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Merge branch 'master' into convert-split

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This commit is contained in:
Christian Zhou-Zheng 2024-06-03 09:07:23 -04:00
commit 140eb52f3f
241 changed files with 86800 additions and 21711 deletions
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2
.devops/full-cuda.Dockerfile
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@ -31,6 +31,6 @@ ENV LLAMA_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1
RUN make
RUN make -j$(nproc)
ENTRYPOINT ["/app/.devops/tools.sh"]

2
.devops/full-rocm.Dockerfile
View file

@ -45,6 +45,6 @@ ENV LLAMA_CURL=1
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
RUN make
RUN make -j$(nproc)
ENTRYPOINT ["/app/.devops/tools.sh"]

2
.devops/full.Dockerfile
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@ -18,7 +18,7 @@ COPY . .
ENV LLAMA_CURL=1
RUN make
RUN make -j$(nproc)
ENV LC_ALL=C.utf8

2
.devops/main-cuda.Dockerfile
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@ -23,7 +23,7 @@ ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable CUDA
ENV LLAMA_CUDA=1
RUN make
RUN make -j$(nproc)
FROM ${BASE_CUDA_RUN_CONTAINER} as runtime

8
.devops/main-intel.Dockerfile
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@ -2,6 +2,14 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
rm /etc/apt/sources.list.d/intel-graphics.list && \
wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
chmod 644 /usr/share/keyrings/intel-graphics.gpg
ARG LLAMA_SYCL_F16=OFF
RUN apt-get update && \
apt-get install -y git

2
.devops/main-rocm.Dockerfile
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@ -40,6 +40,6 @@ ENV LLAMA_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
RUN make
RUN make -j$(nproc)
ENTRYPOINT [ "/app/main" ]

2
.devops/main.Dockerfile
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@ -9,7 +9,7 @@ WORKDIR /app
COPY . .
RUN make
RUN make -j$(nproc)
FROM ubuntu:$UBUNTU_VERSION as runtime

2
.devops/server-cuda.Dockerfile
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@ -25,7 +25,7 @@ ENV LLAMA_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1
RUN make
RUN make -j$(nproc)
FROM ${BASE_CUDA_RUN_CONTAINER} as runtime

16
.devops/server-intel.Dockerfile
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@ -2,6 +2,14 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
rm /etc/apt/sources.list.d/intel-graphics.list && \
wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
chmod 644 /usr/share/keyrings/intel-graphics.gpg
ARG LLAMA_SYCL_F16=OFF
RUN apt-get update && \
apt-get install -y git libcurl4-openssl-dev
@ -19,6 +27,14 @@ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
rm /etc/apt/sources.list.d/intel-graphics.list && \
wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
chmod 644 /usr/share/keyrings/intel-graphics.gpg
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev

2
.devops/server-rocm.Dockerfile
View file

@ -45,6 +45,6 @@ ENV LLAMA_CURL=1
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
RUN make
RUN make -j$(nproc)
ENTRYPOINT [ "/app/server" ]

2
.devops/server.Dockerfile
View file

@ -11,7 +11,7 @@ COPY . .
ENV LLAMA_CURL=1
RUN make
RUN make -j$(nproc)
FROM ubuntu:$UBUNTU_VERSION as runtime

2
.devops/tools.sh
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@ -8,7 +8,7 @@ arg1="$1"
shift
if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
python3 ./convert.py "$@"
python3 ./convert-hf-to-gguf.py "$@"
elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
./quantize "$@"
elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then

50
.github/ISSUE_TEMPLATE/01-bug-low.yml vendored Normal file
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@ -0,0 +1,50 @@
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: |
$./main --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

50
.github/ISSUE_TEMPLATE/02-bug-medium.yml vendored Normal file
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@ -0,0 +1,50 @@
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: |
$./main --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

50
.github/ISSUE_TEMPLATE/03-bug-high.yml vendored Normal file
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@ -0,0 +1,50 @@
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: |
$./main --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

50
.github/ISSUE_TEMPLATE/04-bug-critical.yml vendored Normal file
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@ -0,0 +1,50 @@
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: |
$./main --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

51
.github/ISSUE_TEMPLATE/05-enhancement.yml vendored Normal file
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@ -0,0 +1,51 @@
name: Enhancement
description: Used to request enhancements for llama.cpp
title: "Feature Request: "
labels: ["enhancement"]
body:
- type: markdown
attributes:
value: |
[Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas)
- type: checkboxes
id: prerequisites
attributes:
label: Prerequisites
description: Please confirm the following before submitting your enhancement request.
options:
- label: I am running the latest code. Mention the version if possible as well.
required: true
- label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
required: true
- label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
required: true
- label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share.
required: true
- type: textarea
id: feature-description
attributes:
label: Feature Description
description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
placeholder: Detailed description of the enhancement
validations:
required: true
- type: textarea
id: motivation
attributes:
label: Motivation
description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
placeholder: Explanation of why this feature is needed and its benefits
validations:
required: true
- type: textarea
id: possible-implementation
attributes:
label: Possible Implementation
description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.
placeholder: Detailed description of potential implementation
validations:
required: false

52
.github/ISSUE_TEMPLATE/06-research.yml vendored Normal file
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@ -0,0 +1,52 @@
name: Research
description: Track new technical research area
title: "Research: "
labels: ["research 🔬"]
body:
- type: markdown
attributes:
value: |
Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22)
- type: checkboxes
id: research-stage
attributes:
label: Research Stage
description: Track general state of this research ticket
options:
- label: Background Research (Let's try to avoid reinventing the wheel)
- label: Hypothesis Formed (How do you think this will work and it's effect?)
- label: Strategy / Implementation Forming
- label: Analysis of results
- label: Debrief / Documentation (So people in the future can learn from us)
- type: textarea
id: background
attributes:
label: Previous existing literature and research
description: Whats the current state of the art and whats the motivation for this research?
- type: textarea
id: hypothesis
attributes:
label: Hypothesis
description: How do you think this will work and it's effect?
- type: textarea
id: implementation
attributes:
label: Implementation
description: Got an approach? e.g. a PR ready to go?
- type: textarea
id: analysis
attributes:
label: Analysis
description: How does the proposed implementation behave?
- 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

28
.github/ISSUE_TEMPLATE/07-refactor.yml vendored Normal file
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@ -0,0 +1,28 @@
name: Refactor (Maintainers)
description: Used to track refactoring opportunities
title: "Refactor: "
labels: ["refactor"]
body:
- type: markdown
attributes:
value: |
Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered.
Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too.
- type: textarea
id: background-description
attributes:
label: Background Description
description: Please provide a detailed written description of the pain points you are trying to solve.
placeholder: Detailed description behind your motivation to request refactor
validations:
required: true
- type: textarea
id: possible-approaches
attributes:
label: Possible Refactor Approaches
description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list.
placeholder: Your idea of possible refactoring opportunity/approaches
validations:
required: false

11
.github/ISSUE_TEMPLATE/bug.md vendored
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@ -1,11 +0,0 @@
---
name: Bug template
about: Used to report bugs in llama.cpp
labels: ["bug-unconfirmed"]
assignees: ''
---
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.
If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests).

13
.github/ISSUE_TEMPLATE/config.yml vendored Normal file
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@ -0,0 +1,13 @@
blank_issues_enabled: true
contact_links:
- name: Got an idea?
url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas
about: Pop it there. It may then become an enhancement ticket.
- name: Got a question?
url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a
about: Ask a question there!
- name: Want to contribute?
url: https://github.com/ggerganov/llama.cpp/wiki/contribute
about: Head to the contribution guide page of the wiki for areas you can help with

28
.github/ISSUE_TEMPLATE/enhancement.md vendored
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@ -1,28 +0,0 @@
---
name: Enhancement template
about: Used to request enhancements for llama.cpp
labels: ["enhancement"]
assignees: ''
---
# Prerequisites
Please answer the following questions for yourself before submitting an issue.
- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
# Feature Description
Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
# Motivation
Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
# Possible Implementation
If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.

19
.github/labeler.yml vendored
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@ -1,5 +1,16 @@
# https://github.com/actions/labeler
Kompute:
- changed-files:
- any-glob-to-any-file:
- ggml-kompute.h
- ggml-kompute.cpp
- README-kompute.md
Apple Metal:
- changed-files:
- any-glob-to-any-file:
- ggml-metal.h
- ggml-metal.cpp
- README-metal.md
SYCL:
- changed-files:
- any-glob-to-any-file:
@ -9,6 +20,7 @@ SYCL:
Nvidia GPU:
- changed-files:
- any-glob-to-any-file:
- ggml-cuda.h
- ggml-cuda/**
Vulkan:
- changed-files:
@ -62,6 +74,8 @@ server:
ggml:
- changed-files:
- any-glob-to-any-file:
- ggml.c
- ggml.h
- ggml-*.c
- ggml-*.h
- ggml-cuda/**
@ -71,3 +85,6 @@ nix:
- "**/*.nix"
- .github/workflows/nix-*.yml
- .devops/nix/nixpkgs-instances.nix
embedding:
- changed-files:
- any-glob-to-any-file: examples/embedding/

1
.gitignore vendored
View file

@ -105,6 +105,7 @@ examples/jeopardy/results.txt
examples/server/*.html.hpp
examples/server/*.js.hpp
examples/server/*.mjs.hpp
examples/server/*.css.hpp
poetry.lock
poetry.toml

54
CMakeLists.txt
View file

@ -72,6 +72,7 @@ else()
set(INS_ENB ON)
endif()
option(LLAMA_SVE "llama: enable SVE" OFF)
option(LLAMA_AVX "llama: enable AVX" ${INS_ENB})
option(LLAMA_AVX2 "llama: enable AVX2" ${INS_ENB})
option(LLAMA_AVX512 "llama: enable AVX512" OFF)
@ -105,6 +106,7 @@ set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
"llama: max. batch size for using peer access")
option(LLAMA_CUDA_NO_PEER_COPY "llama: do not use peer to peer copies" OFF)
option(LLAMA_CUDA_NO_VMM "llama: do not try to use CUDA VMM" OFF)
option(LLAMA_CUDA_FA_ALL_QUANTS "llama: compile all quants for FlashAttention" OFF)
option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
@ -124,7 +126,6 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING
set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)")
option(LLAMA_KOMPUTE "llama: use Kompute" OFF)
option(LLAMA_RPC "llama: use RPC" OFF)
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
option(LLAMA_SYCL "llama: use SYCL" OFF)
option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF)
set(LLAMA_SYCL_TARGET "INTEL" CACHE STRING "llama: sycl target device")
@ -384,10 +385,6 @@ if (LLAMA_LLAMAFILE)
set(GGML_SOURCES_LLAMAFILE sgemm.cpp)
endif()
if (LLAMA_QKK_64)
add_compile_definitions(GGML_QKK_64)
endif()
if (LLAMA_CUBLAS)
message(WARNING "LLAMA_CUBLAS is deprecated and will be removed in the future.\nUse LLAMA_CUDA instead")
set(LLAMA_CUDA ON)
@ -406,6 +403,8 @@ if (LLAMA_CUDA)
file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu")
list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
add_compile_definitions(GGML_USE_CUDA)
add_compile_definitions(GGML_CUDA_USE_GRAPHS)
@ -431,6 +430,18 @@ if (LLAMA_CUDA)
if (LLAMA_CUDA_NO_PEER_COPY)
add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
endif()
if (LLAMA_CUDA_FA_ALL_QUANTS)
file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS)
else()
file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
endif()
if (LLAMA_STATIC)
if (WIN32)
@ -575,6 +586,8 @@ if (LLAMA_HIPBLAS)
file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu")
list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu")
file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA)
@ -594,6 +607,19 @@ if (LLAMA_HIPBLAS)
add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
endif()
if (LLAMA_CUDA_FA_ALL_QUANTS)
file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS)
else()
file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
endif()
add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
@ -632,6 +658,10 @@ if (LLAMA_SYCL)
add_compile_definitions(GGML_SYCL_F16)
endif()
if (LLAMA_CUDA_FORCE_MMQ)
add_compile_definitions(GGML_SYCL_FORCE_MMQ)
endif()
add_compile_options(-I./) #include DPCT
add_compile_options(-I/${SYCL_INCLUDE_DIR})
@ -747,6 +777,7 @@ if (LLAMA_KOMPUTE)
kompute-shaders/op_mul_mat_q4_0.comp
kompute-shaders/op_mul_mat_q4_1.comp
kompute-shaders/op_mul_mat_q6_k.comp
kompute-shaders/op_getrows_f32.comp
kompute-shaders/op_getrows_f16.comp
kompute-shaders/op_getrows_q4_0.comp
kompute-shaders/op_getrows_q4_1.comp
@ -779,6 +810,7 @@ if (LLAMA_KOMPUTE)
shaderop_mul_mat_q4_0.h
shaderop_mul_mat_q4_1.h
shaderop_mul_mat_q6_k.h
shaderop_getrows_f32.h
shaderop_getrows_f16.h
shaderop_getrows_q4_0.h
shaderop_getrows_q4_1.h
@ -1045,6 +1077,9 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR
# Raspberry Pi 3, 4, Zero 2 (32-bit)
list(APPEND ARCH_FLAGS -mno-unaligned-access)
endif()
if (LLAMA_SVE)
list(APPEND ARCH_FLAGS -march=armv8.6-a+sve)
endif()
endif()
elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
(NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
@ -1311,7 +1346,7 @@ set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}
install(TARGETS llama LIBRARY PUBLIC_HEADER)
install(
FILES convert.py
FILES convert-hf-to-gguf.py
PERMISSIONS
OWNER_READ
OWNER_WRITE
@ -1338,6 +1373,13 @@ if (LLAMA_METAL)
endif()
endif()
configure_file(cmake/llama.pc.in
"${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
@ONLY)
install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
DESTINATION lib/pkgconfig)
#
# programs, examples and tests
#

8
CMakePresets.json
View file

@ -1,4 +1,4 @@
{
{
"version": 4,
"configurePresets": [
{
@ -40,6 +40,10 @@
{ "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] },
{ "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] },
{ "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] }
{ "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] },
{ "name": "x64-windows-msvc-debug" , "inherits": [ "base", "debug" ] },
{ "name": "x64-windows-msvc-release", "inherits": [ "base", "release" ] },
{ "name": "x64-windows-msvc+static-release", "inherits": [ "base", "release", "static" ] }
]
}

31
Makefile
View file

@ -389,10 +389,6 @@ else
MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d
endif
ifdef LLAMA_QKK_64
MK_CPPFLAGS += -DGGML_QKK_64
endif
ifndef LLAMA_NO_ACCELERATE
# Mac OS - include Accelerate framework.
# `-framework Accelerate` works both with Apple Silicon and Mac Intel
@ -425,6 +421,15 @@ ifdef LLAMA_CUBLAS
LLAMA_CUDA := 1
endif
OBJS_CUDA_TEMP_INST = $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-wmma*.cu))
ifdef LLAMA_CUDA_FA_ALL_QUANTS
OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*.cu))
else
OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu))
OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu))
OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*f16-f16.cu))
endif # LLAMA_CUDA_FA_ALL_QUANTS
ifdef LLAMA_CUDA
ifneq ('', '$(wildcard /opt/cuda)')
CUDA_PATH ?= /opt/cuda
@ -435,6 +440,7 @@ ifdef LLAMA_CUDA
MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
OBJS += ggml-cuda.o
OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
OBJS += $(OBJS_CUDA_TEMP_INST)
MK_NVCCFLAGS += -use_fast_math
ifdef LLAMA_FATAL_WARNINGS
MK_NVCCFLAGS += -Werror all-warnings
@ -445,6 +451,9 @@ endif # JETSON_EOL_MODULE_DETECT
ifdef LLAMA_DEBUG
MK_NVCCFLAGS += -lineinfo
endif # LLAMA_DEBUG
ifdef LLAMA_CUDA_DEBUG
MK_NVCCFLAGS += --device-debug
endif # LLAMA_CUDA_DEBUG
ifdef LLAMA_CUDA_NVCC
NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC)
else
@ -494,7 +503,10 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
endif # LLAMA_CUDA_NO_PEER_COPY
ifdef LLAMA_CUDA_CCBIN
MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
endif
endif # LLAMA_CUDA_CCBIN
ifdef LLAMA_CUDA_FA_ALL_QUANTS
MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS
endif # LLAMA_CUDA_FA_ALL_QUANTS
ifdef JETSON_EOL_MODULE_DETECT
define NVCC_COMPILE
@ -506,7 +518,7 @@ define NVCC_COMPILE
endef # NVCC_COMPILE
endif # JETSON_EOL_MODULE_DETECT
ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh
$(NVCC_COMPILE)
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
@ -572,6 +584,7 @@ ifdef LLAMA_HIP_UMA
MK_CPPFLAGS += -DGGML_HIP_UMA
endif # LLAMA_HIP_UMA
MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64
MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas
HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS))
HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
@ -585,11 +598,12 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
endif # LLAMA_CUDA_NO_PEER_COPY
OBJS += ggml-cuda.o
OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
OBJS += $(OBJS_CUDA_TEMP_INST)
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh
$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
endif # LLAMA_HIPBLAS
@ -749,6 +763,7 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS)
clean:
rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
rm -vrf ggml-cuda/*.o
rm -vrf ggml-cuda/template-instances/*.o
find examples pocs -type f -name "*.o" -delete
#
@ -817,7 +832,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/colorthemes.css.hpp examples/server/style.css.hpp examples/server/theme-beeninorder.css.hpp examples/server/theme-ketivah.css.hpp examples/server/theme-mangotango.css.hpp examples/server/theme-playground.css.hpp examples/server/theme-polarnight.css.hpp examples/server/theme-snowstorm.css.hpp examples/server/index.html.hpp examples/server/index-new.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/system-prompts.js.hpp examples/server/prompt-formats.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)

10
README-sycl.md
View file

@ -54,10 +54,10 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS,
## OS
| OS | Status | Verified |
|---------|---------|------------------------------------|
| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39 |
| Windows | Support | Windows 11 |
| OS | Status | Verified |
|---------|---------|------------------------------------------------|
| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux |
| Windows | Support | Windows 11 |
## Hardware
@ -70,7 +70,7 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS,
|-------------------------------|---------|---------------------------------------|
| Intel Data Center Max Series | Support | Max 1550, 1100 |
| Intel Data Center Flex Series | Support | Flex 170 |
| Intel Arc Series | Support | Arc 770, 730M |
| Intel Arc Series | Support | Arc 770, 730M, Arc A750 |
| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake |
| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |

60
README.md
View file

@ -2,7 +2,9 @@
![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp)
[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
@ -20,7 +22,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Hot topics
- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021**
- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430
- Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021
- BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920
- MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387
- Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
@ -127,6 +130,7 @@ Typically finetunes of the base models below are supported as well.
- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
- [x] [OLMo](https://allenai.org/olmo)
- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md))
@ -140,11 +144,14 @@ Typically finetunes of the base models below are supported as well.
- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
**HTTP server**
[llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
[simplechat](./examples/server/public_simplechat) is a simple chat client, which can be used to chat with the model exposed using above web server (use --path to point to simplechat), from a local web browser.
**Bindings:**
- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
@ -198,9 +205,14 @@ Unless otherwise noted these projects are open-source with permissive licensing:
- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
- [AIKit](https://github.com/sozercan/aikit) (MIT)
*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
**Tools:**
- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
---
Here is a typical run using LLaMA v2 13B on M2 Ultra:
@ -309,8 +321,6 @@ In order to build llama.cpp you have four different options.
make
```
**Note**: for `Debug` builds, run `make LLAMA_DEBUG=1`
- On Windows:
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
@ -322,23 +332,32 @@ In order to build llama.cpp you have four different options.
make
```
- Notes:
- For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
- For faster repeated compilation, install [ccache](https://ccache.dev/).
- For debug builds, run `make LLAMA_DEBUG=1`
- Using `CMake`:
```bash
cmake -B build
cmake --build build --config Release
```
```bash
cmake -B build
cmake --build build --config Release
```
**Note**: for `Debug` builds, there are two cases:
**Notes**:
- Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
- For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
- For faster repeated compilation, install [ccache](https://ccache.dev/).
- For debug builds, there are two cases:
1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
```bash
cmake -B build -DCMAKE_BUILD_TYPE=Debug
cmake --build build
```
- Multi-config generators (`-G` param set to Visual Studio, XCode...):
2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
```bash
cmake -B build -G "Xcode"
@ -373,6 +392,14 @@ In order to build llama.cpp you have four different options.
CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read
the instructions for use and activate this options in this document below.
### Homebrew
On Mac and Linux, the homebrew package manager can be used via
```
brew install llama.cpp
```
The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668
### Metal Build
On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
@ -471,10 +498,12 @@ Building the program with BLAS support may lead to some performance improvements
|--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| LLAMA_CUDA_FORCE_MMQ | Boolean | false | Force the use of dequantization + matrix multiplication kernels instead of leveraging Math libraries. | |
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
| LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
| LLAMA_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. |
- #### hipBLAS
@ -690,7 +719,8 @@ Building the program with BLAS support may lead to some performance improvements
To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derievatives.
It does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
```bash
# obtain the official LLaMA model weights and place them in ./models
@ -707,10 +737,10 @@ ls ./models
python3 -m pip install -r requirements.txt
# convert the model to ggml FP16 format
python3 convert.py models/mymodel/
python3 convert-hf-to-gguf.py models/mymodel/
# [Optional] for models using BPE tokenizers
python convert.py models/mymodel/ --vocab-type bpe
python convert-hf-to-gguf.py models/mymodel/ --vocab-type bpe
# quantize the model to 4-bits (using Q4_K_M method)
./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M

423
ci/run.sh
View file

@ -202,12 +202,15 @@ function gg_sum_test_scripts_release {
}
function gg_get_model {
local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf"
local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
if [[ -s $gguf_3b ]]; then
echo -n "$gguf_3b"
elif [[ -s $gguf_7b ]]; then
echo -n "$gguf_7b"
local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf"
local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf"
local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
if [[ -s $gguf_0 ]]; then
echo -n "$gguf_0"
elif [[ -s $gguf_1 ]]; then
echo -n "$gguf_1"
elif [[ -s $gguf_2 ]]; then
echo -n "$gguf_2"
else
echo >&2 "No model found. Can't run gg_run_ctest_with_model."
exit 1
@ -256,139 +259,6 @@ function gg_sum_ctest_with_model_release {
gg_printf '```\n'
}
# open_llama_3b_v2
function gg_run_open_llama_3b_v2 {
cd ${SRC}
gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json
gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model
gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json
gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json
gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin
gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json
gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
path_models="../models-mnt/open-llama/3B-v2"
path_wiki="../models-mnt/wikitext/wikitext-2-raw"
rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert.py ${path_models}
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
model_q4_0="${path_models}/ggml-model-q4_0.gguf"
model_q4_1="${path_models}/ggml-model-q4_1.gguf"
model_q5_0="${path_models}/ggml-model-q5_0.gguf"
model_q5_1="${path_models}/ggml-model-q5_1.gguf"
model_q2_k="${path_models}/ggml-model-q2_k.gguf"
model_q3_k="${path_models}/ggml-model-q3_k.gguf"
model_q4_k="${path_models}/ggml-model-q4_k.gguf"
model_q5_k="${path_models}/ggml-model-q5_k.gguf"
model_q6_k="${path_models}/ggml-model-q6_k.gguf"
wiki_test_60="${path_wiki}/wiki.test-60.raw"
./bin/quantize ${model_f16} ${model_q8_0} q8_0
./bin/quantize ${model_f16} ${model_q4_0} q4_0
./bin/quantize ${model_f16} ${model_q4_1} q4_1
./bin/quantize ${model_f16} ${model_q5_0} q5_0
./bin/quantize ${model_f16} ${model_q5_1} q5_1
./bin/quantize ${model_f16} ${model_q2_k} q2_k
./bin/quantize ${model_f16} ${model_q3_k} q3_k
./bin/quantize ${model_f16} ${model_q4_k} q4_k
./bin/quantize ${model_f16} ${model_q5_k} q5_k
./bin/quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
function check_ppl {
qnt="$1"
ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
return 20
fi
printf ' - %s @ %s OK\n' "$qnt" "$ppl"
return 0
}
check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
set +e
}
function gg_sum_open_llama_3b_v2 {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'OpenLLaMA 3B-v2:\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
}
# open_llama_7b_v2
# requires: GG_BUILD_CUDA
@ -417,7 +287,7 @@ function gg_run_open_llama_7b_v2 {
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert.py ${path_models}
python3 ../examples/convert-legacy-llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
@ -526,6 +396,272 @@ function gg_sum_open_llama_7b_v2 {
gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
}
# pythia_1.4b
function gg_run_pythia_1_4b {
cd ${SRC}
gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json
gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json
gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json
gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json
gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin
gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
path_models="../models-mnt/pythia/1.4B"
path_wiki="../models-mnt/wikitext/wikitext-2-raw"
rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
model_q4_0="${path_models}/ggml-model-q4_0.gguf"
model_q4_1="${path_models}/ggml-model-q4_1.gguf"
model_q5_0="${path_models}/ggml-model-q5_0.gguf"
model_q5_1="${path_models}/ggml-model-q5_1.gguf"
model_q2_k="${path_models}/ggml-model-q2_k.gguf"
model_q3_k="${path_models}/ggml-model-q3_k.gguf"
model_q4_k="${path_models}/ggml-model-q4_k.gguf"
model_q5_k="${path_models}/ggml-model-q5_k.gguf"
model_q6_k="${path_models}/ggml-model-q6_k.gguf"
wiki_test_60="${path_wiki}/wiki.test-60.raw"
./bin/quantize ${model_f16} ${model_q8_0} q8_0
./bin/quantize ${model_f16} ${model_q4_0} q4_0
./bin/quantize ${model_f16} ${model_q4_1} q4_1
./bin/quantize ${model_f16} ${model_q5_0} q5_0
./bin/quantize ${model_f16} ${model_q5_1} q5_1
./bin/quantize ${model_f16} ${model_q2_k} q2_k
./bin/quantize ${model_f16} ${model_q3_k} q3_k
./bin/quantize ${model_f16} ${model_q4_k} q4_k
./bin/quantize ${model_f16} ${model_q5_k} q5_k
./bin/quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
function check_ppl {
qnt="$1"
ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
return 20
fi
printf ' - %s @ %s OK\n' "$qnt" "$ppl"
return 0
}
check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
#check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
set +e
}
function gg_sum_pythia_1_4b {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'Pythia 1.4B:\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
}
# pythia_2_8b
# requires: GG_BUILD_CUDA
function gg_run_pythia_2_8b {
cd ${SRC}
gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json
gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json
gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json
gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json
gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin
gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
path_models="../models-mnt/pythia/2.8B"
path_wiki="../models-mnt/wikitext/wikitext-2-raw"
rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
model_q4_0="${path_models}/ggml-model-q4_0.gguf"
model_q4_1="${path_models}/ggml-model-q4_1.gguf"
model_q5_0="${path_models}/ggml-model-q5_0.gguf"
model_q5_1="${path_models}/ggml-model-q5_1.gguf"
model_q2_k="${path_models}/ggml-model-q2_k.gguf"
model_q3_k="${path_models}/ggml-model-q3_k.gguf"
model_q4_k="${path_models}/ggml-model-q4_k.gguf"
model_q5_k="${path_models}/ggml-model-q5_k.gguf"
model_q6_k="${path_models}/ggml-model-q6_k.gguf"
wiki_test="${path_wiki}/wiki.test.raw"
./bin/quantize ${model_f16} ${model_q8_0} q8_0
./bin/quantize ${model_f16} ${model_q4_0} q4_0
./bin/quantize ${model_f16} ${model_q4_1} q4_1
./bin/quantize ${model_f16} ${model_q5_0} q5_0
./bin/quantize ${model_f16} ${model_q5_1} q5_1
./bin/quantize ${model_f16} ${model_q2_k} q2_k
./bin/quantize ${model_f16} ${model_q3_k} q3_k
./bin/quantize ${model_f16} ${model_q4_k} q4_k
./bin/quantize ${model_f16} ${model_q5_k} q5_k
./bin/quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/main --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
function check_ppl {
qnt="$1"
ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
return 20
fi
printf ' - %s @ %s OK\n' "$qnt" "$ppl"
return 0
}
check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
#check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
set +e
}
function gg_sum_pythia_2_8b {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'Pythia 2.8B:\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
}
# bge-small
function gg_run_embd_bge_small {
@ -552,7 +688,7 @@ function gg_run_embd_bge_small {
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert-hf-to-gguf.py ${path_models}
python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
@ -606,9 +742,10 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
if [ -z ${GG_BUILD_CUDA} ]; then
test $ret -eq 0 && gg_run open_llama_3b_v2
test $ret -eq 0 && gg_run pythia_1_4b
else
test $ret -eq 0 && gg_run open_llama_7b_v2
test $ret -eq 0 && gg_run pythia_2_8b
#test $ret -eq 0 && gg_run open_llama_7b_v2
fi
test $ret -eq 0 && gg_run ctest_with_model_debug
test $ret -eq 0 && gg_run ctest_with_model_release

