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shanshan shen 2024-11-22 10:01:45 +00:00
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---
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']
...

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.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

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.github/ISSUE_TEMPLATE/010-bug-compilation.yml vendored Normal file
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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

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

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.github/ISSUE_TEMPLATE/019-bug-misc.yml vendored Normal file
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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

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.github/ISSUE_TEMPLATE/02-bug-medium.yml vendored
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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:

9
.github/workflows/build.yml vendored
View file

@ -986,13 +986,14 @@ jobs:
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
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 "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin
cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin
cp "${{ 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

80
cmake/llama-config.cmake.in
View file

@ -3,12 +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_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_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_SHADER_DEBUG_INFO @GGML_VULKAN_SHADER_DEBUG_INFO@)
set(GGML_VULKAN_PERF @GGML_VULKAN_PERF@)
set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@)
set(GGML_OPENMP @GGML_OPENMP@)
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@
@ -20,6 +68,7 @@ find_package(Threads 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}
@ -49,12 +98,26 @@ foreach(backend amx blas cann cpu cuda hip kompute metal musa rpc sycl vulkan)
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)
@ -65,25 +128,33 @@ 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()
if (GGML_OPENMP)
find_package(OpenMP REQUIRED)
endif()
find_library(llama_LIBRARY llama
@ -97,6 +168,7 @@ 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}"

7
docs/backend/SYCL.md
View file

@ -34,9 +34,10 @@ 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

342
examples/llama-bench/llama-bench.cpp
View file

@ -6,21 +6,21 @@
#include <clocale>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <ctime>
#include <cstdlib>
#include <iterator>
#include <map>
#include <numeric>
#include <regex>
#include <sstream>
#include <string>
#include <vector>
#include <thread>
#include <vector>
#include "common.h"
#include "ggml.h"
#include "llama.h"
#include "common.h"
#ifdef _WIN32
# define WIN32_LEAN_AND_MEAN
@ -36,8 +36,7 @@ static uint64_t get_time_ns() {
return std::chrono::nanoseconds(clock::now().time_since_epoch()).count();
}
template<class T>
static std::string join(const std::vector<T> & values, const std::string & delim) {
template <class T> static std::string join(const std::vector<T> & values, const std::string & delim) {
std::ostringstream str;
for (size_t i = 0; i < values.size(); i++) {
str << values[i];
@ -48,15 +47,13 @@ static std::string join(const std::vector<T> & values, const std::string & delim
return str.str();
}
template<typename T, typename F>
static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
template <typename T, typename F> static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
std::vector<std::string> str_values;
std::transform(values.begin(), values.end(), std::back_inserter(str_values), f);
return str_values;
}
template<typename T>
static T avg(const std::vector<T> & v) {
template <typename T> static T avg(const std::vector<T> & v) {
if (v.empty()) {
return 0;
}
@ -64,8 +61,7 @@ static T avg(const std::vector<T> & v) {
return sum / (T) v.size();
}
template<typename T>
static T stdev(const std::vector<T> & v) {
template <typename T> static T stdev(const std::vector<T> & v) {
if (v.size() <= 1) {
return 0;
}
@ -104,13 +100,20 @@ enum output_formats {NONE, CSV, JSON, JSONL, MARKDOWN, SQL};
static const char * output_format_str(output_formats format) {
switch (format) {
case NONE: return "none";
case CSV: return "csv";
case JSON: return "json";
case JSONL: return "jsonl";
case MARKDOWN: return "md";
case SQL: return "sql";
default: GGML_ABORT("invalid output format");
case NONE:
return "none";
case CSV:
return "csv";
case JSON:
return "json";
case JSONL:
return "jsonl";
case MARKDOWN:
return "md";
case SQL:
return "sql";
default:
GGML_ABORT("invalid output format");
}
}
@ -135,10 +138,14 @@ static bool output_format_from_str(const std::string & s, output_formats & forma
static const char * split_mode_str(llama_split_mode mode) {
switch (mode) {
case LLAMA_SPLIT_MODE_NONE: return "none";
case LLAMA_SPLIT_MODE_LAYER: return "layer";
case LLAMA_SPLIT_MODE_ROW: return "row";
default: GGML_ABORT("invalid split mode");
case LLAMA_SPLIT_MODE_NONE:
return "none";
case LLAMA_SPLIT_MODE_LAYER:
return "layer";
case LLAMA_SPLIT_MODE_ROW:
return "row";
default:
GGML_ABORT("invalid split mode");
}
}
@ -218,38 +225,59 @@ static void print_usage(int /* argc */, char ** argv) {
printf("options:\n");
printf(" -h, --help\n");
printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
printf(" -p, --n-prompt <n> (default: %s)\n",
join(cmd_params_defaults.n_prompt, ",").c_str());
printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
printf(" -pg <pp,tg> (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str());
printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
printf(" -ub, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
printf(" -ctk, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
printf(" -ctv, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -C, --cpu-mask <hex,hex> (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str());
printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str());
printf(" -pg <pp,tg> (default: %s)\n",
join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str());
printf(" -b, --batch-size <n> (default: %s)\n",
join(cmd_params_defaults.n_batch, ",").c_str());
printf(" -ub, --ubatch-size <n> (default: %s)\n",
join(cmd_params_defaults.n_ubatch, ",").c_str());
printf(" -ctk, --cache-type-k <t> (default: %s)\n",
join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
printf(" -ctv, --cache-type-v <t> (default: %s)\n",
join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -t, --threads <n> (default: %s)\n",
join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -C, --cpu-mask <hex,hex> (default: %s)\n",
join(cmd_params_defaults.cpu_mask, ",").c_str());
printf(" --cpu-strict <0|1> (default: %s)\n",
join(cmd_params_defaults.cpu_strict, ",").c_str());
printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n",
join(cmd_params_defaults.n_gpu_layers, ",").c_str());
if (llama_supports_rpc()) {
printf(" -rpc, --rpc <rpc_servers> (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str());
printf(" -rpc, --rpc <rpc_servers> (default: %s)\n",
join(cmd_params_defaults.rpc_servers, ",").c_str());
}
printf(" -sm, --split-mode <none|layer|row> (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str());
printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
printf(" -sm, --split-mode <none|layer|row> (default: %s)\n",
join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n",
join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n",
join(cmd_params_defaults.no_kv_offload, ",").c_str());
printf(" -fa, --flash-attn <0|1> (default: %s)\n",
join(cmd_params_defaults.flash_attn, ",").c_str());
printf(" -mmp, --mmap <0|1> (default: %s)\n",
join(cmd_params_defaults.use_mmap, ",").c_str());
printf(" --numa <distribute|isolate|numactl> (default: disabled)\n");
printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
printf(" -embd, --embeddings <0|1> (default: %s)\n",
join(cmd_params_defaults.embeddings, ",").c_str());
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio);
printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay);
printf(" -o, --output <csv|json|jsonl|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
printf(" -oe, --output-err <csv|json|jsonl|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr));
printf(" -o, --output <csv|json|jsonl|md|sql> (default: %s)\n",
output_format_str(cmd_params_defaults.output_format));
printf(" -oe, --output-err <csv|json|jsonl|md|sql> (default: %s)\n",
output_format_str(cmd_params_defaults.output_format_stderr));
printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
printf(" --progress (default: %s)\n", cmd_params_defaults.progress ? "1" : "0");
printf("\n");
printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
printf(
"Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter "
"multiple times.\n");
}
static ggml_type ggml_type_from_name(const std::string & s) {
@ -281,7 +309,6 @@ static ggml_type ggml_type_from_name(const std::string & s) {
return GGML_TYPE_COUNT;
}
static cmd_params parse_cmd_params(int argc, char ** argv) {
cmd_params params;
std::string arg;
@ -476,10 +503,16 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
break;
} else {
std::string value(argv[i]);
/**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
else { invalid_param = true; break; }
/**/ if (value == "distribute" || value == "") {
params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE;
} else if (value == "isolate") {
params.numa = GGML_NUMA_STRATEGY_ISOLATE;
} else if (value == "numactl") {
params.numa = GGML_NUMA_STRATEGY_NUMACTL;
} else {
invalid_param = true;
break;
}
}
} else if (arg == "-fa" || arg == "--flash-attn") {
if (++i >= argc) {
@ -570,27 +603,69 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
// set defaults
if (params.model.empty()) { params.model = cmd_params_defaults.model; }
if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; }
if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; }
if (params.n_pg.empty()) { params.n_pg = cmd_params_defaults.n_pg; }
if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; }
if (params.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; }
if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; }
if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; }
if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
if (params.rpc_servers.empty()) { params.rpc_servers = cmd_params_defaults.rpc_servers; }
if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; }
if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
if (params.flash_attn.empty()) { params.flash_attn = cmd_params_defaults.flash_attn; }
if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; }
if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; }
if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
if (params.cpu_mask.empty()) { params.cpu_mask = cmd_params_defaults.cpu_mask; }
if (params.cpu_strict.empty()) { params.cpu_strict = cmd_params_defaults.cpu_strict; }
if (params.poll.empty()) { params.poll = cmd_params_defaults.poll; }
if (params.model.empty()) {
params.model = cmd_params_defaults.model;
}
if (params.n_prompt.empty()) {
params.n_prompt = cmd_params_defaults.n_prompt;
}
if (params.n_gen.empty()) {
params.n_gen = cmd_params_defaults.n_gen;
}
if (params.n_pg.empty()) {
params.n_pg = cmd_params_defaults.n_pg;
}
if (params.n_batch.empty()) {
params.n_batch = cmd_params_defaults.n_batch;
}
if (params.n_ubatch.empty()) {
params.n_ubatch = cmd_params_defaults.n_ubatch;
}
if (params.type_k.empty()) {
params.type_k = cmd_params_defaults.type_k;
}
if (params.type_v.empty()) {
params.type_v = cmd_params_defaults.type_v;
}
if (params.n_gpu_layers.empty()) {
params.n_gpu_layers = cmd_params_defaults.n_gpu_layers;
}
if (params.rpc_servers.empty()) {
params.rpc_servers = cmd_params_defaults.rpc_servers;
}
if (params.split_mode.empty()) {
params.split_mode = cmd_params_defaults.split_mode;
}
if (params.main_gpu.empty()) {
params.main_gpu = cmd_params_defaults.main_gpu;
}
if (params.no_kv_offload.empty()) {
params.no_kv_offload = cmd_params_defaults.no_kv_offload;
}
if (params.flash_attn.empty()) {
params.flash_attn = cmd_params_defaults.flash_attn;
}
if (params.tensor_split.empty()) {
params.tensor_split = cmd_params_defaults.tensor_split;
}
if (params.use_mmap.empty()) {
params.use_mmap = cmd_params_defaults.use_mmap;
}
if (params.embeddings.empty()) {
params.embeddings = cmd_params_defaults.embeddings;
}
if (params.n_threads.empty()) {
params.n_threads = cmd_params_defaults.n_threads;
}
if (params.cpu_mask.empty()) {
params.cpu_mask = cmd_params_defaults.cpu_mask;
}
if (params.cpu_strict.empty()) {
params.cpu_strict = cmd_params_defaults.cpu_strict;
}
if (params.poll.empty()) {
params.poll = cmd_params_defaults.poll;
}
return params;
}
@ -633,12 +708,8 @@ struct cmd_params_instance {
}
bool equal_mparams(const cmd_params_instance & other) const {
return model == other.model &&
n_gpu_layers == other.n_gpu_layers &&
rpc_servers == other.rpc_servers &&
split_mode == other.split_mode &&
main_gpu == other.main_gpu &&
use_mmap == other.use_mmap &&
return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers == other.rpc_servers &&
split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap &&
tensor_split == other.tensor_split;
}
@ -662,6 +733,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
std::vector<cmd_params_instance> instances;
// this ordering minimizes the number of times that each model needs to be reloaded
// clang-format off
for (const auto & m : params.model)
for (const auto & nl : params.n_gpu_layers)
for (const auto & rpc : params.rpc_servers)
@ -767,6 +839,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
instances.push_back(instance);
}
}
// clang-format on
return instances;
}
@ -834,28 +907,21 @@ struct test {
(void) ctx;
}
uint64_t avg_ns() const {
return ::avg(samples_ns);
}
uint64_t avg_ns() const { return ::avg(samples_ns); }
uint64_t stdev_ns() const {
return ::stdev(samples_ns);
}
uint64_t stdev_ns() const { return ::stdev(samples_ns); }
std::vector<double> get_ts() const {
int n_tokens = n_prompt + n_gen;
std::vector<double> ts;
std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts), [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; });
std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts),
[n_tokens](uint64_t t) { return 1e9 * n_tokens / t; });
return ts;
}
double avg_ts() const {
return ::avg(get_ts());
}
double avg_ts() const { return ::avg(get_ts()); }
double stdev_ts() const {
return ::stdev(get_ts());
}
double stdev_ts() const { return ::stdev(get_ts()); }
static std::string get_backend() {
std::vector<std::string> backends;
@ -871,17 +937,11 @@ struct test {
static const std::vector<std::string> & get_fields() {
static const std::vector<std::string> fields = {
"build_commit", "build_number",
"cpu_info", "gpu_info", "backends",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_ubatch",
"n_threads", "cpu_mask", "cpu_strict", "poll",
"type_k", "type_v",
"n_gpu_layers", "split_mode",
"main_gpu", "no_kv_offload", "flash_attn",
"tensor_split", "use_mmap", "embeddings",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
"build_commit", "build_number", "cpu_info", "gpu_info", "backends", "model_filename",
"model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads",
"cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers",
"split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "use_mmap",
"embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns",
"avg_ts", "stddev_ts",
};
return fields;
@ -890,17 +950,14 @@ struct test {
enum field_type { STRING, BOOL, INT, FLOAT };
static field_type get_field_type(const std::string & field) {
if (field == "build_number" || field == "n_batch" || field == "n_ubatch" ||
field == "n_threads" || field == "poll" ||
field == "model_size" || field == "model_n_params" ||
field == "n_gpu_layers" || field == "main_gpu" ||
field == "n_prompt" || field == "n_gen" ||
field == "avg_ns" || field == "stddev_ns") {
if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || field == "n_threads" ||
field == "poll" || field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" ||
field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "avg_ns" ||
field == "stddev_ns") {
return INT;
}
if (field == "f16_kv" || field == "no_kv_offload" ||
field == "cpu_strict" ||
field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || field == "flash_attn" ||
field == "use_mmap" || field == "embeddings") {
return BOOL;
}
if (field == "avg_ts" || field == "stddev_ts") {
@ -925,20 +982,38 @@ struct test {
tensor_split_str += "/";
}
}
std::vector<std::string> values = {
build_commit, std::to_string(build_number),
cpu_info, gpu_info, get_backend(),
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
std::to_string(n_batch), std::to_string(n_ubatch),
std::to_string(n_threads), cpu_mask, std::to_string(cpu_strict), std::to_string(poll),
ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), split_mode_str(split_mode),
std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn),
tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
std::to_string(avg_ts()), std::to_string(stdev_ts())
};
std::vector<std::string> values = { build_commit,
std::to_string(build_number),
cpu_info,
gpu_info,
get_backend(),
model_filename,
model_type,
std::to_string(model_size),
std::to_string(model_n_params),
std::to_string(n_batch),
std::to_string(n_ubatch),
std::to_string(n_threads),
cpu_mask,
std::to_string(cpu_strict),
std::to_string(poll),
ggml_type_name(type_k),
ggml_type_name(type_v),
std::to_string(n_gpu_layers),
split_mode_str(split_mode),
std::to_string(main_gpu),
std::to_string(no_kv_offload),
std::to_string(flash_attn),
tensor_split_str,
std::to_string(use_mmap),
std::to_string(embeddings),
std::to_string(n_prompt),
std::to_string(n_gen),
test_time,
std::to_string(avg_ns()),
std::to_string(stdev_ns()),
std::to_string(avg_ts()),
std::to_string(stdev_ts()) };
return values;
}
@ -946,8 +1021,8 @@ struct test {
std::map<std::string, std::string> map;
auto fields = get_fields();
auto values = get_values();
std::transform(fields.begin(), fields.end(), values.begin(),
std::inserter(map, map.end()), std::make_pair<const std::string &, const std::string &>);
std::transform(fields.begin(), fields.end(), values.begin(), std::inserter(map, map.end()),
std::make_pair<const std::string &, const std::string &>);
return map;
}
};
@ -961,8 +1036,11 @@ struct printer {
virtual ~printer() {}
FILE * fout;
virtual void print_header(const cmd_params & params) { (void) params; }
virtual void print_test(const test & t) = 0;
virtual void print_footer() {}
};
@ -992,7 +1070,6 @@ struct csv_printer : public printer {
}
};
static std::string escape_json(const std::string & value) {
std::string escaped;
for (auto c : value) {
@ -1033,7 +1110,8 @@ struct json_printer : public printer {
void print_fields(const std::vector<std::string> & fields, const std::vector<std::string> & values) {
assert(fields.size() == values.size());
for (size_t i = 0; i < fields.size(); i++) {
fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(), format_json_value(fields.at(i), values.at(i)).c_str());
fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(),
format_json_value(fields.at(i), values.at(i)).c_str());
}
}
@ -1051,12 +1129,9 @@ struct json_printer : public printer {
fflush(fout);
}
void print_footer() override {
fprintf(fout, "\n]\n");
}
void print_footer() override { fprintf(fout, "\n]\n"); }
};
struct jsonl_printer : public printer {
void print_fields(const std::vector<std::string> & fields, const std::vector<std::string> & values) {
assert(fields.size() == values.size());
@ -1303,7 +1378,8 @@ struct sql_printer : public printer {
std::vector<std::string> fields = test::get_fields();
fprintf(fout, "CREATE TABLE IF NOT EXISTS test (\n");
for (size_t i = 0; i < fields.size(); i++) {
fprintf(fout, " %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(), i < fields.size() - 1 ? "," : "");
fprintf(fout, " %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(),
i < fields.size() - 1 ? "," : "");
}
fprintf(fout, ");\n");
fprintf(fout, "\n");
@ -1505,13 +1581,15 @@ int main(int argc, char ** argv) {
if (t.n_prompt > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps);
fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count,
i + 1, params.reps);
}
test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps);
fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count,
i + 1, params.reps);
}
test_gen(ctx, t.n_gen, t.n_threads);
}

