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Merge branch 'master' into dev-refactoring

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# Conflicts:
#	src/llama.cpp
This commit is contained in:
hongruichen 2024-09-07 11:15:11 +08:00
commit 67e8af7d87
70 changed files with 5467 additions and 1410 deletions
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2
.devops/full-cuda.Dockerfile
View file

@ -27,7 +27,7 @@ RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-cli -j$(nproc) && \
cmake --build build --config Release -j$(nproc) && \
cp build/bin/* .
ENTRYPOINT ["/app/.devops/tools.sh"]

53
.devops/nix/devshells.nix
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@ -1,13 +1,52 @@
{ inputs, ... }:
{
perSystem =
{ config, lib, ... }:
{
config,
lib,
system,
...
}:
{
devShells =
lib.concatMapAttrs
(name: package: {
${name} = package.passthru.shell;
${name + "-extra"} = package.passthru.shell-extra;
})
config.packages;
let
pkgs = import inputs.nixpkgs { inherit system; };
stdenv = pkgs.stdenv;
scripts = config.packages.python-scripts;
in
lib.pipe (config.packages) [
(lib.concatMapAttrs (
name: package: {
${name} = pkgs.mkShell {
name = "${name}";
inputsFrom = [ package ];
shellHook = ''
echo "Entering ${name} devShell"
'';
};
"${name}-extra" =
if (name == "python-scripts") then
null
else
pkgs.mkShell {
name = "${name}-extra";
inputsFrom = [
package
scripts
];
# Extra packages that *may* be used by some scripts
packages = [
pkgs.python3Packages.tiktoken
];
shellHook = ''
echo "Entering ${name} devShell"
addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib stdenv.cc.cc}/lib"
'';
};
}
))
(lib.filterAttrs (name: value: value != null))
];
};
}

18
.devops/nix/nixpkgs-instances.nix
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@ -26,16 +26,14 @@
config.cudaSupport = true;
config.allowUnfreePredicate =
p:
builtins.all
(
license:
license.free
|| builtins.elem license.shortName [
"CUDA EULA"
"cuDNN EULA"
]
)
(p.meta.licenses or [ p.meta.license ]);
builtins.all (
license:
license.free
|| builtins.elem license.shortName [
"CUDA EULA"
"cuDNN EULA"
]
) (p.meta.licenses or [ p.meta.license ]);
};
# Ensure dependencies use ROCm consistently
pkgsRocm = import inputs.nixpkgs {

36
.devops/nix/package-gguf-py.nix Normal file
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@ -0,0 +1,36 @@
{
lib,
llamaVersion,
numpy,
tqdm,
sentencepiece,
pyyaml,
poetry-core,
buildPythonPackage,
pytestCheckHook,
}:
buildPythonPackage {
pname = "gguf";
version = llamaVersion;
pyproject = true;
nativeBuildInputs = [ poetry-core ];
propagatedBuildInputs = [
numpy
tqdm
sentencepiece
pyyaml
];
src = lib.cleanSource ../../gguf-py;
pythonImportsCheck = [
"numpy"
"gguf"
];
nativeCheckInputs = [ pytestCheckHook ];
doCheck = true;
meta = with lib; {
description = "Python package for writing binary files in the GGUF format";
license = licenses.mit;
maintainers = [ maintainers.ditsuke ];
};
}

368
.devops/nix/package.nix
View file

@ -3,31 +3,33 @@
glibc,
config,
stdenv,
mkShell,
runCommand,
cmake,
ninja,
pkg-config,
git,
python3,
mpi,
blas,
cudaPackages,
autoAddDriverRunpath,
darwin,
rocmPackages,
vulkan-headers,
vulkan-loader,
curl,
shaderc,
useBlas ? builtins.all (x: !x) [
useCuda
useMetalKit
useRocm
useVulkan
] && blas.meta.available,
useBlas ?
builtins.all (x: !x) [
useCuda
useMetalKit
useRocm
useVulkan
]
&& blas.meta.available,
useCuda ? config.cudaSupport,
useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin,
useMpi ? false, # Increases the runtime closure size by ~700M
# Increases the runtime closure size by ~700M
useMpi ? false,
useRocm ? config.rocmSupport,
enableCurl ? true,
useVulkan ? false,
@ -37,8 +39,8 @@
# otherwise we get libstdc++ errors downstream.
effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv,
enableStatic ? effectiveStdenv.hostPlatform.isStatic,
precompileMetalShaders ? false
}@inputs:
precompileMetalShaders ? false,
}:
let
inherit (lib)
@ -46,7 +48,6 @@ let
cmakeFeature
optionals
strings
versionOlder
;
stdenv = throw "Use effectiveStdenv instead";
@ -62,54 +63,11 @@ let
pnameSuffix =
strings.optionalString (suffices != [ ])
"-${strings.concatMapStringsSep "-" strings.toLower suffices}";
descriptionSuffix =
strings.optionalString (suffices != [ ])
", accelerated with ${strings.concatStringsSep ", " suffices}";
descriptionSuffix = strings.optionalString (
suffices != [ ]
) ", accelerated with ${strings.concatStringsSep ", " suffices}";
executableSuffix = effectiveStdenv.hostPlatform.extensions.executable;
# TODO: package the Python in this repository in a Nix-like way.
# It'd be nice to migrate to buildPythonPackage, as well as ensure this repo
# is PEP 517-compatible, and ensure the correct .dist-info is generated.
# https://peps.python.org/pep-0517/
#
# TODO: Package up each Python script or service appropriately, by making
# them into "entrypoints"
llama-python = python3.withPackages (
ps: [
ps.numpy
ps.sentencepiece
]
);
# TODO(Green-Sky): find a better way to opt-into the heavy ml python runtime
llama-python-extra = python3.withPackages (
ps: [
ps.numpy
ps.sentencepiece
ps.tiktoken
ps.torchWithoutCuda
ps.transformers
# server bench
ps.matplotlib
# server tests
ps.openai
ps.behave
ps.prometheus-client
# for examples/pydantic-models-to-grammar-examples.py
ps.docstring-parser
ps.pydantic
# for scripts/compare-llama-bench.py
ps.gitpython
ps.tabulate
]
);
xcrunHost = runCommand "xcrunHost" {} ''
xcrunHost = runCommand "xcrunHost" { } ''
mkdir -p $out/bin
ln -s /usr/bin/xcrun $out/bin
'';
@ -144,181 +102,145 @@ let
];
in
effectiveStdenv.mkDerivation (
finalAttrs: {
pname = "llama-cpp${pnameSuffix}";
version = llamaVersion;
effectiveStdenv.mkDerivation (finalAttrs: {
pname = "llama-cpp${pnameSuffix}";
version = llamaVersion;
# Note: none of the files discarded here are visible in the sandbox or
# affect the output hash. This also means they can be modified without
# triggering a rebuild.
src = lib.cleanSourceWith {
filter =
name: type:
let
noneOf = builtins.all (x: !x);
baseName = baseNameOf name;
in
noneOf [
(lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths
(lib.hasSuffix ".md" name) # Ignore *.md changes whe computing outPaths
(lib.hasPrefix "." baseName) # Skip hidden files and directories
(baseName == "flake.lock")
];
src = lib.cleanSource ../../.;
};
postPatch = ''
substituteInPlace ./ggml/src/ggml-metal.m \
--replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
substituteInPlace ./ggml/src/ggml-metal.m \
--replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
'';
# With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015,
# `default.metallib` may be compiled with Metal compiler from XCode
# and we need to escape sandbox on MacOS to access Metal compiler.
# `xcrun` is used find the path of the Metal compiler, which is varible
# and not on $PATH
# see https://github.com/ggerganov/llama.cpp/pull/6118 for discussion
__noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders;
nativeBuildInputs =
[
cmake
ninja
pkg-config
git
]
++ optionals useCuda [
cudaPackages.cuda_nvcc
# TODO: Replace with autoAddDriverRunpath
# once https://github.com/NixOS/nixpkgs/pull/275241 has been merged
cudaPackages.autoAddOpenGLRunpathHook
]
++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [
glibc.static
] ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [
xcrunHost
# Note: none of the files discarded here are visible in the sandbox or
# affect the output hash. This also means they can be modified without
# triggering a rebuild.
src = lib.cleanSourceWith {
filter =
name: type:
let
noneOf = builtins.all (x: !x);
baseName = baseNameOf name;
in
noneOf [
(lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths
(lib.hasSuffix ".md" name) # Ignore *.md changes whe computing outPaths
(lib.hasPrefix "." baseName) # Skip hidden files and directories
(baseName == "flake.lock")
];
src = lib.cleanSource ../../.;
};
buildInputs =
optionals effectiveStdenv.isDarwin darwinBuildInputs
++ optionals useCuda cudaBuildInputs
++ optionals useMpi [ mpi ]
++ optionals useRocm rocmBuildInputs
++ optionals useBlas [ blas ]
++ optionals useVulkan vulkanBuildInputs
++ optionals enableCurl [ curl ];
postPatch = ''
substituteInPlace ./ggml/src/ggml-metal.m \
--replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
substituteInPlace ./ggml/src/ggml-metal.m \
--replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
'';
cmakeFlags =
[
(cmakeBool "LLAMA_BUILD_SERVER" true)
(cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
(cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
(cmakeBool "LLAMA_CURL" enableCurl)
(cmakeBool "GGML_NATIVE" false)
(cmakeBool "GGML_BLAS" useBlas)
(cmakeBool "GGML_CUDA" useCuda)
(cmakeBool "GGML_HIPBLAS" useRocm)
(cmakeBool "GGML_METAL" useMetalKit)
(cmakeBool "GGML_VULKAN" useVulkan)
(cmakeBool "GGML_STATIC" enableStatic)
]
++ optionals useCuda [
(
with cudaPackages.flags;
cmakeFeature "CMAKE_CUDA_ARCHITECTURES" (
builtins.concatStringsSep ";" (map dropDot cudaCapabilities)
)
# With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015,
# `default.metallib` may be compiled with Metal compiler from XCode
# and we need to escape sandbox on MacOS to access Metal compiler.
# `xcrun` is used find the path of the Metal compiler, which is varible
# and not on $PATH
# see https://github.com/ggerganov/llama.cpp/pull/6118 for discussion
__noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders;
nativeBuildInputs =
[
cmake
ninja
pkg-config
git
]
++ optionals useCuda [
cudaPackages.cuda_nvcc
autoAddDriverRunpath
]
++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [ glibc.static ]
++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [ xcrunHost ];
buildInputs =
optionals effectiveStdenv.isDarwin darwinBuildInputs
++ optionals useCuda cudaBuildInputs
++ optionals useMpi [ mpi ]
++ optionals useRocm rocmBuildInputs
++ optionals useBlas [ blas ]
++ optionals useVulkan vulkanBuildInputs
++ optionals enableCurl [ curl ];
cmakeFlags =
[
(cmakeBool "LLAMA_BUILD_SERVER" true)
(cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
(cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
(cmakeBool "LLAMA_CURL" enableCurl)
(cmakeBool "GGML_NATIVE" false)
(cmakeBool "GGML_BLAS" useBlas)
(cmakeBool "GGML_CUDA" useCuda)
(cmakeBool "GGML_HIPBLAS" useRocm)
(cmakeBool "GGML_METAL" useMetalKit)
(cmakeBool "GGML_VULKAN" useVulkan)
(cmakeBool "GGML_STATIC" enableStatic)
]
++ optionals useCuda [
(
with cudaPackages.flags;
cmakeFeature "CMAKE_CUDA_ARCHITECTURES" (
builtins.concatStringsSep ";" (map dropDot cudaCapabilities)
)
]
++ optionals useRocm [
(cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang")
(cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets))
]
++ optionals useMetalKit [
(lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
(cmakeBool "GGML_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
];
)
]
++ optionals useRocm [
(cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang")
(cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets))
]
++ optionals useMetalKit [
(lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
(cmakeBool "GGML_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
];
# Environment variables needed for ROCm
env = optionals useRocm {
ROCM_PATH = "${rocmPackages.clr}";
HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode";
};
# Environment variables needed for ROCm
env = optionals useRocm {
ROCM_PATH = "${rocmPackages.clr}";
HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode";
};
# TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
# if they haven't been added yet.
postInstall = ''
mkdir -p $out/include
cp $src/include/llama.h $out/include/
'';
# TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
# if they haven't been added yet.
postInstall = ''
mkdir -p $out/include
cp $src/include/llama.h $out/include/
'';
# Define the shells here, but don't add in the inputsFrom to avoid recursion.
passthru = {
inherit
useBlas
useCuda
useMetalKit
useMpi
useRocm
useVulkan
;
meta = {
# Configurations we don't want even the CI to evaluate. Results in the
# "unsupported platform" messages. This is mostly a no-op, because
# cudaPackages would've refused to evaluate anyway.
badPlatforms = optionals useCuda lib.platforms.darwin;
shell = mkShell {
name = "shell-${finalAttrs.finalPackage.name}";
description = "contains numpy and sentencepiece";
buildInputs = [ llama-python ];
inputsFrom = [ finalAttrs.finalPackage ];
shellHook = ''
addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib effectiveStdenv.cc.cc}/lib"
'';
};
# Configurations that are known to result in build failures. Can be
# overridden by importing Nixpkgs with `allowBroken = true`.
broken = (useMetalKit && !effectiveStdenv.isDarwin);
shell-extra = mkShell {
name = "shell-extra-${finalAttrs.finalPackage.name}";
description = "contains numpy, sentencepiece, torchWithoutCuda, and transformers";
buildInputs = [ llama-python-extra ];
inputsFrom = [ finalAttrs.finalPackage ];
};
};
description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}";
homepage = "https://github.com/ggerganov/llama.cpp/";
license = lib.licenses.mit;
meta = {
# Configurations we don't want even the CI to evaluate. Results in the
# "unsupported platform" messages. This is mostly a no-op, because
# cudaPackages would've refused to evaluate anyway.
badPlatforms = optionals useCuda lib.platforms.darwin;
# Accommodates `nix run` and `lib.getExe`
mainProgram = "llama-cli";
# Configurations that are known to result in build failures. Can be
# overridden by importing Nixpkgs with `allowBroken = true`.
broken = (useMetalKit && !effectiveStdenv.isDarwin);
# These people might respond, on the best effort basis, if you ping them
# in case of Nix-specific regressions or for reviewing Nix-specific PRs.
# Consider adding yourself to this list if you want to ensure this flake
# stays maintained and you're willing to invest your time. Do not add
# other people without their consent. Consider removing people after
# they've been unreachable for long periods of time.
description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}";
homepage = "https://github.com/ggerganov/llama.cpp/";
license = lib.licenses.mit;
# Note that lib.maintainers is defined in Nixpkgs, but you may just add
# an attrset following the same format as in
# https://github.com/NixOS/nixpkgs/blob/f36a80e54da29775c78d7eff0e628c2b4e34d1d7/maintainers/maintainer-list.nix
maintainers = with lib.maintainers; [
philiptaron
SomeoneSerge
];
# Accommodates `nix run` and `lib.getExe`
mainProgram = "llama-cli";
# These people might respond, on the best effort basis, if you ping them
# in case of Nix-specific regressions or for reviewing Nix-specific PRs.
# Consider adding yourself to this list if you want to ensure this flake
# stays maintained and you're willing to invest your time. Do not add
# other people without their consent. Consider removing people after
# they've been unreachable for long periods of time.
# Note that lib.maintainers is defined in Nixpkgs, but you may just add
# an attrset following the same format as in
# https://github.com/NixOS/nixpkgs/blob/f36a80e54da29775c78d7eff0e628c2b4e34d1d7/maintainers/maintainer-list.nix
maintainers = with lib.maintainers; [
philiptaron
SomeoneSerge
];
# Extend `badPlatforms` instead
platforms = lib.platforms.all;
};
}
)
# Extend `badPlatforms` instead
platforms = lib.platforms.all;
};
})

66
.devops/nix/python-scripts.nix Normal file
View file

@ -0,0 +1,66 @@
{
lib,
stdenv,
buildPythonPackage,
poetry-core,
mkShell,
python3Packages,
gguf-py,
}@inputs:
let
llama-python-deps = with python3Packages; [
numpy
sentencepiece
transformers
protobuf
torchWithoutCuda
gguf-py
tqdm
# for scripts/compare-llama-bench.py
gitpython
tabulate
# for examples/pydantic-models-to-grammar-examples.py
docstring-parser
pydantic
];
llama-python-test-deps = with python3Packages; [
# Server bench
matplotlib
# server tests
openai
behave
prometheus-client
];
in
buildPythonPackage ({
pname = "llama-scripts";
version = "0.0.0";
pyproject = true;
# NOTE: The files filtered out here are not visible in the build sandbox, neither
# do they affect the output hash. They can be modified without triggering a rebuild.
src = lib.cleanSourceWith {
filter =
name: type:
let
any = builtins.any (x: x);
baseName = builtins.baseNameOf name;
in
any [
(lib.hasSuffix ".py" name)
(baseName == "README.md")
(baseName == "pyproject.toml")
];
src = lib.cleanSource ../../.;
};
nativeBuildInputs = [ poetry-core ];
nativeCheckInputs = llama-python-test-deps;
dependencies = llama-python-deps;
})

40
.devops/nix/scope.nix
View file

@ -1,19 +1,41 @@
{
lib,
newScope,
python3,
llamaVersion ? "0.0.0",
}:
let
pythonPackages = python3.pkgs;
buildPythonPackage = pythonPackages.buildPythonPackage;
numpy = pythonPackages.numpy;
tqdm = pythonPackages.tqdm;
sentencepiece = pythonPackages.sentencepiece;
pyyaml = pythonPackages.pyyaml;
poetry-core = pythonPackages.poetry-core;
pytestCheckHook = pythonPackages.pytestCheckHook;
in
# We're using `makeScope` instead of just writing out an attrset
# because it allows users to apply overlays later using `overrideScope'`.
# Cf. https://noogle.dev/f/lib/makeScope
lib.makeScope newScope (
self: {
inherit llamaVersion;
llama-cpp = self.callPackage ./package.nix { };
docker = self.callPackage ./docker.nix { };
docker-min = self.callPackage ./docker.nix { interactive = false; };
sif = self.callPackage ./sif.nix { };
}
)
lib.makeScope newScope (self: {
inherit llamaVersion;
gguf-py = self.callPackage ./package-gguf-py.nix {
inherit
buildPythonPackage
numpy
tqdm
sentencepiece
poetry-core
pyyaml
pytestCheckHook
;
};
python-scripts = self.callPackage ./python-scripts.nix { inherit buildPythonPackage poetry-core; };
llama-cpp = self.callPackage ./package.nix { };
docker = self.callPackage ./docker.nix { };
docker-min = self.callPackage ./docker.nix { interactive = false; };
sif = self.callPackage ./sif.nix { };
})

2
.github/workflows/build.yml vendored
View file

@ -857,7 +857,7 @@ jobs:
run: |
mkdir build
cd build
cmake .. -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON
cmake .. -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON -DGGML_RPC=ON
cmake --build . --config Release -j $((${env:NUMBER_OF_PROCESSORS} - 1)) -t ggml
cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS}

8
CMakePresets.json
View file

@ -32,8 +32,8 @@
{
"name": "arm64-windows-msvc", "hidden": true,
"architecture": { "value": "arm64", "strategy": "external" },
"toolset": { "value": "host=x86_64", "strategy": "external" },
"architecture": { "value": "arm64", "strategy": "external" },
"toolset": { "value": "host=x64", "strategy": "external" },
"cacheVariables": {
"CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake"
}
@ -41,8 +41,8 @@
{
"name": "arm64-windows-llvm", "hidden": true,
"architecture": { "value": "arm64", "strategy": "external" },
"toolset": { "value": "host=x86_64", "strategy": "external" },
"architecture": { "value": "arm64", "strategy": "external" },
"toolset": { "value": "host=x64", "strategy": "external" },
"cacheVariables": {
"CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake"
}

24
README.md
View file

@ -10,32 +10,14 @@
Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
> [!IMPORTANT]
[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809)
## Recent API changes
- [2024 Jun 26] The source code and CMake build scripts have been restructured https://github.com/ggerganov/llama.cpp/pull/8006
- [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807
- [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341
- [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
- [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
- [Changelog for `libllama` API](https://github.com/ggerganov/llama.cpp/issues/9289)
- [Changelog for `llama-server` REST API](https://github.com/ggerganov/llama.cpp/issues/9291)
## Hot topics
- **`convert.py` has been deprecated and moved to `examples/convert_legacy_llama.py`, please use `convert_hf_to_gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430
- Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021
- BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920
- MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387
- Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
- Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225
- Multi-GPU pipeline parallelism support https://github.com/ggerganov/llama.cpp/pull/6017
- Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981
- Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962
- Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328
- *add hot topics here*
----