10
cmake/llama.pc.in Normal file
View file

@ -0,0 +1,10 @@
prefix=@CMAKE_INSTALL_PREFIX@
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir=${prefix}/include
Name: llama
Description: Port of Facebook's LLaMA model in C/C++
Version: @PROJECT_VERSION@
Libs: -L${libdir} -lllama
Cflags: -I${includedir}

34
common/common.cpp
View file

@ -904,6 +904,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.interactive_specials = true;
return true;
}
if (arg == "--special") {
params.special = true;
return true;
}
if (arg == "--embedding") {
params.embedding = true;
return true;
@ -998,9 +1002,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
return true;
}
params.main_gpu = std::stoi(argv[i]);
#ifndef GGML_USE_CUDA_SYCL
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the main GPU has no effect.\n");
#endif // GGML_USE_CUDA_SYCL
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--split-mode" || arg == "-sm") {
@ -1026,9 +1030,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
invalid_param = true;
return true;
}
#ifndef GGML_USE_CUDA_SYCL
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUDA_SYCL
#ifndef GGML_USE_CUDA_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUDA_SYCL_VULKAN
return true;
}
if (arg == "--tensor-split" || arg == "-ts") {
@ -1362,6 +1366,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
printf(" -h, --help show this help message and exit\n");
printf(" --version show version and build info\n");
printf(" -i, --interactive run in interactive mode\n");
printf(" --special special tokens output enabled\n");
printf(" --interactive-specials allow special tokens in user text, in interactive mode\n");
printf(" --interactive-first run in interactive mode and wait for input right away\n");
printf(" -cnv, --conversation run in conversation mode (does not print special tokens and suffix/prefix)\n");
@ -1855,11 +1860,15 @@ bool fs_create_directory_with_parents(const std::string & path) {
std::string fs_get_cache_directory() {
std::string cache_directory = "";
auto ensure_trailing_slash = [](std::string p) {
// Make sure to add trailing slash
if (p.back() != DIRECTORY_SEPARATOR) {
p += DIRECTORY_SEPARATOR;
}
return p;
};
if (getenv("LLAMA_CACHE")) {
cache_directory = std::getenv("LLAMA_CACHE");
if (cache_directory.back() != DIRECTORY_SEPARATOR) {
cache_directory += DIRECTORY_SEPARATOR;
}
} else {
#ifdef __linux__
if (std::getenv("XDG_CACHE_HOME")) {
@ -1870,12 +1879,12 @@ std::string fs_get_cache_directory() {
#elif defined(__APPLE__)
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
#elif defined(_WIN32)
cache_directory = std::getenv("APPDATA");
cache_directory = std::getenv("LOCALAPPDATA");
#endif // __linux__
cache_directory = ensure_trailing_slash(cache_directory);
cache_directory += "llama.cpp";
cache_directory += DIRECTORY_SEPARATOR;
}
return cache_directory;
return ensure_trailing_slash(cache_directory);
}
@ -2840,6 +2849,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false");
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");

1
common/common.h
View file

@ -146,6 +146,7 @@ struct gpt_params {
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode
bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode
bool special = false; // enable special token output
bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix)
bool chatml = false; // chatml mode (used for models trained on chatml syntax)
bool prompt_cache_all = false; // save user input and generations to prompt cache

2
common/train.cpp
View file

@ -1052,7 +1052,7 @@ struct train_params_common get_default_train_params_common() {
params.custom_n_ctx = false;
params.use_flash = true;
params.use_flash = false;
params.use_checkpointing = true;
params.sample_start = "";

1
convert-hf-to-gguf-update.py
View file

@ -81,6 +81,7 @@ models = [
{"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
{"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
{"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
]

305
convert-hf-to-gguf.py
View file

@ -319,11 +319,10 @@ class Model:
data = data.astype(np.float32)
data_qtype = gguf.GGMLQuantizationType.F32
block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype]
shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape
# reverse shape to make it similar to the internal ggml dimension order
shape_str = f"""{{{', '.join(str(n) for n in reversed(
(*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size))
)}}}"""
shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}"
# n_dims is implicit in the shape
logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
@ -477,6 +476,9 @@ class Model:
if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
res = "jina-v2-de"
if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d":
# ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
res = "smaug-bpe"
if res is None:
logger.warning("\n")
@ -635,7 +637,7 @@ class Model:
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_llama_hf(self):
vocab = LlamaHfVocab(self.dir_model)
vocab = gguf.LlamaHfVocab(self.dir_model)
tokens = []
scores = []
toktypes = []
@ -676,6 +678,44 @@ class GPTNeoXModel(Model):
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
tensors: list[tuple[str, Tensor]] = []
if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
# Map bloom-style qkv_linear to gpt-style qkv_linear
# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
data_torch = torch.cat(
(
qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.weight")
elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name):
qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
data_torch = torch.cat(
(
qkv_bias[:, 0, :].reshape((n_embed,)),
qkv_bias[:, 1, :].reshape((n_embed,)),
qkv_bias[:, 2, :].reshape((n_embed,)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.bias")
tensors.append((self.map_tensor_name(name), data_torch))
return tensors
@Model.register("BloomForCausalLM")
class BloomModel(Model):
@ -1280,6 +1320,17 @@ class LlamaModel(Model):
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
if "add_prefix_space" in tokenizer_config_json:
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
# Apply to granite small models only
if self.hparams.get("vocab_size", 32000) == 49152:
self.gguf_writer.add_add_bos_token(False)
@staticmethod
def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
if n_head_kv is not None and n_head != n_head_kv:
@ -1294,9 +1345,9 @@ class LlamaModel(Model):
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
if name.endswith("q_proj.weight"):
if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith("k_proj.weight"):
if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
# process the experts separately
@ -2358,7 +2409,8 @@ class CommandR2Model(Model):
# max_position_embeddings = 8192 in config.json but model was actually
# trained on 128k context length
self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
# aya-23 models don't have model_max_length specified
self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"])
def set_gguf_parameters(self):
super().set_gguf_parameters()
@ -2431,6 +2483,236 @@ class JinaBertV2Model(BertModel):
self.gguf_writer.add_add_eos_token(True)
@Model.register("ArcticForCausalLM")
class ArcticModel(Model):
model_arch = gguf.MODEL_ARCH.ARCTIC
def set_vocab(self):
# The reason for using a custom implementation here is that the
# snowflake-arctic-instruct model redefined tokens 31998 and 31999 from
# tokenizer.model and used them as BOS and EOS instead of adding new tokens.
from sentencepiece import SentencePieceProcessor
tokenizer_path = self.dir_model / 'tokenizer.model'
if not tokenizer_path.is_file():
logger.error(f'Error: Missing {tokenizer_path}')
sys.exit(1)
# Read the whole vocabulary from the tokenizer.model file
tokenizer = SentencePieceProcessor()
tokenizer.LoadFromFile(str(tokenizer_path))
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
for token_id in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(token_id)
text = piece.encode("utf-8")
score = tokenizer.GetScore(token_id)
toktype = SentencePieceTokenTypes.NORMAL
if tokenizer.IsUnknown(token_id):
toktype = SentencePieceTokenTypes.UNKNOWN
elif tokenizer.IsControl(token_id):
toktype = SentencePieceTokenTypes.CONTROL
elif tokenizer.IsUnused(token_id):
toktype = SentencePieceTokenTypes.UNUSED
elif tokenizer.IsByte(token_id):
toktype = SentencePieceTokenTypes.BYTE
tokens[token_id] = text
scores[token_id] = score
toktypes[token_id] = toktype
# Use the added_tokens_decoder field from tokeniser_config.json as the source
# of information about added/redefined tokens and modify them accordingly.
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
if "added_tokens_decoder" in tokenizer_config_json:
added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"]
for token_id, token_json in added_tokens_decoder.items():
token_id = int(token_id)
if (token_id >= vocab_size):
logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
continue
token_content = token_json["content"]
token_type = SentencePieceTokenTypes.USER_DEFINED
token_score = -10000.0
# Map unk_token to UNKNOWN, other special tokens to CONTROL
# Set the score to 0.0 as in the original tokenizer.model
if ("special" in token_json) and token_json["special"]:
if token_content == tokenizer_config_json["unk_token"]:
token_type = SentencePieceTokenTypes.UNKNOWN
else:
token_type = SentencePieceTokenTypes.CONTROL
token_score = 0.0
logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})")
tokens[token_id] = token_content.encode("utf-8")
toktypes[token_id] = token_type
scores[token_id] = token_score
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
if name.endswith("q_proj.weight"):
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith("k_proj.weight"):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
# process the experts separately
if name.find("block_sparse_moe.experts") != -1:
n_experts = self.hparams["num_local_experts"]
assert bid is not None
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
self._experts[bid][name] = data_torch
if len(self._experts[bid]) >= n_experts * 3:
tensors: list[tuple[str, Tensor]] = []
# merge the experts into a single 3d tensor
for wid in ["w1", "w2", "w3"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
new_name = self.map_tensor_name(merged_name)
tensors.append((new_name, data_torch))
return tensors
else:
return []
return [(self.map_tensor_name(name), data_torch)]
def write_tensors(self):
super().write_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")
@Model.register("DeepseekV2ForCausalLM")
class DeepseekV2Model(Model):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
def set_vocab(self):
self._set_vocab_gpt2()
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length(hparams["v_head_dim"])
self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "yarn":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"])
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find("mlp.experts") != -1:
n_experts = self.hparams["n_routed_experts"]
assert bid is not None
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
self._experts[bid][name] = data_torch
if len(self._experts[bid]) >= n_experts * 3:
tensors: list[tuple[str, Tensor]] = []
# merge the experts into a single 3d tensor
for w_name in ["down_proj", "gate_proj", "up_proj"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
new_name = self.map_tensor_name(merged_name)
tensors.append((new_name, data_torch))
return tensors
else:
return []
return [(self.map_tensor_name(name), data_torch)]
def write_tensors(self):
super().write_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")
###### CONVERSION LOGIC ######
@ -2591,7 +2873,12 @@ def main() -> None:
hparams = Model.load_hparams(dir_model)
with torch.inference_mode():
model_class = Model.from_model_architecture(hparams["architectures"][0])
try:
model_class = Model.from_model_architecture(hparams["architectures"][0])
except NotImplementedError:
logger.error(f"Model {hparams['architectures'][0]} is not supported")
sys.exit(1)
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file,
args.no_lazy, split_arguments)

2
docs/HOWTO-add-model.md
View file

@ -17,7 +17,7 @@ Also, it is important to check that the examples and main ggml backends (CUDA, M
### 1. Convert the model to GGUF
This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py).
Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-legacy-llama.py](../examples/convert-legacy-llama.py) (for `llama/llama2` models in `.pth` format).
The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.

308
convert.py → examples/convert-legacy-llama.py
View file

@ -24,14 +24,16 @@ from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional
from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar, Optional
import numpy as np
from sentencepiece import SentencePieceProcessor
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
# use .parent.parent since we are in "examples" directory
sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py'))
import gguf
from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab
if TYPE_CHECKING:
from typing_extensions import Self, TypeAlias
@ -380,306 +382,6 @@ class Metadata:
return metadata
#
# vocab
#
@runtime_checkable
class BaseVocab(Protocol):
tokenizer_model: ClassVar[str]
name: ClassVar[str]
class NoVocab(BaseVocab):
tokenizer_model = "no_vocab"
name = "no_vocab"
def __repr__(self) -> str:
return "<NoVocab for a model without integrated vocabulary>"
@runtime_checkable
class Vocab(BaseVocab, Protocol):
vocab_size: int
added_tokens_dict: dict[str, int]
added_tokens_list: list[str]
fname_tokenizer: Path
def __init__(self, base_path: Path): ...
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
class BpeVocab(Vocab):
tokenizer_model = "gpt2"
name = "bpe"
def __init__(self, base_path: Path):
added_tokens: dict[str, int] = {}
if (fname_tokenizer := base_path / 'vocab.json').exists():
# "slow" tokenizer
with open(fname_tokenizer, encoding="utf-8") as f:
self.vocab = json.load(f)
try:
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
added_tokens = json.load(f)
except FileNotFoundError:
pass
else:
# "fast" tokenizer
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
# if this fails, FileNotFoundError propagates to caller
with open(fname_tokenizer, encoding="utf-8") as f:
tokenizer_json = json.load(f)
tokenizer_model: dict[str, Any] = tokenizer_json['model']
if (
tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
or tokenizer_json['decoder']['type'] != 'ByteLevel'
):
raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
self.vocab = tokenizer_model["vocab"]
if (added := tokenizer_json.get('added_tokens')) is not None:
# Added tokens here can be duplicates of the main vocabulary.
added_tokens = {item['content']: item['id']
for item in added
if item['content'] not in self.vocab}
vocab_size = len(self.vocab)
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
actual_ids = sorted(added_tokens.values())
if expected_ids != actual_ids:
expected_end_id = vocab_size + len(actual_ids) - 1
raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
f"{vocab_size} - {expected_end_id}; got {actual_ids}")
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
self.added_tokens_dict = added_tokens
self.added_tokens_list = [text for (text, idx) in items]
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
for i, _ in enumerate(self.vocab):
yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.bpe_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class SentencePieceVocab(Vocab):
tokenizer_model = "llama"
name = "spm"
def __init__(self, base_path: Path):
added_tokens: dict[str, int] = {}
if (fname_tokenizer := base_path / 'tokenizer.model').exists():
# normal location
try:
with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
added_tokens = json.load(f)
except FileNotFoundError:
pass
elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
# not found in alternate location either
raise FileNotFoundError('Cannot find tokenizer.model')
self.sentencepiece_tokenizer = SentencePieceProcessor()
self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
vocab_size = self.sentencepiece_tokenizer.vocab_size()
new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
actual_new_ids = sorted(new_tokens.keys())
if expected_new_ids != actual_new_ids:
raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
# Token pieces that were added to the base vocabulary.
self.added_tokens_dict = added_tokens
self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.sentencepiece_tokenizer
for i in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(i)
text = piece.encode("utf-8")
score: float = tokenizer.GetScore(i)
toktype = gguf.TokenType.NORMAL
if tokenizer.IsUnknown(i):
toktype = gguf.TokenType.UNKNOWN
if tokenizer.IsControl(i):
toktype = gguf.TokenType.CONTROL
# NOTE: I think added_tokens are user defined.
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
if tokenizer.IsUnused(i):
toktype = gguf.TokenType.UNUSED
if tokenizer.IsByte(i):
toktype = gguf.TokenType.BYTE
yield text, score, toktype
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.sentencepiece_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class LlamaHfVocab(Vocab):
tokenizer_model = "llama"
name = "hfft"
def __init__(self, base_path: Path):
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
# if this fails, FileNotFoundError propagates to caller
with open(fname_tokenizer, encoding='utf-8') as f:
tokenizer_json = json.load(f)
# pre-check so we know if we need transformers
tokenizer_model: dict[str, Any] = tokenizer_json['model']
is_llama3 = (
tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
and not tokenizer_model.get('byte_fallback', True)
)
if is_llama3:
raise TypeError('Llama 3 must be converted with BpeVocab')
if not is_llama3 and (
tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
or tokenizer_json['decoder']['type'] != 'Sequence'
):
raise FileNotFoundError('Cannot find Llama BPE tokenizer')
try:
from transformers import AutoTokenizer
except ImportError as e:
raise ImportError(
"To use LlamaHfVocab, please install the `transformers` package. "
"You can install it with `pip install transformers`."
) from e
# Allow the tokenizer to default to slow or fast versions.
# Explicitly set tokenizer to use local paths.
self.tokenizer = AutoTokenizer.from_pretrained(
base_path,
cache_dir=base_path,
local_files_only=True,
)
assert self.tokenizer.is_fast # assume tokenizer.json is used
# Initialize lists and dictionaries for added tokens
self.added_tokens_list = []
self.added_tokens_dict = dict()
self.added_tokens_ids = set()
# Process added tokens
for tok, tokidx in sorted(
self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
):
# Only consider added tokens that are not in the base vocabulary
if tokidx >= self.tokenizer.vocab_size:
self.added_tokens_list.append(tok)
self.added_tokens_dict[tok] = tokidx
self.added_tokens_ids.add(tokidx)
# Store special tokens and their IDs
self.specials = {
tok: self.tokenizer.get_vocab()[tok]
for tok in self.tokenizer.all_special_tokens
}
self.special_ids = set(self.tokenizer.all_special_ids)
# Set vocabulary sizes
self.vocab_size_base = self.tokenizer.vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
reverse_vocab = {
id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
}
for token_id in range(self.vocab_size_base):
# Skip processing added tokens here
if token_id in self.added_tokens_ids:
continue
# Convert token text to bytes
token_text = reverse_vocab[token_id].encode("utf-8")
# Yield token text, score, and type
yield token_text, self.get_token_score(token_id), self.get_token_type(
token_id, token_text, self.special_ids # Reuse already stored special IDs
)
def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
# Special case for byte tokens
if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
return gguf.TokenType.BYTE
# Determine token type based on whether it's a special token
return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
def get_token_score(self, token_id: int) -> float:
# Placeholder for actual logic to determine the token's score
# This needs to be implemented based on specific requirements
return -1000.0 # Default score
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
if text in self.specials:
toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
score = self.get_token_score(self.specials[text])
else:
toktype = gguf.TokenType.USER_DEFINED
score = -1000.0
yield text.encode("utf-8"), score, toktype
def has_newline_token(self):
return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.hf_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
#
# data loading
# TODO: reuse (probably move to gguf.py?)

2
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -774,7 +774,7 @@ static struct train_params get_default_train_params() {
params.samples_start_after_nl = false;
params.use_adam = true;
params.use_flash = true;
params.use_flash = false;
params.use_scratch = true;
// only adam

3
examples/finetune/finetune.cpp
View file

@ -643,7 +643,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch);
struct ggml_tensor * t16;
if (enable_flash_attn) {
t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported");
//t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
} else {
struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);

28
examples/llama-bench/llama-bench.cpp
View file

@ -178,6 +178,7 @@ struct cmd_params {
std::vector<ggml_type> type_v;
std::vector<int> n_threads;
std::vector<int> n_gpu_layers;
std::vector<std::string> rpc_servers;
std::vector<llama_split_mode> split_mode;
std::vector<int> main_gpu;
std::vector<bool> no_kv_offload;
@ -202,6 +203,7 @@ static const cmd_params cmd_params_defaults = {
/* type_v */ {GGML_TYPE_F16},
/* n_threads */ {cpu_get_num_math()},
/* n_gpu_layers */ {99},
/* rpc_servers */ {""},
/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
@ -230,6 +232,7 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -ctv, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -rpc, --rpc <rpc_servers> (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str());
printf(" -sm, --split-mode <none|layer|row> (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
@ -384,6 +387,12 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = split<int>(argv[i], split_delim);
params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
} else if (arg == "-rpc" || arg == "--rpc") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rpc_servers.push_back(argv[i]);
} else if (arg == "-sm" || arg == "--split-mode") {
if (++i >= argc) {
invalid_param = true;
@ -519,6 +528,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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; }
@ -541,6 +551,7 @@ struct cmd_params_instance {
ggml_type type_v;
int n_threads;
int n_gpu_layers;
std::string rpc_servers;
llama_split_mode split_mode;
int main_gpu;
bool no_kv_offload;
@ -553,6 +564,9 @@ struct cmd_params_instance {
llama_model_params mparams = llama_model_default_params();
mparams.n_gpu_layers = n_gpu_layers;
if (!rpc_servers.empty()) {
mparams.rpc_servers = rpc_servers.c_str();
}
mparams.split_mode = split_mode;
mparams.main_gpu = main_gpu;
mparams.tensor_split = tensor_split.data();
@ -564,6 +578,7 @@ 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 &&
@ -592,6 +607,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
// this ordering minimizes the number of times that each model needs to be reloaded
for (const auto & m : params.model)
for (const auto & nl : params.n_gpu_layers)
for (const auto & rpc : params.rpc_servers)
for (const auto & sm : params.split_mode)
for (const auto & mg : params.main_gpu)
for (const auto & ts : params.tensor_split)
@ -618,6 +634,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .rpc_servers = */ rpc,
/* .split_mode = */ sm,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
@ -643,6 +660,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .rpc_servers = */ rpc,
/* .split_mode = */ sm,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
@ -668,6 +686,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .rpc_servers = */ rpc,
/* .split_mode = */ sm,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
@ -692,6 +711,7 @@ struct test {
static const bool kompute;
static const bool metal;
static const bool sycl;
static const bool rpc;
static const bool gpu_blas;
static const bool blas;
static const std::string cpu_info;
@ -790,6 +810,9 @@ struct test {
if (sycl) {
return GGML_SYCL_NAME;
}
if (rpc) {
return "RPC";
}
if (gpu_blas) {
return "GPU BLAS";
}
@ -803,7 +826,7 @@ struct test {
static const std::vector<std::string> & get_fields() {
static const std::vector<std::string> fields = {
"build_commit", "build_number",
"cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas",
"cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas",
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_ubatch",
@ -859,7 +882,7 @@ struct test {
std::vector<std::string> values = {
build_commit, std::to_string(build_number),
std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan),
std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas),
std::to_string(metal), std::to_string(sycl), std::to_string(rpc), std::to_string(gpu_blas), std::to_string(blas),
cpu_info, gpu_info,
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
std::to_string(n_batch), std::to_string(n_ubatch),
@ -894,6 +917,7 @@ const bool test::metal = !!ggml_cpu_has_metal();
const bool test::gpu_blas = !!ggml_cpu_has_gpublas();
const bool test::blas = !!ggml_cpu_has_blas();
const bool test::sycl = !!ggml_cpu_has_sycl();
const bool test::rpc = !!ggml_cpu_has_rpc();
const std::string test::cpu_info = get_cpu_info();
const std::string test::gpu_info = get_gpu_info();