5
ggml/src/ggml-backend.cpp
View file

@ -252,6 +252,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
}
void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(tensor);
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
if (size == 0) {
@ -266,6 +267,7 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz
}
void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(tensor);
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
if (size == 0) {
@ -884,9 +886,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
int * node_backend_id = &tensor_backend_id(node);
if (ggml_is_view_op(node->op)) {
continue;
}
// do not overwrite user assignments
if (*node_backend_id == -1) {
*node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node);

29
ggml/src/ggml-cann/CMakeLists.txt
View file

@ -3,6 +3,33 @@ if ("cann${CANN_INSTALL_DIR}" STREQUAL "cann" AND DEFINED ENV{ASCEND_TOOLKIT_HOM
message(STATUS "CANN: updated CANN_INSTALL_DIR from ASCEND_TOOLKIT_HOME=$ENV{ASCEND_TOOLKIT_HOME}")
endif()
# Auto-detech Soc type and Soc version, if detect failed, will abort build
set(SOC_VERSION "")
function(detect_ascend_soc_type SOC_VERSION)
execute_process(
COMMAND bash -c "npu-smi info|awk -F' ' 'NF > 0 && NR==7 {print $3}'"
OUTPUT_VARIABLE npu_info
RESULT_VARIABLE npu_result
OUTPUT_STRIP_TRAILING_WHITESPACE
)
if("${npu_info}" STREQUAL "" OR ${npu_result})
message(FATAL_ERROR "Auto-detech ascend soc type failed, please specify manually or check ascend device working normally.")
endif()
set(${SOC_VERSION} "Ascend${npu_info}" PARENT_SCOPE)
endfunction()
if(NOT SOC_TYPE)
detect_ascend_soc_type(SOC_VERSION)
set(SOC_TYPE "${SOC_VERSION}")
message(STATUS "CANN: SOC_VERSION auto-detected is:${SOC_VERSION}")
else()
string(TOLOWER ${SOC_TYPE} SOC_VERSION)
endif()
# Construct Soc specify compile option: ASCEND_#Soc_Major_SN. Such as ASCEND_910B, ASCEND310P.
string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
if (CANN_INSTALL_DIR)
# Only Support Linux.
if (NOT UNIX)
@ -39,6 +66,8 @@ if (CANN_INSTALL_DIR)
target_include_directories(ggml-cann PRIVATE . .. ${CANN_INCLUDE_DIRS})
target_link_directories(ggml-cann PRIVATE ${CANN_INSTALL_DIR}/lib64)
target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
message(STATUS "CANN: CANN_INCLUDE_DIRS = ${CANN_INCLUDE_DIRS}")
message(STATUS "CANN: CANN_LIBRARIES = ${CANN_LIBRARIES}")
else()