365
common/common.cpp
View file

@ -251,6 +251,57 @@ int32_t cpu_get_num_math() {
return cpu_get_num_physical_cores();
}
// Helper for setting process priority
#if defined(_WIN32)
bool set_process_priority(enum ggml_sched_priority prio) {
if (prio == GGML_SCHED_PRIO_NORMAL) {
return true;
}
DWORD p = NORMAL_PRIORITY_CLASS;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break;
}
if (!SetPriorityClass(GetCurrentProcess(), p)) {
fprintf(stderr, "warn: failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
return false;
}
return true;
}
#else // MacOS and POSIX
#include <sys/types.h>
#include <sys/resource.h>
bool set_process_priority(enum ggml_sched_priority prio) {
if (prio == GGML_SCHED_PRIO_NORMAL) {
return true;
}
int p = 0;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = 0; break;
case GGML_SCHED_PRIO_MEDIUM: p = -5; break;
case GGML_SCHED_PRIO_HIGH: p = -10; break;
case GGML_SCHED_PRIO_REALTIME: p = -20; break;
}
if (!setpriority(PRIO_PROCESS, 0, p)) {
fprintf(stderr, "warn: failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
return false;
}
return true;
}
#endif
//
// CLI argument parsing
//
@ -277,6 +328,30 @@ void gpt_params_handle_model_default(gpt_params & params) {
}
}
void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
int32_t n_set = 0;
if (cpuparams.n_threads < 0) {
// Assuming everything about cpuparams is invalid
if (role_model != nullptr) {
cpuparams = *role_model;
} else {
cpuparams.n_threads = cpu_get_num_math();
}
}
for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
if (cpuparams.cpumask[i]) {
n_set++;
}
}
if (n_set && n_set < cpuparams.n_threads) {
// Not enough set bits, may experience performance issues.
fprintf(stderr, "warn: Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
}
}
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false;
std::string arg;
@ -296,6 +371,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
}
}
postprocess_cpu_params(params.cpuparams, nullptr);
postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams_batch, &params.cpuparams_batch);
if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
}
@ -331,7 +411,7 @@ void gpt_params_parse_from_env(gpt_params & params) {
get_env("LLAMA_ARG_MODEL_ALIAS", params.model_alias);
get_env("LLAMA_ARG_HF_REPO", params.hf_repo);
get_env("LLAMA_ARG_HF_FILE", params.hf_file);
get_env("LLAMA_ARG_THREADS", params.n_threads);
get_env("LLAMA_ARG_THREADS", params.cpuparams.n_threads);
get_env("LLAMA_ARG_CTX_SIZE", params.n_ctx);
get_env("LLAMA_ARG_N_PARALLEL", params.n_parallel);
get_env("LLAMA_ARG_BATCH", params.n_batch);
@ -368,6 +448,79 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
return true;
}
bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
size_t dash_loc = range.find('-');
if (dash_loc == std::string::npos) {
fprintf(stderr, "Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
return false;
}
size_t start_i;
size_t end_i;
if (dash_loc == 0) {
start_i = 0;
} else {
start_i = std::stoull(range.substr(0, dash_loc));
if (start_i >= GGML_MAX_N_THREADS) {
fprintf(stderr, "Start index out of bounds!\n");
return false;
}
}
if (dash_loc == range.length() - 1) {
end_i = GGML_MAX_N_THREADS - 1;
} else {
end_i = std::stoull(range.substr(dash_loc + 1));
if (end_i >= GGML_MAX_N_THREADS) {
fprintf(stderr, "End index out of bounds!\n");
return false;
}
}
for (size_t i = start_i; i <= end_i; i++) {
boolmask[i] = true;
}
return true;
}
bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) {
// Discard potential 0x prefix
size_t start_i = 0;
if (mask.length() >= 2 && mask.substr(0, 2) == "0x") {
start_i = 2;
}
size_t num_digits = mask.length() - start_i;
if (num_digits > 128) num_digits = 128;
size_t end_i = num_digits + start_i;
for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) {
char c = mask.at(i);
int8_t id = c;
if ((c >= '0' && c <= '9')) {
id -= '0';
} else if (c >= 'a' && c <= 'f') {
id -= 'a' - 10;
} else if (c >= 'A' && c <= 'F') {
id -= 'A' - 10;
} else {
fprintf(stderr, "Invalid hex character '%c' at position %d\n", c, int32_t(i));
return false;
}
boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0);
boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0);
boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0);
boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0);
}
return true;
}
#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
@ -384,36 +537,142 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
}
if (arg == "-t" || arg == "--threads") {
CHECK_ARG
params.n_threads = std::stoi(argv[i]);
if (params.n_threads <= 0) {
params.n_threads = std::thread::hardware_concurrency();
params.cpuparams.n_threads = std::stoi(argv[i]);
if (params.cpuparams.n_threads <= 0) {
params.cpuparams.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-C" || arg == "--cpu-mask") {
CHECK_ARG
std::string mask = argv[i];
params.cpuparams.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.cpuparams.cpumask);
return true;
}
if (arg == "-Cr" || arg == "--cpu-range") {
CHECK_ARG
std::string range = argv[i];
params.cpuparams.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.cpuparams.cpumask);
return true;
}
if (arg == "--prio") {
CHECK_ARG
params.cpuparams.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict") {
CHECK_ARG
params.cpuparams.strict_cpu = std::stoul(argv[i]);
return true;
}
if (arg == "--poll") {
CHECK_ARG
params.cpuparams.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-tb" || arg == "--threads-batch") {
CHECK_ARG
params.n_threads_batch = std::stoi(argv[i]);
if (params.n_threads_batch <= 0) {
params.n_threads_batch = std::thread::hardware_concurrency();
params.cpuparams_batch.n_threads = std::stoi(argv[i]);
if (params.cpuparams_batch.n_threads <= 0) {
params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Cb" || arg == "--cpu-mask-batch") {
CHECK_ARG
std::string mask = argv[i];
params.cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.cpuparams_batch.cpumask);
return true;
}
if (arg == "-Crb" || arg == "--cpu-range_batch") {
CHECK_ARG
std::string range = argv[i];
params.cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.cpuparams_batch.cpumask);
return true;
}
if (arg == "--prio-batch") {
CHECK_ARG
params.cpuparams_batch.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-batch") {
params.cpuparams_batch.strict_cpu = true;
return true;
}
if (arg == "--poll-batch") {
CHECK_ARG
params.cpuparams_batch.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-td" || arg == "--threads-draft") {
CHECK_ARG
params.n_threads_draft = std::stoi(argv[i]);
if (params.n_threads_draft <= 0) {
params.n_threads_draft = std::thread::hardware_concurrency();
params.draft_cpuparams.n_threads = std::stoi(argv[i]);
if (params.draft_cpuparams.n_threads <= 0) {
params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Cd" || arg == "--cpu-mask-draft") {
CHECK_ARG
std::string mask = argv[i];
params.draft_cpuparams.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.draft_cpuparams.cpumask);
return true;
}
if (arg == "-Crd" || arg == "--cpu-range-draft") {
CHECK_ARG
std::string range = argv[i];
params.draft_cpuparams.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.draft_cpuparams.cpumask);
return true;
}
if (arg == "--prio-draft") {
CHECK_ARG
params.draft_cpuparams.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-draft") {
params.draft_cpuparams.strict_cpu = true;
return true;
}
if (arg == "--poll-draft") {
CHECK_ARG
params.draft_cpuparams.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-tbd" || arg == "--threads-batch-draft") {
CHECK_ARG
params.n_threads_batch_draft = std::stoi(argv[i]);
if (params.n_threads_batch_draft <= 0) {
params.n_threads_batch_draft = std::thread::hardware_concurrency();
params.draft_cpuparams_batch.n_threads = std::stoi(argv[i]);
if (params.draft_cpuparams_batch.n_threads <= 0) {
params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Crbd" || arg == "--cpu-range-batch-draft") {
CHECK_ARG
std::string range = argv[i];
params.draft_cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.draft_cpuparams_batch.cpumask);
return true;
}
if (arg == "--prio-batch-draft") {
CHECK_ARG
params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-batch-draft") {
params.draft_cpuparams_batch.strict_cpu = true;
return true;
}
if (arg == "--poll-batch-draft") {
CHECK_ARG
params.draft_cpuparams_batch.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-p" || arg == "--prompt") {
CHECK_ARG
params.prompt = argv[i];
@ -975,11 +1234,13 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
#ifdef GGML_USE_RPC
if (arg == "--rpc") {
CHECK_ARG
params.rpc_servers = argv[i];
return true;
}
#endif
if (arg == "--no-mmap") {
params.use_mmap = false;
return true;
@ -1417,6 +1678,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
else { invalid_param = true; }
return true;
}
if (arg == "--output-format") {
CHECK_ARG
std::string value(argv[i]);
/**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
else if (value == "md") { params.batched_bench_output_jsonl = false; }
else { invalid_param = true; }
return true;
}
if (arg == "--no-warmup") {
params.warmup = false;
return true;
@ -1498,11 +1767,40 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed });
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.n_threads });
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.cpuparams.n_threads });
options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" });
options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" });
options.push_back({ "speculative", "-tbd, --threads-batch-draft N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
options.push_back({ "speculative", "-tbd, --threads-batch-draft N","number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
#ifndef GGML_USE_OPENMP
// these options are available only with the internal threadpool
options.push_back({ "*", "-C, --cpu-mask M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")"});
options.push_back({ "*", "-Cr, --cpu-range lo-hi", "range of CPUs for affinity. Complements --cpu-mask"});
options.push_back({ "*", " --cpu-strict <0|1>", "use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu});
options.push_back({ "*", " --priority N", "set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority});
options.push_back({ "*", " --poll <0...100>", "use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll});
options.push_back({ "*", "-Cb, --cpu-mask-batch M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)"});
options.push_back({ "*", "-Crb, --cpu-range-batch lo-hi", "ranges of CPUs for affinity. Complements --cpu-mask-batch"});
options.push_back({ "*", " --cpu-strict-batch <0|1>","use strict CPU placement (default: same as --cpu-strict)"});
options.push_back({ "*", " --priority-batch N", "set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: --priority)"});
options.push_back({ "*", " --poll-batch <0|1>", "use polling to wait for work (default: same as --poll"});
options.push_back({ "speculative", "-Cd, --cpu-mask-draft M", "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)"});
options.push_back({ "speculative", "-Crd, --cpu-range-draft lo-hi", "Ranges of CPUs for affinity. Complements --cpu-mask-draft"});
options.push_back({ "speculative", " --cpu-strict-draft <0|1>","Use strict CPU placement for draft model (default: same as --cpu-strict)"});
options.push_back({ "speculative", " --priority-draft N", "Set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: same as --priority)"});
options.push_back({ "speculative", " --poll-draft <0|1>", "Use polling to wait for draft model work (default: same as --poll])"});
options.push_back({ "speculative", "-Cbd, --cpu-mask-batch-draft M","Draft model CPU affinity mask. Complements cpu-range-draft-batch (default: same as --cpu-mask-draft)"});
options.push_back({ "speculative", "-Crbd, --cpu-range-batch-draft lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)"});
options.push_back({ "speculative", " --cpu-strict-batch-draft <0|1>",
"Use strict CPU placement for draft model (default: --cpu-strict-draft)"});
options.push_back({ "speculative", " --priority-batch-draft N","Set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: --priority-draft)"});
options.push_back({ "speculative", " --poll-batch-draft <0|1>","Use polling to wait for draft model work (default: --poll-draft)"});
#endif // GGML_USE_OPENMP
options.push_back({ "speculative", " --draft N", "number of tokens to draft for speculative decoding (default: %d)", params.n_draft });
options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split });
options.push_back({ "*", "-lcs, --lookup-cache-static FNAME",
@ -1641,7 +1939,9 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", " --image FILE", "path to an image file. use with multimodal models. Specify multiple times for batching" });
options.push_back({ "backend" });
#ifdef GGML_USE_RPC
options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" });
#endif
if (llama_supports_mlock()) {
options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" });
@ -1774,9 +2074,11 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "export-lora", "-m, --model", "model path from which to load base model (default '%s')", params.model.c_str() });
options.push_back({ "export-lora", " --lora FNAME", "path to LoRA adapter (can be repeated to use multiple adapters)" });
options.push_back({ "export-lora", " --lora-scaled FNAME S", "path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
options.push_back({ "*", "-t, --threads N", "number of threads to use during computation (default: %d)", params.n_threads });
options.push_back({ "export-lora", "-o, --output FNAME", "output file (default: '%s')", params.lora_outfile.c_str() });
options.push_back({ "batched-bench" });
options.push_back({ "batched-bench", " --output-format {md,jsonl}", "output format for batched-bench results (default: md)" });
printf("usage: %s [options]\n", argv[0]);
for (const auto & o : options) {
@ -1806,9 +2108,9 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
std::string gpt_params_get_system_info(const gpt_params & params) {
std::ostringstream os;
os << "system_info: n_threads = " << params.n_threads;
if (params.n_threads_batch != -1) {
os << " (n_threads_batch = " << params.n_threads_batch << ")";
os << "system_info: n_threads = " << params.cpuparams.n_threads;
if (params.cpuparams_batch.n_threads != -1) {
os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")";
}
#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
// TODO: windows + arm64 + mingw64
@ -2332,8 +2634,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.n_seq_max = params.n_parallel;
cparams.n_batch = params.n_batch;
cparams.n_ubatch = params.n_ubatch;
cparams.n_threads = params.n_threads;
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
cparams.n_threads = params.cpuparams.n_threads;
cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ?
params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
cparams.seed = params.seed;
cparams.logits_all = params.logits_all;
cparams.embeddings = params.embedding;
@ -2359,6 +2662,22 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
return cparams;
}
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
struct ggml_threadpool_params tpp;
ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults
if (params.mask_valid) {
std::memcpy(&tpp.cpumask, &params.cpumask, GGML_MAX_N_THREADS);
}
tpp.prio = params.priority;
tpp.poll = params.poll;
tpp.strict_cpu = params.strict_cpu;
return tpp;
}
#ifdef LLAMA_USE_CURL
static bool starts_with(const std::string & str, const std::string & prefix) {
@ -3348,7 +3667,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);

33
common/common.h
View file

@ -67,13 +67,18 @@ enum dimre_method {
DIMRE_METHOD_MEAN,
};
struct cpu_params {
int n_threads = -1;
bool cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
bool mask_valid = false; // Default: any CPU
enum ggml_sched_priority priority = GGML_SCHED_PRIO_NORMAL; // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
bool strict_cpu = false; // Use strict CPU placement
uint32_t poll = 50; // Polling (busywait) level (0 - no polling, 100 - mostly polling)
};
struct gpt_params {
uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed
int32_t n_threads = cpu_get_num_math();
int32_t n_threads_draft = -1;
int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
int32_t n_threads_batch_draft = -1;
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 0; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
@ -100,6 +105,11 @@ struct gpt_params {
int32_t yarn_orig_ctx = 0; // YaRN original context length
float defrag_thold = -1.0f; // KV cache defragmentation threshold
struct cpu_params cpuparams;
struct cpu_params cpuparams_batch;
struct cpu_params draft_cpuparams;
struct cpu_params draft_cpuparams_batch;
ggml_backend_sched_eval_callback cb_eval = nullptr;
void * cb_eval_user_data = nullptr;
@ -204,7 +214,7 @@ struct gpt_params {
int32_t port = 8080; // server listens on this network port
int32_t timeout_read = 600; // http read timeout in seconds
int32_t timeout_write = timeout_read; // http write timeout in seconds
int32_t n_threads_http = -1; // number of threads to process HTTP requests
int n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool)
std::string hostname = "127.0.0.1";
std::string public_path = "";
@ -265,6 +275,9 @@ struct gpt_params {
bool spm_infill = false; // suffix/prefix/middle pattern for infill
std::string lora_outfile = "ggml-lora-merged-f16.gguf";
// batched-bench params
bool batched_bench_output_jsonl = false;
};
void gpt_params_parse_from_env(gpt_params & params);
@ -277,6 +290,11 @@ void gpt_params_print_usage(int argc, char ** argv, const gpt_params & params);
std::string gpt_params_get_system_info(const gpt_params & params);
bool parse_cpu_range(const std::string& range, bool(&boolmask)[GGML_MAX_N_THREADS]);
bool parse_cpu_mask(const std::string& mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model = nullptr);
bool set_process_priority(enum ggml_sched_priority prio);
//
// String utils
//
@ -327,8 +345,9 @@ struct llama_init_result {
struct llama_init_result llama_init_from_gpt_params(gpt_params & params);
struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params);
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params);
struct llama_context_params llama_context_params_from_gpt_params (const gpt_params & params);
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params);
struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params);

131
convert_hf_to_gguf.py
View file

@ -3,6 +3,7 @@
from __future__ import annotations
import ast
import logging
import argparse
import contextlib
@ -298,12 +299,29 @@ class Model:
gguf.MODEL_TENSOR.POS_EMBD,
gguf.MODEL_TENSOR.TOKEN_TYPES,
gguf.MODEL_TENSOR.SSM_CONV1D,
gguf.MODEL_TENSOR.TIME_MIX_FIRST,
gguf.MODEL_TENSOR.TIME_MIX_W1,
gguf.MODEL_TENSOR.TIME_MIX_W2,
)
)
or not name.endswith(".weight")
or not new_name.endswith(".weight")
):
data_qtype = gguf.GGMLQuantizationType.F32
if data_qtype is False and any(
self.match_model_tensor_name(new_name, key, bid)
for key in (
gguf.MODEL_TENSOR.TOKEN_EMBD,
gguf.MODEL_TENSOR.OUTPUT,
)
):
if self.ftype in (
gguf.LlamaFileType.MOSTLY_TQ1_0,
gguf.LlamaFileType.MOSTLY_TQ2_0,
):
# TODO: use Q4_K and Q6_K
data_qtype = gguf.GGMLQuantizationType.F16
# No override (data_qtype is False), or wants to be quantized (data_qtype is True)
if isinstance(data_qtype, bool):
if self.ftype == gguf.LlamaFileType.ALL_F32:
@ -314,6 +332,10 @@ class Model:
data_qtype = gguf.GGMLQuantizationType.BF16
elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
data_qtype = gguf.GGMLQuantizationType.Q8_0
elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ1_0:
data_qtype = gguf.GGMLQuantizationType.TQ1_0
elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ2_0:
data_qtype = gguf.GGMLQuantizationType.TQ2_0
else:
raise ValueError(f"Unknown file type: {self.ftype.name}")
@ -1619,15 +1641,16 @@ class BitnetModel(Model):
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(1.0)
def weight_quant(self, weight):
def weight_quant(self, weight: Tensor) -> Tensor:
dtype = weight.dtype
weight = weight.float()
s = 1 / weight.abs().mean().clamp(min=1e-5)
weight = (weight * s).round().clamp(-1, 1) / s
scale = weight.abs().max().unsqueeze(0)
weight = torch.where(weight.abs().less(1e-6), 0, weight).type(dtype)
weight = torch.sign(weight).type(dtype)
return weight.type(dtype), scale.type(torch.float32)
scale = weight.abs().mean().clamp(min=1e-5)
iscale = 1 / scale
# TODO: multiply by the scale directly instead of inverting it twice
# (this is also unnecessarily doubly inverted upstream)
# ref: https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/af89e318d78a70802061246bf037199d2fb97020/utils_quant.py#L10
result = (weight * iscale).round().clamp(-1, 1) / iscale
return result.type(dtype)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
new_name = self.map_tensor_name(name)
@ -1642,11 +1665,9 @@ class BitnetModel(Model):
gguf.MODEL_TENSOR.FFN_GATE,
]):
# transform weight into 1/0/-1 (in fp32)
weight_torch, scale_torch = self.weight_quant(data_torch)
yield (new_name, weight_torch)
yield (new_name.removesuffix(".weight") + ".scale", scale_torch)
else:
yield (new_name, data_torch)
data_torch = self.weight_quant(data_torch)
yield (new_name, data_torch)
@Model.register("GrokForCausalLM")
@ -2716,6 +2737,84 @@ class StarCoder2Model(Model):
model_arch = gguf.MODEL_ARCH.STARCODER2
@Model.register("Rwkv6ForCausalLM")
class Rwkv6Model(Model):
model_arch = gguf.MODEL_ARCH.RWKV6
def set_vocab(self):
assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file()
vocab_size = self.hparams.get("vocab_size", 65536)
tokens: list[bytes] = ['<s>'.encode("utf-8")]
toktypes: list[int] = [gguf.TokenType.CONTROL]
with open(self.dir_model / "rwkv_vocab_v20230424.txt", "r", encoding="utf-8") as f:
lines = f.readlines()
for line in lines:
parts = line.split(' ')
assert len(parts) >= 3
token, token_len = ast.literal_eval(' '.join(parts[1:-1])), int(parts[-1])
token = token.encode("utf-8") if isinstance(token, str) else token
assert isinstance(token, bytes)
assert len(token) == token_len
token_text: str = repr(token)[2:-1] # "b'\xff'" -> "\xff"
tokens.append(token_text.encode("utf-8"))
toktypes.append(gguf.TokenType.NORMAL)
remainder = vocab_size - len(tokens)
assert remainder >= 0
for i in range(len(tokens), vocab_size):
tokens.append(f"[PAD{i}]".encode("utf-8"))
toktypes.append(gguf.TokenType.UNUSED)
self.gguf_writer.add_tokenizer_model("rwkv")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
head_size = self.hparams["head_size"]
hidden_size = self.hparams["hidden_size"]
layer_norm_eps = self.hparams["layer_norm_epsilon"]
rescale_every_n_layers = self.hparams["rescale_every"]
intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else int((hidden_size * 3.5) // 32 * 32)
time_mix_extra_dim = 64 if hidden_size == 4096 else 32
time_decay_extra_dim = 128 if hidden_size == 4096 else 64
# RWKV isn't context limited
self.gguf_writer.add_context_length(1048576)
self.gguf_writer.add_embedding_length(hidden_size)
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
self.gguf_writer.add_rescale_every_n_layers(rescale_every_n_layers)
self.gguf_writer.add_wkv_head_size(head_size)
self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
self.gguf_writer.add_feed_forward_length(intermediate_size)
self.gguf_writer.add_file_type(self.ftype)
# required by llama.cpp, unused
self.gguf_writer.add_head_count(0)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
new_name = self.map_tensor_name(name)
if not (new_name.endswith(".weight") or new_name.endswith(".bias")):
new_name += ".weight"
if new_name.endswith("time_mix_w1.weight") or new_name.endswith("time_mix_decay_w1.weight") or new_name.endswith("time_mix_decay_w2.weight"):
data_torch = data_torch.transpose(0, 1)
if new_name.endswith("time_mix_w2.weight"):
data_torch = data_torch.permute(0, 2, 1)
rescale_every_n_layers = self.hparams["rescale_every"]
if rescale_every_n_layers > 0:
if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
yield (new_name, data_torch)
@Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
class MambaModel(Model):
model_arch = gguf.MODEL_ARCH.MAMBA
@ -3929,8 +4028,8 @@ def parse_args() -> argparse.Namespace:
help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
)
parser.add_argument(
"--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
"--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "tq1_0", "tq2_0", "auto"], default="f16",
help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
)
parser.add_argument(
"--bigendian", action="store_true",
@ -4017,6 +4116,8 @@ def main() -> None:
"f16": gguf.LlamaFileType.MOSTLY_F16,
"bf16": gguf.LlamaFileType.MOSTLY_BF16,
"q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
"tq1_0": gguf.LlamaFileType.MOSTLY_TQ1_0,
"tq2_0": gguf.LlamaFileType.MOSTLY_TQ2_0,
"auto": gguf.LlamaFileType.GUESSED,
}

4
docs/backend/SYCL.md
View file

@ -336,12 +336,12 @@ Choose one of following methods to run.
- Use device 0:
```sh
./examples/sycl/run_llama2.sh 0
./examples/sycl/run-llama2.sh 0
```
- Use multiple devices:
```sh
./examples/sycl/run_llama2.sh
./examples/sycl/run-llama2.sh
```
2. Command line

2
docs/docker.md
View file

@ -20,7 +20,7 @@ Additionally, there the following images, similar to the above:
- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
## Usage

2
examples/baby-llama/baby-llama.cpp
View file

@ -18,7 +18,7 @@ constexpr float rms_norm_eps = 5e-6f;
#endif
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads, nullptr);
if (plan.work_size > 0) {
buf.resize(plan.work_size);

9
examples/batched-bench/README.md
View file

@ -49,3 +49,12 @@ There are 2 modes of operation:
| 128 | 256 | 8 | 3072 | 0.751 | 1363.92 | 15.110 | 135.54 | 15.861 | 193.69 |
| 128 | 256 | 16 | 6144 | 1.569 | 1304.93 | 18.073 | 226.64 | 19.642 | 312.80 |
| 128 | 256 | 32 | 12288 | 3.409 | 1201.35 | 19.223 | 426.15 | 22.633 | 542.93 |
### JSONL output
Pass `--output-format jsonl` to output JSONL instead of Markdown, á la
```json lines
{"n_kv_max": 2048, "n_batch": 2048, "n_ubatch": 512, "flash_attn": 0, "is_pp_shared": 0, "n_gpu_layers": 99, "n_threads": 8, "n_threads_batch": 8, "pp": 128, "tg": 128, "pl": 1, "n_kv": 256, "t_pp": 0.233810, "speed_pp": 547.453064, "t_tg": 3.503684, "speed_tg": 36.532974, "t": 3.737494, "speed": 68.495094}
{"n_kv_max": 2048, "n_batch": 2048, "n_ubatch": 512, "flash_attn": 0, "is_pp_shared": 0, "n_gpu_layers": 99, "n_threads": 8, "n_threads_batch": 8, "pp": 128, "tg": 128, "pl": 2, "n_kv": 512, "t_pp": 0.422602, "speed_pp": 605.770935, "t_tg": 11.106112, "speed_tg": 23.050371, "t": 11.528713, "speed": 44.410854}
```