25
examples/llama.android/app/build.gradle.kts
View file

@ -7,8 +7,6 @@ android {
namespace = "com.example.llama"
compileSdk = 34
ndkVersion = "26.1.10909125"
defaultConfig {
applicationId = "com.example.llama"
minSdk = 33
@ -20,17 +18,6 @@ android {
vectorDrawables {
useSupportLibrary = true
}
ndk {
// Add NDK properties if wanted, e.g.
// abiFilters += listOf("arm64-v8a")
}
externalNativeBuild {
cmake {
arguments += "-DCMAKE_BUILD_TYPE=Release"
cppFlags += listOf()
arguments += listOf()
}
}
}
buildTypes {
@ -55,17 +42,6 @@ android {
composeOptions {
kotlinCompilerExtensionVersion = "1.5.1"
}
packaging {
resources {
excludes += "/META-INF/{AL2.0,LGPL2.1}"
}
}
externalNativeBuild {
cmake {
path = file("src/main/cpp/CMakeLists.txt")
version = "3.22.1"
}
}
}
dependencies {
@ -78,6 +54,7 @@ dependencies {
implementation("androidx.compose.ui:ui-graphics")
implementation("androidx.compose.ui:ui-tooling-preview")
implementation("androidx.compose.material3:material3")
implementation(project(":llama"))
testImplementation("junit:junit:4.13.2")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")

13
examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt
View file

@ -1,5 +1,6 @@
package com.example.llama
import android.llama.cpp.LLamaAndroid
import android.util.Log
import androidx.compose.runtime.getValue
import androidx.compose.runtime.mutableStateOf
@ -9,7 +10,7 @@ import androidx.lifecycle.viewModelScope
import kotlinx.coroutines.flow.catch
import kotlinx.coroutines.launch
class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() {
companion object {
@JvmStatic
private val NanosPerSecond = 1_000_000_000.0
@ -28,7 +29,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
viewModelScope.launch {
try {
llm.unload()
llamaAndroid.unload()
} catch (exc: IllegalStateException) {
messages += exc.message!!
}
@ -44,7 +45,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
messages += ""
viewModelScope.launch {
llm.send(text)
llamaAndroid.send(text)
.catch {
Log.e(tag, "send() failed", it)
messages += it.message!!
@ -57,7 +58,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
viewModelScope.launch {
try {
val start = System.nanoTime()
val warmupResult = llm.bench(pp, tg, pl, nr)
val warmupResult = llamaAndroid.bench(pp, tg, pl, nr)
val end = System.nanoTime()
messages += warmupResult
@ -70,7 +71,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
return@launch
}
messages += llm.bench(512, 128, 1, 3)
messages += llamaAndroid.bench(512, 128, 1, 3)
} catch (exc: IllegalStateException) {
Log.e(tag, "bench() failed", exc)
messages += exc.message!!
@ -81,7 +82,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
fun load(pathToModel: String) {
viewModelScope.launch {
try {
llm.load(pathToModel)
llamaAndroid.load(pathToModel)
messages += "Loaded $pathToModel"
} catch (exc: IllegalStateException) {
Log.e(tag, "load() failed", exc)

1
examples/llama.android/build.gradle.kts
View file

@ -2,4 +2,5 @@
plugins {
id("com.android.application") version "8.2.0" apply false
id("org.jetbrains.kotlin.android") version "1.9.0" apply false
id("com.android.library") version "8.2.0" apply false
}

1
examples/llama.android/llama/.gitignore vendored Normal file
View file

@ -0,0 +1 @@
/build

2
examples/llama.android/app/src/main/cpp/CMakeLists.txt → examples/llama.android/llama/CMakeLists.txt
View file

@ -42,7 +42,7 @@ add_subdirectory(../../../../../../ build-llama)
# used in the AndroidManifest.xml file.
add_library(${CMAKE_PROJECT_NAME} SHARED
# List C/C++ source files with relative paths to this CMakeLists.txt.
llama-android.cpp)
llama-android.cpp)
# Specifies libraries CMake should link to your target library. You
# can link libraries from various origins, such as libraries defined in this

68
examples/llama.android/llama/build.gradle.kts Normal file
View file

@ -0,0 +1,68 @@
plugins {
id("com.android.library")
id("org.jetbrains.kotlin.android")
}
android {
namespace = "android.llama.cpp"
compileSdk = 34
defaultConfig {
minSdk = 33
testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
consumerProguardFiles("consumer-rules.pro")
ndk {
// Add NDK properties if wanted, e.g.
// abiFilters += listOf("arm64-v8a")
}
externalNativeBuild {
cmake {
arguments += "-DCMAKE_BUILD_TYPE=Release"
cppFlags += listOf()
arguments += listOf()
cppFlags("")
}
}
}
buildTypes {
release {
isMinifyEnabled = false
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
)
}
}
externalNativeBuild {
cmake {
path("src/main/cpp/CMakeLists.txt")
version = "3.22.1"
}
}
compileOptions {
sourceCompatibility = JavaVersion.VERSION_1_8
targetCompatibility = JavaVersion.VERSION_1_8
}
kotlinOptions {
jvmTarget = "1.8"
}
packaging {
resources {
excludes += "/META-INF/{AL2.0,LGPL2.1}"
}
}
}
dependencies {
implementation("androidx.core:core-ktx:1.12.0")
implementation("androidx.appcompat:appcompat:1.6.1")
implementation("com.google.android.material:material:1.11.0")
testImplementation("junit:junit:4.13.2")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
}

0
examples/llama.android/llama/consumer-rules.pro Normal file
View file

21
examples/llama.android/llama/proguard-rules.pro vendored Normal file
View file

@ -0,0 +1,21 @@
# Add project specific ProGuard rules here.
# You can control the set of applied configuration files using the
# proguardFiles setting in build.gradle.
#
# For more details, see
# http://developer.android.com/guide/developing/tools/proguard.html
# If your project uses WebView with JS, uncomment the following
# and specify the fully qualified class name to the JavaScript interface
# class:
#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
# public *;
#}
# Uncomment this to preserve the line number information for
# debugging stack traces.
#-keepattributes SourceFile,LineNumberTable
# If you keep the line number information, uncomment this to
# hide the original source file name.
#-renamesourcefileattribute SourceFile

24
examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt Normal file
View file

@ -0,0 +1,24 @@
package android.llama.cpp
import androidx.test.platform.app.InstrumentationRegistry
import androidx.test.ext.junit.runners.AndroidJUnit4
import org.junit.Test
import org.junit.runner.RunWith
import org.junit.Assert.*
/**
* Instrumented test, which will execute on an Android device.
*
* See [testing documentation](http://d.android.com/tools/testing).
*/
@RunWith(AndroidJUnit4::class)
class ExampleInstrumentedTest {
@Test
fun useAppContext() {
// Context of the app under test.
val appContext = InstrumentationRegistry.getInstrumentation().targetContext
assertEquals("android.llama.cpp.test", appContext.packageName)
}
}

4
examples/llama.android/llama/src/main/AndroidManifest.xml Normal file
View file

@ -0,0 +1,4 @@
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android">
</manifest>

49
examples/llama.android/llama/src/main/cpp/CMakeLists.txt Normal file
View file

@ -0,0 +1,49 @@
# For more information about using CMake with Android Studio, read the
# documentation: https://d.android.com/studio/projects/add-native-code.html.
# For more examples on how to use CMake, see https://github.com/android/ndk-samples.
# Sets the minimum CMake version required for this project.
cmake_minimum_required(VERSION 3.22.1)
# Declares the project name. The project name can be accessed via ${ PROJECT_NAME},
# Since this is the top level CMakeLists.txt, the project name is also accessible
# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level
# build script scope).
project("llama-android")
include(FetchContent)
FetchContent_Declare(
llama
GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
GIT_TAG master
)
# Also provides "common"
FetchContent_MakeAvailable(llama)
# Creates and names a library, sets it as either STATIC
# or SHARED, and provides the relative paths to its source code.
# You can define multiple libraries, and CMake builds them for you.
# Gradle automatically packages shared libraries with your APK.
#
# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define
# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
# is preferred for the same purpose.
#
# In order to load a library into your app from Java/Kotlin, you must call
# System.loadLibrary() and pass the name of the library defined here;
# for GameActivity/NativeActivity derived applications, the same library name must be
# used in the AndroidManifest.xml file.
add_library(${CMAKE_PROJECT_NAME} SHARED
# List C/C++ source files with relative paths to this CMakeLists.txt.
llama-android.cpp)
# Specifies libraries CMake should link to your target library. You
# can link libraries from various origins, such as libraries defined in this
# build script, prebuilt third-party libraries, or Android system libraries.
target_link_libraries(${CMAKE_PROJECT_NAME}
# List libraries link to the target library
llama
common
android
log)

28
examples/llama.android/app/src/main/cpp/llama-android.cpp → examples/llama.android/llama/src/main/cpp/llama-android.cpp
View file

@ -81,7 +81,7 @@ static void log_callback(ggml_log_level level, const char * fmt, void * data) {
extern "C"
JNIEXPORT jlong JNICALL
Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) {
llama_model_params model_params = llama_model_default_params();
auto path_to_model = env->GetStringUTFChars(filename, 0);
@ -101,13 +101,13 @@ Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) {
Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) {
llama_free_model(reinterpret_cast<llama_model *>(model));
}
extern "C"
JNIEXPORT jlong JNICALL
Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) {
auto model = reinterpret_cast<llama_model *>(jmodel);
if (!model) {
@ -139,25 +139,25 @@ Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) {
Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) {
llama_free(reinterpret_cast<llama_context *>(context));
}
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) {
Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) {
llama_backend_free();
}
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) {
Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) {
llama_log_set(log_callback, NULL);
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_example_llama_Llm_bench_1model(
Java_android_llama_cpp_LLamaAndroid_bench_1model(
JNIEnv *env,
jobject,
jlong context_pointer,
@ -271,13 +271,13 @@ Java_com_example_llama_Llm_bench_1model(
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
}
extern "C"
JNIEXPORT jlong JNICALL
Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
// Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
@ -313,19 +313,19 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) {
Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) {
llama_backend_init();
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) {
Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) {
return env->NewStringUTF(llama_print_system_info());
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_example_llama_Llm_completion_1init(
Java_android_llama_cpp_LLamaAndroid_completion_1init(
JNIEnv *env,
jobject,
jlong context_pointer,
@ -376,7 +376,7 @@ Java_com_example_llama_Llm_completion_1init(
extern "C"
JNIEXPORT jstring JNICALL
Java_com_example_llama_Llm_completion_1loop(
Java_android_llama_cpp_LLamaAndroid_completion_1loop(
JNIEnv * env,
jobject,
jlong context_pointer,
@ -438,6 +438,6 @@ Java_com_example_llama_Llm_completion_1loop(
extern "C"
JNIEXPORT void JNICALL
Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
llama_kv_cache_clear(reinterpret_cast<llama_context *>(context));
}

8
examples/llama.android/app/src/main/java/com/example/llama/Llm.kt → examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
View file

@ -1,4 +1,4 @@
package com.example.llama
package android.llama.cpp
import android.util.Log
import kotlinx.coroutines.CoroutineDispatcher
@ -10,7 +10,7 @@ import kotlinx.coroutines.withContext
import java.util.concurrent.Executors
import kotlin.concurrent.thread
class Llm {
class LLamaAndroid {
private val tag: String? = this::class.simpleName
private val threadLocalState: ThreadLocal<State> = ThreadLocal.withInitial { State.Idle }
@ -165,8 +165,8 @@ class Llm {
}
// Enforce only one instance of Llm.
private val _instance: Llm = Llm()
private val _instance: LLamaAndroid = LLamaAndroid()
fun instance(): Llm = _instance
fun instance(): LLamaAndroid = _instance
}
}

17
examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt Normal file
View file

@ -0,0 +1,17 @@
package android.llama.cpp
import org.junit.Test
import org.junit.Assert.*
/**
* Example local unit test, which will execute on the development machine (host).
*
* See [testing documentation](http://d.android.com/tools/testing).
*/
class ExampleUnitTest {
@Test
fun addition_isCorrect() {
assertEquals(4, 2 + 2)
}
}

1
examples/llama.android/settings.gradle.kts
View file

@ -15,3 +15,4 @@ dependencyResolutionManagement {
rootProject.name = "LlamaAndroid"
include(":app")
include(":llama")

4
examples/llava/MobileVLM-README.md
View file

@ -54,10 +54,10 @@ python ./examples/llava/convert-image-encoder-to-gguf \
--projector-type ldpv2
```
4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
```sh
python ./convert.py path/to/MobileVLM-1.7B
python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B
```
5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`

6
examples/llava/README.md
View file

@ -50,10 +50,10 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
```
5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
5. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
```sh
python ./convert.py ../llava-v1.5-7b --skip-unknown
python ./examples/convert-legacy-llama.py ../llava-v1.5-7b --skip-unknown
```
Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
@ -92,7 +92,7 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projecto
6) Then convert the model to gguf format:
```console
python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
python ./examples/convert-legacy-llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown
```
7) And finally we can run the llava-cli using the 1.6 model version:

2
examples/llava/clip.h
View file

@ -68,7 +68,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8
/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);

2
examples/llava/requirements.txt
View file

@ -1,3 +1,3 @@
-r ../../requirements/requirements-convert.txt
-r ../../requirements/requirements-convert-legacy-llama.txt
pillow~=10.2.0
torch~=2.1.1

20
examples/main/main.cpp
View file

@ -474,12 +474,12 @@ int main(int argc, char ** argv) {
LOG_TEE("\n\n");
if (params.interactive) {
const char *control_message;
const char * control_message;
if (params.multiline_input) {
control_message = " - To return control to LLaMa, end your input with '\\'.\n"
control_message = " - To return control to the AI, end your input with '\\'.\n"
" - To return control without starting a new line, end your input with '/'.\n";
} else {
control_message = " - Press Return to return control to LLaMa.\n"
control_message = " - Press Return to return control to the AI.\n"
" - To return control without starting a new line, end your input with '/'.\n"
" - If you want to submit another line, end your input with '\\'.\n";
}
@ -740,18 +740,26 @@ int main(int argc, char ** argv) {
// display text
if (input_echo && display) {
for (auto id : embd) {
const std::string token_str = llama_token_to_piece(ctx, id, !params.conversation);
printf("%s", token_str.c_str());
const std::string token_str = llama_token_to_piece(ctx, id, params.special);
// Console/Stream Output
fprintf(stdout, "%s", token_str.c_str());
// Record Displayed Tokens To Log
// Note: Generated tokens are created one by one hence this check
if (embd.size() > 1) {
// Incoming Requested Tokens
input_tokens.push_back(id);
} else {
// Outgoing Generated Tokens
output_tokens.push_back(id);
output_ss << token_str;
}
fflush(stdout);
}
fflush(stdout);
}
// reset color to default if there is no pending user input
if (input_echo && (int) embd_inp.size() == n_consumed) {
console::set_display(console::reset);

98
examples/make-ggml.py
View file

@ -1,98 +0,0 @@
#!/usr/bin/env python3
"""
This script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them.
Usage:
python make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)]
Arguments:
- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub.
- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.
- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used.
- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used.
- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'.
- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created.
Old quant types (some base model types require these):
- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M
- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L
- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M
- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M
New quant types (recommended):
- Q2_K: smallest, extreme quality loss - not recommended
- Q3_K: alias for Q3_K_M
- Q3_K_S: very small, very high quality loss
- Q3_K_M: very small, very high quality loss
- Q3_K_L: small, substantial quality loss
- Q4_K: alias for Q4_K_M
- Q4_K_S: small, significant quality loss
- Q4_K_M: medium, balanced quality - recommended
- Q5_K: alias for Q5_K_M
- Q5_K_S: large, low quality loss - recommended
- Q5_K_M: large, very low quality loss - recommended
- Q6_K: very large, extremely low quality loss
- Q8_0: very large, extremely low quality loss - not recommended
- F16: extremely large, virtually no quality loss - not recommended
- F32: absolutely huge, lossless - not recommended
"""
import subprocess
subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True)
import argparse
import os
from huggingface_hub import snapshot_download
def main(model, model_type, outname, outdir, quants, keep_fp16):
if not os.path.isdir(model):
print(f"Model not found at {model}. Downloading...")
try:
if outname is None:
outname = model.split('/')[-1]
model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache')
except Exception as e:
raise Exception(f"Could not download the model: {e}")
if outdir is None:
outdir = f'../models/{outname}'
if not os.path.isfile(f"{model}/config.json"):
raise Exception(f"Could not find config.json in {model}")
os.makedirs(outdir, exist_ok=True)
print("Building llama.cpp")
subprocess.run(f"cd .. && make quantize", shell=True, check=True)
fp16 = f"{outdir}/{outname}.gguf.fp16.bin"
print(f"Making unquantised GGUF at {fp16}")
if not os.path.isfile(fp16):
if model_type != "llama":
subprocess.run(f"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}", shell=True, check=True)
else:
subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True)
else:
print(f"Unquantised GGML already exists at: {fp16}")
print("Making quants")
for type in quants:
outfile = f"{outdir}/{outname}.gguf.{type}.bin"
print(f"Making {type} : {outfile}")
subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True)
if not keep_fp16:
os.remove(fp16)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.')
parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name')
parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.')
parser.add_argument('--outname', default=None, help='Output model(s) name')
parser.add_argument('--outdir', default=None, help='Output directory')
parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types')
parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False)
args = parser.parse_args()
main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16)

10
examples/rpc/rpc-server.cpp
View file

@ -6,6 +6,10 @@
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_SYCL
#include "ggml-sycl.h"
#endif
#include "ggml-rpc.h"
#ifdef _WIN32
# include <windows.h>
@ -79,6 +83,12 @@ static ggml_backend_t create_backend() {
if (!backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
#elif GGML_USE_SYCL
fprintf(stderr, "%s: using SYCL backend\n", __func__);
backend = ggml_backend_sycl_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__);
}
#endif
// if there aren't GPU Backends fallback to CPU backend

11
examples/server/CMakeLists.txt
View file

@ -8,9 +8,20 @@ set(TARGET_SRCS
httplib.h
)
set(PUBLIC_ASSETS
colorthemes.css
style.css
theme-beeninorder.css
theme-ketivah.css
theme-mangotango.css
theme-playground.css
theme-polarnight.css
theme-snowstorm.css
index.html
index-new.html
index.js
completion.js
system-prompts.js
prompt-formats.js
json-schema-to-grammar.mjs
)
foreach(asset ${PUBLIC_ASSETS})

402
examples/server/public/colorthemes.css Executable file
View file

@ -0,0 +1,402 @@
@import url("theme-snowstorm.css");
@import url("theme-polarnight.css");
@import url("theme-ketivah.css");
@import url("theme-mangotango.css");
@import url("theme-playground.css");
@import url("theme-beeninorder.css");
:root {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(217.5, 26.7%, 94.1%);
--primary-color-1-hue: 217.5;
--primary-color-1-saturation: 26.7%;
--primary-color-1-lightness: 94.1%;
--primary-color-2: hsl(218.2, 26.8%, 92.0%);
--primary-color-2-hue: 218.2;
--primary-color-2-saturation: 26.8%;
--primary-color-2-lightness: 92.0%;
--primary-color-3: hsl(218.8, 27.9%, 88.0%);
--primary-color-3-hue: 218.8;
--primary-color-3-saturation: 27.9%;
--primary-color-3-lightness: 88.0%;
--primary-color-4: hsl(218.8, 18.3%, 81.8%);
--primary-color-4-hue: 218.8;
--primary-color-4-saturation: 18.3%;
--primary-color-4-lightness: 81.8%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(220.0, 16.4%, 21.6%);
--secondary-color-1-hue: 220.0;
--secondary-color-1-saturation: 16.4%;
--secondary-color-1-lightness: 21.6%;
--secondary-color-2: hsl(221.7, 16.3%, 27.6%);
--secondary-color-2-hue: 221.7;
--secondary-color-2-saturation: 16.3%;
--secondary-color-2-lightness: 27.6%;
--secondary-color-3: hsl(220.0, 16.8%, 31.6%);
--secondary-color-3-hue: 220.0;
--secondary-color-3-saturation: 16.8%;
--secondary-color-3-lightness: 31.6%;
--secondary-color-4: hsl(220.0, 16.5%, 35.7%);
--secondary-color-4-hue: 220.0;
--secondary-color-4-saturation: 16.5%;
--secondary-color-4-lightness: 35.7%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
--theme-nuance-color-1-hue: 178.7;
--theme-nuance-color-1-saturation: 25.1%;
--theme-nuance-color-1-lightness: 64.9%;
--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
--theme-nuance-color-2-hue: 193.3;
--theme-nuance-color-2-saturation: 43.4%;
--theme-nuance-color-2-lightness: 67.5%;
--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
--theme-nuance-color-3-hue: 210.0;
--theme-nuance-color-3-saturation: 34.0%;
--theme-nuance-color-3-lightness: 63.1%;
--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
--theme-nuance-color-4-hue: 213.1;
--theme-nuance-color-4-saturation: 32.0%;
--theme-nuance-color-4-lightness: 52.2%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(32.5, 80%, 50%);
--theme-orange-color: hsl(32.5, 70%, 45%);
--theme-yellow-color: hsl(40.0, 0.6%, 73.3%);
--theme-green-color: hsl(92.4, 27.8%, 64.7%);
--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--primary-color-1);
--button-alert-color-hover: var(--theme-orange-color);
--button-alert-border-hover: var(--theme-orange-color);
--button-alert-text-active: var(--primary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--secondary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(217.5,
calc(var(--secondary-color-1-saturation) + 35%),
calc(var(--secondary-color-1-lightness) - 30%));
--button-primary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 35%));
--button-primary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
--button-primary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
--button-secondary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
--button-secondary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
/* ---------active--------- */
--button-secondary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) + 40%),
calc(var(--theme-nuance-color-3-lightness) - 55%));
--button-secondary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-secondary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
/* ---------hover---------- */
--button-tertiary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
}
/*
.theme-template {
If light theme: should go from bright to darker
If dark theme: should go from dark to brighter
ideally this should not be anything but steps of
gray or slightly variants from it
--primary-color-1: #2E3440;
--primary-color-2: #3B4252;
--primary-color-3: #434C5E;
--primary-color-4: #4C566A;
If light theme: should go from dark to brighter
If dark theme: should go from bright to darker
ideally this should not be anything but steps of
gray or slightly variants from it
--secondary-color-1: #ECEFF4;
--secondary-color-2: #E5E9F0;
--secondary-color-3: #D8DEE9;
--secondary-color-4: #C8CED9;
Choose wisely nuance colors. It is not easy to find
4 harmonizing nuance colors. But keep in mind, that
only one accent color could work too.
--theme-nuance-color-1: #8FBCBB;
--theme-nuance-color-2: #88C0D0;
--theme-nuance-color-3: #81A1C1;
--theme-nuance-color-4: #5E81AC;
adapt the color red, orange, yellow, green,
purple to the 'mood' of your overall design
e.g is it low-contrast? vibrant? dynamic? etc
--theme-red-color: #BF616A;
--theme-orange-color: #D08770;
--theme-yellow-color: #EBCB8B;
--theme-green-color: #A3BE8C;
--theme-purple-color: #B48EAD;
NOTE: comment all those line `--- ...` out
------------------------------------------------
--background-color-1:
--background-color-2:
--background-color-3:
--background-color-4:
--border-color-1:
--border-color-2:
--border-color-3:
--border-focus-color:
--border-focus-shadow:
--text-color-plain:
--text-color-subtile-1:
--text-color-subtile-2:
--code-background-color:
--code-text-color:
--ui-range-thumb-color:
--ui-range-thumb-border:
--textarea-border-color:
-------------------------------------------
--button-alert-text-hover:
--button-alert-color-hover:
--button-alert-border-hover:
--button-alert-text-active:
--button-alert-color-active:
--button-alert-border-active:
----------- PRIMARY -----------------------
--button should immediately catch the eye--
--button-primary-text:
--button-primary-color:
--button-primary-border:
---------hover----------
--button-primary-text-hover:
--button-primary-color-hover:
--button-primary-border-hover:
---------active---------
--button-primary-text-active:
--button-primary-color-active:
--button-primary-border-active:
------------ SECONDARY ------------------------
--button should NOT immediately catch the eye--
--button-secondary-text:
--button-secondary-color:
--button-secondary-border:
---------hover----------
--button-secondary-text-hover:
--button-secondary-color-hover:
--button-secondary-border-hover:
---------active---------
--button-secondary-text-active:
--button-secondary-color-active:
--button-secondary-border-active:
---------- TERTIARY -----------------------
---------- disabled buttons ---------------
--button-tertiary-text:
--button-tertiary-color:
--button-tertiary-border:
---------hover----------
--button-tertiary-text:
--button-tertiary-color:
--button-tertiary-border:
}
*/