18
ggml/src/ggml-cann/aclnn_ops.cpp
View file

@ -2314,6 +2314,14 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
switch (src0->type) {
case GGML_TYPE_F32:
{
#ifdef ASCEND_310P
// Special operation for get_row_f32 kernel of 310P: clear the content of dest data buffer when row is not aligned to 32 bytes
if ((src0->ne[0] % 8) != 0) {
size_t dst_len = src1->ne[0] * src1->ne[1] * src1->ne[2] * src0->ne[0] * ggml_type_size(GGML_TYPE_F32);
ACL_CHECK(aclrtMemset((char*)dst->data, dst_len, 0, dst_len));
}
#endif
aclrtlaunch_ascendc_get_row_f32(
24, ctx.stream(), src0->data, src1->data, dst->data,
((ggml_tensor*)src0->extra)->ne,
@ -2322,7 +2330,16 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
((ggml_tensor*)dst->extra)->nb);
break;
}
case GGML_TYPE_F16:
{
#ifdef ASCEND_310P
// Special operation for get_row_f16 kernel of 310P: clear the content of dest data buffer when row is not aligned to 32 bytes
if ((src0->ne[0] % 16) != 0) {
size_t dst_len = src1->ne[0] * src1->ne[1] * src1->ne[2] * src0->ne[0] * ggml_type_size(GGML_TYPE_F32); // out is also f32, even input is f16
ACL_CHECK(aclrtMemset((char*)dst->data, dst_len, 0, dst_len));
}
#endif
aclrtlaunch_ascendc_get_row_f16(
24, ctx.stream(), src0->data, src1->data, dst->data,
((ggml_tensor*)src0->extra)->ne,
@ -2331,6 +2348,7 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
((ggml_tensor*)dst->extra)->nb);
break;
}
case GGML_TYPE_Q4_0:
aclrtlaunch_ascendc_get_row_q4_0(
24, ctx.stream(), src0->data, src1->data, dst->data,

7
ggml/src/ggml-cann/kernels/CMakeLists.txt
View file

@ -1,7 +1,3 @@
if (NOT SOC_TYPE)
set (SOC_TYPE "Ascend910B3")
endif()
file(GLOB SRC_FILES
get_row_f32.cpp
get_row_f16.cpp
@ -13,7 +9,6 @@ file(GLOB SRC_FILES
dup.cpp
)
string(TOLOWER ${SOC_TYPE} SOC_VERSION)
set(ASCEND_CANN_PACKAGE_PATH ${CANN_INSTALL_DIR})
set(RUN_MODE "npu" CACHE STRING "run mode: npu/sim")
@ -30,4 +25,6 @@ ascendc_library(ascendc_kernels STATIC
${SRC_FILES}
)
message(STATUS "CANN: compile ascend kernels witch SOC_VERSION:${SOC_VERSION}.")
ascendc_compile_definitions(ascendc_kernels PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
# ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP)

32
ggml/src/ggml-cann/kernels/dup.cpp
View file

@ -5,6 +5,7 @@
using namespace AscendC;
#define BUFFER_NUM 2
const int64_t SUPPORTED_MAX_DIM = 65535; // currently the limit of max block dim supportted by dup kernel is 65535template <typename SRC_T, typename DST_T>
template <typename SRC_T, typename DST_T>
class DupByRows {
@ -19,6 +20,7 @@ class DupByRows {
// Input has four dims.
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
assert(op_block_idx < SUPPORTED_MAX_DIM && op_block_idx >= 0, "Invalid block index:%d, max is:%d\n", op_block_idx, SUPPORTED_MAX_DIM);
// param
num_rows = input_ne_ub[1] * input_ne_ub[2] * input_ne_ub[3];
@ -51,24 +53,36 @@ class DupByRows {
__aicore__ inline void copy_in() {
LocalTensor<SRC_T> src_local = src_queue.AllocTensor<SRC_T>();
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = num_elem * sizeof(SRC_T);
DataCopyPadExtParams<SRC_T> padParams;
DataCopyPad(src_local, src_gm, dataCopyParams, padParams);
const size_t elem_per_block = 32 / sizeof(SRC_T);
size_t tail = num_elem % elem_per_block;
size_t cpy_elements_len = tail > 0 ? num_elem + 1 : num_elem;
DataCopy(src_local, src_gm, cpy_elements_len);
src_queue.EnQue(src_local);
}
__aicore__ inline void copy_out() {
LocalTensor<DST_T> dst_local = dst_queue.DeQue<DST_T>();
#ifdef ASCEND_310P
const size_t elem_per_block = 32 / sizeof(DST_T);
size_t tail = num_elem % elem_per_block;
size_t len = num_elem & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(dst_gm, dst_local, len);
}
if(tail != 0) {
for (size_t i = tail; i < elem_per_block; i++) {
dst_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(dst_gm[len], dst_local[len], elem_per_block);
SetAtomicNone();
}
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = num_elem * sizeof(DST_T);
DataCopyPad(dst_gm, dst_local, dataCopyParams);
#endif
dst_queue.FreeTensor(dst_local);
}

35
ggml/src/ggml-cann/kernels/get_row_f16.cpp
View file

@ -14,7 +14,7 @@ class GET_ROW_F16 {
int64_t *output_ne_ub, size_t *output_nb_ub) {
// TODO, use template for F16/f32
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
@ -59,32 +59,42 @@ class GET_ROW_F16 {
}
__aicore__ inline void copy_in(uint32_t offset, size_t len) {
size_t origin_len = len;
LocalTensor<half> input_local = input_queue.AllocTensor<half>();
size_t tail = len % 32;
len = len & ~31;
DataCopy(input_local, input_gm[offset], len);
const size_t elem_per_block = 32 / sizeof(half);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if(tail != 0) {
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(half);
DataCopyPadExtParams<half> padParams;
DataCopyPad(input_local[len], input_gm[offset + len],
dataCopyParams, padParams);
len += elem_per_block;
}
DataCopy(input_local, input_gm[offset], len);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset, size_t len) {
LocalTensor<float> output_local = output_queue.DeQue<float>();
size_t tail = len % 32;
len = len & ~31;
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(output_gm[offset], output_local, len);
}
if(tail != 0) {
#ifdef ASCEND_310P
for (size_t i = tail; i < elem_per_block; i++) {
output_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(output_gm[offset + len], output_local[len], elem_per_block);
SetAtomicNone();
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPad(output_gm[offset + len], output_local[len],
dataCopyParams);
#endif
}
output_queue.FreeTensor(output_local);
}
@ -150,6 +160,7 @@ class GET_ROW_F16 {
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
int64_t op_block_idx;
};
template <typename T>

34
ggml/src/ggml-cann/kernels/get_row_f32.cpp
View file

@ -13,7 +13,7 @@ class GET_ROW_F32 {
int64_t *indices_ne_ub, size_t *indices_nb_ub,
int64_t *output_ne_ub, size_t *output_nb_ub) {
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
@ -55,31 +55,40 @@ class GET_ROW_F32 {
__aicore__ inline void copy_in(uint32_t offset, size_t len) {
LocalTensor<float> input_local = input_queue.AllocTensor<float>();
size_t tail = len % 32;
len = len & ~31;
DataCopy(input_local, input_gm[offset], len);
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if(tail != 0) {
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPadExtParams<float> padParams;
DataCopyPad(input_local[len], input_gm[offset + len],
dataCopyParams, padParams);
len += elem_per_block;
}
DataCopy(input_local, input_gm[offset], len);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset, size_t len) {
LocalTensor<float> output_local = output_queue.DeQue<float>();
size_t tail = len % 32;
len = len & ~31;
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(output_gm[offset], output_local, len);
}
if(tail != 0) {
#ifdef ASCEND_310P
for (size_t i = tail; i < elem_per_block; i++) {
output_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(output_gm[offset + len], output_local[len], elem_per_block);
SetAtomicNone();
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPad(output_gm[offset + len], output_local[len],
dataCopyParams);
#endif
}
output_queue.FreeTensor(output_local);
}
@ -144,6 +153,7 @@ class GET_ROW_F32 {
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
int64_t op_block_idx;
};
template <typename T>

5
ggml/src/ggml-cann/kernels/get_row_q4_0.cpp
View file

@ -110,9 +110,12 @@ class GET_ROW_Q4_0 {
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
// TODO: cast more data to speed up.
#ifdef ASCEND_310P
// TODO: 310P support quantification
#else
Cast(cast_local, input_local, RoundMode::CAST_NONE, QK4_0);
Cast(output_local, cast_local, RoundMode::CAST_NONE, QK4_0);
#endif
// Only mul need compile by group.
half scale = scale_gm.GetValue(scale_offset);

94
ggml/src/ggml-cuda/argmax.cu
View file

@ -1,57 +1,69 @@
#include "common.cuh"
#include "argmax.cuh"
#include "sum.cuh"
#include <algorithm>
#include <cstdint>
static __global__ void argmax_f32(
const float * x, int32_t * dst, const int64_t ncols, const int64_t nrows) {
#include "argmax.cuh"
#include "common.cuh"
#include "sum.cuh"
int argmax_thread = 0;
const int64_t row0 = (int64_t)blockIdx.x*WARP_SIZE;
#pragma unroll
for (int64_t row1 = 0; row1 < WARP_SIZE; ++row1) {
const int64_t row = row0 + row1;
if (row >= nrows) {
break;
}
static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __restrict__ dst, const int64_t ncols) {
const int64_t row = blockIdx.x;
float maxval = -FLT_MAX;
int argmax = -1;
const float * rowx = x + row * ncols;
for (int32_t col = threadIdx.x; col < ncols; col += WARP_SIZE) {
const float val = x[row*ncols + col];
const int bigger = val > maxval;
const int not_bigger = bigger ^ 0x00000001;
maxval = maxval*not_bigger + val*bigger;
argmax = argmax*not_bigger + col*bigger;
for (int32_t col = threadIdx.x; col < ncols; col += blockDim.x) {
const float val = rowx[col];
if (val > maxval) {
maxval = val;
argmax = col;
}
}
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, mask, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, mask, WARP_SIZE);
const int bigger = val > maxval;
const int not_bigger = bigger ^ 0x00000001;
maxval = maxval*not_bigger + val*bigger;
argmax = argmax*not_bigger + col*bigger;
for (int offset = 16; offset > 0; offset >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
if (val > maxval) {
maxval = val;
argmax = col;
}
}
const int store = row1 == threadIdx.x;
argmax_thread += store*argmax;
const int n_warps = blockDim.x / WARP_SIZE;
const int lane_id = threadIdx.x % WARP_SIZE;
const int warp_id = threadIdx.x / WARP_SIZE;
if (n_warps > 1) {
constexpr int max_warps = 1024 / WARP_SIZE;
__shared__ float shared_maxval[max_warps];
__shared__ int shared_argmax[max_warps];
if (lane_id == 0) {
shared_maxval[warp_id] = maxval;
shared_argmax[warp_id] = argmax;
}
const int row = row0 + threadIdx.x;
__syncthreads();
if (row >= nrows) {
return;
if (warp_id == 0) {
if (lane_id < n_warps) {
maxval = shared_maxval[lane_id];
argmax = shared_argmax[lane_id];
}
#pragma unroll
for (int offset = 16; offset > 0; offset >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
if (val > maxval) {
maxval = val;
argmax = col;
}
}
}
}
dst[row] = argmax_thread;
if (warp_id == 0 && lane_id == 0) {
dst[row] = argmax;
}
}
void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@ -70,10 +82,10 @@ void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
cudaStream_t stream = ctx.stream();
const int64_t num_blocks = (nrows + WARP_SIZE - 1) / WARP_SIZE;
const dim3 blocks_dim(WARP_SIZE, 1, 1);
const int64_t num_blocks = nrows;
const int64_t num_threads = std::min<int64_t>(1024, (ne00 + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE);
const dim3 blocks_dim(num_threads, 1, 1);
const dim3 blocks_num(num_blocks, 1, 1);
argmax_f32<<<blocks_num, blocks_dim, 0, stream>>>(src0_d, dst_d, ne00, nrows);
argmax_f32<<<blocks_num, blocks_dim, 0, stream>>>(src0_d, dst_d, ne00);
}

