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Merge branch 'master' into compilade/mamba2
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This commit is contained in:
Francis Couture-Harpin 2024-11-25 12:04:23 -05:00
commit 1ee6c482d0
343 changed files with 61682 additions and 30750 deletions
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161
.clang-format Normal file
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@ -0,0 +1,161 @@
---
Language: Cpp
AlignAfterOpenBracket: Align
AlignArrayOfStructures: Left
AlignConsecutiveAssignments: AcrossComments
AlignConsecutiveBitFields: AcrossComments
AlignConsecutiveDeclarations: AcrossComments
AlignConsecutiveMacros: AcrossComments
# AlignConsecutiveShortCaseStatements: AcrossComments
AlignEscapedNewlines: Left # LeftWithLastLine
AlignOperands: Align
AlignTrailingComments:
Kind: Always
OverEmptyLines: 1
AllowAllArgumentsOnNextLine: true
AllowAllParametersOfDeclarationOnNextLine: false
# AllowBreakBeforeNoexceptSpecifier: OnlyWithParen
AllowShortBlocksOnASingleLine: Never
AllowShortCaseLabelsOnASingleLine: false
AllowShortFunctionsOnASingleLine: Inline
AllowShortIfStatementsOnASingleLine: Never
AllowShortLambdasOnASingleLine: Inline
AllowShortLoopsOnASingleLine: false
AlwaysBreakBeforeMultilineStrings: true
BinPackArguments: true
BinPackParameters: true # OnePerLine
BitFieldColonSpacing: Both
BreakBeforeBraces: Custom # Attach
BraceWrapping:
AfterCaseLabel: true
AfterClass: false
AfterControlStatement: false
AfterEnum: false
AfterFunction: false
AfterNamespace: false
AfterObjCDeclaration: false
AfterStruct: false
AfterUnion: false
AfterExternBlock: false
BeforeCatch: false
BeforeElse: false
BeforeLambdaBody: false
BeforeWhile: false
IndentBraces: false
SplitEmptyFunction: false
SplitEmptyRecord: false
SplitEmptyNamespace: false
# BreakAdjacentStringLiterals: true
BreakAfterAttributes: Never
BreakBeforeBinaryOperators: None
BreakBeforeInlineASMColon: OnlyMultiline
BreakBeforeTernaryOperators: false
# BreakBinaryOperations: Never
BreakConstructorInitializers: AfterColon
# BreakFunctionDefinitionParameters: false
BreakInheritanceList: AfterComma
BreakStringLiterals: true
# BreakTemplateDeclarations: Yes
ColumnLimit: 120
CommentPragmas: '^ IWYU pragma:'
CompactNamespaces: false
ConstructorInitializerIndentWidth: 4
ContinuationIndentWidth: 4
Cpp11BracedListStyle: false
DerivePointerAlignment: false
DisableFormat: false
EmptyLineBeforeAccessModifier: Leave
EmptyLineAfterAccessModifier: Never
ExperimentalAutoDetectBinPacking: false
FixNamespaceComments: true
IncludeBlocks: Regroup
IncludeCategories:
- Regex: '^<.*\.h>'
Priority: 1
SortPriority: 0
- Regex: '^<.*'
Priority: 2
SortPriority: 0
- Regex: '.*'
Priority: 3
SortPriority: 0
IncludeIsMainRegex: '([-_](test|unittest))?$'
IncludeIsMainSourceRegex: ''
IndentAccessModifiers: false
IndentCaseBlocks: true
IndentCaseLabels: true
IndentExternBlock: NoIndent
IndentGotoLabels: false
IndentPPDirectives: AfterHash
IndentWidth: 4
IndentWrappedFunctionNames: false
InsertBraces: true # NOTE: may lead to incorrect formatting
InsertNewlineAtEOF: true
JavaScriptQuotes: Leave
JavaScriptWrapImports: true
KeepEmptyLinesAtTheStartOfBlocks: false
LambdaBodyIndentation: Signature
LineEnding: LF
MacroBlockBegin: ''
MacroBlockEnd: ''
MaxEmptyLinesToKeep: 1
NamespaceIndentation: None
ObjCBinPackProtocolList: Auto
ObjCBlockIndentWidth: 4
ObjCSpaceAfterProperty: true
ObjCSpaceBeforeProtocolList: true
PPIndentWidth: -1
PackConstructorInitializers: CurrentLine
PenaltyBreakAssignment: 2
PenaltyBreakBeforeFirstCallParameter: 1
PenaltyBreakComment: 300
PenaltyBreakFirstLessLess: 120
PenaltyBreakString: 1000
PenaltyBreakTemplateDeclaration: 10
PenaltyExcessCharacter: 1000000
PenaltyReturnTypeOnItsOwnLine: 200
PointerAlignment: Middle
QualifierAlignment: Left
#QualifierOrder: ['static', 'inline', 'friend', 'constexpr', 'const', 'volatile', 'type', 'restrict']
RawStringFormats:
- Language: Cpp
Delimiters:
- cc
- CC
- cpp
- Cpp
- CPP
- 'c++'
- 'C++'
CanonicalDelimiter: ''
ReferenceAlignment: Middle
ReflowComments: false # IndentOnly
SeparateDefinitionBlocks: Always
SortIncludes: CaseInsensitive
SortUsingDeclarations: LexicographicNumeric
SpaceAfterCStyleCast: true
SpaceAfterLogicalNot: false
SpaceAfterTemplateKeyword: true
SpaceBeforeAssignmentOperators: true
SpaceBeforeCpp11BracedList: false
SpaceBeforeCtorInitializerColon: true
SpaceBeforeInheritanceColon: true
SpaceBeforeParens: ControlStatements
SpaceBeforeRangeBasedForLoopColon: true
SpaceInEmptyBlock: false
SpaceInEmptyParentheses: false
SpacesBeforeTrailingComments: 2
SpacesInAngles: Never
SpacesInContainerLiterals: true
SpacesInLineCommentPrefix:
Minimum: 1
Maximum: -1
SpacesInParentheses: false
SpacesInSquareBrackets: false
SpaceBeforeSquareBrackets: false
Standard: c++17
TabWidth: 4
UseTab: Never
WhitespaceSensitiveMacros: ['STRINGIZE']
...

2
.devops/full-cuda.Dockerfile
View File

@ -26,7 +26,7 @@ COPY . .
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 -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release -j$(nproc) && \
cp build/bin/* .

2
.devops/full-musa.Dockerfile
View File

@ -19,7 +19,7 @@ WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release -j$(nproc) && \
cp build/bin/* .

6
.devops/llama-cli-cann.Dockerfile
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@ -1,6 +1,6 @@
ARG ASCEND_VERSION=8.0.rc2.alpha003-910b-openeuler22.03-py3.8
FROM cosdt/cann:$ASCEND_VERSION AS build
FROM ascendai/cann:$ASCEND_VERSION AS build
WORKDIR /app
@ -22,11 +22,11 @@ ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
RUN echo "Building with static libs" && \
source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \
cmake -B build -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF && \
cmake --build build --config Release --target llama-cli
# TODO: use image with NNRT
FROM cosdt/cann:$ASCEND_VERSION AS runtime
FROM ascendai/cann:$ASCEND_VERSION AS runtime
COPY --from=build /app/build/bin/llama-cli /llama-cli
ENV LC_ALL=C.utf8

11
.devops/llama-cli-cuda.Dockerfile
View File

@ -22,16 +22,17 @@ COPY . .
RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-cli -j$(nproc)
cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-cli -j$(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libgomp1
COPY --from=build /app/build/ggml/src/libggml.so /libggml.so
COPY --from=build /app/build/src/libllama.so /libllama.so
COPY --from=build /app/build/bin/llama-cli /llama-cli
COPY --from=build /app/lib/ /
COPY --from=build /app/build/bin/llama-cli /
ENTRYPOINT [ "/llama-cli" ]

4
.devops/llama-cli-intel.Dockerfile
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@ -1,4 +1,4 @@
ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
@ -15,7 +15,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
fi && \
echo "Building with static libs" && \
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \
cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \
${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \
cmake --build build --config Release --target llama-cli

9
.devops/llama-cli-musa.Dockerfile
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@ -15,16 +15,17 @@ WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-cli -j$(nproc)
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-cli -j$(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libgomp1
COPY --from=build /app/build/ggml/src/libggml.so /libggml.so
COPY --from=build /app/build/src/libllama.so /libllama.so
COPY --from=build /app/lib/ /
COPY --from=build /app/build/bin/llama-cli /llama-cli
ENTRYPOINT [ "/llama-cli" ]

2
.devops/llama-cli-vulkan.Dockerfile
View File

@ -14,7 +14,7 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
# Build it
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_VULKAN=1 && \
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 && \
cmake --build build --config Release --target llama-cli
# Clean up

9
.devops/llama-server-cuda.Dockerfile
View File

@ -22,16 +22,17 @@ COPY . .
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 ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-server -j$(nproc)
cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-server -j$(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev libgomp1 curl
COPY --from=build /app/build/ggml/src/libggml.so /libggml.so
COPY --from=build /app/build/src/libllama.so /libllama.so
COPY --from=build /app/lib/ /
COPY --from=build /app/build/bin/llama-server /llama-server
# Must be set to 0.0.0.0 so it can listen to requests from host machine

4
.devops/llama-server-intel.Dockerfile
View File

@ -1,4 +1,4 @@
ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
@ -15,7 +15,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
fi && \
echo "Building with dynamic libs" && \
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake --build build --config Release --target llama-server
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime

9
.devops/llama-server-musa.Dockerfile
View File

@ -15,16 +15,17 @@ WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-server -j$(nproc)
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-server -j$(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev libgomp1 curl
COPY --from=build /app/build/ggml/src/libggml.so /libggml.so
COPY --from=build /app/build/src/libllama.so /libllama.so
COPY --from=build /app/lib/ /
COPY --from=build /app/build/bin/llama-server /llama-server
# Must be set to 0.0.0.0 so it can listen to requests from host machine

2
.devops/llama-server-vulkan.Dockerfile
View File

@ -14,7 +14,7 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
# Build it
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
cmake --build build --config Release --target llama-server
# Clean up

6
.devops/nix/package.nix
View File

@ -126,9 +126,9 @@ effectiveStdenv.mkDerivation (finalAttrs: {
};
postPatch = ''
substituteInPlace ./ggml/src/ggml-metal.m \
substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \
--replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
substituteInPlace ./ggml/src/ggml-metal.m \
substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \
--replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
'';
@ -173,7 +173,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
(cmakeBool "GGML_NATIVE" false)
(cmakeBool "GGML_BLAS" useBlas)
(cmakeBool "GGML_CUDA" useCuda)
(cmakeBool "GGML_HIPBLAS" useRocm)
(cmakeBool "GGML_HIP" useRocm)
(cmakeBool "GGML_METAL" useMetalKit)
(cmakeBool "GGML_VULKAN" useVulkan)
(cmakeBool "GGML_STATIC" enableStatic)

10
.editorconfig
View File

@ -24,6 +24,16 @@ insert_final_newline = unset
[examples/server/public/*]
indent_size = 2
[examples/server/public/deps_*]
trim_trailing_whitespace = unset
indent_style = unset
indent_size = unset
[examples/server/deps_*]
trim_trailing_whitespace = unset
indent_style = unset
indent_size = unset
[examples/llama.swiftui/llama.swiftui.xcodeproj/*]
indent_style = tab

50
.github/ISSUE_TEMPLATE/01-bug-low.yml vendored
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@ -1,50 +0,0 @@
name: Low Severity Bugs
description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches)
title: "Bug: "
labels: ["bug-unconfirmed", "low severity"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
Please include information about your system, the steps to reproduce the bug,
and the version of llama.cpp that you are using.
If possible, please provide a minimal code example that reproduces the bug.
- type: textarea
id: what-happened
attributes:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: version
attributes:
label: Name and Version
description: Which executable and which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./llama-cli --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: What operating system are you seeing the problem on?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

73
.github/ISSUE_TEMPLATE/010-bug-compilation.yml vendored Normal file
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@ -0,0 +1,73 @@
name: Bug (compilation)
description: Something goes wrong when trying to compile llama.cpp.
title: "Compile bug: "
labels: ["bug-unconfirmed", "compilation"]
body:
- type: markdown
attributes:
value: >
Thanks for taking the time to fill out this bug report!
This issue template is intended for bug reports where the compilation of llama.cpp fails.
Before opening an issue, please confirm that the compilation still fails with `-DGGML_CCACHE=OFF`.
If the compilation succeeds with ccache disabled you should be able to permanently fix the issue
by clearing `~/.cache/ccache` (on Linux).
- type: textarea
id: commit
attributes:
label: Git commit
description: Which commit are you trying to compile?
placeholder: |
$git rev-parse HEAD
84a07a17b1b08cf2b9747c633a2372782848a27f
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: Which operating systems do you know to be affected?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: true
- type: dropdown
id: backends
attributes:
label: GGML backends
description: Which GGML backends do you know to be affected?
options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan]
multiple: true
- type: textarea
id: steps_to_reproduce
attributes:
label: Steps to Reproduce
description: >
Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it.
If you can narrow down the bug to specific compile flags, that information would be very much appreciated by us.
placeholder: >
Here are the exact commands that I used: ...
validations:
required: true
- type: textarea
id: first_bad_commit
attributes:
label: First Bad Commit
description: >
If the bug was not present on an earlier version: when did it start appearing?
If possible, please do a git bisect and identify the exact commit that introduced the bug.
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: >
Please copy and paste any relevant log output, including the command that you entered and any generated text.
This will be automatically formatted into code, so no need for backticks.
render: shell
validations:
required: true

98
.github/ISSUE_TEMPLATE/011-bug-results.yml vendored Normal file
View File

@ -0,0 +1,98 @@
name: Bug (model use)
description: Something goes wrong when using a model (in general, not specific to a single llama.cpp module).
title: "Eval bug: "
labels: ["bug-unconfirmed", "model evaluation"]
body:
- type: markdown
attributes:
value: >
Thanks for taking the time to fill out this bug report!
This issue template is intended for bug reports where the model evaluation results
(i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation.
If you encountered the issue while using an external UI (e.g. ollama),
please reproduce your issue using one of the examples/binaries in this repository.
The `llama-cli` binary can be used for simple and reproducible model inference.
- type: textarea
id: version
attributes:
label: Name and Version
description: Which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./llama-cli --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: Which operating systems do you know to be affected?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: true
- type: dropdown
id: backends
attributes:
label: GGML backends
description: Which GGML backends do you know to be affected?
options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan]
multiple: true
- type: textarea
id: hardware
attributes:
label: Hardware
description: Which CPUs/GPUs are you using?
placeholder: >
e.g. Ryzen 5950X + 2x RTX 4090
validations:
required: true
- type: textarea
id: model
attributes:
label: Model
description: >
Which model at which quantization were you using when encountering the bug?
If you downloaded a GGUF file off of Huggingface, please provide a link.
placeholder: >
e.g. Meta LLaMA 3.1 Instruct 8b q4_K_M
validations:
required: false
- type: textarea
id: steps_to_reproduce
attributes:
label: Steps to Reproduce
description: >
Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it.
If you can narrow down the bug to specific hardware, compile flags, or command line arguments,
that information would be very much appreciated by us.
placeholder: >
e.g. when I run llama-cli with -ngl 99 I get garbled outputs.
When I use -ngl 0 it works correctly.
Here are the exact commands that I used: ...
validations:
required: true
- type: textarea
id: first_bad_commit
attributes:
label: First Bad Commit
description: >
If the bug was not present on an earlier version: when did it start appearing?
If possible, please do a git bisect and identify the exact commit that introduced the bug.
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: >
Please copy and paste any relevant log output, including the command that you entered and any generated text.
This will be automatically formatted into code, so no need for backticks.
render: shell
validations:
required: true

78
.github/ISSUE_TEMPLATE/019-bug-misc.yml vendored Normal file
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@ -0,0 +1,78 @@
name: Bug (misc.)
description: Something is not working the way it should (and it's not covered by any of the above cases).
title: "Misc. bug: "
labels: ["bug-unconfirmed"]
body:
- type: markdown
attributes:
value: >
Thanks for taking the time to fill out this bug report!
This issue template is intended for miscellaneous bugs that don't fit into any other category.
If you encountered the issue while using an external UI (e.g. ollama),
please reproduce your issue using one of the examples/binaries in this repository.
- type: textarea
id: version
attributes:
label: Name and Version
description: Which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./llama-cli --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: Which operating systems do you know to be affected?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: true
- type: dropdown
id: module
attributes:
label: Which llama.cpp modules do you know to be affected?
multiple: true
options:
- libllama (core library)
- llama-cli
- llama-server
- llama-bench
- llama-quantize
- Python/Bash scripts
- Other (Please specify in the next section)
validations:
required: true
- type: textarea
id: steps_to_reproduce
attributes:
label: Steps to Reproduce
description: >
Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it.
validations:
required: true
- type: textarea
id: first_bad_commit
attributes:
label: First Bad Commit
description: >
If the bug was not present on an earlier version: when did it start appearing?
If possible, please do a git bisect and identify the exact commit that introduced the bug.
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: >
Please copy and paste any relevant log output, including the command that you entered and any generated text.
This will be automatically formatted into code, so no need for backticks.
render: shell
validations:
required: true

50
.github/ISSUE_TEMPLATE/02-bug-medium.yml vendored
View File

@ -1,50 +0,0 @@
name: Medium Severity Bug
description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable)
title: "Bug: "
labels: ["bug-unconfirmed", "medium severity"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
Please include information about your system, the steps to reproduce the bug,
and the version of llama.cpp that you are using.
If possible, please provide a minimal code example that reproduces the bug.
- type: textarea
id: what-happened
attributes:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: version
attributes:
label: Name and Version
description: Which executable and which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./llama-cli --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: What operating system are you seeing the problem on?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

2
.github/ISSUE_TEMPLATE/05-enhancement.yml → .github/ISSUE_TEMPLATE/020-enhancement.yml vendored
View File

@ -1,5 +1,5 @@
name: Enhancement
description: Used to request enhancements for llama.cpp
description: Used to request enhancements for llama.cpp.
title: "Feature Request: "
labels: ["enhancement"]
body:

50
.github/ISSUE_TEMPLATE/03-bug-high.yml vendored
View File

@ -1,50 +0,0 @@
name: High Severity Bug
description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow)
title: "Bug: "
labels: ["bug-unconfirmed", "high severity"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
Please include information about your system, the steps to reproduce the bug,
and the version of llama.cpp that you are using.
If possible, please provide a minimal code example that reproduces the bug.
- type: textarea
id: what-happened
attributes:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: version
attributes:
label: Name and Version
description: Which executable and which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./llama-cli --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: What operating system are you seeing the problem on?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

2
.github/ISSUE_TEMPLATE/06-research.yml → .github/ISSUE_TEMPLATE/030-research.yml vendored
View File

@ -1,5 +1,5 @@
name: Research
description: Track new technical research area
description: Track new technical research area.
title: "Research: "
labels: ["research 🔬"]
body:

50
.github/ISSUE_TEMPLATE/04-bug-critical.yml vendored
View File

@ -1,50 +0,0 @@
name: Critical Severity Bug
description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss)
title: "Bug: "
labels: ["bug-unconfirmed", "critical severity"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this bug report!
Please include information about your system, the steps to reproduce the bug,
and the version of llama.cpp that you are using.
If possible, please provide a minimal code example that reproduces the bug.
- type: textarea
id: what-happened
attributes:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
validations:
required: true
- type: textarea
id: version
attributes:
label: Name and Version
description: Which executable and which version of our software are you running? (use `--version` to get a version string)
placeholder: |
$./llama-cli --version
version: 2999 (42b4109e)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
validations:
required: true
- type: dropdown
id: operating-system
attributes:
label: What operating system are you seeing the problem on?
multiple: true
options:
- Linux
- Mac
- Windows
- BSD
- Other? (Please let us know in description)
validations:
required: false
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

