mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 18:34:36 +00:00
ggml : quantization refactoring (#3833)
* ggml : factor all quantization code in ggml-quants ggml-ci * ggml-quants : fix Zig and Swift builds + quantize tool ggml-ci * quantize : --pure option for disabling k-quant mixtures --------- Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
This commit is contained in:
Georgi Gerganov
GitHub
parent
ff3bad83e2
commit
d69d777c02
@ -94,7 +94,6 @@ option(LLAMA_CLBLAST "llama: use CLBlast"
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option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT})
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option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF)
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option(LLAMA_MPI "llama: use MPI" OFF)
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option(LLAMA_K_QUANTS "llama: use k-quants" ON)
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option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
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option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
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@ -278,13 +277,8 @@ if (LLAMA_BLAS)
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endif()
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endif()
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if (LLAMA_K_QUANTS)
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set(GGML_HEADERS_EXTRA k_quants.h)
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set(GGML_SOURCES_EXTRA k_quants.c)
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add_compile_definitions(GGML_USE_K_QUANTS)
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if (LLAMA_QKK_64)
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if (LLAMA_QKK_64)
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add_compile_definitions(GGML_QKK_64)
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endif()
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endif()
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if (LLAMA_CUBLAS)
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@ -673,6 +667,8 @@ add_library(ggml OBJECT
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ggml-alloc.h
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ggml-backend.c
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ggml-backend.h
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ggml-quants.c
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ggml-quants.h
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${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}
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${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}
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${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}
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14
Makefile
14
Makefile
@ -342,13 +342,9 @@ else
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MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d
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endif
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ifndef LLAMA_NO_K_QUANTS
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MK_CPPFLAGS += -DGGML_USE_K_QUANTS
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OBJS += k_quants.o
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ifdef LLAMA_QKK_64
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MK_CPPFLAGS += -DGGML_QKK_64
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endif
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endif
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ifndef LLAMA_NO_ACCELERATE
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# Mac OS - include Accelerate framework.
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@ -497,11 +493,6 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h
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$(CC) $(CFLAGS) -c $< -o $@
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endif # LLAMA_MPI
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ifndef LLAMA_NO_K_QUANTS
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k_quants.o: k_quants.c k_quants.h
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$(CC) $(CFLAGS) -c $< -o $@
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endif # LLAMA_NO_K_QUANTS
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# combine build flags with cmdline overrides
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override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
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override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
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@ -542,7 +533,10 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
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ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
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$(CC) $(CFLAGS) -c $< -o $@
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OBJS += ggml-alloc.o ggml-backend.o
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ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h
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$(CC) $(CFLAGS) -c $< -o $@
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OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o
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llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
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$(CXX) $(CXXFLAGS) -c $< -o $@
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@ -42,13 +42,12 @@ let package = Package(
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"llama.cpp",
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"ggml-alloc.c",
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"ggml-backend.c",
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"k_quants.c",
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"ggml-quants.c",
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] + additionalSources,
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resources: resources,
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publicHeadersPath: "spm-headers",
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cSettings: [
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.unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
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.define("GGML_USE_K_QUANTS"),
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.define("GGML_USE_ACCELERATE")
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// NOTE: NEW_LAPACK will required iOS version 16.4+
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// We should consider add this in the future when we drop support for iOS 14
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21
build.zig
21
build.zig
@ -116,15 +116,10 @@ pub fn build(b: *std.build.Builder) !void {
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var make = try Maker.init(b);
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make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false;
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if (b.option(bool, "k-quants", "Enable K-quants, (default: true)") orelse true) {
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try make.addFlag("-DGGML_USE_K_QUANTS");
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const k_quants = make.obj("k_quants", "k_quants.c");
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try make.objs.append(k_quants);
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}
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const ggml = make.obj("ggml", "ggml.c");
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const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
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const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
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const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
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const llama = make.obj("llama", "llama.cpp");
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const common = make.obj("common", "common/common.cpp");
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const console = make.obj("console", "common/console.cpp");
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@ -133,14 +128,14 @@ pub fn build(b: *std.build.Builder) !void {
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const train = make.obj("train", "common/train.cpp");
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const clip = make.obj("clip", "examples/llava/clip.cpp");
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_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, console, grammar_parser });
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_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
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_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
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_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
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_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train });
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_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train });
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_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, console, grammar_parser });
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_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
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_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
