||
enum ggml_sched_priority prio; // thread priority
|
||||
uint32_t poll; // polling level (0 - no polling, 100 - aggressive polling)
|
||||
bool strict_cpu; // strict cpu placement
|
||||
bool paused; // start in paused state
|
||||
};
|
||||
|
||||
struct ggml_threadpool; // forward declaration, see ggml.c
|
||||
|
||||
typedef struct ggml_threadpool * ggml_threadpool_t;
|
||||
|
||||
// the compute plan that needs to be prepared for ggml_graph_compute()
|
||||
// since https://github.com/ggerganov/ggml/issues/287
|
||||
struct ggml_cplan {
|
||||
size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()`
|
||||
uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
|
||||
|
||||
int n_threads;
|
||||
struct ggml_threadpool * threadpool;
|
||||
|
||||
// abort ggml_graph_compute when true
|
||||
ggml_abort_callback abort_callback;
|
||||
void * abort_callback_data;
|
||||
};
|
||||
|
||||
// numa strategies
|
||||
enum ggml_numa_strategy {
|
||||
GGML_NUMA_STRATEGY_DISABLED = 0,
|
||||
GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
|
||||
GGML_NUMA_STRATEGY_ISOLATE = 2,
|
||||
GGML_NUMA_STRATEGY_NUMACTL = 3,
|
||||
GGML_NUMA_STRATEGY_MIRROR = 4,
|
||||
GGML_NUMA_STRATEGY_COUNT
|
||||
};
|
||||
|
||||
GGML_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems
|
||||
GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
|
||||
GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
|
||||
GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
|
||||
|
||||
GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
|
||||
GGML_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
|
||||
|
||||
GGML_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
|
||||
GGML_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value);
|
||||
|
||||
GGML_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
|
||||
GGML_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
|
||||
|
||||
GGML_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
|
||||
GGML_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value);
|
||||
|
||||
GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads);
|
||||
GGML_API void ggml_threadpool_params_init (struct ggml_threadpool_params * p, int n_threads);
|
||||
GGML_API bool ggml_threadpool_params_match (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1);
|
||||
GGML_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params);
|
||||
GGML_API void ggml_threadpool_free (struct ggml_threadpool * threadpool);
|
||||
GGML_API int ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool);
|
||||
GGML_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool);
|
||||
GGML_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool);
|
||||
|
||||
// ggml_graph_plan() has to be called before ggml_graph_compute()
|
||||
// when plan.work_size > 0, caller must allocate memory for plan.work_data
|
||||
GGML_API struct ggml_cplan ggml_graph_plan(
|
||||
const struct ggml_cgraph * cgraph,
|
||||
int n_threads, /* = GGML_DEFAULT_N_THREADS */
|
||||
struct ggml_threadpool * threadpool /* = NULL */ );
|
||||
GGML_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
|
||||
|
||||
// same as ggml_graph_compute() but the work data is allocated as a part of the context
|
||||
// note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
|
||||
GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
|
||||
|
||||
// TODO: move to backend interface
|
||||
GGML_API int ggml_cpu_has_neon (void);
|
||||
GGML_API int ggml_cpu_has_sve (void);
|
||||
GGML_API int ggml_cpu_has_matmul_int8(void);
|
||||
// get the sve vector length in bytes
|
||||
GGML_API int ggml_cpu_get_sve_cnt(void);
|
||||
|
||||
// Internal types and functions exposed for tests and benchmarks
|
||||
|
||||
typedef void (*ggml_from_float_to_mat_t)
|
||||
(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs);
|
||||
typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
|
||||
const void * GGML_RESTRICT y, size_t by, int nrc);
|
||||
typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
|
||||
const void * GGML_RESTRICT y, int nr, int nc);
|
||||
typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
|
||||
const void * GGML_RESTRICT y, int nr, int nc);
|
||||
|
||||
struct ggml_type_traits_cpu {
|
||||
ggml_from_float_to_mat_t from_float_to_mat;
|
||||
ggml_vec_dot_t vec_dot;
|
||||
enum ggml_type vec_dot_type;
|
||||
int64_t nrows; // number of rows to process simultaneously
|
||||
int64_t ncols; // number of columns to process simultaneously
|
||||
ggml_gemv_t gemv;
|
||||
ggml_gemm_t gemm;
|
||||
};
|
||||
|
||||
GGML_API const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type);
|
||||
|
||||
GGML_API void ggml_cpu_init(void);
|
||||
|
||||
//
|
||||
// CPU backend
|
||||
//
|
||||
|
||||
GGML_API ggml_backend_t ggml_backend_cpu_init(void);
|
||||
|
||||
GGML_API bool ggml_backend_is_cpu (ggml_backend_t backend);
|
||||
GGML_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads);
|
||||
GGML_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
|
||||
GGML_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
|
||||
|
||||
GGML_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
|
||||
|
||||
#ifdef GGML_USE_CPU_HBM
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
|
||||
#endif
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
+132
-19
@@ -573,13 +573,6 @@ extern "C" {
|
||||
GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up)
|
||||
};
|
||||
|
||||
struct ggml_init_params {
|
||||
// memory pool
|
||||
size_t mem_size; // bytes
|
||||
void * mem_buffer; // if NULL, memory will be allocated internally
|
||||
bool no_alloc; // don't allocate memory for the tensor data
|
||||
};
|
||||
|
||||
// n-dimensional tensor
|
||||
struct ggml_tensor {
|
||||
enum ggml_type type;
|
||||
@@ -625,6 +618,59 @@ extern "C" {
|
||||
// If it returns true, the computation is aborted
|
||||
typedef bool (*ggml_abort_callback)(void * data);
|
||||
|
||||
// Scheduling priorities
|
||||
enum ggml_sched_priority {
|
||||
GGML_SCHED_PRIO_NORMAL,
|
||||
GGML_SCHED_PRIO_MEDIUM,
|
||||
GGML_SCHED_PRIO_HIGH,
|
||||
GGML_SCHED_PRIO_REALTIME
|
||||
};
|
||||
|
||||
// Threadpool params
|
||||
// Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults
|
||||
struct ggml_threadpool_params {
|
||||
bool cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings)
|
||||
int n_threads; // number of threads
|
||||
enum ggml_sched_priority prio; // thread priority
|
||||
uint32_t poll; // polling level (0 - no polling, 100 - aggressive polling)
|
||||
bool strict_cpu; // strict cpu placement
|
||||
bool paused; // start in paused state
|
||||
};
|
||||
|
||||
struct ggml_threadpool; // forward declaration, see ggml.c
|
||||
|
||||
typedef struct ggml_threadpool * ggml_threadpool_t;
|
||||
|
||||
// the compute plan that needs to be prepared for ggml_graph_compute()
|
||||
// since https://github.com/ggerganov/ggml/issues/287
|
||||
struct ggml_cplan {
|
||||
size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()`
|