2
.github/ISSUE_TEMPLATE/07-refactor.yml → .github/ISSUE_TEMPLATE/040-refactor.yml vendored
View File

@ -1,5 +1,5 @@
name: Refactor (Maintainers)
description: Used to track refactoring opportunities
description: Used to track refactoring opportunities.
title: "Refactor: "
labels: ["refactor"]
body:

81
.github/workflows/build.yml vendored
View File

@ -55,7 +55,13 @@ jobs:
sysctl -a
mkdir build
cd build
cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF ..
cmake .. \
-DLLAMA_FATAL_WARNINGS=ON \
-DLLAMA_CURL=ON \
-DGGML_METAL_USE_BF16=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DGGML_RPC=ON \
-DBUILD_SHARED_LIBS=OFF
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -92,7 +98,7 @@ jobs:
name: llama-bin-macos-arm64.zip
macOS-latest-cmake-x64:
runs-on: macos-12
runs-on: macos-13
steps:
- name: Clone
@ -113,7 +119,12 @@ jobs:
sysctl -a
# Metal is disabled due to intermittent failures with Github runners not having a GPU:
# https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313
cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF
cmake -B build \
-DLLAMA_FATAL_WARNINGS=ON \
-DLLAMA_CURL=ON \
-DGGML_METAL=OFF \
-DGGML_RPC=ON \
-DBUILD_SHARED_LIBS=OFF
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -394,15 +405,36 @@ jobs:
- name: Build with native CMake HIP support
id: cmake_build
run: |
cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIPBLAS=ON
cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIP=ON
cmake --build build --config Release -j $(nproc)
- name: Build with legacy HIP support
id: cmake_build_legacy_hip
run: |
cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIPBLAS=ON
cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIP=ON
cmake --build build2 --config Release -j $(nproc)
ubuntu-22-cmake-musa:
runs-on: ubuntu-22.04
container: mthreads/musa:rc3.1.0-devel-ubuntu22.04
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Dependencies
id: depends
run: |
apt-get update
apt-get install -y build-essential git cmake libcurl4-openssl-dev
- name: Build with native CMake MUSA support
id: cmake_build
run: |
cmake -B build -S . -DGGML_MUSA=ON
cmake --build build --config Release -j $(nproc)
ubuntu-22-cmake-sycl:
runs-on: ubuntu-22.04
@ -569,6 +601,7 @@ jobs:
mkdir build
cd build
cmake -G Xcode .. \
-DGGML_METAL_USE_BF16=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
@ -599,6 +632,7 @@ jobs:
mkdir build
cd build
cmake -G Xcode .. \
-DGGML_METAL_USE_BF16=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
@ -734,7 +768,7 @@ jobs:
id: clone_kompute
if: ${{ matrix.build == 'kompute-x64' }}
run: |
git submodule update --init ggml/src/kompute
git submodule update --init ggml/src/ggml-kompute/kompute
- name: Download OpenBLAS
id: get_openblas
@ -917,8 +951,8 @@ jobs:
shell: bash
env:
WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7dff44ba-e3af-4448-841c-0d616c8da6e7/w_BaseKit_p_2024.1.0.595_offline.exe
WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel
WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b380d914-366b-4b77-a74a-05e3c38b3514/intel-oneapi-base-toolkit-2025.0.0.882_offline.exe
WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
steps:
- name: Clone
@ -928,7 +962,8 @@ jobs:
fetch-depth: 0
- name: Install
run: scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
run: |
scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
- name: Build
id: cmake_build
@ -947,26 +982,34 @@ jobs:
echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
fi
- name: Pack artifacts
- name: Build the release package
id: pack_artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
if: ${{ ( github.event_name == 'pull_request' && github.base_ref == 'master' ) }}
run: |
echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin"
cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.4.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.5.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_win_proxy_loader.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_level_zero.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl7.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_opencl.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl8.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/dnnl/latest/bin/dnnl.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin
echo "cp oneAPI running time dll files to ./build/bin done"
7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/*
- name: Upload artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
- name: Upload the release package
if: ${{ ( github.event_name == 'pull_request' && github.base_ref == 'master' ) }}
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip
@ -1001,7 +1044,7 @@ jobs:
run: |
$env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
$env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON
cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON
cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
windows-latest-cmake-hip-release:
@ -1037,7 +1080,7 @@ jobs:
run: |
$env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
$env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON
cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON
cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
md "build\bin\rocblas\library\"
cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"

5
.gitignore vendored
View File

@ -3,6 +3,7 @@
*.a
*.bat
*.bin
*.d
*.dll
*.dot
*.etag
@ -133,3 +134,7 @@ poetry.toml
# Test models for lora adapters
/lora-tests
# Local scripts
/run-vim.sh
/run-chat.sh

2
.gitmodules vendored
View File

@ -1,3 +1,3 @@
[submodule "kompute"]
path = ggml/src/kompute
path = ggml/src/ggml-kompute/kompute
url = https://github.com/nomic-ai/kompute.git

8
CMakeLists.txt
View File

@ -46,6 +46,13 @@ if (WIN32)
add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
endif()
if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC")
add_compile_options("$<$<COMPILE_LANGUAGE:C>:/source-charset:utf-8>")
add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/source-charset:utf-8>")
add_compile_options("$<$<COMPILE_LANGUAGE:C>:/execution-charset:utf-8>")
add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/execution-charset:utf-8>")
endif()
#
# option list
#
@ -140,7 +147,6 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o
set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files")
set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files")
# At the moment some compile definitions are placed within the ggml/src
# directory but not exported on the `ggml` target. This could be improved by
# determining _precisely_ which defines are necessary for the llama-config

34
CMakePresets.json
View File

@ -24,11 +24,12 @@
"CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
}
},
{ "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } },
{ "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
{ "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
{ "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
{ "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } },
{ "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } },
{ "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
{ "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
{ "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
{ "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } },
{ "name": "vulkan", "hidden": true, "cacheVariables": { "GGML_VULKAN": "ON" } },
{
"name": "arm64-windows-msvc", "hidden": true,
@ -57,25 +58,28 @@
}
},
{ "name": "arm64-windows-llvm-debug" , "inherits": [ "base", "arm64-windows-llvm", "debug" ] },
{ "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] },
{ "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] },
{ "name": "arm64-windows-llvm-debug", "inherits": [ "base", "arm64-windows-llvm", "debug" ] },
{ "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] },
{ "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] },
{ "name": "arm64-apple-clang-debug" , "inherits": [ "base", "arm64-apple-clang", "debug" ] },
{ "name": "arm64-apple-clang-release" , "inherits": [ "base", "arm64-apple-clang", "reldbg" ] },
{ "name": "arm64-apple-clang+static-release" , "inherits": [ "base", "arm64-apple-clang", "reldbg", "static" ] },
{ "name": "arm64-apple-clang-debug", "inherits": [ "base", "arm64-apple-clang", "debug" ] },
{ "name": "arm64-apple-clang-release", "inherits": [ "base", "arm64-apple-clang", "reldbg" ] },
{ "name": "arm64-apple-clang+static-release", "inherits": [ "base", "arm64-apple-clang", "reldbg", "static" ] },
{ "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] },
{ "name": "arm64-windows-msvc-debug", "inherits": [ "base", "arm64-windows-msvc", "debug" ] },
{ "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg" ] },
{ "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg", "static" ] },
{ "name": "x64-windows-msvc-debug" , "inherits": [ "base", "debug" ] },
{ "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] },
{ "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
{ "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
{ "name": "x64-windows-sycl-debug" , "inherits": [ "sycl-base", "debug" ] },
{ "name": "x64-windows-sycl-debug", "inherits": [ "sycl-base", "debug" ] },
{ "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] },
{ "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] },
{ "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] }
{ "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] },
{ "name": "x64-windows-vulkan-debug", "inherits": [ "base", "vulkan", "debug" ] },
{ "name": "x64-windows-vulkan-release", "inherits": [ "base", "vulkan", "release" ] }
]
}