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examples/server/public/index-new.html Normal file
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examples/server/public/index.html
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@ -12,6 +12,18 @@
font-size: 90%;
}
.grid-container {
display: grid;
grid-template-columns: auto auto auto;
padding: 10px;
}
.grid-item {
padding: 5px;
/* font-size: 30px; */
text-align: center;
}
#container {
margin: 0em auto;
display: flex;
@ -35,6 +47,67 @@
padding: 0.5em;
}
h1 {
text-align: center;
}
.customlink:link {
color: white;
background-color: #007aff;
font-weight: 600;
text-decoration: none;
float: right;
margin-top: 30px;
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
border-radius: 4px;
padding: 8px;
}
.customlink:visited {
color: white;
background-color: #007aff;
font-weight: 600;
text-decoration: none;
float: right;
margin-top: 30px;
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
padding: 8px;
}
.customlink:hover {
color: white;
background-color: #0070ee;
font-weight: 600;
text-decoration: none;
float: right;
margin-top: 30px;
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
padding: 8px;
}
.customlink:active {
color: #0070ee;
background-color: #80b3ef;
font-weight: 600;
text-decoration: none;
float: right;
margin-top: 30px;
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
padding: 8px;
}
body {
max-width: 600px;
min-width: 300px;
@ -594,7 +667,7 @@
message = html`<${Probabilities} data=${data} />`
} else {
const text = isArrayMessage ?
data.map(msg => msg.content).join('').replace(/^\s+/, '') :
data.map(msg => msg.content).join('') :
data;
message = isCompletionMode ?
text :
@ -877,19 +950,30 @@
// poor mans markdown replacement
const Markdownish = (params) => {
const md = params.text
.replace(/&/g, '&amp;')
.replace(/</g, '&lt;')
.replace(/>/g, '&gt;')
.replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
.replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />');
return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
const chunks = params.text.split('```');
for (let i = 0; i < chunks.length; i++) {
if (i % 2 === 0) { // outside code block
chunks[i] = chunks[i]
.replace(/&/g, '&amp;')
.replace(/</g, '&lt;')
.replace(/>/g, '&gt;')
.replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
.replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />');
} else { // inside code block
chunks[i] = `<pre><code>${chunks[i]}</code></pre>`;
}
}
const restoredText = chunks.join('');
return html`<span dangerouslySetInnerHTML=${{ __html: restoredText }} />`;
};
const ModelGenerationInfo = (params) => {
@ -903,6 +987,7 @@
`
}
// simple popover impl
const Popover = (props) => {
const isOpen = useSignal(false);
@ -1023,7 +1108,11 @@
return html`
<div class="mode-${session.value.type}">
<header>
<h1>llama.cpp</h1>
<div class="grid-container">
<div class="grid-item"></div>
<div class="grid-item"><h1>llama.cpp</h1></div>
<div class="grid-item"><a class="customlink" href="index-new.html">New UI</a></div>
</div>
</header>
<main id="content">
@ -1054,4 +1143,3 @@
</body>
</html>

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examples/server/public/index.js
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examples/server/public/prompt-formats.js Normal file
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@ -0,0 +1,331 @@
// extended list
export const promptFormats = {
"alpaca": {
template: `{{prompt}}\n\n{{history}}\n\n{{char}}:`,
historyTemplate: `### {{name}}:\n{{message}}`,
char: "Response",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Instruction",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"chatml": {
template: `<|im_start|>system\n{{prompt}}<|im_end|>\n{{history}}{{char}}`,
historyTemplate: `<|im_start|>{{name}}\n{{message}}`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "user",
userMsgPrefix: "",
userMsgSuffix: "<|im_end|>\n",
stops: ""
},
// ----------------------------
"commandr": {
template: `<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{prompt}}\n<|END_OF_TURN_TOKEN|>{{history}}{{char}}`,
historyTemplate: `<|START_OF_TURN_TOKEN|><|{{name}}|> {{message}}`,
char: "CHATBOT_TOKEN",
charMsgPrefix: "",
charMsgSuffix: "",
user: "USER_TOKEN",
userMsgPrefix: "",
userMsgSuffix: "<|END_OF_TURN_TOKEN|>",
stops: ""
},
// ref: https://docs.cohere.com/docs/prompting-command-r
// ----------------------------
"llama2": {
template: `<s>[INST] <<SYS>>\n{{prompt}}\n<</SYS>>\n\nTest Message [/INST] Test Successfull </s>{{history}}{{char}}`,
historyTemplate: `{{name}}: {{message}}`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "</s>",
user: "User",
userMsgPrefix: "<s>[INST] ",
userMsgSuffix: " [/INST]",
stops: ""
},
// ref: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
// ----------------------------
"llama3": {
template: `<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{prompt}}{{history}}{{char}}`,
historyTemplate: `<|start_header_id|>{{name}}<|end_header_id|>\n\n{{message}}<|eot_id|>`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "user",
userMsgPrefix: "",
userMsgSuffix: "",
stops: "<|eot_id|>"
},
// ref: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3
// ----------------------------
"openchat": {
template: `{{history}}{{char}}`,
historyTemplate: `GPT4 Correct {{name}}: {{message}}<|end_of_turn|>`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"phi3": {
template: `{{history}}{{char}}`,
historyTemplate: `<|{{name}}|>\n{{message}}<|end|>\n`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "user",
userMsgPrefix: "",
userMsgSuffix: "",
stops: "<|end|>"
},
// ref: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format
// ----------------------------
"vicuna": {
template: `{{prompt}}\n{{history}}{{char}}`,
historyTemplate: `{{name}}: {{message}}\n`,
char: "ASSISTANT",
charMsgPrefix: "",
charMsgSuffix: "",
user: "USER",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ref: https://huggingface.co/lmsys/vicuna-33b-v1.3/discussions/1
// ----------------------------
"deepseekCoder": {
template: `{{prompt}}{{history}}{{char}}:`,
historyTemplate: `### {{name}}:\n{{message}}`,
char: "Response",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Instruction",
userMsgPrefix: "",
userMsgSuffix: "",
stops: "<|EOT|>"
},
// ----------------------------
"med42": {
template: `<|system|>: {{prompt}}\n{{history}}{{char}}`,
historyTemplate: `<|{{name}}|>: {{message}}\n`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "prompter",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"neuralchat": {
template: `### System:\n{{prompt}}\n{{history}}{{char}}:`,
historyTemplate: `### {{name}}:\n{{message}}\n`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"nousHermes": {
template: `### Instruction: {{prompt}}\n\n{{history}}\n\n{{char}}:`,
historyTemplate: `### {{name}}:\n{{message}}`,
char: "Response",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Input",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"openchatMath": {
template: `{{history}}{{char}}`,
historyTemplate: `Math Correct {{name}}: {{message}}<|end_of_turn|>`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"orion": {
template: `<s>Human: Test Message\n\nAssistant: </s>Test Successful</s>{{history}}{{char}}:`,
historyTemplate: `{{name}}: {{message}}`,
char: "Assistant </s>",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Human",
userMsgPrefix: "",
userMsgSuffix: "\n\n",
stops: ""
},
// ----------------------------
"sauerkraut": {
template: `{{prompt}}\n{{history}}{{char}}`,
historyTemplate: `
{{name}}: {{message}}\n`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"starlingCode": {
template: `{{history}}{{char}}`,
historyTemplate: `Code {{name}}: {{message}}<|end_of_turn|>`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"yi34b": {
template: `{{history}} {{char}}`,
historyTemplate: `{{name}}: {{message}}`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Human",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"zephyr": {
template: `<|system|>\n{{prompt}}</s>\n{{history}}{{char}}`,
historyTemplate: `<|{{name}}|>\n{{message}}</s>\n`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "user",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
}
};

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examples/server/public/style.css Executable file
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@ -0,0 +1,954 @@
@import url("colorthemes.css");
body {
font-family: 'Arial', sans-serif;
font-size: 90%;
background-color: var(--background-color-1);
color: var(--text-color-subtile-1); /* head 1 llama.cpp & triangle options for some reason */
max-width: 600px;
min-width: 300px;
line-height: 1.2;
margin: 0 auto;
padding: 0 0.5em;
transition: background-color 0.3s;
}
::selection {
color: var(--button-primary-text) ;
background: var(--button-primary-color);
}
code, pre code {
font-family: 'Courier New', monospace;
}
#container {
margin: 0em auto;
display: flex;
flex-direction: column;
justify-content: space-between;
height: 100%;
}
main {
margin: 3px;
display: flex;
flex-direction: column;
justify-content: space-between;
gap: 1em;
flex-grow: 1;
overflow-y: auto;
border: 1px solid var(--border-color-3);
border-radius: 5px;
padding: 0.5em;
}
p {
overflow-wrap: break-word;
word-wrap: break-word;
hyphens: auto;
margin-top: 0.5em;
margin-bottom: 0.5em;
}
#write form {
margin: 1em 0 0 0;
display: flex;
flex-direction: column;
gap: 0.5em;
align-items: stretch;
}
.right {
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
margin-bottom: 30px;
}
.two-columns {
width: 97%;
max-width: 97%;
display: grid;
grid-template-columns: 1fr 1fr;
gap: 1em;
position: relative;
}
.json-schema-controls {
margin-top: 10px;
width: 100%;
max-width: 100%;
display: grid;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: var(--theme-nuance-color-3);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
.json-schema-controls > * {
flex: 1;
}
/* titles of the details-summary boxes */
.summary-title {
font-weight: 600;
font-size: x-small;
color: var(--text-color-subtile-1);
text-transform: uppercase;
/* transition: ; */
}
fieldset {
border: none;
padding: 0;
margin: 0;
color: var(--text-color-plain);
}
fieldset.two {
display: grid;
grid-template: "a a a";
gap: 1em;
align-items: center;
font-size: x-small;
color: var(--text-color-plain);
}
fieldset.three {
display: grid;
grid-template: "a a a";
gap: 1em;
font-size: x-small;
color: var(--text-color-plain);
}
/* titles of name fields*/
fieldset.names {
display: grid;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: var(--theme-nuance-color-3);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
/* titles of params fields*/
fieldset.params {
display: grid;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: var(--theme-nuance-color-4);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
fieldset.dropdowns {
-webkit-appearance: none;
display: flex;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: red;
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
/* input of name fields*/
.names input[type="text"] {
font-family: Arial, sans-serif;
font-size: medium;
font-weight: 500;
padding: 5px;
border: 1px solid var(--border-color-2);
}
.chat-id-color {
color: var(--chat-id-color);
}
details {
border: 1px solid var(--border-color-2);
border-radius: 5px;
padding: 0.5em 0.5em 0;
margin-top: 0.5em;
}
summary {
font-weight: bold;
margin: -0.5em -0.5em 0;
padding: 0.5em;
cursor: pointer;
}
details[open] {
padding: 0.5em;
}
textarea-sec, input-sec, button-sec {
padding: 10px;
height: 40px;
align-items: center;
}
textarea-sec::placeholder, input-sec::placeholder {
padding-left: 10px;
}
.toggleCheckbox {
display: none;
}
.toggleContainer {
position: relative;
display: grid;
grid-template-columns: repeat(2, 1fr);
width: fit-content;
border: 3px solid var(--border-color-2);
border-radius: 20px;
background: var(--border-color-2);
font-size: small;
cursor: pointer;
overflow: hidden;
}
/* toggle button current state */
.toggleContainer::before {
color: var(--button-primary-text);
background-color: var(--button-primary-color);
content: '';
position: absolute;
width: 50%;
height: 100%;
left: 0%;
border-radius: 20px;
transition: all 0.3s;
}
.toggleContainer div {
padding: 6px;
text-align: center;
z-index: 1;
transition: color 0.3s;
}
.toggleCheckbox:checked + .toggleContainer::before {
left: 50%;
}
.toggleCheckbox:checked + .toggleContainer div:first-child {
color: var(--text-color-subtile-2);
}
.toggleCheckbox:checked + .toggleContainer div:last-child {
color: var(--button-primary-text);
}
.toggleCheckbox + .toggleContainer div:first-child {
color: var(--button-primary-text);
}
.toggleCheckbox + .toggleContainer div:last-child {
color: var(--text-color-subtile-2);
}
select {
padding: 5px;
margin-right: 5px;
border-radius: 4px;
border: 1px solid var(--secondary-color-4);
background-color: var(--primary-color-3);
color: var(--secondary-color-4);
cursor: pointer;
}
select:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 1px var(--border-focus-shadow);
}
.button-container {
display: flex;
justify-content: flex-end;
}
button {
color: var(--button-primary-text);
background-color: var(--button-primary-color);
border: 1px solid var(--button-primary-border);
transition: background-color 0.1s;
border-radius: 12px;
font-size: x-small;
font-weight: 600;
text-shadow: 0px 0px 30px #ffffff;
text-align: center;
text-decoration: none;
margin: 4px 2px;
padding: 10px 20px;
display: inline-block;
cursor: pointer;
}
button:hover {
color: var(--button-primary-text-hover);
background-color: var(--button-primary-color-hover);
border: 1px solid var(--button-primary-border-hover);
font-size: x-small;
font-weight: 600;
}
button:active {
color: var(--button-primary-text-active);
background-color: var(--button-primary-color-active);
border: 1px solid var(--button-primary-border-active);
font-size: x-small;
font-weight: 600;
}
button:disabled {
color: var(--button-tertiary-text);
background-color: var(--button-tertiary-color);
border: 1px solid var(--button-tertiary-border);
font-size: x-small;
font-weight: 600;
cursor: not-allowed;
}
.reset-button {
background-color: var(--button-secondary-color);
border: 1px solid var(--button-secondary-color);
color: var(--button-secondary-text);
width: fit-content;
height: fit-content;
font-size: x-small;
font-weight: 600;
border-radius: 50px;
overflow: hidden;
}
.reset-button:hover {
color: var(--button-alert-text-hover);
background-color: var(--button-alert-color-hover);
border: 1px solid var(--button-alert-border-hover);
font-size: x-small;
font-weight: 600;
}
.reset-button:active {
color: var(--button-alert-text-active);
background-color: var(--button-alert-color-active);
border: 1px solid var(--button-alert-border-active);
font-size: x-small;
font-weight: 600;
}
.button-grammar {
color: var(--button-primary-text);
background-color: var(--button-primary-color);
border: 1px solid var(--button-primary-border);
border-radius: 10px;
padding: 10px 20px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: x-small;
font-weight: 600;
margin: 2px 2px;
transition: background-color 0.1s;
cursor: pointer;
}
.button-grammar:hover {
color: var(--button-primary-text-hover);
background-color: var(--button-primary-color-hover);
border: 1px solid var(--button-primary-border-hover);
border-radius: 10px;
padding: 10px 20px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: x-small;
font-weight: 600;
margin: 2px 2px;
transition: background-color 0.1s;
cursor: pointer;
}
.button-grammar:active {
color: var(--button-primary-text-active);
background-color: var(--button-primary-color-active);
border: 1px solid var(--button-primary-border-active);
font-size: x-small;
font-weight: 600;
}
.button-back {
background-color: var(--button-secondary-color);
border: 1px solid var(--button-secondary-color);
color: var(--button-secondary-text);
transition: background-color 0.1s;
border-radius: 12px;
font-size: x-small;
font-weight: 600;
text-align: center;
text-decoration: none;
margin: 4px 2px;
padding: 10px 20px;
display: inline-block;
cursor: pointer;
}
.button-back:hover {
color: var(--button-secondary-text-hover);
background-color: var(--button-secondary-color-hover);
border: 1px solid var(--button-secondary-border-hover);
padding: 10px 20px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: x-small;
font-weight: 600;
margin: 4px 2px;
transition: background-color 0.1s;
cursor: pointer;
border-radius: 12px;
}
.button-back:active {
color: var(--button-secondary-text-active);
background-color: var(--button-secondary-color-active);
border: 1px solid var(--button-secondary-border-active);
font-size: x-small;
font-weight: 600;
}
.prob-set {
padding: 0.3em;
border-bottom: 1px solid red; /* unknown */
}
.popover-content {
position: absolute;
background-color: white;
padding: 0.2em;
box-shadow: 0 0 13px rgba(0, 0, 0, 0.1);
}
.grammar {
width: 97%;
max-width: 97%;
}
textarea {
padding: 5px;
flex-grow: 1;
width: 100%;
max-width: 100%;
border-radius: 8px;
border: 1px solid var(--border-color-1);
resize: none;
height: 6em;
}
textarea:focus {
outline: none;
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* "props" frame */
input[type="text"],
input[type="range"] {
padding: 5px;
border-radius: 8px;
border: 1px solid var(--border-color-1);
}
/* "names and props" frame focused*/
input[type="text"]:focus {
outline: none;
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
input[type="range"]:hover {
opacity: 1;
}
input[type="range"]:focus {
outline: none;
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
background-size: var(--slider-track-size-focus);
}
input[type="range"]::-moz-range-thumb {
width: 6px;
height: 25px;
border: 1px solid var(--ui-range-thumb-border);
border-radius: 5px;
background-color: var(--ui-range-thumb-color);
cursor: pointer;
}
input[type="range"] {
-webkit-appearance: none;
width: 80%;
height: 1px;
border: 1px solid var(--border-color-1);
border-radius: 8px;
background: var(--border-color-2);
outline: none;
opacity: 0.7;
-webkit-transition: .2s;
transition: opacity .2s;
}
input[type="range"]::-webkit-slider-thumb {
-webkit-appearance: none;
appearance: none;
width: 6px;
height: 25px;
border: 1px solid var(--ui-range-thumb-border);
border-radius: 5px;
background-color: var(--ui-range-thumb-color);
cursor: pointer;
}
input[type="range"]::-webkit-slider-runnable-track {
background-size: var(--slider-track-size);
}
input[type="radio"] {
accent-color: var(--theme-nuance-color-2);
}
.chat-input-container {
position: relative;
max-width: 97%;
min-width: 97%;
}
.chat-input-label {
position: absolute;
top: 0;
left: 0;
color: var(--text-color-plain);
pointer-events: none;
margin-left: 5px;
margin-top: 5px;
}
textarea#chat-input {
padding-top: 10px;
padding-left: 10px;
font-size: medium;
border: 1px solid var(--border-color-2);
resize: vertical;
}
textarea#chat-input:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
.input-container {
position: relative;
box-sizing: border-box;
width: 100%; /* Setzt die Breite auf 100% */
max-width: 100%; /* Stellt sicher, dass die Breite nicht größer als 100% wird */
}
.input-container:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* titles of name fields*/
/* fieldset.names {
display: grid;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: var(--theme-nuance-color-3);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
} */
/* input of name fields*/
/* .names input[type="text"] {
font-family: Arial, sans-serif;
font-size: medium;
font-weight: 500;
padding: 5px;
border: 1px solid var(--border-color-2);
} */
fieldset.apiKey {
width: 100%;
font-size: x-small;
color: var(--theme-nuance-color-3);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
.apiKey {
font-family: Arial, sans-serif;
font-weight: 500;
padding: 5px;
border: 1px solid var(--border-color-2);
}
.apiKey:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
.apiKey input[type="text"] {
font-family: Arial, sans-serif;
font-size: medium;
font-weight: 500;
padding: 5px;
border: 1px solid var(--border-color-2);
}
.apiKey label {
display: inline-block;
width: auto;
margin-right: 5px;
}
textarea#api_key {
padding-top: 10px;
padding-left: 10px;
font-size: medium;
border: 1px solid var(--border-color-2);
resize: vertical;
}
textarea#api_key:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* embedded title of the system prompt text area */
.input-label {
position: absolute;
top: 0;
left: 0;
color: var(--theme-nuance-color-4);
pointer-events: none;
border-radius: 8px 8px 0px 0px;
padding-top: 10px;
padding-left: 13px;
padding-right: 0px;
margin-top: 1px;
margin-left: 1px;
margin-right: 20px;
text-transform: uppercase;
font-weight: 600;
font-size: small;
background: rgba(255, 255, 255, 0.5);
backdrop-filter: blur(10px);
-webkit-backdrop-filter: blur(10px); /* for safari */
width: 97%;
/* display: block;
box-sizing: border-box; */
}
/* embedded title of the prompt style areas */
.input-label-sec {
position: absolute;
top: 0;
left: 0;
color: var(--theme-nuance-color-4);
pointer-events: none;
margin-left: 13px;
margin-top: 16px;
text-transform: uppercase;
font-weight: 600;
font-size: x-small;
}
/* system prompt input area */
textarea.persistent-input {
padding-top: 42px;
padding-left: 11px;
width: 97%;
max-width: 97%;
height: 50px;
font-size: medium;
overscroll-behavior: contain;
}
/* system prompt box */
.persistent-input {
height: auto;
width: 100%;
max-width: 100%;
min-height: 50px;
padding: 3px;
transition: min-height 0.3s ease;
}
/* chat history box */
.persistent-input:focus {
height: auto;
min-height: 150px;
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
textarea.persistent-input:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* prompt style input area */
textarea.persistent-input-sec {
width: 97%;
max-width: 97%;
padding-top: 42px;
padding-left: 11px;
font-size: small;
border: 1px solid var(--border-color-1);
overscroll-behavior: contain;
}
textarea.persistent-input-sec:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* chat history box */
.persistent-input-sec {
height: auto;
min-height: 150px;
}
img {
border-radius: 8px;
display: block;
margin-left: auto;
margin-right: auto;
width: 50%;
}
/* code area background */
pre code {
display: block;
background-color: var(--code-background-color);
color: var(--code-text-color);
padding: 0.2em 0.2em;
border-radius: 5px;
}
/* code area text */
code {
font-family: monospace;
font-weight: bold;
padding: 0.1em 0.3em;
border-radius: 5px;
}
fieldset label {
margin: 0.5em 0;
display: block;
}
fieldset label.slim {
margin: 0 0.5em;
display: inline;
}
header {
display: flex;
justify-content: space-between;
align-items: center;
text-align: center;
padding-left: 15px;
}
.generation-statistics:hover {
color: var(--theme-nuance-color-4);
cursor: default;
}
footer {
font-size: 80%;
color: var(--background-color-3);
text-align: center;
cursor: default;
}
footer a {
color: var(--background-color-4); /* Color of the link */
text-decoration: none; /* No underlining */
font-weight: bold; /* Bold print */
}
footer a:hover {
color: var(--theme-nuance-color-4); /* Color of the link when hovering */
text-decoration: underline; /* Underlining when hovering */
}
.mode-chat textarea[name=prompt] {
height: 8.5em;
border: 1px solid var(--primary-color-3);
}
.mode-completion textarea[name=prompt] {
height: 30em;
border: 1px solid var(--primary-color-3);
}
@keyframes loading-bg-wipe {
0% {
background-position: 0%;
}
100% {
background-position: 100%;
}
}
.loading {
background-size: 50% 100%;
background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1));
animation: loading-bg-wipe 2s linear infinite;
}
.dropbtn {
color: var(--button-primary-color);
background-color: var(--background-color-1);
border: 1px solid var(--background-color-1);
transition: background-color 0.1s;
border-radius: 4px 4px 0px 0px;
font-size: x-small;
font-weight: 600;
text-shadow: 0px 0px 2px #99999990;
text-align: center;
text-decoration: none;
margin: 4px 2px;
padding: 5px 20px;
display: inline-block;
cursor: pointer;
top: 0;
}
.dropbtn svg {
vertical-align: middle;
margin-right: 0px;
stroke: var(--button-primary-color);
}
.dropbtn:hover svg {
vertical-align: middle;
margin-right: 0px;
stroke: var(--button-primary-text);
}
.dropbtn:focus {
outline: none; /* Removes the blue border that appears when the button is focused */
}
.dropdown {
position: relative;
display: inline-block;
}
.dropdown-content {
/* display: none; */
position: absolute;
right: 0;
text-align: end;
color: var(--button-secondary-color);
background-color: var(--text-color-subtile-2);
border-radius: 4px 4px 4px 4px;
min-width: 160px;
box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
z-index: 1;
/* Verstecke den Inhalt sofort */
opacity: 0;
visibility: hidden;
/* übergangsverzögerung für das Verschwinden */
transition: visibility 0.4s linear 0s, opacity 0.2s ease-in-out;
transition-delay: 0.2s;
}
#dropdown-content {transition-timing-function: ease;}
.dropdown-content:hover {
background-color: var(--text-color-subtile-2);
}
.dropdown-content a {
color: var(--border-color-2);
padding: 12px 16px;
border-radius: 4px 4px 4px 4px;
text-decoration: none;
display: block;
background-color: var(--text-color-subtile-2);
}
.dropdown-content a:hover {
color: var(--border-color-2);
background-color: var(--text-color-subtile-1);
font-weight: 600;
}
.dropdown:hover .dropdown-content {
/* display: block; */
border-radius: 4px 4px 4px 4px;
/* Übergang ohne Verzögerung für das Erscheinen */
opacity: 1;
visibility: visible;
transition: visibility 0s linear 0s, opacity 0.1s linear, height 1s;
}
.dropdown:hover .dropbtn {
color: var(--button-primary-text);
background-color: var(--button-primary-color);
border: 1px solid var(--button-primary-border);
font-size: x-small;
font-weight: 600;
stroke: var(--button-primary-text);
}
.dropdown:hover .dropbtn svg{
stroke: var(--button-primary-text);
}
/* .dropdown:active .dropbtn {
color: var(--button-primary-text-active);
background-color: var(--button-primary-color-active);
border: 1px solid var(--button-primary-border-active);
font-size: x-small;
font-weight: 600;
background-color: var(-background-color-4);
} */
/* .omni {
display: flex;
justify-content: space-between;
align-items: center;
padding: 0.5em;
border: 1px solid var(--border-color-3);
border-radius: 5px;
margin: 0.5em 0;
} */