||||
uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
|
||||
|
||||
int n_threads;
|
||||
struct ggml_threadpool * threadpool;
|
||||
|
||||
// abort ggml_graph_compute when true
|
||||
ggml_abort_callback abort_callback;
|
||||
void * abort_callback_data;
|
||||
};
|
||||
|
||||
struct ggml_init_params {
|
||||
// memory pool
|
||||
size_t mem_size; // bytes
|
||||
void * mem_buffer; // if NULL, memory will be allocated internally
|
||||
bool no_alloc; // don't allocate memory for the tensor data
|
||||
};
|
||||
|
||||
// numa strategies
|
||||
enum ggml_numa_strategy {
|
||||
GGML_NUMA_STRATEGY_DISABLED = 0,
|
||||
GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
|
||||
GGML_NUMA_STRATEGY_ISOLATE = 2,
|
||||
GGML_NUMA_STRATEGY_NUMACTL = 3,
|
||||
GGML_NUMA_STRATEGY_MIRROR = 4,
|
||||
GGML_NUMA_STRATEGY_COUNT
|
||||
};
|
||||
|
||||
//
|
||||
// GUID
|
||||
@@ -647,6 +693,9 @@ extern "C" {
|
||||
// accepts a UTF-8 path, even on Windows
|
||||
GGML_API FILE * ggml_fopen(const char * fname, const char * mode);
|
||||
|
||||
GGML_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems
|
||||
GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node
|
||||
|
||||
GGML_API void ggml_print_object (const struct ggml_object * obj);
|
||||
GGML_API void ggml_print_objects(const struct ggml_context * ctx);
|
||||
|
||||
@@ -748,7 +797,8 @@ extern "C" {
|
||||
int64_t ne2,
|
||||
int64_t ne3);
|
||||
|
||||
GGML_API void * ggml_new_buffer(struct ggml_context * ctx, size_t nbytes);
|
||||
GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
|
||||
GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
|
||||
GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
|
||||
@@ -758,25 +808,35 @@ extern "C" {
|
||||
GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
|
||||
|
||||
// Converts a flat index into coordinates
|
||||
GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3);
|
||||
GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
|
||||
GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
|
||||
|
||||
GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
|
||||
// Converts a flat index into coordinates
|
||||
GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3);
|
||||
|
||||
GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
|
||||
GGML_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
|
||||
|
||||
GGML_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
|
||||
GGML_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value);
|
||||
|
||||
GGML_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
|
||||
GGML_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
|
||||
|
||||
GGML_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
|
||||
GGML_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value);
|
||||
|
||||
GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
|
||||
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name);
|
||||
GGML_ATTRIBUTE_FORMAT(2, 3)
|
||||
GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...);
|
||||
|
||||
// Tensor flags
|
||||
GGML_API void ggml_set_input(struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_set_output(struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
|
||||
|
||||
//
|
||||
// operations on tensors with backpropagation
|
||||
//
|
||||
@@ -1992,6 +2052,9 @@ extern "C" {
|
||||
// automatic differentiation
|
||||
//
|
||||
|
||||
GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate);
|
||||
|
||||
@@ -2023,6 +2086,27 @@ extern "C" {
|
||||
GGML_API size_t ggml_graph_overhead(void);
|
||||
GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads);
|
||||
|
||||
GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads);
|
||||
GGML_API void ggml_threadpool_params_init (struct ggml_threadpool_params * p, int n_threads);
|
||||
GGML_API bool ggml_threadpool_params_match (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1);
|
||||
GGML_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params);
|
||||
GGML_API void ggml_threadpool_free (struct ggml_threadpool * threadpool);
|
||||
GGML_API int ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool);
|
||||
GGML_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool);
|
||||
GGML_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool);
|
||||
|
||||
// ggml_graph_plan() has to be called before ggml_graph_compute()
|
||||
// when plan.work_size > 0, caller must allocate memory for plan.work_data
|
||||
GGML_API struct ggml_cplan ggml_graph_plan(
|
||||
const struct ggml_cgraph * cgraph,
|
||||
int n_threads, /* = GGML_DEFAULT_N_THREADS */
|
||||
struct ggml_threadpool * threadpool /* = NULL */ );
|
||||
GGML_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
|
||||
|
||||
// same as ggml_graph_compute() but the work data is allocated as a part of the context
|
||||
// note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
|
||||
GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
|
||||
|
||||
GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);
|
||||
@@ -2193,8 +2277,6 @@ extern "C" {
|
||||
} lbfgs;
|
||||
};
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
|
||||
|
||||
// optimize the function defined by the tensor f
|
||||
@@ -2226,6 +2308,12 @@ extern "C" {
|
||||
ggml_opt_callback callback,
|
||||
void * callback_data);
|
||||
|
||||
//
|
||||
// tensor flags
|
||||
//
|
||||
GGML_API void ggml_set_input(struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_set_output(struct ggml_tensor * tensor);
|
||||
|
||||
//
|
||||
// quantization
|
||||
//
|
||||
@@ -2394,6 +2482,8 @@ extern "C" {
|
||||
GGML_API int ggml_cpu_has_avx512_bf16(void);
|
||||
GGML_API int ggml_cpu_has_amx_int8 (void);
|
||||
GGML_API int ggml_cpu_has_fma (void);
|
||||
GGML_API int ggml_cpu_has_neon (void);
|
||||
GGML_API int ggml_cpu_has_sve (void);
|
||||
GGML_API int ggml_cpu_has_arm_fma (void);
|
||||
GGML_API int ggml_cpu_has_metal (void);
|
||||
GGML_API int ggml_cpu_has_f16c (void);
|
||||
@@ -2410,9 +2500,17 @@ extern "C" {
|
||||
GGML_API int ggml_cpu_has_sycl (void);
|
||||
GGML_API int ggml_cpu_has_rpc (void);
|
||||
GGML_API int ggml_cpu_has_vsx (void);
|
||||
GGML_API int ggml_cpu_has_matmul_int8(void);
|
||||
GGML_API int ggml_cpu_has_cann (void);
|
||||
GGML_API int ggml_cpu_has_llamafile (void);
|
||||
|
||||
// get the sve vector length in bytes
|
||||
GGML_API int ggml_cpu_get_sve_cnt(void);
|
||||
|
||||
//
|
||||
// Internal types and functions exposed for tests and benchmarks
|
||||
//
|
||||
|
||||
#ifdef __cplusplus
|
||||
// restrict not standard in C++
|
||||
#define GGML_RESTRICT
|
||||
@@ -2421,6 +2519,14 @@ extern "C" {
|
||||
#endif
|
||||
typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
typedef void (*ggml_from_float_to_mat_t)
|
||||
(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs);
|
||||
typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
|
||||
const void * GGML_RESTRICT y, size_t by, int nrc);
|
||||
typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
|
||||