620
Makefile
View File

@ -48,7 +48,6 @@ TEST_TARGETS = \
tests/test-backend-ops \
tests/test-chat-template \
tests/test-double-float \
tests/test-grad0 \
tests/test-grammar-integration \
tests/test-grammar-parser \
tests/test-json-schema-to-grammar \
@ -252,7 +251,7 @@ endif
#
# keep standard at C11 and C++11
MK_CPPFLAGS = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon
MK_CPPFLAGS = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon -DGGML_USE_CPU
MK_CFLAGS = -std=c11 -fPIC
MK_CXXFLAGS = -std=c++11 -fPIC
MK_NVCCFLAGS = -std=c++11
@ -291,6 +290,7 @@ endif
# some memory allocation are available on Linux through GNU extensions in libc
ifeq ($(UNAME_S),Linux)
MK_CPPFLAGS += -D_GNU_SOURCE
MK_LDFLAGS += -ldl
endif
# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1,
@ -359,6 +359,10 @@ ifdef LLAMA_SERVER_SSL
MK_LDFLAGS += -lssl -lcrypto
endif
ifndef GGML_NO_CPU_AARCH64
MK_CPPFLAGS += -DGGML_USE_CPU_AARCH64
endif
# warnings
WARN_FLAGS = \
-Wall \
@ -523,70 +527,59 @@ ifndef GGML_NO_ACCELERATE
# Mac OS - include Accelerate framework.
# `-framework Accelerate` works both with Apple Silicon and Mac Intel
ifeq ($(UNAME_S),Darwin)
MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS
MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK
MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64
MK_LDFLAGS += -framework Accelerate
OBJ_GGML += ggml/src/ggml-blas.o
MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS -DGGML_BLAS_USE_ACCELERATE
MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK
MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64
MK_LDFLAGS += -framework Accelerate
OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
endif
endif # GGML_NO_ACCELERATE
ifdef GGML_MUSA
CC := clang
CXX := clang++
GGML_CUDA := 1
MK_CPPFLAGS += -DGGML_USE_MUSA
endif
ifndef GGML_NO_OPENMP
MK_CPPFLAGS += -DGGML_USE_OPENMP
MK_CFLAGS += -fopenmp
MK_CXXFLAGS += -fopenmp
ifdef GGML_MUSA
MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp
MK_LDFLAGS += -L/usr/lib/llvm-10/lib
endif # GGML_MUSA
endif # GGML_NO_OPENMP
ifdef GGML_OPENBLAS
MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas)
MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas)
MK_LDFLAGS += $(shell pkg-config --libs openblas)
OBJ_GGML += ggml/src/ggml-blas.o
MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas)
MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas)
MK_LDFLAGS += $(shell pkg-config --libs openblas)
OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
endif # GGML_OPENBLAS
ifdef GGML_OPENBLAS64
MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64)
MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas64)
MK_LDFLAGS += $(shell pkg-config --libs openblas64)
OBJ_GGML += ggml/src/ggml-blas.o
MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64)
MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas64)
MK_LDFLAGS += $(shell pkg-config --libs openblas64)
OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
endif # GGML_OPENBLAS64
ifdef GGML_BLIS
MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis
MK_LDFLAGS += -lblis -L/usr/local/lib
OBJ_GGML += ggml/src/ggml-blas.o
MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis
MK_LDFLAGS += -lblis -L/usr/local/lib
OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
endif # GGML_BLIS
ifdef GGML_NVPL
MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas
MK_LDFLAGS += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp
OBJ_GGML += ggml/src/ggml-blas.o
MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas
MK_LDFLAGS += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp
OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o
endif # GGML_NVPL
ifndef GGML_NO_LLAMAFILE
MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
OBJ_GGML += ggml/src/llamafile/sgemm.o
MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
OBJ_GGML_EXT += ggml/src/ggml-cpu/llamafile/sgemm.o
endif
ifndef GGML_NO_AMX
MK_CPPFLAGS += -DGGML_USE_AMX
OBJ_GGML += ggml/src/ggml-amx.o ggml/src/ggml-amx/mmq.o
OBJ_GGML_EXT += ggml/src/ggml-amx/ggml-amx.o ggml/src/ggml-amx/mmq.o
endif
ifdef GGML_RPC
MK_CPPFLAGS += -DGGML_USE_RPC
OBJ_GGML += ggml/src/ggml-rpc.o
MK_CPPFLAGS += -DGGML_USE_RPC
OBJ_GGML_EXT += ggml/src/ggml-rpc.o
endif # GGML_RPC
OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu))
@ -601,41 +594,27 @@ else
endif # GGML_CUDA_FA_ALL_QUANTS
ifdef GGML_CUDA
ifdef GGML_MUSA
ifneq ('', '$(wildcard /opt/musa)')
CUDA_PATH ?= /opt/musa
else
CUDA_PATH ?= /usr/local/musa
endif
MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include
MK_LDFLAGS += -lmusa -lmublas -lmusart -lpthread -ldl -lrt -L$(CUDA_PATH)/lib -L/usr/lib64
MK_NVCCFLAGS += -x musa -mtgpu --cuda-gpu-arch=mp_21 --cuda-gpu-arch=mp_22
ifneq ('', '$(wildcard /opt/cuda)')
CUDA_PATH ?= /opt/cuda
else
ifneq ('', '$(wildcard /opt/cuda)')
CUDA_PATH ?= /opt/cuda
else
CUDA_PATH ?= /usr/local/cuda
endif
CUDA_PATH ?= /usr/local/cuda
endif
MK_CPPFLAGS += -DGGML_USE_CUDA -DGGML_CUDA_USE_GRAPHS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib
MK_NVCCFLAGS += -use_fast_math
endif # GGML_MUSA
MK_CPPFLAGS += -DGGML_USE_CUDA -DGGML_CUDA_USE_GRAPHS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib
MK_NVCCFLAGS += -use_fast_math
OBJ_GGML += ggml/src/ggml-cuda.o
OBJ_GGML += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
OBJ_GGML += $(OBJ_CUDA_TMPL)
OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o
OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
OBJ_GGML_EXT += $(OBJ_CUDA_TMPL)
ifdef LLAMA_FATAL_WARNINGS
MK_NVCCFLAGS += -Werror all-warnings
endif # LLAMA_FATAL_WARNINGS
ifndef GGML_MUSA
ifndef JETSON_EOL_MODULE_DETECT
MK_NVCCFLAGS += --forward-unknown-to-host-compiler
endif # JETSON_EOL_MODULE_DETECT
endif # GGML_MUSA
ifdef LLAMA_DEBUG
MK_NVCCFLAGS += -lineinfo
@ -648,11 +627,7 @@ endif # GGML_CUDA_DEBUG
ifdef GGML_CUDA_NVCC
NVCC = $(CCACHE) $(GGML_CUDA_NVCC)
else
ifdef GGML_MUSA
NVCC = $(CCACHE) mcc
else
NVCC = $(CCACHE) nvcc
endif # GGML_MUSA
NVCC = $(CCACHE) nvcc
endif # GGML_CUDA_NVCC
ifdef CUDA_DOCKER_ARCH
@ -661,10 +636,6 @@ else ifndef CUDA_POWER_ARCH
MK_NVCCFLAGS += -arch=native
endif # CUDA_DOCKER_ARCH
ifdef GGML_CUDA_FORCE_DMMV
MK_NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
endif # GGML_CUDA_FORCE_DMMV
ifdef GGML_CUDA_FORCE_MMQ
MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ
endif # GGML_CUDA_FORCE_MMQ
@ -673,20 +644,6 @@ ifdef GGML_CUDA_FORCE_CUBLAS
MK_NVCCFLAGS += -DGGML_CUDA_FORCE_CUBLAS
endif # GGML_CUDA_FORCE_CUBLAS
ifdef GGML_CUDA_DMMV_X
MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(GGML_CUDA_DMMV_X)
else
MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
endif # GGML_CUDA_DMMV_X
ifdef GGML_CUDA_MMV_Y
MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_MMV_Y)
else ifdef GGML_CUDA_DMMV_Y
MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_DMMV_Y) # for backwards compatibility
else
MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
endif # GGML_CUDA_MMV_Y
ifdef GGML_CUDA_F16
MK_NVCCFLAGS += -DGGML_CUDA_F16
endif # GGML_CUDA_F16
@ -695,12 +652,6 @@ ifdef GGML_CUDA_DMMV_F16
MK_NVCCFLAGS += -DGGML_CUDA_F16
endif # GGML_CUDA_DMMV_F16
ifdef GGML_CUDA_KQUANTS_ITER
MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(GGML_CUDA_KQUANTS_ITER)
else
MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
endif
ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE
MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE)
else
@ -724,15 +675,9 @@ define NVCC_COMPILE
$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
endef # NVCC_COMPILE
else
ifdef GGML_MUSA
define NVCC_COMPILE
$(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -c $< -o $@
endef # NVCC_COMPILE
else
define NVCC_COMPILE
$(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
endef # NVCC_COMPILE
endif # GGML_MUSA
endif # JETSON_EOL_MODULE_DETECT
ggml/src/ggml-cuda/%.o: \
@ -742,8 +687,8 @@ ggml/src/ggml-cuda/%.o: \
ggml/src/ggml-cuda/common.cuh
$(NVCC_COMPILE)
ggml/src/ggml-cuda.o: \
ggml/src/ggml-cuda.cu \
ggml/src/ggml-cuda/ggml-cuda.o: \
ggml/src/ggml-cuda/ggml-cuda.cu \
ggml/include/ggml-cuda.h \
ggml/include/ggml.h \
ggml/include/ggml-backend.h \
@ -754,9 +699,9 @@ ggml/src/ggml-cuda.o: \
endif # GGML_CUDA
ifdef GGML_VULKAN
MK_CPPFLAGS += -DGGML_USE_VULKAN
MK_LDFLAGS += $(shell pkg-config --libs vulkan)
OBJ_GGML += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o
MK_CPPFLAGS += -DGGML_USE_VULKAN
MK_LDFLAGS += $(shell pkg-config --libs vulkan)
OBJ_GGML_EXT += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o
ifdef GGML_VULKAN_CHECK_RESULTS
MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS
@ -786,10 +731,10 @@ GLSLC_CMD = glslc
_ggml_vk_genshaders_cmd = $(shell pwd)/vulkan-shaders-gen
_ggml_vk_header = ggml/src/ggml-vulkan-shaders.hpp
_ggml_vk_source = ggml/src/ggml-vulkan-shaders.cpp
_ggml_vk_input_dir = ggml/src/vulkan-shaders
_ggml_vk_input_dir = ggml/src/ggml-vulkan/vulkan-shaders
_ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp)
ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source)
ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source)
$(CXX) $(CXXFLAGS) $(shell pkg-config --cflags vulkan) -c $< -o $@
$(_ggml_vk_header): $(_ggml_vk_source)
@ -801,8 +746,8 @@ $(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen
--target-hpp $(_ggml_vk_header) \
--target-cpp $(_ggml_vk_source)
vulkan-shaders-gen: ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
$(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
vulkan-shaders-gen: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
$(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
endif # GGML_VULKAN
@ -815,11 +760,7 @@ ifdef GGML_HIPBLAS
AMDGPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
endif
GGML_CUDA_DMMV_X ?= 32
GGML_CUDA_MMV_Y ?= 1
GGML_CUDA_KQUANTS_ITER ?= 2
MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUDA
MK_CPPFLAGS += -DGGML_USE_HIP -DGGML_USE_CUDA
ifdef GGML_HIP_UMA
MK_CPPFLAGS += -DGGML_HIP_UMA
@ -832,13 +773,6 @@ endif # GGML_HIP_UMA
HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc
HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS))
HIPFLAGS += -DGGML_CUDA_DMMV_X=$(GGML_CUDA_DMMV_X)
HIPFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_MMV_Y)
HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(GGML_CUDA_KQUANTS_ITER)
ifdef GGML_CUDA_FORCE_DMMV
HIPFLAGS += -DGGML_CUDA_FORCE_DMMV
endif # GGML_CUDA_FORCE_DMMV
ifdef GGML_CUDA_FORCE_MMQ
HIPFLAGS += -DGGML_CUDA_FORCE_MMQ
@ -852,12 +786,12 @@ ifdef GGML_CUDA_NO_PEER_COPY
HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY
endif # GGML_CUDA_NO_PEER_COPY
OBJ_GGML += ggml/src/ggml-cuda.o
OBJ_GGML += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
OBJ_GGML += $(OBJ_CUDA_TMPL)
OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o
OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
OBJ_GGML_EXT += $(OBJ_CUDA_TMPL)
ggml/src/ggml-cuda.o: \
ggml/src/ggml-cuda.cu \
ggml/src/ggml-cuda/ggml-cuda.o: \
ggml/src/ggml-cuda/ggml-cuda.cu \
ggml/include/ggml-cuda.h \
ggml/include/ggml.h \
ggml/include/ggml-backend.h \
@ -874,70 +808,168 @@ ggml/src/ggml-cuda/%.o: \
$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
endif # GGML_HIPBLAS
ifdef GGML_MUSA
ifeq ($(wildcard /opt/musa),)
MUSA_PATH ?= /usr/local/musa
else
MUSA_PATH ?= /opt/musa
endif
MTGPU_TARGETS ?= mp_21 mp_22
MK_CPPFLAGS += -DGGML_USE_MUSA -DGGML_USE_CUDA
MK_LDFLAGS += -L$(MUSA_PATH)/lib -Wl,-rpath=$(MUSA_PATH)/lib
MK_LDFLAGS += -lmusa -lmusart -lmublas
ifndef GGML_NO_OPENMP
# For Ubuntu Focal
MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp
MK_LDFLAGS += -L/usr/lib/llvm-10/lib
# For Ubuntu Jammy
MK_CPPFLAGS += -I/usr/lib/llvm-14/lib/clang/14.0.0/include
MK_LDFLAGS += -L/usr/lib/llvm-14/lib
endif # GGML_NO_OPENMP
CC := $(MUSA_PATH)/bin/clang
CXX := $(MUSA_PATH)/bin/clang++
MCC := $(CCACHE) $(MUSA_PATH)/bin/mcc
MUSAFLAGS += $(addprefix --cuda-gpu-arch=, $(MTGPU_TARGETS))
ifdef GGML_CUDA_FORCE_MMQ
MUSAFLAGS += -DGGML_CUDA_FORCE_MMQ
endif # GGML_CUDA_FORCE_MMQ
ifdef GGML_CUDA_FORCE_CUBLAS
MUSAFLAGS += -DGGML_CUDA_FORCE_CUBLAS
endif # GGML_CUDA_FORCE_CUBLAS
ifdef GGML_CUDA_F16
MUSAFLAGS += -DGGML_CUDA_F16
endif # GGML_CUDA_F16
ifdef GGML_CUDA_DMMV_F16
MUSAFLAGS += -DGGML_CUDA_F16
endif # GGML_CUDA_DMMV_F16
ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE
MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE)
else
MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
endif # GGML_CUDA_PEER_MAX_BATCH_SIZE
ifdef GGML_CUDA_NO_PEER_COPY
MUSAFLAGS += -DGGML_CUDA_NO_PEER_COPY
endif # GGML_CUDA_NO_PEER_COPY
ifdef GGML_CUDA_FA_ALL_QUANTS
MUSAFLAGS += -DGGML_CUDA_FA_ALL_QUANTS
endif # GGML_CUDA_FA_ALL_QUANTS
OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o
OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu))
OBJ_GGML_EXT += $(OBJ_CUDA_TMPL)
ggml/src/ggml-cuda/ggml-cuda.o: \
ggml/src/ggml-cuda/ggml-cuda.cu \
ggml/include/ggml-cuda.h \
ggml/include/ggml.h \
ggml/include/ggml-backend.h \
ggml/src/ggml-backend-impl.h \
ggml/src/ggml-common.h \
$(wildcard ggml/src/ggml-cuda/*.cuh)
$(MCC) $(CXXFLAGS) $(MUSAFLAGS) -x musa -mtgpu -c -o $@ $<
ggml/src/ggml-cuda/%.o: \
ggml/src/ggml-cuda/%.cu \
ggml/include/ggml.h \
ggml/src/ggml-common.h \
ggml/src/ggml-cuda/common.cuh
$(MCC) $(CXXFLAGS) $(MUSAFLAGS) -x musa -mtgpu -c -o $@ $<
endif # GGML_MUSA
ifdef GGML_METAL
MK_CPPFLAGS += -DGGML_USE_METAL
MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
OBJ_GGML += ggml/src/ggml-metal.o
MK_CPPFLAGS += -DGGML_USE_METAL
MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
OBJ_GGML_EXT += ggml/src/ggml-metal/ggml-metal.o
ifdef GGML_METAL_USE_BF16
MK_CPPFLAGS += -DGGML_METAL_USE_BF16
endif # GGML_METAL_USE_BF16
ifdef GGML_METAL_NDEBUG
MK_CPPFLAGS += -DGGML_METAL_NDEBUG
endif
ifdef GGML_METAL_EMBED_LIBRARY
MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY
OBJ_GGML += ggml/src/ggml-metal-embed.o
MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY
OBJ_GGML_EXT += ggml/src/ggml-metal-embed.o
endif
endif # GGML_METAL
ifdef GGML_METAL
ggml/src/ggml-metal.o: \
ggml/src/ggml-metal.m \
ggml/src/ggml-metal/ggml-metal.o: \
ggml/src/ggml-metal/ggml-metal.m \
ggml/src/ggml-metal/ggml-metal-impl.h \
ggml/include/ggml-metal.h \
ggml/include/ggml.h
$(CC) $(CFLAGS) -c $< -o $@
ifdef GGML_METAL_EMBED_LIBRARY
ggml/src/ggml-metal-embed.o: \
ggml/src/ggml-metal.metal \
ggml/src/ggml-metal/ggml-metal.metal \
ggml/src/ggml-metal/ggml-metal-impl.h \
ggml/src/ggml-common.h
@echo "Embedding Metal library"
@sed -e '/#include "ggml-common.h"/r ggml/src/ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml/src/ggml-metal.metal > ggml/src/ggml-metal-embed.metal
@sed -e '/__embed_ggml-common.h__/r ggml/src/ggml-common.h' -e '/__embed_ggml-common.h__/d' < ggml/src/ggml-metal/ggml-metal.metal > ggml/src/ggml-metal/ggml-metal-embed.metal.tmp
@sed -e '/#include "ggml-metal-impl.h"/r ggml/src/ggml-metal/ggml-metal-impl.h' -e '/#include "ggml-metal-impl.h"/d' < ggml/src/ggml-metal/ggml-metal-embed.metal.tmp > ggml/src/ggml-metal/ggml-metal-embed.metal
$(eval TEMP_ASSEMBLY=$(shell mktemp -d))
@echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo ".incbin \"ggml/src/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo ".incbin \"ggml/src/ggml-metal/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
@echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s
$(CC) $(CFLAGS) -c $(TEMP_ASSEMBLY)/ggml-metal-embed.s -o $@
@rm -f ${TEMP_ASSEMBLY}/ggml-metal-embed.s
@rmdir ${TEMP_ASSEMBLY}
endif
endif # GGML_METAL
OBJ_GGML += \
ggml/src/ggml.o \
ggml/src/ggml-cpu.o \
ggml/src/ggml-alloc.o \
ggml/src/ggml-backend.o \
ggml/src/ggml-quants.o \
ggml/src/ggml-aarch64.o
DIR_GGML = ggml
DIR_LLAMA = src
DIR_COMMON = common
OBJ_GGML = \
$(DIR_GGML)/src/ggml.o \
$(DIR_GGML)/src/ggml-aarch64.o \
$(DIR_GGML)/src/ggml-alloc.o \
$(DIR_GGML)/src/ggml-backend.o \
$(DIR_GGML)/src/ggml-backend-reg.o \
$(DIR_GGML)/src/ggml-opt.o \
$(DIR_GGML)/src/ggml-quants.o \
$(DIR_GGML)/src/ggml-threading.o \
$(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \
$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o \
$(DIR_GGML)/src/ggml-cpu/ggml-cpu-aarch64.o \
$(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \
$(OBJ_GGML_EXT)
OBJ_LLAMA = \
src/llama.o \
src/llama-vocab.o \
src/llama-grammar.o \
src/llama-sampling.o \
src/unicode.o \
src/unicode-data.o
$(DIR_LLAMA)/llama.o \
$(DIR_LLAMA)/llama-vocab.o \
$(DIR_LLAMA)/llama-grammar.o \
$(DIR_LLAMA)/llama-sampling.o \
$(DIR_LLAMA)/unicode.o \
$(DIR_LLAMA)/unicode-data.o
OBJ_COMMON = \
common/common.o \
common/arg.o \
common/log.o \
common/console.o \
common/ngram-cache.o \
common/sampling.o \
common/build-info.o \
common/json-schema-to-grammar.o
$(DIR_COMMON)/common.o \
$(DIR_COMMON)/arg.o \
$(DIR_COMMON)/log.o \
$(DIR_COMMON)/console.o \
$(DIR_COMMON)/ngram-cache.o \
$(DIR_COMMON)/sampling.o \
$(DIR_COMMON)/speculative.o \
$(DIR_COMMON)/build-info.o \
$(DIR_COMMON)/json-schema-to-grammar.o
OBJ_ALL = $(OBJ_GGML) $(OBJ_LLAMA) $(OBJ_COMMON)
@ -993,7 +1025,6 @@ $(info I CXX: $(shell $(CXX) --version | head -n 1))
ifdef GGML_CUDA
$(info I NVCC: $(shell $(NVCC) --version | tail -n 1))
CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])')
ifndef GGML_MUSA
ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1)
ifndef CUDA_DOCKER_ARCH
@ -1003,7 +1034,6 @@ endif # CUDA_POWER_ARCH
endif # CUDA_DOCKER_ARCH
endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1)
endif # GGML_MUSA
endif # GGML_CUDA
$(info )
@ -1040,224 +1070,78 @@ endif
# Build libraries
#
# ggml
# Libraries
LIB_GGML = libggml.so
LIB_GGML_S = libggml.a
ggml/src/ggml.o: \
ggml/src/ggml.c \
ggml/include/ggml.h
$(CC) $(CFLAGS) -c $< -o $@
LIB_LLAMA = libllama.so
LIB_LLAMA_S = libllama.a
ggml/src/ggml-cpu.o: \
ggml/src/ggml-cpu.c \
ggml/include/ggml.h \
ggml/src/ggml-common.h
$(CC) $(CFLAGS) -c $< -o $@
LIB_COMMON = libcommon.so
LIB_COMMON_S = libcommon.a
ggml/src/ggml-alloc.o: \
ggml/src/ggml-alloc.c \
ggml/include/ggml.h \
ggml/include/ggml-alloc.h
$(CC) $(CFLAGS) -c $< -o $@
# Targets
BUILD_TARGETS += $(LIB_GGML) $(LIB_GGML_S) $(LIB_LLAMA) $(LIB_LLAMA_S) $(LIB_COMMON) $(LIB_COMMON_S)
ggml/src/ggml-backend.o: \
ggml/src/ggml-backend.cpp \
ggml/src/ggml-backend-impl.h \
ggml/include/ggml.h \
ggml/include/ggml-backend.h
$(CXX) $(CXXFLAGS) -c $< -o $@
# Dependency files
DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d)
ggml/src/ggml-quants.o: \
ggml/src/ggml-quants.c \
ggml/include/ggml.h \
ggml/src/ggml-quants.h \
ggml/src/ggml-common.h
$(CC) $(CFLAGS) -c $< -o $@
# Default target
all: $(BUILD_TARGETS)
ggml/src/ggml-aarch64.o: \
ggml/src/ggml-aarch64.c \
ggml/include/ggml.h \
ggml/src/ggml-aarch64.h \
ggml/src/ggml-common.h
$(CC) $(CFLAGS) -c $< -o $@
ggml/src/ggml-blas.o: \
ggml/src/ggml-blas.cpp \
ggml/include/ggml-blas.h
$(CXX) $(CXXFLAGS) -c $< -o $@
ifndef GGML_NO_LLAMAFILE
ggml/src/llamafile/sgemm.o: \
ggml/src/llamafile/sgemm.cpp \
ggml/src/llamafile/sgemm.h \
ggml/include/ggml.h
$(CXX) $(CXXFLAGS) -c $< -o $@
endif # GGML_NO_LLAMAFILE
ifndef GGML_NO_AMX
ggml/src/ggml-amx.o: \
ggml/src/ggml-amx.cpp \
ggml/include/ggml-amx.h
$(CXX) $(CXXFLAGS) -c $< -o $@
ggml/src/ggml-amx/mmq.o: \
ggml/src/ggml-amx/mmq.cpp \
ggml/src/ggml-amx/mmq.h \
ggml/include/ggml.h
$(CXX) $(CXXFLAGS) -c $< -o $@
endif
ifdef GGML_RPC
ggml/src/ggml-rpc.o: \
ggml/src/ggml-rpc.cpp \
ggml/include/ggml-rpc.h
$(CXX) $(CXXFLAGS) -c $< -o $@
endif # GGML_RPC
$(LIB_GGML): \
$(OBJ_GGML)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
$(LIB_GGML_S): \
$(OBJ_GGML)
ar rcs $(LIB_GGML_S) $^
# llama
src/unicode.o: \
src/unicode.cpp \
src/unicode.h
$(CXX) $(CXXFLAGS) -c $< -o $@
src/unicode-data.o: \
src/unicode-data.cpp \
src/unicode-data.h
$(CXX) $(CXXFLAGS) -c $< -o $@
src/llama.o: \
src/llama.cpp \
src/llama-impl.h \
src/llama-vocab.h \
src/llama-grammar.h \
src/llama-sampling.h \
src/unicode.h \
include/llama.h \
ggml/include/ggml-cuda.h \
ggml/include/ggml-metal.h \
# Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files
# g++ -M -I ./ggml/include/ -I ./ggml/src ggml/src/ggml-cpu/ggml-cpu.cpp | grep ggml
$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o: \
ggml/src/ggml-cpu/ggml-cpu.cpp \
ggml/include/ggml-backend.h \
ggml/include/ggml.h \
ggml/include/ggml-alloc.h \
ggml/include/ggml-backend.h
$(CXX) $(CXXFLAGS) -c $< -o $@
ggml/src/ggml-backend-impl.h \
ggml/include/ggml-cpu.h \
ggml/src/ggml-impl.h
$(CXX) $(CXXFLAGS) -c $< -o $@
src/llama-vocab.o: \
src/llama-vocab.cpp \
src/llama-vocab.h \
src/llama-impl.h \
include/llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
# Rules for building object files
$(DIR_GGML)/%.o: $(DIR_GGML)/%.c
$(CC) $(CFLAGS) -MMD -c $< -o $@
src/llama-grammar.o: \
src/llama-grammar.cpp \
src/llama-grammar.h \
src/llama-impl.h \
src/llama-vocab.h \
src/llama-sampling.h \
include/llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
$(DIR_GGML)/%.o: $(DIR_GGML)/%.cpp
$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
src/llama-sampling.o: \
src/llama-sampling.cpp \
src/llama-sampling.h \
src/llama-impl.h \
include/llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
$(DIR_LLAMA)/%.o: $(DIR_LLAMA)/%.cpp
$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
$(LIB_LLAMA): \
$(OBJ_LLAMA) \
$(LIB_GGML)
$(DIR_COMMON)/%.o: $(DIR_COMMON)/%.cpp
$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
# Rules for building libraries
$(LIB_GGML): $(OBJ_GGML)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
$(LIB_LLAMA_S): \
$(OBJ_LLAMA)
$(LIB_GGML_S): $(OBJ_GGML)
ar rcs $(LIB_GGML_S) $^
$(LIB_LLAMA): $(OBJ_LLAMA) $(LIB_GGML)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
$(LIB_LLAMA_S): $(OBJ_LLAMA)
ar rcs $(LIB_LLAMA_S) $^
# common
common/common.o: \
common/common.cpp \
common/common.h \
common/console.h \
common/sampling.h \
common/json.hpp \
common/json-schema-to-grammar.h \
include/llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common/arg.o: \
common/arg.cpp \
common/arg.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common/log.o: \
common/log.cpp \
common/log.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common/sampling.o: \
common/sampling.cpp \
common/sampling.h \
include/llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common/console.o: \
common/console.cpp \
common/console.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common/json-schema-to-grammar.o: \
common/json-schema-to-grammar.cpp \
common/json-schema-to-grammar.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common/ngram-cache.o: \
common/ngram-cache.cpp \
common/ngram-cache.h
$(CXX) $(CXXFLAGS) -c $< -o $@
$(LIB_COMMON): \
$(OBJ_COMMON) \
$(LIB_LLAMA) \
$(LIB_GGML)
$(LIB_COMMON): $(OBJ_COMMON) $(LIB_LLAMA) $(LIB_GGML)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
$(LIB_COMMON_S): \
$(OBJ_COMMON)
$(LIB_COMMON_S): $(OBJ_COMMON)
ar rcs $(LIB_COMMON_S) $^
# Include dependency files
-include $(DEP_FILES)
# Clean rule
clean:
rm -vrf *.dot $(BUILD_TARGETS) $(TEST_TARGETS)
rm -rvf src/*.o
rm -rvf tests/*.o
rm -rvf examples/*.o
rm -rvf common/*.o
rm -rvf *.a
rm -rvf *.dll
rm -rvf *.so
rm -rvf *.dot
rm -rvf ggml/*.a
rm -rvf ggml/*.dll
rm -rvf ggml/*.so
rm -vrf ggml/src/*.o
rm -rvf ggml/src/llamafile/*.o
rm -rvf common/build-info.cpp
rm -vrf ggml/src/ggml-metal-embed.metal
rm -vrf ggml/src/ggml-cuda/*.o
rm -vrf ggml/src/ggml-cuda/template-instances/*.o
rm -vrf ggml/src/ggml-amx/*.o
rm -rvf $(BUILD_TARGETS)
rm -rvf $(TEST_TARGETS)
rm -f vulkan-shaders-gen ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders.cpp
rm -rvf $(LEGACY_TARGETS_CLEAN)
find examples pocs -type f -name "*.o" -delete
rm -vrf $(BUILD_TARGETS) $(TEST_TARGETS)
rm -rvf *.a *.dll *.so *.dot
find ggml src common tests examples pocs -type f -name "*.o" -delete
find ggml src common tests examples pocs -type f -name "*.d" -delete
#
# Examples
@ -1455,22 +1339,13 @@ llama-server: \
examples/server/server.cpp \
examples/server/utils.hpp \
examples/server/httplib.h \
examples/server/colorthemes.css.hpp \
examples/server/style.css.hpp \
examples/server/theme-beeninorder.css.hpp \
examples/server/theme-ketivah.css.hpp \
examples/server/theme-mangotango.css.hpp \
examples/server/theme-playground.css.hpp \
examples/server/theme-polarnight.css.hpp \
examples/server/theme-snowstorm.css.hpp \
examples/server/index.html.hpp \
examples/server/index-new.html.hpp \
examples/server/index.js.hpp \
examples/server/completion.js.hpp \
examples/server/system-prompts.js.hpp \
examples/server/prompt-formats.js.hpp \
examples/server/json-schema-to-grammar.mjs.hpp \
examples/server/loading.html.hpp \
examples/server/deps_daisyui.min.css.hpp \
examples/server/deps_markdown-it.js.hpp \
examples/server/deps_tailwindcss.js.hpp \
examples/server/deps_vue.esm-browser.js.hpp \
common/json.hpp \
common/stb_image.h \
$(OBJ_ALL)
@ -1572,11 +1447,6 @@ tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \
$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-grad0: tests/test-grad0.cpp \
$(OBJ_GGML)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-opt: tests/test-opt.cpp \
$(OBJ_GGML)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)