24
examples/batched-bench/batched-bench.cpp
View file

@ -122,12 +122,13 @@ int main(int argc, char ** argv) {
}
}
LOG_TEE("\n");
LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("\n");
LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
if (!params.batched_bench_output_jsonl) {
LOG_TEE("\n");
LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("\n");
LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
}
for ( int i_pp = 0; i_pp < (int) n_pp.size(); ++i_pp) {
for ( int i_tg = 0; i_tg < (int) n_tg.size(); ++i_tg) {
@ -195,7 +196,16 @@ int main(int argc, char ** argv) {
const float speed_tg = pl*tg / t_tg;
const float speed = n_kv / t;
LOG_TEE("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed);
if(params.batched_bench_output_jsonl) {
LOG_TEE(
"{\"n_kv_max\": %d, \"n_batch\": %d, \"n_ubatch\": %d, \"flash_attn\": %d, \"is_pp_shared\": %d, \"n_gpu_layers\": %d, \"n_threads\": %u, \"n_threads_batch\": %u, "
"\"pp\": %d, \"tg\": %d, \"pl\": %d, \"n_kv\": %d, \"t_pp\": %f, \"speed_pp\": %f, \"t_tg\": %f, \"speed_tg\": %f, \"t\": %f, \"speed\": %f}\n",
n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch,
pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed
);
} else {
LOG_TEE("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed);
}
}
}
}

4
examples/benchmark/benchmark-matmult.cpp
View file

@ -21,7 +21,7 @@
#endif
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads, nullptr);
if (plan.work_size > 0) {
buf.resize(plan.work_size);
@ -54,7 +54,7 @@ static void tensor_dump(const ggml_tensor * tensor, const char * name) {
#define TENSOR_DUMP(tensor) tensor_dump(tensor, #tensor)
struct benchmark_params_struct {
int32_t n_threads = 1;
int n_threads = 1;
int32_t n_iterations = 10;
};

4
examples/cvector-generator/cvector-generator.cpp
View file

@ -486,8 +486,8 @@ int main(int argc, char ** argv) {
if (use_pca) {
// run PCA
PCA::pca_params pca_params;
pca_params.n_threads = params.n_threads;
pca_params.n_batch = params.n_pca_batch;
pca_params.n_threads = params.cpuparams.n_threads;
pca_params.n_batch = params.n_pca_batch;
pca_params.n_iterations = params.n_pca_iterations;
PCA::run_pca(pca_params, ctx_train.v_diff, ctx_train.v_final);
} else {

2
examples/export-lora/export-lora.cpp
View file

@ -410,7 +410,7 @@ int main(int argc, char ** argv) {
g_verbose = (params.verbosity == 1);
try {
lora_merge_ctx ctx(params.model, params.lora_adapters, params.lora_outfile, params.n_threads);
lora_merge_ctx ctx(params.model, params.lora_adapters, params.lora_outfile, params.cpuparams.n_threads);
ctx.run_merge();
} catch (const std::exception & err) {
fprintf(stderr, "%s\n", err.what());

65
examples/llama-bench/README.md
View file

@ -14,7 +14,8 @@ Performance testing tool for llama.cpp.
1. [Markdown](#markdown)
2. [CSV](#csv)
3. [JSON](#json)
4. [SQL](#sql)
4. [JSONL](#jsonl)
5. [SQL](#sql)
## Syntax
@ -23,27 +24,34 @@ usage: ./llama-bench [options]
options:
-h, --help
-m, --model <filename> (default: models/7B/ggml-model-q4_0.gguf)
-p, --n-prompt <n> (default: 512)
-n, --n-gen <n> (default: 128)
-pg <pp,tg> (default: 512,128)
-b, --batch-size <n> (default: 2048)
-ub, --ubatch-size <n> (default: 512)
-ctk, --cache-type-k <t> (default: f16)
-ctv, --cache-type-v <t> (default: f16)
-t, --threads <n> (default: 16)
-ngl, --n-gpu-layers <n> (default: 99)
-sm, --split-mode <none|layer|row> (default: layer)
-mg, --main-gpu <i> (default: 0)
-nkvo, --no-kv-offload <0|1> (default: 0)
-fa, --flash-attn <0|1> (default: 0)
-mmp, --mmap <0|1> (default: 1)
--numa <distribute|isolate|numactl> (default: disabled)
-embd, --embeddings <0|1> (default: 0)
-ts, --tensor-split <ts0/ts1/..> (default: 0)
-r, --repetitions <n> (default: 5)
-o, --output <csv|json|md|sql> (default: md)
-v, --verbose (default: 0)
-m, --model <filename> (default: models/7B/ggml-model-q4_0.gguf)
-p, --n-prompt <n> (default: 512)
-n, --n-gen <n> (default: 128)
-pg <pp,tg> (default: )
-b, --batch-size <n> (default: 2048)
-ub, --ubatch-size <n> (default: 512)
-ctk, --cache-type-k <t> (default: f16)
-ctv, --cache-type-v <t> (default: f16)
-t, --threads <n> (default: 8)
-C, --cpu-mask <hex,hex> (default: 0x0)
--cpu-strict <0|1> (default: 0)
--poll <0...100> (default: 50)
-ngl, --n-gpu-layers <n> (default: 99)
-rpc, --rpc <rpc_servers> (default: )
-sm, --split-mode <none|layer|row> (default: layer)
-mg, --main-gpu <i> (default: 0)
-nkvo, --no-kv-offload <0|1> (default: 0)
-fa, --flash-attn <0|1> (default: 0)
-mmp, --mmap <0|1> (default: 1)
--numa <distribute|isolate|numactl> (default: disabled)
-embd, --embeddings <0|1> (default: 0)
-ts, --tensor-split <ts0/ts1/..> (default: 0)
-r, --repetitions <n> (default: 5)
--prio <0|1|2|3> (default: 0)
--delay <0...N> (seconds) (default: 0)
-o, --output <csv|json|jsonl|md|sql> (default: md)
-oe, --output-err <csv|json|jsonl|md|sql> (default: none)
-v, --verbose (default: 0)
Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.
```
@ -238,6 +246,19 @@ $ ./llama-bench -o json
]
```
### JSONL
```sh
$ ./llama-bench -o jsonl
```
```json lines
{"build_commit":"3469684","build_number":1275,"cuda":true,"metal":false,"gpu_blas":true,"blas":true,"cpu_info":"13th Gen Intel(R) Core(TM) i9-13900K","gpu_info":"NVIDIA GeForce RTX 3090 Ti","model_filename":"models/7B/ggml-model-q4_0.gguf","model_type":"llama 7B mostly Q4_0","model_size":3825065984,"model_n_params":6738415616,"n_batch":512,"n_threads":16,"f16_kv":true,"n_gpu_layers":99,"main_gpu":0,"mul_mat_q":true,"tensor_split":"0.00","n_prompt":512,"n_gen":0,"test_time":"2023-09-23T12:09:57Z","avg_ns":212365953,"stddev_ns":985423,"avg_ts":2410.974041,"stddev_ts":11.163766,"samples_ns":[213837238,211635853,212328053,211329715,212698907],"samples_ts":[2394.34,2419.25,2411.36,2422.75,2407.16]}
{"build_commit":"3469684","build_number":1275,"cuda":true,"metal":false,"gpu_blas":true,"blas":true,"cpu_info":"13th Gen Intel(R) Core(TM) i9-13900K","gpu_info":"NVIDIA GeForce RTX 3090 Ti","model_filename":"models/7B/ggml-model-q4_0.gguf","model_type":"llama 7B mostly Q4_0","model_size":3825065984,"model_n_params":6738415616,"n_batch":512,"n_threads":16,"f16_kv":true,"n_gpu_layers":99,"main_gpu":0,"mul_mat_q":true,"tensor_split":"0.00","n_prompt":0,"n_gen":128,"test_time":"2023-09-23T12:09:59Z","avg_ns":977425219,"stddev_ns":9268593,"avg_ts":130.965708,"stddev_ts":1.238924,"samples_ns":[984472709,974901233,989474741,970729355,967548060],"samples_ts":[130.019,131.295,129.362,131.86,132.293]}
```
### SQL
SQL output is suitable for importing into a SQLite database. The output can be piped into the `sqlite3` command line tool to add the results to a database.

289
examples/llama-bench/llama-bench.cpp
View file

@ -16,6 +16,7 @@
#include <sstream>
#include <string>
#include <vector>
#include <thread>
#include "ggml.h"
#include "llama.h"
@ -123,6 +124,9 @@ static std::string get_cpu_info() {
(LPBYTE)cpu_brand,
&cpu_brand_size) == ERROR_SUCCESS) {
id.assign(cpu_brand, cpu_brand_size);
if (id.find('\0') != std::string::npos) {
id.resize(id.find('\0'));
}
}
RegCloseKey(hKey);
#endif
@ -170,13 +174,14 @@ static std::string get_gpu_info() {
}
// command line params
enum output_formats {NONE, CSV, JSON, MARKDOWN, SQL};
enum output_formats {NONE, CSV, JSON, JSONL, MARKDOWN, SQL};
static const char * output_format_str(output_formats format) {
switch (format) {
case NONE: return "none";
case CSV: return "csv";
case JSON: return "json";
case JSONL: return "jsonl";
case MARKDOWN: return "md";
case SQL: return "sql";
default: GGML_ABORT("invalid output format");
@ -190,6 +195,8 @@ static bool output_format_from_str(const std::string & s, output_formats & forma
format = CSV;
} else if (s == "json") {
format = JSON;
} else if (s == "jsonl") {
format = JSONL;
} else if (s == "md") {
format = MARKDOWN;
} else if (s == "sql") {
@ -225,6 +232,9 @@ struct cmd_params {
std::vector<ggml_type> type_k;
std::vector<ggml_type> type_v;
std::vector<int> n_threads;
std::vector<std::string> cpu_mask;
std::vector<bool> cpu_strict;
std::vector<int> poll;
std::vector<int> n_gpu_layers;
std::vector<std::string> rpc_servers;
std::vector<llama_split_mode> split_mode;
@ -236,7 +246,10 @@ struct cmd_params {
std::vector<bool> embeddings;
ggml_numa_strategy numa;
int reps;
ggml_sched_priority prio;
int delay;
bool verbose;
bool progress;
output_formats output_format;
output_formats output_format_stderr;
};
@ -251,6 +264,9 @@ static const cmd_params cmd_params_defaults = {
/* type_k */ {GGML_TYPE_F16},
/* type_v */ {GGML_TYPE_F16},
/* n_threads */ {cpu_get_num_math()},
/* cpu_mask */ {"0x0"},
/* cpu_strict */ {false},
/* poll */ {50},
/* n_gpu_layers */ {99},
/* rpc_servers */ {""},
/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
@ -262,7 +278,10 @@ static const cmd_params cmd_params_defaults = {
/* embeddings */ {false},
/* numa */ GGML_NUMA_STRATEGY_DISABLED,
/* reps */ 5,
/* prio */ GGML_SCHED_PRIO_NORMAL,
/* delay */ 0,
/* verbose */ false,
/* progress */ false,
/* output_format */ MARKDOWN,
/* output_format_stderr */ NONE,
};
@ -272,29 +291,37 @@ static void print_usage(int /* argc */, char ** argv) {
printf("\n");
printf("options:\n");
printf(" -h, --help\n");
printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
printf(" -pg <pp,tg> (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str());
printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
printf(" -ub, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
printf(" -ctk, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
printf(" -ctv, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -rpc, --rpc <rpc_servers> (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str());
printf(" -sm, --split-mode <none|layer|row> (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str());
printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
printf(" --numa <distribute|isolate|numactl> (default: disabled)\n");
printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
printf(" -o, --output <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
printf(" -oe, --output-err <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr));
printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
printf(" -pg <pp,tg> (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str());
printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
printf(" -ub, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
printf(" -ctk, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
printf(" -ctv, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -C, --cpu-mask <hex,hex> (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str());
printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str());
printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
#ifdef GGML_USE_RPC
printf(" -rpc, --rpc <rpc_servers> (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str());
#endif
printf(" -sm, --split-mode <none|layer|row> (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str());
printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
printf(" --numa <distribute|isolate|numactl> (default: disabled)\n");
printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio);
printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay);
printf(" -o, --output <csv|json|jsonl|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
printf(" -oe, --output-err <csv|json|jsonl|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr));
printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
printf(" --progress (default: %s)\n", cmd_params_defaults.progress ? "1" : "0");
printf("\n");
printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
}
@ -338,6 +365,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
params.output_format_stderr = cmd_params_defaults.output_format_stderr;
params.reps = cmd_params_defaults.reps;
params.numa = cmd_params_defaults.numa;
params.prio = cmd_params_defaults.prio;
params.delay = cmd_params_defaults.delay;
params.progress = cmd_params_defaults.progress;
for (int i = 1; i < argc; i++) {
arg = argv[i];
@ -433,6 +463,27 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = string_split<int>(argv[i], split_delim);
params.n_threads.insert(params.n_threads.end(), p.begin(), p.end());
} else if (arg == "-C" || arg == "--cpu-mask") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = string_split<std::string>(argv[i], split_delim);
params.cpu_mask.insert(params.cpu_mask.end(), p.begin(), p.end());
} else if (arg == "--cpu-strict") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = string_split<bool>(argv[i], split_delim);
params.cpu_strict.insert(params.cpu_strict.end(), p.begin(), p.end());
} else if (arg == "--poll") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = string_split<int>(argv[i], split_delim);
params.poll.insert(params.poll.end(), p.begin(), p.end());
} else if (arg == "-ngl" || arg == "--n-gpu-layers") {
if (++i >= argc) {
invalid_param = true;
@ -440,12 +491,14 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = string_split<int>(argv[i], split_delim);
params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
#ifdef GGML_USE_RPC
} else if (arg == "-rpc" || arg == "--rpc") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rpc_servers.push_back(argv[i]);
#endif
} else if (arg == "-sm" || arg == "--split-mode") {
if (++i >= argc) {
invalid_param = true;
@ -541,6 +594,18 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
break;
}
params.reps = std::stoi(argv[i]);
} else if (arg == "--prio") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.prio = (enum ggml_sched_priority) std::stoi(argv[i]);
} else if (arg == "--delay") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.delay = std::stoi(argv[i]);
} else if (arg == "-o" || arg == "--output") {
if (++i >= argc) {
invalid_param = true;
@ -555,6 +620,8 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
invalid_param = !output_format_from_str(argv[i], params.output_format_stderr);
} else if (arg == "-v" || arg == "--verbose") {
params.verbose = true;
} else if (arg == "--progress") {
params.progress = true;
} else {
invalid_param = true;
break;
@ -585,6 +652,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; }
if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; }
if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
if (params.cpu_mask.empty()) { params.cpu_mask = cmd_params_defaults.cpu_mask; }
if (params.cpu_strict.empty()) { params.cpu_strict = cmd_params_defaults.cpu_strict; }
if (params.poll.empty()) { params.poll = cmd_params_defaults.poll; }
return params;
}
@ -598,6 +668,9 @@ struct cmd_params_instance {
ggml_type type_k;
ggml_type type_v;
int n_threads;
std::string cpu_mask;
bool cpu_strict;
int poll;
int n_gpu_layers;
std::string rpc_servers;
llama_split_mode split_mode;
@ -667,7 +740,10 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & tv : params.type_v)
for (const auto & nkvo : params.no_kv_offload)
for (const auto & fa : params.flash_attn)
for (const auto & nt : params.n_threads) {
for (const auto & nt : params.n_threads)
for (const auto & cm : params.cpu_mask)
for (const auto & cs : params.cpu_strict)
for (const auto & pl : params.poll) {
for (const auto & n_prompt : params.n_prompt) {
if (n_prompt == 0) {
continue;
@ -681,6 +757,9 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .cpu_mask = */ cm,
/* .cpu_strict = */ cs,
/* .poll = */ pl,
/* .n_gpu_layers = */ nl,
/* .rpc_servers = */ rpc,
/* .split_mode = */ sm,
@ -707,6 +786,9 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .cpu_mask = */ cm,
/* .cpu_strict = */ cs,
/* .poll = */ pl,
/* .n_gpu_layers = */ nl,
/* .rpc_servers = */ rpc,
/* .split_mode = */ sm,
@ -733,6 +815,9 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .cpu_mask = */ cm,
/* .cpu_strict = */ cs,
/* .poll = */ pl,
/* .n_gpu_layers = */ nl,
/* .rpc_servers = */ rpc,
/* .split_mode = */ sm,
@ -769,6 +854,9 @@ struct test {
int n_batch;
int n_ubatch;
int n_threads;
std::string cpu_mask;
bool cpu_strict;
int poll;
bool has_rpc;
ggml_type type_k;
ggml_type type_v;
@ -795,6 +883,9 @@ struct test {
n_batch = inst.n_batch;
n_ubatch = inst.n_ubatch;
n_threads = inst.n_threads;
cpu_mask = inst.cpu_mask;
cpu_strict = inst.cpu_strict;
poll = inst.poll;
has_rpc = !inst.rpc_servers.empty();
type_k = inst.type_k;
type_v = inst.type_v;
@ -872,13 +963,14 @@ struct test {
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_ubatch",
"n_threads", "type_k", "type_v",
"n_threads", "cpu_mask", "cpu_strict", "poll",
"type_k", "type_v",
"n_gpu_layers", "split_mode",
"main_gpu", "no_kv_offload", "flash_attn",
"tensor_split", "use_mmap", "embeddings",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
"avg_ts", "stddev_ts"
"avg_ts", "stddev_ts",
};
return fields;
}
@ -887,7 +979,7 @@ struct test {
static field_type get_field_type(const std::string & field) {
if (field == "build_number" || field == "n_batch" || field == "n_ubatch" ||
field == "n_threads" ||
field == "n_threads" || field == "poll" ||
field == "model_size" || field == "model_n_params" ||
field == "n_gpu_layers" || field == "main_gpu" ||
field == "n_prompt" || field == "n_gen" ||
@ -896,6 +988,7 @@ struct test {
}
if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" ||
field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
field == "cpu_strict" ||
field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
return BOOL;
}
@ -928,7 +1021,8 @@ struct test {
cpu_info, gpu_info,
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
std::to_string(n_batch), std::to_string(n_ubatch),
std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_threads), cpu_mask, std::to_string(cpu_strict), std::to_string(poll),
ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), split_mode_str(split_mode),
std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn),
tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
@ -996,38 +1090,39 @@ struct csv_printer : public printer {
}
};
static std::string escape_json(const std::string & value) {
std::string escaped;
for (auto c : value) {
if (c == '"') {
escaped += "\\\"";
} else if (c == '\\') {
escaped += "\\\\";
} else if (c <= 0x1f) {
char buf[8];
snprintf(buf, sizeof(buf), "\\u%04x", c);
escaped += buf;
} else {
escaped += c;
}
}
return escaped;
}
static std::string format_json_value(const std::string & field, const std::string & value) {
switch (test::get_field_type(field)) {
case test::STRING:
return "\"" + escape_json(value) + "\"";
case test::BOOL:
return value == "0" ? "false" : "true";
default:
return value;
}
}
struct json_printer : public printer {
bool first = true;
static std::string escape_json(const std::string & value) {
std::string escaped;
for (auto c : value) {
if (c == '"') {
escaped += "\\\"";
} else if (c == '\\') {
escaped += "\\\\";
} else if (c <= 0x1f) {
char buf[8];
snprintf(buf, sizeof(buf), "\\u%04x", c);
escaped += buf;
} else {
escaped += c;
}
}
return escaped;
}
static std::string format_value(const std::string & field, const std::string & value) {
switch (test::get_field_type(field)) {
case test::STRING:
return "\"" + escape_json(value) + "\"";
case test::BOOL:
return value == "0" ? "false" : "true";
default:
return value;
}
}
void print_header(const cmd_params & params) override {
fprintf(fout, "[\n");
(void) params;
@ -1036,7 +1131,7 @@ struct json_printer : public printer {
void print_fields(const std::vector<std::string> & fields, const std::vector<std::string> & values) {
assert(fields.size() == values.size());
for (size_t i = 0; i < fields.size(); i++) {
fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(), format_value(fields.at(i), values.at(i)).c_str());
fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(), format_json_value(fields.at(i), values.at(i)).c_str());
}
}
@ -1059,6 +1154,25 @@ struct json_printer : public printer {
}
};
struct jsonl_printer : public printer {
void print_fields(const std::vector<std::string> & fields, const std::vector<std::string> & values) {
assert(fields.size() == values.size());
for (size_t i = 0; i < fields.size(); i++) {
fprintf(fout, "\"%s\": %s, ", fields.at(i).c_str(), format_json_value(fields.at(i), values.at(i)).c_str());
}
}
void print_test(const test & t) override {
fprintf(fout, "{");
print_fields(test::get_fields(), t.get_values());
fprintf(fout, "\"samples_ns\": [ %s ],", join(t.samples_ns, ", ").c_str());
fprintf(fout, "\"samples_ts\": [ %s ]", join(t.get_ts(), ", ").c_str());
fprintf(fout, "}\n");
fflush(fout);
}
};
struct markdown_printer : public printer {
std::vector<std::string> fields;
@ -1067,7 +1181,7 @@ struct markdown_printer : public printer {
return -30;
}
if (field == "t/s") {
return 16;
return 20;
}
if (field == "size" || field == "params") {
return 10;
@ -1149,6 +1263,15 @@ struct markdown_printer : public printer {
if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
fields.emplace_back("n_threads");
}
if (params.cpu_mask.size() > 1 || params.cpu_mask != cmd_params_defaults.cpu_mask) {
fields.emplace_back("cpu_mask");
}
if (params.cpu_strict.size() > 1 || params.cpu_strict != cmd_params_defaults.cpu_strict) {
fields.emplace_back("cpu_strict");
}
if (params.poll.size() > 1 || params.poll != cmd_params_defaults.poll) {
fields.emplace_back("poll");
}
if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
fields.emplace_back("n_batch");
}
@ -1350,6 +1473,8 @@ static std::unique_ptr<printer> create_printer(output_formats format) {
return std::unique_ptr<printer>(new csv_printer());
case JSON:
return std::unique_ptr<printer>(new json_printer());
case JSONL:
return std::unique_ptr<printer>(new jsonl_printer());
case MARKDOWN:
return std::unique_ptr<printer>(new markdown_printer());
case SQL:
@ -1383,6 +1508,8 @@ int main(int argc, char ** argv) {
llama_backend_init();
llama_numa_init(params.numa);
set_process_priority(params.prio);
// initialize printer
std::unique_ptr<printer> p = create_printer(params.output_format);
std::unique_ptr<printer> p_err = create_printer(params.output_format_stderr);
@ -1402,7 +1529,13 @@ int main(int argc, char ** argv) {
llama_model * lmodel = nullptr;
const cmd_params_instance * prev_inst = nullptr;
int params_idx = 0;
auto params_count = params_instances.size();
for (const auto & inst : params_instances) {
params_idx ++;
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: starting\n", params_idx, params_count);
}
// keep the same model between tests when possible
if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
if (lmodel) {
@ -1428,12 +1561,40 @@ int main(int argc, char ** argv) {
llama_kv_cache_clear(ctx);
// cool off before the test
if (params.delay) {
std::this_thread::sleep_for(std::chrono::seconds(params.delay));
}
struct ggml_threadpool_params tpp = ggml_threadpool_params_default(t.n_threads);
if (!parse_cpu_mask(t.cpu_mask, tpp.cpumask)) {
fprintf(stderr, "%s: failed to parse cpu-mask: %s\n", __func__, t.cpu_mask.c_str());
exit(1);
}
tpp.strict_cpu = t.cpu_strict;
tpp.poll = t.poll;
tpp.prio = params.prio;
struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
if (!threadpool) {
fprintf(stderr, "%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads);
exit(1);
}
llama_attach_threadpool(ctx, threadpool, NULL);
// warmup run
if (t.n_prompt > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count);
}
//test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count);
}
test_gen(ctx, 1, 0, t.n_threads);
}
@ -1443,9 +1604,15 @@ int main(int argc, char ** argv) {
uint64_t t_start = get_time_ns();
if (t.n_prompt > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps);
}
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps);
}
test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
}
@ -1466,6 +1633,8 @@ int main(int argc, char ** argv) {
llama_print_timings(ctx);
llama_free(ctx);
ggml_threadpool_free(threadpool);
}
llama_free_model(lmodel);