30
ggml/src/ggml-cuda/common.cuh
View file

@ -180,8 +180,8 @@ static __device__ __forceinline__ int warp_reduce_sum(int x) {
return __reduce_add_sync(0xffffffff, x);
#else
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
for (int offset = 16; offset > 0; offset >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, offset, 32);
}
return x;
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE
@ -189,17 +189,17 @@ static __device__ __forceinline__ int warp_reduce_sum(int x) {
static __device__ __forceinline__ float warp_reduce_sum(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
for (int offset = 16; offset > 0; offset >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, offset, 32);
}
return x;
}
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
for (int offset = 16; offset > 0; offset >>= 1) {
a.x += __shfl_xor_sync(0xffffffff, a.x, offset, 32);
a.y += __shfl_xor_sync(0xffffffff, a.y, offset, 32);
}
return a;
}
@ -209,16 +209,16 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
const half2 a_other = __shfl_xor_sync(0xffffffff, a, mask, 32);
for (int offset = 16; offset > 0; offset >>= 1) {
const half2 a_other = __shfl_xor_sync(0xffffffff, a, offset, 32);
reinterpret_cast<half&>(a.x) += __low2half(a_other);
reinterpret_cast<half&>(a.y) += __high2half(a_other);
}
return a;
#else
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
for (int offset = 16; offset > 0; offset >>= 1) {
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, 32));
}
return a;
#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
@ -231,8 +231,8 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
static __device__ __forceinline__ float warp_reduce_max(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
for (int offset = 16; offset > 0; offset >>= 1) {
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, 32));
}
return x;
}
@ -275,8 +275,8 @@ static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const hal
static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
for (int offset = 16; offset > 0; offset >>= 1) {
x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, 32));
}
return x;
#else

8
ggml/src/ggml-cuda/quantize.cu
View file

@ -69,8 +69,8 @@ static __global__ void quantize_mmq_q8_1(
// Exchange max. abs. value between vals_per_scale/4 threads.
#pragma unroll
for (int mask = vals_per_scale/8; mask > 0; mask >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, WARP_SIZE));
for (int offset = vals_per_scale/8; offset > 0; offset >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, offset, WARP_SIZE));
}
float sum;
@ -79,8 +79,8 @@ static __global__ void quantize_mmq_q8_1(
// Exchange calculate sum across vals_per_sum/4 threads.
#pragma unroll
for (int mask = vals_per_sum/8; mask > 0; mask >>= 1) {
sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, WARP_SIZE);
for (int offset = vals_per_sum/8; offset > 0; offset >>= 1) {
sum += __shfl_xor_sync(0xFFFFFFFF, sum, offset, WARP_SIZE);
}
}

3
ggml/src/ggml-impl.h
View file

@ -295,6 +295,9 @@ struct ggml_cgraph {
enum ggml_cgraph_eval_order order;
};
// returns a slice of cgraph with nodes [i0, i1)
// the slice does not have leafs or gradients
// if you need the gradients, get them from the original graph
struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
// Memory allocation

121
ggml/src/ggml-opt.cpp
View file

@ -14,51 +14,51 @@
#include <vector>
struct ggml_opt_dataset {
struct ggml_context * ctx;
ggml_backend_buffer_t buf;
struct ggml_tensor * data;
struct ggml_tensor * labels;
struct ggml_context * ctx = nullptr;
ggml_backend_buffer_t buf = nullptr;
struct ggml_tensor * data = nullptr;
struct ggml_tensor * labels = nullptr;
int64_t ndata;
int64_t ndata_shard;
size_t nbs_data;
size_t nbs_labels;
int64_t ndata = -1;
int64_t ndata_shard = -1;
size_t nbs_data = -1;
size_t nbs_labels = -1;
std::vector<int64_t> permutation;
};
struct ggml_opt_context {
ggml_backend_sched_t backend_sched;
ggml_cgraph * allocated_graph;
ggml_cgraph * allocated_graph_copy;
struct ggml_context * ctx_static;
struct ggml_context * ctx_static_cpu;
struct ggml_context * ctx_compute;
struct ggml_context * ctx_copy;
ggml_backend_buffer_t buf_static;
ggml_backend_buffer_t buf_static_cpu;
ggml_backend_sched_t backend_sched = nullptr;
ggml_cgraph * allocated_graph = nullptr;
ggml_cgraph * allocated_graph_copy = nullptr;
struct ggml_context * ctx_static = nullptr;
struct ggml_context * ctx_static_cpu = nullptr;
struct ggml_context * ctx_compute = nullptr;
struct ggml_context * ctx_copy = nullptr;
ggml_backend_buffer_t buf_static = nullptr;
ggml_backend_buffer_t buf_static_cpu = nullptr;
std::mt19937 rng;
struct ggml_tensor * inputs;
struct ggml_tensor * outputs;
struct ggml_tensor * labels;
struct ggml_tensor * inputs = nullptr;
struct ggml_tensor * outputs = nullptr;
struct ggml_tensor * labels = nullptr;
struct ggml_tensor * loss;
struct ggml_tensor * pred;
struct ggml_tensor * ncorrect;
struct ggml_tensor * loss = nullptr;
struct ggml_tensor * pred = nullptr;
struct ggml_tensor * ncorrect = nullptr;
struct ggml_cgraph * gf;
struct ggml_cgraph * gb_grad;
struct ggml_cgraph * gb_opt;
struct ggml_cgraph * gf = nullptr;
struct ggml_cgraph * gb_grad = nullptr;
struct ggml_cgraph * gb_opt = nullptr;
int64_t iter;
int32_t opt_period;
int32_t opt_i;
bool loss_per_datapoint;
int64_t iter = 1;
int32_t opt_period = 1;
int32_t opt_i = 0;
bool loss_per_datapoint = false;
ggml_opt_get_optimizer_params get_opt_pars;
void * get_opt_pars_ud;
struct ggml_tensor * adamw_params;
ggml_opt_get_optimizer_params get_opt_pars = nullptr;
void * get_opt_pars_ud = nullptr;
struct ggml_tensor * adamw_params = nullptr;
};
struct ggml_opt_result {
@ -67,8 +67,8 @@ struct ggml_opt_result {
std::vector<int32_t> pred;
int64_t ncorrect = 0;
bool loss_per_datapoint = false;
int64_t opt_period = -1;
bool loss_per_datapoint = false;
};
// ====== Dataset ======
@ -237,25 +237,33 @@ static ggml_tensor * map_tensor(std::map<ggml_tensor *, ggml_tensor *> & tensor_
return new_tensor;
}
static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * graph) {
static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) {
std::map<ggml_tensor *, ggml_tensor *> tensor_map;
ggml_cgraph * new_graph = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true);
ggml_cgraph * dst = ggml_new_graph_custom(ctx, src->size, /*grads =*/ true);
for (int i = 0; i < graph->n_leafs; i++) {
ggml_build_forward_expand(new_graph, map_tensor(tensor_map, ctx, graph->leafs[i]));
for (int i = 0; i < src->n_leafs; i++) {
ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->leafs[i]));
}
for (int i = 0; i < graph->n_nodes; i++) {
ggml_build_forward_expand(new_graph, map_tensor(tensor_map, ctx, graph->nodes[i]));
GGML_ASSERT(dst->n_leafs == src->n_leafs);
for (int i = 0; i < src->n_nodes; i++) {
ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->nodes[i]));
}
for (int i = 0; i < graph->n_nodes; ++i) {
const size_t igrad_src = ggml_hash_find(&graph->visited_hash_set, graph->nodes[i]);
const size_t igrad_dst = ggml_hash_find(&new_graph->visited_hash_set, new_graph->nodes[i]);
graph->grads[igrad_dst] = new_graph->grads[igrad_src];
graph->grad_accs[igrad_dst] = new_graph->grad_accs[igrad_src];
GGML_ASSERT(dst->n_nodes == src->n_nodes);
for (int i = 0; i < src->n_nodes; ++i) {
const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]);
const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]);
GGML_ASSERT(igrad_src != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src));
GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst));
dst->grads[igrad_dst] = src->grads[igrad_src];
dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src];
}
return new_graph;
return dst;
}
static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph) {
@ -285,15 +293,10 @@ static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph
ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
ggml_opt_context_t result = new struct ggml_opt_context;
result->backend_sched = params.backend_sched;
result->allocated_graph = nullptr;
result->allocated_graph_copy = nullptr;
result->ctx_compute = params.ctx_compute;
result->ctx_copy = nullptr;
result->inputs = params.inputs;
result->outputs = params.outputs;
result->iter = 1;
result->opt_period = params.opt_period;
result->opt_i = 0;
result->get_opt_pars = params.get_opt_pars;
result->get_opt_pars_ud = params.get_opt_pars_ud;
@ -348,7 +351,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
switch (params.loss_type) {
case GGML_OPT_LOSS_TYPE_MEAN: {
result->labels = nullptr;
result->loss = ggml_sum(result->ctx_static, result->outputs);
ggml_set_name(result->loss, "loss_sum");
const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs));
@ -358,7 +360,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
break;
}
case GGML_OPT_LOSS_TYPE_SUM: {
result->labels = nullptr;
result->loss = ggml_sum(result->ctx_static, result->outputs);
ggml_set_name(result->loss, "loss_sum");
result->loss_per_datapoint = false;
@ -413,14 +414,7 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
}
if (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
result->gb_grad = nullptr;
result->gb_opt = nullptr;
result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
result->buf_static_cpu = nullptr;
ggml_opt_alloc_graph(result, result->gf);
return result;
}
@ -429,14 +423,8 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate);
if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) {
result->gb_opt = nullptr;
result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
result->buf_static_cpu = nullptr;
ggml_opt_alloc_graph(result, result->gb_grad);
ggml_graph_reset(result->gb_grad);
return result;
}
@ -466,7 +454,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type());
ggml_opt_alloc_graph(result, result->gb_opt);
ggml_graph_reset(result->gb_opt);
return result;