const void * GGML_RESTRICT y, int nr, int nc);
|
||||
typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
|
||||
const void * GGML_RESTRICT y, int nr, int nc);
|
||||
|
||||
struct ggml_type_traits {
|
||||
const char * type_name;
|
||||
@@ -2431,6 +2537,13 @@ extern "C" {
|
||||
ggml_to_float_t to_float;
|
||||
ggml_from_float_t from_float;
|
||||
ggml_from_float_t from_float_ref;
|
||||
ggml_from_float_to_mat_t from_float_to_mat;
|
||||
ggml_vec_dot_t vec_dot;
|
||||
enum ggml_type vec_dot_type;
|
||||
int64_t nrows; // number of rows to process simultaneously
|
||||
int64_t ncols; // number of columns to process simultaneously
|
||||
ggml_gemv_t gemv;
|
||||
ggml_gemm_t gemm;
|
||||
};
|
||||
|
||||
GGML_API const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type);
|
||||
|
||||
@@ -1366,12 +1366,10 @@ endif()
|
||||
|
||||
add_library(ggml
|
||||
../include/ggml.h
|
||||
../include/ggml-cpu.h
|
||||
../include/ggml-alloc.h
|
||||
../include/ggml-backend.h
|
||||
../include/ggml-cpp.h
|
||||
ggml.c
|
||||
ggml-cpu.c
|
||||
ggml-alloc.c
|
||||
ggml-backend.cpp
|
||||
ggml-quants.c
|
||||
@@ -1396,7 +1394,7 @@ if (EMSCRIPTEN)
|
||||
endif()
|
||||
|
||||
target_compile_definitions(ggml PUBLIC ${GGML_CDEF_PUBLIC})
|
||||
target_include_directories(ggml PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)
|
||||
target_include_directories(ggml PUBLIC ../include)
|
||||
target_include_directories(ggml PRIVATE . ${GGML_EXTRA_INCLUDES})
|
||||
target_link_directories (ggml PRIVATE ${GGML_EXTRA_LIBDIRS})
|
||||
target_compile_features (ggml PRIVATE c_std_11) # don't bump
|
||||
|
||||
@@ -7,7 +7,6 @@
|
||||
|
||||
#include "ggml-quants.h"
|
||||
#include "ggml-impl.h"
|
||||
#include "ggml-cpu.h"
|
||||
#include "ggml-cpu-impl.h"
|
||||
|
||||
#include <math.h>
|
||||
|
||||
+610
-627
File diff suppressed because it is too large
Load Diff
@@ -1227,6 +1227,7 @@ static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggm
|
||||
|
||||
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(hostPtr, size);
|
||||
buffer->buft = buft;
|
||||
buffer->iface.get_name = ggml_backend_cann_host_buffer_name;
|
||||
buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free;
|
||||
|
||||
return buffer;
|
||||
|
||||
-13715
File diff suppressed because it is too large
Load Diff
@@ -1297,17 +1297,11 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
|
||||
cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0);
|
||||
if (err != cudaErrorPeerAccessAlreadyEnabled) {
|
||||
CUDA_CHECK(err);
|
||||
} else {
|
||||
// reset the error
|
||||
cudaGetLastError();
|
||||
}
|
||||
} else {
|
||||
cudaError_t err = cudaDeviceDisablePeerAccess(id_other);
|
||||
if (err != cudaErrorPeerAccessNotEnabled) {
|
||||
CUDA_CHECK(err);
|
||||
} else {
|
||||
// reset the error
|
||||
cudaGetLastError();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -8,7 +8,6 @@
|
||||
#include <stdlib.h> // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/
|
||||
#include <stdbool.h>
|
||||
#include <stdint.h>
|
||||
#include <string.h>
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
@@ -37,20 +36,6 @@ extern "C" {
|
||||
#endif
|
||||
#endif
|
||||
|
||||
static inline int ggml_up32(int n) {
|
||||
return (n + 31) & ~31;
|
||||
}
|
||||
|
||||
//static inline int ggml_up64(int n) {
|
||||
// return (n + 63) & ~63;
|
||||
//}
|
||||
|
||||
static inline int ggml_up(int n, int m) {
|
||||
// assert m is a power of 2
|
||||
GGML_ASSERT((m & (m - 1)) == 0);
|
||||
return (n + m - 1) & ~(m - 1);
|
||||
}
|
||||
|
||||
//
|
||||
// logging
|
||||
//
|
||||
@@ -66,74 +51,6 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi
|
||||
#define GGML_LOG_DEBUG(...) ggml_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
|
||||
#define GGML_LOG_CONT(...) ggml_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__)
|
||||
|
||||
#define GGML_DEBUG 0
|
||||
|
||||
#if (GGML_DEBUG >= 1)
|
||||
#define GGML_PRINT_DEBUG(...) GGML_LOG_DEBUG(__VA_ARGS__)
|
||||
#else
|
||||
#define GGML_PRINT_DEBUG(...)
|
||||
#endif
|
||||
|
||||
#if (GGML_DEBUG >= 5)
|
||||
#define GGML_PRINT_DEBUG_5(...) GGML_LOG_DEBUG(__VA_ARGS__)
|
||||
#else
|
||||
#define GGML_PRINT_DEBUG_5(...)
|
||||
#endif
|
||||
|
||||
#if (GGML_DEBUG >= 10)
|
||||
#define GGML_PRINT_DEBUG_10(...) GGML_LOG_DEBUG(__VA_ARGS__)
|
||||
#else
|
||||
#define GGML_PRINT_DEBUG_10(...)
|
||||
#endif
|
||||
|
||||
// tensor params
|
||||
|
||||
static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
|
||||
GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings
|
||||
assert(params_size <= GGML_MAX_OP_PARAMS);
|
||||
memcpy(tensor->op_params, params, params_size);
|
||||
}
|
||||
|
||||
static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) {
|
||||
assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
|
||||
return ((const int32_t *)(tensor->op_params))[i];
|
||||
}
|
||||
|
||||
static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) {
|
||||
assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
|
||||
return ((const float *)(tensor->op_params))[i];
|
||||
}
|
||||
|
||||
static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
|
||||
assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
|
||||
((int32_t *)(tensor->op_params))[i] = value;
|
||||
}
|
||||
|
||||
static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) {
|
||||
assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
|
||||
((float *)(tensor->op_params))[i] = value;
|
||||
}
|
||||
|
||||
struct ggml_map_custom1_op_params {
|
||||
ggml_custom1_op_t fun;
|
||||
int n_tasks;
|
||||
void * userdata;
|
||||
};
|
||||
|
||||
|
||||
struct ggml_map_custom2_op_params {
|
||||
ggml_custom2_op_t fun;
|
||||
int n_tasks;
|
||||
void * userdata;
|
||||
};
|
||||
|
||||
|
||||
struct ggml_map_custom3_op_params {
|
||||
ggml_custom3_op_t fun;
|
||||
int n_tasks;
|
||||
void * userdata;
|
||||
};
|
||||
|
||||
// bitset
|
||||
|
||||
typedef uint32_t ggml_bitset_t;
|
||||
@@ -287,10 +204,6 @@ struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
|
||||
void * ggml_aligned_malloc(size_t size);
|
||||
void ggml_aligned_free(void * ptr, size_t size);
|
||||
|
||||
// TODO: move to threading file
|
||||
void ggml_critical_section_start(void);
|
||||
void ggml_critical_section_end(void);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
@@ -450,14 +450,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
|
||||
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
#if !__has_feature(objc_arc)
|
||||
[options release];
|
||||
#endif
|
||||
}
|
||||
#if GGML_METAL_EMBED_LIBRARY
|
||||
[src release];
|
||||
#endif // GGML_METAL_EMBED_LIBRARY
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
+447
-438
@@ -12,436 +12,6 @@ using namespace metal;
|
||||
|
||||
#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
|
||||
|
||||
constexpr constant static float kvalues_iq4nl_f[16] = {
|
||||
-127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
|
||||
};
|
||||
|
||||