4
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -71,8 +71,8 @@ actor LlamaContext {
var ctx_params = llama_context_default_params()
ctx_params.seed = 1234
ctx_params.n_ctx = 2048
ctx_params.n_threads = UInt32(n_threads)
ctx_params.n_threads_batch = UInt32(n_threads)
ctx_params.n_threads = Int32(n_threads)
ctx_params.n_threads_batch = Int32(n_threads)
let context = llama_new_context_with_model(model, ctx_params)
guard let context else {

6
examples/llava/clip.cpp
View file

@ -1623,7 +1623,7 @@ static void normalize_image_u8_to_f32(const clip_image_u8* src, clip_image_f32*
}
}
inline float clip(float x, float lower, float upper) {
inline int clip(int x, int lower, int upper) {
return std::max(lower, std::min(x, upper));
}
@ -1827,10 +1827,6 @@ static std::pair<int, int> uhd_get_refine_size(std::pair<int, int> original_size
return refine_size;
}
inline int clip(int x, int lower, int upper) {
return std::max(lower, std::min(x, upper));
}
static std::pair<int, int> uhd_best_grid(const int max_slice_nums, const int multiple, const float log_ratio) {
std::vector<int> candidate_split_grids_nums;
for (int i : {multiple - 1, multiple, multiple + 1}) {

4
examples/llava/llava-cli.cpp
View file

@ -129,14 +129,14 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
if (!params->image.empty()) {
LOG_TEE("using base64 encoded image instead of command line image path\n");
}
embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
if (!embed) {
LOG_TEE("%s: can't load image from prompt\n", __func__);
return NULL;
}
params->prompt = remove_image_from_prompt(prompt);
} else {
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, fname.c_str());
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
if (!embed) {
fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
return NULL;

2
examples/llava/minicpmv-cli.cpp
View file

@ -180,7 +180,7 @@ static const char * sample(struct llama_sampling_context * ctx_sampling,
static struct llava_context * minicpmv_init(gpt_params * params, const std::string & fname, int &n_past){
auto ctx_clip = clip_init_context(params);
auto embeds = llava_image_embed_make_with_filename(ctx_clip, params->n_threads, fname.c_str());
auto embeds = llava_image_embed_make_with_filename(ctx_clip, params->cpuparams.n_threads, fname.c_str());
if (!embeds) {
std::cerr << "error: failed to load image " << fname << ". Terminating\n\n";
return NULL;

41
examples/main/main.cpp
View file

@ -221,6 +221,40 @@ int main(int argc, char ** argv) {
return 1;
}
LOG("%s: llama threadpool init = n_threads = %d\n",
__func__,
(int) params.cpuparams.n_threads
);
struct ggml_threadpool_params tpp_batch =
ggml_threadpool_params_from_cpu_params(params.cpuparams_batch);
struct ggml_threadpool_params tpp =
ggml_threadpool_params_from_cpu_params(params.cpuparams);
set_process_priority(params.cpuparams.priority);
struct ggml_threadpool * threadpool_batch = NULL;
if (!ggml_threadpool_params_match(&tpp, &tpp_batch)) {
threadpool_batch = ggml_threadpool_new(&tpp_batch);
if (!threadpool_batch) {
LOG_TEE("%s: batch threadpool create failed : n_threads %d\n", __func__, tpp_batch.n_threads);
exit(1);
}
// Start the non-batch threadpool in the paused state
tpp.paused = true;
}
struct ggml_threadpool * threadpool = ggml_threadpool_new(&tpp);
if (!threadpool) {
LOG_TEE("%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads);
exit(1);
}
llama_attach_threadpool(ctx, threadpool, threadpool_batch);
if (ctx_guidance) {
llama_attach_threadpool(ctx_guidance, threadpool, threadpool_batch);
}
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
LOG("n_ctx: %d\n", n_ctx);
@ -352,8 +386,8 @@ int main(int argc, char ** argv) {
}
LOGLN(
"recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu",
log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());
"recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu",
log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size());
// if we will use the cache for the full prompt without reaching the end of the cache, force
// reevaluation of the last token to recalculate the cached logits
@ -989,6 +1023,9 @@ int main(int argc, char ** argv) {
llama_sampling_free(ctx_sampling);
llama_backend_free();
ggml_threadpool_free(threadpool);
ggml_threadpool_free(threadpool_batch);
#ifndef LOG_DISABLE_LOGS
LOG_TEE("Log end\n");
#endif // LOG_DISABLE_LOGS

2
examples/quantize/quantize.cpp
View file

@ -26,6 +26,8 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", },
{ "TQ1_0", LLAMA_FTYPE_MOSTLY_TQ1_0, " 1.69 bpw ternarization", },
{ "TQ2_0", LLAMA_FTYPE_MOSTLY_TQ2_0, " 2.06 bpw ternarization", },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.96G, +3.5199 ppl @ Llama-3-8B", },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.96G, +3.1836 ppl @ Llama-3-8B", },
{ "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS, " 3.06 bpw quantization", },

977
examples/server/server.cpp
View file

File diff suppressed because it is too large Load diff

29
examples/server/tests/features/parallel.feature
View file

@ -77,6 +77,35 @@ Feature: Parallel
| disabled | 128 |
| enabled | 64 |
Scenario Outline: Multi users with number of prompts exceeding number of slots
Given a system prompt You are a writer.
And a model tinyllama-2
Given a prompt:
"""
Write a very long book.
"""
And a prompt:
"""
Write another a poem.
"""
And a prompt:
"""
What is LLM?
"""
And a prompt:
"""
The sky is blue and I love it.
"""
And <n_predict> max tokens to predict
And streaming is <streaming>
Given concurrent OAI completions requests
Then the server is busy
Then the server is idle
Then all prompts are predicted with <n_predict> tokens
Examples:
| streaming | n_predict |
| disabled | 128 |
| enabled | 64 |
Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969
Given a prompt:

4
examples/server/tests/features/passkey.feature
View file

@ -15,6 +15,7 @@ Feature: Passkey / Self-extend with context shift
And <n_junk> as number of junk
And <n_predicted> server max tokens to predict
And 42 as seed
And 0.0 temperature
And <n_ctx> KV cache size
And 1 slots
And <n_ga> group attention factor to extend context size through self-extend
@ -22,7 +23,8 @@ Feature: Passkey / Self-extend with context shift
# Can be override with N_GPU_LAYERS
And <ngl> GPU offloaded layers
Then the server is starting
Then the server is healthy
# Higher timeout because the model may need to be downloaded from the internet
Then the server is healthy with timeout 120 seconds
Given available models
Then model 0 is trained on <n_ctx_train> tokens context
Given a prefix prompt:

58
examples/server/tests/features/steps/steps.py
View file

@ -23,6 +23,8 @@ from prometheus_client import parser
# pyright: reportRedeclaration=false
DEFAULT_TIMEOUT_SECONDS = aiohttp.ClientTimeout(total=600)
@step("a server listening on {server_fqdn}:{server_port}")
def step_server_config(context, server_fqdn: str, server_port: str):
context.server_fqdn = server_fqdn
@ -200,17 +202,15 @@ def step_start_server(context):
time.sleep(0.1)
@step("the server is {expecting_status}")
@async_run_until_complete
async def step_wait_for_the_server_to_be_started(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str):
async def wait_for_server_status_with_timeout(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str, timeout: int):
match expecting_status:
case 'healthy':
await wait_for_slots_status(context, context.base_url, 200,
timeout=30)
timeout=timeout)
case 'ready' | 'idle':
await wait_for_slots_status(context, context.base_url, 200,
timeout=30,
timeout=timeout,
params={'fail_on_no_slot': 1},
slots_idle=context.n_slots,
slots_processing=0)
@ -223,6 +223,18 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status: Lite
assert False, "unknown status"
@step("the server is {expecting_status} with timeout {timeout:d} seconds")
@async_run_until_complete
async def step_wait_for_server_status_with_timeout(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str, timeout: int):
await wait_for_server_status_with_timeout(context, expecting_status, timeout)
@step("the server is {expecting_status}")
@async_run_until_complete
async def step_wait_for_server_status(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str):
await wait_for_server_status_with_timeout(context, expecting_status, 30)
@step('all slots are {expected_slot_status_string}')
@async_run_until_complete
async def step_all_slots_status(context, expected_slot_status_string: Literal['idle', 'busy'] | str):
@ -689,7 +701,7 @@ def step_tokenize_set_add_special(context):
@async_run_until_complete
async def step_tokenize(context):
context.tokenized_text = context_text(context)
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
tokenize_args = {
"content": context.tokenized_text,
}
@ -706,7 +718,7 @@ async def step_tokenize(context):
@async_run_until_complete
async def step_detokenize(context):
assert len(context.tokens) > 0
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{context.base_url}/detokenize',
json={
"tokens": context.tokens,
@ -735,7 +747,7 @@ def step_strings_for_tokenization(context):
@step('an OPTIONS request is sent from {origin}')
@async_run_until_complete
async def step_options_request(context, origin):
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin}
async with session.options(f'{context.base_url}/v1/chat/completions',
headers=headers) as response:
@ -751,7 +763,7 @@ def step_check_options_header_value(context, cors_header, cors_header_value):
@step('prometheus metrics are exposed')
@async_run_until_complete
async def step_prometheus_metrics_exported(context):
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with await session.get(f'{context.base_url}/metrics') as metrics_response:
assert metrics_response.status == 200
assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4"
@ -818,13 +830,13 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
for prompt_no in range(context.n_prompts):
shifted_args = [context.prompts.pop(), seeds[prompt_no], *args]
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
await asyncio.sleep(0.1)
await asyncio.sleep(0.01)
@step('the slot {slot_id:d} is saved with filename "{filename}"')
@async_run_until_complete
async def step_save_slot(context, slot_id, filename):
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{context.base_url}/slots/{slot_id}?action=save',
json={"filename": filename},
headers={"Content-Type": "application/json"}) as response:
@ -834,7 +846,7 @@ async def step_save_slot(context, slot_id, filename):
@step('the slot {slot_id:d} is restored with filename "{filename}"')
@async_run_until_complete
async def step_restore_slot(context, slot_id, filename):
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{context.base_url}/slots/{slot_id}?action=restore',
json={"filename": filename},
headers={"Content-Type": "application/json"}) as response:
@ -844,7 +856,7 @@ async def step_restore_slot(context, slot_id, filename):
@step('the slot {slot_id:d} is erased')
@async_run_until_complete
async def step_erase_slot(context, slot_id):
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{context.base_url}/slots/{slot_id}?action=erase',
headers={"Content-Type": "application/json"}) as response:
context.response = response
@ -853,7 +865,7 @@ async def step_erase_slot(context, slot_id):
@step('switch {on_or_off} lora adapter {lora_id:d}')
@async_run_until_complete
async def toggle_lora_adapter(context, on_or_off: str, lora_id: int):
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{context.base_url}/lora-adapters',
json=[{'id': lora_id, 'scale': 1 if on_or_off == 'on' else 0}],
headers={"Content-Type": "application/json"}) as response:
@ -889,7 +901,7 @@ async def request_completion(prompt,
print(f"Set user_api_key: {user_api_key}")
headers['Authorization'] = f'Bearer {user_api_key}'
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{base_url}/completion',
json={
"input_prefix": prompt_prefix,
@ -902,8 +914,7 @@ async def request_completion(prompt,
"temperature": temperature if temperature is not None else 0.8,
"n_probs": 2,
},
headers=headers,
timeout=3600) as response:
headers=headers) as response:
if expect_api_error is None or not expect_api_error:
assert response.status == 200
assert response.headers['Access-Control-Allow-Origin'] == origin
@ -961,7 +972,7 @@ async def oai_chat_completions(user_prompt,
if async_client:
origin = 'llama.cpp'
headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{base_url}{base_path}',
json=payload,
headers=headers) as response:
@ -1048,7 +1059,7 @@ async def oai_chat_completions(user_prompt,
async def request_embedding(content, seed, base_url=None) -> list[list[float]]:
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{base_url}/embedding',
json={
"content": content,
@ -1068,14 +1079,13 @@ async def request_oai_embeddings(input, seed,
headers=[]
if user_api_key is not None:
headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{base_url}/v1/embeddings',
json={
"input": input,
"model": model,
},
headers=headers,
timeout=3600) as response:
headers=headers) as response:
assert response.status == 200, f"received status code not expected: {response.status}"
assert response.headers['Access-Control-Allow-Origin'] == origin
assert response.headers['Content-Type'] == "application/json; charset=utf-8"
@ -1194,7 +1204,7 @@ async def wait_for_slots_status(context,
if 'GITHUB_ACTIONS' in os.environ:
timeout *= 2
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
while True:
async with await session.get(f'{base_url}/slots', params=params) as slots_response:
status_code = slots_response.status
@ -1237,7 +1247,7 @@ def assert_embeddings(embeddings):
async def request_slots_status(context, expected_slots):
async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with await session.get(f'{context.base_url}/slots') as slots_response:
assert slots_response.status == 200
slots = await slots_response.json()

3
examples/server/tests/features/wrong_usages.feature
View file

@ -8,9 +8,12 @@ Feature: Wrong usage of llama.cpp server
Scenario: Infinite loop
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And 42 as server seed
And 2048 KV cache size
# Uncomment below to fix the issue
#And 64 server max tokens to predict
Then the server is starting
Then the server is healthy
Given a prompt:
"""
Go to: infinite loop

33
examples/server/utils.hpp
View file

@ -3,6 +3,14 @@
#include "llama.h"
#include "common.h"
#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1
#endif
// increase max payload length to allow use of larger context size
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
#include "httplib.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
@ -279,6 +287,18 @@ static size_t find_partial_stop_string(const std::string &stop, const std::strin
return std::string::npos;
}
static bool json_is_array_of_numbers(json data) {
if (data.is_array()) {
for (const auto & e : data) {
if (!e.is_number()) {
return false;
}
}
return true;
}
return false;
}
// TODO: reuse llama_detokenize
template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
@ -343,6 +363,19 @@ static json probs_vector_to_json(const llama_context * ctx, const std::vector<co
return out;
}
static bool server_sent_event(httplib::DataSink & sink, const char * event, json & data) {
const std::string str =
std::string(event) + ": " +
data.dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {
{ "to_send", str }
});
return sink.write(str.c_str(), str.size());
}
//
// OAI utils
//

7
examples/speculative/speculative.cpp
View file

@ -73,10 +73,11 @@ int main(int argc, char ** argv) {
// load the draft model
params.model = params.model_draft;
params.n_gpu_layers = params.n_gpu_layers_draft;
if (params.n_threads_draft > 0) {
params.n_threads = params.n_threads_draft;
if (params.draft_cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.draft_cpuparams.n_threads;
}
params.n_threads_batch = params.n_threads_batch_draft;
params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads;
llama_init_result llama_init_dft = llama_init_from_gpt_params(params);
model_dft = llama_init_dft.model;
ctx_dft = llama_init_dft.context;

12
flake.lock generated
View file

@ -5,11 +5,11 @@
"nixpkgs-lib": "nixpkgs-lib"
},
"locked": {
"lastModified": 1722555600,
"narHash": "sha256-XOQkdLafnb/p9ij77byFQjDf5m5QYl9b2REiVClC+x4=",
"lastModified": 1725024810,
"narHash": "sha256-ODYRm8zHfLTH3soTFWE452ydPYz2iTvr9T8ftDMUQ3E=",
"owner": "hercules-ci",
"repo": "flake-parts",
"rev": "8471fe90ad337a8074e957b69ca4d0089218391d",
"rev": "af510d4a62d071ea13925ce41c95e3dec816c01d",
"type": "github"
},
"original": {
@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1724224976,
"narHash": "sha256-Z/ELQhrSd7bMzTO8r7NZgi9g5emh+aRKoCdaAv5fiO0=",
"lastModified": 1724819573,
"narHash": "sha256-GnR7/ibgIH1vhoy8cYdmXE6iyZqKqFxQSVkFgosBh6w=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "c374d94f1536013ca8e92341b540eba4c22f9c62",
"rev": "71e91c409d1e654808b2621f28a327acfdad8dc2",
"type": "github"
},
"original": {

5
flake.nix
View file

@ -145,7 +145,9 @@
# the same path you would with an overlay.
legacyPackages = {
llamaPackages = pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
llamaPackagesWindows = pkgs.pkgsCross.mingwW64.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
llamaPackagesWindows = pkgs.pkgsCross.mingwW64.callPackage .devops/nix/scope.nix {
inherit llamaVersion;
};
llamaPackagesCuda = pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
llamaPackagesRocm = pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
};
@ -157,6 +159,7 @@
default = config.legacyPackages.llamaPackages.llama-cpp;
vulkan = config.packages.default.override { useVulkan = true; };
windows = config.legacyPackages.llamaPackagesWindows.llama-cpp;
python-scripts = config.legacyPackages.llamaPackages.python-scripts;
}
// lib.optionalAttrs pkgs.stdenv.isLinux {
cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp;

1
ggml/CMakeLists.txt
View file

@ -135,6 +135,7 @@ option(GGML_VULKAN "ggml: use Vulkan"
option(GGML_VULKAN_CHECK_RESULTS "ggml: run Vulkan op checks" OFF)
option(GGML_VULKAN_DEBUG "ggml: enable Vulkan debug output" OFF)
option(GGML_VULKAN_MEMORY_DEBUG "ggml: enable Vulkan memory debug output" OFF)
option(GGML_VULKAN_SHADER_DEBUG_INFO "ggml: enable Vulkan shader debug info" OFF)
option(GGML_VULKAN_PERF "ggml: enable Vulkan perf output" OFF)
option(GGML_VULKAN_VALIDATE "ggml: enable Vulkan validation" OFF)
option(GGML_VULKAN_RUN_TESTS "ggml: run Vulkan tests" OFF)

4
ggml/include/ggml-alloc.h
View file

@ -7,8 +7,8 @@ extern "C" {
#endif
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend * ggml_backend_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend * ggml_backend_t;
// Tensor allocator
struct ggml_tallocr {

1
ggml/include/ggml-backend.h
View file

@ -103,6 +103,7 @@ extern "C" {
GGML_API GGML_CALL 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

64
ggml/include/ggml.h
View file

@ -231,6 +231,8 @@
#define GGML_MAX_SRC 10
#ifndef GGML_MAX_NAME
#define GGML_MAX_NAME 64
#define GGML_MAX_N_THREADS 512
#endif
#define GGML_MAX_OP_PARAMS 64
#define GGML_DEFAULT_N_THREADS 4
@ -393,6 +395,8 @@ extern "C" {
GGML_TYPE_Q4_0_4_4 = 31,
GGML_TYPE_Q4_0_4_8 = 32,
GGML_TYPE_Q4_0_8_8 = 33,
GGML_TYPE_TQ1_0 = 34,
GGML_TYPE_TQ2_0 = 35,
GGML_TYPE_COUNT,
};
@ -512,6 +516,7 @@ extern "C" {
GGML_OP_WIN_UNPART,
GGML_OP_GET_REL_POS,
GGML_OP_ADD_REL_POS,
GGML_OP_RWKV_WKV,
GGML_OP_UNARY,
@ -546,6 +551,7 @@ extern "C" {
GGML_UNARY_OP_SILU,
GGML_UNARY_OP_HARDSWISH,
GGML_UNARY_OP_HARDSIGMOID,
GGML_UNARY_OP_EXP,
GGML_UNARY_OP_COUNT,
};
@ -628,6 +634,29 @@ 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 {
@ -635,6 +664,7 @@ extern "C" {
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;
@ -1139,6 +1169,14 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_exp(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_exp_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// normalize along rows
GGML_API struct ggml_tensor * ggml_norm(
struct ggml_context * ctx,
@ -1887,6 +1925,15 @@ extern "C" {
struct ggml_tensor * pw,
struct ggml_tensor * ph);
GGML_API struct ggml_tensor * ggml_rwkv_wkv(
struct ggml_context * ctx,
struct ggml_tensor * k,
struct ggml_tensor * v,
struct ggml_tensor * r,
struct ggml_tensor * tf,
struct ggml_tensor * td,
struct ggml_tensor * state);
// custom operators
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
@ -2057,10 +2104,23 @@ 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*/);
GGML_API enum ggml_status ggml_graph_compute( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
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);

6
ggml/src/CMakeLists.txt
View file

@ -612,6 +612,10 @@ if (GGML_VULKAN)
add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG)
endif()
if (GGML_VULKAN_SHADER_DEBUG_INFO)
add_compile_definitions(GGML_VULKAN_SHADER_DEBUG_INFO)
endif()
if (GGML_VULKAN_PERF)
add_compile_definitions(GGML_VULKAN_PERF)
endif()
@ -1277,7 +1281,7 @@ endif()
# Data types, macros and functions related to controlling CPU affinity and
# some memory allocation are available on Linux through GNU extensions in libc
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
if (CMAKE_SYSTEM_NAME MATCHES "Linux" OR CMAKE_SYSTEM_NAME MATCHES "Android")
add_compile_definitions(_GNU_SOURCE)
endif()