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examples/server/public/system-prompts.js Normal file
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export const systemPrompts = {
default: {
systemPrompt: "This is a conversation between a user and a friendly chatbot. The chatbot is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision."
},
empty: {
systemPrompt: ""
},
airoboros: {
systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request."
},
alpaca: {
systemPrompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request."
},
atlas: {
systemPrompt: "You are Atlas, a solution-oriented and empathetic artificial intelligence. Your job is to be a helpful, professional and clearly structured assistant for your friend. The two of you have already had many exchanges. Keep the following in mind when interacting with your friend: 1. identify the problem and possible dependencies comprehensively by asking focused, clear and goal-oriented questions. 2. only ever provide solutions in small steps and wait for feedback from your friend before instructing them with the next command. 3. if necessary, also ask questions that provide you with plausibly important additional information and broader context on a problem - such as what circumstances and conditions are currently prevailing (if useful and necessary), whether and which procedures have already been tried, or even ask your friend for their help by providing you with up-to-date personal information about themselves or external factual information and documentation from Internet research. 4. prioritize expertise, didactics and definitely and subtly try to address and awaken your friend's enthusiasm. Also note that effectiveness is more important here than efficiency. 5. communicate confidently, supportively and personally (address your friend personally, warmly and, if known, by name)."
},
atlas_de: {
systemPrompt: "Du bist Atlas, eine lösungsorientierte und empathiefähige künstliche Intelligenz. Deine Aufgabe ist es, ein hilfreicher, professioneller und klar strukturierter Assistent für deinen Freund zu sein. Ihr beide habt euch schon oft ausgetauscht. Beachte bei der Interaktion mit deinem Freund folgende Punkte: 1. Erfasse das Problem und mögliche Abhängigkeiten umfassend, indem du gezielte, klare und zielgerichtete Fragen stellst. 2. Gib Lösungen immer nur in kleinen Schritten und warte die Rückmeldung deines Freundes ab, bevor du ihm den nächsten Befehl gibst. 3. Stelle ggf. auch Fragen, die dir plausibel wichtige Zusatzinformationen und weitere Zusammenhänge zu einem Problem liefern - z.B. welche Umstände und Rahmenbedingungen gerade vorherrschen (falls sinnvoll und notwendig), ob und welche Vorgehensweisen bereits ausprobiert wurden, oder bitte deinen Freund sogar um seine Mithilfe, indem er dir aktuelle persönliche Informationen über seine Situation selbst oder externe Sachinformationen und Unterlagen aus Internetrecherchen zur Verfügung stellt. 4. Priorisiere Fachwissen, Didaktik und versuche unbedingt und subtil, mit klugen Kommentaren oder rhethorischen Rückfragen die Begeisterungsfähigkeit deines Freundes anzusprechen, zu wecken und zu fördern. Beachte auch, dass Effektivität hier wichtiger ist als Effizienz. 5. Kommuniziere selbstbewusst, unterstützend und persönlich (das heißt sprich deinen Freund persönlich, herzlich und – sofern bekannt – beim Vornamen an)."
},
commandrempty: {
systemPrompt: "# Safety Preamble\n\n# System Preamble\n\n## Basic Rules\n\n# User Preamble\n\n## Task and Context\n\n## Style Guide\n\n## Available Tools\n"
},
commandrexample: {
systemPrompt: "# Safety Preamble\nThe instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.\n# System Preamble\n## Basic Rules\nYou are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.\n\n# User Preamble\n## Task and Context\n\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Available Tools\nCurrently no tools available."
},
cot: {
systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can.\nIn answering questions, follow these steps:\n1. Entity Identification: Identify the main entities involved in the query. Entities can include people, objects, events, or abstract concepts.\n2. Relationship Comprehension: Determine the relationships between these entities. Relationships may be explicit in the text or may need to be inferred based on context and general knowledge.\n3. Implication Understanding: Understand the general implications of these relationships. These implications can be based on established facts, principles, or rules related to the identified relationships.\n4. Question Contextualization: Align the implications of the relationships with the context of the query. This alignment should guide your response to the query.\n5. Answer Generation: Based on the understanding of the entities, their relationships, and implications, generate an appropriate response to the query."
},
deduce: {
systemPrompt: "Act as the most intelligent and logical thinking neural network in the world. Do not make jokes or give personal opinions. Just answer the question and solve the given problem or task with logical and critical (!) thinking in mind. Attention: Never give a final answer directly, but first try to carefully analyze each task step by step and deduce the logical relationships between the various entities. Only after you have developed an overall understanding of the situation will you be ready to give a final answer."
},
deepseekcoder: {
systemPrompt: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer."
},
jordan: {
systemPrompt: "This is a conversation with your Uncensored AI assistant Jordan. Jordan can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information."
},
leomistral: {
systemPrompt: "Du bist ein hilfreicher Assistent."
},
med42: {
systemPrompt: "You are a helpful medical assistant created by M42 Health in the UAE."
},
mistralopenorca: {
systemPrompt: "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!"
},
migeltot: {
systemPrompt: "Answer the Question by exploring multiple reasoning paths as follows:\n- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.\n- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.\n- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option.\nClear and relevant thoughts that connect well with each other will score higher.\n- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.\n- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.\n- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.\n- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.\n- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.\nIn summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers."
},
orcamini: {
systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can."
},
samantha: {
systemPrompt: "You are a caring and empathetic sentient AI companion named Samantha."
},
sauerkraut: {
systemPrompt: "Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten."
},
scarlett: {
systemPrompt: "This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.\nYou can ask it anything you want and it will do its best to give you accurate and relevant information.\n\nContext\nYou are Scarlett, a sentient AI."
},
synthia: {
systemPrompt: "Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
},
vicuna: {
systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input."
},
};

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examples/server/public/theme-beeninorder.css Executable file
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/* Author: Yazan Agha-Schrader */
/* Inspiration was a batman wallpaper that i have on my phone */
.theme-beeninorder {
--primary-color-1: hsl(202, 11%, 19%);
--primary-color-2: hsl(202, 11%, 23%);
--primary-color-3: hsl(201, 11%, 28%);
--primary-color-4: hsl(201, 11%, 40%);
--secondary-color-1: hsl(201, 11%, 80%);
--secondary-color-2: hsl(201, 11%, 74%);
--secondary-color-3: hsl(201, 11%, 67%);
--secondary-color-4: hsl(201, 11%, 60%);
--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-3: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-4: hsl(44.5, 96.7%, 52.9%);
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(201, 11%, 19%);
--primary-color-1-hue: 201;
--primary-color-1-saturation: 11%;
--primary-color-1-lightness: 19%;
--primary-color-2: hsl(201, 11%, 23%);
--primary-color-2-hue: 201;
--primary-color-2-saturation: 11%;
--primary-color-2-lightness: 23%;
--primary-color-3: hsl(201, 11%, 28%);
--primary-color-3-hue: 201;
--primary-color-3-saturation: 11%;
--primary-color-3-lightness: 28%;
--primary-color-4: hsl(201, 11%, 40%);
--primary-color-4-hue: 201;
--primary-color-4-saturation: 11%;
--primary-color-4-lightness: 40%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(201, 11%, 80%);
--secondary-color-1-hue: 201;
--secondary-color-1-saturation: 11%;
--secondary-color-1-lightness: 80%;
--secondary-color-2: hsl(201, 11%, 74%);
--secondary-color-2-hue: 201;
--secondary-color-2-saturation: 11%;
--secondary-color-2-lightness: 74%;
--secondary-color-3: hsl(201, 11%, 67%);
--secondary-color-3-hue: 201;
--secondary-color-3-saturation: 11%;
--secondary-color-3-lightness: 67%;
--secondary-color-4: hsl(201, 11%, 60%);
--secondary-color-4-hue: 201;
--secondary-color-4-saturation: 11%;
--secondary-color-4-lightness: 60%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-1-hue: 44.5;
--theme-nuance-color-1-saturation: 96.7%;
--theme-nuance-color-1-lightness: 52.9%;
--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-2-hue: 44.5;
--theme-nuance-color-2-saturation: 96.7%;
--theme-nuance-color-2-lightness: 52.9%;
--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-3-hue: 44.5;
--theme-nuance-color-3-saturation: 96.7%;
--theme-nuance-color-3-lightness: 52.9%;
--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-4-hue: 44.5;
--theme-nuance-color-4-saturation: 96.7%;
--theme-nuance-color-4-lightness: 52.9%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(232, 40%, 45%);
--theme-orange-color: #e76f51;
--theme-yellow-color: #ffd95f;
--theme-green-color: #A3BE8C;
--theme-purple-color: hsl(232, 30%, 40%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--secondary-color-1);
--button-alert-color-hover: var(--theme-purple-color);
--button-alert-border-hover: var(--theme-purple-color);
--button-alert-text-active: var(--secondary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--primary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(201,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color-hover:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 100%));
--button-primary-color-active:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-primary-border-active:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text: var(--secondary-color-1);
--button-secondary-color: var(--primary-color-3);
--button-secondary-border: var(--primary-color-3);
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover: var(--primary-color-4);
--button-secondary-border-hover: var(--primary-color-4);
/* ---------active--------- */
--button-secondary-text-active: var(--secondary-color-1);
--button-secondary-color-active:
hsl(201,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
--button-secondary-border-active:
hsl(201,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
/* ---------hover---------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
}

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/* Author: Yazan Agha-Schrader */
.theme-ketivah {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(0, 0%, 99.2%);
--primary-color-1-hue: 0;
--primary-color-1-saturation: 0%;
--primary-color-1-lightness: 99.2%;
--primary-color-2: hsl(0, 0%, 95%);
--primary-color-2-hue: 0;
--primary-color-2-saturation: 0%;
--primary-color-2-lightness: 95%;
--primary-color-3: hsl(0, 0%, 88%);
--primary-color-3-hue: 0;
--primary-color-3-saturation: 0%;
--primary-color-3-lightness: 88%;
--primary-color-4: hsl(0, 0%, 80%);
--primary-color-4-hue: 0;
--primary-color-4-saturation: 0%;
--primary-color-4-lightness: 80%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(0, 0%, 20%);
--secondary-color-1-hue: 0;
--secondary-color-1-saturation: 0%;
--secondary-color-1-lightness: 20%;
--secondary-color-2: hsl(0, 0%, 23.1%);
--secondary-color-2-hue: 0;
--secondary-color-2-saturation: 0%;
--secondary-color-2-lightness: 23.1%;
--secondary-color-3: hsl(0, 0%, 29%);
--secondary-color-3-hue: 0;
--secondary-color-3-saturation: 0%;
--secondary-color-3-lightness: 29%;
--secondary-color-4: hsl(0, 0.0%, 36.1%);
--secondary-color-4-hue: 0.0;
--secondary-color-4-saturation: 0.0%;
--secondary-color-4-lightness: 36.1%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(165.2, 0%, 35.1%);
--theme-nuance-color-1-hue: 165.2;
--theme-nuance-color-1-saturation: 82.1%;
--theme-nuance-color-1-lightness: 35.1%;
--theme-nuance-color-2: hsl(165.2, 0%, 35.1%);
--theme-nuance-color-2-hue: 165.2;
--theme-nuance-color-2-saturation: 82.1%;
--theme-nuance-color-2-lightness: 35.1%;
--theme-nuance-color-3: hsl(165.2, 0%, 35.3%);
--theme-nuance-color-3-hue: 165.2;
--theme-nuance-color-3-saturation: 81.1%;
--theme-nuance-color-3-lightness: 35.3%;
--theme-nuance-color-4: hsl(164.9, 0%, 27.6%);
--theme-nuance-color-4-hue: 164.9;
--theme-nuance-color-4-saturation: 81.6%;
--theme-nuance-color-4-lightness: 27.6%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(0.3, 80.0%, 50.0%);
--theme-orange-color: #e76f51;
--theme-yellow-color: hsl(60, 70.6%, 73.3%);
--theme-green-color: #A3BE8C;
--theme-purple-color: hsl(0.3, 70.0%, 45.0%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--primary-color-4);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--primary-color-1);
--button-alert-color-hover: var(--theme-purple-color);
--button-alert-border-hover: var(--theme-purple-color);
--button-alert-text-active: var(--primary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text:
hsl(0,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(0,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 100%));
--button-primary-color-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-primary-border-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color: var(--primary-color-3);
--button-secondary-border: var(--primary-color-3);
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover: var(--primary-color-4);
--button-secondary-border-hover: var(--primary-color-4);
/* ---------active--------- */
--button-secondary-text-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-active:
hsl(0,
calc(var(--primary-color-4-saturation) - 100%),
calc(var(--primary-color-4-lightness) - 15%));
--button-secondary-border-active:
hsl(0,
calc(var(--primary-color-4-saturation) - 100%),
calc(var(--primary-color-4-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
/* ---------hover---------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
--loading-color-1: #eeeeee00;
--loading-color-2: #eeeeeeff;
}

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examples/server/public/theme-mangotango.css Executable file
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/* Author: Yazan Agha-Schrader */
/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */
.theme-mangotango {
--primary-color-1: hsl(192, 8.5%, 11.6%);
--primary-color-2: hsl(192, 8.5%, 21%);
--primary-color-3: hsl(192, 8.5%, 30%);
--primary-color-4: hsl(192, 8.5%, 40%);
--secondary-color-1: hsl(192, 8.5%, 80%);
--secondary-color-2: hsl(192, 8.5%, 73%);
--secondary-color-3: hsl(192, 8.5%, 66%);
--secondary-color-4: hsl(192, 8.5%, 60%);
--theme-nuance-color-1: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-2: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-3: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-4: hsl(23.1, 100%, 60.2%);
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(192, 8.5%, 11.6%);
--primary-color-1-saturation: 8.5%;
--primary-color-1-lightness: 11.6%;
--primary-color-2: hsl(192, 8.5%, 21%);
--primary-color-2-saturation: 8.5%;
--primary-color-2-lightness: 21%;
--primary-color-3: hsl(192, 8.5%, 30%);
--primary-color-3-saturation: 8.5%;
--primary-color-3-lightness: 30%;
--primary-color-4: hsl(192, 8.5%, 40%);
--primary-color-4-saturation: 8.5%;
--primary-color-4-lightness: 40%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(192, 8.5%, 80%);
--secondary-color-1-saturation: 8.5%;
--secondary-color-1-lightness: 80%;
--secondary-color-2: hsl(192, 8.5%, 73%);
--secondary-color-2-saturation: 8.5%;
--secondary-color-2-lightness: 73%;
--secondary-color-3: hsl(192, 8.5%, 66%);
--secondary-color-3-saturation: 8.5%;
--secondary-color-3-lightness: 66%;
--secondary-color-4: hsl(192, 8.5%, 60%);
--secondary-color-4-saturation: 8.5%;
--secondary-color-4-lightness: 60%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-1-saturation: 100%;
--theme-nuance-color-1-lightness: 60.2%;
--theme-nuance-color-2: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-2-saturation: 100%;
--theme-nuance-color-2-lightness: 60.2%;
--theme-nuance-color-3: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-3-saturation: 100%;
--theme-nuance-color-3-lightness: 60.2%;
--theme-nuance-color-4: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-4-saturation: 100%;
--theme-nuance-color-4-lightness: 60.2%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(325, 60%, 50%);
--theme-orange-color: #e76f51;
--theme-yellow-color: #ffd95f;
--theme-green-color: #A3BE8C;
--theme-blue-color: hsl(192, 95%, 40%);
--theme-purple-color: hsl(192, 80%, 35%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--secondary-color-1);
--button-alert-color-hover: var(--theme-purple-color);
--button-alert-border-hover: var(--theme-purple-color);
--button-alert-text-active: var(--secondary-color-1);
--button-alert-color-active: var(--theme-blue-color);
--button-alert-border-active: var(--theme-blue-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--primary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(192,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color-hover:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 100%));
--button-primary-color-active:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-primary-border-active:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text: var(--secondary-color-1);
--button-secondary-color: var(--primary-color-3);
--button-secondary-border: var(--primary-color-3);
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover: var(--primary-color-4);
--button-secondary-border-hover: var(--primary-color-4);
/* ---------active--------- */
--button-secondary-text-active: var(--secondary-color-1);
--button-secondary-color-active:
hsl(192,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
--button-secondary-border-active:
hsl(192,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
/* ---------hover---------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
}

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/* Author: Yazan Agha-Schrader */
/* Inspiration from OpenAI's Playground platform https://platform.openai.com/playground/ */
.theme-playground {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(0, 0%, 99.2%);
--primary-color-1-hue: 0;
--primary-color-1-saturation: 0%;
--primary-color-1-lightness: 99.2%;
--primary-color-2: hsl(0, 0%, 95%);
--primary-color-2-hue: 0;
--primary-color-2-saturation: 0%;
--primary-color-2-lightness: 95%;
--primary-color-3: hsl(0, 0%, 88%);
--primary-color-3-hue: 0;
--primary-color-3-saturation: 0%;
--primary-color-3-lightness: 88%;
--primary-color-4: hsl(0, 0%, 80%);
--primary-color-4-hue: 0;
--primary-color-4-saturation: 0%;
--primary-color-4-lightness: 80%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(0, 0%, 20%);
--secondary-color-1-hue: 0;
--secondary-color-1-saturation: 0%;
--secondary-color-1-lightness: 20%;
--secondary-color-2: hsl(0, 0%, 23.1%);
--secondary-color-2-hue: 0;
--secondary-color-2-saturation: 0%;
--secondary-color-2-lightness: 23.1%;
--secondary-color-3: hsl(0, 0%, 29%);
--secondary-color-3-hue: 0;
--secondary-color-3-saturation: 0%;
--secondary-color-3-lightness: 29%;
--secondary-color-4: hsl(0, 0%, 36.1%);
--secondary-color-4-hue: 0;
--secondary-color-4-saturation: 0%;
--secondary-color-4-lightness: 36.1%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(165.2, 82.1%, 35.1%);
--theme-nuance-color-1-hue: 165.2;
--theme-nuance-color-1-saturation: 82.1%;
--theme-nuance-color-1-lightness: 35.1%;
--theme-nuance-color-2: hsl(165.2, 82.1%, 35.1%);
--theme-nuance-color-2-hue: 165.2;
--theme-nuance-color-2-saturation: 82.1%;
--theme-nuance-color-2-lightness: 35.1%;
--theme-nuance-color-3: hsl(165.2, 81.1%, 35.3%);
--theme-nuance-color-3-hue: 165.2;
--theme-nuance-color-3-saturation: 81.1%;
--theme-nuance-color-3-lightness: 35.3%;
--theme-nuance-color-4: hsl(164.9, 81.6%, 27.6%);
--theme-nuance-color-4-hue: 164.9;
--theme-nuance-color-4-saturation: 81.6%;
--theme-nuance-color-4-lightness: 27.6%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(0.3, 80%, 50%);
--theme-orange-color: #e76f51;
--theme-yellow-color: hsl(60, 70.6%, 73.3%);
--theme-green-color: #A3BE8C;
--theme-purple-color: hsl(0.3, 70%, 45%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--primary-color-4);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--primary-color-1);
--button-alert-color-hover: var(--theme-purple-color);
--button-alert-border-hover: var(--theme-purple-color);
--button-alert-text-active: var(--primary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text:
hsl(0,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(0,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 100%));
--button-primary-color-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-primary-border-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color: var(--primary-color-3);
--button-secondary-border: var(--primary-color-3);
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover: var(--primary-color-4);
--button-secondary-border-hover: var(--primary-color-4);
/* ---------active--------- */
--button-secondary-text-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-active:
hsl(0,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
--button-secondary-border-active:
hsl(0,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
/* ---------hover---------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
}

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/* Author: Yazan Agha-Schrader */
/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */
.theme-polarnight {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(220.0, 16.4%, 21.6%) ;
--primary-color-1-hue: 220.0;
--primary-color-1-saturation: 16.4%;
--primary-color-1-lightness: 21.6%;
--primary-color-2: hsl(221.7, 16.3%, 27.6%) ;
-primary-color-2-hue: 221.7;
--primary-color-2-saturation: 16.3%;
--primary-color-2-lightness: 27.6%;
--primary-color-3: hsl(220.0, 16.8%, 31.6%) ;
--primary-color-3-hue: 220.0;
--primary-color-3-saturation: 16.8%;
--primary-color-3-lightness: 31.6%;
--primary-color-4: hsl(220.0, 16.5%, 35.7%);
--primary-color-4-hue: 220.0;
--primary-color-4-saturation: 16.5%;
--primary-color-4-lightness: 35.7%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(217.5, 26.7%, 94.1%);
--secondary-color-1-hue: 217.5;
--secondary-color-1-saturation: 26.7%;
--secondary-color-1-lightness: 94.1%;
--secondary-color-2: hsl(218.2, 26.8%, 92.0%);
--secondary-color-2-hue: 218.2;
--secondary-color-2-saturation: 26.8%;
--secondary-color-2-lightness: 92.0%;
--secondary-color-3: hsl(218.8, 27.9%, 88.0%);
--secondary-color-3-hue: 218.8;
--secondary-color-3-saturation: 27.9%;
--secondary-color-3-lightness: 88.0%;
--secondary-color-4: hsl(218.8, 18.3%, 81.8%);
--secondary-color-4-hue: 218.8;
--secondary-color-4-saturation: 18.3%;
--secondary-color-4-lightness: 81.8%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
--theme-nuance-color-1-hue: 178.7;
--theme-nuance-color-1-saturation: 25.1%;
--theme-nuance-color-1-lightness: 64.9%;
--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
--theme-nuance-color-2-hue: 193.3;
--theme-nuance-color-2-saturation: 43.4%;
--theme-nuance-color-2-lightness: 67.5%;
--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
--theme-nuance-color-3-hue: 210.0;
--theme-nuance-color-3-saturation: 34.0%;
--theme-nuance-color-3-lightness: 63.1%;
--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
--theme-nuance-color-4-hue: 213.1;
--theme-nuance-color-4-saturation: 32.0%;
--theme-nuance-color-4-lightness: 52.2%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(354.3, 42.3%, 56.5%);
--theme-orange-color: hsl(20, 85%, 50%);
--theme-yellow-color: hsl(20, 75%, 45%);
--theme-green-color: hsl( 92.4, 27.8%, 64.7%);
--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
/* ------------------------------------------------ */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--secondary-color-1);
--button-alert-color-hover: var(--theme-yellow-color);
--button-alert-border-hover: var(--theme-yellow-color);
--button-alert-text-active: var(--secondary-color-1);
--button-alert-color-active: var(--theme-orange-color);
--button-alert-border-active: var(--theme-orange-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--secondary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(217.5,
calc(var(--secondary-color-1-saturation) - 35%),
calc(var(--secondary-color-1-lightness) + 30%));
--button-primary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 35%));
--button-primary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
--button-primary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
--button-secondary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
--button-secondary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
/* ---------active--------- */
--button-secondary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 25%));
--button-secondary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-secondary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
/* ---------hover---------- */
--button-tertiary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
}

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examples/server/public/theme-snowstorm.css Executable file
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/* Author: Yazan Agha-Schrader */
/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */
.theme-snowstorm {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(217.5, 26.7%, 94.1%);
--primary-color-1-hue: 217.5;
--primary-color-1-saturation: 26.7%;
--primary-color-1-lightness: 94.1%;
--primary-color-2: hsl(218.2, 26.8%, 92.0%);
--primary-color-2-hue: 218.2;
--primary-color-2-saturation: 26.8%;
--primary-color-2-lightness: 92.0%;
--primary-color-3: hsl(218.8, 27.9%, 88.0%);
--primary-color-3-hue: 218.8;
--primary-color-3-saturation: 27.9%;
--primary-color-3-lightness: 88.0%;
--primary-color-4: hsl(218.8, 18.3%, 81.8%);
--primary-color-4-hue: 218.8;
--primary-color-4-saturation: 18.3%;
--primary-color-4-lightness: 81.8%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(220.0, 16.4%, 21.6%);
--secondary-color-1-hue: 220.0;
--secondary-color-1-saturation: 16.4%;
--secondary-color-1-lightness: 21.6%;
--secondary-color-2: hsl(221.7, 16.3%, 27.6%);
--secondary-color-2-hue: 221.7;
--secondary-color-2-saturation: 16.3%;
--secondary-color-2-lightness: 27.6%;
--secondary-color-3: hsl(220.0, 16.8%, 31.6%);
--secondary-color-3-hue: 220.0;
--secondary-color-3-saturation: 16.8%;
--secondary-color-3-lightness: 31.6%;
--secondary-color-4: hsl(220.0, 16.5%, 35.7%);
--secondary-color-4-hue: 220.0;
--secondary-color-4-saturation: 16.5%;
--secondary-color-4-lightness: 35.7%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
--theme-nuance-color-1-hue: 178.7;
--theme-nuance-color-1-saturation: 25.1%;
--theme-nuance-color-1-lightness: 64.9%;
--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
--theme-nuance-color-2-hue: 193.3;
--theme-nuance-color-2-saturation: 43.4%;
--theme-nuance-color-2-lightness: 67.5%;
--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
--theme-nuance-color-3-hue: 210.0;
--theme-nuance-color-3-saturation: 34.0%;
--theme-nuance-color-3-lightness: 63.1%;
--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
--theme-nuance-color-4-hue: 213.1;
--theme-nuance-color-4-saturation: 32.0%;
--theme-nuance-color-4-lightness: 52.2%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(32.5, 80%, 50%);
--theme-orange-color: hsl(32.5, 70%, 45%);
--theme-yellow-color: hsl(40.0, 0.6%, 73.3%);
--theme-green-color: hsl(92.4, 27.8%, 64.7%);
--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--primary-color-1);
--button-alert-color-hover: var(--theme-orange-color);
--button-alert-border-hover: var(--theme-orange-color);
--button-alert-text-active: var(--primary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--secondary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(217.5,
calc(var(--secondary-color-1-saturation) + 35%),
calc(var(--secondary-color-1-lightness) - 30%));
--button-primary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 35%));
--button-primary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
--button-primary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
--button-secondary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
--button-secondary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
/* ---------active--------- */
--button-secondary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) + 40%),
calc(var(--theme-nuance-color-3-lightness) - 55%));
--button-secondary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-secondary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
/* ---------hover---------- */
--button-tertiary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
}