101
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View file

@ -158,6 +158,7 @@ struct vk_device_struct {
std::string name;
uint64_t max_memory_allocation_size;
bool fp16;
bool pipeline_robustness;
vk::Device device;
uint32_t vendor_id;
vk_queue compute_queue;
@ -654,7 +655,7 @@ static uint32_t compile_count = 0;
static std::mutex compile_count_mutex;
static std::condition_variable compile_count_cond;
static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants, uint32_t align) {
static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants, uint32_t align, bool disable_robustness) {
VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")");
GGML_ASSERT(parameter_count > 0);
GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
@ -724,6 +725,15 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
vk::PipelineCreateFlags(),
pipeline_shader_create_info,
pipeline->layout);
vk::PipelineRobustnessCreateInfoEXT rci;
if (device->pipeline_robustness && disable_robustness) {
rci.storageBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled;
rci.uniformBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled;
compute_pipeline_create_info.setPNext(&rci);
}
pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
{
@ -1261,7 +1271,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL] = std::make_shared<vk_matmul_pipeline_struct>();
std::vector<std::future<void>> compiles;
auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants, uint32_t align) {
auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants, uint32_t align, bool disable_robustness = false) {
{
// wait until fewer than N compiles are in progress
uint32_t N = std::max(1u, std::thread::hardware_concurrency());
@ -1271,7 +1281,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
}
compile_count++;
}
compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align));
compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align, disable_robustness));
};
if (device->fp16) {
@ -1370,45 +1380,45 @@ static void ggml_vk_load_shaders(vk_device& device) {
// computing two rows per workgroup is a benefit for Q4_0 -> Q5_1, but not for Q8_0.
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true);
// dequant shaders
ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@ -1591,12 +1601,15 @@ static vk_device ggml_vk_get_device(size_t idx) {
bool fp16_storage = false;
bool fp16_compute = false;
bool pipeline_robustness = false;
for (const auto& properties : ext_props) {
if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
fp16_storage = true;
} else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
fp16_compute = true;
} else if (strcmp("VK_EXT_pipeline_robustness", properties.extensionName) == 0) {
pipeline_robustness = true;
}
}
@ -1642,10 +1655,22 @@ static vk_device ggml_vk_get_device(size_t idx) {
vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
vk11_features.pNext = &vk12_features;
VkPhysicalDevicePipelineRobustnessFeaturesEXT pl_robustness_features;
pl_robustness_features.pNext = nullptr;
pl_robustness_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_ROBUSTNESS_FEATURES_EXT;
pl_robustness_features.pipelineRobustness = VK_FALSE;
if (pipeline_robustness) {
vk12_features.pNext = &pl_robustness_features;
device_extensions.push_back("VK_EXT_pipeline_robustness");
}
vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2);
device->fp16 = device->fp16 && vk12_features.shaderFloat16;
device->pipeline_robustness = pl_robustness_features.pipelineRobustness;
if (!vk11_features.storageBuffer16BitAccess) {
std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
throw std::runtime_error("Unsupported device");
@ -3190,7 +3215,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
if (ne01 > max_groups_x) {
groups_z = 64;
groups_x /= groups_z;
groups_x = CEIL_DIV(groups_x, groups_z);
}
// compute
@ -3767,7 +3792,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
if (ne01 > max_groups_x) {
groups_z = 64;
groups_x /= groups_z;
groups_x = CEIL_DIV(groups_x, groups_z);
}
// compute

48
ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp
View file

@ -2,6 +2,15 @@
#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
#endif
#include "types.comp"
#if defined(A_TYPE_PACKED16)
layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];};
#endif
#if defined(A_TYPE_PACKED32)
layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];};
#endif
#if defined(DATA_A_F32)
vec2 dequantize(uint ib, uint iqs, uint a_offset) {
return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]);
@ -20,6 +29,11 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) {
const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
}
vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
const float d = float(data_a_packed16[a_offset + ib].d);
const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
return (vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, (vui >> 12) & 0xF) - 8.0f) * d;
}
#endif
#if defined(DATA_A_Q4_1)
@ -29,6 +43,12 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) {
const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
return vec2(vui & 0xF, vui >> 4) * d + m;
}
vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
const float d = float(data_a_packed16[a_offset + ib].d);
const float m = float(data_a_packed16[a_offset + ib].m);
const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
return vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, (vui >> 12) & 0xF) * d + m;
}
#endif
#if defined(DATA_A_Q5_0)
@ -39,6 +59,14 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) {
const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
}
vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
const float d = float(data_a_packed16[a_offset + ib].d);
const uint uint_qh = uint(data_a_packed16[a_offset + ib].qh[1]) << 16 | data_a_packed16[a_offset + ib].qh[0];
const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10);
const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
return (vec4(((vui >> 0) & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) - 16.0f) * d;
}
#endif
#if defined(DATA_A_Q5_1)
@ -50,6 +78,15 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) {
const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
}
vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
const float d = float(data_a_packed16[a_offset + ib].d);
const float m = float(data_a_packed16[a_offset + ib].m);
const uint uint_qh = data_a_packed16[a_offset + ib].qh;
const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10);
const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
return vec4(((vui >> 0) & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) * d + m;
}
#endif
#if defined(DATA_A_Q8_0)
@ -57,6 +94,12 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) {
const float d = float(data_a[a_offset + ib].d);
return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d;
}
vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
const float d = float(data_a_packed16[a_offset + ib].d);
uint32_t v0 = data_a_packed16[a_offset + ib].qs[iqs/2];
uint32_t v1 = data_a_packed16[a_offset + ib].qs[iqs/2 + 1];
return vec4(int8_t(v0 & 0xFF), int8_t((v0 >> 8) & 0xFF), int8_t(v1 & 0xFF), int8_t((v1 >> 8) & 0xFF)) * d;
}
#endif
#if defined(DATA_A_IQ4_NL)
@ -65,4 +108,9 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) {
const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d;
}
vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
const float d = float(data_a_packed16[a_offset + ib].d);
const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]);
return vec4(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[(vui >> 4) & 0xF], kvalues_iq4nl[(vui >> 8) & 0xF], kvalues_iq4nl[(vui >> 12) & 0xF]) * d;
}
#endif

2
ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp
View file

@ -10,6 +10,8 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
void main() {
const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
init_iq4nl_shmem();
const uint tid = gl_LocalInvocationID.x % 64;
const uint il = tid/32;
const uint ir = tid%32;

4
ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp
View file

@ -12,6 +12,10 @@ void main() {
const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
#if defined(DATA_A_IQ4_NL)
init_iq4nl_shmem();
#endif
if (i00 >= p.ne00) {
return;
}

86
ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp
View file

@ -3,7 +3,7 @@
#ifdef FLOAT16
#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
#endif
#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
#extension GL_EXT_shader_explicit_arithmetic_types : require
#include "mul_mat_vec_base.comp"
@ -12,16 +12,48 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
layout (constant_id = 0) const uint BLOCK_SIZE = 32;
layout (constant_id = 1) const uint NUM_ROWS = 1;
#if !defined(DATA_A_F32) && !defined(DATA_A_F16)
#define K_PER_ITER 8
#else
#define K_PER_ITER 2
#endif
uint a_offset, b_offset, d_offset, y_offset;
shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE];
void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter)
{
const uint col = i*BLOCK_SIZE + 2*tid;
const uint col = i*BLOCK_SIZE + K_PER_ITER*tid;
const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
const uint iybs = col - col%QUANT_K; // y block start index
#if K_PER_ITER == 8
#if QUANT_R == 2
B_TYPE_VEC4 bv02 = data_b_v4[(b_offset + iybs + iqs) / 4];
B_TYPE_VEC4 bv13 = data_b_v4[(b_offset + iybs + iqs + y_offset) / 4];
FLOAT_TYPE b0 = FLOAT_TYPE(bv02.x);
FLOAT_TYPE b1 = FLOAT_TYPE(bv13.x);
FLOAT_TYPE b2 = FLOAT_TYPE(bv02.y);
FLOAT_TYPE b3 = FLOAT_TYPE(bv13.y);
FLOAT_TYPE b4 = FLOAT_TYPE(bv02.z);
FLOAT_TYPE b5 = FLOAT_TYPE(bv13.z);
FLOAT_TYPE b6 = FLOAT_TYPE(bv02.w);
FLOAT_TYPE b7 = FLOAT_TYPE(bv13.w);
#else
B_TYPE_VEC4 bv0 = data_b_v4[(b_offset + iybs + iqs) / 4];
B_TYPE_VEC4 bv1 = data_b_v4[(b_offset + iybs + iqs) / 4 + 1];
FLOAT_TYPE b0 = FLOAT_TYPE(bv0.x);
FLOAT_TYPE b1 = FLOAT_TYPE(bv0.y);
FLOAT_TYPE b2 = FLOAT_TYPE(bv0.z);
FLOAT_TYPE b3 = FLOAT_TYPE(bv0.w);
FLOAT_TYPE b4 = FLOAT_TYPE(bv1.x);
FLOAT_TYPE b5 = FLOAT_TYPE(bv1.y);
FLOAT_TYPE b6 = FLOAT_TYPE(bv1.z);
FLOAT_TYPE b7 = FLOAT_TYPE(bv1.w);
#endif
#else
// Check if the second of the pair of elements is OOB, and don't fetch B or
// accumulate it. We still fetch a pair of elements for A, which is fine for
// quantized formats since they'll be within the same block. We should
@ -34,9 +66,24 @@ void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_
if (!OOB) {
b1 = FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]);
}
#endif
[[unroll]] for (uint n = 0; n < num_rows; ++n) {
const uint ib = ((first_row + n)*p.ncols + col)/QUANT_K; // block index
#if K_PER_ITER == 8
const vec4 v = dequantize4(ib, iqs, a_offset);
const vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset);
// matrix multiplication
temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]);
temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]);
temp[n] = fma(FLOAT_TYPE(v.z), b2, temp[n]);
temp[n] = fma(FLOAT_TYPE(v.w), b3, temp[n]);
temp[n] = fma(FLOAT_TYPE(v2.x), b4, temp[n]);
temp[n] = fma(FLOAT_TYPE(v2.y), b5, temp[n]);
temp[n] = fma(FLOAT_TYPE(v2.z), b6, temp[n]);
temp[n] = fma(FLOAT_TYPE(v2.w), b7, temp[n]);
#else
const vec2 v = dequantize(ib, iqs, a_offset);
// matrix multiplication
@ -44,6 +91,7 @@ void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_
if (!OOB) {
temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]);
}
#endif
}
}
@ -61,22 +109,33 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
temp[i] = FLOAT_TYPE(0);
}
const int unroll_count = 8;
const uint num_iters = (p.ncols >= 2*tid) ? ((p.ncols - 2*tid + BLOCK_SIZE - 1) / BLOCK_SIZE) : 0;
const uint unrolled_iters = num_iters & ~(2*unroll_count - 1);
uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE);
if (num_iters * K_PER_ITER * BLOCK_SIZE + K_PER_ITER*tid < p.ncols) {
num_iters++;
}
int unroll_count = 4;
uint unrolled_iters = num_iters & ~(unroll_count - 1);
uint i = 0;
while (i < unrolled_iters) {
// Manually partially unroll the loop
[[unroll]] for (uint k = 0; k < unroll_count; ++k) {
iter(temp, first_row, num_rows, tid, i, false);
i += 2;
iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false);
i++;
}
}
unroll_count = 2;
unrolled_iters = num_iters & ~(unroll_count - 1);
while (i < unrolled_iters) {
// Manually partially unroll the loop
[[unroll]] for (uint k = 0; k < unroll_count; ++k) {
iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false);
i++;
}
}
while (i < num_iters) {
iter(temp, first_row, num_rows, tid, i, true);
i += 2;
iter(temp, first_row, num_rows, tid, i*K_PER_ITER, true);
i++;
}
// sum up partial sums and write back result
@ -102,10 +161,17 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
void main() {
const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
#if defined(DATA_A_IQ4_NL)
init_iq4nl_shmem();
#endif
// do NUM_ROWS at a time, unless there aren't enough remaining rows
if (first_row + NUM_ROWS <= p.stride_d) {
compute_outputs(first_row, NUM_ROWS);
} else {
if (first_row >= p.stride_d) {
return;
}
compute_outputs(first_row, p.stride_d - first_row);
}
}