// NOTE: this is not dequantizing - we are simply fitting the template
|
||||
template <typename type4x4>
|
||||
void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
|
||||
reg = (type4x4)(*src);
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) {
|
||||
reg = (type4x4)(*src);
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 1);
|
||||
const float d1 = il ? (xb->d / 16.h) : xb->d;
|
||||
const float d2 = d1 / 256.f;
|
||||
const float md = -8.h * xb->d;
|
||||
const ushort mask0 = il ? 0x00F0 : 0x000F;
|
||||
const ushort mask1 = mask0 << 8;
|
||||
|
||||
for (int i=0;i<8;i++) {
|
||||
reg[i/2][2*(i%2)+0] = d1 * (qs[i] & mask0) + md;
|
||||
reg[i/2][2*(i%2)+1] = d2 * (qs[i] & mask1) + md;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 2);
|
||||
const float d1 = il ? (xb->d / 16.h) : xb->d;
|
||||
const float d2 = d1 / 256.f;
|
||||
const float m = xb->m;
|
||||
const ushort mask0 = il ? 0x00F0 : 0x000F;
|
||||
const ushort mask1 = mask0 << 8;
|
||||
|
||||
for (int i=0;i<8;i++) {
|
||||
reg[i/2][2*(i%2)+0] = ((qs[i] & mask0) * d1) + m;
|
||||
reg[i/2][2*(i%2)+1] = ((qs[i] & mask1) * d2) + m;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_0(device const block_q5_0 *xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 3);
|
||||
const float d = xb->d;
|
||||
const float md = -16.h * xb->d;
|
||||
const ushort mask = il ? 0x00F0 : 0x000F;
|
||||
|
||||
const uint32_t qh = *((device const uint32_t *)xb->qh);
|
||||
|
||||
const int x_mv = il ? 4 : 0;
|
||||
|
||||
const int gh_mv = il ? 12 : 0;
|
||||
const int gh_bk = il ? 0 : 4;
|
||||
|
||||
for (int i = 0; i < 8; i++) {
|
||||
// extract the 5-th bits for x0 and x1
|
||||
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
|
||||
|
||||
// combine the 4-bits from qs with the 5th bit
|
||||
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
|
||||
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
|
||||
|
||||
reg[i/2][2*(i%2)+0] = d * x0 + md;
|
||||
reg[i/2][2*(i%2)+1] = d * x1 + md;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_1(device const block_q5_1 *xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 4);
|
||||
const float d = xb->d;
|
||||
const float m = xb->m;
|
||||
const ushort mask = il ? 0x00F0 : 0x000F;
|
||||
|
||||
const uint32_t qh = *((device const uint32_t *)xb->qh);
|
||||
|
||||
const int x_mv = il ? 4 : 0;
|
||||
|
||||
const int gh_mv = il ? 12 : 0;
|
||||
const int gh_bk = il ? 0 : 4;
|
||||
|
||||
for (int i = 0; i < 8; i++) {
|
||||
// extract the 5-th bits for x0 and x1
|
||||
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
|
||||
|
||||
// combine the 4-bits from qs with the 5th bit
|
||||
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
|
||||
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
|
||||
|
||||
reg[i/2][2*(i%2)+0] = d * x0 + m;
|
||||
reg[i/2][2*(i%2)+1] = d * x1 + m;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) {
|
||||
device const int8_t * qs = ((device const int8_t *)xb->qs);
|
||||
const half d = xb->d;
|
||||
|
||||
for (int i = 0; i < 16; i++) {
|
||||
reg[i/4][i%4] = (qs[i + 16*il] * d);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) {
|
||||
const float d = xb->d;
|
||||
const float min = xb->dmin;
|
||||
device const uint8_t * q = (device const uint8_t *)xb->qs;
|
||||
float dl, ml;
|
||||
uint8_t sc = xb->scales[il];
|
||||
|
||||
q = q + 32*(il/8) + 16*(il&1);
|
||||
il = (il/2)%4;
|
||||
|
||||
half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
|
||||
uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
|
||||
dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4);
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) {
|
||||
const half d_all = xb->d;
|
||||
device const uint8_t * q = (device const uint8_t *)xb->qs;
|
||||
device const uint8_t * h = (device const uint8_t *)xb->hmask;
|
||||
device const int8_t * scales = (device const int8_t *)xb->scales;
|
||||
|
||||
q = q + 32 * (il/8) + 16 * (il&1);
|
||||
h = h + 16 * (il&1);
|
||||
uint8_t m = 1 << (il/2);
|
||||
uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \
|
||||
((il/4)>0 ? 12 : 3);
|
||||
uint16_t kmask2 = il/8 ? 0xF0 : 0x0F;
|
||||
uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4];
|
||||
int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2)
|
||||
: (scale_2&kmask2) | ((scale_1&kmask1) << 4);
|
||||
float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f);
|
||||
const float ml = 4.f * dl;
|
||||
|
||||
il = (il/2) & 3;
|
||||
const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
|
||||
const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
|
||||
dl *= coef;
|
||||
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml);
|
||||
}
|
||||
}
|
||||
|
||||
static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) {
|
||||
return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)}
|
||||
: uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))};
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) {
|
||||
device const uchar * q = xb->qs;
|
||||
|
||||
short is = (il/4) * 2;
|
||||
q = q + (il/4) * 32 + 16 * (il&1);
|
||||
il = il & 3;
|
||||
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
|
||||
const float d = il < 2 ? xb->d : xb->d / 16.h;
|
||||
const float min = xb->dmin;
|
||||
const float dl = d * sc[0];
|
||||
const float ml = min * sc[1];
|
||||
|
||||
const ushort mask = il<2 ? 0x0F : 0xF0;
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) {
|
||||
device const uint8_t * q = xb->qs;
|
||||
device const uint8_t * qh = xb->qh;
|
||||
|
||||
short is = (il/4) * 2;
|
||||
q = q + 32 * (il/4) + 16 * (il&1);
|
||||
qh = qh + 16 * (il&1);
|
||||
uint8_t ul = 1 << (il/2);
|
||||
il = il & 3;
|
||||
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
|
||||
const float d = il < 2 ? xb->d : xb->d / 16.f;
|
||||
const float min = xb->dmin;
|
||||
const float dl = d * sc[0];
|
||||
const float ml = min * sc[1];
|
||||
|
||||
const ushort mask = il<2 ? 0x0F : 0xF0;
|
||||
const float qh_val = il<2 ? 16.f : 256.f;
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) {
|
||||
const half d_all = xb->d;
|
||||
device const uint8_t * ql = (device const uint8_t *)xb->ql;
|
||||
device const uint8_t * qh = (device const uint8_t *)xb->qh;
|
||||
device const int8_t * scales = (device const int8_t *)xb->scales;
|
||||
|
||||
ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1);
|
||||
qh = qh + 32*(il/8) + 16*(il&1);
|
||||
float sc = scales[(il%2) + 2 * ((il/2))];
|
||||
il = (il/2) & 3;
|
||||
|
||||
const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
|
||||
const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F;
|
||||
const float coef = il>1 ? 1.f/16.f : 1.f;
|
||||
const float ml = d_all * sc * 32.f;
|
||||
const float dl = d_all * sc * coef;
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2))
|
||||
: ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4));
|
||||
reg[i/4][i%4] = dl * q - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
// each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's.