22
Package.swift
View File

@ -10,11 +10,16 @@ var sources = [
"src/unicode.cpp",
"src/unicode-data.cpp",
"ggml/src/ggml.c",
"ggml/src/ggml-cpu.c",
"ggml/src/ggml-aarch64.c",
"ggml/src/ggml-alloc.c",
"ggml/src/ggml-backend.cpp",
"ggml/src/ggml-backend-reg.cpp",
"ggml/src/ggml-cpu/ggml-cpu.c",
"ggml/src/ggml-cpu/ggml-cpu.cpp",
"ggml/src/ggml-cpu/ggml-cpu-aarch64.c",
"ggml/src/ggml-cpu/ggml-cpu-quants.c",
"ggml/src/ggml-threading.cpp",
"ggml/src/ggml-quants.c",
"ggml/src/ggml-aarch64.c",
]
var resources: [Resource] = []
@ -22,6 +27,7 @@ var linkerSettings: [LinkerSetting] = []
var cSettings: [CSetting] = [
.unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
.unsafeFlags(["-fno-objc-arc"]),
.headerSearchPath("ggml/src"),
// NOTE: NEW_LAPACK will required iOS version 16.4+
// We should consider add this in the future when we drop support for iOS 14
// (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
@ -30,13 +36,15 @@ var cSettings: [CSetting] = [
]
#if canImport(Darwin)
sources.append("ggml/src/ggml-metal.m")
resources.append(.process("ggml/src/ggml-metal.metal"))
sources.append("ggml/src/ggml-common.h")
sources.append("ggml/src/ggml-metal/ggml-metal.m")
resources.append(.process("ggml/src/ggml-metal/ggml-metal.metal"))
linkerSettings.append(.linkedFramework("Accelerate"))
cSettings.append(
contentsOf: [
.define("GGML_USE_ACCELERATE"),
.define("GGML_USE_METAL")
.define("GGML_USE_METAL"),
.define("GGML_USE_CPU")
]
)
#endif
@ -61,13 +69,15 @@ let package = Package(
name: "llama",
path: ".",
exclude: [
"build",
"cmake",
"examples",
"scripts",
"models",
"tests",
"CMakeLists.txt",
"Makefile"
"Makefile",
"ggml/src/ggml-metal-embed.metal"
],
sources: sources,
resources: resources,

5
README.md
View File

@ -131,6 +131,7 @@ Typically finetunes of the base models below are supported as well.
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama)
- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)
- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp)
- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift)
@ -458,14 +459,14 @@ To learn more how to measure perplexity using llama.cpp, [read this documentatio
- Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205)
- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)
## Other documentations
## Other documentation
- [main (cli)](./examples/main/README.md)
- [server](./examples/server/README.md)
- [jeopardy](./examples/jeopardy/README.md)
- [GBNF grammars](./grammars/README.md)
**Development documentations**
**Development documentation**
- [How to build](./docs/build.md)
- [Running on Docker](./docs/docker.md)

2
ci/run.sh
View File

@ -39,7 +39,7 @@ SRC=`pwd`
CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON"
if [ ! -z ${GG_BUILD_METAL} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON"
fi
if [ ! -z ${GG_BUILD_CUDA} ]; then

202
cmake/llama-config.cmake.in
View File

@ -3,18 +3,60 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
set(GGML_BLAS @GGML_BLAS@)
set(GGML_CUDA @GGML_CUDA@)
set(GGML_METAL @GGML_METAL@)
set(GGML_HIPBLAS @GGML_HIPBLAS@)
set(GGML_STATIC @GGML_STATIC@)
set(GGML_NATIVE @GGML_NATIVE@)
set(GGML_LTO @GGML_LTO@)
set(GGML_CCACHE @GGML_CCACHE@)
set(GGML_AVX @GGML_AVX@)
set(GGML_AVX2 @GGML_AVX2@)
set(GGML_AVX512 @GGML_AVX512@)
set(GGML_AVX512_VBMI @GGML_AVX512_VBMI@)
set(GGML_AVX512_VNNI @GGML_AVX512_VNNI@)
set(GGML_AVX512_BF16 @GGML_AVX512_BF16@)
set(GGML_AMX_TILE @GGML_AMX_TILE@)
set(GGML_AMX_INT8 @GGML_AMX_INT8@)
set(GGML_AMX_BF16 @GGML_AMX_BF16@)
set(GGML_FMA @GGML_FMA@)
set(GGML_LASX @GGML_LASX@)
set(GGML_LSX @GGML_LSX@)
set(GGML_RVV @GGML_RVV@)
set(GGML_SVE @GGML_SVE@)
set(GGML_ACCELERATE @GGML_ACCELERATE@)
set(GGML_VULKAN @GGML_VULKAN@)
set(GGML_OPENMP @GGML_OPENMP@)
set(GGML_CPU_HBM @GGML_CPU_HBM@)
set(GGML_BLAS_VENDOR @GGML_BLAS_VENDOR@)
set(GGML_CUDA_FORCE_MMQ @GGML_CUDA_FORCE_MMQ@)
set(GGML_CUDA_FORCE_CUBLAS @GGML_CUDA_FORCE_CUBLAS@)
set(GGML_CUDA_F16 @GGML_CUDA_F16@)
set(GGML_CUDA_PEER_MAX_BATCH_SIZE @GGML_CUDA_PEER_MAX_BATCH_SIZE@)
set(GGML_CUDA_NO_PEER_COPY @GGML_CUDA_NO_PEER_COPY@)
set(GGML_CUDA_NO_VMM @GGML_CUDA_NO_VMM@)
set(GGML_CUDA_FA_ALL_QUANTS @GGML_CUDA_FA_ALL_QUANTS@)
set(GGML_CUDA_GRAPHS @GGML_CUDA_GRAPHS@)
set(GGML_HIP_UMA @GGML_HIP_UMA@)
set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@)
set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@)
set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@)
set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@)
set(GGML_SYCL @GGML_SYCL@)
set(GGML_OPENMP @GGML_OPENMP@)
set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@)
set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@)
set(GGML_VULKAN_SHADER_DEBUG_INFO @GGML_VULKAN_SHADER_DEBUG_INFO@)
set(GGML_VULKAN_PERF @GGML_VULKAN_PERF@)
set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@)
set(GGML_VULKAN_RUN_TESTS @GGML_VULKAN_RUN_TESTS@)
set(GGML_METAL_USE_BF16 @GGML_METAL_USE_BF16@)
set(GGML_METAL_NDEBUG @GGML_METAL_NDEBUG@)
set(GGML_METAL_SHADER_DEBUG @GGML_METAL_SHADER_DEBUG@)
set(GGML_METAL_EMBED_LIBRARY @GGML_METAL_EMBED_LIBRARY@)
set(GGML_METAL_MACOSX_VERSION_MIN @GGML_METAL_MACOSX_VERSION_MIN@)
set(GGML_METAL_STD @GGML_METAL_STD@)
set(GGML_SYCL_F16 @GGML_SYCL_F16@)
set(GGML_SYCL_TARGET @GGML_SYCL_TARGET@)
set(GGML_SYCL_DEVICE_ARCH @GGML_SYCL_DEVICE_ARCH@)
@PACKAGE_INIT@
@ -22,65 +64,111 @@ set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
# Ensure transient dependencies satisfied
find_package(Threads REQUIRED)
if (APPLE AND GGML_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@")
set(_llama_link_deps "")
set(_llama_link_opts "")
foreach(_ggml_lib ggml ggml-base)
string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY")
find_library(${_ggml_lib_var} ${_ggml_lib}
REQUIRED
HINTS ${LLAMA_LIB_DIR}
NO_CMAKE_FIND_ROOT_PATH
)
list(APPEND _llama_link_deps "${${_ggml_lib_var}}")
message(STATUS "Found ${${_ggml_lib_var}}")
endforeach()
foreach(backend amx blas cann cpu cuda hip kompute metal musa rpc sycl vulkan)
string(TOUPPER "GGML_${backend}" backend_id)
set(_ggml_lib "ggml-${backend}")
string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY")
find_library(${_ggml_lib_var} ${_ggml_lib}
HINTS ${LLAMA_LIB_DIR}
NO_CMAKE_FIND_ROOT_PATH
)
if(${_ggml_lib_var})
list(APPEND _llama_link_deps "${${_ggml_lib_var}}")
set(${backend_id} ON)
message(STATUS "Found backend ${${_ggml_lib_var}}")
else()
set(${backend_id} OFF)
endif()
endforeach()
if (NOT LLAMA_SHARED_LIB)
if (APPLE AND GGML_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
list(APPEND _llama_link_deps ${ACCELERATE_FRAMEWORK})
endif()
if (GGML_OPENMP)
find_package(OpenMP REQUIRED)
list(APPEND _llama_link_deps OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
endif()
if (GGML_CPU_HBM)
find_library(memkind memkind REQUIRED)
list(APPEND _llama_link_deps memkind)
endif()
if (GGML_BLAS)
find_package(BLAS REQUIRED)
list(APPEND _llama_link_deps ${BLAS_LIBRARIES})
list(APPEND _llama_link_opts ${BLAS_LINKER_FLAGS})
endif()
if (GGML_CUDA)
find_package(CUDAToolkit REQUIRED)
endif()
if (GGML_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
list(APPEND _llama_link_deps ${FOUNDATION_LIBRARY}
${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK})
endif()
if (GGML_VULKAN)
find_package(Vulkan REQUIRED)
list(APPEND _llama_link_deps Vulkan::Vulkan)
endif()
if (GGML_HIP)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
list(APPEND _llama_link_deps hip::host roc::rocblas roc::hipblas)
endif()
if (GGML_SYCL)
find_package(DNNL)
if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
list(APPEND _llama_link_deps DNNL::dnnl)
endif()
if (WIN32)
find_package(IntelSYCL REQUIRED)
find_package(MKL REQUIRED)
list(APPEND _llama_link_deps IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
endif()
endif()
endif()
if (GGML_BLAS)
find_package(BLAS REQUIRED)
endif()
if (GGML_CUDA)
find_package(CUDAToolkit REQUIRED)
endif()
if (GGML_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
endif()
if (GGML_VULKAN)
find_package(Vulkan REQUIRED)
endif()
if (GGML_HIPBLAS)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
endif()
if (GGML_SYCL)
find_package(IntelSYCL REQUIRED)
find_package(MKL REQUIRED)
endif()
if (GGML_OPENMP)
find_package(OpenMP REQUIRED)
endif()
find_library(ggml_LIBRARY ggml
REQUIRED
HINTS ${LLAMA_LIB_DIR})
find_library(llama_LIBRARY llama
REQUIRED
HINTS ${LLAMA_LIB_DIR})
set(_llama_link_deps "${ggml_LIBRARY}" "@GGML_LINK_LIBRARIES@")
set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@")
HINTS ${LLAMA_LIB_DIR}
NO_CMAKE_FIND_ROOT_PATH
)
add_library(llama UNKNOWN IMPORTED)
set_target_properties(llama
PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
INTERFACE_LINK_LIBRARIES "${_llama_link_deps}"
INTERFACE_LINK_OPTIONS "${_llama_link_opts}"
INTERFACE_COMPILE_DEFINITIONS "${_llama_transient_defines}"
IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
IMPORTED_LOCATION "${llama_LIBRARY}"

2
common/CMakeLists.txt
View File

@ -66,6 +66,8 @@ add_library(${TARGET} STATIC
ngram-cache.h
sampling.cpp
sampling.h
speculative.cpp
speculative.h
)
if (BUILD_SHARED_LIBS)

449
common/arg.cpp
View File

@ -233,10 +233,11 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
}
}
postprocess_cpu_params(params.cpuparams, nullptr);
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);
postprocess_cpu_params(params.speculative.cpuparams, &params.cpuparams);
postprocess_cpu_params(params.speculative.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");
@ -251,7 +252,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
for (auto & antiprompt : params.antiprompt) {
string_process_escapes(antiprompt);
}
for (auto & seq_breaker : params.sparams.dry_sequence_breakers) {
for (auto & seq_breaker : params.sampling.dry_sequence_breakers) {
string_process_escapes(seq_breaker);
}
}
@ -329,7 +330,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
std::string sampler_type_chars;
std::string sampler_type_names;
for (const auto & sampler : params.sparams.samplers) {
for (const auto & sampler : params.sampling.samplers) {
sampler_type_chars += common_sampler_type_to_chr(sampler);
sampler_type_names += common_sampler_type_to_str(sampler) + ";";
}
@ -407,26 +408,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
));
add_opt(common_arg(
{"-td", "--threads-draft"}, "N",
"number of threads to use during generation (default: same as --threads)",
[](common_params & params, int value) {
params.draft_cpuparams.n_threads = value;
if (params.draft_cpuparams.n_threads <= 0) {
params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-tbd", "--threads-batch-draft"}, "N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)",
[](common_params & params, int value) {
params.draft_cpuparams_batch.n_threads = value;
if (params.draft_cpuparams_batch.n_threads <= 0) {
params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-C", "--cpu-mask"}, "M",
"CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")",
@ -515,108 +496,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.cpuparams_batch.poll = value;
}
));
add_opt(common_arg(
{"-Cd", "--cpu-mask-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](common_params & params, const std::string & mask) {
params.draft_cpuparams.mask_valid = true;
if (!parse_cpu_mask(mask, params.draft_cpuparams.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Crd", "--cpu-range-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft",
[](common_params & params, const std::string & range) {
params.draft_cpuparams.mask_valid = true;
if (!parse_cpu_range(range, params.draft_cpuparams.cpumask)) {
throw std::invalid_argument("invalid range");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--cpu-strict-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: same as --cpu-strict)",
[](common_params & params, int value) {
params.draft_cpuparams.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--prio-draft"}, "N",
string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority),
[](common_params & params, int prio) {
if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value");
}
params.draft_cpuparams.priority = (enum ggml_sched_priority) prio;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--poll-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: same as --poll])",
[](common_params & params, int value) {
params.draft_cpuparams.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Cbd", "--cpu-mask-batch-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](common_params & params, const std::string & mask) {
params.draft_cpuparams_batch.mask_valid = true;
if (!parse_cpu_mask(mask, params.draft_cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
[](common_params & params, const std::string & range) {
params.draft_cpuparams_batch.mask_valid = true;
if (!parse_cpu_range(range, params.draft_cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--cpu-strict-batch-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: --cpu-strict-draft)",
[](common_params & params, int value) {
params.draft_cpuparams_batch.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--prio-batch-draft"}, "N",
string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams_batch.priority),
[](common_params & params, int prio) {
if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value");
}
params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) prio;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--poll-batch-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: --poll-draft)",
[](common_params & params, int value) {
params.draft_cpuparams_batch.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--draft"}, "N",
string_format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
[](common_params & params, int value) {
params.n_draft = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
add_opt(common_arg(
{"-ps", "--p-split"}, "N",
string_format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
[](common_params & params, const std::string & value) {
params.p_split = std::stof(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-lcs", "--lookup-cache-static"}, "FNAME",
"path to static lookup cache to use for lookup decoding (not updated by generation)",
@ -701,7 +580,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
[](common_params & params) {
params.no_perf = true;
params.sparams.no_perf = true;
params.sampling.no_perf = true;
}
).set_env("LLAMA_ARG_NO_PERF"));
add_opt(common_arg(
@ -883,155 +762,155 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
[](common_params & params, const std::string & value) {
const auto sampler_names = string_split<std::string>(value, ';');
params.sparams.samplers = common_sampler_types_from_names(sampler_names, true);
params.sampling.samplers = common_sampler_types_from_names(sampler_names, true);
}
).set_sparam());
add_opt(common_arg(
{"-s", "--seed"}, "SEED",
string_format("RNG seed (default: %d, use random seed for %d)", params.sparams.seed, LLAMA_DEFAULT_SEED),
string_format("RNG seed (default: %d, use random seed for %d)", params.sampling.seed, LLAMA_DEFAULT_SEED),
[](common_params & params, const std::string & value) {
params.sparams.seed = std::stoul(value);
params.sampling.seed = std::stoul(value);
}
).set_sparam());
add_opt(common_arg(
{"--sampling-seq"}, "SEQUENCE",
string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
[](common_params & params, const std::string & value) {
params.sparams.samplers = common_sampler_types_from_chars(value);
params.sampling.samplers = common_sampler_types_from_chars(value);
}
).set_sparam());
add_opt(common_arg(
{"--ignore-eos"},
"ignore end of stream token and continue generating (implies --logit-bias EOS-inf)",
[](common_params & params) {
params.sparams.ignore_eos = true;
params.sampling.ignore_eos = true;
}
).set_sparam());
add_opt(common_arg(
{"--penalize-nl"},
string_format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"),
[](common_params & params) {
params.sparams.penalize_nl = true;
params.sampling.penalize_nl = true;
}
).set_sparam());
add_opt(common_arg(
{"--temp"}, "N",
string_format("temperature (default: %.1f)", (double)params.sparams.temp),
string_format("temperature (default: %.1f)", (double)params.sampling.temp),
[](common_params & params, const std::string & value) {
params.sparams.temp = std::stof(value);
params.sparams.temp = std::max(params.sparams.temp, 0.0f);
params.sampling.temp = std::stof(value);
params.sampling.temp = std::max(params.sampling.temp, 0.0f);
}
).set_sparam());
add_opt(common_arg(
{"--top-k"}, "N",
string_format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
string_format("top-k sampling (default: %d, 0 = disabled)", params.sampling.top_k),
[](common_params & params, int value) {
params.sparams.top_k = value;
params.sampling.top_k = value;
}
).set_sparam());
add_opt(common_arg(
{"--top-p"}, "N",
string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p),
[](common_params & params, const std::string & value) {
params.sparams.top_p = std::stof(value);
params.sampling.top_p = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--min-p"}, "N",
string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sampling.min_p),
[](common_params & params, const std::string & value) {
params.sparams.min_p = std::stof(value);
params.sampling.min_p = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--xtc-probability"}, "N",
string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_probability),
string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability),
[](common_params & params, const std::string & value) {
params.sparams.xtc_probability = std::stof(value);
params.sampling.xtc_probability = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--xtc-threshold"}, "N",
string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sparams.xtc_threshold),
string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sampling.xtc_threshold),
[](common_params & params, const std::string & value) {
params.sparams.xtc_threshold = std::stof(value);
params.sampling.xtc_threshold = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--typical"}, "N",
string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sampling.typ_p),
[](common_params & params, const std::string & value) {
params.sparams.typ_p = std::stof(value);
params.sampling.typ_p = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--repeat-last-n"}, "N",
string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n),
string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
[](common_params & params, int value) {
params.sparams.penalty_last_n = value;
params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n);
params.sampling.penalty_last_n = value;
params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
}
).set_sparam());
add_opt(common_arg(
{"--repeat-penalty"}, "N",
string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sampling.penalty_repeat),
[](common_params & params, const std::string & value) {
params.sparams.penalty_repeat = std::stof(value);
params.sampling.penalty_repeat = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--presence-penalty"}, "N",
string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_present),
[](common_params & params, const std::string & value) {
params.sparams.penalty_present = std::stof(value);
params.sampling.penalty_present = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--frequency-penalty"}, "N",
string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_freq),
[](common_params & params, const std::string & value) {
params.sparams.penalty_freq = std::stof(value);
params.sampling.penalty_freq = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dry-multiplier"}, "N",
string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sparams.dry_multiplier),
string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sampling.dry_multiplier),
[](common_params & params, const std::string & value) {
params.sparams.dry_multiplier = std::stof(value);
params.sampling.dry_multiplier = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dry-base"}, "N",
string_format("set DRY sampling base value (default: %.2f)", (double)params.sparams.dry_base),
string_format("set DRY sampling base value (default: %.2f)", (double)params.sampling.dry_base),
[](common_params & params, const std::string & value) {
float potential_base = std::stof(value);
if (potential_base >= 1.0f)
{
params.sparams.dry_base = potential_base;
params.sampling.dry_base = potential_base;
}
}
).set_sparam());
add_opt(common_arg(
{"--dry-allowed-length"}, "N",
string_format("set allowed length for DRY sampling (default: %d)", params.sparams.dry_allowed_length),
string_format("set allowed length for DRY sampling (default: %d)", params.sampling.dry_allowed_length),
[](common_params & params, int value) {
params.sparams.dry_allowed_length = value;
params.sampling.dry_allowed_length = value;
}
).set_sparam());
add_opt(common_arg(
{"--dry-penalty-last-n"}, "N",
string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sparams.dry_penalty_last_n),
string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
[](common_params & params, int value) {
params.sparams.dry_penalty_last_n = value;
params.sampling.dry_penalty_last_n = value;
}
).set_sparam());
add_opt(common_arg(
{"--dry-sequence-breaker"}, "STRING",
string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n",
params.sparams.dry_sequence_breakers.empty() ? "none" :
std::accumulate(std::next(params.sparams.dry_sequence_breakers.begin()),
params.sparams.dry_sequence_breakers.end(),
std::string("'") + (params.sparams.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sparams.dry_sequence_breakers[0]) + "'",
params.sampling.dry_sequence_breakers.empty() ? "none" :
std::accumulate(std::next(params.sampling.dry_sequence_breakers.begin()),
params.sampling.dry_sequence_breakers.end(),
std::string("'") + (params.sampling.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sampling.dry_sequence_breakers[0]) + "'",
[](const std::string& a, const std::string& b) {
std::string formatted_b = (b == "\n") ? "\\n" : b;
return a + ", '" + formatted_b + "'";
@ -1040,51 +919,51 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
static bool defaults_cleared = false;
if (!defaults_cleared) {
params.sparams.dry_sequence_breakers.clear();
params.sampling.dry_sequence_breakers.clear();
defaults_cleared = true;
}
if (value == "none") {
params.sparams.dry_sequence_breakers.clear();
params.sampling.dry_sequence_breakers.clear();
} else {
params.sparams.dry_sequence_breakers.emplace_back(value);
params.sampling.dry_sequence_breakers.emplace_back(value);
}
}
).set_sparam());
add_opt(common_arg(
{"--dynatemp-range"}, "N",
string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range),
[](common_params & params, const std::string & value) {
params.sparams.dynatemp_range = std::stof(value);
params.sampling.dynatemp_range = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dynatemp-exp"}, "N",
string_format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
string_format("dynamic temperature exponent (default: %.1f)", (double)params.sampling.dynatemp_exponent),
[](common_params & params, const std::string & value) {
params.sparams.dynatemp_exponent = std::stof(value);
params.sampling.dynatemp_exponent = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--mirostat"}, "N",
string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n"
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat),
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sampling.mirostat),
[](common_params & params, int value) {
params.sparams.mirostat = value;
params.sampling.mirostat = value;
}
).set_sparam());
add_opt(common_arg(
{"--mirostat-lr"}, "N",
string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sampling.mirostat_eta),
[](common_params & params, const std::string & value) {
params.sparams.mirostat_eta = std::stof(value);
params.sampling.mirostat_eta = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--mirostat-ent"}, "N",
string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sampling.mirostat_tau),
[](common_params & params, const std::string & value) {
params.sparams.mirostat_tau = std::stof(value);
params.sampling.mirostat_tau = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
@ -1100,7 +979,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
try {
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
params.sparams.logit_bias.push_back({key, bias});
params.sampling.logit_bias.push_back({key, bias});
} else {
throw std::invalid_argument("invalid input format");
}
@ -1111,9 +990,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam());
add_opt(common_arg(
{"--grammar"}, "GRAMMAR",
string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sampling.grammar.c_str()),
[](common_params & params, const std::string & value) {
params.sparams.grammar = value;
params.sampling.grammar = value;
}
).set_sparam());
add_opt(common_arg(
@ -1127,7 +1006,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(params.sparams.grammar)
std::back_inserter(params.sampling.grammar)
);
}
).set_sparam());
@ -1135,7 +1014,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"-j", "--json-schema"}, "SCHEMA",
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
[](common_params & params, const std::string & value) {
params.sparams.grammar = json_schema_to_grammar(json::parse(value));
params.sampling.grammar = json_schema_to_grammar(json::parse(value));
}
).set_sparam());
add_opt(common_arg(
@ -1444,17 +1323,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
).set_env("LLAMA_ARG_N_GPU_LAYERS"));
add_opt(common_arg(
{"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
"number of layers to store in VRAM for the draft model",
[](common_params & params, int value) {
params.n_gpu_layers_draft = value;
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-sm", "--split-mode"}, "{none,layer,row}",
"how to split the model across multiple GPUs, one of:\n"
@ -1593,13 +1461,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.model = value;
}
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
add_opt(common_arg(
{"-md", "--model-draft"}, "FNAME",
"draft model for speculative decoding (default: unused)",
[](common_params & params, const std::string & value) {
params.model_draft = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-mu", "--model-url"}, "MODEL_URL",
"model download url (default: unused)",
@ -1939,17 +1800,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.simple_io = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
add_opt(common_arg(
{"-ld", "--logdir"}, "LOGDIR",
"path under which to save YAML logs (no logging if unset)",
[](common_params & params, const std::string & value) {
params.logdir = value;
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
params.logdir += DIRECTORY_SEPARATOR;
}
}
));
add_opt(common_arg(
{"--positive-file"}, "FNAME",
string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
@ -2048,5 +1898,168 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_env("LLAMA_LOG_TIMESTAMPS"));
// speculative parameters
add_opt(common_arg(
{"-td", "--threads-draft"}, "N",
"number of threads to use during generation (default: same as --threads)",
[](common_params & params, int value) {
params.speculative.cpuparams.n_threads = value;
if (params.speculative.cpuparams.n_threads <= 0) {
params.speculative.cpuparams.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-tbd", "--threads-batch-draft"}, "N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)",
[](common_params & params, int value) {
params.speculative.cpuparams_batch.n_threads = value;
if (params.speculative.cpuparams_batch.n_threads <= 0) {
params.speculative.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Cd", "--cpu-mask-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](common_params & params, const std::string & mask) {
params.speculative.cpuparams.mask_valid = true;
if (!parse_cpu_mask(mask, params.speculative.cpuparams.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Crd", "--cpu-range-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft",
[](common_params & params, const std::string & range) {
params.speculative.cpuparams.mask_valid = true;
if (!parse_cpu_range(range, params.speculative.cpuparams.cpumask)) {
throw std::invalid_argument("invalid range");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--cpu-strict-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: same as --cpu-strict)",
[](common_params & params, int value) {
params.speculative.cpuparams.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--prio-draft"}, "N",
string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams.priority),
[](common_params & params, int prio) {
if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value");
}
params.speculative.cpuparams.priority = (enum ggml_sched_priority) prio;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--poll-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: same as --poll])",
[](common_params & params, int value) {
params.speculative.cpuparams.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Cbd", "--cpu-mask-batch-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](common_params & params, const std::string & mask) {
params.speculative.cpuparams_batch.mask_valid = true;
if (!parse_cpu_mask(mask, params.speculative.cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
[](common_params & params, const std::string & range) {
params.speculative.cpuparams_batch.mask_valid = true;
if (!parse_cpu_range(range, params.speculative.cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--cpu-strict-batch-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: --cpu-strict-draft)",
[](common_params & params, int value) {
params.speculative.cpuparams_batch.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--prio-batch-draft"}, "N",
string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams_batch.priority),
[](common_params & params, int prio) {
if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value");
}
params.speculative.cpuparams_batch.priority = (enum ggml_sched_priority) prio;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--poll-batch-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: --poll-draft)",
[](common_params & params, int value) {
params.speculative.cpuparams_batch.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--draft-max", "--draft", "--draft-n"}, "N",
string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max),
[](common_params & params, int value) {
params.speculative.n_max = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--draft-min", "--draft-n-min"}, "N",
string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min),
[](common_params & params, int value) {
params.speculative.n_min = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--draft-p-split"}, "P",
string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split),
[](common_params & params, const std::string & value) {
params.speculative.p_split = std::stof(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--draft-p-min"}, "P",
string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min),
[](common_params & params, const std::string & value) {
params.speculative.p_min = std::stof(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-cd", "--ctx-size-draft"}, "N",
string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),
[](common_params & params, int value) {
params.speculative.n_ctx = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
"number of layers to store in VRAM for the draft model",
[](common_params & params, int value) {
params.speculative.n_gpu_layers = value;
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-md", "--model-draft"}, "FNAME",
"draft model for speculative decoding (default: unused)",
[](common_params & params, const std::string & value) {
params.speculative.model = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
return ctx_arg;
}