3
ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp
View file

@ -12,6 +12,9 @@
layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
layout (binding = 1) readonly buffer BV2 {B_TYPE_VEC2 data_b_v2[];};
layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];};
layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
#ifdef MUL_MAT_ID
layout (binding = 3) readonly buffer IDS {int data_ids[];};

4
ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp
View file

@ -9,6 +9,10 @@ shared FLOAT_TYPE tmp[32];
void main() {
const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
if (row >= p.stride_d) {
return;
}
uint a_offset, b_offset, d_offset;
get_offsets(a_offset, b_offset, d_offset);

4
ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp
View file

@ -9,6 +9,10 @@ shared FLOAT_TYPE tmp[32];
void main() {
const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
if (row >= p.stride_d) {
return;
}
uint a_offset, b_offset, d_offset;
get_offsets(a_offset, b_offset, d_offset);

34
ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp
View file

@ -8,30 +8,14 @@ layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
shared FLOAT_TYPE tmp[32];
// Declare aliased versions of A and B bindings that can use 16b/32b loads for
// the quantized values, and vec4 loads for B.
struct block_q4_K_u32
{
f16vec2 d;
uint32_t scales[3*QUANT_K/64/4];
uint32_t qs[QUANT_K/2/4];
};
struct block_q4_K_u16
{
f16vec2 d;
uint16_t scales[3*QUANT_K/64/2];
uint16_t qs[QUANT_K/2/2];
};
layout (binding = 0) readonly buffer A_u32 {block_q4_K_u32 data_a_u32[];};
layout (binding = 0) readonly buffer A_u16 {block_q4_K_u16 data_a_u16[];};
layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];};
// This shader assumes K_QUANTS_PER_ITERATION == 2 for alignment of loads
void main() {
const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
if (row >= p.stride_d) {
return;
}
uint a_offset, b_offset, d_offset;
get_offsets(a_offset, b_offset, d_offset);
@ -64,9 +48,9 @@ void main() {
const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
uint32_t scale0_u32 = data_a_u16[ib0 + i].scales[v_im ];
uint32_t scale4_u32 = data_a_u16[ib0 + i].scales[v_im + 2];
uint32_t scale8_u32 = data_a_u16[ib0 + i].scales[v_im + 4];
uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ];
uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
uvec4 scale0 = uvec4(unpack8(scale0_u32));
uvec4 scale4 = uvec4(unpack8(scale4_u32));
uvec4 scale8 = uvec4(unpack8(scale8_u32));
@ -80,8 +64,8 @@ void main() {
const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2));
const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2));
uint32_t qs0_u32 = data_a_u32[ib0 + i].qs[q_offset / 4];
uint32_t qs64_u32 = data_a_u32[ib0 + i].qs[q_offset / 4 + 16];
uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4];
uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16];
uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F;
uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F;

140
ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp
View file

@ -1,5 +1,7 @@
#version 450
#extension GL_EXT_shader_explicit_arithmetic_types : require
#include "mul_mat_vec_base.comp"
layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
@ -9,6 +11,10 @@ shared FLOAT_TYPE tmp[32];
void main() {
const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
if (row >= p.stride_d) {
return;
}
uint a_offset, b_offset, d_offset;
get_offsets(a_offset, b_offset, d_offset);
@ -31,70 +37,106 @@ void main() {
const uint8_t hm1 = uint8_t(1 << (2*v_im));
const uint8_t hm2 = uint8_t(hm1 << 4);
tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp
[[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) {
const uint y1_idx = i * QUANT_K + y_offset;
const uint y2_idx = y1_idx + 128;
const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
f16vec2 d = data_a[ib0 + i].d;
const FLOAT_TYPE dall = FLOAT_TYPE(d.x);
const FLOAT_TYPE dmin = FLOAT_TYPE(d.y);
const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f);
const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f);
const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f);
const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f);
const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2));
const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ];
uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2];
uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4];
uvec4 scale0 = uvec4(unpack8(scale0_u32));
uvec4 scale4 = uvec4(unpack8(scale4_u32));
uvec4 scale8 = uvec4(unpack8(scale8_u32));
const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf);
const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf);
const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf);
const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf);
const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4);
const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4);
const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] >> 4);
const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] >> 4);
const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf);
const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf);
const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4);
const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4);
const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] >> 4);
const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] >> 4);
const uint32_t sc0 = ( scale0.x & 0x3f);
const uint32_t sc1 = ( scale0.y & 0x3f);
const uint32_t sc2 = ( scale4.x & 0x3f);
const uint32_t sc3 = ( scale4.y & 0x3f);
const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2));
const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2));
const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2));
const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2));
uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16);
uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16);
uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F;
uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F;
uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F;
uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F;
uvec4 qs0_16_lo4 = uvec4(unpack8(qs0_16_u32_lo4));
uvec4 qs64_80_lo4 = uvec4(unpack8(qs64_80_u32_lo4));
uvec4 qs0_16_hi4 = uvec4(unpack8(qs0_16_u32_hi4));
uvec4 qs64_80_hi4 = uvec4(unpack8(qs64_80_u32_hi4));
const uint32_t q4_0 = qs0_16_lo4.x;
const uint32_t q4_1 = qs0_16_lo4.y;
const uint32_t q4_2 = qs0_16_lo4.z;
const uint32_t q4_3 = qs0_16_lo4.w;
const uint32_t q4_4 = qs0_16_hi4.x;
const uint32_t q4_5 = qs0_16_hi4.y;
const uint32_t q4_6 = qs0_16_hi4.z;
const uint32_t q4_7 = qs0_16_hi4.w;
const uint32_t q4_8 = qs64_80_lo4.x;
const uint32_t q4_9 = qs64_80_lo4.y;
const uint32_t q4_10 = qs64_80_lo4.z;
const uint32_t q4_11 = qs64_80_lo4.w;
const uint32_t q4_12 = qs64_80_hi4.x;
const uint32_t q4_13 = qs64_80_hi4.y;
const uint32_t q4_14 = qs64_80_hi4.z;
const uint32_t q4_15 = qs64_80_hi4.w;
B_TYPE_VEC2 by10 = data_b_v2[(b_offset + y1_idx) / 2];
B_TYPE_VEC2 by116 = data_b_v2[(b_offset + y1_idx) / 2 + 8];
B_TYPE_VEC2 by132 = data_b_v2[(b_offset + y1_idx) / 2 + 16];
B_TYPE_VEC2 by148 = data_b_v2[(b_offset + y1_idx) / 2 + 24];
B_TYPE_VEC2 by20 = data_b_v2[(b_offset + y2_idx) / 2];
B_TYPE_VEC2 by216 = data_b_v2[(b_offset + y2_idx) / 2 + 8];
B_TYPE_VEC2 by232 = data_b_v2[(b_offset + y2_idx) / 2 + 16];
B_TYPE_VEC2 by248 = data_b_v2[(b_offset + y2_idx) / 2 + 24];
uint32_t qh0 = data_a_packed16[ib0 + i].qh[l0 / 2];
uint32_t qh1 = qh0 >> 8;
uint32_t qh16 = data_a_packed16[ib0 + i].qh[l0 / 2 + 8];
uint32_t qh17 = qh16 >> 8;
const FLOAT_TYPE sx =
fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 16]), (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)),
FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0)))));
fma(FLOAT_TYPE(by10.x), (q4_0 + (((qh0 & hm1) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(by10.y), (q4_1 + (((qh1 & hm1) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(by116.x), (q4_2 + (((qh16 & hm1) != 0) ? 16 : 0)),
FLOAT_TYPE(by116.y) * (q4_3 + (((qh17 & hm1) != 0) ? 16 : 0)))));
const FLOAT_TYPE sy =
fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 48]), (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)),
FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0)))));
fma(FLOAT_TYPE(by132.x), (q4_4 + (((qh0 & (hm1 << 1)) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(by132.y), (q4_5 + (((qh1 & (hm1 << 1)) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(by148.x), (q4_6 + (((qh16 & (hm1 << 1)) != 0) ? 16 : 0)),
FLOAT_TYPE(by148.y) * (q4_7 + (((qh17 & (hm1 << 1)) != 0) ? 16 : 0)))));
const FLOAT_TYPE sz =
fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 16]), (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)),
FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0)))));
fma(FLOAT_TYPE(by20.x), (q4_8 + (((qh0 & hm2) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(by20.y), (q4_9 + (((qh1 & hm2) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(by216.x), (q4_10 + (((qh16 & hm2) != 0) ? 16 : 0)),
FLOAT_TYPE(by216.y) * (q4_11 + (((qh17 & hm2) != 0) ? 16 : 0)))));
const FLOAT_TYPE sw =
fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 48]), (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)),
FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0)))));
fma(FLOAT_TYPE(by232.x), (q4_12 + (((qh0 & (hm2 << 1)) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(by232.y), (q4_13 + (((qh1 & (hm2 << 1)) != 0) ? 16 : 0)),
fma(FLOAT_TYPE(by248.x), (q4_14 + (((qh16 & (hm2 << 1)) != 0) ? 16 : 0)),
FLOAT_TYPE(by248.y) * (q4_15 + (((qh17 & (hm2 << 1)) != 0) ? 16 : 0)))));
const FLOAT_TYPE smin =
fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]), sc2,
fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]), sc3,
fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]), sc6,
(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7)));
const uint tmp_idx = 16 * ix + tid;
tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx]));
fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2,
fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3,
fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6,
(FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7)));
temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp));
}
tmp[gl_LocalInvocationID.x] = temp;
// sum up partial sums and write back result
barrier();
[[unroll]] for (uint s = 16; s > 0; s >>= 1) {