|
||||
device const uint16_t * q2 = xb->qs + 4*ib32;
|
||||
const uint32_t aux32_g = q2[0] | (q2[1] << 16);
|
||||
const uint32_t aux32_s = q2[2] | (q2[3] << 16);
|
||||
thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g;
|
||||
const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f;
|
||||
constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
|
||||
uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
|
||||
signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint16_t * q2 = xb->qs + 4*ib32;
|
||||
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
|
||||
constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511));
|
||||
uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511));
|
||||
signs = ksigns_iq2xs[q2[2*il+1] >> 9];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * q3 = xb->qs + 8*ib32;
|
||||
device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32;
|
||||
const uint32_t aux32 = gas[0] | (gas[1] << 16);
|
||||
const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]);
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]);
|
||||
uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
|
||||
reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
|
||||
}
|
||||
grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]);
|
||||
grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]);
|
||||
signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
|
||||
reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * qs = xb->qs + 8*ib32;
|
||||
device const uint8_t * signs = xb->signs + 4*ib32 + 2*il;
|
||||
const uint8_t qh = xb->qh[ib32] >> 4*il;
|
||||
const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf));
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]);
|
||||
reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]);
|
||||
}
|
||||
grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256)));
|
||||
grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]);
|
||||
reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint8_t * signs = qs + QK_K/8;
|
||||
const uint8_t qh = xb->qh[ib32] >> 4*il;
|
||||
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300)));
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]);
|
||||
reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
const float d = xb->d;
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint16_t * qh = xb->qh;
|
||||
const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1);
|
||||
const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA);
|
||||
const uint16_t h = qh[ib32] >> 6*il;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * (grid1[i] & 0xf) + ml;
|
||||
reg[1][i] = dl * (grid1[i] >> 4) + ml;
|
||||
reg[2][i] = dl * (grid2[i] & 0xf) + ml;
|
||||
reg[3][i] = dl * (grid2[i] >> 4) + ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
device const uint16_t * sc = (device const uint16_t *)xb->scales;
|
||||
|
||||
iq1m_scale_t scale;
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
const float d = scale.f16;
|
||||
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint8_t * qh = xb->qh + 2*ib32 + il;
|
||||
|
||||
const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
|
||||
const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * (grid1[i] & 0xf) + ml1;
|
||||
reg[1][i] = dl * (grid1[i] >> 4) + ml1;
|
||||
reg[2][i] = dl * (grid2[i] & 0xf) + ml2;
|
||||
reg[3][i] = dl * (grid2[i] >> 4) + ml2;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * q4 = (device const uint16_t *)xb->qs;
|
||||
const float d = xb->d;
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f;
|
||||
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
|
||||
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
|
||||
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
|
||||
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
|
||||
const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4);
|
||||
const float d = (float)xb->d * (ls - 32);
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
|
||||
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
|
||||
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
|
||||
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
|
||||
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
|
||||
}
|
||||
}
|
||||
|
||||
enum ggml_sort_order {
|
||||
GGML_SORT_ORDER_ASC,
|
||||
GGML_SORT_ORDER_DESC,
|
||||
@@ -3206,11 +2776,11 @@ kernel void kernel_flash_attn_ext_vec_f16(
|
||||
const short iv3 = iq3 / rv3;
|
||||
|
||||
// load the queries from shared memory into local memory
|
||||
float4 mq[D4/NW];
|
||||
float4 mq[D4];
|
||||
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
short i = ii + tiisg;
|
||||
mq[ii/NW] = (float4) sq4[i];
|
||||
mq[i] = (float4) sq4[i];
|
||||
}
|
||||
|
||||
// pointer to the mask
|
||||
@@ -3242,7 +2812,7 @@ kernel void kernel_flash_attn_ext_vec_f16(
|
||||
mk[2] = (float4) pk4[i + 2*(nb11/8)];
|
||||
mk[3] = (float4) pk4[i + 3*(nb11/8)];
|
||||
|
||||
mqk += (float4) (mq[ii/NW] * mk);
|
||||
mqk += (float4) (mq[i] * mk);
|
||||
}
|
||||
|
||||
// reduce the results from the threads in the simdgroup
|
||||
@@ -3287,7 +2857,8 @@ kernel void kernel_flash_attn_ext_vec_f16(
|
||||
// O = diag(ms)*O
|
||||
#pragma unroll
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
lo[ii/NW] *= ms;
|
||||
const short i = ii + tiisg;
|
||||
lo[i/NW] *= ms;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -3301,10 +2872,10 @@ kernel void kernel_flash_attn_ext_vec_f16(
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
const short i = ii + tiisg;
|
||||
|
||||
lo[ii/NW] += pv4[i + 0*(nb21/8)] * ss[4*cc + 0];
|
||||
lo[ii/NW] += pv4[i + 1*(nb21/8)] * ss[4*cc + 1];
|
||||
lo[ii/NW] += pv4[i + 2*(nb21/8)] * ss[4*cc + 2];
|
||||
lo[ii/NW] += pv4[i + 3*(nb21/8)] * ss[4*cc + 3];
|
||||
lo[i/NW] += pv4[i + 0*(nb21/8)] * ss[4*cc + 0];
|
||||
lo[i/NW] += pv4[i + 1*(nb21/8)] * ss[4*cc + 1];
|
||||
lo[i/NW] += pv4[i + 2*(nb21/8)] * ss[4*cc + 2];
|
||||
lo[i/NW] += pv4[i + 3*(nb21/8)] * ss[4*cc + 3];
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -3769,6 +3340,10 @@ static inline int best_index_int8(int n, constant float * val, float x) {
|
||||
return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
|
||||
}
|
||||
|
||||
constexpr constant static float kvalues_iq4nl_f[16] = {
|
||||
-127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
|
||||
};
|
||||
|
||||
kernel void kernel_cpy_f32_iq4_nl(
|
||||
device const float * src0,
|
||||
device void * dst,
|
||||
@@ -5883,6 +5458,440 @@ kernel void kernel_mul_mv_iq4_xs_f32(
|
||||
kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
|
||||
}
|
||||
|
||||
//============================= templates and their specializations =============================
|
||||
|
||||
// NOTE: this is not dequantizing - we are simply fitting the template
|
||||
template <typename type4x4>
|
||||
void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
|
||||
float4x4 temp = *(((device float4x4 *)src));
|
||||
for (int i = 0; i < 16; i++){