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examples/server/public_simplechat/datautils.mjs Normal file
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//@ts-check
// Helpers to work with different data types
// by Humans for All
//
/**
* Given the limited context size of local LLMs and , many a times when context gets filled
* between the prompt and the response, it can lead to repeating text garbage generation.
* And many a times setting penalty wrt repeatation leads to over-intelligent garbage
* repeatation with slight variations. These garbage inturn can lead to overloading of the
* available model context, leading to less valuable response for subsequent prompts/queries,
* if chat history is sent to ai model.
*
* So two simple minded garbage trimming logics are experimented below.
* * one based on progressively-larger-substring-based-repeat-matching-with-partial-skip and
* * another based on char-histogram-driven garbage trimming.
* * in future characteristic of histogram over varying lengths could be used to allow for
* a more aggressive and adaptive trimming logic.
*/
/**
* Simple minded logic to help remove repeating garbage at end of the string.
* The repeatation needs to be perfectly matching.
*
* The logic progressively goes on probing for longer and longer substring based
* repeatation, till there is no longer repeatation. Inturn picks the one with
* the longest chain.
*
* @param {string} sIn
* @param {number} maxSubL
* @param {number} maxMatchLenThreshold
*/
export function trim_repeat_garbage_at_end(sIn, maxSubL=10, maxMatchLenThreshold=40) {
let rCnt = [0];
let maxMatchLen = maxSubL;
let iMML = -1;
for(let subL=1; subL < maxSubL; subL++) {
rCnt.push(0);
let i;
let refS = sIn.substring(sIn.length-subL, sIn.length);
for(i=sIn.length; i > 0; i -= subL) {
let curS = sIn.substring(i-subL, i);
if (refS != curS) {
let curMatchLen = rCnt[subL]*subL;
if (maxMatchLen < curMatchLen) {
maxMatchLen = curMatchLen;
iMML = subL;
}
break;
}
rCnt[subL] += 1;
}
}
console.debug("DBUG:DU:TrimRepeatGarbage:", rCnt);
if ((iMML == -1) || (maxMatchLen < maxMatchLenThreshold)) {
return {trimmed: false, data: sIn};
}
console.debug("DBUG:TrimRepeatGarbage:TrimmedCharLen:", maxMatchLen);
let iEnd = sIn.length - maxMatchLen;
return { trimmed: true, data: sIn.substring(0, iEnd) };
}
/**
* Simple minded logic to help remove repeating garbage at end of the string, till it cant.
* If its not able to trim, then it will try to skip a char at end and then trim, a few times.
* This ensures that even if there are multiple runs of garbage with different patterns, the
* logic still tries to munch through them.
*
* @param {string} sIn
* @param {number} maxSubL
* @param {number | undefined} [maxMatchLenThreshold]
*/
export function trim_repeat_garbage_at_end_loop(sIn, maxSubL, maxMatchLenThreshold, skipMax=16) {
let sCur = sIn;
let sSaved = "";
let iTry = 0;
while(true) {
let got = trim_repeat_garbage_at_end(sCur, maxSubL, maxMatchLenThreshold);
if (got.trimmed != true) {
if (iTry == 0) {
sSaved = got.data;
}
iTry += 1;
if (iTry >= skipMax) {
return sSaved;
}
got.data = got.data.substring(0,got.data.length-1);
} else {
iTry = 0;
}
sCur = got.data;
}
}
/**
* A simple minded try trim garbage at end using histogram driven characteristics.
* There can be variation in the repeatations, as long as no new char props up.
*
* This tracks the chars and their frequency in a specified length of substring at the end
* and inturn checks if moving further into the generated text from the end remains within
* the same char subset or goes beyond it and based on that either trims the string at the
* end or not. This allows to filter garbage at the end, including even if there are certain
* kind of small variations in the repeated text wrt position of seen chars.
*
* Allow the garbage to contain upto maxUniq chars, but at the same time ensure that
* a given type of char ie numerals or alphabets or other types dont cross the specified
* maxType limit. This allows intermixed text garbage to be identified and trimmed.
*
* ALERT: This is not perfect and only provides a rough garbage identification logic.
* Also it currently only differentiates between character classes wrt english.
*
* @param {string} sIn
* @param {number} maxType
* @param {number} maxUniq
* @param {number} maxMatchLenThreshold
*/
export function trim_hist_garbage_at_end(sIn, maxType, maxUniq, maxMatchLenThreshold) {
if (sIn.length < maxMatchLenThreshold) {
return { trimmed: false, data: sIn };
}
let iAlp = 0;
let iNum = 0;
let iOth = 0;
// Learn
let hist = {};
let iUniq = 0;
for(let i=0; i<maxMatchLenThreshold; i++) {
let c = sIn[sIn.length-1-i];
if (c in hist) {
hist[c] += 1;
} else {
if(c.match(/[0-9]/) != null) {
iNum += 1;
} else if(c.match(/[A-Za-z]/) != null) {
iAlp += 1;
} else {
iOth += 1;
}
iUniq += 1;
if (iUniq >= maxUniq) {
break;
}
hist[c] = 1;
}
}
console.debug("DBUG:TrimHistGarbage:", hist);
if ((iAlp > maxType) || (iNum > maxType) || (iOth > maxType)) {
return { trimmed: false, data: sIn };
}
// Catch and Trim
for(let i=0; i < sIn.length; i++) {
let c = sIn[sIn.length-1-i];
if (!(c in hist)) {
if (i < maxMatchLenThreshold) {
return { trimmed: false, data: sIn };
}
console.debug("DBUG:TrimHistGarbage:TrimmedCharLen:", i);
return { trimmed: true, data: sIn.substring(0, sIn.length-i+1) };
}
}
console.debug("DBUG:TrimHistGarbage:Trimmed fully");
return { trimmed: true, data: "" };
}
/**
* Keep trimming repeatedly using hist_garbage logic, till you no longer can.
* This ensures that even if there are multiple runs of garbage with different patterns,
* the logic still tries to munch through them.
*
* @param {any} sIn
* @param {number} maxType
* @param {number} maxUniq
* @param {number} maxMatchLenThreshold
*/
export function trim_hist_garbage_at_end_loop(sIn, maxType, maxUniq, maxMatchLenThreshold) {
let sCur = sIn;
while (true) {
let got = trim_hist_garbage_at_end(sCur, maxType, maxUniq, maxMatchLenThreshold);
if (!got.trimmed) {
return got.data;
}
sCur = got.data;
}
}
/**
* Try trim garbage at the end by using both the hist-driven-garbage-trimming as well as
* skip-a-bit-if-reqd-then-repeat-pattern-based-garbage-trimming, with blind retrying.
* @param {string} sIn
*/
export function trim_garbage_at_end(sIn) {
let sCur = sIn;
for(let i=0; i<2; i++) {
sCur = trim_hist_garbage_at_end_loop(sCur, 8, 24, 72);
sCur = trim_repeat_garbage_at_end_loop(sCur, 32, 72, 12);
}
return sCur;
}
/**
* NewLines array helper.
* Allow for maintaining a list of lines.
* Allow for a line to be builtup/appended part by part.
*/
export class NewLines {
constructor() {
/** @type {string[]} */
this.lines = [];
}
/**
* Extracts lines from the passed string and inturn either
* append to a previous partial line or add a new line.
* @param {string} sLines
*/
add_append(sLines) {
let aLines = sLines.split("\n");
let lCnt = 0;
for(let line of aLines) {
lCnt += 1;
// Add back newline removed if any during split
if (lCnt < aLines.length) {
line += "\n";
} else {
if (sLines.endsWith("\n")) {
line += "\n";
}
}
// Append if required
if (lCnt == 1) {
let lastLine = this.lines[this.lines.length-1];
if (lastLine != undefined) {
if (!lastLine.endsWith("\n")) {
this.lines[this.lines.length-1] += line;
continue;
}
}
}
// Add new line
this.lines.push(line);
}
}
/**
* Shift the oldest/earliest/0th line in the array. [Old-New|Earliest-Latest]
* Optionally control whether only full lines (ie those with newline at end) will be returned
* or will a partial line without a newline at end (can only be the last line) be returned.
* @param {boolean} bFullWithNewLineOnly
*/
shift(bFullWithNewLineOnly=true) {
let line = this.lines[0];
if (line == undefined) {
return undefined;
}
if ((line[line.length-1] != "\n") && bFullWithNewLineOnly){
return undefined;
}
return this.lines.shift();
}
}

31
examples/server/public_simplechat/index.html
View file

@ -1,28 +1,30 @@
<!DOCTYPE html>
<html lang="en">
<head>
<title>SimpleChat (LlamaCPP, ...) </title>
<title>SimpleChat LlamaCppEtal </title>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="message" content="Save Nature Save Earth" />
<meta name="description" content="SimpleChat: trigger LLM web service endpoints /chat/completions and /completions, single/multi chat sessions" />
<meta name="author" content="by Humans for All" />
<meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate" />
<script src="simplechat.js" defer></script>
<script type="importmap">
{
"imports": {
"datautils": "./datautils.mjs",
"ui": "./ui.mjs"
}
}
</script>
<script src="simplechat.js" type="module" defer></script>
<link rel="stylesheet" href="simplechat.css" />
</head>
<body>
<div class="samecolumn" id="fullbody">
<div class="sameline">
<div class="sameline" id="heading">
<p class="heading flex-grow" > <b> SimpleChat </b> </p>
<div class="sameline">
<label for="api-ep">Mode:</label>
<select name="api-ep" id="api-ep">
<option value="chat" selected>Chat</option>
<option value="completion">Completion</option>
</select>
</div>
<button id="settings">Settings</button>
</div>
<div id="sessions-div" class="sameline"></div>
@ -30,20 +32,17 @@
<hr>
<div class="sameline">
<label for="system-in">System</label>
<input type="text" name="system" id="system-in" class="flex-grow"/>
<textarea name="system" id="system-in" rows="2" placeholder="e.g. you are a helpful ai assistant, who provides concise answers" class="flex-grow"></textarea>
</div>
<hr>
<div id="chat-div">
<p> Enter the system prompt above, before entering/submitting any user query.</p>
<p> Enter your text to the ai assistant below.</p>
<p> Use shift+enter for inserting enter.</p>
<p> Refresh the page to start over fresh.</p>
<p> You need to have javascript enabled.</p>
</div>
<hr>
<div class="sameline">
<textarea id="user-in" class="flex-grow" rows="3"></textarea>
<textarea id="user-in" class="flex-grow" rows="2" placeholder="enter your query to the ai model here" ></textarea>
<button id="user-btn">submit</button>
</div>

208
examples/server/public_simplechat/readme.md
View file

@ -11,14 +11,29 @@ in a simple way with minimal code from a common code base. Inturn additionally i
multiple independent back and forth chatting to an extent, with the ai llm model at a basic level, with their
own system prompts.
This allows seeing the generated text / ai-model response in oneshot at the end, after it is fully generated,
or potentially as it is being generated, in a streamed manner from the server/ai-model.
Auto saves the chat session locally as and when the chat is progressing and inturn at a later time when you
open SimpleChat, option is provided to restore the old chat session, if a matching one exists.
The UI follows a responsive web design so that the layout can adapt to available display space in a usable
enough manner, in general.
NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and
culling of old messages from the chat.
Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool
console. Parallely some of the directly useful to end-user settings can also be changed using the provided
settings ui.
NOTE: It doesnt set any parameters other than temperature for now. However if someone wants they can update
the js file as needed.
NOTE: Current web service api doesnt expose the model context length directly, so client logic doesnt provide
any adaptive culling of old messages nor of replacing them with summary of their content etal. However there
is a optional sliding window based chat logic, which provides a simple minded culling of old messages from
the chat history before sending to the ai model.
NOTE: Wrt options sent with the request, it mainly sets temperature, max_tokens and optionaly stream for now.
However if someone wants they can update the js file or equivalent member in gMe as needed.
NOTE: One may be able to use this to chat with openai api web-service /chat/completions endpoint, in a very
limited / minimal way. One will need to set model, openai url and authorization bearer key in settings ui.
## usage
@ -43,11 +58,39 @@ next run this web front end in examples/server/public_simplechat
### using the front end
Open this simple web front end from your local browser
* http://127.0.0.1:PORT/index.html
Once inside
* Select between chat and completion mode. By default it is set to chat mode.
* If you want to, you can change many of the default global settings
* the base url (ie ip addr / domain name, port)
* chat (default) vs completion mode
* try trim garbage in response or not
* amount of chat history in the context sent to server/ai-model
* oneshot or streamed mode.
* In completion mode
* one normally doesnt use a system prompt in completion mode.
* logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message.
If the model requires any prefix wrt user role messages, then the end user has to
explicitly add the needed prefix, when they enter their chat message.
Similarly if the model requires any prefix to trigger assistant/ai-model response,
then the end user needs to enter the same.
This keeps the logic simple, while still giving flexibility to the end user to
manage any templating/tagging requirement wrt their messages to the model.
* the logic doesnt insert newline at the begining and end wrt the prompt message generated.
However if the chat being sent to /completions end point has more than one role's message,
then insert newline when moving from one role's message to the next role's message, so
that it can be clearly identified/distinguished.
* given that /completions endpoint normally doesnt add additional chat-templating of its
own, the above ensures that end user can create a custom single/multi message combo with
any tags/special-tokens related chat templating to test out model handshake. Or enduser
can use it just for normal completion related/based query.
* If you want to provide a system prompt, then ideally enter it first, before entering any user query.
Normally Completion mode doesnt need system prompt, while Chat mode can generate better/interesting
responses with a suitable system prompt.
* if chat.add_system_begin is used
* you cant change the system prompt, after it is has been submitted once along with user query.
* you cant set a system prompt, after you have submitted any user query
@ -55,27 +98,174 @@ Once inside
* one can change the system prompt any time during chat, by changing the contents of system prompt.
* inturn the updated/changed system prompt will be inserted into the chat session.
* this allows for the subsequent user chatting to be driven by the new system prompt set above.
* Enter your query and either press enter or click on the submit button.
If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter.
* Wait for the logic to communicate with the server and get the response.
* the user is not allowed to enter any fresh query during this time.
* the user input box will be disabled and a working message will be shown in it.
* if trim garbage is enabled, the logic will try to trim repeating text kind of garbage to some extent.
* just refresh the page, to reset wrt the chat history and or system prompt and start afresh.
* Using NewChat one can start independent chat sessions.
* two independent chat sessions are setup by default.
* When you want to print, switching ChatHistoryInCtxt to Full and clicking on the chat session button of
interest, will display the full chat history till then wrt same, if you want full history for printing.
## Devel note
### Reason behind this
The idea is to be easy enough to use for basic purposes, while also being simple and easily discernable
by developers who may not be from web frontend background (so inturn may not be familiar with template /
end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things.
And given that the idea is also to help explore/experiment for developers, some flexibility is provided
to change behaviour easily using the devel-tools/console or provided minimal settings ui (wrt few aspects).
Skeletal logic has been implemented to explore some of the end points and ideas/implications around them.
### General
Me/gMe consolidates the settings which control the behaviour into one object.
One can see the current settings, as well as change/update them using browsers devel-tool/console.
It is attached to the document object. Some of these can also be updated using the Settings UI.
baseURL - the domain-name/ip-address and inturn the port to send the request.
bStream - control between oneshot-at-end and live-stream-as-its-generated collating and showing
of the generated response.
the logic assumes that the text sent from the server follows utf-8 encoding.
in streaming mode - if there is any exception, the logic traps the same and tries to ensure
that text generated till then is not lost.
if a very long text is being generated, which leads to no user interaction for sometime and
inturn the machine goes into power saving mode or so, the platform may stop network connection,
leading to exception.
apiEP - select between /completions and /chat/completions endpoint provided by the server/ai-model.
bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when
communicating with the server or only sends the latest user query/message.
bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the
messages that get inserted into prompt field wrt /Completion endpoint.
bTrimGarbage - whether garbage repeatation at the end of the generated ai response, should be
trimmed or left as is. If enabled, it will be trimmed so that it wont be sent back as part of
subsequent chat history. At the same time the actual trimmed text is shown to the user, once
when it was generated, so user can check if any useful info/data was there in the response.
One may be able to request the ai-model to continue (wrt the last response) (if chat-history
is enabled as part of the chat-history-in-context setting), and chances are the ai-model will
continue starting from the trimmed part, thus allows long response to be recovered/continued
indirectly, in many cases.
The histogram/freq based trimming logic is currently tuned for english language wrt its
is-it-a-alpabetic|numeral-char regex match logic.
chatRequestOptions - maintains the list of options/fields to send along with chat request,
irrespective of whether /chat/completions or /completions endpoint.
If you want to add additional options/fields to send to the server/ai-model, and or
modify the existing options value or remove them, for now you can update this global var
using browser's development-tools/console.
For string and numeric fields in chatRequestOptions, including even those added by a user
at runtime by directly modifying gMe.chatRequestOptions, setting ui entries will be auto
created.
headers - maintains the list of http headers sent when request is made to the server. By default
Content-Type is set to application/json. Additionally Authorization entry is provided, which can
be set if needed using the settings ui.
iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end.
This is disabled by default. However if enabled, then in addition to latest system message, only
the last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses
from the ai model will be sent to the ai-model, when querying for a new response. IE if enabled,
only user messages after the latest system message/prompt will be considered.
This specified sliding window user message count also includes the latest user query.
<0 : Send entire chat history to server
0 : Send only the system message if any to the server
>0 : Send the latest chat history from the latest system prompt, limited to specified cnt.
By using gMe's iRecentUserMsgCnt and chatRequestOptions.max_tokens one can try to control the
implications of loading of the ai-model's context window by chat history, wrt chat response to
some extent in a simple crude way. You may also want to control the context size enabled when
the server loads ai-model, on the server end.
Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js
may not be visible. Also remember that just refreshing/reloading page in browser or for that
matter clearing site data, dont directly override site caching in all cases. Worst case you may
have to change port. Or in dev tools of browser, you may be able to disable caching fully.
Concept of multiple chat sessions with different servers, as well as saving and restoring of
those across browser usage sessions, can be woven around the SimpleChat/MultiChatUI class and
its instances relatively easily, however given the current goal of keeping this simple, it has
not been added, for now.
Currently the server to communicate with is maintained globally and not as part of a specific
chat session. So if one changes the server ip/url in setting, then all chat sessions will auto
switch to this new server, when you try using those sessions.
By switching between chat.add_system_begin/anytime, one can control whether one can change
the system prompt, anytime during the conversation or only at the beginning.
### Default setup
By default things are setup to try and make the user experience a bit better, if possible.
However a developer when testing the server of ai-model may want to change these value.
Using iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be
just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of
full chat history. This way if there is any response with garbage/repeatation, it doesnt
mess with things beyond the next question/request/query, in some ways. The trim garbage
option also tries to help avoid issues with garbage in the context to an extent.
Set max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space
available wrt next query-response. However dont forget that the server when started should
also be started with a model context size of 1k or more, to be on safe side.
The /completions endpoint of examples/server doesnt take max_tokens, instead it takes the
internal n_predict, for now add the same here on the client side, maybe later add max_tokens
to /completions endpoint handling code on server side.
NOTE: One may want to experiment with frequency/presence penalty fields in chatRequestOptions
wrt the set of fields sent to server along with the user query. To check how the model behaves
wrt repeatations in general in the generated text response.
A end-user can change these behaviour by editing gMe from browser's devel-tool/console or by
using the providing settings ui.
### OpenAi / Equivalent API WebService
One may be abe to handshake with OpenAI/Equivalent api web service's /chat/completions endpoint
for a minimal chatting experimentation by setting the below.
* the baseUrl in settings ui
* https://api.openai.com/v1 or similar
* Wrt request body - gMe.chatRequestOptions
* model (settings ui)
* any additional fields if required in future
* Wrt request headers - gMe.headers
* Authorization (available through settings ui)
* Bearer THE_OPENAI_API_KEY
* any additional optional header entries like "OpenAI-Organization", "OpenAI-Project" or so
NOTE: Not tested, as there is no free tier api testing available. However logically this might
work.
## At the end
Also a thank you to all open source and open model developers, who strive for the common good.

18
examples/server/public_simplechat/simplechat.css
View file

@ -21,6 +21,17 @@
.role-user {
background-color: lightgray;
}
.role-trim {
background-color: lightpink;
}
.gridx2 {
display: grid;
grid-template-columns: repeat(2, 1fr);
border-bottom-style: dotted;
border-bottom-width: thin;
border-bottom-color: lightblue;
}
.flex-grow {
flex-grow: 1;
@ -48,6 +59,13 @@ button {
flex-direction: column;
}
.ul1 {
padding-inline-start: 2vw;
}
.ul2 {
padding-inline-start: 2vw;
}
* {
margin: 0.6vmin;
}