67
ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp
View file

@ -1,5 +1,7 @@
#version 450
#extension GL_EXT_shader_explicit_arithmetic_types : require
#include "mul_mat_vec_base.comp"
layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
@ -9,6 +11,10 @@ shared FLOAT_TYPE tmp[32];
void main() {
const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
if (row >= p.stride_d) {
return;
}
uint a_offset, b_offset, d_offset;
get_offsets(a_offset, b_offset, d_offset);
@ -36,35 +42,60 @@ void main() {
const uint s_offset = 8*v_im + is;
const uint y_offset = 128*v_im + l0;
tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp
[[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const uint y_idx = i * QUANT_K + y_offset;
const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
#if K_QUANTS_PER_ITERATION == 1
const uint tmp_idx = 16 * ix + tid;
tmp[tmp_idx] = fma(FLOAT_TYPE(data_b[b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32), tmp[tmp_idx]))))))));
#else
FLOAT_TYPE scales[4];
scales[0] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]);
scales[1] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]);
scales[2] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]);
scales[3] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]);
uint32_t ql0_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16);
uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16);
uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F;
uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F;
uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F;
uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F;
uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16);
uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4;
uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2;
uint32_t qh4_u32 = (qh_u32 & 0x30303030) << 0;
uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2;
uint32_t q0_u32 = ql0_u32_lo4 | qh0_u32;
uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32;
uint32_t q2_u32 = ql0_u32_hi4 | qh4_u32;
uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32;
uvec4 q0 = uvec4(unpack8(q0_u32));
uvec4 q1 = uvec4(unpack8(q1_u32));
uvec4 q2 = uvec4(unpack8(q2_u32));
uvec4 q3 = uvec4(unpack8(q3_u32));
B_TYPE_VEC4 by0 = data_b_v4[(b_offset + y_idx) / 4];
B_TYPE_VEC4 by32 = data_b_v4[(b_offset + y_idx) / 4 + 8];
B_TYPE_VEC4 by64 = data_b_v4[(b_offset + y_idx) / 4 + 16];
B_TYPE_VEC4 by96 = data_b_v4[(b_offset + y_idx) / 4 + 24];
FLOAT_TYPE sum = FLOAT_TYPE(0.0);
[[unroll]] for (int l = 0; l < 4; ++l) {
sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32),
fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32), sum))));
sum = fma(FLOAT_TYPE(by0[l]) * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32),
fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32),
fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32),
fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum))));
}
tmp[16 * ix + tid] += sum;
#endif
temp += sum * d;
}
tmp[gl_LocalInvocationID.x] = temp;
// sum up partial sums and write back result
barrier();
[[unroll]] for (uint s = 16; s > 0; s >>= 1) {

4
ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
View file

@ -75,6 +75,10 @@ shared u16vec2 row_ids[3072];
#endif
void main() {
#if defined(DATA_A_IQ4_NL)
init_iq4nl_shmem();
#endif
#ifdef MUL_MAT_ID
const uint expert_idx = gl_GlobalInvocationID.z;
#else

2
ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp
View file

@ -73,7 +73,9 @@ void soft_max(uint num_iters) {
FLOAT_TYPE v = a * p.scale + slope * b;
if (col < p.KX) {
max_val = max(max_val, v);
}
if (idx < DATA_CACHE_SIZE) {
data_cache[idx] = v;

128
ggml/src/ggml-vulkan/vulkan-shaders/types.comp
View file

@ -1,6 +1,8 @@
#if !defined(DATA_A_F32) && !defined(DATA_A_F16)
#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
#endif
#if !defined(GGML_TYPES_COMP)
#define GGML_TYPES_COMP
#extension GL_EXT_shader_explicit_arithmetic_types : require
#if defined(DATA_A_F32)
#define QUANT_K 1
@ -38,8 +40,14 @@ struct block_q4_0
float16_t d;
uint8_t qs[16];
};
struct block_q4_0_packed16
{
float16_t d;
uint16_t qs[16/2];
};
#define A_TYPE block_q4_0
#define A_TYPE_PACKED16 block_q4_0_packed16
#endif
#if defined(DATA_A_Q4_1)
@ -54,7 +62,15 @@ struct block_q4_1
uint8_t qs[16];
};
struct block_q4_1_packed16
{
float16_t d;
float16_t m;
uint16_t qs[16/2];
};
#define A_TYPE block_q4_1
#define A_TYPE_PACKED16 block_q4_1_packed16
#endif
#if defined(DATA_A_Q5_0)
@ -70,7 +86,15 @@ struct block_q5_0
uint8_t qs[16];
};
struct block_q5_0_packed16
{
float16_t d;
uint16_t qh[2];
uint16_t qs[16/2];
};
#define A_TYPE block_q5_0
#define A_TYPE_PACKED16 block_q5_0_packed16
#endif
#if defined(DATA_A_Q5_1)
@ -87,7 +111,16 @@ struct block_q5_1
uint8_t qs[16];
};
struct block_q5_1_packed16
{
float16_t d;
float16_t m;
uint qh;
uint16_t qs[16/2];
};
#define A_TYPE block_q5_1
#define A_TYPE_PACKED16 block_q5_1_packed16
#endif
#if defined(DATA_A_Q8_0)
@ -100,8 +133,14 @@ struct block_q8_0
float16_t d;
int8_t qs[32];
};
struct block_q8_0_packed16
{
float16_t d;
uint16_t qs[32/2];
};
#define A_TYPE block_q8_0
#define A_TYPE_PACKED16 block_q8_0_packed16
#endif
// K-quants
@ -116,7 +155,23 @@ struct block_q2_K
f16vec2 d;
};
struct block_q2_K_packed16
{
uint16_t scales[QUANT_K/16/2];
uint16_t qs[QUANT_K/4/2];
f16vec2 d;
};
struct block_q2_K_packed32
{
uint32_t scales[QUANT_K/16/4];
uint32_t qs[QUANT_K/4/4];
f16vec2 d;
};
#define A_TYPE block_q2_K
#define A_TYPE_PACKED16 block_q2_K_packed16
#define A_TYPE_PACKED32 block_q2_K_packed32
#endif
#if defined(DATA_A_Q3_K)
@ -131,7 +186,16 @@ struct block_q3_K
float16_t d;
};
struct block_q3_K_packed16
{
uint16_t hmask[QUANT_K/8/2];
uint16_t qs[QUANT_K/4/2];
uint16_t scales[12/2];
float16_t d;
};
#define A_TYPE block_q3_K
#define A_TYPE_PACKED16 block_q3_K_packed16
#endif
#if defined(DATA_A_Q4_K)
@ -145,7 +209,23 @@ struct block_q4_K
uint8_t qs[QUANT_K/2];
};
struct block_q4_K_packed16
{
f16vec2 d;
uint16_t scales[3*QUANT_K/64/2];
uint16_t qs[QUANT_K/2/2];
};
struct block_q4_K_packed32
{
f16vec2 d;
uint32_t scales[3*QUANT_K/64/4];
uint32_t qs[QUANT_K/2/4];
};
#define A_TYPE block_q4_K
#define A_TYPE_PACKED16 block_q4_K_packed16
#define A_TYPE_PACKED32 block_q4_K_packed32
#endif
#if defined(DATA_A_Q5_K)
@ -160,7 +240,16 @@ struct block_q5_K
uint8_t qs[QUANT_K/2];
};
struct block_q5_K_packed16
{
f16vec2 d;
uint16_t scales[12/2];
uint16_t qh[QUANT_K/8/2];
uint16_t qs[QUANT_K/2/2];
};
#define A_TYPE block_q5_K
#define A_TYPE_PACKED16 block_q5_K_packed16
#endif
#if defined(DATA_A_Q6_K)
@ -175,7 +264,16 @@ struct block_q6_K
float16_t d;
};
struct block_q6_K_packed16
{
uint16_t ql[QUANT_K/2/2];
uint16_t qh[QUANT_K/4/2];
int8_t scales[QUANT_K/16];
float16_t d;
};
#define A_TYPE block_q6_K
#define A_TYPE_PACKED16 block_q6_K_packed16
#endif
// IQuants
@ -191,10 +289,30 @@ struct block_iq4_nl
uint8_t qs[QUANT_K/2];
};
#define A_TYPE block_iq4_nl
struct block_iq4_nl_packed16
{
float16_t d;
uint16_t qs[QUANT_K/2/2];
};
const int8_t kvalues_iq4nl[16] = {
#define A_TYPE block_iq4_nl
#define A_TYPE_PACKED16 block_iq4_nl_packed16
const int8_t kvalues_iq4nl_const[16] = {
int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10),
int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113)
};
shared FLOAT_TYPE kvalues_iq4nl[16];
void init_iq4nl_shmem()
{
// copy the table into shared memory and sync
if (gl_LocalInvocationIndex.x < 16) {
kvalues_iq4nl[gl_LocalInvocationIndex.x] = FLOAT_TYPE(kvalues_iq4nl_const[gl_LocalInvocationIndex.x]);
}
barrier();
}
#endif
#endif // !defined(GGML_TYPES_COMP)

12
ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
View file

@ -317,10 +317,10 @@ void process_shaders() {
std::string data_a_key = "DATA_A_" + to_uppercase(tname);
std::string shader = (string_ends_with(tname, "_k")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp";
string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}}));
string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC2", "f16vec2"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}}));
string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
// Dequant shaders
if (tname != "f16") {
@ -331,11 +331,11 @@ void process_shaders() {
shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp";
if (tname == "f16") {
string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}));
} else {
string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}});
string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}));
}
string_to_spv("get_rows_" + tname + "_f32", shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}});
string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}));
}
}