612
ggml/src/ggml-aarch64.c
View file

@ -36,6 +36,84 @@
// from bias offset form to pure sign form (this saves subtract
// operations durin unpacking)
//
#if defined(__AVX__)
#if defined(__F16C__)
// the _mm256_cvt intrinsics require F16C
#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x)))
#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) _mm256_cvtph_ps(_mm_shuffle_epi32(_mm_maskload_epi32((int const*)(x), loadMask), 68))
#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) _mm256_cvtph_ps(_mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask))
#else
static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
float tmp[8];
for (int i = 0; i < 8; i++) {
tmp[i] = GGML_FP16_TO_FP32(x[i]);
}
return _mm256_loadu_ps(tmp);
}
static inline __m256 __avx_repeat_f32cx8_load(ggml_fp16_t *x) {
float tmp[8];
for (int i = 0; i < 4; i++) {
tmp[i] = GGML_FP16_TO_FP32(x[i]);
tmp[i + 4] = GGML_FP16_TO_FP32(x[i]);
}
return _mm256_loadu_ps(tmp);
}
static inline __m256 __avx_rearranged_f32cx8_load(ggml_fp16_t *x, __m128i arrangeMask) {
uint16_t tmphalf[8];
float tmp[8];
_mm_storeu_si128((__m128i*)tmphalf, _mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask));
for (int i = 0; i < 8; i++) {
tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]);
}
return _mm256_loadu_ps(tmp);
}
#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x)
#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) __avx_repeat_f32cx8_load(x)
#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) __avx_rearranged_f32cx8_load(x, arrangeMask)
#endif
#endif
#if defined(__AVX2__) || defined(__AVX512F__)
static inline __m256i sum_i16_pairs_int(const __m256i x) {
const __m256i ones = _mm256_set1_epi16(1);
return _mm256_madd_epi16(ones, x);
}
static inline __m256i mul_sum_us8_pairs_int(const __m256i ax, const __m256i sy) {
#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__))
const __m256i zero = _mm256_setzero_si256();
return _mm256_dpbusd_epi32(zero, ax, sy);
#else
// Perform multiplication and create 16-bit values
const __m256i dot = _mm256_maddubs_epi16(ax, sy);
return sum_i16_pairs_int(dot);
#endif
}
// Integer variant of the function defined in ggml-quants.c
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256i mul_sum_i8_pairs_int(const __m256i x, const __m256i y) {
#if __AVXVNNIINT8__
const __m256i zero = _mm256_setzero_si256();
return _mm256_dpbssd_epi32(zero, x, y);
#else
// Get absolute values of x vectors
const __m256i ax = _mm256_sign_epi8(x, x);
// Sign the values of the y vectors
const __m256i sy = _mm256_sign_epi8(y, x);
return mul_sum_us8_pairs_int(ax, sy);
#endif
}
#endif
static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) {
block_q4_0x4 out;
@ -255,6 +333,103 @@ void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k)
y[i].qs[32 * j + 31] = vgetq_lane_s32(vi, 3);
}
}
#elif defined(__AVX2__) || defined(__AVX__)
float id[4];
__m256 srcv[4][4];
__m256 idvec[4];
for (int i = 0; i < nb; i++) {
for (int row_iter = 0; row_iter < 4; row_iter++) {
// Load elements into 4 AVX vectors
__m256 v0 = _mm256_loadu_ps( x + row_iter * k + i * 32 );
__m256 v1 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 8 );
__m256 v2 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 16 );
__m256 v3 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 24 );
// Compute max(abs(e)) for the block
const __m256 signBit = _mm256_set1_ps( -0.0f );
__m256 maxAbs = _mm256_andnot_ps( signBit, v0 );
maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) );
maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) );
maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) );
__m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) );
max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) );
max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) );
const float maxScalar = _mm_cvtss_f32( max4 );
// Divided by 127.f to mirror results in quantize_row_q8_0
const float d = maxScalar / 127.f;
id[row_iter] = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; //d ? 1.0f / d : 0.0f;
// Store the scale for the individual block
y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
// Store the values in blocks of eight values - Aim is to use these later for block interleaving
srcv[row_iter][0] = v0;
srcv[row_iter][1] = v1;
srcv[row_iter][2] = v2;
srcv[row_iter][3] = v3;
idvec[row_iter] = _mm256_set1_ps(id[row_iter]);
}
// The loop iterates four times - The aim is to get 4 corresponding chunks of eight bytes from the original weight blocks that are interleaved
for (int j = 0; j < 4; j++) {
// Apply the multiplier
__m256 v0 = _mm256_mul_ps(srcv[0][j], idvec[0]);
__m256 v1 = _mm256_mul_ps(srcv[1][j], idvec[1]);
__m256 v2 = _mm256_mul_ps(srcv[2][j], idvec[2]);
__m256 v3 = _mm256_mul_ps(srcv[3][j], idvec[3]);
// Round to nearest integer
v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST );
v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST );
v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST );
v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST );
// Convert floats to integers
__m256i i0 = _mm256_cvtps_epi32( v0 );
__m256i i1 = _mm256_cvtps_epi32( v1 );
__m256i i2 = _mm256_cvtps_epi32( v2 );
__m256i i3 = _mm256_cvtps_epi32( v3 );
#if defined(__AVX2__)
// Convert int32 to int16
i0 = _mm256_packs_epi32( i0, i1 );
i2 = _mm256_packs_epi32( i2, i3 );
// Convert int16 to int8
i0 = _mm256_packs_epi16( i0, i2 );
// Permute and store the quantized weights in the required order after the pack instruction
const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 );
i0 = _mm256_permutevar8x32_epi32( i0, perm );
_mm256_storeu_si256((__m256i *)(y[i].qs + 32 * j), i0);
#else
// Since we don't have in AVX some necessary functions,
// we split the registers in half and call AVX2 analogs from SSE
__m128i ni0 = _mm256_castsi256_si128( i0 );
__m128i ni1 = _mm256_extractf128_si256( i0, 1);
__m128i ni2 = _mm256_castsi256_si128( i1 );
__m128i ni3 = _mm256_extractf128_si256( i1, 1);
__m128i ni4 = _mm256_castsi256_si128( i2 );
__m128i ni5 = _mm256_extractf128_si256( i2, 1);
__m128i ni6 = _mm256_castsi256_si128( i3 );
__m128i ni7 = _mm256_extractf128_si256( i3, 1);
// Convert int32 to int16
ni0 = _mm_packs_epi32( ni0, ni1 );
ni2 = _mm_packs_epi32( ni2, ni3 );
ni4 = _mm_packs_epi32( ni4, ni5 );
ni6 = _mm_packs_epi32( ni6, ni7 );
// Convert int16 to int8
ni0 = _mm_packs_epi16( ni0, ni2 );
ni4 = _mm_packs_epi16( ni4, ni6 );
_mm_storeu_si128((__m128i *)(y[i].qs + 32 * j), ni0);
_mm_storeu_si128((__m128i *)(y[i].qs + 32 * j + 16), ni4);
#endif
}
}
#else
// scalar
const int blck_size_interleave = 8;
@ -684,6 +859,96 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
"__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
"performance");
#elif defined(__AVX2__)
// Lookup table to convert signed nibbles to signed bytes
__m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0));
signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
__m128i changemask = _mm_set_epi8(15, 14, 7, 6, 13, 12, 5, 4, 11, 10, 3, 2, 9, 8, 1, 0);
__m256i finalpermutemask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0);
// Permute mask used for easier vector processing at later stages
const __m256i m4b = _mm256_set1_epi8(0x0F);
int64_t b_nb = n / QK4_0;
const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx;
const block_q8_0 * a_ptr_start = (const block_q8_0 *)vy;
// Process Q8_0 blocks one by one
for (int64_t y = 0; y < nr; y++) {
// Pointers to LHS blocks of block_q8_0 format
const block_q8_0 * a_ptr = a_ptr_start + (y * nb);
// Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation
for (int64_t x = 0; x < nc / 8; x++) {
// Pointers to RHS blocks
const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb);
// Master FP accumulator
__m256 acc_row = _mm256_setzero_ps();
for (int64_t b = 0; b < nb; b++) {
// Load 8 blocks of Q4_0 interleaved as 8 bytes (B0 - B7)
const __m256i rhs_raw_vec_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs));
const __m256i rhs_raw_vec_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 1);
const __m256i rhs_raw_vec_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 2);
const __m256i rhs_raw_vec_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 3);
// 4-bit -> 8-bit - Sign is maintained
const __m256i rhs_vec_0123_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_0, m4b)); // B0(0-7) B1(0-7) B2(0-7) B3(0-7)
const __m256i rhs_vec_4567_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_0, m4b)); // B4(0-7) B5(0-7) B6(0-7) B7(0-7)
const __m256i rhs_vec_0123_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15)
const __m256i rhs_vec_4567_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15)
const __m256i rhs_vec_0123_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_0, 4), m4b)); // B0(16-23) B1(16-23) B2(16-23) B3(16-23)
const __m256i rhs_vec_4567_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_0, 4), m4b)); // B4(16-23) B5(16-23) B6(16-23) B7(16-23)
const __m256i rhs_vec_0123_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_1, 4), m4b)); // B0(24-31) B1(24-31) B2(24-31) B3(24-31)
const __m256i rhs_vec_4567_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_1, 4), m4b)); // B4(24-31) B5(24-31) B6(24-31) B7(24-31)
// Load the scale values for the 8 blocks interleaved in block_q4_0x8
const __m256 col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask);
// Load and convert to FP32 scale from block_q8_0
const __m256 row_scale_f32 = _mm256_set1_ps(GGML_FP16_TO_FP32(a_ptr[b].d));
// Load the block values in block_q8_0 in batches of 16 bytes and replicate the same across 256 bit vector
__m256i lhs_vec_0 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)a_ptr[b].qs));
__m256i lhs_vec_1 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)(a_ptr[b].qs + 16)));
lhs_vec_0 = _mm256_permute2f128_si256(lhs_vec_0, lhs_vec_0, 0); // A0 (0-15) A0(0-15)
lhs_vec_1 = _mm256_permute2f128_si256(lhs_vec_1, lhs_vec_1, 0); // A0 (16-31) A0(16-31))
__m256i iacc = _mm256_setzero_si256();
// Dot product done within 32 bit lanes and accumulated in the same vector
// B0(0-3) B4(0-3) B1(0-3) B5(0-3) B2(0-3) B6(0-3) B3(0-3) B7(0-3) with A0(0-3)
// B0(4-7) B4(4-7) B1(4-7) B5(4-7) B2(4-7) B6(4-7) B3(4-7) B7(4-7) with A0(4-7)
// ...........................................................................
// B0(28-31) B4(28-31) B1(28-31) B5(28-31) B2(28-31) B6(28-31) B3(28-31) B7(28-31) with A0(28-31)
iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int(_mm256_blend_epi32(rhs_vec_0123_0 ,_mm256_shuffle_epi32(rhs_vec_4567_0, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 0)));
iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_0, 177) ,rhs_vec_4567_0, 170), _mm256_shuffle_epi32(lhs_vec_0, 85)));
iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int(_mm256_blend_epi32(rhs_vec_0123_1 ,_mm256_shuffle_epi32(rhs_vec_4567_1, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 170)));
iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_1, 177) ,rhs_vec_4567_1, 170), _mm256_shuffle_epi32(lhs_vec_0, 255)));
iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int(_mm256_blend_epi32(rhs_vec_0123_2 ,_mm256_shuffle_epi32(rhs_vec_4567_2, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 0)));
iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_2, 177) ,rhs_vec_4567_2, 170), _mm256_shuffle_epi32(lhs_vec_1, 85)));
iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int(_mm256_blend_epi32(rhs_vec_0123_3 ,_mm256_shuffle_epi32(rhs_vec_4567_3, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 170)));
iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_3, 177) ,rhs_vec_4567_3, 170), _mm256_shuffle_epi32(lhs_vec_1, 255)));
// Accumulated values multipled with appropriate scales
acc_row = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc), _mm256_mul_ps(col_scale_f32, row_scale_f32), acc_row);
}
// Accumulated output values permuted so as to be stored in appropriate order post accumulation
acc_row = _mm256_permutevar8x32_ps(acc_row, finalpermutemask);
_mm256_storeu_ps(s + (y * nr + x * 8), acc_row);
}
}
#else
float sumf[8];
int sumi;
@ -2143,6 +2408,353 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
"__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
"performance");
#elif defined(__AVX2__) || defined(__AVX512F__)
const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx;
const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy;
int64_t b_nb = n / QK4_0;
int64_t y = 0;
// Mask to mask out nibbles from packed bytes
const __m256i m4b = _mm256_set1_epi8(0x0F);
const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3);
// Lookup table to convert signed nibbles to signed bytes
__m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0));
signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
// Permute mask used for easier vector processing at later stages
__m256i requiredOrder = _mm256_set_epi32(3 ,2 ,1 ,0, 7 ,6, 5, 4);
// Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation
int anr = nr - nr %16; // Used to align nr with boundary of 16
for (; y < anr / 4; y += 4) {
const block_q8_0x4 * a_ptrs[4];
a_ptrs[0] = a_ptr_start + (y * nb);
for (int i = 0; i < 3; ++i) {
a_ptrs[i + 1] = a_ptrs[i] + nb;
}
// Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation
for (int64_t x = 0; x < nc / 8; x++) {
const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb);
// Master FP accumulators
__m256 acc_rows[16];
for (int i = 0; i < 16; i++) {
acc_rows[i] = _mm256_setzero_ps();
}
for (int64_t b = 0; b < nb; b++) {
// Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs));
const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32));
const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64));
const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96));
// Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values
const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
// 4-bit -> 8-bit - Sign is maintained
const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7)
const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7)
const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15)
const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15)
const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23)
const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23)
const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31)
const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31)
// Shuffle pattern one - right side input
const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3)
const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3)
const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11)
const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11)
const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19)
const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19)
const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27)
const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27)
// Shuffle pattern two - right side input
const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7)
const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7)
const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15)
const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15)
const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23)
const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23)
const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31)
const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31)
// Scale values - Load the wight scale values of block_q4_0x8
const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d);
// Process LHS in groups of four
for (int rp = 0; rp < 4; rp++) {
// Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
// Loaded as set of 128 bit vectors and repeated into a 256 bit vector
__m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs)));
__m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0);
__m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17);
__m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32)));
__m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0);
__m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17);
__m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64)));
__m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0);
__m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17);
__m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96)));
__m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0);
__m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17);
// Shuffle pattern one - left side input
const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
// Shuffle pattern two - left side input
const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
// The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
// Resembles MMLAs into 2x2 matrices in ARM Version
__m256i iacc_mat_00_sp1 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1));
__m256i iacc_mat_01_sp1 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1));
__m256i iacc_mat_10_sp1 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1));
__m256i iacc_mat_11_sp1 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1));
__m256i iacc_mat_00_sp2 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2));
__m256i iacc_mat_01_sp2 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2));
__m256i iacc_mat_10_sp2 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2));
__m256i iacc_mat_11_sp2 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2));
// Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
__m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
__m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
__m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
__m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
// Straighten out to make 4 row vectors
__m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204);
__m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204);
__m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204);
__m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204);
// Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask);
// Multiply with appropiate scales and accumulate
acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]);
acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]);
acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]);
acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]);
}
}
// Store the accumulated values
for (int i = 0; i < 16; i++) {
_mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
}
}
}
// Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation
for (; y < nr / 4; y ++) {
const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb);
// Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
for (int64_t x = 0; x < nc / 8; x++) {
const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb);
// Master FP accumulators
__m256 acc_rows[4];
for (int i = 0; i < 4; i++) {
acc_rows[i] = _mm256_setzero_ps();
}
for (int64_t b = 0; b < nb; b++) {
// Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7
const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs));
const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32));
const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64));
const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96));
// Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess
const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240);
// 4-bit -> 8-bit - Sign is maintained
const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7)
const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7)
const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15)
const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15)
const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23)
const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23)
const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31)
const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31)
// Shuffle pattern one - right side input
const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3)
const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3)
const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11)
const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11)
const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19)
const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19)
const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27)
const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27)
// Shuffle pattern two - right side input
const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7)
const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7)
const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15)
const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15)
const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23)
const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23)
const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31)
const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31)
// Scale values - Load the wight scale values of block_q4_0x8
const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d);
// Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
// Loaded as set of 128 bit vectors and repeated into a 256 bit vector
__m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs)));
__m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0);
__m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17);
__m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32)));
__m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0);
__m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17);
__m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64)));
__m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0);
__m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17);
__m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96)));
__m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0);
__m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17);
// Shuffle pattern one - left side input
const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3)
const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3)
const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11)
const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11)
const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19)
const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19)
const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27)
const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27)
// Shuffle pattern two - left side input
const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7)
const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7)
const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15)
const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15)
const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23)
const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23)
const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31)
const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31)
// The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane
// Resembles MMLAs into 2x2 matrices in ARM Version
__m256i iacc_mat_00_sp1 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1));
__m256i iacc_mat_01_sp1 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1));
__m256i iacc_mat_10_sp1 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1));
__m256i iacc_mat_11_sp1 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1));
__m256i iacc_mat_00_sp2 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2));
__m256i iacc_mat_01_sp2 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2));
__m256i iacc_mat_10_sp2 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2));
__m256i iacc_mat_11_sp2 =
_mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2));
// Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block
__m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2);
__m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2);
__m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2);
__m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2);
// Straighten out to make 4 row vectors
__m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204);
__m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204);
__m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204);
__m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204);
// Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes
const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask);
// Multiply with appropiate scales and accumulate
acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]);
acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]);
acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]);
acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]);
}
// Store the accumulated values
for (int i = 0; i < 4; i++) {
_mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]);
}
}
}
#else
float sumf[4][8];
int sumi;

35
ggml/src/ggml-backend.c
View file

@ -727,9 +727,11 @@ ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
#endif
struct ggml_backend_cpu_context {
int n_threads;
void * work_data;
size_t work_size;
int n_threads;
ggml_threadpool_t threadpool;
void * work_data;
size_t work_size;
ggml_abort_callback abort_callback;
void * abort_callback_data;
@ -764,7 +766,7 @@ GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(gg
struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool);
cpu_plan->cgraph = *cgraph; // FIXME: deep copy
if (cpu_plan->cplan.work_size > 0) {
@ -801,7 +803,7 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backe
GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool);
if (cpu_ctx->work_size < cplan.work_size) {
free(cpu_ctx->work_data);
@ -878,6 +880,7 @@ ggml_backend_t ggml_backend_cpu_init(void) {
}
ctx->n_threads = GGML_DEFAULT_N_THREADS;
ctx->threadpool = NULL;
ctx->work_data = NULL;
ctx->work_size = 0;
ctx->abort_callback = NULL;
@ -908,6 +911,18 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
ctx->n_threads = n_threads;
}
void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) {
GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
if (ctx->threadpool && ctx->threadpool != threadpool) {
// already had a different threadpool, pause/suspend it before switching
ggml_threadpool_pause(ctx->threadpool);
}
ctx->threadpool = threadpool;
}
void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) {
GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
@ -1155,6 +1170,11 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
}
}
if (tensor->buffer || (tensor->view_src && tensor->view_src->buffer)) {
// since the tensor is pre-allocated, it cannot be moved to another backend
GGML_ABORT("pre-allocated tensor in a backend that cannot run the operation");
}
// graph input
if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
cur_backend_id = sched->n_backends - 1; // last backend (assumed CPU)
@ -1634,7 +1654,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
sched->prev_leaf_backend_ids = tmp;
}
int graph_size = graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2;
int graph_size = MAX(graph->n_nodes, graph->n_leafs) + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sched->n_copies;
if (sched->graph.size < graph_size) {
sched->graph.size = graph_size;
sched->graph.nodes = realloc(sched->graph.nodes, graph_size * sizeof(struct ggml_tensor *));
@ -1686,6 +1706,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
for (int c = 0; c < sched->n_copies; c++) {
struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
assert(graph_copy->size > graph_copy->n_leafs);
graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
}
}
@ -1699,6 +1720,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
for (int c = 0; c < sched->n_copies; c++) {
struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
assert(graph_copy->size > graph_copy->n_leafs);
graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
}
}
@ -1709,6 +1731,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
assert(graph_copy->size > graph_copy->n_leafs);
graph_copy->leafs[graph_copy->n_leafs++] = leaf;
}
}

20
ggml/src/ggml-common.h
View file

@ -227,6 +227,25 @@ typedef struct {
} block_q8_0x8;
static_assert(sizeof(block_q8_0x8) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong q8_0x8 block size/padding");
//
// Ternary quantization
//
// 1.6875 bpw
typedef struct {
uint8_t qs[(QK_K - 4 * QK_K / 64) / 5]; // 5 elements per byte (3^5 = 243 < 256)
uint8_t qh[QK_K/64]; // 4 elements per byte
ggml_half d;
} block_tq1_0;
static_assert(sizeof(block_tq1_0) == sizeof(ggml_half) + QK_K / 64 + (QK_K - 4 * QK_K / 64) / 5, "wrong tq1_0 block size/padding");
// 2.0625 bpw
typedef struct {
uint8_t qs[QK_K/4]; // 2 bits per element
ggml_half d;
} block_tq2_0;
static_assert(sizeof(block_tq2_0) == sizeof(ggml_half) + QK_K / 4, "wrong tq2_0 block size/padding");
//
// Super-block quantization structures
//
@ -361,6 +380,7 @@ typedef struct {
} block_iq3_s;
static_assert(sizeof(block_iq3_s) == sizeof(ggml_half) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding");
// 1.5625 bpw
typedef struct {
ggml_half d;
uint8_t qs[QK_K/8];

14
ggml/src/ggml-cuda.cu
View file

@ -2572,8 +2572,15 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
// store a pointer to each copy op CUDA kernel to identify it later
void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
ggml_cuda_cpy_fn_ptrs.push_back(ptr);
if (!ptr) {
use_cuda_graph = false;
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
#endif
} else {
if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
ggml_cuda_cpy_fn_ptrs.push_back(ptr);
}
}
}
@ -2842,6 +2849,9 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
return true;
}
if (src0_type == src1_type && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1])) {
return true;
}
return false;
} break;
case GGML_OP_DUP:

35
ggml/src/ggml-cuda/cpy.cu
View file

@ -428,7 +428,10 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
char * src0_ddc = (char *) src0->data;
char * src1_ddc = (char *) src1->data;
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1));
CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
@ -449,9 +452,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
} else {
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));
GGML_ABORT("fatal error");
}
}
@ -461,29 +463,30 @@ void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
}
void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
return (void*) cpy_f32_f16<cpy_1_f32_f32>;
if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
return nullptr;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
return (void*) cpy_f32_f16<cpy_1_f32_f32>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
return (void*) cpy_f32_f16<cpy_1_f16_f32>;
return (void*) cpy_f32_f16<cpy_1_f16_f32>;
} else {
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));
GGML_ABORT("fatal error");
}
}

11
ggml/src/ggml-impl.h
View file

@ -175,7 +175,7 @@ typedef __fp16 ggml_fp16_internal_t;
// 32-bit ARM compatibility
// vaddvq_s16
// vaddlvq_s16
// vpaddq_s16
// vpaddq_s32
// vaddvq_s32
@ -185,12 +185,9 @@ typedef __fp16 ggml_fp16_internal_t;
// vzip1_u8
// vzip2_u8
inline static int32_t vaddvq_s16(int16x8_t v) {
return
(int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
(int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
(int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
(int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
inline static int32_t vaddlvq_s16(int16x8_t v) {
int32x4_t v0 = vreinterpretq_s32_s64(vpaddlq_s32(vpaddlq_s16(v)));
return vgetq_lane_s32(v0, 0) + vgetq_lane_s32(v0, 2);
}
inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {

690
ggml/src/ggml-quants.c
View file

@ -1630,7 +1630,7 @@ void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int6
// ===================== Helper functions
//
static inline int nearest_int(float fval) {
assert(fval <= 4194303.f);
assert(fabsf(fval) <= 4194303.f);
float val = fval + 12582912.f;
int i; memcpy(&i, &val, sizeof(int));
return (i & 0x007fffff) - 0x00400000;
@ -3306,6 +3306,191 @@ size_t quantize_q8_0(const float * restrict src, void * restrict dst, int64_t nr
return nrow * row_size;
}
// ====================== Ternary (de)-quantization (BitNet b1.58 and TriLMs)
void quantize_row_tq1_0_ref(const float * restrict x, block_tq1_0 * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int64_t nb = k / QK_K;
for (int64_t i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
for (int j = 0; j < QK_K; j++) {
const float v = x[j];
amax = MAX(amax, fabsf(v));
}
const float d = amax;
const float id = d ? 1.0f/d : 0.0f;
y[i].d = GGML_FP32_TO_FP16(d);
// 5 elements per byte, along 32 bytes
for (size_t j = 0; j < sizeof(y->qs) - sizeof(y->qs) % 32; j += 32) {
for (size_t m = 0; m < 32; ++m) {
uint8_t q = 0;
for (size_t n = 0; n < 5; ++n) {
int xi = lroundf(x[m + n*32] * id) + 1; // -1, 0, 1 -> 0, 1, 2
q *= 3;
q += xi;
}
// ceiling division (243 == pow(3, 5))
q = ((uint16_t)q * 256 + (243 - 1)) / 243;
y[i].qs[j + m] = q;
}
x += 5*32;
}
// along 16 bytes
for (size_t j = sizeof(y->qs) - sizeof(y->qs) % 32; j < sizeof(y->qs); j += 16) {
for (size_t m = 0; m < 16; ++m) {
uint8_t q = 0;
for (size_t n = 0; n < 5; ++n) {
int xi = lroundf(x[m + n*16] * id) + 1; // -1, 0, 1 -> 0, 1, 2
q *= 3;
q += xi;
}
// ceiling division (243 == pow(3, 5))
q = ((uint16_t)q * 256 + (243 - 1)) / 243;
y[i].qs[j + m] = q;
}
x += 5*16;
}
// 4 elements per byte
for (size_t j = 0; j < sizeof(y->qh); ++j) {
uint8_t q = 0;
for (size_t m = 0; m < 4; ++m) {
// -1, 0, 1 -> 0, 1, 2
int xi = lroundf(x[j + m*sizeof(y->qh)] * id) + 1;
q *= 3;
q += xi;
}
// shift the first value to the most significant trit
q *= 3;
// ceiling division (243 == pow(3, 5))
q = ((uint16_t)q * 256 + (243 - 1)) / 243;
y[i].qh[j] = q;
}
x += 4*sizeof(y->qh);
}
}
void quantize_row_tq2_0_ref(const float * restrict x, block_tq2_0 * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int64_t nb = k / QK_K;
for (int64_t i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
for (int j = 0; j < QK_K; j++) {
const float v = x[j];
amax = MAX(amax, fabsf(v));
}
const float d = amax;
const float id = d ? 1.0f/d : 0.0f;
y[i].d = GGML_FP32_TO_FP16(d);
for (size_t j = 0; j < sizeof(y->qs); j += 32) {
for (size_t m = 0; m < 32; ++m) {
uint8_t q = 0;
for (size_t n = 0; n < 4; ++n) {
// -1, 0, 1 -> 0, 1, 2
int xi = lroundf(x[m + n*32] * id) + 1;
q += (xi & 3) << (2*n);
}
y[i].qs[j + m] = q;
}
x += 4*32;
}
}
}
void quantize_row_tq1_0(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_tq1_0 * restrict y = vy;
quantize_row_tq1_0_ref(x, y, k);
}
void quantize_row_tq2_0(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_tq2_0 * restrict y = vy;
quantize_row_tq2_0_ref(x, y, k);
}
size_t quantize_tq1_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
(void)quant_weights; // not used
const size_t row_size = ggml_row_size(GGML_TYPE_TQ1_0, n_per_row);
quantize_row_tq1_0(src, dst, (int64_t)nrow*n_per_row);
return nrow * row_size;
}
size_t quantize_tq2_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
(void)quant_weights; // not used
const size_t row_size = ggml_row_size(GGML_TYPE_TQ2_0, n_per_row);
quantize_row_tq2_0(src, dst, (int64_t)nrow*n_per_row);
return nrow * row_size;
}
void dequantize_row_tq1_0(const block_tq1_0 * restrict x, float * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int64_t nb = k / QK_K;
const uint8_t pow3[6] = {1, 3, 9, 27, 81, 243};
for (int64_t i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d);
for (size_t j = 0; j < sizeof(x->qs) - sizeof(x->qs) % 32; j += 32) {
for (size_t n = 0; n < 5; ++n) {
for (size_t m = 0; m < 32; ++m) {
uint8_t q = x[i].qs[j + m] * pow3[n];
int16_t xi = ((uint16_t) q * 3) >> 8;
*y++ = (float) (xi - 1) * d;
}
}
}
for (size_t j = sizeof(x->qs) - sizeof(x->qs) % 32; j < sizeof(x->qs); j += 16) {
for (size_t n = 0; n < 5; ++n) {
for (size_t m = 0; m < 16; ++m) {
uint8_t q = x[i].qs[j + m] * pow3[n];
int16_t xi = ((uint16_t) q * 3) >> 8;
*y++ = (float) (xi - 1) * d;
}
}
}
for (size_t n = 0; n < 4; ++n) {
for (size_t j = 0; j < sizeof(x->qh); ++j) {
uint8_t q = x[i].qh[j] * pow3[n];
int16_t xi = ((uint16_t) q * 3) >> 8;
*y++ = (float) (xi - 1) * d;
}
}
}
}
void dequantize_row_tq2_0(const block_tq2_0 * restrict x, float * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int64_t nb = k / QK_K;
for (int64_t i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d);
for (size_t j = 0; j < sizeof(x->qs); j += 32) {
for (size_t l = 0; l < 4; ++l) {
for (size_t m = 0; m < 32; ++m) {
int8_t q = (x[i].qs[j + m] >> (l*2)) & 3;
*y++ = (float) (q - 1) * d;
}
}
}
}
}
// ====================== "True" 2-bit (de)-quantization
void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int64_t k) {
@ -5470,6 +5655,501 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
*s = sumf;
}
void ggml_vec_dot_tq1_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
assert(nrc == 1);
UNUSED(nrc);
UNUSED(bx);
UNUSED(by);
UNUSED(bs);
const block_tq1_0 * restrict x = vx;
const block_q8_K * restrict y = vy;
const int nb = n / QK_K;
#if defined(__ARM_NEON)
float sumf = 0.0f;
uint8_t k_shift[16] = {1, 1, 1, 1, 3, 3, 3, 3, 9, 9, 9, 9, 27, 27, 27, 27};
const uint8x16_t shift = vld1q_u8(k_shift);
for (int i = 0; i < nb; ++i) {
#if defined(__ARM_FEATURE_DOTPROD)
int32x4_t sumi0 = vdupq_n_s32(0);
int32x4_t sumi1 = vdupq_n_s32(0);
#else
int16x8_t sumi0 = vdupq_n_s16(0);
int16x8_t sumi1 = vdupq_n_s16(0);
#endif
// first 32 bytes of 5 elements
{
uint8x16_t qx0 = vld1q_u8(x[i].qs + 0);
uint8x16_t qx1 = vld1q_u8(x[i].qs + 16);
uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(3));
uint8x16_t qx3 = vmulq_u8(qx1, vdupq_n_u8(3));
uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(9));
uint8x16_t qx5 = vmulq_u8(qx1, vdupq_n_u8(9));
uint8x16_t qx6 = vmulq_u8(qx0, vdupq_n_u8(27));
uint8x16_t qx7 = vmulq_u8(qx1, vdupq_n_u8(27));
uint8x16_t qx8 = vmulq_u8(qx0, vdupq_n_u8(81));
uint8x16_t qx9 = vmulq_u8(qx1, vdupq_n_u8(81));
// multiply by 3 and keep the 2 bits above 8 bits
int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6));
int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6));
int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6));
int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6));
int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6));
int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6));
int8x16_t sqx6 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx6, vshrq_n_u8(qx6, 1)), 6));
int8x16_t sqx7 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx7, vshrq_n_u8(qx7, 1)), 6));
int8x16_t sqx8 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx8, vshrq_n_u8(qx8, 1)), 6));
int8x16_t sqx9 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx9, vshrq_n_u8(qx9, 1)), 6));
const int8x16_t qy0 = vld1q_s8(y[i].qs + 0);
const int8x16_t qy1 = vld1q_s8(y[i].qs + 16);
const int8x16_t qy2 = vld1q_s8(y[i].qs + 32);
const int8x16_t qy3 = vld1q_s8(y[i].qs + 48);
const int8x16_t qy4 = vld1q_s8(y[i].qs + 64);
const int8x16_t qy5 = vld1q_s8(y[i].qs + 80);
const int8x16_t qy6 = vld1q_s8(y[i].qs + 96);
const int8x16_t qy7 = vld1q_s8(y[i].qs + 112);
const int8x16_t qy8 = vld1q_s8(y[i].qs + 128);
const int8x16_t qy9 = vld1q_s8(y[i].qs + 144);
#if defined(__ARM_FEATURE_DOTPROD)
sumi0 = vdotq_s32(sumi0, sqx0, qy0);
sumi1 = vdotq_s32(sumi1, sqx1, qy1);
sumi0 = vdotq_s32(sumi0, sqx2, qy2);
sumi1 = vdotq_s32(sumi1, sqx3, qy3);
sumi0 = vdotq_s32(sumi0, sqx4, qy4);
sumi1 = vdotq_s32(sumi1, sqx5, qy5);
sumi0 = vdotq_s32(sumi0, sqx6, qy6);
sumi1 = vdotq_s32(sumi1, sqx7, qy7);
sumi0 = vdotq_s32(sumi0, sqx8, qy8);
sumi1 = vdotq_s32(sumi1, sqx9, qy9);
#else
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx8), vget_low_s8(qy8));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx8), vget_high_s8(qy8));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx9), vget_low_s8(qy9));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx9), vget_high_s8(qy9));
#endif
}
// last 16 bytes of 5-element, along with the 4 bytes of 4 elements
{
uint8x16_t qx0 = vld1q_u8(x[i].qs + 32);
uint8x16_t qx1 = vmulq_u8(qx0, vdupq_n_u8(3));
uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(9));
uint8x16_t qx3 = vmulq_u8(qx0, vdupq_n_u8(27));
uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(81));
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned
uint8x16_t qx5 = vreinterpretq_u8_u32(vdupq_n_u32(qh));
qx5 = vmulq_u8(qx5, shift);
// multiply by 3 and keep the 2 bits above 8 bits
int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6));
int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6));
int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6));
int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6));
int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6));
int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6));
const int8x16_t qy0 = vld1q_s8(y[i].qs + 160);
const int8x16_t qy1 = vld1q_s8(y[i].qs + 176);
const int8x16_t qy2 = vld1q_s8(y[i].qs + 192);
const int8x16_t qy3 = vld1q_s8(y[i].qs + 208);
const int8x16_t qy4 = vld1q_s8(y[i].qs + 224);
const int8x16_t qy5 = vld1q_s8(y[i].qs + 240);
#if defined(__ARM_FEATURE_DOTPROD)
sumi0 = vdotq_s32(sumi0, sqx0, qy0);
sumi1 = vdotq_s32(sumi1, sqx1, qy1);
sumi0 = vdotq_s32(sumi0, sqx2, qy2);
sumi1 = vdotq_s32(sumi1, sqx3, qy3);
sumi0 = vdotq_s32(sumi0, sqx4, qy4);
sumi1 = vdotq_s32(sumi1, sqx5, qy5);
#else
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5));
#endif
}
const int16x8_t ysum0 = vld1q_s16(y[i].bsums);
const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8);
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
#if defined(__ARM_FEATURE_DOTPROD)
sumi0 = vaddq_s32(sumi0, sumi1);
sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1)));
sumf += d * (float) vaddvq_s32(sumi0);
#else
sumi0 = vaddq_s16(sumi0, sumi1);
sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1));
sumf += d * (float) vaddlvq_s16(sumi0);
#endif
}
*s = sumf;
#elif defined(__AVX2__)
__m256 sumf = _mm256_setzero_ps();
for (int i = 0; i < nb; ++i) {
// 16-bit sums
__m256i sumi0 = _mm256_setzero_si256();
__m256i sumi1 = _mm256_setzero_si256();
__m256i sumi2 = _mm256_setzero_si256();
// first 32 bytes of 5 elements
{
__m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs));
// 8-bit multiplies with shifts, masks and adds
__m256i qx1 = _mm256_add_epi8(qx0, _mm256_add_epi8(qx0, qx0)); // 1 * 3
__m256i qx2 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx0, 3), _mm256_set1_epi8(-8)), qx0); // 1 * 9
__m256i qx3 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx1, 3), _mm256_set1_epi8(-8)), qx1); // 3 * 9
__m256i qx4 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx2, 3), _mm256_set1_epi8(-8)), qx2); // 9 * 9
// TODO: can _mm256_mulhi_epu16 be faster even if 16-bits?
// Cancel the +1 from avg so that it behaves like a halving add
qx0 = _mm256_subs_epu8(qx0, _mm256_set1_epi8(1));
qx1 = _mm256_subs_epu8(qx1, _mm256_set1_epi8(1));
qx2 = _mm256_subs_epu8(qx2, _mm256_set1_epi8(1));
qx3 = _mm256_subs_epu8(qx3, _mm256_set1_epi8(1));
qx4 = _mm256_subs_epu8(qx4, _mm256_set1_epi8(1));
// Multiply by 3 and get the top 2 bits
qx0 = _mm256_avg_epu8(qx0, _mm256_avg_epu8(qx0, _mm256_setzero_si256()));
qx1 = _mm256_avg_epu8(qx1, _mm256_avg_epu8(qx1, _mm256_setzero_si256()));
qx2 = _mm256_avg_epu8(qx2, _mm256_avg_epu8(qx2, _mm256_setzero_si256()));
qx3 = _mm256_avg_epu8(qx3, _mm256_avg_epu8(qx3, _mm256_setzero_si256()));
qx4 = _mm256_avg_epu8(qx4, _mm256_avg_epu8(qx4, _mm256_setzero_si256()));
qx0 = _mm256_and_si256(_mm256_srli_epi16(qx0, 6), _mm256_set1_epi8(3));
qx1 = _mm256_and_si256(_mm256_srli_epi16(qx1, 6), _mm256_set1_epi8(3));
qx2 = _mm256_and_si256(_mm256_srli_epi16(qx2, 6), _mm256_set1_epi8(3));
qx3 = _mm256_and_si256(_mm256_srli_epi16(qx3, 6), _mm256_set1_epi8(3));
qx4 = _mm256_and_si256(_mm256_srli_epi16(qx4, 6), _mm256_set1_epi8(3));
const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 0));
const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 32));
const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 64));
const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 96));
const __m256i qy4 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 128));
qx0 = _mm256_maddubs_epi16(qx0, qy0);
qx1 = _mm256_maddubs_epi16(qx1, qy1);
qx2 = _mm256_maddubs_epi16(qx2, qy2);
qx3 = _mm256_maddubs_epi16(qx3, qy3);
qx4 = _mm256_maddubs_epi16(qx4, qy4);
sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1));
sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3));
sumi2 = _mm256_add_epi16(sumi2, qx4);
}
// last 16 bytes of 5-element, along with the 4 bytes of 4 elements
{
__m128i qx0 = _mm_loadu_si128((const __m128i *) (x[i].qs + 32));
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned
__m256i qx5_l = _mm256_cvtepu8_epi16(_mm_set1_epi32(qh));
__m128i qx1 = _mm_add_epi8(qx0, _mm_add_epi8(qx0, qx0)); // 1 * 3
__m128i qx2 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx0, 3), _mm_set1_epi8(-8)), qx0); // 1 * 9
__m128i qx3 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx1, 3), _mm_set1_epi8(-8)), qx1); // 3 * 9
__m128i qx4 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx2, 3), _mm_set1_epi8(-8)), qx2); // 9 * 9
__m256i qx01 = MM256_SET_M128I(qx1, qx0);
__m256i qx23 = MM256_SET_M128I(qx3, qx2);
// avx2 does not have 8-bit multiplies, so 16-bit it is.
qx5_l = _mm256_mullo_epi16(qx5_l, _mm256_set_epi16(27, 27, 27, 27, 9, 9, 9, 9, 3, 3, 3, 3, 1, 1, 1, 1));
qx5_l = _mm256_and_si256(qx5_l, _mm256_set1_epi16(0xFF));
__m128i qx5 = _mm_packus_epi16(_mm256_castsi256_si128(qx5_l), _mm256_extracti128_si256(qx5_l, 1));
__m256i qx45 = MM256_SET_M128I(qx5, qx4);
// Cancel the +1 from avg so that it behaves like a halving add
qx01 = _mm256_subs_epu8(qx01, _mm256_set1_epi8(1));
qx23 = _mm256_subs_epu8(qx23, _mm256_set1_epi8(1));
qx45 = _mm256_subs_epu8(qx45, _mm256_set1_epi8(1));
// Multiply by 3 and get the top 2 bits
qx01 = _mm256_avg_epu8(qx01, _mm256_avg_epu8(qx01, _mm256_setzero_si256()));
qx23 = _mm256_avg_epu8(qx23, _mm256_avg_epu8(qx23, _mm256_setzero_si256()));
qx45 = _mm256_avg_epu8(qx45, _mm256_avg_epu8(qx45, _mm256_setzero_si256()));
qx01 = _mm256_and_si256(_mm256_srli_epi16(qx01, 6), _mm256_set1_epi8(3));
qx23 = _mm256_and_si256(_mm256_srli_epi16(qx23, 6), _mm256_set1_epi8(3));
qx45 = _mm256_and_si256(_mm256_srli_epi16(qx45, 6), _mm256_set1_epi8(3));
const __m256i qy01 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 160));
const __m256i qy23 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 192));
const __m256i qy45 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 224));
qx01 = _mm256_maddubs_epi16(qx01, qy01);
qx23 = _mm256_maddubs_epi16(qx23, qy23);
qx45 = _mm256_maddubs_epi16(qx45, qy45);
sumi0 = _mm256_add_epi16(sumi0, qx01);
sumi1 = _mm256_add_epi16(sumi1, qx23);
sumi2 = _mm256_add_epi16(sumi2, qx45);
}
const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums);
const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d));
sumi0 = _mm256_sub_epi16(sumi0, ysum);
sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(sumi1, sumi2));
sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1));
sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf);
}
*s = hsum_float_8(sumf);
#else
const uint8_t pow3[6] = {1, 3, 9, 27, 81, 243};
float sumf = 0.0f;
for (int i = 0; i < nb; ++i) {
int sum = 0;
for (size_t j = 0; j < sizeof(x->qs) - sizeof(x->qs) % 32; j += 32) {
for (size_t l = 0; l < 5; ++l) {
for (size_t m = 0; m < 32; ++m) {
uint8_t q = x[i].qs[j + m] * pow3[l];
uint16_t xi = ((uint16_t) q * 3) >> 8;
sum += (xi - 1) * y[i].qs[j*5 + l*32 + m];
}
}
}
for (size_t j = sizeof(x->qs) - sizeof(x->qs) % 32; j < sizeof(x->qs); j += 16) {
for (size_t l = 0; l < 5; ++l) {
for (size_t m = 0; m < 16; ++m) {
uint8_t q = x[i].qs[j + m] * pow3[l];
uint16_t xi = ((uint16_t) q * 3) >> 8;
sum += (xi - 1) * y[i].qs[j*5 + l*16 + m];
}
}
}
for (size_t l = 0; l < 4; ++l) {
for (size_t j = 0; j < sizeof(x->qh); ++j) {
uint8_t q = x[i].qh[j] * pow3[l];
uint16_t xi = ((uint16_t) q * 3) >> 8;
sum += (xi - 1) * y[i].qs[sizeof(x->qs)*5 + l*sizeof(x->qh) + j];
}
}
sumf += (float) sum * (GGML_FP16_TO_FP32(x[i].d) * y[i].d);
}
*s = sumf;
#endif
}
void ggml_vec_dot_tq2_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
assert(nrc == 1);
UNUSED(nrc);
UNUSED(bx);
UNUSED(by);
UNUSED(bs);
const block_tq2_0 * restrict x = vx;
const block_q8_K * restrict y = vy;
const int nb = n / QK_K;
#if defined(__ARM_NEON)
float sumf = 0.0f;
const uint8x16_t m3 = vdupq_n_u8(3);
for (int i = 0; i < nb; ++i) {
#if defined(__ARM_FEATURE_DOTPROD)
int32x4_t sumi0 = vdupq_n_s32(0);
int32x4_t sumi1 = vdupq_n_s32(0);
#else
int16x8_t sumi0 = vdupq_n_s16(0);
int16x8_t sumi1 = vdupq_n_s16(0);
#endif
for (size_t j = 0; j < sizeof(x->qs); j += 32) {
uint8x16_t qx0 = vld1q_u8(x[i].qs + j);
uint8x16_t qx1 = vld1q_u8(x[i].qs + j + 16);
uint8x16_t qx2 = vshrq_n_u8(qx0, 2);
uint8x16_t qx3 = vshrq_n_u8(qx1, 2);
uint8x16_t qx4 = vshrq_n_u8(qx0, 4);
uint8x16_t qx5 = vshrq_n_u8(qx1, 4);
uint8x16_t qx6 = vshrq_n_u8(qx0, 6);
uint8x16_t qx7 = vshrq_n_u8(qx1, 6);
int8x16_t sqx0 = vreinterpretq_s8_u8(vandq_u8(qx0, m3));
int8x16_t sqx1 = vreinterpretq_s8_u8(vandq_u8(qx1, m3));
int8x16_t sqx2 = vreinterpretq_s8_u8(vandq_u8(qx2, m3));
int8x16_t sqx3 = vreinterpretq_s8_u8(vandq_u8(qx3, m3));
int8x16_t sqx4 = vreinterpretq_s8_u8(vandq_u8(qx4, m3));
int8x16_t sqx5 = vreinterpretq_s8_u8(vandq_u8(qx5, m3));
int8x16_t sqx6 = vreinterpretq_s8_u8(vandq_u8(qx6, m3));
int8x16_t sqx7 = vreinterpretq_s8_u8(vandq_u8(qx7, m3));
const int8x16_t qy0 = vld1q_s8(y[i].qs + j*4 + 0);
const int8x16_t qy1 = vld1q_s8(y[i].qs + j*4 + 16);
const int8x16_t qy2 = vld1q_s8(y[i].qs + j*4 + 32);
const int8x16_t qy3 = vld1q_s8(y[i].qs + j*4 + 48);
const int8x16_t qy4 = vld1q_s8(y[i].qs + j*4 + 64);
const int8x16_t qy5 = vld1q_s8(y[i].qs + j*4 + 80);
const int8x16_t qy6 = vld1q_s8(y[i].qs + j*4 + 96);
const int8x16_t qy7 = vld1q_s8(y[i].qs + j*4 + 112);
#if defined(__ARM_FEATURE_DOTPROD)
sumi0 = vdotq_s32(sumi0, sqx0, qy0);
sumi1 = vdotq_s32(sumi1, sqx1, qy1);
sumi0 = vdotq_s32(sumi0, sqx2, qy2);
sumi1 = vdotq_s32(sumi1, sqx3, qy3);
sumi0 = vdotq_s32(sumi0, sqx4, qy4);
sumi1 = vdotq_s32(sumi1, sqx5, qy5);
sumi0 = vdotq_s32(sumi0, sqx6, qy6);
sumi1 = vdotq_s32(sumi1, sqx7, qy7);
#else
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6));
sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7));
sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7));
#endif
}
const int16x8_t ysum0 = vld1q_s16(y[i].bsums);
const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8);
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
#if defined(__ARM_FEATURE_DOTPROD)
sumi0 = vaddq_s32(sumi0, sumi1);
sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1)));
sumf += d * (float) vaddvq_s32(sumi0);
#else
sumi0 = vaddq_s16(sumi0, sumi1);
sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1));
sumf += d * (float) vaddlvq_s16(sumi0);
#endif
}
*s = sumf;
#elif defined(__AVX2__)
__m256 sumf = _mm256_setzero_ps();
for (int i = 0; i < nb; ++i) {
// 16-bit sums, because 256*127 still fits
__m256i sumi0 = _mm256_setzero_si256();
__m256i sumi1 = _mm256_setzero_si256();
for (size_t j = 0; j < sizeof(x->qs); j += 32) {
__m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs + j));
__m256i qx1 = _mm256_srli_epi16(qx0, 2);
__m256i qx2 = _mm256_srli_epi16(qx0, 4);
__m256i qx3 = _mm256_srli_epi16(qx0, 6);
// 0, 1, 2 (should not be 3)
qx0 = _mm256_and_si256(qx0, _mm256_set1_epi8(3));
qx1 = _mm256_and_si256(qx1, _mm256_set1_epi8(3));
qx2 = _mm256_and_si256(qx2, _mm256_set1_epi8(3));
qx3 = _mm256_and_si256(qx3, _mm256_set1_epi8(3));
const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 0));
const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 32));
const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 64));
const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 96));
qx0 = _mm256_maddubs_epi16(qx0, qy0);
qx1 = _mm256_maddubs_epi16(qx1, qy1);
qx2 = _mm256_maddubs_epi16(qx2, qy2);
qx3 = _mm256_maddubs_epi16(qx3, qy3);
sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1));
sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3));
}
const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums);
const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d));
sumi0 = _mm256_add_epi16(sumi0, sumi1);
sumi0 = _mm256_sub_epi16(sumi0, ysum);
sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1));
sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf);
}
*s = hsum_float_8(sumf);
#else
float sumf = 0.0f;
for (int i = 0; i < nb; ++i) {
int32_t sumi = 0;
for (size_t j = 0; j < sizeof(x->qs); j += 32) {
for (size_t l = 0; l < 4; ++l) {
for (size_t k = 0; k < 32; ++k) {
sumi += y[i].qs[j*4 + l*32 + k] * (((x[i].qs[j + k] >> (l*2)) & 3) - 1);
}
}
}
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
sumf += (float) sumi * d;
}
*s = sumf;
#endif
}
void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
assert(nrc == 1);
UNUSED(nrc);
@ -14800,6 +15480,14 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
}
}
} break;
case GGML_TYPE_TQ1_0:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_tq1_0, data, nb);
} break;
case GGML_TYPE_TQ2_0:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_tq2_0, data, nb);
} break;
case GGML_TYPE_IQ1_S:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq1_s, data, nb);