|
||||
reg[i/4][i%4] = temp[i/4][i%4];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) {
|
||||
half4x4 temp = *(((device half4x4 *)src));
|
||||
for (int i = 0; i < 16; i++){
|
||||
reg[i/4][i%4] = temp[i/4][i%4];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 1);
|
||||
const float d1 = il ? (xb->d / 16.h) : xb->d;
|
||||
const float d2 = d1 / 256.f;
|
||||
const float md = -8.h * xb->d;
|
||||
const ushort mask0 = il ? 0x00F0 : 0x000F;
|
||||
const ushort mask1 = mask0 << 8;
|
||||
|
||||
for (int i=0;i<8;i++) {
|
||||
reg[i/2][2*(i%2)+0] = d1 * (qs[i] & mask0) + md;
|
||||
reg[i/2][2*(i%2)+1] = d2 * (qs[i] & mask1) + md;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 2);
|
||||
const float d1 = il ? (xb->d / 16.h) : xb->d;
|
||||
const float d2 = d1 / 256.f;
|
||||
const float m = xb->m;
|
||||
const ushort mask0 = il ? 0x00F0 : 0x000F;
|
||||
const ushort mask1 = mask0 << 8;
|
||||
|
||||
for (int i=0;i<8;i++) {
|
||||
reg[i/2][2*(i%2)+0] = ((qs[i] & mask0) * d1) + m;
|
||||
reg[i/2][2*(i%2)+1] = ((qs[i] & mask1) * d2) + m;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_0(device const block_q5_0 *xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 3);
|
||||
const float d = xb->d;
|
||||
const float md = -16.h * xb->d;
|
||||
const ushort mask = il ? 0x00F0 : 0x000F;
|
||||
|
||||
const uint32_t qh = *((device const uint32_t *)xb->qh);
|
||||
|
||||
const int x_mv = il ? 4 : 0;
|
||||
|
||||
const int gh_mv = il ? 12 : 0;
|
||||
const int gh_bk = il ? 0 : 4;
|
||||
|
||||
for (int i = 0; i < 8; i++) {
|
||||
// extract the 5-th bits for x0 and x1
|
||||
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
|
||||
|
||||
// combine the 4-bits from qs with the 5th bit
|
||||
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
|
||||
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
|
||||
|
||||
reg[i/2][2*(i%2)+0] = d * x0 + md;
|
||||
reg[i/2][2*(i%2)+1] = d * x1 + md;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_1(device const block_q5_1 *xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 4);
|
||||
const float d = xb->d;
|
||||
const float m = xb->m;
|
||||
const ushort mask = il ? 0x00F0 : 0x000F;
|
||||
|
||||
const uint32_t qh = *((device const uint32_t *)xb->qh);
|
||||
|
||||
const int x_mv = il ? 4 : 0;
|
||||
|
||||
const int gh_mv = il ? 12 : 0;
|
||||
const int gh_bk = il ? 0 : 4;
|
||||
|
||||
for (int i = 0; i < 8; i++) {
|
||||
// extract the 5-th bits for x0 and x1
|
||||
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
|
||||
|
||||
// combine the 4-bits from qs with the 5th bit
|
||||
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
|
||||
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
|
||||
|
||||
reg[i/2][2*(i%2)+0] = d * x0 + m;
|
||||
reg[i/2][2*(i%2)+1] = d * x1 + m;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) {
|
||||
device const int8_t * qs = ((device const int8_t *)xb->qs);
|
||||
const half d = xb->d;
|
||||
|
||||
for (int i = 0; i < 16; i++) {
|
||||
reg[i/4][i%4] = (qs[i + 16*il] * d);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) {
|
||||
const float d = xb->d;
|
||||
const float min = xb->dmin;
|
||||
device const uint8_t * q = (device const uint8_t *)xb->qs;
|
||||
float dl, ml;
|
||||
uint8_t sc = xb->scales[il];
|
||||
|
||||
q = q + 32*(il/8) + 16*(il&1);
|
||||
il = (il/2)%4;
|
||||
|
||||
half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
|
||||
uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
|
||||
dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4);
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) {
|
||||
const half d_all = xb->d;
|
||||
device const uint8_t * q = (device const uint8_t *)xb->qs;
|
||||
device const uint8_t * h = (device const uint8_t *)xb->hmask;
|
||||
device const int8_t * scales = (device const int8_t *)xb->scales;
|
||||
|
||||
q = q + 32 * (il/8) + 16 * (il&1);
|
||||
h = h + 16 * (il&1);
|
||||
uint8_t m = 1 << (il/2);
|
||||
uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \
|
||||
((il/4)>0 ? 12 : 3);
|
||||
uint16_t kmask2 = il/8 ? 0xF0 : 0x0F;
|
||||
uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4];
|
||||
int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2)
|
||||
: (scale_2&kmask2) | ((scale_1&kmask1) << 4);
|
||||
float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f);
|
||||
const float ml = 4.f * dl;
|
||||
|
||||
il = (il/2) & 3;
|
||||
const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
|
||||
const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
|
||||
dl *= coef;
|
||||
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml);
|
||||
}
|
||||
}
|
||||
|
||||
static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) {
|
||||
return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)}
|
||||
: uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))};
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) {
|
||||
device const uchar * q = xb->qs;
|
||||
|
||||
short is = (il/4) * 2;
|
||||
q = q + (il/4) * 32 + 16 * (il&1);
|
||||
il = il & 3;
|
||||
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
|
||||
const float d = il < 2 ? xb->d : xb->d / 16.h;
|
||||
const float min = xb->dmin;
|
||||
const float dl = d * sc[0];
|
||||
const float ml = min * sc[1];
|
||||
|
||||
const ushort mask = il<2 ? 0x0F : 0xF0;
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) {
|
||||
device const uint8_t * q = xb->qs;
|
||||
device const uint8_t * qh = xb->qh;
|
||||
|
||||
short is = (il/4) * 2;
|
||||
q = q + 32 * (il/4) + 16 * (il&1);
|
||||
qh = qh + 16 * (il&1);
|
||||
uint8_t ul = 1 << (il/2);
|
||||
il = il & 3;
|
||||
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
|
||||
const float d = il < 2 ? xb->d : xb->d / 16.f;
|
||||
const float min = xb->dmin;
|
||||
const float dl = d * sc[0];
|
||||
const float ml = min * sc[1];
|
||||
|
||||
const ushort mask = il<2 ? 0x0F : 0xF0;
|
||||
const float qh_val = il<2 ? 16.f : 256.f;
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) {
|
||||
const half d_all = xb->d;
|
||||
device const uint8_t * ql = (device const uint8_t *)xb->ql;
|
||||
device const uint8_t * qh = (device const uint8_t *)xb->qh;
|
||||
device const int8_t * scales = (device const int8_t *)xb->scales;
|
||||
|
||||
ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1);
|
||||
qh = qh + 32*(il/8) + 16*(il&1);
|
||||
float sc = scales[(il%2) + 2 * ((il/2))];
|
||||
il = (il/2) & 3;
|
||||
|
||||
const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
|
||||
const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F;
|
||||
const float coef = il>1 ? 1.f/16.f : 1.f;
|
||||
const float ml = d_all * sc * 32.f;
|
||||
const float dl = d_all * sc * coef;
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2))
|
||||
: ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4));
|
||||
reg[i/4][i%4] = dl * q - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
// each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's.