303
common/common.cpp
View File

@ -377,6 +377,9 @@ void common_init() {
#endif
LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type);
// load dynamic backends
ggml_backend_load_all();
}
std::string common_params_get_system_info(const common_params & params) {
@ -536,12 +539,12 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat
[](const unsigned char c) { return !std::isprint(c); }),
detokenized.end());
buf << "\n" << std::to_string(i)
<< ":token '" << detokenized << "'"
<< ":pos " << std::to_string(batch.pos[i])
<< ":n_seq_id " << std::to_string(batch.n_seq_id[i])
<< ":seq_id " << std::to_string(batch.seq_id[i][0])
<< ":logits " << std::to_string(batch.logits[i]);
buf << "\n" << std::to_string(i)
<< ", token '" << detokenized << "'"
<< ", pos " << std::to_string(batch.pos[i])
<< ", n_seq_id " << std::to_string(batch.n_seq_id[i])
<< ", seq_id " << std::to_string(batch.seq_id[i][0])
<< ", logits " << std::to_string(batch.logits[i]);
}
buf << " ]";
@ -875,6 +878,12 @@ struct common_init_result common_init_from_params(common_params & params) {
return iparams;
}
if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) {
LOG_ERR("%s: KV cache shifting is not supported for this model (--no-context-shift to disable)'\n", __func__);
llama_free_model(model);
return iparams;
}
if (!params.control_vectors.empty()) {
if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
@ -919,9 +928,9 @@ struct common_init_result common_init_from_params(common_params & params) {
common_lora_adapters_apply(lctx, iparams.lora_adapters);
}
if (params.sparams.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sparams.ignore_eos = false;
params.sampling.ignore_eos = false;
}
if (params.warmup) {
@ -1003,6 +1012,9 @@ static ggml_type kv_cache_type_from_str(const std::string & s) {
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "bf16") {
return GGML_TYPE_BF16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
@ -1481,6 +1493,66 @@ void common_batch_add(
batch.n_tokens++;
}
//
// Token utils
//
size_t common_lcp(const llama_tokens & a, const llama_tokens & b) {
size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
return i;
}
size_t common_lcs(const llama_tokens & a, const llama_tokens & b) {
// check for empty sequences
if (a.empty() || b.empty()) {
return 0;
}
// get the lengths of the input sequences
size_t a_len = a.size();
size_t b_len = b.size();
// initialize the maximum length of the longest common subsequence (LCS)
size_t max_length = 0;
// use two rows instead of a 2D matrix to optimize space
std::vector<size_t> prev_row(b_len + 1, 0);
std::vector<size_t> curr_row(b_len + 1, 0);
// iterate through the elements of a
for (size_t i = 1; i <= a_len; i++) {
// iterate through the elements of b
for (size_t j = 1; j <= b_len; j++) {
// if elements at the current positions match
if (a[i - 1] == b[j - 1]) {
// if it's the first element of either sequences, set LCS length to 1
if (i == 1 || j == 1) {
curr_row[j] = 1;
} else {
// increment LCS length by 1 compared to the previous element
curr_row[j] = prev_row[j - 1] + 1;
}
// update max_length if necessary
if (curr_row[j] > max_length) {
max_length = curr_row[j];
}
} else {
// reset LCS length if elements don't match
curr_row[j] = 0;
}
}
// update the previous row for the next iteration
prev_row = curr_row;
}
// return the maximum length of the LCS
return max_length;
}
//
// Vocab utils
//
@ -1887,218 +1959,3 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
return result;
}
//
// YAML utils
//
void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%e, ", data[i]);
}
fprintf(stream, "%e]\n", data.back());
}
void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%d, ", data[i]);
}
fprintf(stream, "%d]\n", data.back());
}
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
std::string data_str(data == NULL ? "" : data);
if (data_str.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
size_t pos_start = 0;
size_t pos_found = 0;
if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
data_str = "\"" + data_str + "\"";
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
if (data_str.find('\n') == std::string::npos) {
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
fprintf(stream, "%s: |\n", prop_name);
while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
pos_start = pos_found + 1;
}
}
void yaml_dump_non_result_info(FILE * stream, const common_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
ggml_cpu_init(); // some ARM features are detected at runtime
const auto & sparams = params.sparams;
fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT);
fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER);
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false");
fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false");
fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false");
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false");
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
fprintf(stream, "cpu_has_riscv_v: %s\n", ggml_cpu_has_riscv_v() ? "true" : "false");
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
#ifdef NDEBUG
fprintf(stream, "debug: false\n");
#else
fprintf(stream, "debug: true\n");
#endif // NDEBUG
fprintf(stream, "model_desc: %s\n", model_desc);
fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));
#ifdef __OPTIMIZE__
fprintf(stream, "optimize: true\n");
#else
fprintf(stream, "optimize: false\n");
#endif // __OPTIMIZE__
fprintf(stream, "time: %s\n", timestamp.c_str());
fprintf(stream, "\n");
fprintf(stream, "###############\n");
fprintf(stream, "# User Inputs #\n");
fprintf(stream, "###############\n");
fprintf(stream, "\n");
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "dry_allowed_length: %d # default: 2\n", sparams.dry_allowed_length);
fprintf(stream, "dry_base: %.2f # default: 1.75\n", sparams.dry_base);
fprintf(stream, "dry_multiplier: %.1f # default: 0.0\n", sparams.dry_multiplier);
fprintf(stream, "dry_penalty_last_n: %d # default: -1 (0 = disable, -1 = context size)\n", sparams.dry_penalty_last_n);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str());
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
fprintf(stream, "ignore_eos: %s # default: false\n", sparams.ignore_eos ? "true" : "false");
yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
fprintf(stream, "logit_bias:\n");
for (const auto & logit_bias : sparams.logit_bias) {
fprintf(stream, " %d: %f", logit_bias.token, logit_bias.bias);
}
fprintf(stream, "lora:\n");
for (auto & la : params.lora_adapters) {
if (la.scale == 1.0f) {
fprintf(stream, " - %s\n", la.path.c_str());
}
}
fprintf(stream, "lora_scaled:\n");
for (auto & la : params.lora_adapters) {
if (la.scale != 1.0f) {
fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale);
}
}
fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);
fprintf(stream, "reverse_prompt:\n");
for (std::string ap : params.antiprompt) {
size_t pos = 0;
while ((pos = ap.find('\n', pos)) != std::string::npos) {
ap.replace(pos, 1, "\\n");
pos += 1;
}
fprintf(stream, " - %s\n", ap.c_str());
}
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
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);
fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability);
fprintf(stream, "xtc_threshold: %f # default: 0.1\n", sparams.xtc_threshold);
fprintf(stream, "typ_p: %f # default: 1.0\n", sparams.typ_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
}

56
common/common.h
View File

@ -33,6 +33,8 @@ struct common_lora_adapter_container : common_lora_adapter_info {
struct llama_lora_adapter * adapter;
};
using llama_tokens = std::vector<llama_token>;
// build info
extern int LLAMA_BUILD_NUMBER;
extern char const * LLAMA_COMMIT;
@ -101,8 +103,8 @@ enum dimre_method {
DIMRE_METHOD_MEAN,
};
// sampler parameters
struct common_sampler_params {
// sampling parameters
struct common_params_sampling {
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
int32_t n_prev = 64; // number of previous tokens to remember
@ -153,19 +155,30 @@ struct common_sampler_params {
std::string print() const;
};
struct common_params_speculative {
int32_t n_ctx = 0; // draft context size
int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding
int32_t n_min = 5; // minimum number of draft tokens to use for speculative decoding
int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
float p_split = 0.1f; // speculative decoding split probability
float p_min = 0.9f; // minimum speculative decoding probability (greedy)
struct cpu_params cpuparams;
struct cpu_params cpuparams_batch;
std::string model = ""; // draft model for speculative decoding // NOLINT
};
struct common_params {
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 4096; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 5; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
float p_split = 0.1f; // speculative decoding split probability
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
int32_t grp_attn_n = 1; // group-attention factor
@ -178,12 +191,10 @@ struct common_params {
float yarn_beta_fast = 32.0f; // YaRN low correction dim
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
float defrag_thold = -1.0f; // KV cache defragmentation threshold
float defrag_thold = 0.1f; // 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;
@ -195,10 +206,10 @@ struct common_params {
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
struct common_sampler_params sparams;
struct common_params_sampling sampling;
struct common_params_speculative speculative;
std::string model = ""; // model path // NOLINT
std::string model_draft = ""; // draft model for speculative decoding // NOLINT
std::string model_alias = "unknown"; // model alias // NOLINT
std::string model_url = ""; // model url to download // NOLINT
std::string hf_token = ""; // HF token // NOLINT
@ -209,7 +220,6 @@ struct common_params {
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state // NOLINT
std::string input_prefix = ""; // string to prefix user inputs with // NOLINT
std::string input_suffix = ""; // string to suffix user inputs with // NOLINT
std::string logdir = ""; // directory in which to save YAML log files // NOLINT
std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT
std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT
std::string logits_file = ""; // file for saving *all* logits // NOLINT
@ -462,7 +472,9 @@ struct llama_model * common_load_model_from_hf(const char * repo, const char * f
// clear LoRA adapters from context, then apply new list of adapters
void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters);
//
// Batch utils
//
void common_batch_clear(struct llama_batch & batch);
@ -473,6 +485,16 @@ void common_batch_add(
const std::vector<llama_seq_id> & seq_ids,
bool logits);
//
// Token utils
//
// longest common prefix
size_t common_lcp(const llama_tokens & a, const llama_tokens & b);
// longet common subsequence
size_t common_lcs(const llama_tokens & a, const llama_tokens & b);
//
// Vocab utils
//
@ -584,15 +606,3 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
static const char * const LLM_KV_SPLIT_NO = "split.no";
static const char * const LLM_KV_SPLIT_COUNT = "split.count";
static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
//
// YAML utils
//
void yaml_dump_vector_float (FILE * stream, const char * prop_name, const std::vector<float> & data);
void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std::vector<int> & data);
void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data);
void yaml_dump_non_result_info(
FILE * stream, const common_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);

45
common/sampling.cpp
View File

@ -99,7 +99,7 @@ struct ring_buffer {
};
struct common_sampler {
common_sampler_params params;
common_params_sampling params;
struct llama_sampler * grmr;
struct llama_sampler * chain;
@ -125,7 +125,7 @@ struct common_sampler {
}
};
std::string common_sampler_params::print() const {
std::string common_params_sampling::print() const {
char result[1024];
snprintf(result, sizeof(result),
@ -141,7 +141,7 @@ std::string common_sampler_params::print() const {
return std::string(result);
}
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params) {
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
lparams.no_perf = params.no_perf;
@ -320,6 +320,45 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
return cur_p.data[cur_p.selected].id;
}
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
std::vector<llama_token> result;
result.reserve(idxs.size());
size_t i = 0;
for (; i < draft.size(); i++) {
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
common_sampler_accept(gsmpl, id, true);
result.push_back(id);
if (draft[i] != id) {
break;
}
}
if (i == draft.size()) {
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
common_sampler_accept(gsmpl, id, true);
result.push_back(id);
}
return result;
}
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
std::vector<int> idxs(draft.size() + 1);
for (size_t i = 0; i < idxs.size(); ++i) {
idxs[i] = i;
}
return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
}
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
return llama_sampler_get_seed(gsmpl->chain);
}