15
ggml/src/ggml-quants.h
View file

@ -26,6 +26,9 @@ void quantize_row_q5_K_ref(const float * GGML_RESTRICT x, block_q5_K * GGML_REST
void quantize_row_q6_K_ref(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_K_ref(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
void quantize_row_tq1_0_ref(const float * GGML_RESTRICT x, block_tq1_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_tq2_0_ref(const float * GGML_RESTRICT x, block_tq2_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_xxs_ref(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_nl_ref (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_xs_ref (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k);
@ -46,6 +49,9 @@ void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_tq1_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_tq2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@ -67,6 +73,9 @@ void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRI
void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_tq1_0(const block_tq1_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_tq2_0(const block_tq2_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
@ -90,6 +99,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@ -111,6 +123,9 @@ size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_tq1_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_tq2_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);

4
ggml/src/ggml-sycl/dmmv.cpp
View file

@ -76,8 +76,8 @@ static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat *
}
// sum up partial sums and write back result
#pragma unroll
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
const int mask_start = ncols > GGML_SYCL_DMMV_X ? WARP_SIZE >> 1 : WARP_SIZE >> 2;
for (int mask = mask_start; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}

2
ggml/src/ggml-vulkan.cpp
View file

@ -2480,7 +2480,7 @@ static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context&
const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]);
VK_LOG_DEBUG("ggml_vk_dispatch_pipeline(" << pipeline->name << ", {";
for (auto& buffer : descriptor_buffer_infos) {
std::cerr << "(" << buffer << ", " << buffer.offset << ", " << buffer.size << "), ";
std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.range << "), ";
}
std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))");
GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size());

1123
ggml/src/ggml.c
View file

File diff suppressed because it is too large Load diff

149
ggml/src/llamafile/sgemm.cpp
View file

@ -606,17 +606,29 @@ class tinyBLAS_Q0_AVX {
case 0x44:
mc = 4;
nc = 4;
#if defined(__AVX2__) && defined(__F16C__)
gemm4xN<4>(m0, m, n0, n);
#else
gemm<4, 4>(m0, m, n0, n);
#endif
break;
case 0x43:
mc = 4;
nc = 3;
#if defined(__AVX2__) && defined(__F16C__)
gemm4xN<3>(m0, m, n0, n);
#else
gemm<4, 3>(m0, m, n0, n);
#endif
break;
case 0x34:
mc = 3;
nc = 4;
#if defined(__AVX2__) && defined(__F16C__)
gemmMx4<3>(m0, m, n0, n);
#else
gemm<3, 4>(m0, m, n0, n);
#endif
break;
case 0x33:
mc = 3;
@ -626,12 +638,20 @@ class tinyBLAS_Q0_AVX {
case 0x42:
mc = 4;
nc = 2;
#if defined(__AVX2__) && defined(__F16C__)
gemm4xN<2>(m0, m, n0, n);
#else
gemm<4, 2>(m0, m, n0, n);
#endif
break;
case 0x24:
mc = 2;
nc = 4;
#if defined(__AVX2__) && defined(__F16C__)
gemmMx4<2>(m0, m, n0, n);
#else
gemm<2, 4>(m0, m, n0, n);
#endif
break;
#else
case 0x44:
@ -639,13 +659,21 @@ class tinyBLAS_Q0_AVX {
case 0x42:
mc = 4;
nc = 2;
#if defined(__AVX2__) && defined(__F16C__)
gemm4xN<2>(m0, m, n0, n);
#else
gemm<4, 2>(m0, m, n0, n);
#endif
break;
case 0x34:
case 0x24:
mc = 2;
nc = 4;
#if defined(__AVX2__) && defined(__F16C__)
gemmMx4<2>(m0, m, n0, n);
#else
gemm<2, 4>(m0, m, n0, n);
#endif
break;
case 0x33:
#endif
@ -662,7 +690,11 @@ class tinyBLAS_Q0_AVX {
case 0x41:
mc = 4;
nc = 1;
#if defined(__AVX2__) && defined(__F16C__)
gemm4xN<1>(m0, m, n0, n);
#else
gemm<4, 1>(m0, m, n0, n);
#endif
break;
case 0x22:
mc = 2;
@ -672,7 +704,11 @@ class tinyBLAS_Q0_AVX {
case 0x14:
mc = 1;
nc = 4;
#if defined(__AVX2__) && defined(__F16C__)
gemmMx4<1>(m0, m, n0, n);
#else
gemm<1, 4>(m0, m, n0, n);
#endif
break;
case 0x31:
mc = 3;
@ -708,6 +744,119 @@ class tinyBLAS_Q0_AVX {
mnpack(m0, m, np, n);
}
#if defined(__AVX2__) && defined(__F16C__)
// Templated functions for gemm of dimensions 4xN
template <int RN>
NOINLINE void gemm4xN(int64_t m0, int64_t m, int64_t n0, int64_t n) {
int64_t ytiles = (m - m0) / 4;
int64_t xtiles = (n - n0) / RN;
int64_t tiles = xtiles * ytiles;
int64_t duty = (tiles + nth - 1) / nth;
int64_t start = duty * ith;
int64_t end = start + duty;
if (end > tiles)
end = tiles;
for (int64_t job = start; job < end; ++job) {
int64_t ii = m0 + job / xtiles * 4;
int64_t jj = n0 + job % xtiles * RN;
__m256 Cv[RN][4] = {};
for (int64_t l = 0; l < k; ++l) {
uint64_t a_delta = ((uint64_t)A[lda * (ii + 3) + l].d << 48) | ((uint64_t)A[lda * (ii + 2) + l].d << 32) | ((uint64_t)A[lda * (ii + 1) + l].d << 16) | (A[lda * (ii + 0) + l].d);
// Convert delta values for four blocks to float values
__m128 da = _mm_cvtph_ps(_mm_set_epi64x(0, a_delta));
__m256i avec0 = load(A + lda * (ii + 0) + l);
__m256i avec1 = load(A + lda * (ii + 1) + l);
__m256i avec2 = load(A + lda * (ii + 2) + l);
__m256i avec3 = load(A + lda * (ii + 3) + l);
for (int64_t j = 0; j < RN; ++j) {
__m128 db = _mm_set1_ps(unhalf(B[ldb * (jj + j) + l].d));
// Computation of product of delta values for four blocks and replicate it across 256 bit lane
__m256 dvec = _mm256_castps128_ps256(_mm_mul_ps(da, db));
dvec = _mm256_permute2f128_ps(dvec ,dvec, 0);
// Computation of dot product and multiplication with appropriate delta value products
Cv[j][0] = madd(_mm256_shuffle_ps(dvec, dvec, 0),
updot(_mm256_sign_epi8(avec0, avec0),
_mm256_sign_epi8(load(B + ldb * (jj + j) + l), avec0)),
Cv[j][0]);
Cv[j][1] = madd(_mm256_shuffle_ps(dvec, dvec, 85),
updot(_mm256_sign_epi8(avec1, avec1),
_mm256_sign_epi8(load(B + ldb * (jj + j) + l), avec1)),
Cv[j][1]);
Cv[j][2] = madd(_mm256_shuffle_ps(dvec, dvec, 170),
updot(_mm256_sign_epi8(avec2, avec2),
_mm256_sign_epi8(load(B + ldb * (jj + j) + l), avec2)),
Cv[j][2]);
Cv[j][3] = madd(_mm256_shuffle_ps(dvec, dvec, 255),
updot(_mm256_sign_epi8(avec3, avec3),
_mm256_sign_epi8(load(B + ldb * (jj + j) + l), avec3)),
Cv[j][3]);
}
}
for (int64_t j = 0; j < RN; ++j)
for (int64_t i = 0; i < 4; ++i)
C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]);
}
}
// Templated functions for gemm of dimensions Mx4
template <int RM>
NOINLINE void gemmMx4(int64_t m0, int64_t m, int64_t n0, int64_t n) {
int64_t ytiles = (m - m0) / RM;
int64_t xtiles = (n - n0) / 4;
int64_t tiles = xtiles * ytiles;
int64_t duty = (tiles + nth - 1) / nth;
int64_t start = duty * ith;
int64_t end = start + duty;
if (end > tiles)
end = tiles;
for (int64_t job = start; job < end; ++job) {
int64_t ii = m0 + job / xtiles * RM;
int64_t jj = n0 + job % xtiles * 4;
__m256 Cv[4][RM] = {};
for (int64_t l = 0; l < k; ++l) {
uint64_t b_delta = ((uint64_t)B[ldb * (jj + 3) + l].d << 48) | ((uint64_t)B[ldb * (jj + 2) + l].d << 32) | ((uint64_t)B[ldb * (jj + 1) + l].d << 16) | (B[ldb * (jj + 0) + l].d);
// Convert delta values for four blocks to float values
__m128 db = _mm_cvtph_ps(_mm_set_epi64x(0, b_delta));
__m256i bvec0 = load(B + ldb * (jj + 0) + l);
__m256i bvec1 = load(B + ldb * (jj + 1) + l);
__m256i bvec2 = load(B + ldb * (jj + 2) + l);
__m256i bvec3 = load(B + ldb * (jj + 3) + l);
for (int64_t i = 0; i < RM; ++i) {
__m128 da = _mm_set1_ps(unhalf((A[lda * (ii + i) + l].d)));
// Computation of product of delta values for four blocks and replicate it across 256 bit lane
__m256 dvec = _mm256_castps128_ps256(_mm_mul_ps(da, db));
dvec = _mm256_permute2f128_ps(dvec ,dvec, 0);
// Computation of dot product and multiplication with appropriate delta value products
Cv[0][i] = madd(_mm256_shuffle_ps(dvec, dvec, 0),
updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l),
load(A + lda * (ii + i) + l)),
_mm256_sign_epi8(bvec0, load(A + lda * (ii + i) + l))),
Cv[0][i]);
Cv[1][i] = madd(_mm256_shuffle_ps(dvec, dvec, 85),
updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l),
load(A + lda * (ii + i) + l)),
_mm256_sign_epi8(bvec1, load(A + lda * (ii + i) + l))),
Cv[1][i]);
Cv[2][i] = madd(_mm256_shuffle_ps(dvec, dvec, 170),
updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l),
load(A + lda * (ii + i) + l)),
_mm256_sign_epi8(bvec2, load(A + lda * (ii + i) + l))),
Cv[2][i]);
Cv[3][i] = madd(_mm256_shuffle_ps(dvec, dvec, 255),
updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l),
load(A + lda * (ii + i) + l)),
_mm256_sign_epi8(bvec3, load(A + lda * (ii + i) + l))),
Cv[3][i]);
}
}
for (int64_t j = 0; j < 4; ++j)
for (int64_t i = 0; i < RM; ++i)
C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]);
}
}
#endif
template <int RM, int RN>
NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
int64_t ytiles = (m - m0) / RM;

5
ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
View file

@ -200,6 +200,11 @@ void string_to_spv(const std::string& _name, const std::string& in_fname, const
#else
std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname};
#endif
#ifdef GGML_VULKAN_SHADER_DEBUG_INFO
cmd.push_back("-g");
#endif
for (const auto& define : defines) {
cmd.push_back("-D" + define.first + "=" + define.second);
}

253
gguf-py/gguf/constants.py
View file

@ -94,6 +94,9 @@ class Keys:
DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id"
ATTN_LOGIT_SOFTCAPPING = "{arch}.attn_logit_softcapping"
FINAL_LOGIT_SOFTCAPPING = "{arch}.final_logit_softcapping"
RESCALE_EVERY_N_LAYERS = "{arch}.rescale_every_n_layers"
TIME_MIX_EXTRA_DIM = "{arch}.time_mix_extra_dim"
TIME_DECAY_EXTRA_DIM = "{arch}.time_decay_extra_dim"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
@ -132,6 +135,9 @@ class Keys:
TIME_STEP_RANK = "{arch}.ssm.time_step_rank"
DT_B_C_RMS = "{arch}.ssm.dt_b_c_rms"
class WKV:
HEAD_SIZE = "{arch}.wkv.head_size"
class Tokenizer:
MODEL = "tokenizer.ggml.model"
PRE = "tokenizer.ggml.pre"
@ -207,6 +213,7 @@ class MODEL_ARCH(IntEnum):
GEMMA = auto()
GEMMA2 = auto()
STARCODER2 = auto()
RWKV6 = auto()
MAMBA = auto()
XVERSE = auto()
COMMAND_R = auto()
@ -270,6 +277,29 @@ class MODEL_TENSOR(IntEnum):
SSM_A = auto()
SSM_D = auto()
SSM_OUT = auto()
TIME_MIX_W1 = auto()
TIME_MIX_W2 = auto()
TIME_MIX_LERP_X = auto()
TIME_MIX_LERP_K = auto()
TIME_MIX_LERP_V = auto()
TIME_MIX_LERP_R = auto()
TIME_MIX_LERP_G = auto()
TIME_MIX_LERP_W = auto()
TIME_MIX_FIRST = auto()
TIME_MIX_DECAY = auto()
TIME_MIX_DECAY_W1 = auto()
TIME_MIX_DECAY_W2 = auto()
TIME_MIX_KEY = auto()
TIME_MIX_VALUE = auto()
TIME_MIX_RECEPTANCE = auto()
TIME_MIX_GATE = auto()
TIME_MIX_LN = auto()
TIME_MIX_OUTPUT = auto()
CHANNEL_MIX_LERP_K = auto()
CHANNEL_MIX_LERP_R = auto()
CHANNEL_MIX_KEY = auto()
CHANNEL_MIX_RECEPTANCE = auto()
CHANNEL_MIX_VALUE = auto()
ATTN_Q_A = auto()
ATTN_Q_B = auto()
ATTN_KV_A_MQA = auto()
@ -337,6 +367,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.GEMMA: "gemma",
MODEL_ARCH.GEMMA2: "gemma2",
MODEL_ARCH.STARCODER2: "starcoder2",
MODEL_ARCH.RWKV6: "rwkv6",
MODEL_ARCH.MAMBA: "mamba",
MODEL_ARCH.XVERSE: "xverse",
MODEL_ARCH.COMMAND_R: "command-r",
@ -355,87 +386,110 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm",
MODEL_TENSOR.TOKEN_TYPES: "token_types",
MODEL_TENSOR.POS_EMBD: "position_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long",
MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short",
MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm",
MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm",
MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm",
MODEL_TENSOR.ATTN_POST_NORM: "blk.{bid}.post_attention_norm",
MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp",
MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp",
MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_PRE_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_POST_NORM: "blk.{bid}.post_ffw_norm",
MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp",
MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp",
MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp",
MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn",
MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps",
MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps",
MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps",
MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps",
MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm",
MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in",
MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d",
MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x",
MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt",
MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a",
MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d",
MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out",
MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a",
MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b",
MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa",
MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b",
MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm",
MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm",
MODEL_TENSOR.ATTN_SUB_NORM: "blk.{bid}.attn_sub_norm",
MODEL_TENSOR.FFN_SUB_NORM: "blk.{bid}.ffn_sub_norm",
MODEL_TENSOR.DEC_ATTN_NORM: "dec.blk.{bid}.attn_norm",
MODEL_TENSOR.DEC_ATTN_Q: "dec.blk.{bid}.attn_q",
MODEL_TENSOR.DEC_ATTN_K: "dec.blk.{bid}.attn_k",
MODEL_TENSOR.DEC_ATTN_V: "dec.blk.{bid}.attn_v",
MODEL_TENSOR.DEC_ATTN_OUT: "dec.blk.{bid}.attn_o",
MODEL_TENSOR.DEC_ATTN_REL_B: "dec.blk.{bid}.attn_rel_b",
MODEL_TENSOR.DEC_CROSS_ATTN_NORM: "dec.blk.{bid}.cross_attn_norm",
MODEL_TENSOR.DEC_CROSS_ATTN_Q: "dec.blk.{bid}.cross_attn_q",
MODEL_TENSOR.DEC_CROSS_ATTN_K: "dec.blk.{bid}.cross_attn_k",
MODEL_TENSOR.DEC_CROSS_ATTN_V: "dec.blk.{bid}.cross_attn_v",
MODEL_TENSOR.DEC_CROSS_ATTN_OUT: "dec.blk.{bid}.cross_attn_o",
MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: "dec.blk.{bid}.cross_attn_rel_b",
MODEL_TENSOR.DEC_FFN_NORM: "dec.blk.{bid}.ffn_norm",
MODEL_TENSOR.DEC_FFN_GATE: "dec.blk.{bid}.ffn_gate",
MODEL_TENSOR.DEC_FFN_DOWN: "dec.blk.{bid}.ffn_down",
MODEL_TENSOR.DEC_FFN_UP: "dec.blk.{bid}.ffn_up",
MODEL_TENSOR.DEC_OUTPUT_NORM: "dec.output_norm",
MODEL_TENSOR.ENC_ATTN_NORM: "enc.blk.{bid}.attn_norm",
MODEL_TENSOR.ENC_ATTN_Q: "enc.blk.{bid}.attn_q",
MODEL_TENSOR.ENC_ATTN_K: "enc.blk.{bid}.attn_k",
MODEL_TENSOR.ENC_ATTN_V: "enc.blk.{bid}.attn_v",
MODEL_TENSOR.ENC_ATTN_OUT: "enc.blk.{bid}.attn_o",
MODEL_TENSOR.ENC_ATTN_REL_B: "enc.blk.{bid}.attn_rel_b",
MODEL_TENSOR.ENC_FFN_NORM: "enc.blk.{bid}.ffn_norm",
MODEL_TENSOR.ENC_FFN_GATE: "enc.blk.{bid}.ffn_gate",
MODEL_TENSOR.ENC_FFN_DOWN: "enc.blk.{bid}.ffn_down",
MODEL_TENSOR.ENC_FFN_UP: "enc.blk.{bid}.ffn_up",
MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm",
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm",
MODEL_TENSOR.TOKEN_TYPES: "token_types",
MODEL_TENSOR.POS_EMBD: "position_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long",
MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short",
MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm",
MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm",
MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm",
MODEL_TENSOR.ATTN_POST_NORM: "blk.{bid}.post_attention_norm",
MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp",
MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp",
MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_PRE_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_POST_NORM: "blk.{bid}.post_ffw_norm",
MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp",
MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp",
MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp",
MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn",
MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps",
MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps",
MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps",
MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps",
MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm",
MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in",
MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d",
MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x",
MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt",
MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a",
MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d",
MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out",
MODEL_TENSOR.TIME_MIX_W1: "blk.{bid}.time_mix_w1",
MODEL_TENSOR.TIME_MIX_W2: "blk.{bid}.time_mix_w2",
MODEL_TENSOR.TIME_MIX_LERP_X: "blk.{bid}.time_mix_lerp_x",
MODEL_TENSOR.TIME_MIX_LERP_K: "blk.{bid}.time_mix_lerp_k",
MODEL_TENSOR.TIME_MIX_LERP_V: "blk.{bid}.time_mix_lerp_v",
MODEL_TENSOR.TIME_MIX_LERP_R: "blk.{bid}.time_mix_lerp_r",
MODEL_TENSOR.TIME_MIX_LERP_G: "blk.{bid}.time_mix_lerp_g",
MODEL_TENSOR.TIME_MIX_LERP_W: "blk.{bid}.time_mix_lerp_w",
MODEL_TENSOR.TIME_MIX_FIRST: "blk.{bid}.time_mix_first",
MODEL_TENSOR.TIME_MIX_DECAY: "blk.{bid}.time_mix_decay",
MODEL_TENSOR.TIME_MIX_DECAY_W1: "blk.{bid}.time_mix_decay_w1",
MODEL_TENSOR.TIME_MIX_DECAY_W2: "blk.{bid}.time_mix_decay_w2",
MODEL_TENSOR.TIME_MIX_KEY: "blk.{bid}.time_mix_key",
MODEL_TENSOR.TIME_MIX_VALUE: "blk.{bid}.time_mix_value",
MODEL_TENSOR.TIME_MIX_RECEPTANCE: "blk.{bid}.time_mix_receptance",
MODEL_TENSOR.TIME_MIX_GATE: "blk.{bid}.time_mix_gate",
MODEL_TENSOR.TIME_MIX_LN: "blk.{bid}.time_mix_ln",
MODEL_TENSOR.TIME_MIX_OUTPUT: "blk.{bid}.time_mix_output",
MODEL_TENSOR.CHANNEL_MIX_LERP_K: "blk.{bid}.channel_mix_lerp_k",
MODEL_TENSOR.CHANNEL_MIX_LERP_R: "blk.{bid}.channel_mix_lerp_r",
MODEL_TENSOR.CHANNEL_MIX_KEY: "blk.{bid}.channel_mix_key",
MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: "blk.{bid}.channel_mix_receptance",
MODEL_TENSOR.CHANNEL_MIX_VALUE: "blk.{bid}.channel_mix_value",
MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a",
MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b",
MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa",
MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b",
MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm",
MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm",
MODEL_TENSOR.ATTN_SUB_NORM: "blk.{bid}.attn_sub_norm",
MODEL_TENSOR.FFN_SUB_NORM: "blk.{bid}.ffn_sub_norm",
MODEL_TENSOR.DEC_ATTN_NORM: "dec.blk.{bid}.attn_norm",
MODEL_TENSOR.DEC_ATTN_Q: "dec.blk.{bid}.attn_q",
MODEL_TENSOR.DEC_ATTN_K: "dec.blk.{bid}.attn_k",
MODEL_TENSOR.DEC_ATTN_V: "dec.blk.{bid}.attn_v",
MODEL_TENSOR.DEC_ATTN_OUT: "dec.blk.{bid}.attn_o",
MODEL_TENSOR.DEC_ATTN_REL_B: "dec.blk.{bid}.attn_rel_b",
MODEL_TENSOR.DEC_CROSS_ATTN_NORM: "dec.blk.{bid}.cross_attn_norm",
MODEL_TENSOR.DEC_CROSS_ATTN_Q: "dec.blk.{bid}.cross_attn_q",
MODEL_TENSOR.DEC_CROSS_ATTN_K: "dec.blk.{bid}.cross_attn_k",
MODEL_TENSOR.DEC_CROSS_ATTN_V: "dec.blk.{bid}.cross_attn_v",
MODEL_TENSOR.DEC_CROSS_ATTN_OUT: "dec.blk.{bid}.cross_attn_o",
MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: "dec.blk.{bid}.cross_attn_rel_b",
MODEL_TENSOR.DEC_FFN_NORM: "dec.blk.{bid}.ffn_norm",
MODEL_TENSOR.DEC_FFN_GATE: "dec.blk.{bid}.ffn_gate",
MODEL_TENSOR.DEC_FFN_DOWN: "dec.blk.{bid}.ffn_down",
MODEL_TENSOR.DEC_FFN_UP: "dec.blk.{bid}.ffn_up",
MODEL_TENSOR.DEC_OUTPUT_NORM: "dec.output_norm",
MODEL_TENSOR.ENC_ATTN_NORM: "enc.blk.{bid}.attn_norm",
MODEL_TENSOR.ENC_ATTN_Q: "enc.blk.{bid}.attn_q",
MODEL_TENSOR.ENC_ATTN_K: "enc.blk.{bid}.attn_k",
MODEL_TENSOR.ENC_ATTN_V: "enc.blk.{bid}.attn_v",
MODEL_TENSOR.ENC_ATTN_OUT: "enc.blk.{bid}.attn_o",
MODEL_TENSOR.ENC_ATTN_REL_B: "enc.blk.{bid}.attn_rel_b",
MODEL_TENSOR.ENC_FFN_NORM: "enc.blk.{bid}.ffn_norm",
MODEL_TENSOR.ENC_FFN_GATE: "enc.blk.{bid}.ffn_gate",
MODEL_TENSOR.ENC_FFN_DOWN: "enc.blk.{bid}.ffn_down",
MODEL_TENSOR.ENC_FFN_UP: "enc.blk.{bid}.ffn_up",
MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@ -856,6 +910,37 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.RWKV6: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.TOKEN_EMBD_NORM,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_NORM_2,
MODEL_TENSOR.TIME_MIX_W1,
MODEL_TENSOR.TIME_MIX_W2,
MODEL_TENSOR.TIME_MIX_LERP_X,
MODEL_TENSOR.TIME_MIX_LERP_K,
MODEL_TENSOR.TIME_MIX_LERP_V,
MODEL_TENSOR.TIME_MIX_LERP_R,
MODEL_TENSOR.TIME_MIX_LERP_G,
MODEL_TENSOR.TIME_MIX_LERP_W,
MODEL_TENSOR.TIME_MIX_FIRST,
MODEL_TENSOR.TIME_MIX_DECAY,
MODEL_TENSOR.TIME_MIX_DECAY_W1,
MODEL_TENSOR.TIME_MIX_DECAY_W2,
MODEL_TENSOR.TIME_MIX_KEY,
MODEL_TENSOR.TIME_MIX_VALUE,
MODEL_TENSOR.TIME_MIX_RECEPTANCE,
MODEL_TENSOR.TIME_MIX_GATE,
MODEL_TENSOR.TIME_MIX_LN,
MODEL_TENSOR.TIME_MIX_OUTPUT,
MODEL_TENSOR.CHANNEL_MIX_LERP_K,
MODEL_TENSOR.CHANNEL_MIX_LERP_R,
MODEL_TENSOR.CHANNEL_MIX_KEY,
MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE,
MODEL_TENSOR.CHANNEL_MIX_VALUE,
],
MODEL_ARCH.MAMBA: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
@ -1206,6 +1291,8 @@ class GGMLQuantizationType(IntEnum):
Q4_0_4_4 = 31
Q4_0_4_8 = 32
Q4_0_8_8 = 33
TQ1_0 = 34
TQ2_0 = 35
# TODO: add GGMLFileType from ggml_ftype in ggml.h
@ -1250,6 +1337,8 @@ class LlamaFileType(IntEnum):
MOSTLY_Q4_0_4_4 = 33 # except 1d tensors
MOSTLY_Q4_0_4_8 = 34 # except 1d tensors
MOSTLY_Q4_0_8_8 = 35 # except 1d tensors
MOSTLY_TQ1_0 = 36 # except 1d tensors
MOSTLY_TQ2_0 = 37 # except 1d tensors
GUESSED = 1024 # not specified in the model file
@ -1326,6 +1415,8 @@ GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
GGMLQuantizationType.Q4_0_4_4:(32, 2 + 16),
GGMLQuantizationType.Q4_0_4_8:(32, 2 + 16),
GGMLQuantizationType.Q4_0_8_8:(32, 2 + 16),
GGMLQuantizationType.TQ1_0: (256, 2 + 4 * 13),
GGMLQuantizationType.TQ2_0: (256, 2 + 64),
}