|
||||
device const uint16_t * q2 = xb->qs + 4*ib32;
|
||||
const uint32_t aux32_g = q2[0] | (q2[1] << 16);
|
||||
const uint32_t aux32_s = q2[2] | (q2[3] << 16);
|
||||
thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g;
|
||||
const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f;
|
||||
constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
|
||||
uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
|
||||
signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint16_t * q2 = xb->qs + 4*ib32;
|
||||
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
|
||||
constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511));
|
||||
uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511));
|
||||
signs = ksigns_iq2xs[q2[2*il+1] >> 9];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * q3 = xb->qs + 8*ib32;
|
||||
device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32;
|
||||
const uint32_t aux32 = gas[0] | (gas[1] << 16);
|
||||
const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]);
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]);
|
||||
uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
|
||||
reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
|
||||
}
|
||||
grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]);
|
||||
grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]);
|
||||
signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
|
||||
reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * qs = xb->qs + 8*ib32;
|
||||
device const uint8_t * signs = xb->signs + 4*ib32 + 2*il;
|
||||
const uint8_t qh = xb->qh[ib32] >> 4*il;
|
||||
const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf));
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]);
|
||||
reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]);
|
||||
}
|
||||
grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256)));
|
||||
grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]);
|
||||
reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint8_t * signs = qs + QK_K/8;
|
||||
const uint8_t qh = xb->qh[ib32] >> 4*il;
|
||||
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300)));
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]);
|
||||
reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
const float d = xb->d;
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint16_t * qh = xb->qh;
|
||||
const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1);
|
||||
const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA);
|
||||
const uint16_t h = qh[ib32] >> 6*il;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * (grid1[i] & 0xf) + ml;
|
||||
reg[1][i] = dl * (grid1[i] >> 4) + ml;
|
||||
reg[2][i] = dl * (grid2[i] & 0xf) + ml;
|
||||
reg[3][i] = dl * (grid2[i] >> 4) + ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
device const uint16_t * sc = (device const uint16_t *)xb->scales;
|
||||
|
||||
iq1m_scale_t scale;
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
const float d = scale.f16;
|
||||
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint8_t * qh = xb->qh + 2*ib32 + il;
|
||||
|
||||
const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
|
||||
const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * (grid1[i] & 0xf) + ml1;
|
||||
reg[1][i] = dl * (grid1[i] >> 4) + ml1;
|
||||
reg[2][i] = dl * (grid2[i] & 0xf) + ml2;
|
||||
reg[3][i] = dl * (grid2[i] >> 4) + ml2;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * q4 = (device const uint16_t *)xb->qs;
|
||||
const float d = xb->d;
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f;
|
||||
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
|
||||
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
|
||||
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
|
||||
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
|
||||
const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4);
|
||||
const float d = (float)xb->d * (ls - 32);
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
|
||||
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
|
||||
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
|
||||
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
|
||||
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
|
||||
kernel void kernel_get_rows_q(
|
||||
device const void * src0,
|
||||
|
||||
@@ -1296,6 +1296,13 @@ static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_b
|
||||
UNUSED(dev);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_rpc_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
|
||||
return ggml_backend_cpu_buffer_from_ptr(ptr, size);
|
||||
|
||||
UNUSED(dev);
|
||||
UNUSED(max_tensor_size);
|
||||
}
|
||||
|
||||
static bool ggml_backend_rpc_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
|
||||
UNUSED(dev);
|
||||
UNUSED(op);
|
||||
@@ -1321,7 +1328,7 @@ static const struct ggml_backend_device_i ggml_backend_rpc_device_i = {
|
||||
/* .init_backend = */ ggml_backend_rpc_device_init,
|
||||
/* .get_buffer_type = */ ggml_backend_rpc_device_get_buffer_type,
|
||||
/* .get_host_buffer_type = */ NULL,
|
||||
/* .buffer_from_host_ptr = */ NULL,
|
||||
/* .buffer_from_host_ptr = */ ggml_backend_rpc_device_buffer_from_ptr,
|
||||
/* .supports_op = */ ggml_backend_rpc_device_supports_op,
|
||||
/* .supports_buft = */ ggml_backend_rpc_device_supports_buft,
|
||||
/* .offload_op = */ NULL,
|
||||
|
||||
+15158
-105
File diff suppressed because it is too large
Load Diff
@@ -2,7 +2,6 @@
|
||||
#define LLAMA_H
|
||||
|
||||
#include "ggml.h"
|
||||
#include "ggml-cpu.h"
|
||||
#include "ggml-backend.h"
|
||||
|
||||
#include <stddef.h>
|
||||
|
||||
+1
-2
@@ -11,7 +11,6 @@
|
||||
#include <type_traits>
|
||||
|
||||
#include <ggml.h>
|
||||
#include <ggml-cpu.h>
|
||||
|
||||
constexpr int kVecSize = 1 << 16;
|
||||
|
||||
@@ -137,7 +136,7 @@ int main(int argc, char** argv) {
|
||||
|
||||
auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1;
|
||||
|
||||
const auto * funcs = ggml_get_type_traits_cpu(ggml_type);
|
||||
const auto * funcs = ggml_get_type_traits(ggml_type);
|
||||
|
||||
Stat simple, ggml;
|
||||
|
||||
|
||||
+4
-6
@@ -9,7 +9,6 @@
|
||||
#include <array>
|
||||
|
||||
#include <ggml.h>
|
||||
#include <ggml-cpu.h>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
@@ -237,8 +236,7 @@ int main(int argc, char** argv) {
|
||||
int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64);
|
||||
int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64);
|
||||
|
||||
const auto * funcs = ggml_get_type_traits(useQ4_1 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q4_0);
|
||||
const auto * funcs_cpu = ggml_get_type_traits_cpu(useQ4_1 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q4_0);
|
||||
const auto * funcs = useQ4_1 ? ggml_get_type_traits(GGML_TYPE_Q4_1) : ggml_get_type_traits(GGML_TYPE_Q4_0);
|
||||
|
||||
std::vector<block_q4_0> q40;
|
||||
std::vector<block_q4_1> q41;
|
||||
@@ -284,10 +282,10 @@ int main(int argc, char** argv) {
|
||||
dot_q4_q8(kVecSize, &result, q40.data(), q8.data());
|
||||
}
|
||||
else {
|
||||
const auto * vdot = ggml_get_type_traits(funcs_cpu->vec_dot_type);
|
||||
const auto * vdot = ggml_get_type_traits(funcs->vec_dot_type);
|
||||
vdot->from_float(y1.data(), q8.data(), kVecSize);
|
||||
if (useQ4_1) funcs_cpu->vec_dot(kVecSize, &result, 0, q41.data(), 0, q8.data(), 0, 1);
|
||||