23
common/sampling.h
View File

@ -36,7 +36,7 @@ struct common_sampler;
// llama_sampler API overloads
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params);
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
void common_sampler_free(struct common_sampler * gsmpl);
@ -60,6 +60,27 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
//
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
// generalized version of common_sampler_sample
//
// will cross-reference the sampled tokens with a batch of draft tokens and accept those that match
// if the sampler disagrees at some point, we stop and return the accepted tokens up to now
//
// common_sampler_sample_n(gsmpl, ctx, { idx }, {});
//
// is equivalent to
//
// common_sampler_sample(gsmpl, ctx, idx);
// common_sampler_accept(gsmpl, token, true);
//
// requires: idxs.size() == draft.size() + 1
//
// returns at least 1 token, up to idxs.size()
//
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
// assume idxs == [ 0, 1, 2, ..., draft.size() ]
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
// helpers

269
common/speculative.cpp Normal file
View File

@ -0,0 +1,269 @@
#include "speculative.h"
#include "log.h"
#include "common.h"
#include "sampling.h"
#include <cstring>
#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128
#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
struct common_speculative {
struct llama_context * ctx;
struct common_sampler * smpl;
llama_batch batch;
llama_tokens prompt;
};
struct common_speculative * common_speculative_init(
struct llama_context * ctx_dft) {
auto * result = new common_speculative {
/* .ctx = */ ctx_dft,
/* .smpl = */ nullptr,
/* .batch = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
/* .prompt = */ {},
};
// TODO: optimize or pass from outside?
#if 0
{
common_params_sampling params;
params.no_perf = false;
params.top_k = 40;
params.top_p = 0.9;
params.samplers = {
COMMON_SAMPLER_TYPE_TOP_K,
COMMON_SAMPLER_TYPE_TOP_P,
COMMON_SAMPLER_TYPE_INFILL,
};
result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
}
#else
{
common_params_sampling params;
params.no_perf = false;
params.top_k = 10;
params.samplers = {
COMMON_SAMPLER_TYPE_TOP_K,
};
result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
}
#endif
return result;
}
void common_speculative_free(struct common_speculative * spec) {
common_sampler_free(spec->smpl);
llama_batch_free(spec->batch);
delete spec;
}
bool common_speculative_are_compatible(
const struct llama_context * ctx_tgt,
const struct llama_context * ctx_dft) {
const struct llama_model * model_tgt = llama_get_model(ctx_tgt);
const struct llama_model * model_dft = llama_get_model(ctx_dft);
const bool vocab_type_tgt = llama_vocab_type(model_tgt);
LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
const bool vocab_type_dft = llama_vocab_type(model_dft);
LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
if (vocab_type_tgt != vocab_type_dft) {
LOG_ERR("%s: draft model vocab type must match target model to use speculation but "
"vocab_type_dft = %d while vocab_type_tgt = %d\n", __func__, vocab_type_dft, vocab_type_tgt);
return false;
}
if (llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) ||
llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) ||
llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
llama_token_eos(model_tgt) != llama_token_eos(model_dft)
) {
LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
return false;
}
{
const int n_vocab_tgt = llama_n_vocab(model_tgt);
const int n_vocab_dft = llama_n_vocab(model_dft);
const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft);
if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
LOG_ERR("%s: draft model vocab must closely match target model to use speculation but "
"target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
__func__, n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
return false;
}
for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
const char * token_text_tgt = llama_token_get_text(model_tgt, i);
const char * token_text_dft = llama_token_get_text(model_dft, i);
if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
LOG_ERR("%s: draft model vocab must match target model to use speculation but "
"token %d content differs - target '%s', draft '%s'\n", __func__, i,
common_token_to_piece(ctx_tgt, i).c_str(),
common_token_to_piece(ctx_dft, i).c_str());
return false;
}
}
}
return true;
}
llama_tokens common_speculative_gen_draft(
struct common_speculative * spec,
struct common_speculative_params params,
const llama_tokens & prompt_tgt,
llama_token id_last) {
auto & batch = spec->batch;
auto & ctx = spec->ctx;
auto & smpl = spec->smpl;
auto & prompt = spec->prompt;
int reuse_i = 0;
int reuse_n = 0;
const int n_ctx = llama_n_ctx(ctx) - params.n_draft;
const int i_start = std::max<int>(0, (int) prompt_tgt.size() - n_ctx);
// reuse as much as possible from the old draft context
// ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
for (int i = 0; i < (int) prompt.size(); ++i) {
int cur = 0;
while (i_start + cur < (int) prompt_tgt.size() &&
i + cur < (int) prompt.size() &&
prompt_tgt[i_start + cur] == prompt[i + cur]) {
cur++;
}
if ((cur >= params.n_reuse || n_ctx >= (int) prompt_tgt.size()) && cur > reuse_n) {
reuse_i = i;
reuse_n = cur;
}
}
LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt.size());
llama_tokens result;
result.reserve(params.n_draft);
if (reuse_n == 0) {
llama_kv_cache_clear(ctx);
prompt.clear();
} else {
// this happens when a previous draft has been discarded (for example, due to being too small), but the
// target model agreed with it. in this case, we simply pass back the previous results to save compute
if (reuse_i + reuse_n < (int) prompt.size() && prompt[reuse_i + reuse_n] == id_last) {
for (int i = reuse_i + reuse_n + 1; i < (int) prompt.size(); ++i) {
result.push_back(prompt[i]);
if (params.n_draft <= (int) result.size()) {
break;
}
}
return result;
}
if (reuse_i > 0) {
llama_kv_cache_seq_rm (ctx, 0, 0, reuse_i);
llama_kv_cache_seq_add(ctx, 0, reuse_i, -1, -reuse_i);
prompt.erase(prompt.begin(), prompt.begin() + reuse_i);
}
if (reuse_n < (int) prompt.size()) {
llama_kv_cache_seq_rm (ctx, 0, reuse_n, -1);
prompt.erase(prompt.begin() + reuse_n, prompt.end());
}
}
// prepare a batch to evaluate any new tokens in the prompt
common_batch_clear(batch);
for (size_t i = i_start + reuse_n; i < prompt_tgt.size(); ++i) {
//LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]);
common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, false);
prompt.push_back(prompt_tgt[i]);
}
// we should rarely end-up here during normal decoding
if (batch.n_tokens > 0) {
//LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
llama_decode(ctx, batch);
}
const llama_pos n_past = prompt.size();
LOG_DBG("%s: n_past = %d\n", __func__, n_past);
common_batch_clear(batch);
common_batch_add (batch, id_last, n_past, { 0 }, true);
prompt.push_back(id_last);
//LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx, prompt).c_str());
llama_decode(ctx, batch);
common_sampler_reset(smpl);
// sample n_draft tokens from the draft model
for (int i = 0; i < params.n_draft; ++i) {
common_batch_clear(batch);
common_sampler_sample(smpl, ctx, 0, true);
const auto * cur_p = common_sampler_get_candidates(smpl);
for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx, cur_p->data[k].id).c_str());
}
// add drafted token for each sequence
const llama_token id = cur_p->data[0].id;
// only collect very high-confidence draft tokens
if (cur_p->data[0].p < params.p_min) {
break;
}
common_sampler_accept(smpl, id, true);
result.push_back(id);
if (params.n_draft <= (int) result.size()) {
break;
}
common_batch_add(batch, id, n_past + i + 1, { 0 }, true);
// evaluate the drafted tokens on the draft model
llama_decode(ctx, batch);
prompt.push_back(id);
}
return result;
}

28
common/speculative.h Normal file
View File

@ -0,0 +1,28 @@
#pragma once
#include "llama.h"
#include "common.h"
struct common_speculative;
struct common_speculative_params {
int n_draft = 16; // max drafted tokens
int n_reuse = 256;
float p_min = 0.9f; // min probabiliy required to accept a token in the draft
};
struct common_speculative * common_speculative_init(struct llama_context * ctx_dft);
void common_speculative_free(struct common_speculative * spec);
bool common_speculative_are_compatible(
const struct llama_context * ctx_tgt,
const struct llama_context * ctx_dft);
// sample up to n_draft tokens and add them to the batch using the draft model
llama_tokens common_speculative_gen_draft(
struct common_speculative * spec,
struct common_speculative_params params,
const llama_tokens & prompt,
llama_token id_last);

13
convert_hf_to_gguf.py
View File

@ -2713,7 +2713,7 @@ class XLMRobertaModel(BertModel):
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
self.gguf_writer.add_add_space_prefix(add_prefix)
self.gguf_writer.add_token_type_count(1)
self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
if precompiled_charsmap:
self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
@ -3134,6 +3134,11 @@ class OlmoModel(Model):
return [(self.map_tensor_name(name), data_torch)]
@Model.register("Olmo1124ForCausalLM")
class Olmo1124Model(Model):
model_arch = gguf.MODEL_ARCH.OLMO_1124
@Model.register("OlmoeForCausalLM")
class OlmoeModel(Model):
model_arch = gguf.MODEL_ARCH.OLMOE
@ -3842,10 +3847,7 @@ class JaisModel(Model):
# Embeddings scale
self.embeddings_scale = 1.0
# note: For some JAIS flavors, output is tied to (same as) wte in original model
self.output_is_wte = False
if 'mup_embeddings_scale' in self.hparams:
self.output_is_wte = True # Hack (?)
self.embeddings_scale = self.hparams['mup_embeddings_scale']
elif 'embeddings_scale' in self.hparams:
self.embeddings_scale = self.hparams['embeddings_scale']
@ -3902,10 +3904,7 @@ class JaisModel(Model):
if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
tensors.append((new_name, data_torch * self.embeddings_scale))
if self.output_is_wte:
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale))
elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
assert not self.output_is_wte
tensors.append((new_name, data_torch * self.width_scale))
else:
tensors.append((new_name, data_torch))

23
docs/backend/SYCL.md
View File

@ -34,13 +34,16 @@ The SYCL backend would be broken by some PRs due to no online CI.
The following release is verified with good quality:
|Commit ID|Tag|Release|Verified Platform|
|-|-|-|-|
|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1|
|Commit ID|Tag|Release|Verified Platform| Update date|
|-|-|-|-|-|
|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19|
|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1||
## News
- 2024.11
- Use syclcompat to improve the performance on some platforms. This requires to use oneAPI 2025.0 or newer.
- 2024.8
- Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs.
@ -310,12 +313,14 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_
export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
# Build LLAMA with Nvidia BLAS acceleration through SYCL
# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance
GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 2: Use FP16
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
# build all binary
cmake --build build --config Release -j -v
@ -333,8 +338,9 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithrocBLAS/include:$CPLUS_INCLUDE
## AMD
# Use FP32, FP16 is not supported
# Find your GGML_SYCL_HIP_TARGET with rocminfo, under the key 'Name:'
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_HIP_TARGET=${GGML_SYCL_HIP_TARGET} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:'
GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# build all binary
cmake --build build --config Release -j -v
@ -377,7 +383,7 @@ found 2 SYCL devices:
|Chosen Device ID|Setting|
|-|-|
|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:0"` or no action|
|1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
|0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
@ -644,6 +650,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
|--------------------|---------------------------------------|---------------------------------------------|
| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path.<br>FP32 path - recommended for better perforemance than FP16 on quantized model|
| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA \| AMD | Set the SYCL target device type. |
| GGML_SYCL_DEVICE_ARCH | Optional (except for AMD) | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
| GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. |
| CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. |
| CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |

46
docs/build.md
View File

@ -186,13 +186,9 @@ The following compilation options are also available to tweak performance:
| Option | Legal values | Default | Description |
|-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| GGML_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. |
| GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models |
| GGML_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
| GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. |
@ -230,7 +226,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm
- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
```bash
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
&& cmake --build build --config Release -- -j 16
```
On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
@ -247,7 +243,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm
```bash
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
&& cmake --build build -- -j 16
```
@ -259,7 +255,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm
- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
```bash
set PATH=%HIP_PATH%\bin;%PATH%
cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
cmake --build build
```
Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
@ -268,13 +264,6 @@ You can download it from your Linux distro's package manager or from here: [ROCm
The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
| Option | Legal values | Default | Description |
|------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
### Vulkan
@ -282,9 +271,9 @@ The following compilation options are also available to tweak performance (yes,
#### w64devkit
Download and extract [w64devkit](https://github.com/skeeto/w64devkit/releases).
Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases).
Download and install the [Vulkan SDK](https://vulkan.lunarg.com/sdk/home#windows). When selecting components, only the Vulkan SDK Core is required.
Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings.
Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies:
```sh
@ -302,6 +291,29 @@ EOF
```
Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`.
#### Git Bash MINGW64
Download and install [`Git-SCM`](https://git-scm.com/downloads/win) with the default settings
Download and install [`Visual Studio Community Edition`](https://visualstudio.microsoft.com/) and make sure you select `C++`
Download and install [`CMake`](https://cmake.org/download/) with the default settings
Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings.
Go into your `llama.cpp` directory and right click, select `Open Git Bash Here` and then run the following commands
```
cmake -B build -DGGML_VULKAN=ON
cmake --build build --config Release
```
Now you can load the model in conversation mode using `Vulkan`
```
build/bin/release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv
```
#### MSYS2
Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies.
```sh
@ -375,7 +387,7 @@ cmake --build build --config release
You can test with:
`./build/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32`
`./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32`
If the fllowing info is output on screen, you are using `llama.cpp by CANN backend`:
```bash

28
examples/CMakeLists.txt
View File

@ -12,13 +12,10 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR})
if (EMSCRIPTEN)
else()
add_subdirectory(cvector-generator)
add_subdirectory(batched-bench)
add_subdirectory(batched)
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(embedding)
add_subdirectory(eval-callback)
add_subdirectory(export-lora)
add_subdirectory(gbnf-validator)
add_subdirectory(gguf-hash)
add_subdirectory(gguf-split)
@ -27,28 +24,35 @@ else()
add_subdirectory(imatrix)
add_subdirectory(infill)
add_subdirectory(llama-bench)
add_subdirectory(llava)
add_subdirectory(lookahead)
add_subdirectory(lookup)
add_subdirectory(main)
add_subdirectory(parallel)
add_subdirectory(passkey)
add_subdirectory(perplexity)
add_subdirectory(quantize-stats)
add_subdirectory(quantize)
add_subdirectory(retrieval)
if (GGML_RPC)
add_subdirectory(rpc)
endif()
if (LLAMA_BUILD_SERVER)
add_subdirectory(server)
endif()
if (GGML_SYCL)
add_subdirectory(sycl)
add_subdirectory(server)
endif()
add_subdirectory(save-load-state)
add_subdirectory(simple)
add_subdirectory(simple-chat)
add_subdirectory(speculative)
add_subdirectory(speculative-simple)
add_subdirectory(tokenize)
if (NOT GGML_BACKEND_DL)
# these examples use the backends directly and cannot be built with dynamic loading
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(cvector-generator)
add_subdirectory(export-lora)
add_subdirectory(quantize-stats)
add_subdirectory(llava)
if (GGML_RPC)
add_subdirectory(rpc)
endif()
if (GGML_SYCL)
add_subdirectory(sycl)
endif()
endif()
endif()

8
examples/batched/batched.cpp
View File

@ -68,10 +68,10 @@ int main(int argc, char ** argv) {
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sparams.top_k));
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sparams.top_p, params.sparams.min_keep));
llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sparams.temp));
llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sparams.seed));
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
if (ctx == NULL) {
LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);

12
examples/chat-persistent.sh
View File

@ -23,8 +23,9 @@ CUR_PROMPT_CACHE="${CHAT_SAVE_DIR}/current-cache.bin"
NEXT_PROMPT_FILE="${CHAT_SAVE_DIR}/next-prompt.txt"
NEXT_PROMPT_CACHE="${CHAT_SAVE_DIR}/next-cache.bin"
SESSION_SIZE_MSG_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+'
SAMPLE_TIME_MSG_PATTERN='sample time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+'
SESSION_AND_SAMPLE_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+'\
'|'\
'sampling time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+'
SED_DELETE_MESSAGES="/^(${USER_NAME}:|${AI_NAME}:|\\.\\.\\.)/,\$d"
CTX_SIZE=2048
@ -129,15 +130,12 @@ while read -e line; do
printf ' '
# HACK get num tokens from debug message
# TODO get both messages in one go
if ! session_size_msg="$(tail -n30 "$LOG" | grep -oE "$SESSION_SIZE_MSG_PATTERN")" ||
! sample_time_msg="$(tail -n10 "$LOG" | grep -oE "$SAMPLE_TIME_MSG_PATTERN")"; then
if ! session_and_sample_msg=$(tail -n30 "$LOG" | grep -oE "$SESSION_AND_SAMPLE_PATTERN"); then
echo >&2 "Couldn't get number of tokens from ./llama-cli output!"
exit 1
fi
n_tokens=$(($(cut -d/ -f2 <<<"$session_size_msg") + $(cut -d/ -f2 <<<"$sample_time_msg")))
n_tokens=$(awk '{sum+=$1} END {print sum}' <<< "$(cut -d/ -f2 <<< "$session_and_sample_msg")")
if ((n_tokens > CTX_ROTATE_POINT)); then
tail -c+$((n_prompt_len_pre + 1)) "$CUR_PROMPT_FILE" >>"$NEXT_PROMPT_FILE"

26
examples/convert_legacy_llama.py
View File

@ -840,6 +840,8 @@ class OutputFile:
self.gguf.add_base_model_version(key, base_model_entry["version"])
if "organization" in base_model_entry:
self.gguf.add_base_model_organization(key, base_model_entry["organization"])
if "description" in base_model_entry:
self.gguf.add_base_model_description(key, base_model_entry["description"])
if "url" in base_model_entry:
self.gguf.add_base_model_url(key, base_model_entry["url"])
if "doi" in base_model_entry:
@ -849,12 +851,32 @@ class OutputFile:
if "repo_url" in base_model_entry:
self.gguf.add_base_model_repo_url(key, base_model_entry["repo_url"])
if metadata.datasets is not None:
self.gguf.add_dataset_count(len(metadata.datasets))
for key, dataset_entry in enumerate(metadata.datasets):
if "name" in dataset_entry:
self.gguf.add_dataset_name(key, dataset_entry["name"])
if "author" in dataset_entry:
self.gguf.add_dataset_author(key, dataset_entry["author"])
if "version" in dataset_entry:
self.gguf.add_dataset_version(key, dataset_entry["version"])
if "organization" in dataset_entry:
self.gguf.add_dataset_organization(key, dataset_entry["organization"])
if "description" in dataset_entry:
self.gguf.add_dataset_description(key, dataset_entry["description"])
if "url" in dataset_entry:
self.gguf.add_dataset_url(key, dataset_entry["url"])
if "doi" in dataset_entry:
self.gguf.add_dataset_doi(key, dataset_entry["doi"])
if "uuid" in dataset_entry:
self.gguf.add_dataset_uuid(key, dataset_entry["uuid"])
if "repo_url" in dataset_entry:
self.gguf.add_dataset_repo_url(key, dataset_entry["repo_url"])
if metadata.tags is not None:
self.gguf.add_tags(metadata.tags)
if metadata.languages is not None:
self.gguf.add_languages(metadata.languages)
if metadata.datasets is not None:
self.gguf.add_datasets(metadata.datasets)
def add_meta_arch(self, params: Params) -> None:
# Metadata About The Neural Architecture Itself

3
examples/eval-callback/CMakeLists.txt
View File

@ -5,5 +5,6 @@ target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
set(TEST_TARGET test-eval-callback)
add_test(NAME ${TEST_TARGET} COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
add_test(NAME ${TEST_TARGET}
COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl)