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_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
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_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train });
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_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train });
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const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser, clip });
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const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, grammar_parser, clip });
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if (server.target.isWindows()) {
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server.linkSystemLibrary("ws2_32");
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}
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@ -18,7 +18,6 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", },
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{ "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", },
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{ "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
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#ifdef GGML_USE_K_QUANTS
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{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
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{ "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
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{ "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
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@ -31,7 +30,6 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", },
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{ "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
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{ "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 5.15G, -0.0008 ppl @ LLaMA-v1-7B", },
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#endif
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{ "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
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{ "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 7B", },
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{ "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", },
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@ -70,13 +68,14 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
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}
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// usage:
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// ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
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// ./quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
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//
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[[noreturn]]
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static void usage(const char * executable) {
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printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
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printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
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printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
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printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
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printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
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printf("\nAllowed quantization types:\n");
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for (auto & it : QUANT_OPTIONS) {
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if (it.name != "COPY") {
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@ -103,6 +102,8 @@ int main(int argc, char ** argv) {
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params.quantize_output_tensor = false;
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} else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
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params.allow_requantize = true;
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} else if (strcmp(argv[arg_idx], "--pure") == 0) {
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params.pure = true;
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} else {
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usage(argv[0]);
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}
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File diff suppressed because it is too large
Load Diff
@ -1,20 +1,14 @@
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#pragma once
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// This is a private API for quantization and dequantization
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// Should not be used directly, use ggml.h instead
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#include "ggml.h"
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#include <stdint.h>
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#include <assert.h>
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#include <stddef.h>
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// Super-block size
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#ifdef GGML_QKK_64
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#define QK_K 64
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#define K_SCALE_SIZE 4
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#else
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#define QK_K 256
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#define K_SCALE_SIZE 12
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#endif
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#ifndef static_assert
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#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
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#define static_assert(cond, msg) _Static_assert(cond, msg)
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@ -23,10 +17,66 @@
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#endif
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#endif
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#define QK4_0 32
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typedef struct {
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ggml_fp16_t d; // delta
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uint8_t qs[QK4_0 / 2]; // nibbles / quants
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} block_q4_0;
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static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
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#define QK4_1 32
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typedef struct {
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ggml_fp16_t d; // delta
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ggml_fp16_t m; // min
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uint8_t qs[QK4_1 / 2]; // nibbles / quants
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} block_q4_1;
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static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
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#define QK5_0 32
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typedef struct {
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ggml_fp16_t d; // delta
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uint8_t qh[4]; // 5-th bit of quants
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uint8_t qs[QK5_0 / 2]; // nibbles / quants
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} block_q5_0;
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static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
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#define QK5_1 32
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typedef struct {
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ggml_fp16_t d; // delta
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ggml_fp16_t m; // min
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uint8_t qh[4]; // 5-th bit of quants
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uint8_t qs[QK5_1 / 2]; // nibbles / quants
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} block_q5_1;
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static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
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#define QK8_0 32
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typedef struct {
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ggml_fp16_t d; // delta
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int8_t qs[QK8_0]; // quants
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} block_q8_0;
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static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
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#define QK8_1 32
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typedef struct {
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float d; // delta
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float s; // d * sum(qs[i])
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int8_t qs[QK8_1]; // quants
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} block_q8_1;
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static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
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//
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// Super-block quantization structures