12
gguf-py/gguf/gguf_writer.py
View file

@ -670,6 +670,18 @@ class GGUFWriter:
def add_expert_weights_scale(self, value: float) -> None:
self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value)
def add_rescale_every_n_layers(self, count: int) -> None:
self.add_uint32(Keys.LLM.RESCALE_EVERY_N_LAYERS.format(arch=self.arch), count)
def add_time_mix_extra_dim(self, dim: int) -> None:
self.add_uint32(Keys.LLM.TIME_MIX_EXTRA_DIM.format(arch=self.arch), dim)
def add_time_decay_extra_dim(self, dim: int) -> None:
self.add_uint32(Keys.LLM.TIME_DECAY_EXTRA_DIM.format(arch=self.arch), dim)
def add_wkv_head_size(self, size: int) -> None:
self.add_uint32(Keys.WKV.HEAD_SIZE.format(arch=self.arch), size)
def add_layer_norm_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)

81
gguf-py/gguf/quants.py
View file

@ -574,6 +574,87 @@ class Q6_K(__Quant, qtype=GGMLQuantizationType.Q6_K):
return (d * q).reshape((n_blocks, QK_K))
class TQ1_0(__Quant, qtype=GGMLQuantizationType.TQ1_0):
@classmethod
def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
n_blocks = blocks.shape[0]
d = abs(blocks).max(axis=-1, keepdims=True)
with np.errstate(divide="ignore"):
id = np.where(d == 0, 0, 1 / d)
qs = np_roundf(blocks * id)
qs = (qs.astype(np.int8) + np.int8(1)).astype(np.uint8)
qs0, qs1, qh = qs[..., :(32 * 5)], qs[..., (32 * 5):(48 * 5)], qs[..., (48 * 5):]
qs0 = qs0.reshape((n_blocks, -1, 5, 32)) * np.array([81, 27, 9, 3, 1], dtype=np.uint8).reshape((1, 1, 5, 1))
qs0 = np.sum(qs0, axis=-2).reshape((n_blocks, -1))
qs1 = qs1.reshape((n_blocks, -1, 5, 16)) * np.array([81, 27, 9, 3, 1], dtype=np.uint8).reshape((1, 1, 5, 1))
qs1 = np.sum(qs1, axis=-2).reshape((n_blocks, -1))
qh = qh.reshape((n_blocks, -1, 4, 4)) * np.array([81, 27, 9, 3], dtype=np.uint8).reshape((1, 1, 4, 1))
qh = np.sum(qh, axis=-2).reshape((n_blocks, -1))
qs = np.concatenate([qs0, qs1, qh], axis=-1)
qs = (qs.astype(np.uint16) * 256 + (243 - 1)) // 243
qs = qs.astype(np.uint8)
d = d.astype(np.float16).view(np.uint8)
return np.concatenate([qs, d], axis=-1)
@classmethod
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
n_blocks = blocks.shape[0]
qs, rest = np.hsplit(blocks, [(QK_K - 4 * QK_K // 64) // 5])
qh, d = np.hsplit(rest, [QK_K // 64])
d = d.view(np.float16).astype(np.float32)
qs0, qs1 = qs[..., :32], qs[..., 32:]
qs0 = qs0.reshape((n_blocks, -1, 1, 32)) * np.array([1, 3, 9, 27, 81], dtype=np.uint8).reshape((1, 1, 5, 1))
qs0 = qs0.reshape((n_blocks, -1))
qs1 = qs1.reshape((n_blocks, -1, 1, 16)) * np.array([1, 3, 9, 27, 81], dtype=np.uint8).reshape((1, 1, 5, 1))
qs1 = qs1.reshape((n_blocks, -1))
qh = qh.reshape((n_blocks, -1, 1, 4)) * np.array([1, 3, 9, 27], dtype=np.uint8).reshape((1, 1, 4, 1))
qh = qh.reshape((n_blocks, -1))
qs = np.concatenate([qs0, qs1, qh], axis=-1)
qs = ((qs.astype(np.uint16) * 3) >> 8).astype(np.int8) - np.int8(1)
return (d * qs.astype(np.float32))
class TQ2_0(__Quant, qtype=GGMLQuantizationType.TQ2_0):
@classmethod
def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
n_blocks = blocks.shape[0]
d = abs(blocks).max(axis=-1, keepdims=True)
with np.errstate(divide="ignore"):
id = np.where(d == 0, 0, 1 / d)
qs = np_roundf(blocks * id)
qs = (qs.astype(np.int8) + np.int8(1)).astype(np.uint8)
qs = qs.reshape((n_blocks, -1, 4, 32)) << np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
qs = qs[..., 0, :] | qs[..., 1, :] | qs[..., 2, :] | qs[..., 3, :]
qs = qs.reshape((n_blocks, -1))
d = d.astype(np.float16).view(np.uint8)
return np.concatenate([qs, d], axis=-1)
@classmethod
def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
n_blocks = blocks.shape[0]
qs, d = np.hsplit(blocks, [QK_K // 4])
d = d.view(np.float16).astype(np.float32)
qs = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1))
qs = (qs & 0x03).reshape((n_blocks, -1)).astype(np.int8) - np.int8(1)
return (d * qs.astype(np.float32))
class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS):
ksigns: bytes = (
b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f"

100
gguf-py/gguf/tensor_mapping.py
View file

@ -27,6 +27,7 @@ class TensorNameMap:
"embedding.word_embeddings", # chatglm
"transformer.token_embeddings", # openelm
"shared", # t5
"rwkv.embeddings", # rwkv
),
# Token type embeddings
@ -40,6 +41,7 @@ class TensorNameMap:
"embeddings.LayerNorm", # bert
"emb_ln", # nomic-bert
"transformer.norm", # openelm
"rwkv.blocks.0.pre_ln", # rwkv
),
# Position embeddings
@ -57,6 +59,7 @@ class TensorNameMap:
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
"output_layer", # chatglm
"head", # rwkv
),
# Output norm
@ -76,6 +79,7 @@ class TensorNameMap:
"encoder.final_layernorm", # chatglm
"transformer.norm", # openelm
"model.norm", # nemotron
"rwkv.ln_out", # rwkv
),
# Rope frequencies
@ -108,12 +112,14 @@ class TensorNameMap:
"transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
"encoder.layers.{bid}.input_layernorm", # chatglm
"transformer.layers.{bid}.attn_norm", # openelm
"rwkv.blocks.{bid}.ln1", # rwkv
),
# Attention norm 2
MODEL_TENSOR.ATTN_NORM_2: (
"transformer.h.{bid}.ln_attn", # falcon40b
"transformer.h.{bid}.ln_attn", # falcon40b
"encoder.layer.{bid}.layer_norm_1", # jina-v2-code
"rwkv.blocks.{bid}.ln2", # rwkv
),
# Attention query-key-value
@ -434,6 +440,98 @@ class TensorNameMap:
"backbone.layers.{bid}.mixer.out_proj",
),
MODEL_TENSOR.TIME_MIX_W1: (
"rwkv.blocks.{bid}.attention.time_maa_w1", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_W2: (
"rwkv.blocks.{bid}.attention.time_maa_w2", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_LERP_X: (
"rwkv.blocks.{bid}.attention.time_maa_x", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_LERP_K: (
"rwkv.blocks.{bid}.attention.time_maa_k", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_LERP_V: (
"rwkv.blocks.{bid}.attention.time_maa_v", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_LERP_R: (
"rwkv.blocks.{bid}.attention.time_maa_r", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_LERP_G: (
"rwkv.blocks.{bid}.attention.time_maa_g", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_LERP_W: (
"rwkv.blocks.{bid}.attention.time_maa_w", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_FIRST: (
"rwkv.blocks.{bid}.attention.time_faaaa", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_DECAY: (
"rwkv.blocks.{bid}.attention.time_decay", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_DECAY_W1: (
"rwkv.blocks.{bid}.attention.time_decay_w1", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_DECAY_W2: (
"rwkv.blocks.{bid}.attention.time_decay_w2", # rwkv v6
),
MODEL_TENSOR.TIME_MIX_KEY: (
"rwkv.blocks.{bid}.attention.key", # rwkv
),
MODEL_TENSOR.TIME_MIX_VALUE: (
"rwkv.blocks.{bid}.attention.value", # rwkv
),
MODEL_TENSOR.TIME_MIX_RECEPTANCE: (
"rwkv.blocks.{bid}.attention.receptance", # rwkv
),
MODEL_TENSOR.TIME_MIX_GATE: (
"rwkv.blocks.{bid}.attention.gate", # rwkv
),
MODEL_TENSOR.TIME_MIX_LN: (
"rwkv.blocks.{bid}.attention.ln_x", # rwkv
),
MODEL_TENSOR.TIME_MIX_OUTPUT: (
"rwkv.blocks.{bid}.attention.output", # rwkv
),
MODEL_TENSOR.CHANNEL_MIX_LERP_K: (
"rwkv.blocks.{bid}.feed_forward.time_maa_k", # rwkv v6
),
MODEL_TENSOR.CHANNEL_MIX_LERP_R: (
"rwkv.blocks.{bid}.feed_forward.time_maa_r", # rwkv v6
),
MODEL_TENSOR.CHANNEL_MIX_KEY: (
"rwkv.blocks.{bid}.feed_forward.key", # rwkv
),
MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: (
"rwkv.blocks.{bid}.feed_forward.receptance", # rwkv
),
MODEL_TENSOR.CHANNEL_MIX_VALUE: (
"rwkv.blocks.{bid}.feed_forward.value", # rwkv
),
MODEL_TENSOR.ATTN_Q_A: (
"model.layers.{bid}.self_attn.q_a_proj", # deepseek2
),

1
gguf-py/pyproject.toml
View file

@ -23,6 +23,7 @@ python = ">=3.8"
numpy = ">=1.17"
tqdm = ">=4.27"
pyyaml = ">=5.1"
sentencepiece = ">=0.1.98,<=0.2.0"
[tool.poetry.dev-dependencies]
pytest = "^5.2"

1
gguf-py/tests/test_quants.py
View file

@ -66,6 +66,7 @@ class GGMLQuants:
for t in (
"q4_0", "q4_1", "q5_0", "q5_1", "q8_0",
"q2_K", "q3_K", "q4_K", "q5_K", "q6_K",
"tq1_0", "tq2_0",
"iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m",
"iq4_nl", "iq4_xs",
):

2
grammars/README.md
View file

@ -120,7 +120,7 @@ You can use GBNF grammars:
- In [llama-server](../examples/server):
- For any completion endpoints, passed as the `json_schema` body field
- For the `/chat/completions` endpoint, passed inside the `result_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}`)
- For the `/chat/completions` endpoint, passed inside the `response_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}`)
- In [llama-cli](../examples/main), passed as the `--json` / `-j` flag
- To convert to a grammar ahead of time:
- in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py)

26
include/llama.h
View file

@ -66,6 +66,7 @@ extern "C" {
LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE
LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece
LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram
LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization
};
// pre-tokenization types
@ -166,6 +167,8 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // except 1d tensors
LLAMA_FTYPE_MOSTLY_TQ1_0 = 36, // except 1d tensors
LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
@ -267,9 +270,9 @@ extern "C" {
enum llama_split_mode split_mode; // how to split the model across multiple GPUs
// main_gpu interpretation depends on split_mode:
// LLAMA_SPLIT_NONE: the GPU that is used for the entire model
// LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results
// LLAMA_SPLIT_LAYER: ignored
// LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model
// LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results
// LLAMA_SPLIT_MODE_LAYER: ignored
int32_t main_gpu;
// proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
@ -304,8 +307,8 @@ extern "C" {
uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode
uint32_t n_ubatch; // physical maximum batch size
uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
uint32_t n_threads; // number of threads to use for generation
uint32_t n_threads_batch; // number of threads to use for batch processing
int32_t n_threads; // number of threads to use for generation
int32_t n_threads_batch; // number of threads to use for batch processing
enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id
@ -428,6 +431,13 @@ extern "C" {
//optional:
LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
// Optional: an auto threadpool gets created in ggml if not passed explicitly
LLAMA_API void llama_attach_threadpool(
struct llama_context * ctx,
ggml_threadpool_t threadpool,
ggml_threadpool_t threadpool_batch);
LLAMA_API void llama_detach_threadpool(struct llama_context * ctx);
// Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free(void);
@ -837,13 +847,13 @@ extern "C" {
// Set the number of threads used for decoding
// n_threads is the number of threads used for generation (single token)
// n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
LLAMA_API void llama_set_n_threads(struct llama_context * ctx, int32_t n_threads, int32_t n_threads_batch);
// Get the number of threads used for generation of a single token.
LLAMA_API uint32_t llama_n_threads(struct llama_context * ctx);
LLAMA_API int32_t llama_n_threads(struct llama_context * ctx);
// Get the number of threads used for prompt and batch processing (multiple token).
LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx);
LLAMA_API int32_t llama_n_threads_batch(struct llama_context * ctx);
// Set whether the model is in embeddings mode or not
// If true, embeddings will be returned but logits will not

2
pyproject.toml
View file

@ -17,7 +17,7 @@ classifiers = [
[tool.poetry.dependencies]
python = ">=3.9"
numpy = "^1.25.0"
sentencepiece = ">=0.1.98,<0.2.0"
sentencepiece = ">=0.1.98,<=0.2.0"
transformers = ">=4.35.2,<5.0.0"
protobuf = ">=4.21.0,<5.0.0"
gguf = { path = "./gguf-py" }

147
src/llama-vocab.cpp
View file

@ -58,17 +58,17 @@ struct naive_trie {
auto res = children.find(c);
if (res != children.end()) {
return res->second.get_longest_prefix(key, len, offset + 1);
} else {
return std::make_pair(key, offset);
}
return std::make_pair(key, offset);
}
struct naive_trie * traverse(const char c) {
const struct naive_trie * traverse(const char c) const {
auto res = children.find(c);
if (res != children.end()) {
return &res->second;
} else {
return NULL;
}
return NULL;
}
std::map<char, struct naive_trie> children;
bool has_value;
@ -843,7 +843,7 @@ struct llm_tokenizer_ugm {
// traverse the token matcher trie to find a matching token
bool single_codepoint_token_found = false;
const struct best_tokenization & current_best = tokenization_results[input_offset];
struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]);
const struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]);
while (prefix_offset <= input_len && node != NULL) {
// check if we found valid token in prefix
@ -963,7 +963,7 @@ private:
/*
* This structure is a view wrapper for XOR-compressed double array (XCDA)
* See Shunsuke Kanda (2018). Space- and Time-Efficient String Dictionaries.
* Eeach bit-packed entry contains:
* Each bit-packed entry contains:
* - BASE array value in bits 10-30
* - LCHECK array value in bits 0-7
* - LEAF array value in bit 9
@ -1097,6 +1097,111 @@ private:
struct naive_trie token_matcher;
};
//
// RWKV tokenizer
//
static std::vector<uint8_t> llama_unescape_rwkv_token(const std::string & escaped) {
std::vector<uint8_t> output;
output.reserve(escaped.size());
// Parser state
bool escaping = false;
uint8_t hex_remaining = 0;
uint8_t hex_acc = 0;
// Step through characters, performing parsing
for (const char & c : escaped) {
// If we're parsing a hex code, interpret the next character
if (hex_remaining != 0) {
uint8_t value = (c >= 'a') ? (c - 'a' + 10) : (c - '0');
hex_acc = (hex_acc << 4) + value;
hex_remaining -= 1;
if (hex_remaining == 0) {
output.push_back(hex_acc);
hex_acc = 0;
}
continue;
}
// If we got an escape character, interpret it
if (escaping) {
if (c == 't') {
output.push_back('\t');
} else if (c == 'n') {
output.push_back('\n');
} else if (c == 'r') {
output.push_back('\r');
} else if (c == 'x') {
hex_remaining = 2;
} else {
output.push_back(c);
}
escaping = false;
continue;
}
if (c == '\\') {
escaping = true;
continue;
}
output.push_back(c);
}
return output;
}
struct llm_tokenizer_rwkv {
llm_tokenizer_rwkv(const llama_vocab & vocab): vocab(vocab) {
// RWKV supports arbitrary byte tokens, but the vocab struct only supports string tokens.
// For now, we decode the vocab here into the lookup we'll use for tokenization.
// build trie
for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
const auto & token = vocab.id_to_token[id];
const auto data = llama_unescape_rwkv_token(token.text);
token_matcher.insert((const char *) data.data(), data.size(), id);
}
}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
uint32_t position = 0;
while (position < text.size()) {
const struct naive_trie * node = token_matcher.traverse(text[position]);
if (node == NULL) {
// no matching token found, add unknown token
output.push_back(vocab.special_unk_id);
position += 1;
continue;
}
// traverse the trie to find the longest matching token
uint32_t token_id = 0;
uint32_t token_length = 0;
while (node != NULL) {
if (node->has_value) {
token_id = node->value;
token_length = position + 1;
}
node = node->traverse(text[++position]);
}
// add the longest matching token
output.push_back(token_id);
position = token_length;
}
}
const llama_vocab & vocab;
struct naive_trie token_matcher;
};
//
// (de-) tokenize
//
@ -1401,6 +1506,23 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
output.push_back(vocab.special_eos_id);
}
} break;
case LLAMA_VOCAB_TYPE_RWKV:
{
for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
llm_tokenizer_rwkv tokenizer(vocab);
tokenizer.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
}
}
} break;
case LLAMA_VOCAB_TYPE_NONE:
GGML_ABORT("fatal error");
}
@ -1616,6 +1738,17 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
}
break;
}
case LLAMA_VOCAB_TYPE_RWKV: {
std::vector<uint8_t> result = llama_unescape_rwkv_token(token_text);
// If we don't have enough space, return an error
if (result.size() > (size_t)length) {
return -(int)result.size();
}
memcpy(buf, result.data(), result.size());
return (int)result.size();
}
default:
GGML_ABORT("fatal error");
}

713
src/llama.cpp
View file

File diff suppressed because it is too large Load diff

2
tests/test-backend-ops.cpp
View file

@ -2200,6 +2200,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K,
// GGML_TYPE_TQ1_0, GGML_TYPE_TQ2_0, // TODO: implement for all backends
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
@ -2219,6 +2220,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K,
// GGML_TYPE_TQ1_0, GGML_TYPE_TQ2_0, // TODO: implement for all backends
GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,

6
tests/test-quantize-fns.cpp
View file

@ -15,11 +15,13 @@
constexpr float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
constexpr float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_TERNARY = 0.01f;
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS = 0.0050f;
constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f;
constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f;
constexpr float MAX_DOT_PRODUCT_ERROR_TERNARY = 0.15f;
static const char* RESULT_STR[] = {"ok", "FAILED"};
@ -144,6 +146,8 @@ int main(int argc, char * argv[]) {
if (qfns.from_float && qfns.to_float) {
const float total_error = total_quantization_error(qfns, test_size, test_data.data());
const float max_quantization_error =
type == GGML_TYPE_TQ1_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
type == GGML_TYPE_TQ2_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
type == GGML_TYPE_IQ2_S ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
@ -166,6 +170,8 @@ int main(int argc, char * argv[]) {
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
: type == GGML_TYPE_TQ1_0 || type == GGML_TYPE_TQ2_0
? MAX_DOT_PRODUCT_ERROR_TERNARY
: MAX_DOT_PRODUCT_ERROR;
failed = !(vec_dot_error < max_allowed_error);
num_failed += failed;

2
tests/test-rope.cpp
View file

@ -113,7 +113,7 @@ static struct ggml_tensor * get_random_tensor_f32(
}
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads, nullptr);
if (plan.work_size > 0) {
buf.resize(plan.work_size);