48
examples/infill/infill.cpp
View File

@ -43,50 +43,6 @@ static std::vector<llama_token> * g_output_tokens;
static bool is_interacting = false;
static void write_logfile(
const llama_context * ctx, const common_params & params, const llama_model * model,
const std::vector<llama_token> & input_tokens, const std::string & output,
const std::vector<llama_token> & output_tokens
) {
if (params.logdir.empty()) {
return;
}
const std::string timestamp = string_get_sortable_timestamp();
const bool success = fs_create_directory_with_parents(params.logdir);
if (!success) {
LOG_ERR("%s: warning: failed to create logdir %s, cannot write logfile\n",
__func__, params.logdir.c_str());
return;
}
const std::string logfile_path = params.logdir + timestamp + ".yml";
FILE * logfile = fopen(logfile_path.c_str(), "w");
if (logfile == NULL) {
LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
return;
}
fprintf(logfile, "binary: infill\n");
char model_desc[128];
llama_model_desc(model, model_desc, sizeof(model_desc));
yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc);
fprintf(logfile, "\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "# Generation Results #\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "\n");
yaml_dump_string_multiline(logfile, "output", output.c_str());
yaml_dump_vector_int(logfile, "output_tokens", output_tokens);
llama_perf_dump_yaml(logfile, ctx);
fclose(logfile);
}
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
static void sigint_handler(int signo) {
if (signo == SIGINT) {
@ -96,7 +52,6 @@ static void sigint_handler(int signo) {
console::cleanup();
LOG("\n");
common_perf_print(*g_ctx, *g_smpl);
write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
// make sure all logs are flushed
LOG("Interrupted by user\n");
@ -118,7 +73,7 @@ int main(int argc, char ** argv) {
common_init();
auto & sparams = params.sparams;
auto & sparams = params.sampling;
console::init(params.simple_io, params.use_color);
atexit([]() { console::cleanup(); });
@ -625,7 +580,6 @@ int main(int argc, char ** argv) {
LOG("\n");
common_perf_print(ctx, smpl);
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
llama_free(ctx);
llama_free_model(model);

742
examples/llama-bench/llama-bench.cpp
View File

File diff suppressed because it is too large Load Diff

2
examples/llava/llava-cli.cpp
View File

@ -191,7 +191,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
LOG("\n");
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams);
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
if (!smpl) {
LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
exit(1);

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

@ -237,7 +237,7 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm
LOG_INF("\n");
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams);
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
return smpl;
}

2
examples/lookahead/lookahead.cpp
View File

@ -115,7 +115,7 @@ int main(int argc, char ** argv) {
llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
// target model sampling context
struct common_sampler * smpl = common_sampler_init(model, params.sparams);
struct common_sampler * smpl = common_sampler_init(model, params.sampling);
// verification n-grams
std::vector<ngram_data> ngrams_cur(G);

3
examples/lookup/lookup-stats.cpp
View File

@ -21,7 +21,7 @@ int main(int argc, char ** argv){
common_init();
const int n_draft = params.n_draft;
const int n_draft = params.speculative.n_max;
// init llama.cpp
llama_backend_init();
@ -40,6 +40,7 @@ int main(int argc, char ** argv){
common_ngram_cache ngram_cache_context;
common_ngram_cache ngram_cache_dynamic;
common_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;

4
examples/lookup/lookup.cpp
View File

@ -22,7 +22,7 @@ int main(int argc, char ** argv){
common_init();
// max. number of additional tokens to draft if match is found
const int n_draft = params.n_draft;
const int n_draft = params.speculative.n_max;
const bool dump_kv_cache = params.dump_kv_cache;
@ -102,7 +102,7 @@ int main(int argc, char ** argv){
bool has_eos = false;
struct common_sampler * smpl = common_sampler_init(model, params.sparams);
struct common_sampler * smpl = common_sampler_init(model, params.sampling);
std::vector<llama_token> draft;

59
examples/main/main.cpp
View File

@ -62,49 +62,6 @@ static bool file_is_empty(const std::string & path) {
return f.tellg() == 0;
}
static void write_logfile(
const llama_context * ctx, const common_params & params, const llama_model * model,
const std::vector<llama_token> & input_tokens, const std::string & output,
const std::vector<llama_token> & output_tokens
) {
if (params.logdir.empty()) {
return;
}
const std::string timestamp = string_get_sortable_timestamp();
const bool success = fs_create_directory_with_parents(params.logdir);
if (!success) {
LOG_ERR("%s: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str());
return;
}
const std::string logfile_path = params.logdir + timestamp + ".yml";
FILE * logfile = fopen(logfile_path.c_str(), "w");
if (logfile == NULL) {
LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
return;
}
fprintf(logfile, "binary: main\n");
char model_desc[128];
llama_model_desc(model, model_desc, sizeof(model_desc));
yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc);
fprintf(logfile, "\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "# Generation Results #\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "\n");
yaml_dump_string_multiline(logfile, "output", output.c_str());
yaml_dump_vector_int(logfile, "output_tokens", output_tokens);
llama_perf_dump_yaml(logfile, ctx);
fclose(logfile);
}
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
static void sigint_handler(int signo) {
if (signo == SIGINT) {
@ -115,7 +72,6 @@ static void sigint_handler(int signo) {
console::cleanup();
LOG("\n");
common_perf_print(*g_ctx, *g_smpl);
write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
// make sure all logs are flushed
LOG("Interrupted by user\n");
@ -144,7 +100,7 @@ int main(int argc, char ** argv) {
common_init();
auto & sparams = params.sparams;
auto & sparams = params.sampling;
// save choice to use color for later
// (note for later: this is a slightly awkward choice)
@ -209,6 +165,10 @@ int main(int argc, char ** argv) {
LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads);
auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU));
auto * ggml_threadpool_new_fn = (decltype(ggml_threadpool_new) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_new");
auto * ggml_threadpool_free_fn = (decltype(ggml_threadpool_free) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_free");
struct ggml_threadpool_params tpp_batch =
ggml_threadpool_params_from_cpu_params(params.cpuparams_batch);
struct ggml_threadpool_params tpp =
@ -218,7 +178,7 @@ int main(int argc, char ** argv) {
struct ggml_threadpool * threadpool_batch = NULL;
if (!ggml_threadpool_params_match(&tpp, &tpp_batch)) {
threadpool_batch = ggml_threadpool_new(&tpp_batch);
threadpool_batch = ggml_threadpool_new_fn(&tpp_batch);
if (!threadpool_batch) {
LOG_ERR("%s: batch threadpool create failed : n_threads %d\n", __func__, tpp_batch.n_threads);
return 1;
@ -228,7 +188,7 @@ int main(int argc, char ** argv) {
tpp.paused = true;
}
struct ggml_threadpool * threadpool = ggml_threadpool_new(&tpp);
struct ggml_threadpool * threadpool = ggml_threadpool_new_fn(&tpp);
if (!threadpool) {
LOG_ERR("%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads);
return 1;
@ -926,7 +886,6 @@ int main(int argc, char ** argv) {
LOG("\n\n");
common_perf_print(ctx, smpl);
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
common_sampler_free(smpl);
@ -935,8 +894,8 @@ int main(int argc, char ** argv) {
llama_backend_free();
ggml_threadpool_free(threadpool);
ggml_threadpool_free(threadpool_batch);
ggml_threadpool_free_fn(threadpool);
ggml_threadpool_free_fn(threadpool_batch);
return 0;
}

2
examples/parallel/parallel.cpp
View File

@ -160,7 +160,7 @@ int main(int argc, char ** argv) {
for (size_t i = 0; i < clients.size(); ++i) {
auto & client = clients[i];
client.id = i;
client.smpl = common_sampler_init(model, params.sparams);
client.smpl = common_sampler_init(model, params.sampling);
}
std::vector<llama_token> tokens_system;

51
examples/perplexity/perplexity.cpp
View File

@ -34,55 +34,6 @@ struct results_log_softmax {
float prob;
};
static void write_logfile(
const llama_context * ctx, const common_params & params, const llama_model * model,
const struct results_perplexity & results
) {
if (params.logdir.empty()) {
return;
}
if (params.hellaswag) {
LOG_WRN("%s: logging results is not implemented for HellaSwag. No files will be written.\n", __func__);
return;
}
const std::string timestamp = string_get_sortable_timestamp();
const bool success = fs_create_directory_with_parents(params.logdir);
if (!success) {
LOG_WRN("%s: failed to create logdir %s, cannot write logfile\n",
__func__, params.logdir.c_str());
return;
}
const std::string logfile_path = params.logdir + timestamp + ".yml";
FILE * logfile = fopen(logfile_path.c_str(), "w");
if (logfile == NULL) {
LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
return;
}
fprintf(logfile, "binary: main\n");
char model_desc[128];
llama_model_desc(model, model_desc, sizeof(model_desc));
yaml_dump_non_result_info(logfile, params, ctx, timestamp, results.tokens, model_desc);
fprintf(logfile, "\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "# Perplexity Results #\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "\n");
yaml_dump_vector_float(logfile, "logits", results.logits);
fprintf(logfile, "ppl_value: %f\n", results.ppl_value);
yaml_dump_vector_float(logfile, "probs", results.probs);
llama_perf_dump_yaml(logfile, ctx);
fclose(logfile);
}
static std::vector<float> softmax(const std::vector<float>& logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];
@ -2072,8 +2023,6 @@ int main(int argc, char ** argv) {
LOG("\n");
llama_perf_context_print(ctx);
write_logfile(ctx, params, model, results);
llama_free(ctx);
llama_free_model(model);

19
examples/quantize-stats/quantize-stats.cpp
View File

@ -142,7 +142,7 @@ static bool tensor_is_contiguous(const struct ggml_tensor * tensor) {
}
static void test_roundtrip_on_chunk(
const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits & qfns, bool use_reference,
const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits & qfns, const ggml_type_traits_cpu & qfns_cpu, bool use_reference,
float * input_scratch, char * quantized_scratch, float * output_scratch, error_stats & stats
) {
if (layer->type == GGML_TYPE_F16) {
@ -156,7 +156,7 @@ static void test_roundtrip_on_chunk(
if (use_reference) {
qfns.from_float_ref(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.from_float(input_scratch, quantized_scratch, chunk_size);
qfns_cpu.from_float(input_scratch, quantized_scratch, chunk_size);
}
qfns.to_float(quantized_scratch, output_scratch, chunk_size);
@ -166,7 +166,7 @@ static void test_roundtrip_on_chunk(
// Run quantization function for a single layer and update error stats
static void test_roundtrip_on_layer(
std::string & name, bool print_layer_stats, const ggml_type_traits & qfns, bool use_reference,
std::string & name, bool print_layer_stats, const ggml_type_traits & qfns, const ggml_type_traits_cpu & qfns_cpu, bool use_reference,
const ggml_tensor * layer, std::vector<float> & input_scratch, std::vector<char> & quantized_scratch,
std::vector<float> & output_scratch, error_stats & total_error, int max_thread = 0
) {
@ -187,13 +187,13 @@ static void test_roundtrip_on_layer(
int num_chunks = (nelements + chunk_size - 1)/chunk_size;
if (num_chunks < 2 || max_thread < 2) {
test_roundtrip_on_chunk(layer, 0, nelements, qfns, use_reference, input_scratch_ptr, quantized_scratch.data(),
test_roundtrip_on_chunk(layer, 0, nelements, qfns, qfns_cpu, use_reference, input_scratch_ptr, quantized_scratch.data(),
output_scratch.data(), print_layer_stats ? layer_error : total_error);
} else {
auto & stats = print_layer_stats ? layer_error : total_error;
std::mutex mutex;
uint64_t counter = 0;
auto compute = [&mutex, &counter, &stats, &qfns, nelements, layer, use_reference, input_scratch_ptr,
auto compute = [&mutex, &counter, &stats, &qfns, &qfns_cpu, nelements, layer, use_reference, input_scratch_ptr,
&quantized_scratch, &output_scratch, chunk_size] () {
error_stats local_stats {};
while (true) {
@ -205,7 +205,7 @@ static void test_roundtrip_on_layer(
}
lock.unlock();
uint64_t chunk = offset + chunk_size < nelements ? chunk_size : nelements - offset;
test_roundtrip_on_chunk(layer, offset, chunk, qfns, use_reference, input_scratch_ptr + offset,
test_roundtrip_on_chunk(layer, offset, chunk, qfns, qfns_cpu, use_reference, input_scratch_ptr + offset,
quantized_scratch.data() + 4*offset, output_scratch.data() + offset, local_stats);
}
};
@ -371,8 +371,9 @@ int main(int argc, char ** argv) {
if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
continue;
}
const auto * qfns = ggml_get_type_traits(type);
if (qfns->from_float && qfns->to_float) {
const auto * qfns = ggml_get_type_traits(type);
const auto * qfns_cpu = ggml_get_type_traits_cpu(type);
if (qfns_cpu->from_float && qfns->to_float) {
if (params.verbose) {
printf("testing %s ...\n", ggml_type_name(type));
}
@ -393,7 +394,7 @@ int main(int argc, char ** argv) {
test_roundtrip_on_layer(
layer_name,
params.per_layer_stats,
*qfns,
*qfns, *qfns_cpu,
params.reference,
kv_tensor.second,
input_scratch,

4
examples/retrieval/retrieval.cpp
View File

@ -282,8 +282,8 @@ int main(int argc, char ** argv) {
return a.second > b.second;
});
LOG("Top %d similar chunks:\n", params.sparams.top_k);
for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
LOG("Top %d similar chunks:\n", params.sampling.top_k);
for (int i = 0; i < std::min(params.sampling.top_k, (int) chunks.size()); i++) {
LOG("filename: %s\n", chunks[similarities[i].first].filename.c_str());
LOG("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
LOG("similarity: %f\n", similarities[i].second);

8
examples/save-load-state/save-load-state.cpp
View File

@ -9,7 +9,7 @@ int main(int argc, char ** argv) {
common_params params;
params.prompt = "The quick brown fox";
params.sparams.seed = 1234;
params.sampling.seed = 1234;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
@ -42,7 +42,7 @@ int main(int argc, char ** argv) {
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed));
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed));
// tokenize prompt
auto tokens = common_tokenize(ctx, params.prompt, true);
@ -106,7 +106,7 @@ int main(int argc, char ** argv) {
llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sparams.seed));
llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sampling.seed));
printf("\nsecond run: %s", params.prompt.c_str());
@ -169,7 +169,7 @@ int main(int argc, char ** argv) {
llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sparams.seed));
llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sampling.seed));
printf("\nsingle seq run: %s", params.prompt.c_str());

17
examples/server/CMakeLists.txt
View File

@ -15,22 +15,13 @@ set(TARGET_SRCS
httplib.h
)
set(PUBLIC_ASSETS
colorthemes.css
style.css
theme-beeninorder.css
theme-ketivah.css
theme-mangotango.css
theme-playground.css
theme-polarnight.css
theme-snowstorm.css
index.html
index-new.html
index.js
completion.js
system-prompts.js
prompt-formats.js
json-schema-to-grammar.mjs
loading.html
deps_daisyui.min.css
deps_markdown-it.js
deps_tailwindcss.js
deps_vue.esm-browser.js
)
foreach(asset ${PUBLIC_ASSETS})