78
ggml/src/ggml.c
View file

@ -2255,6 +2255,7 @@ struct ggml_tensor * ggml_argmax(
struct ggml_context * ctx,
struct ggml_tensor * a) {
GGML_ASSERT(ggml_is_matrix(a));
GGML_ASSERT(a->ne[0] <= INT32_MAX);
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, a->ne[1]);
@ -4138,6 +4139,7 @@ struct ggml_tensor * ggml_argsort(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_sort_order order) {
GGML_ASSERT(a->ne[0] <= INT32_MAX);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, GGML_MAX_DIMS, a->ne);
ggml_set_op_params_i32(result, 0, (int32_t) order);
@ -5019,8 +5021,10 @@ static void ggml_hash_map_free(struct hash_map * map) {
}
// utility functions to change gradients
// if a is in acc_table, modify gradients in-place and mark result as gradient accumulator
// else if a is in zero_table, replace a
// isrc is the index of tensor in cgraph->visited_has_set.keys
// the corresponding gradient (accumulators) are also at position isrc
// if tensor has a gradient accumulator, modify that accumulator in-place
// else if there is no gradient for tensor, set the corresponding value
// else, just add/subtract/etc. the gradients
static void ggml_add_or_set(
@ -5028,11 +5032,14 @@ static void ggml_add_or_set(
struct ggml_cgraph * cgraph,
size_t isrc,
struct ggml_tensor * tensor) {
struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc];
GGML_ASSERT(src);
if (cgraph->grads[isrc]) {
cgraph->grads[isrc] = ggml_add_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]);
cgraph->grads[isrc] = ggml_add_impl(ctx, cgraph->grads[isrc], tensor, /*inplace =*/ cgraph->grad_accs[isrc]);
} else {
cgraph->grads[isrc] = tensor;
}
ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name);
ggml_build_forward_expand(cgraph, cgraph->grads[isrc]);
}
@ -5040,18 +5047,20 @@ static void ggml_acc_or_set(
struct ggml_context * ctx,
struct ggml_cgraph * cgraph,
size_t isrc,
struct ggml_tensor * src,
struct ggml_tensor * tensor,
const size_t nb1,
const size_t nb2,
const size_t nb3,
const size_t offset) {
struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc];
GGML_ASSERT(src);
if (cgraph->grads[isrc]) {
cgraph->grads[isrc] = ggml_acc_impl(ctx, cgraph->grads[isrc], tensor, nb1, nb2, nb3, offset, cgraph->grad_accs[isrc]);
} else {
struct ggml_tensor * a_zero = ggml_scale(ctx, src, 0.0f); // FIXME this is going to produce NaN if a contains inf/NaN
cgraph->grads[isrc] = ggml_acc_impl(ctx, a_zero, tensor, nb1, nb2, nb3, offset, false);
}
ggml_format_name(cgraph->grads[isrc], "grad for %s", cgraph->visited_hash_set.keys[isrc]->name);
ggml_build_forward_expand(cgraph, cgraph->grads[isrc]);
}
@ -5059,13 +5068,15 @@ static void ggml_add1_or_set(
struct ggml_context * ctx,
struct ggml_cgraph * cgraph,
size_t isrc,
struct ggml_tensor * src,
struct ggml_tensor * tensor) {
struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc];
GGML_ASSERT(src);
if (cgraph->grads[isrc]) {
cgraph->grads[isrc] = ggml_add1_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]);
} else {
cgraph->grads[isrc] = ggml_repeat(ctx, tensor, src);
}
ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name);
ggml_build_forward_expand(cgraph, cgraph->grads[isrc]);
}
@ -5074,11 +5085,14 @@ static void ggml_sub_or_set(
struct ggml_cgraph * cgraph,
size_t isrc,
struct ggml_tensor * tensor) {
struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc];
GGML_ASSERT(src);
if (cgraph->grads[isrc]) {
cgraph->grads[isrc] = ggml_sub_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]);
} else {
cgraph->grads[isrc] = ggml_neg(ctx, tensor);
}
ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name);
ggml_build_forward_expand(cgraph, cgraph->grads[isrc]);
}
@ -5095,12 +5109,12 @@ static void ggml_compute_backward(
struct ggml_tensor * src1 = tensor->src[1];
struct ggml_tensor * src2 = tensor->src[2];
struct ggml_hash_set * hash_set = &cgraph->visited_hash_set;
const size_t isrc0 = ggml_hash_find(hash_set, src0);
const size_t isrc1 = ggml_hash_find(hash_set, src1);
const size_t isrc2 = ggml_hash_find(hash_set, src2);
const bool src0_needs_grads = isrc0 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc0) && grads_needed[isrc0];
const bool src1_needs_grads = isrc1 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc1) && grads_needed[isrc1];
const bool src2_needs_grads = isrc2 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc2) && grads_needed[isrc2];
const size_t isrc0 = src0 ? ggml_hash_find(hash_set, src0) : (size_t) -1;
const size_t isrc1 = src1 ? ggml_hash_find(hash_set, src1) : (size_t) -1;
const size_t isrc2 = src2 ? ggml_hash_find(hash_set, src2) : (size_t) -1;
const bool src0_needs_grads = src0 && isrc0 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc0) && grads_needed[isrc0];
const bool src1_needs_grads = src1 && isrc1 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc1) && grads_needed[isrc1];
const bool src2_needs_grads = src2 && isrc2 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc2) && grads_needed[isrc2];
switch (tensor->op) {
case GGML_OP_DUP: {
@ -5200,7 +5214,7 @@ static void ggml_compute_backward(
} break;
case GGML_OP_SUM: {
if (src0_needs_grads) {
ggml_add1_or_set(ctx, cgraph, isrc0, src0, grad);
ggml_add1_or_set(ctx, cgraph, isrc0, grad);
}
} break;
case GGML_OP_SUM_ROWS: {
@ -5210,7 +5224,7 @@ static void ggml_compute_backward(
} break;
case GGML_OP_MEAN: {
if (src0_needs_grads) {
ggml_add1_or_set(ctx, cgraph, isrc0, src0, ggml_scale_impl(ctx, grad, 1.0f/src0->ne[0], false));
ggml_add1_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, 1.0f/src0->ne[0], false));
}
} break;
case GGML_OP_REPEAT: {
@ -5363,7 +5377,7 @@ static void ggml_compute_backward(
nb3 = (nb3 / n0) * ng;
}
ggml_acc_or_set(ctx, cgraph, isrc0, src0, grad, nb1, nb2, nb3, offset);
ggml_acc_or_set(ctx, cgraph, isrc0, grad, nb1, nb2, nb3, offset);
}
} break;
case GGML_OP_PERMUTE: {
@ -5597,10 +5611,9 @@ void ggml_build_backward_expand(
const int n_nodes_f = cgraph->n_nodes;
const size_t hash_size = ggml_hash_size(2*cgraph->size);
memset(cgraph->grads, 0, hash_size*sizeof(struct ggml_tensor *));
memset(cgraph->grad_accs, 0, hash_size*sizeof(struct ggml_tensor *));
bool * grads_needed = calloc(hash_size, sizeof(bool));
memset(cgraph->grads, 0, cgraph->visited_hash_set.size*sizeof(struct ggml_tensor *));
memset(cgraph->grad_accs, 0, cgraph->visited_hash_set.size*sizeof(struct ggml_tensor *));
bool * grads_needed = calloc(cgraph->visited_hash_set.size, sizeof(bool));
{
bool any_params = false;
@ -5621,7 +5634,7 @@ void ggml_build_backward_expand(
continue;
}
bool node_needs_grad = node->flags & GGML_TENSOR_FLAG_PARAM;
bool node_needs_grad = (node->flags & GGML_TENSOR_FLAG_PARAM) || (node->flags & GGML_TENSOR_FLAG_LOSS);
bool ignore_src[GGML_MAX_SRC] = {false};
switch (node->op) {
// gradients in node->src[0] for one reason or another have no effect on output gradients
@ -5665,9 +5678,12 @@ void ggml_build_backward_expand(
node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE);
const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
GGML_ASSERT(igrad != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, igrad));
if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) {
cgraph->grads[igrad] = ggml_dup_tensor(ctx_static, node);
cgraph->grad_accs[igrad] = cgraph->grads[igrad];
cgraph->grad_accs[igrad] = ggml_dup_tensor(ctx_static, node);
cgraph->grads[igrad] = cgraph->grad_accs[igrad];
ggml_format_name(cgraph->grad_accs[igrad], "grad acc for %s", node->name);
}
grads_needed[igrad] = true;
}
@ -5765,10 +5781,10 @@ struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1)
/*.n_nodes =*/ i1 - i0,
/*.n_leafs =*/ 0,
/*.nodes =*/ cgraph0->nodes + i0,
/*.grads =*/ cgraph0->grads ? cgraph0->grads + i0 : NULL,
/*.grad_accs =*/ cgraph0->grad_accs ? cgraph0->grad_accs + i0 : NULL,
/*.grads =*/ NULL, // gradients would need visited_hash_set
/*.grad_accs =*/ NULL,
/*.leafs =*/ NULL,
/*.hash_table =*/ { 0, NULL, NULL },
/*.visited_hash_set =*/ { 0, NULL, NULL },
/*.order =*/ cgraph0->order,
};
@ -5799,12 +5815,22 @@ void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) {
}
}
if (dst->grads) {
memset(dst->grads, 0, dst->visited_hash_set.size*sizeof(struct ggml_tensor *));
memset(dst->grad_accs, 0, dst->visited_hash_set.size*sizeof(struct ggml_tensor *));
}
if (src->grads) {
GGML_ASSERT(dst->grads != NULL);
GGML_ASSERT(dst->grad_accs != NULL);
for (int i = 0; i < src->n_nodes; ++i) {
const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]);
const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]);
GGML_ASSERT(igrad_src != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src));
GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL);
GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst));
dst->grads[igrad_dst] = src->grads[igrad_src];
dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src];
}
@ -5839,13 +5865,9 @@ void ggml_graph_reset(struct ggml_cgraph * cgraph) {
if (node->op == GGML_OP_OPT_STEP_ADAMW) {
// clear momenta
if (node->src[2]->data) {
ggml_set_zero(node->src[2]);
}
if (node->src[3]->data) {
ggml_set_zero(node->src[3]);
}
}
// initial gradients of loss should be 1, 0 otherwise
if (grad_acc) {

2
scripts/sync-ggml.last
View file

@ -1 +1 @@
2884dd72fea8922910fe53387c3d17ab928d3a8e
6fcbd60bc72ac3f7ad43f78c87e535f2e6206f58

10
src/llama.cpp
View file

@ -18211,13 +18211,13 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
static void llama_kv_cache_update_internal(struct llama_context & lctx) {
bool need_reserve = false;
// apply K-shift if needed
if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
if (lctx.kv_self.has_shift) {
if (!llama_kv_cache_can_shift(&lctx)) {
GGML_ABORT("Deepseek2 does not support K-shift");
GGML_ABORT("The current context does not support K-shift");
}
{
// apply K-shift if needed
if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE) {
ggml_backend_sched_reset(lctx.sched.get());
ggml_cgraph * gf = llama_build_graph_k_shift(lctx);
@ -20463,7 +20463,7 @@ void llama_kv_cache_update(struct llama_context * ctx) {
}
bool llama_kv_cache_can_shift(struct llama_context * ctx) {
return ctx->model.arch != LLM_ARCH_DEEPSEEK2; // not supported due to MLA
return !ctx->kv_self.recurrent && ctx->model.arch != LLM_ARCH_DEEPSEEK2; // not supported due to MLA
}
// deprecated

30
tests/test-backend-ops.cpp
View file

@ -819,7 +819,6 @@ struct test_case {
}
}
// TODO: refactor so that this check is only needed once
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (!ggml_backend_supports_op(backend, t)) {
printf("not supported [%s] ", ggml_backend_name(backend));
@ -1155,6 +1154,26 @@ struct test_argmax : public test_case {
return out;
}
void initialize_tensors(ggml_context * ctx) override {
std::random_device rd;
std::default_random_engine rng(rd());
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_F32) {
// initialize with unique values to avoid ties
for (int64_t r = 0; r < ggml_nrows(t); r++) {
std::vector<float> data(t->ne[0]);
for (int i = 0; i < t->ne[0]; i++) {
data[i] = i;
}
std::shuffle(data.begin(), data.end(), rng);
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float));
}
} else {
init_tensor_uniform(t);
}
}
}
double max_nmse_err() override {
return 0.0;
}
@ -3441,6 +3460,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
test_cases.emplace_back(new test_argmax());
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32, 1, 1, 1}));
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {100, 10, 1, 1}));
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1}));
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {2000, 10, 1, 1}));
test_cases.emplace_back(new test_count_equal());
for (int ne3 : {1, 3}) { // CUDA backward pass only supports ne3 == 1
@ -3831,6 +3855,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {64, 64, 20, 1}, false, 1.0f, 0.0f));
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 64, 20, 1}, false, 1.0f, 0.0f));
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32, 10, 1, 1}));
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1}));
test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32000, 512, 1, 1}));
for (int bs : {1, 512}) {
for (ggml_type type_a : all_types) {
for (ggml_type type_b : {GGML_TYPE_F32}) {