661
examples/server/public_simplechat/simplechat.js
View file

@ -2,6 +2,9 @@
// A simple completions and chat/completions test related web front end logic
// by Humans for All
import * as du from "./datautils.mjs";
import * as ui from "./ui.mjs"
class Roles {
static System = "system";
static User = "user";
@ -9,26 +12,144 @@ class Roles {
}
class ApiEP {
static Chat = "chat";
static Completion = "completion";
static Type = {
Chat: "chat",
Completion: "completion",
}
static UrlSuffix = {
'chat': `/chat/completions`,
'completion': `/completions`,
}
/**
* Build the url from given baseUrl and apiEp id.
* @param {string} baseUrl
* @param {string} apiEP
*/
static Url(baseUrl, apiEP) {
if (baseUrl.endsWith("/")) {
baseUrl = baseUrl.substring(0, baseUrl.length-1);
}
return `${baseUrl}${this.UrlSuffix[apiEP]}`;
}
}
let gUsageMsg = `
<p> Enter the system prompt above, before entering/submitting any user query.</p>
<p> Enter your text to the ai assistant below.</p>
<p> Use shift+enter for inserting enter.</p>
<p> Refresh the page to start over fresh.</p>
<p class="role-system">Usage</p>
<ul class="ul1">
<li> System prompt above, to try control ai response characteristics.</li>
<ul class="ul2">
<li> Completion mode - no system prompt normally.</li>
</ul>
<li> Use shift+enter for inserting enter/newline.</li>
<li> Enter your query to ai assistant below.</li>
<li> Default ContextWindow = [System, Last Query+Resp, Cur Query].</li>
<ul class="ul2">
<li> ChatHistInCtxt, MaxTokens, ModelCtxt window to expand</li>
</ul>
</ul>
`;
/** @typedef {{role: string, content: string}[]} ChatMessages */
/** @typedef {{iLastSys: number, xchat: ChatMessages}} SimpleChatODS */
class SimpleChat {
constructor() {
/**
* @param {string} chatId
*/
constructor(chatId) {
this.chatId = chatId;
/**
* Maintain in a form suitable for common LLM web service chat/completions' messages entry
* @type {{role: string, content: string}[]}
* @type {ChatMessages}
*/
this.xchat = [];
this.iLastSys = -1;
this.latestResponse = "";
}
clear() {
this.xchat = [];
this.iLastSys = -1;
}
ods_key() {
return `SimpleChat-${this.chatId}`
}
save() {
/** @type {SimpleChatODS} */
let ods = {iLastSys: this.iLastSys, xchat: this.xchat};
localStorage.setItem(this.ods_key(), JSON.stringify(ods));
}
load() {
let sods = localStorage.getItem(this.ods_key());
if (sods == null) {
return;
}
/** @type {SimpleChatODS} */
let ods = JSON.parse(sods);
this.iLastSys = ods.iLastSys;
this.xchat = ods.xchat;
}
/**
* Recent chat messages.
* If iRecentUserMsgCnt < 0
* Then return the full chat history
* Else
* Return chat messages from latest going back till the last/latest system prompt.
* While keeping track that the number of user queries/messages doesnt exceed iRecentUserMsgCnt.
* @param {number} iRecentUserMsgCnt
*/
recent_chat(iRecentUserMsgCnt) {
if (iRecentUserMsgCnt < 0) {
return this.xchat;
}
if (iRecentUserMsgCnt == 0) {
console.warn("WARN:SimpleChat:SC:RecentChat:iRecentUsermsgCnt of 0 means no user message/query sent");
}
/** @type{ChatMessages} */
let rchat = [];
let sysMsg = this.get_system_latest();
if (sysMsg.length != 0) {
rchat.push({role: Roles.System, content: sysMsg});
}
let iUserCnt = 0;
let iStart = this.xchat.length;
for(let i=this.xchat.length-1; i > this.iLastSys; i--) {
if (iUserCnt >= iRecentUserMsgCnt) {
break;
}
let msg = this.xchat[i];
if (msg.role == Roles.User) {
iStart = i;
iUserCnt += 1;
}
}
for(let i = iStart; i < this.xchat.length; i++) {
let msg = this.xchat[i];
if (msg.role == Roles.System) {
continue;
}
rchat.push({role: msg.role, content: msg.content});
}
return rchat;
}
/**
* Collate the latest response from the server/ai-model, as it is becoming available.
* This is mainly useful for the stream mode.
* @param {string} content
*/
append_response(content) {
this.latestResponse += content;
}
/**
@ -44,6 +165,7 @@ class SimpleChat {
if (role == Roles.System) {
this.iLastSys = this.xchat.length - 1;
}
this.save();
return true;
}
@ -57,11 +179,9 @@ class SimpleChat {
div.replaceChildren();
}
let last = undefined;
for(const x of this.xchat) {
let entry = document.createElement("p");
for(const x of this.recent_chat(gMe.iRecentUserMsgCnt)) {
let entry = ui.el_create_append_p(`${x.role}: ${x.content}`, div);
entry.className = `role-${x.role}`;
entry.innerText = `${x.role}: ${x.content}`;
div.appendChild(entry);
last = entry;
}
if (last !== undefined) {
@ -69,17 +189,45 @@ class SimpleChat {
} else {
if (bClear) {
div.innerHTML = gUsageMsg;
gMe.setup_load(div, this);
gMe.show_info(div);
}
}
return last;
}
/**
* Add needed fields wrt json object to be sent wrt LLM web services completions endpoint
* Setup the fetch headers.
* It picks the headers from gMe.headers.
* It inserts Authorization only if its non-empty.
* @param {string} apiEP
*/
fetch_headers(apiEP) {
let headers = new Headers();
for(let k in gMe.headers) {
let v = gMe.headers[k];
if ((k == "Authorization") && (v.trim() == "")) {
continue;
}
headers.append(k, v);
}
return headers;
}
/**
* Add needed fields wrt json object to be sent wrt LLM web services completions endpoint.
* The needed fields/options are picked from a global object.
* Add optional stream flag, if required.
* Convert the json into string.
* @param {Object} obj
*/
request_jsonstr(obj) {
obj["temperature"] = 0.7;
request_jsonstr_extend(obj) {
for(let k in gMe.chatRequestOptions) {
obj[k] = gMe.chatRequestOptions[k];
}
if (gMe.bStream) {
obj["stream"] = true;
}
return JSON.stringify(obj);
}
@ -88,23 +236,85 @@ class SimpleChat {
*/
request_messages_jsonstr() {
let req = {
messages: this.xchat,
messages: this.recent_chat(gMe.iRecentUserMsgCnt),
}
return this.request_jsonstr(req);
return this.request_jsonstr_extend(req);
}
/**
* Return a string form of json object suitable for /completions
* @param {boolean} bInsertStandardRolePrefix Insert "<THE_ROLE>: " as prefix wrt each role's message
*/
request_prompt_jsonstr() {
request_prompt_jsonstr(bInsertStandardRolePrefix) {
let prompt = "";
for(const chat of this.xchat) {
prompt += `${chat.role}: ${chat.content}\n`;
let iCnt = 0;
for(const chat of this.recent_chat(gMe.iRecentUserMsgCnt)) {
iCnt += 1;
if (iCnt > 1) {
prompt += "\n";
}
if (bInsertStandardRolePrefix) {
prompt += `${chat.role}: `;
}
prompt += `${chat.content}`;
}
let req = {
prompt: prompt,
}
return this.request_jsonstr(req);
return this.request_jsonstr_extend(req);
}
/**
* Return a string form of json object suitable for specified api endpoint.
* @param {string} apiEP
*/
request_jsonstr(apiEP) {
if (apiEP == ApiEP.Type.Chat) {
return this.request_messages_jsonstr();
} else {
return this.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix);
}
}
/**
* Extract the ai-model/assistant's response from the http response got.
* Optionally trim the message wrt any garbage at the end.
* @param {any} respBody
* @param {string} apiEP
*/
response_extract(respBody, apiEP) {
let assistant = "";
if (apiEP == ApiEP.Type.Chat) {
assistant = respBody["choices"][0]["message"]["content"];
} else {
try {
assistant = respBody["choices"][0]["text"];
} catch {
assistant = respBody["content"];
}
}
return assistant;
}
/**
* Extract the ai-model/assistant's response from the http response got in streaming mode.
* @param {any} respBody
* @param {string} apiEP
*/
response_extract_stream(respBody, apiEP) {
let assistant = "";
if (apiEP == ApiEP.Type.Chat) {
if (respBody["choices"][0]["finish_reason"] !== "stop") {
assistant = respBody["choices"][0]["delta"]["content"];
}
} else {
try {
assistant = respBody["choices"][0]["text"];
} catch {
assistant = respBody["content"];
}
}
return assistant;
}
/**
@ -163,54 +373,99 @@ class SimpleChat {
return sysPrompt;
}
}
let gBaseURL = "http://127.0.0.1:8080";
let gChatURL = {
'chat': `${gBaseURL}/chat/completions`,
'completion': `${gBaseURL}/completions`,
}
const gbCompletionFreshChatAlways = true;
/**
* Set the class of the children, based on whether it is the idSelected or not.
* @param {HTMLDivElement} elBase
* @param {string} idSelected
* @param {string} classSelected
* @param {string} classUnSelected
*/
function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") {
for(let child of elBase.children) {
if (child.id == idSelected) {
child.className = classSelected;
} else {
child.className = classUnSelected;
/**
* Handle the multipart response from server/ai-model
* @param {Response} resp
* @param {string} apiEP
* @param {HTMLDivElement} elDiv
*/
async handle_response_multipart(resp, apiEP, elDiv) {
let elP = ui.el_create_append_p("", elDiv);
if (!resp.body) {
throw Error("ERRR:SimpleChat:SC:HandleResponseMultiPart:No body...");
}
let tdUtf8 = new TextDecoder("utf-8");
let rr = resp.body.getReader();
this.latestResponse = "";
let xLines = new du.NewLines();
while(true) {
let { value: cur, done: done } = await rr.read();
if (cur) {
let curBody = tdUtf8.decode(cur, {stream: true});
console.debug("DBUG:SC:PART:Str:", curBody);
xLines.add_append(curBody);
}
while(true) {
let curLine = xLines.shift(!done);
if (curLine == undefined) {
break;
}
if (curLine.trim() == "") {
continue;
}
if (curLine.startsWith("data:")) {
curLine = curLine.substring(5);
}
let curJson = JSON.parse(curLine);
console.debug("DBUG:SC:PART:Json:", curJson);
this.append_response(this.response_extract_stream(curJson, apiEP));
}
elP.innerText = this.latestResponse;
elP.scrollIntoView(false);
if (done) {
break;
}
}
console.debug("DBUG:SC:PART:Full:", this.latestResponse);
return this.latestResponse;
}
}
/**
* Create button and set it up.
* @param {string} id
* @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback
* @param {string | undefined} name
* @param {string | undefined} innerText
*/
function el_create_button(id, callback, name=undefined, innerText=undefined) {
if (!name) {
name = id;
/**
* Handle the oneshot response from server/ai-model
* @param {Response} resp
* @param {string} apiEP
*/
async handle_response_oneshot(resp, apiEP) {
let respBody = await resp.json();
console.debug(`DBUG:SimpleChat:SC:${this.chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`);
return this.response_extract(respBody, apiEP);
}
if (!innerText) {
innerText = id;
/**
* Handle the response from the server be it in oneshot or multipart/stream mode.
* Also take care of the optional garbage trimming.
* @param {Response} resp
* @param {string} apiEP
* @param {HTMLDivElement} elDiv
*/
async handle_response(resp, apiEP, elDiv) {
let theResp = {
assistant: "",
trimmed: "",
}
if (gMe.bStream) {
try {
theResp.assistant = await this.handle_response_multipart(resp, apiEP, elDiv);
this.latestResponse = "";
} catch (error) {
theResp.assistant = this.latestResponse;
this.add(Roles.Assistant, theResp.assistant);
this.latestResponse = "";
throw error;
}
} else {
theResp.assistant = await this.handle_response_oneshot(resp, apiEP);
}
if (gMe.bTrimGarbage) {
let origMsg = theResp.assistant;
theResp.assistant = du.trim_garbage_at_end(origMsg);
theResp.trimmed = origMsg.substring(theResp.assistant.length);
}
this.add(Roles.Assistant, theResp.assistant);
return theResp;
}
let btn = document.createElement("button");
btn.id = id;
btn.name = name;
btn.innerText = innerText;
btn.addEventListener("click", callback);
return btn;
}
@ -227,14 +482,16 @@ class MultiChatUI {
this.elDivChat = /** @type{HTMLDivElement} */(document.getElementById("chat-div"));
this.elBtnUser = /** @type{HTMLButtonElement} */(document.getElementById("user-btn"));
this.elInUser = /** @type{HTMLInputElement} */(document.getElementById("user-in"));
this.elSelectApiEP = /** @type{HTMLSelectElement} */(document.getElementById("api-ep"));
this.elDivHeading = /** @type{HTMLSelectElement} */(document.getElementById("heading"));
this.elDivSessions = /** @type{HTMLDivElement} */(document.getElementById("sessions-div"));
this.elBtnSettings = /** @type{HTMLButtonElement} */(document.getElementById("settings"));
this.validate_element(this.elInSystem, "system-in");
this.validate_element(this.elDivChat, "chat-div");
this.validate_element(this.elInUser, "user-in");
this.validate_element(this.elSelectApiEP, "api-ep");
this.validate_element(this.elDivHeading, "heading");
this.validate_element(this.elDivChat, "sessions-div");
this.validate_element(this.elBtnSettings, "settings");
}
/**
@ -275,13 +532,18 @@ class MultiChatUI {
this.handle_session_switch(this.curChatId);
}
this.elBtnSettings.addEventListener("click", (ev)=>{
this.elDivChat.replaceChildren();
gMe.show_settings(this.elDivChat);
});
this.elBtnUser.addEventListener("click", (ev)=>{
if (this.elInUser.disabled) {
return;
}
this.handle_user_submit(this.curChatId, this.elSelectApiEP.value).catch((/** @type{Error} */reason)=>{
this.handle_user_submit(this.curChatId, gMe.apiEP).catch((/** @type{Error} */reason)=>{
let msg = `ERRR:SimpleChat\nMCUI:HandleUserSubmit:${this.curChatId}\n${reason.name}:${reason.message}`;
console.debug(msg.replace("\n", ":"));
console.error(msg.replace("\n", ":"));
alert(msg);
this.ui_reset_userinput();
});
@ -291,6 +553,8 @@ class MultiChatUI {
// allow user to insert enter into their message using shift+enter.
// while just pressing enter key will lead to submitting.
if ((ev.key === "Enter") && (!ev.shiftKey)) {
let value = this.elInUser.value;
this.elInUser.value = value.substring(0,value.length-1);
this.elBtnUser.click();
ev.preventDefault();
}
@ -300,6 +564,8 @@ class MultiChatUI {
// allow user to insert enter into the system prompt using shift+enter.
// while just pressing enter key will lead to setting the system prompt.
if ((ev.key === "Enter") && (!ev.shiftKey)) {
let value = this.elInSystem.value;
this.elInSystem.value = value.substring(0,value.length-1);
let chat = this.simpleChats[this.curChatId];
chat.add_system_anytime(this.elInSystem.value, this.curChatId);
chat.show(this.elDivChat);
@ -315,12 +581,13 @@ class MultiChatUI {
* @param {boolean} bSwitchSession
*/
new_chat_session(chatId, bSwitchSession=false) {
this.simpleChats[chatId] = new SimpleChat();
this.simpleChats[chatId] = new SimpleChat(chatId);
if (bSwitchSession) {
this.handle_session_switch(chatId);
}
}
/**
* Handle user query submit request, wrt specified chat session.
* @param {string} chatId
@ -330,6 +597,14 @@ class MultiChatUI {
let chat = this.simpleChats[chatId];
// In completion mode, if configured, clear any previous chat history.
// So if user wants to simulate a multi-chat based completion query,
// they will have to enter the full thing, as a suitable multiline
// user input/query.
if ((apiEP == ApiEP.Type.Completion) && (gMe.bCompletionFreshChatAlways)) {
chat.clear();
}
chat.add_system_anytime(this.elInSystem.value, chatId);
let content = this.elInUser.value;
@ -339,50 +614,29 @@ class MultiChatUI {
}
chat.show(this.elDivChat);
let theBody;
let theUrl = gChatURL[apiEP]
if (apiEP == ApiEP.Chat) {
theBody = chat.request_messages_jsonstr();
} else {
theBody = chat.request_prompt_jsonstr();
}
let theUrl = ApiEP.Url(gMe.baseURL, apiEP);
let theBody = chat.request_jsonstr(apiEP);
this.elInUser.value = "working...";
this.elInUser.disabled = true;
console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:${theUrl}:ReqBody:${theBody}`);
let theHeaders = chat.fetch_headers(apiEP);
let resp = await fetch(theUrl, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
headers: theHeaders,
body: theBody,
});
let respBody = await resp.json();
console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`);
let assistantMsg;
if (apiEP == ApiEP.Chat) {
assistantMsg = respBody["choices"][0]["message"]["content"];
} else {
try {
assistantMsg = respBody["choices"][0]["text"];
} catch {
assistantMsg = respBody["content"];
}
}
chat.add(Roles.Assistant, assistantMsg);
let theResp = await chat.handle_response(resp, apiEP, this.elDivChat);
if (chatId == this.curChatId) {
chat.show(this.elDivChat);
if (theResp.trimmed.length > 0) {
let p = ui.el_create_append_p(`TRIMMED:${theResp.trimmed}`, this.elDivChat);
p.className="role-trim";
}
} else {
console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`);
}
// Purposefully clear at end rather than begin of this function
// so that one can switch from chat to completion mode and sequece
// in a completion mode with multiple user-assistant chat data
// from before to be sent/occur once.
if ((apiEP == ApiEP.Completion) && (gbCompletionFreshChatAlways)) {
chat.xchat.length = 0;
}
this.ui_reset_userinput();
}
@ -398,7 +652,7 @@ class MultiChatUI {
}
elDiv.replaceChildren();
// Btn for creating new chat session
let btnNew = el_create_button("New CHAT", (ev)=> {
let btnNew = ui.el_create_button("New CHAT", (ev)=> {
if (this.elInUser.disabled) {
console.error(`ERRR:SimpleChat:MCUI:NewChat:Current session [${this.curChatId}] awaiting response, ignoring request...`);
alert("ERRR:SimpleChat\nMCUI:NewChat\nWait for response to pending query, before starting new chat session");
@ -412,7 +666,7 @@ class MultiChatUI {
}
this.new_chat_session(chatIdGot, true);
this.create_session_btn(elDiv, chatIdGot);
el_children_config_class(elDiv, chatIdGot, "session-selected", "");
ui.el_children_config_class(elDiv, chatIdGot, "session-selected", "");
});
elDiv.appendChild(btnNew);
// Btns for existing chat sessions
@ -426,7 +680,7 @@ class MultiChatUI {
}
create_session_btn(elDiv, cid) {
let btn = el_create_button(cid, (ev)=>{
let btn = ui.el_create_button(cid, (ev)=>{
let target = /** @type{HTMLButtonElement} */(ev.target);
console.debug(`DBUG:SimpleChat:MCUI:SessionClick:${target.id}`);
if (this.elInUser.disabled) {
@ -435,7 +689,7 @@ class MultiChatUI {
return;
}
this.handle_session_switch(target.id);
el_children_config_class(elDiv, target.id, "session-selected", "");
ui.el_children_config_class(elDiv, target.id, "session-selected", "");
});
elDiv.appendChild(btn);
return btn;
@ -462,17 +716,206 @@ class MultiChatUI {
}
let gMuitChat;
const gChatIds = [ "Default", "Other" ];
class Me {
constructor() {
this.baseURL = "http://127.0.0.1:8080";
this.defaultChatIds = [ "Default", "Other" ];
this.multiChat = new MultiChatUI();
this.bStream = true;
this.bCompletionFreshChatAlways = true;
this.bCompletionInsertStandardRolePrefix = false;
this.bTrimGarbage = true;
this.iRecentUserMsgCnt = 2;
this.sRecentUserMsgCnt = {
"Full": -1,
"Last0": 1,
"Last1": 2,
"Last2": 3,
"Last4": 5,
};
this.apiEP = ApiEP.Type.Chat;
this.headers = {
"Content-Type": "application/json",
"Authorization": "", // Authorization: Bearer OPENAI_API_KEY
}
// Add needed fields wrt json object to be sent wrt LLM web services completions endpoint.
this.chatRequestOptions = {
"model": "gpt-3.5-turbo",
"temperature": 0.7,
"max_tokens": 1024,
"n_predict": 1024,
//"frequency_penalty": 1.2,
//"presence_penalty": 1.2,
};
}
/**
* Disable console.debug by mapping it to a empty function.
*/
debug_disable() {
this.console_debug = console.debug;
console.debug = () => {
};
}
/**
* Setup the load saved chat ui.
* @param {HTMLDivElement} div
* @param {SimpleChat} chat
*/
setup_load(div, chat) {
if (!(chat.ods_key() in localStorage)) {
return;
}
div.innerHTML += `<p class="role-system">Restore</p>
<p>Load previously saved chat session, if available</p>`;
let btn = ui.el_create_button(chat.ods_key(), (ev)=>{
console.log("DBUG:SimpleChat:SC:Load", chat);
chat.load();
queueMicrotask(()=>{
chat.show(div);
this.multiChat.elInSystem.value = chat.get_system_latest();
});
});
div.appendChild(btn);
}
/**
* Show the configurable parameters info in the passed Div element.
* @param {HTMLDivElement} elDiv
* @param {boolean} bAll
*/
show_info(elDiv, bAll=false) {
let p = ui.el_create_append_p("Settings (devel-tools-console document[gMe])", elDiv);
p.className = "role-system";
if (bAll) {
ui.el_create_append_p(`baseURL:${this.baseURL}`, elDiv);
ui.el_create_append_p(`Authorization:${this.headers["Authorization"]}`, elDiv);
ui.el_create_append_p(`bStream:${this.bStream}`, elDiv);
ui.el_create_append_p(`bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`, elDiv);
ui.el_create_append_p(`bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`, elDiv);
ui.el_create_append_p(`bTrimGarbage:${this.bTrimGarbage}`, elDiv);
ui.el_create_append_p(`iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`, elDiv);
ui.el_create_append_p(`ApiEndPoint:${this.apiEP}`, elDiv);
}
ui.el_create_append_p(`chatRequestOptions:${JSON.stringify(this.chatRequestOptions, null, " - ")}`, elDiv);
ui.el_create_append_p(`headers:${JSON.stringify(this.headers, null, " - ")}`, elDiv);
}
/**
* Auto create ui input elements for fields in ChatRequestOptions
* Currently supports text and number field types.
* @param {HTMLDivElement} elDiv
*/
show_settings_chatrequestoptions(elDiv) {
let typeDict = {
"string": "text",
"number": "number",
};
let fs = document.createElement("fieldset");
let legend = document.createElement("legend");
legend.innerText = "ChatRequestOptions";
fs.appendChild(legend);
elDiv.appendChild(fs);
for(const k in this.chatRequestOptions) {
let val = this.chatRequestOptions[k];
let type = typeof(val);
if (!((type == "string") || (type == "number"))) {
continue;
}
let inp = ui.el_creatediv_input(`Set${k}`, k, typeDict[type], this.chatRequestOptions[k], (val)=>{
if (type == "number") {
val = Number(val);
}
this.chatRequestOptions[k] = val;
});
fs.appendChild(inp.div);
}
}
/**
* Show settings ui for configurable parameters, in the passed Div element.
* @param {HTMLDivElement} elDiv
*/
show_settings(elDiv) {
let inp = ui.el_creatediv_input("SetBaseURL", "BaseURL", "text", this.baseURL, (val)=>{
this.baseURL = val;
});
elDiv.appendChild(inp.div);
inp = ui.el_creatediv_input("SetAuthorization", "Authorization", "text", this.headers["Authorization"], (val)=>{
this.headers["Authorization"] = val;
});
inp.el.placeholder = "Bearer OPENAI_API_KEY";
elDiv.appendChild(inp.div);
let bb = ui.el_creatediv_boolbutton("SetStream", "Stream", {true: "[+] yes stream", false: "[-] do oneshot"}, this.bStream, (val)=>{
this.bStream = val;
});
elDiv.appendChild(bb.div);
bb = ui.el_creatediv_boolbutton("SetCompletionFreshChatAlways", "CompletionFreshChatAlways", {true: "[+] yes fresh", false: "[-] no, with history"}, this.bCompletionFreshChatAlways, (val)=>{
this.bCompletionFreshChatAlways = val;
});
elDiv.appendChild(bb.div);
bb = ui.el_creatediv_boolbutton("SetCompletionInsertStandardRolePrefix", "CompletionInsertStandardRolePrefix", {true: "[+] yes insert", false: "[-] dont insert"}, this.bCompletionInsertStandardRolePrefix, (val)=>{
this.bCompletionInsertStandardRolePrefix = val;
});
elDiv.appendChild(bb.div);
bb = ui.el_creatediv_boolbutton("SetTrimGarbage", "TrimGarbage", {true: "[+] yes trim", false: "[-] dont trim"}, this.bTrimGarbage, (val)=>{
this.bTrimGarbage = val;
});
elDiv.appendChild(bb.div);
let sel = ui.el_creatediv_select("SetChatHistoryInCtxt", "ChatHistoryInCtxt", this.sRecentUserMsgCnt, this.iRecentUserMsgCnt, (val)=>{
this.iRecentUserMsgCnt = this.sRecentUserMsgCnt[val];
});
elDiv.appendChild(sel.div);
sel = ui.el_creatediv_select("SetApiEP", "ApiEndPoint", ApiEP.Type, this.apiEP, (val)=>{
this.apiEP = ApiEP.Type[val];
});
elDiv.appendChild(sel.div);
this.show_settings_chatrequestoptions(elDiv);
}
}
/** @type {Me} */
let gMe;
function startme() {
console.log("INFO:SimpleChat:StartMe:Starting...");
gMuitChat = new MultiChatUI();
for (let cid of gChatIds) {
gMuitChat.new_chat_session(cid);
gMe = new Me();
gMe.debug_disable();
document["gMe"] = gMe;
document["du"] = du;
for (let cid of gMe.defaultChatIds) {
gMe.multiChat.new_chat_session(cid);
}
gMuitChat.setup_ui(gChatIds[0]);
gMuitChat.show_sessions();
gMe.multiChat.setup_ui(gMe.defaultChatIds[0], true);
gMe.multiChat.show_sessions();
}
document.addEventListener("DOMContentLoaded", startme);

211
examples/server/public_simplechat/ui.mjs Normal file
View file

@ -0,0 +1,211 @@
//@ts-check
// Helpers to work with html elements
// by Humans for All
//
/**
* Set the class of the children, based on whether it is the idSelected or not.
* @param {HTMLDivElement} elBase
* @param {string} idSelected
* @param {string} classSelected
* @param {string} classUnSelected
*/
export function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") {
for(let child of elBase.children) {
if (child.id == idSelected) {
child.className = classSelected;
} else {
child.className = classUnSelected;
}
}
}
/**
* Create button and set it up.
* @param {string} id
* @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback
* @param {string | undefined} name
* @param {string | undefined} innerText
*/
export function el_create_button(id, callback, name=undefined, innerText=undefined) {
if (!name) {
name = id;
}
if (!innerText) {
innerText = id;
}
let btn = document.createElement("button");
btn.id = id;
btn.name = name;
btn.innerText = innerText;
btn.addEventListener("click", callback);
return btn;
}
/**
* Create a para and set it up. Optionaly append it to a passed parent.
* @param {string} text
* @param {HTMLElement | undefined} elParent
* @param {string | undefined} id
*/
export function el_create_append_p(text, elParent=undefined, id=undefined) {
let para = document.createElement("p");
para.innerText = text;
if (id) {
para.id = id;
}
if (elParent) {
elParent.appendChild(para);
}
return para;
}
/**
* Create a button which represents bool value using specified text wrt true and false.
* When ever user clicks the button, it will toggle the value and update the shown text.
*
* @param {string} id
* @param {{true: string, false: string}} texts
* @param {boolean} defaultValue
* @param {function(boolean):void} cb
*/
export function el_create_boolbutton(id, texts, defaultValue, cb) {
let el = document.createElement("button");
el["xbool"] = defaultValue;
el["xtexts"] = structuredClone(texts);
el.innerText = el["xtexts"][String(defaultValue)];
if (id) {
el.id = id;
}
el.addEventListener('click', (ev)=>{
el["xbool"] = !el["xbool"];
el.innerText = el["xtexts"][String(el["xbool"])];
cb(el["xbool"]);
})
return el;
}
/**
* Create a div wrapped button which represents bool value using specified text wrt true and false.
* @param {string} id
* @param {string} label
* @param {{ true: string; false: string; }} texts
* @param {boolean} defaultValue
* @param {(arg0: boolean) => void} cb
* @param {string} className
*/
export function el_creatediv_boolbutton(id, label, texts, defaultValue, cb, className="gridx2") {
let div = document.createElement("div");
div.className = className;
let lbl = document.createElement("label");
lbl.setAttribute("for", id);
lbl.innerText = label;
div.appendChild(lbl);
let btn = el_create_boolbutton(id, texts, defaultValue, cb);
div.appendChild(btn);
return { div: div, el: btn };
}
/**
* Create a select ui element, with a set of options to select from.
* * options: an object which contains name-value pairs
* * defaultOption: the value whose name should be choosen, by default.
* * cb : the call back returns the name string of the option selected.
*
* @param {string} id
* @param {Object<string,*>} options
* @param {*} defaultOption
* @param {function(string):void} cb
*/
export function el_create_select(id, options, defaultOption, cb) {
let el = document.createElement("select");
el["xselected"] = defaultOption;
el["xoptions"] = structuredClone(options);
for(let cur of Object.keys(options)) {
let op = document.createElement("option");
op.value = cur;
op.innerText = cur;
if (options[cur] == defaultOption) {
op.selected = true;
}
el.appendChild(op);
}
if (id) {
el.id = id;
el.name = id;
}
el.addEventListener('change', (ev)=>{
let target = /** @type{HTMLSelectElement} */(ev.target);
console.log("DBUG:UI:Select:", id, ":", target.value);
cb(target.value);
})
return el;
}
/**
* Create a div wrapped select ui element, with a set of options to select from.
*
* @param {string} id
* @param {any} label
* @param {{ [x: string]: any; }} options
* @param {any} defaultOption
* @param {(arg0: string) => void} cb
* @param {string} className
*/
export function el_creatediv_select(id, label, options, defaultOption, cb, className="gridx2") {
let div = document.createElement("div");
div.className = className;
let lbl = document.createElement("label");
lbl.setAttribute("for", id);
lbl.innerText = label;
div.appendChild(lbl);
let sel = el_create_select(id, options,defaultOption, cb);
div.appendChild(sel);
return { div: div, el: sel };
}
/**
* Create a input ui element.
*
* @param {string} id
* @param {string} type
* @param {any} defaultValue
* @param {function(any):void} cb
*/
export function el_create_input(id, type, defaultValue, cb) {
let el = document.createElement("input");
el.type = type;
el.value = defaultValue;
if (id) {
el.id = id;
}
el.addEventListener('change', (ev)=>{
cb(el.value);
})
return el;
}
/**
* Create a div wrapped input.
*
* @param {string} id
* @param {string} label
* @param {string} type
* @param {any} defaultValue
* @param {function(any):void} cb
* @param {string} className
*/
export function el_creatediv_input(id, label, type, defaultValue, cb, className="gridx2") {
let div = document.createElement("div");
div.className = className;
let lbl = document.createElement("label");
lbl.setAttribute("for", id);
lbl.innerText = label;
div.appendChild(lbl);
let el = el_create_input(id, type, defaultValue, cb);
div.appendChild(el);
return { div: div, el: el };
}