37
examples/server/README.md
View File

@ -39,7 +39,7 @@ The project is under active development, and we are [looking for feedback and co
| `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) |
| `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
| `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) |
| `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE) |
| `-c, --ctx-size N` | size of the prompt context (default: 4096, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE) |
| `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)<br/>(env: LLAMA_ARG_N_PREDICT) |
| `-b, --batch-size N` | logical maximum batch size (default: 2048)<br/>(env: LLAMA_ARG_BATCH) |
| `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
@ -64,7 +64,7 @@ The project is under active development, and we are [looking for feedback and co
| `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: 0.1, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
| `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_NO_MMAP) |
@ -85,7 +85,6 @@ The project is under active development, and we are [looking for feedback and co
| `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
| `-hff, --hf-file FILE` | Hugging Face model file (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)<br/>(env: HF_TOKEN) |
| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) |
| `--log-disable` | Log disable |
| `--log-file FNAME` | Log to file |
| `--log-colors` | Enable colored logging<br/>(env: LLAMA_LOG_COLORS) |
@ -99,25 +98,27 @@ The project is under active development, and we are [looking for feedback and co
| Argument | Explanation |
| -------- | ----------- |
| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;typ_p;top_p;min_p;temperature) |
| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: dry;top_k;typ_p;top_p;min_p;xtc;temperature) |
| `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) |
| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) |
| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
| `--penalize-nl` | penalize newline tokens (default: false) |
| `--temp N` | temperature (default: 0.8) |
| `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
| `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) |
| `--xtc-probability N` | xtc probability (default: 0.0, 0.0 = disabled) |
| `--xtc-threshold N` | xtc threshold (default: 0.1, 1.0 = disabled) |
| `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) |
| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) |
| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) |
| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) |
| `--dry-multiplier N` | DRY sampling multiplier (default: 0.0, 0.0 = disabled) |
| `--dry-base N` | DRY sampling base value (default: 1.75) |
| `--dry-allowed-length N` | allowed length for DRY sampling (default: 2) |
| `--dry-penalty-last-n N` | DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) |
| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers (`['\n', ':', '"', '*']`) in the process; use `"none"` to not use any sequence breakers
| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.0, 0.0 = disabled) |
| `--dry-base N` | set DRY sampling base value (default: 1.75) |
| `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) |
| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) |
| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers<br/> |
| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) |
| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) |
| `--mirostat N` | use Mirostat sampling.<br/>Top K, Nucleus and Locally Typical samplers are ignored if used.<br/>(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) |
@ -381,6 +382,10 @@ node index.js
`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']`
`xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled.
`xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC)
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
@ -409,7 +414,7 @@ node index.js
`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false`
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values.
**Response format**
@ -928,6 +933,16 @@ Apart from error types supported by OAI, we also have custom types that are spec
}
```
### Legacy completion web UI
A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggerganov/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./examples/server/public_legacy`
For example:
```sh
./llama-server -m my_model.gguf -c 8192 --path ./examples/server/public_legacy
```
### Extending or building alternative Web Front End
You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.

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examples/server/chat.mjs
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@ -1,7 +1,7 @@
import * as readline from 'node:readline'
import { stdin, stdout } from 'node:process'
import { readFileSync } from 'node:fs'
import { SchemaConverter } from './public/json-schema-to-grammar.mjs'
import { SchemaConverter } from './public_legacy/json-schema-to-grammar.mjs'
const args = process.argv.slice(2);
const grammarJsonSchemaFile = args.find(

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examples/server/deps.sh
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@ -6,5 +6,20 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
PUBLIC=$DIR/public
echo "download js bundle files"
curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
echo >> $PUBLIC/index.js # add newline
# Note for contributors: Always pin to a specific version "maj.min.patch" to avoid breaking the CI
curl -L https://cdn.tailwindcss.com/3.4.14 > $PUBLIC/deps_tailwindcss.js
echo >> $PUBLIC/deps_tailwindcss.js # add newline
curl -L https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/styled.min.css > $PUBLIC/deps_daisyui.min.css
curl -L https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/themes.min.css >> $PUBLIC/deps_daisyui.min.css
echo >> $PUBLIC/deps_daisyui.min.css # add newline
curl -L https://unpkg.com/vue@3.5.12/dist/vue.esm-browser.js > $PUBLIC/deps_vue.esm-browser.js
echo >> $PUBLIC/deps_vue.esm-browser.js # add newline
curl -L https://cdnjs.cloudflare.com/ajax/libs/markdown-it/13.0.2/markdown-it.js > $PUBLIC/deps_markdown-it.js
echo >> $PUBLIC/deps_markdown-it.js # add newline
ls -lah $PUBLIC

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examples/server/public/completion.js
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@ -1,12 +1,16 @@
const paramDefaults = {
stream: true,
n_predict: 500,
temperature: 0.2,
stop: ["</s>"]
};
let generation_settings = null;
export class CompletionError extends Error {
constructor(message, name, data) {
super(message);
this.name = name;
}
};
// Completes the prompt as a generator. Recommended for most use cases.
//
@ -29,7 +33,7 @@ export async function* llama(prompt, params = {}, config = {}) {
const completionParams = { ...paramDefaults, ...params, prompt };
const response = await fetch(`${api_url}/completion`, {
const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, {
method: 'POST',
body: JSON.stringify(completionParams),
headers: {
@ -41,6 +45,18 @@ export async function* llama(prompt, params = {}, config = {}) {
signal: controller.signal,
});
const status = response.status;
if (status !== 200) {
try {
const body = await response.json();
if (body && body.error && body.error.message) {
throw new CompletionError(body.error.message, 'ServerError');
}
} catch (err) {
throw new CompletionError(err.message, 'ServerError');
}
}
const reader = response.body.getReader();
const decoder = new TextDecoder();
@ -78,7 +94,12 @@ export async function* llama(prompt, params = {}, config = {}) {
for (const line of lines) {
const match = regex.exec(line);
if (match) {
result[match[1]] = match[2]
result[match[1]] = match[2];
if (result.data === '[DONE]') {
cont = false;
break;
}
// since we know this is llama.cpp, let's just decode the json in data
if (result.data) {
result.data = JSON.parse(result.data);

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@ -0,0 +1,209 @@
const paramDefaults = {
stream: true,
n_predict: 500,
temperature: 0.2,
stop: ["</s>"]
};
let generation_settings = null;
// Completes the prompt as a generator. Recommended for most use cases.
//
// Example:
//
// import { llama } from '/completion.js'
//
// const request = llama("Tell me a joke", {n_predict: 800})
// for await (const chunk of request) {
// document.write(chunk.data.content)
// }
//
export async function* llama(prompt, params = {}, config = {}) {
let controller = config.controller;
const api_url = config.api_url?.replace(/\/+$/, '') || "";
if (!controller) {
controller = new AbortController();
}
const completionParams = { ...paramDefaults, ...params, prompt };
const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, {
method: 'POST',
body: JSON.stringify(completionParams),
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream',
...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
},
signal: controller.signal,
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
let content = "";
let leftover = ""; // Buffer for partially read lines
try {
let cont = true;
while (cont) {
const result = await reader.read();
if (result.done) {
break;
}
// Add any leftover data to the current chunk of data
const text = leftover + decoder.decode(result.value);
// Check if the last character is a line break
const endsWithLineBreak = text.endsWith('\n');
// Split the text into lines
let lines = text.split('\n');
// If the text doesn't end with a line break, then the last line is incomplete
// Store it in leftover to be added to the next chunk of data
if (!endsWithLineBreak) {
leftover = lines.pop();
} else {
leftover = ""; // Reset leftover if we have a line break at the end
}
// Parse all sse events and add them to result
const regex = /^(\S+):\s(.*)$/gm;
for (const line of lines) {
const match = regex.exec(line);
if (match) {
result[match[1]] = match[2];
if (result.data === '[DONE]') {
cont = false;
break;
}
// since we know this is llama.cpp, let's just decode the json in data
if (result.data) {
result.data = JSON.parse(result.data);
content += result.data.content;
// yield
yield result;
// if we got a stop token from server, we will break here
if (result.data.stop) {
if (result.data.generation_settings) {
generation_settings = result.data.generation_settings;
}
cont = false;
break;
}
}
if (result.error) {
try {
result.error = JSON.parse(result.error);
if (result.error.message.includes('slot unavailable')) {
// Throw an error to be caught by upstream callers
throw new Error('slot unavailable');
} else {
console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
}
} catch(e) {
console.error(`llama.cpp error ${result.error}`)
}
}
}
}
}
} catch (e) {
if (e.name !== 'AbortError') {
console.error("llama error: ", e);
}
throw e;
}
finally {
controller.abort();
}
return content;
}
// Call llama, return an event target that you can subscribe to
//
// Example:
//
// import { llamaEventTarget } from '/completion.js'
//
// const conn = llamaEventTarget(prompt)
// conn.addEventListener("message", (chunk) => {
// document.write(chunk.detail.content)
// })
//
export const llamaEventTarget = (prompt, params = {}, config = {}) => {
const eventTarget = new EventTarget();
(async () => {
let content = "";
for await (const chunk of llama(prompt, params, config)) {
if (chunk.data) {
content += chunk.data.content;
eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
}
if (chunk.data.generation_settings) {
eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
}
if (chunk.data.timings) {
eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
}
}
eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
})();
return eventTarget;
}
// Call llama, return a promise that resolves to the completed text. This does not support streaming
//
// Example:
//
// llamaPromise(prompt).then((content) => {
// document.write(content)
// })
//
// or
//
// const content = await llamaPromise(prompt)
// document.write(content)
//
export const llamaPromise = (prompt, params = {}, config = {}) => {
return new Promise(async (resolve, reject) => {
let content = "";
try {
for await (const chunk of llama(prompt, params, config)) {
content += chunk.data.content;
}
resolve(content);
} catch (error) {
reject(error);
}
});
};
/**
* (deprecated)
*/
export const llamaComplete = async (params, controller, callback) => {
for await (const chunk of llama(params.prompt, params, { controller })) {
callback(chunk);
}
}
// Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async (config = {}) => {
if (!generation_settings) {
const api_url = config.api_url?.replace(/\/+$/, '') || "";
const props = await fetch(`${api_url}/props`).then(r => r.json());
generation_settings = props.default_generation_settings;
}
return generation_settings;
}

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@ -0,0 +1,12 @@
<!DOCTYPE html>
<html>
<head>
<meta http-equiv="refresh" content="5">
</head>
<body>
<div id="loading">
The model is loading. Please wait.<br/>
The user interface will appear soon.
</div>
</body>
</html>

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@ -64,5 +64,5 @@ Feature: Security
| localhost | Access-Control-Allow-Origin | localhost |
| web.mydomain.fr | Access-Control-Allow-Origin | web.mydomain.fr |
| origin | Access-Control-Allow-Credentials | true |
| web.mydomain.fr | Access-Control-Allow-Methods | POST |
| web.mydomain.fr | Access-Control-Allow-Methods | GET, POST |
| web.mydomain.fr | Access-Control-Allow-Headers | * |

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examples/server/utils.hpp
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@ -24,7 +24,6 @@
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
using json = nlohmann::ordered_json;
using llama_tokens = std::vector<llama_token>;
#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
@ -439,62 +438,6 @@ static std::string gen_chatcmplid() {
// other common utils
//
static size_t longest_common_prefix(const llama_tokens & a, const llama_tokens & b) {
size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
return i;
}
static size_t longest_common_subsequence(const llama_tokens & a, const llama_tokens & b) {
// check for empty sequences
if (a.empty() || b.empty()) {
return 0;
}
// get the lengths of the input sequences
size_t a_len = a.size();
size_t b_len = b.size();
// initialize the maximum length of the longest common subsequence (LCS)
size_t max_length = 0;
// use two rows instead of a 2D matrix to optimize space
std::vector<size_t> prev_row(b_len + 1, 0);
std::vector<size_t> curr_row(b_len + 1, 0);
// iterate through the elements of a
for (size_t i = 1; i <= a_len; i++) {
// iterate through the elements of b
for (size_t j = 1; j <= b_len; j++) {
// if elements at the current positions match
if (a[i - 1] == b[j - 1]) {
// if it's the first element of either sequences, set LCS length to 1
if (i == 1 || j == 1) {
curr_row[j] = 1;
} else {
// increment LCS length by 1 compared to the previous element
curr_row[j] = prev_row[j - 1] + 1;
}
// update max_length if necessary
if (curr_row[j] > max_length) {
max_length = curr_row[j];
}
} else {
// reset LCS length if elements don't match
curr_row[j] = 0;
}
}
// update the previous row for the next iteration
prev_row = curr_row;
}
// return the maximum length of the LCS
return max_length;
}
static bool ends_with(const std::string & str, const std::string & suffix) {
return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
}

3
examples/simple-chat/simple-chat.cpp
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@ -62,6 +62,9 @@ int main(int argc, char ** argv) {
}
}, nullptr);
// load dynamic backends
ggml_backend_load_all();
// initialize the model
llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = ngl;

4
examples/simple/simple.cpp
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@ -74,6 +74,10 @@ int main(int argc, char ** argv) {
}
}
// load dynamic backends
ggml_backend_load_all();
// initialize the model
llama_model_params model_params = llama_model_default_params();

5
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@ -0,0 +1,5 @@
set(TARGET llama-speculative-simple)
add_executable(${TARGET} speculative-simple.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

12
examples/speculative-simple/README.md Normal file
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@ -0,0 +1,12 @@
# llama.cpp/examples/speculative-simple
Demonstration of basic greedy speculative decoding
```bash
./bin/llama-speculative-simple \
-m ../models/qwen2.5-32b-coder-instruct/ggml-model-q8_0.gguf \
-md ../models/qwen2.5-1.5b-coder-instruct/ggml-model-q4_0.gguf \
-f test.txt -c 0 -ngl 99 --color \
--sampling-seq k --top-k 1 -fa --temp 0.0 \
-ngld 99 --draft-max 16 --draft-min 5 --draft-p-min 0.9
```

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@ -0,0 +1,273 @@
#include "arg.h"
#include "common.h"
#include "sampling.h"
#include "speculative.h"
#include "log.h"
#include "llama.h"
#include <cstdio>
#include <cstring>
#include <string>
#include <vector>
int main(int argc, char ** argv) {
common_params params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
return 1;
}
if (params.n_predict < -1) {
LOG_ERR("%s: --n-predict must be >= -1\n", __func__);
return 1;
}
common_init();
if (params.speculative.model.empty()) {
LOG_ERR("%s: --model-draft is required\n", __func__);
return 1;
}
// init llama.cpp
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model_tgt = NULL;
llama_model * model_dft = NULL;
llama_context * ctx_tgt = NULL;
llama_context * ctx_dft = NULL;
// load the target model
common_init_result llama_init_tgt = common_init_from_params(params);
model_tgt = llama_init_tgt.model;
ctx_tgt = llama_init_tgt.context;
// load the draft model
params.model = params.speculative.model;
params.n_ctx = params.speculative.n_ctx;
params.n_batch = params.speculative.n_ctx > 0 ? params.speculative.n_ctx : params.n_batch;
params.n_gpu_layers = params.speculative.n_gpu_layers;
if (params.speculative.cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
}
params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
common_init_result llama_init_dft = common_init_from_params(params);
model_dft = llama_init_dft.model;
ctx_dft = llama_init_dft.context;
if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
return 1;
}
// Tokenize the prompt
std::vector<llama_token> inp;
inp = common_tokenize(ctx_tgt, params.prompt, true, true);
if (llama_n_ctx(ctx_tgt) < (int) inp.size()) {
LOG_ERR("%s: the prompt exceeds the context size (%d tokens, ctx %d)\n", __func__, (int) inp.size(), llama_n_ctx(ctx_tgt));
return 1;
}
if (llama_n_batch(ctx_tgt) < (int) inp.size()) {
LOG_ERR("%s: the prompt exceeds the batch size (%d tokens, batch %d)\n", __func__, (int) inp.size(), llama_n_batch(ctx_tgt));
return 1;
}
LOG("\n\n");
for (auto id : inp) {
LOG("%s", common_token_to_piece(ctx_tgt, id).c_str());
}
// how many tokens to draft each time
int n_draft = params.speculative.n_max;
int n_draft_min = params.speculative.n_min;
float p_min = params.speculative.p_min;
int n_predict = 0;
int n_drafted = 0;
int n_accept = 0;
// used to determine end of generation
bool has_eos = false;
// ================================================
// everything until here is standard initialization
// the relevant stuff for speculative decoding starts here
const auto t_enc_start = ggml_time_us();
// target model sampling context
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling);
// eval the prompt
llama_decode(ctx_tgt, llama_batch_get_one(inp.data(), inp.size() - 1));
// note: keep the last token separate!
llama_token id_last = inp.back();
// all tokens currently in the target context
auto prompt_tgt = std::vector<llama_token>(inp.begin(), inp.end() - 1);
int n_past = inp.size() - 1;
// init the speculator
struct common_speculative_params params_spec;
params_spec.n_draft = n_draft;
params_spec.n_reuse = llama_n_ctx(ctx_dft) - n_draft;
params_spec.p_min = p_min;
struct common_speculative * spec = common_speculative_init(ctx_dft);
llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
const auto t_enc_end = ggml_time_us();
const auto t_dec_start = ggml_time_us();
while (true) {
// optionally, generate draft tokens that can be appended to the target batch
//
// this is the most important part of the speculation. the more probable tokens that are provided here
// the better the performance will be. in theory, this computation can be performed asynchronously and even
// offloaded to a remote device. it doesn't even have to be based on an LLM. instead, it can provide tokens
// from a cache or lookup tables.
//
llama_tokens draft = common_speculative_gen_draft(spec, params_spec, prompt_tgt, id_last);
//LOG_DBG("draft: %s\n", string_from(ctx_dft, draft).c_str());
// always have a token to evaluate from before - id_last
common_batch_clear(batch_tgt);
common_batch_add (batch_tgt, id_last, n_past++, { 0 }, true);
// evaluate the target model on [id_last, draft0, draft1, ..., draftN-1]
{
// do not waste time on small drafts
if (draft.size() < n_draft_min) {
draft.clear();
}
for (size_t i = 0; i < draft.size(); ++i) {
common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
}
//LOG_DBG("target batch: %s\n", string_from(ctx_tgt, batch_tgt).c_str());
llama_decode(ctx_tgt, batch_tgt);
}
// sample from the full target batch and return the accepted tokens based on the target sampler
//
// for each token to be accepted, the sampler would have to sample that same token
// in such cases, instead of decoding the sampled token as we normally do, we simply continue with the
// available logits from the batch and sample the next token until we run out of logits or the sampler
// disagrees with the draft
//
const auto ids = common_sampler_sample_and_accept_n(smpl, ctx_tgt, draft);
//LOG_DBG("ids: %s\n", string_from(ctx_tgt, ids).c_str());
GGML_ASSERT(ids.size() > 0); // there will always be at least one accepted token
n_past += ids.size() - 1;
n_drafted += batch_tgt.n_tokens - 1;
n_accept += ids.size() - 1;
// process the accepted tokens and update contexts
//
// this is the standard token post-processing that we normally do
// in this case, we do it for a group of accepted tokens at once
//
{
llama_token id;
std::string token_str;
for (size_t i = 0; i < ids.size(); ++i) {
id = ids[i];
++n_predict;
if (llama_token_is_eog(model_tgt, id)) {
has_eos = true;
break;
}
token_str = common_token_to_piece(ctx_tgt, id);
if (params.use_color && i + 1 < ids.size()) {
LOG("\u001b[%dm%s\u001b[37m", (36 - 0 % 6), token_str.c_str());
} else {
LOG("%s", token_str.c_str());
}
}
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
break;
}
LOG_DBG("accepted %d/%d draft tokens, the last target token is: (%d, '%s')\n", (int) ids.size() - 1, (int) draft.size(), id, token_str.c_str());
{
LOG_DBG("clear kv cache from any extra tokens, n_past = %d\n", n_past);
llama_kv_cache_seq_rm(ctx_tgt, 0, n_past, -1);
}
prompt_tgt.push_back(id_last);
prompt_tgt.insert(prompt_tgt.end(), ids.begin(), ids.end() - 1);
// remember the last accepted token for the next iteration
id_last = id;
}
}
auto t_dec_end = ggml_time_us();
const int n_input = inp.size();
LOG("\n\n");
LOG_INF("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
LOG_INF("\n");
LOG_INF("n_draft = %d\n", n_draft);
LOG_INF("n_predict = %d\n", n_predict);
LOG_INF("n_drafted = %d\n", n_drafted);
LOG_INF("n_accept = %d\n", n_accept);
LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_INF("\n");
LOG_INF("draft:\n\n");
llama_perf_context_print(ctx_dft);
LOG_INF("\n");
LOG_INF("target:\n\n");
common_perf_print(ctx_tgt, smpl);
common_sampler_free(smpl);
common_speculative_free(spec);
llama_free(ctx_tgt);
llama_free_model(model_tgt);
llama_free(ctx_dft);
llama_free_model(model_dft);
llama_backend_free();
LOG("\n\n");
return 0;
}

35
examples/speculative/speculative.cpp
View File

@ -12,7 +12,7 @@
#include <string>
#include <vector>
#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100
#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128
#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
struct seq_draft {
@ -33,7 +33,7 @@ int main(int argc, char ** argv) {
common_params params;
// needed to get candidate probs even for temp <= 0.0
params.sparams.n_probs = 128;
params.sampling.n_probs = 128;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
return 1;
@ -46,7 +46,7 @@ int main(int argc, char ** argv) {
common_init();
if (params.model_draft.empty()) {
if (params.speculative.model.empty()) {
LOG_ERR("%s: --model-draft is required\n", __func__);
return 1;
}
@ -55,9 +55,9 @@ int main(int argc, char ** argv) {
const int n_seq_dft = params.n_parallel;
// probability threshold for splitting a draft branch (only for n_seq_dft > 1)
const float p_split = params.p_split;
const float p_draft_split = params.speculative.p_split;
std::default_random_engine rng(params.sparams.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sparams.seed);
std::default_random_engine rng(params.sampling.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sampling.seed);
std::uniform_real_distribution<> u_dist;
// init llama.cpp
@ -76,13 +76,13 @@ int main(int argc, char ** argv) {
ctx_tgt = llama_init_tgt.context;
// load the draft model
params.model = params.model_draft;
params.n_gpu_layers = params.n_gpu_layers_draft;
if (params.draft_cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.draft_cpuparams.n_threads;
params.model = params.speculative.model;
params.n_gpu_layers = params.speculative.n_gpu_layers;
if (params.speculative.cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
}
params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads;
params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
common_init_result llama_init_dft = common_init_from_params(params);
model_dft = llama_init_dft.model;
ctx_dft = llama_init_dft.context;
@ -170,7 +170,7 @@ int main(int argc, char ** argv) {
//GGML_ASSERT(n_vocab == llama_n_vocab(model_dft));
// how many tokens to draft each time
int n_draft = params.n_draft;
int n_draft = params.speculative.n_max;
int n_predict = 0;
int n_drafted = 0;
@ -183,14 +183,14 @@ int main(int argc, char ** argv) {
bool has_eos = false;
// target model sampling context (reuse the llama_context's sampling instance)
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams);
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling);
// draft sequence data
std::vector<seq_draft> drafts(n_seq_dft);
for (int s = 0; s < n_seq_dft; ++s) {
// allocate llama_sampler for each draft sequence
drafts[s].smpl = common_sampler_init(model_dft, params.sparams);
drafts[s].smpl = common_sampler_init(model_dft, params.sampling);
}
llama_batch batch_dft = llama_batch_init(llama_n_batch(ctx_dft), 0, 1);
@ -230,7 +230,7 @@ int main(int argc, char ** argv) {
// for stochastic sampling, attempt to match the token with the drafted tokens
{
bool accept = false;
if (params.sparams.temp > 0) {
if (params.sampling.temp > 0) {
// stochastic verification
common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
@ -267,11 +267,12 @@ int main(int argc, char ** argv) {
for (size_t i = 0; i < dist_tgt.size; i++) {
if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) {
p_tgt = dist_tgt.data[i].p;
break;
}
}
for (size_t i = 0; i < dist_dft.size; i++) {
if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) {
p_dft = dist_dft.data[i].p;
}
if (p_tgt && p_dft) {
break;
}
}
@ -493,7 +494,7 @@ int main(int argc, char ** argv) {
// attempt to split the branch if the probability is high enough
for (int f = 1; f < 8; ++f) {
if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_split) {
if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_draft_split) {
LOG_DBG("splitting seq %3d into %3d\n", s, n_seq_cur);
llama_kv_cache_seq_rm(ctx_dft, n_seq_cur, -1, -1);

6
flake.lock
View File

@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1730200266,
"narHash": "sha256-l253w0XMT8nWHGXuXqyiIC/bMvh1VRszGXgdpQlfhvU=",
"lastModified": 1732014248,
"narHash": "sha256-y/MEyuJ5oBWrWAic/14LaIr/u5E0wRVzyYsouYY3W6w=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "807e9154dcb16384b1b765ebe9cd2bba2ac287fd",
"rev": "23e89b7da85c3640bbc2173fe04f4bd114342367",
"type": "github"
},
"original": {

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