else funcs_cpu->vec_dot(kVecSize, &result, 0, q40.data(), 0, q8.data(), 0, 1);
|
||||
if (useQ4_1) funcs->vec_dot(kVecSize, &result, 0, q41.data(), 0, q8.data(), 0, 1);
|
||||
else funcs->vec_dot(kVecSize, &result, 0, q40.data(), 0, q8.data(), 0, 1);
|
||||
}
|
||||
sumq += result;
|
||||
t2 = std::chrono::high_resolution_clock::now();
|
||||
|
||||
@@ -1 +1 @@
|
||||
a099cb514d6687e436a5a423d1fb0448be0feb20
|
||||
bb78a40dc60e04c626bac2b65840b509988e990d
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
../ggml/include/ggml-cpu.h
|
||||
+7
-22
@@ -19440,26 +19440,12 @@ struct llama_context * llama_new_context_with_model(
|
||||
cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL;
|
||||
}
|
||||
|
||||
const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max;
|
||||
|
||||
LLAMA_LOG_INFO("%s: n_seq_max = %u\n", __func__, cparams.n_seq_max);
|
||||
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
|
||||
LLAMA_LOG_INFO("%s: n_ctx_per_seq = %u\n", __func__, n_ctx_per_seq);
|
||||
LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
|
||||
LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
|
||||
LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn);
|
||||
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
|
||||
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
|
||||
|
||||
if (n_ctx_per_seq < hparams.n_ctx_train) {
|
||||
LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
|
||||
__func__, n_ctx_per_seq, hparams.n_ctx_train);
|
||||
}
|
||||
|
||||
if (n_ctx_per_seq > hparams.n_ctx_train) {
|
||||
LLAMA_LOG_WARN("%s: n_ctx_pre_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n",
|
||||
__func__, n_ctx_per_seq, hparams.n_ctx_train);
|
||||
}
|
||||
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
|
||||
LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
|
||||
LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
|
||||
LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn);
|
||||
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
|
||||
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
|
||||
|
||||
ctx->abort_callback = params.abort_callback;
|
||||
ctx->abort_callback_data = params.abort_callback_data;
|
||||
@@ -19534,6 +19520,7 @@ struct llama_context * llama_new_context_with_model(
|
||||
|
||||
if (!llama_kv_cache_init(ctx->kv_self, ctx, type_k, type_v, kv_size, cparams.offload_kqv)) {
|
||||
LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
|
||||
LLAMA_LOG_ERROR("%s: suggestion: try using a smaller context size (-c command line option or llama_context_params.n_ctx)\n", __func__);
|
||||
llama_free(ctx);
|
||||
return nullptr;
|
||||
}
|
||||
@@ -21900,8 +21887,6 @@ int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int
|
||||
}
|
||||
|
||||
const char * llama_print_system_info(void) {
|
||||
ggml_cpu_init(); // some ARM features are detected at runtime
|
||||
|
||||
static std::string s;
|
||||
|
||||
s = "";
|
||||
|
||||
@@ -16,7 +16,6 @@
|
||||
|
||||
|
||||
#include <ggml.h>
|
||||
#include <ggml-cpu.h>
|
||||
#include <ggml-alloc.h>
|
||||
#include <ggml-backend.h>
|
||||
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
#include "ggml.h"
|
||||
#include "ggml-cpu.h"
|
||||
#include "ggml-backend.h"
|
||||
|
||||
#include <chrono>
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows
|
||||
#include "ggml.h"
|
||||
#include "ggml-cpu.h"
|
||||
|
||||
#include <cfloat>
|
||||
#include <cmath>
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
// Unit tests for quantization specific functions - quantize, dequantize and dot product
|
||||
|
||||
#include "ggml.h"
|
||||
#include "ggml-cpu.h"
|
||||
|
||||
#undef NDEBUG
|
||||
#include <assert.h>
|
||||
@@ -79,18 +78,18 @@ static float dot_product(const float * a1, const float * a2, size_t test_size) {
|
||||
|
||||
// Total dot product error
|
||||
static float dot_product_error(
|
||||
const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, size_t test_size, const float * test_data1, const float *test_data2
|
||||
const ggml_type_traits * qfns, size_t test_size, const float * test_data1, const float *test_data2
|
||||
) {
|
||||
std::vector<uint8_t> tmp_q1(2*test_size);
|
||||
std::vector<uint8_t> tmp_q2(2*test_size);
|
||||
|
||||
const auto * vdot = ggml_get_type_traits(qfns_cpu->vec_dot_type);
|
||||
const auto * vdot = ggml_get_type_traits(qfns->vec_dot_type);
|
||||
|
||||
qfns->from_float(test_data1, tmp_q1.data(), test_size);
|
||||
vdot->from_float(test_data2, tmp_q2.data(), test_size);
|
||||
|
||||
float result = INFINITY;
|
||||
qfns_cpu->vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1);
|
||||
qfns->vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1);
|
||||
|
||||
const float dot_ref = dot_product(test_data1, test_data2, test_size);
|
||||
|
||||
@@ -133,7 +132,6 @@ int main(int argc, char * argv[]) {
|
||||
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
|
||||
ggml_type type = (ggml_type) i;
|
||||
const auto * qfns = ggml_get_type_traits(type);
|
||||
const auto * qfns_cpu = ggml_get_type_traits_cpu(type);
|
||||
|
||||
// deprecated - skip
|
||||
if (qfns->blck_size == 0) {
|
||||
@@ -168,7 +166,7 @@ int main(int argc, char * argv[]) {
|
||||
printf("%5s reference implementation error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], reference_error);
|
||||
}
|
||||
|
||||
const float vec_dot_error = dot_product_error(qfns, qfns_cpu, test_size, test_data.data(), test_data2.data());
|
||||
const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data());
|
||||
const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS ||
|
||||
type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S
|
||||
? MAX_DOT_PRODUCT_ERROR_LOWBIT
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
// Benchmark quantization specific functions on synthetic data
|
||||
|
||||
#include "ggml.h"
|
||||
#include "ggml-cpu.h"
|
||||
|
||||
#undef NDEBUG
|
||||
#include <algorithm>
|
||||
@@ -272,7 +271,6 @@ int main(int argc, char * argv[]) {
|
||||
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
|
||||
ggml_type type = (ggml_type) i;
|
||||
const auto * qfns = ggml_get_type_traits(type);
|
||||
const auto * qfns_cpu = ggml_get_type_traits_cpu(type);
|
||||
if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
|
||||
continue;
|
||||
}
|
||||
@@ -330,7 +328,7 @@ int main(int argc, char * argv[]) {
|
||||
for (size_t size : params.test_sizes) {
|
||||
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
|
||||
auto quantize_fn = [&](void) -> float {
|
||||
const auto * vdot = ggml_get_type_traits(qfns_cpu->vec_dot_type);
|
||||
const auto * vdot = ggml_get_type_traits(qfns->vec_dot_type);
|
||||
vdot->from_float(test_data1, test_q1, size);
|
||||
return test_q1[0];
|
||||
};
|
||||
@@ -348,7 +346,7 @@ int main(int argc, char * argv[]) {
|
||||
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
|
||||
auto quantize_fn = [&](void) -> float {
|
||||
float result;
|
||||
qfns_cpu->vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1);
|
||||
qfns->vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1);
|
||||
return result;
|
||||
};
|
||||
size_t quantized_size = ggml_row_size(type, size);
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
#include "ggml.h"
|
||||
#include "ggml-cpu.h"
|
||||
|
||||
#include <cmath>
|
||||
#include <cstdio>
|
||||
|
||||
Reference in New Issue
Block a user