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//
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// Super-block size
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#ifdef GGML_QKK_64
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#define QK_K 64
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#define K_SCALE_SIZE 4
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#else
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#define QK_K 256
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#define K_SCALE_SIZE 12
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#endif
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// 2-bit quantization
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// weight is represented as x = a * q + b
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// 16 blocks of 16 elements each
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@ -127,6 +177,13 @@ static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_
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// Quantization
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void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k);
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void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k);
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void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k);
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void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k);
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void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k);
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void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k);
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void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
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void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
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void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
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@ -134,6 +191,13 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
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void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
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void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
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void quantize_row_q4_0(const float * restrict x, void * restrict y, int k);
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void quantize_row_q4_1(const float * restrict x, void * restrict y, int k);
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void quantize_row_q5_0(const float * restrict x, void * restrict y, int k);
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void quantize_row_q5_1(const float * restrict x, void * restrict y, int k);
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void quantize_row_q8_0(const float * restrict x, void * restrict y, int k);
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void quantize_row_q8_1(const float * restrict x, void * restrict y, int k);
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void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
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void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
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void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
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@ -142,6 +206,13 @@ void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
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void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
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// Dequantization
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void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k);
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void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k);
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void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k);
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void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k);
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void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k);
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//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k);
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void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
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void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
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void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
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@ -150,16 +221,14 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int
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void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
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// Dot product
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void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
|
||||
void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
|
||||
// Quantization with histogram collection
|
||||
size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
7
ggml.h
7
ggml.h
@ -1930,12 +1930,19 @@ extern "C" {
|
||||
// quantization
|
||||
//
|
||||
|
||||
// TODO: these would probably get removed in favor of the more general ggml_quantize_chunk
|
||||
GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
|
||||
|
||||
//
|
||||
|
||||
24
llama.cpp
24
llama.cpp
@ -19,14 +19,12 @@
|
||||
#ifdef GGML_USE_MPI
|
||||
# include "ggml-mpi.h"
|
||||
#endif
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
# ifndef QK_K
|
||||
#ifndef QK_K
|
||||
# ifdef GGML_QKK_64
|
||||
# define QK_K 64
|
||||
# else
|
||||
# define QK_K 256
|
||||
# endif
|
||||
# endif
|
||||
#endif
|
||||
|
||||
#ifdef __has_include
|
||||
@ -8052,7 +8050,7 @@ struct no_init {
|
||||
struct quantize_state_internal {
|
||||
const llama_model & model;
|
||||
const llama_model_quantize_params * params;
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
|
||||
int n_attention_wv = 0;
|
||||
int n_feed_forward_w2 = 0;
|
||||
int i_attention_wv = 0;
|
||||
@ -8060,7 +8058,7 @@ struct quantize_state_internal {
|
||||
|
||||
int n_k_quantized = 0;
|
||||
int n_fallback = 0;
|
||||
#endif
|
||||
|
||||
quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
|
||||
: model(model)
|
||||
, params(params)
|
||||
@ -8125,7 +8123,6 @@ static void llama_convert_tensor_internal(
|
||||
workers.clear();
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
static ggml_type get_k_quant_type(
|
||||
quantize_state_internal & qs,
|
||||
ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype
|
||||
@ -8237,7 +8234,6 @@ static ggml_type get_k_quant_type(
|
||||
|
||||
return new_type;
|
||||
}
|
||||
#endif
|
||||
|
||||
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
|
||||
ggml_type quantized_type;
|
||||
@ -8252,7 +8248,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
case LLAMA_FTYPE_MOSTLY_F16: quantized_type = GGML_TYPE_F16; break;
|
||||
case LLAMA_FTYPE_ALL_F32: quantized_type = GGML_TYPE_F32; break;
|
||||
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
// K-quants
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
|
||||
@ -8263,7 +8258,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
|
||||
#endif
|
||||
|
||||
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
|
||||
}
|
||||
|
||||
@ -8304,7 +8299,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
|
||||
gguf_set_val_u32(ctx_out, "general.file_type", ftype);
|
||||
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
for (int i = 0; i < ml.n_tensors; ++i) {
|
||||
struct ggml_tensor * meta = ml.get_tensor_meta(i);
|
||||
|
||||
@ -8322,7 +8316,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
|
||||
__func__, qs.n_attention_wv, qs.n_feed_forward_w2, model.hparams.n_layer);
|
||||
}
|
||||
#endif
|
||||
|
||||
size_t total_size_org = 0;
|
||||
size_t total_size_new = 0;
|
||||
@ -8387,9 +8380,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
|
||||
if (quantize) {
|
||||
new_type = quantized_type;
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
if (!params->pure) {
|
||||
new_type = get_k_quant_type(qs, new_type, tensor, ftype);
|
||||
#endif
|
||||
}
|
||||
|
||||
// If we've decided to quantize to the same type the tensor is already
|
||||
// in then there's nothing to do.
|
||||
quantize = tensor->type != new_type;
|
||||
@ -8514,12 +8508,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
LLAMA_LOG_INFO("\n");
|
||||
}
|
||||
}
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
|
||||
if (qs.n_fallback > 0) {
|
||||
LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n",
|
||||
__func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
static int llama_apply_lora_from_file_internal(
|
||||
@ -8844,6 +8837,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
|
||||
/*.allow_requantize =*/ false,
|
||||
/*.quantize_output_tensor =*/ true,
|
||||
/*.only_copy =*/ false,
|
||||
/*.pure =*/ false,
|
||||
};
|
||||
|
||||
return result;
|
||||
|
||||
1
llama.h
1
llama.h
@ -191,6 +191,7 @@ extern "C" {
|
||||
bool allow_requantize; // allow quantizing non-f32/f16 tensors
|
||||
bool quantize_output_tensor; // quantize output.weight
|
||||
bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
||||
bool pure; // disable k-quant mixtures and quantize all tensors to the same type
|
||||
} llama_model_quantize_params;
|
||||
|
||||
// grammar types
|
||||
|
||||
Loading…
Reference in New Issue
Block a user