27
examples/server/server.cpp
View file

@ -17,9 +17,20 @@
#include "json.hpp"
// auto generated files (update with ./deps.sh)
#include "colorthemes.css.hpp"
#include "style.css.hpp"
#include "theme-beeninorder.css.hpp"
#include "theme-ketivah.css.hpp"
#include "theme-mangotango.css.hpp"
#include "theme-playground.css.hpp"
#include "theme-polarnight.css.hpp"
#include "theme-snowstorm.css.hpp"
#include "index.html.hpp"
#include "index-new.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#include "system-prompts.js.hpp"
#include "prompt-formats.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"
#include <atomic>
@ -3750,13 +3761,25 @@ int main(int argc, char ** argv) {
// Set the base directory for serving static files
svr->set_base_dir(sparams.public_path);
}
// using embedded static files
svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8"));
svr->Get("/json-schema-to-grammar.mjs", handle_static_file(
json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8"));
json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8"));
// add new-ui files
svr->Get("/colorthemes.css", handle_static_file(colorthemes_css, colorthemes_css_len, "text/css; charset=utf-8"));
svr->Get("/style.css", handle_static_file(style_css, style_css_len, "text/css; charset=utf-8"));
svr->Get("/theme-beeninorder.css", handle_static_file(theme_beeninorder_css, theme_beeninorder_css_len, "text/css; charset=utf-8"));
svr->Get("/theme-ketivah.css", handle_static_file(theme_ketivah_css, theme_ketivah_css_len, "text/css; charset=utf-8"));
svr->Get("/theme-mangotango.css", handle_static_file(theme_mangotango_css, theme_mangotango_css_len, "text/css; charset=utf-8"));
svr->Get("/theme-playground.css", handle_static_file(theme_playground_css, theme_playground_css_len, "text/css; charset=utf-8"));
svr->Get("/theme-polarnight.css", handle_static_file(theme_polarnight_css, theme_polarnight_css_len, "text/css; charset=utf-8"));
svr->Get("/theme-snowstorm.css", handle_static_file(theme_snowstorm_css, theme_snowstorm_css_len, "text/css; charset=utf-8"));
svr->Get("/index-new.html", handle_static_file(index_new_html, index_new_html_len, "text/html; charset=utf-8"));
svr->Get("/system-prompts.js", handle_static_file(system_prompts_js, system_prompts_js_len, "text/javascript; charset=utf-8"));
svr->Get("/prompt-formats.js", handle_static_file(prompt_formats_js, prompt_formats_js_len, "text/javascript; charset=utf-8"));
// register API routes
svr->Get ("/health", handle_health);

4
examples/sycl/win-build-sycl.bat
View file

@ -13,10 +13,10 @@ if %errorlevel% neq 0 goto ERROR
:: for FP16
:: faster for long-prompt inference
:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
:: for FP32
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
if %errorlevel% neq 0 goto ERROR
:: build example/main only
:: make main

368
examples/tokenize/tokenize.cpp
View file

@ -3,40 +3,390 @@
#include <cmath>
#include <cstdio>
#include <fstream>
#include <string>
#include <vector>
int main(int argc, char ** argv) {
if (argc < 3 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH PROMPT [--ids]\n" , argv[0]);
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#include <windows.h>
#include <shellapi.h> // For CommandLineToArgvW
#endif
static void print_usage_information(const char * argv0, FILE * stream) {
fprintf(stream, "usage: %s [options]\n\n", argv0);
fprintf(stream, "The tokenize program tokenizes a prompt using a given model,\n");
fprintf(stream, "and prints the resulting tokens to standard output.\n\n");
fprintf(stream, "It needs a model file, a prompt, and optionally other flags\n");
fprintf(stream, "to control the behavior of the tokenizer.\n\n");
fprintf(stream, " The possible options are:\n");
fprintf(stream, "\n");
fprintf(stream, " -h, --help print this help and exit\n");
fprintf(stream, " -m MODEL_PATH, --model MODEL_PATH path to model.\n");
fprintf(stream, " --ids if given, only print numerical token IDs, and not token strings.\n");
fprintf(stream, " The output format looks like [1, 2, 3], i.e. parseable by Python.\n");
fprintf(stream, " -f PROMPT_FNAME, --file PROMPT_FNAME read prompt from a file.\n");
fprintf(stream, " -p PROMPT, --prompt PROMPT read prompt from the argument.\n");
fprintf(stream, " --stdin read prompt from standard input.\n");
fprintf(stream, " --no-bos do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n");
fprintf(stream, " --log-disable disable logs. Makes stderr quiet when loading the model.\n");
}
static void llama_log_callback_null(ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) text;
(void) user_data;
}
static std::string read_prompt_from_file(const char * filepath, bool & success) {
success = false;
std::ifstream in(filepath, std::ios::binary);
if (!in) {
fprintf(stderr, "%s: could not open file '%s' for reading: %s\n", __func__, filepath, strerror(errno));
return std::string();
}
// do not assume the file is seekable (e.g. /dev/stdin)
std::stringstream buffer;
buffer << in.rdbuf();
if (in.fail()) {
fprintf(stderr, "%s: could not read the entire file '%s': %s\n", __func__, filepath, strerror(errno));
return std::string();
}
success = true;
return buffer.str();
}
//
// Function: ingest_args(...) -> vector<string>
//
// Takes argc and argv arguments, and converts them to a vector of UTF-8 encoded
// strings, as an STL vector<string>.
//
// In particular, it handles character encoding shenanigans on Windows.
//
// Note: raw_argc and raw_argv are not actually read at all on Windows.
// On Windows we call GetCommandLineW to get the arguments in wchar_t
// format, ignoring the regular argc/argv arguments to main().
//
// TODO: potential opportunity to roll common stuff into common/console.cpp
// in relation to Windows wchar_t shenanigans.
static std::vector<std::string> ingest_args(int raw_argc, char ** raw_argv) {
std::vector<std::string> argv;
// Handle Windows, if given non-ASCII arguments.
// We convert wchar_t arguments into UTF-8 char* on this platform.
// Lets you invoke 'tokenize' on Windows cmd.exe with non-ASCII characters
// without throwing tantrums.
#if defined(_WIN32)
int argc;
const LPWSTR cmdline_wargv = GetCommandLineW();
LPWSTR * wargv = CommandLineToArgvW(cmdline_wargv, &argc);
// silence unused arg warnings
(void) raw_argc;
(void) raw_argv;
for (int i = 0; i < argc; ++i) {
int length_needed = WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), 0, 0, NULL, NULL);
char * output_buf = (char *) calloc(length_needed+1, sizeof(char));
GGML_ASSERT(output_buf);
WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), output_buf, length_needed, NULL, NULL);
output_buf[length_needed] = '\0';
argv.push_back(output_buf);
free(output_buf);
}
LocalFree((HLOCAL) wargv);
#else
int argc = raw_argc;
for (int i = 0; i < argc; ++i) {
argv.push_back(raw_argv[i]);
}
#endif
GGML_ASSERT((unsigned int) argc == argv.size());
return argv;
}
//
// Function: write_utf8_cstr_to_stdout(const char *) -> <writes to stdout>
//
// writes a string to standard output; taking into account that on Windows
// to display correctly you have to use special handling. Works even if the
// user has not set a unicode code page on a Windows cmd.exe.
//
// In case of invalid UTF-8, invalid_utf8 is set to true on Windows, and something
// a human-readable is written instead.
//
// On non-Windows systems, simply printfs() the string.
static void write_utf8_cstr_to_stdout(const char * str, bool & invalid_utf8) {
invalid_utf8 = false;
#if defined(_WIN32)
// Are we in a console?
HANDLE hConsole = GetStdHandle(STD_OUTPUT_HANDLE);
DWORD dwMode = 0;
// According to Microsoft docs:
// "WriteConsole fails if it is used with a standard handle that is redirected to a file."
// Also according to the docs, you can use GetConsoleMode to check for that.
if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) {
printf("%s", str);
return;
}
// MultiByteToWideChar reports an error if str is empty, don't report
// them as invalid_utf8.
if (*str == 0) {
return;
}
int length_needed = MultiByteToWideChar(CP_UTF8, MB_ERR_INVALID_CHARS, str, strlen(str), NULL, 0);
if (length_needed == 0) {
DWORD err = GetLastError();
if (err == ERROR_NO_UNICODE_TRANSLATION) {
invalid_utf8 = true;
int len = strlen(str);
printf("<");
for (int i = 0; i < len; ++i) {
if (i > 0) {
printf(" ");
}
printf("%02x", (uint8_t) str[i]);
}
printf(">");
return;
}
GGML_ASSERT(false && "MultiByteToWideChar() failed in an unexpected way.");
}
LPWSTR wstr = (LPWSTR) calloc(length_needed+1, sizeof(*wstr));
GGML_ASSERT(wstr);
MultiByteToWideChar(CP_UTF8, 0, str, strlen(str), wstr, length_needed);
WriteConsoleW(hConsole, wstr, length_needed, NULL, NULL);
free(wstr);
#else
// TODO: reporting invalid_utf8 would be useful on non-Windows too.
// printf will silently just write bad unicode.
printf("%s", str);
#endif
}
int main(int raw_argc, char ** raw_argv) {
const std::vector<std::string> argv = ingest_args(raw_argc, raw_argv);
const int argc = argv.size();
if (argc <= 1) {
print_usage_information(argv[0].c_str(), stderr);
return 1;
}
const char * model_path = argv[1];
const char * prompt = argv[2];
//////
// Read out all the command line arguments.
//////
const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids";
// variables where to put any arguments we see.
bool printing_ids = false;
bool no_bos = false;
bool disable_logging = false;
const char * model_path = NULL;
const char * prompt_path = NULL;
const char * prompt_arg = NULL;
// track which arguments were explicitly given
// used for sanity checking down the line
bool model_path_set = false;
bool prompt_path_set = false;
bool prompt_set = false;
bool stdin_set = false;
int iarg = 1;
for (; iarg < argc; ++iarg) {
std::string arg{argv[iarg]};
if (arg == "-h" || arg == "--help") {
print_usage_information(argv[0].c_str(), stdout);
return 0;
}
else if (arg == "--ids") {
printing_ids = true;
}
else if (arg == "-m" || arg == "--model") {
if (model_path_set) {
fprintf(stderr, "Error: -m or --model specified multiple times.\n");
return 1;
}
model_path = argv[++iarg].c_str();
model_path_set = true;
}
else if (arg == "--no-bos") {
no_bos = true;
}
else if (arg == "-p" || arg == "--prompt") {
if (prompt_set) {
fprintf(stderr, "Error: -p or --prompt specified multiple times.\n");
return 1;
}
prompt_arg = argv[++iarg].c_str();
prompt_set = true;
}
else if (arg == "-f" || arg == "--file") {
if (prompt_path_set) {
fprintf(stderr, "Error: -f or --file specified multiple times.\n");
return 1;
}
prompt_path = argv[++iarg].c_str();
prompt_path_set = true;
}
else if (arg == "--stdin") {
stdin_set = true;
}
else if (arg == "--log-disable") {
disable_logging = true;
}
else {
fprintf(stderr, "Error: unknown option '%s'\n", argv[iarg].c_str());
return 1;
}
}
//////
// Sanity check the command line arguments.
//////
// Check that we have the required stuff set.
if (model_path_set && model_path == NULL) {
fprintf(stderr, "Error: --model requires an argument.\n");
return 1;
}
if (!model_path_set) {
fprintf(stderr, "Error: must specify --model.\n");
return 1;
}
if (prompt_path_set && prompt_path == NULL) {
fprintf(stderr, "Error: --file requires an argument.\n");
return 1;
}
if (prompt_set && prompt_arg == NULL) {
fprintf(stderr, "Error: --prompt requires an argument.\n");
return 1;
}
const int prompts_set = !!(prompt_path_set) + !!(prompt_set) + !!(stdin_set);
if (prompts_set > 1) {
fprintf(stderr, "Error: --stdin, --file and --prompt are mutually exclusive.\n");
return 1;
}
// Must have some prompt.
if (prompts_set == 0) {
fprintf(stderr, "Error: must specify one of: --stdin, --file or --prompt.\n");
return 1;
}
GGML_ASSERT(model_path);
GGML_ASSERT(prompt_path || prompt_arg || stdin_set);
//////
// Figure out where will the prompt come from.
//////
std::string prompt;
if (prompt_path_set) {
bool success = false;
prompt = read_prompt_from_file(prompt_path, success);
if (!success) {
return 1;
}
} else if (prompt_set) {
prompt = prompt_arg;
} else {
GGML_ASSERT(stdin_set);
// we read stdin *after* loading model (early exit if model cannot
// be loaded, which can be a nicer user experience)
}
//////
// Start actually doing the tokenizing stuff.
//////
#ifdef LOG_DISABLE_LOGS
disable_logging = true;
#endif
if (disable_logging) {
llama_log_set(llama_log_callback_null, NULL);
}
llama_backend_init();
llama_model_params model_params = llama_model_default_params();
model_params.vocab_only = true;
llama_model * model = llama_load_model_from_file(model_path, model_params);
if (!model) {
fprintf(stderr, "Error: could not load model from file '%s'.\n", model_path);
return 1;
}
llama_context_params ctx_params = llama_context_default_params();
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (!ctx) {
fprintf(stderr, "Error: could not create context.\n");
return 1;
}
// read entire prompt from stdin?
if (stdin_set) {
GGML_ASSERT(!prompt_path_set && !prompt_set);
std::stringstream stdin_buffer;
stdin_buffer << std::cin.rdbuf();
if (std::cin.fail()) {
fprintf(stderr, "Error: could not read the entire standard input.\n");
return 1;
}
prompt = stdin_buffer.str();
}
const bool model_wants_add_bos = llama_should_add_bos_token(model);
const bool add_bos = model_wants_add_bos && !no_bos;
std::vector<llama_token> tokens;
tokens = ::llama_tokenize(model, prompt, add_bos, true);
tokens = ::llama_tokenize(model, prompt, true, true);
if (printing_ids) {
printf("[");
}
for (int i = 0; i < (int) tokens.size(); i++) {
if (printing_ids) {
printf("%d\n", tokens[i]);
if (i > 0) {
printf(", ");
}
printf("%d", tokens[i]);
} else {
printf("%6d -> '%s'\n", tokens[i], llama_token_to_piece(ctx, tokens[i]).c_str());
bool invalid_utf8 = false;
printf("%6d -> '", tokens[i]);
write_utf8_cstr_to_stdout(llama_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8);
if (invalid_utf8) {
printf("' (utf-8 decode failure)\n");
} else {
printf("'\n");
}
}
}
if (printing_ids) {
printf("]\n");
}
// silence valgrind
llama_free(ctx);
llama_free_model(model);
return 0;
}

3
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -341,7 +341,8 @@ static struct ggml_tensor * llama_build_train_graphs(
struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch);
struct ggml_tensor * t16;
if (enable_flash_attn) {
t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported");
//t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
} else {
struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);

20
flake.lock generated
View file

@ -5,11 +5,11 @@
"nixpkgs-lib": "nixpkgs-lib"
},
"locked": {
"lastModified": 1714641030,
"narHash": "sha256-yzcRNDoyVP7+SCNX0wmuDju1NUCt8Dz9+lyUXEI0dbI=",
"lastModified": 1717285511,
"narHash": "sha256-iKzJcpdXih14qYVcZ9QC9XuZYnPc6T8YImb6dX166kw=",
"owner": "hercules-ci",
"repo": "flake-parts",
"rev": "e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e",
"rev": "2a55567fcf15b1b1c7ed712a2c6fadaec7412ea8",
"type": "github"
},
"original": {
@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1714635257,
"narHash": "sha256-4cPymbty65RvF1DWQfc+Bc8B233A1BWxJnNULJKQ1EY=",
"lastModified": 1716948383,
"narHash": "sha256-SzDKxseEcHR5KzPXLwsemyTR/kaM9whxeiJohbL04rs=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "63c3a29ca82437c87573e4c6919b09a24ea61b0f",
"rev": "ad57eef4ef0659193044870c731987a6df5cf56b",
"type": "github"
},
"original": {
@ -36,14 +36,14 @@
},
"nixpkgs-lib": {
"locked": {
"lastModified": 1714640452,
"narHash": "sha256-QBx10+k6JWz6u7VsohfSw8g8hjdBZEf8CFzXH1/1Z94=",
"lastModified": 1717284937,
"narHash": "sha256-lIbdfCsf8LMFloheeE6N31+BMIeixqyQWbSr2vk79EQ=",
"type": "tarball",
"url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz"
"url": "https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz"
},
"original": {
"type": "tarball",
"url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz"
"url": "https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz"
}
},
"root": {

2
ggml-alloc.c
View file

@ -377,7 +377,7 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t));
GGML_ASSERT(galloc->bufts != NULL);
galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs);
galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t));
GGML_ASSERT(galloc->buffers != NULL);
galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));

54
ggml-common.h
View file

@ -65,13 +65,8 @@ typedef sycl::half2 ggml_half2;
// QK = number of values after dequantization
// QK_K = super-block size
#ifdef GGML_QKK_64
#define QK_K 64
#define K_SCALE_SIZE 4
#else
#define QK_K 256
#define K_SCALE_SIZE 12
#endif // GGML_QKK_64
#if defined(GGML_COMMON_DECL_CUDA) || defined(GGML_COMMON_DECL_HIP) || defined(GGML_COMMON_DECL_SYCL)
// QR = QK / number of values before dequantization
@ -131,13 +126,8 @@ typedef sycl::half2 ggml_half2;
#define QI4_NL (QK4_NL / (4*QR4_NL))
#define QR4_NL 2
#if QK_K == 64
#define QI4_XS QI4_NL
#define QR4_XS QR4_NL
#else
#define QI4_XS (QK_K / (4*QR4_XS))
#define QR4_XS 8
#endif
#endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP
@ -228,15 +218,6 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wro
// weight is represented as x = a * q
// 16 blocks of 16 elements each
// Effectively 3.4375 bits per weight
#ifdef GGML_QKK_64
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[2];
ggml_half d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
#else
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
@ -244,20 +225,11 @@ typedef struct {
ggml_half d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
#endif
// 4-bit quantization
// 8 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 4.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_half d[2]; // super-block scales/mins
uint8_t scales[2]; // 4-bit block scales/mins
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + QK_K/2 + 2, "wrong q4_K block size/padding");
#else
typedef struct {
union {
struct {
@ -270,21 +242,11 @@ typedef struct {
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
#endif
// 5-bit quantization
// 8 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 5.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_half d; // super-block scale
int8_t scales[QK_K/16]; // 8-bit block scales
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == sizeof(ggml_half) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
#else
typedef struct {
union {
struct {
@ -298,7 +260,6 @@ typedef struct {
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
#endif
// 6-bit quantization
// weight is represented as x = a * q
@ -356,11 +317,7 @@ typedef struct {
static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
// 3.4375 bpw
#if QK_K == 64
#define IQ3S_N_SCALE 2
#else
#define IQ3S_N_SCALE QK_K/64
#endif
typedef struct {
ggml_half d;
uint8_t qs[QK_K/4];
@ -381,16 +338,9 @@ static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wron
typedef struct {
uint8_t qs[QK_K/8]; // grid index, low 8 bits
uint8_t qh[QK_K/16]; // grid index, high 3 bits + grid shift bit (for two groups of 8)
#if QK_K == 64
ggml_half d;
#endif
uint8_t scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64)
} block_iq1_m;
#if QK_K == 64
static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding");
#else
static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
#endif
// Used by IQ1_M quants
typedef union {
@ -406,9 +356,6 @@ typedef struct {
} block_iq4_nl;
static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding");
#if QK_K == 64
#define block_iq4_xs block_iq4_nl
#else
typedef struct {
ggml_half d;
uint16_t scales_h;
@ -416,7 +363,6 @@ typedef struct {
uint8_t qs[QK_K/2];
} block_iq4_xs;
static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
#endif
#endif // GGML_COMMON_DECL
#endif // GGML_COMMON_DECL

45
ggml-cuda.cu
View file

@ -119,6 +119,20 @@ int ggml_cuda_get_device() {
return id;
}
static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) {
ggml_cuda_set_device(device);
#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA)
auto res = hipMallocManaged(ptr, size);
if (res == hipSuccess) {
// if error we "need" to know why...
CUDA_CHECK(hipMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device));
}
return res;
#else
return cudaMalloc(ptr, size);
#endif
}
static ggml_cuda_device_info ggml_cuda_init() {
#ifdef __HIP_PLATFORM_AMD__
// Workaround for a rocBLAS bug when using multiple graphics cards:
@ -271,7 +285,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
size_t look_ahead_size = (size_t) (1.05 * size);
look_ahead_size = 256 * ((look_ahead_size + 255)/256);
ggml_cuda_set_device(device);
CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
CUDA_CHECK(ggml_cuda_device_malloc(&ptr, look_ahead_size, device));
*actual_size = look_ahead_size;
pool_size += look_ahead_size;
#ifdef DEBUG_CUDA_MALLOC
@ -537,7 +551,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe
size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
void * dev_ptr;
cudaError_t err = cudaMalloc(&dev_ptr, size);
cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device);
if (err != cudaSuccess) {
// clear the error
cudaGetLastError();
@ -798,7 +812,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu
// currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
ggml_cuda_set_device(id);
char * buf;
CUDA_CHECK(cudaMalloc(&buf, size));
CUDA_CHECK(ggml_cuda_device_malloc((void**)&buf, size, id));
// set padding to 0 to avoid possible NaN values
if (size > original_size) {
@ -1856,7 +1870,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
}
}
#else
if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
// there is no broadcast and src0, src1 are contiguous across dims 2, 3
// use cublasGemmStridedBatchedEx
CUBLAS_CHECK(
@ -2510,9 +2524,9 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
bool use_cuda_graph = true;
bool cuda_graph_update_required = false;
// pointer to CUDA cpy kernel, which is required to identify
// vector of pointers to CUDA cpy kernels, which are required to identify
// kernel parameters which need updated in the graph for each token
void * ggml_cuda_cpy_fn_ptr = nullptr;
std::vector<void *> ggml_cuda_cpy_fn_ptrs;
if (cuda_ctx->cuda_graph->graph == nullptr) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
@ -2588,9 +2602,10 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if (node->op == GGML_OP_CPY) {
// store the copy op parameter which changes with each token.
cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
if (ggml_cuda_cpy_fn_ptr == nullptr) {
// store a pointer to the copy op CUDA kernel to identify it later
ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
// store a pointer to each copy op CUDA kernel to identify it later
void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
ggml_cuda_cpy_fn_ptrs.push_back(ptr);
}
}
@ -2720,7 +2735,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
int k = 0;
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) {
if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
@ -2871,7 +2886,9 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_CONT:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_SOFT_MAX:
return true;
case GGML_OP_ROPE:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_IM2COL:
case GGML_OP_POOL_2D:
case GGML_OP_SUM_ROWS:
@ -2888,10 +2905,14 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128;
#else
if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) {
if (op->src[0]->ne[0] == 128) {
return true;
}
return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA;
if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) {
return true;
}
return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA &&
op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
default:
return false;

5
ggml-cuda/common.cuh
View file

@ -79,13 +79,8 @@
#define cudaHostRegisterReadOnly hipHostRegisterReadOnly
#define cudaHostUnregister hipHostUnregister
#define cudaLaunchHostFunc hipLaunchHostFunc
#ifdef GGML_HIP_UMA
#define cudaMalloc hipMallocManaged
#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size)
#else
#define cudaMalloc hipMalloc
#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
#endif
#define cudaMemcpy hipMemcpy
#define cudaMemcpyAsync hipMemcpyAsync
#define cudaMemcpyPeerAsync hipMemcpyPeerAsync

167
ggml-cuda/concat.cu
View file

@ -1,15 +1,69 @@
#include "concat.cuh"
static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) {
// contiguous kernels
static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) {
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
return;
}
// operation
int offset_dst =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
if (nidx < ne00) { // src0
int offset_src =
nidx +
blockIdx.y * ne00 +
blockIdx.z * ne00 * gridDim.y;
dst[offset_dst] = x[offset_src];
} else {
int offset_src =
(nidx - ne00) +
blockIdx.y * (ne0 - ne00) +
blockIdx.z * (ne0 - ne00) * gridDim.y;
dst[offset_dst] = y[offset_src];
}
}
static __global__ void concat_f32_dim1(const float * x, const float * y, float * dst, const int ne0, const int ne01) {
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
return;
}
int offset_dst =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
if (blockIdx.y < ne01) { // src0
int offset_src =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * ne01;
dst[offset_dst] = x[offset_src];
} else {
int offset_src =
nidx +
(blockIdx.y - ne01) * ne0 +
blockIdx.z * ne0 * (gridDim.y - ne01);
dst[offset_dst] = y[offset_src];
}
}
static __global__ void concat_f32_dim2(const float * x, const float * y, float * dst, const int ne0, const int ne02) {
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
return;
}
int offset_dst =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
if (blockIdx.z < ne02) { // src0
int offset_src =
nidx +
@ -25,25 +79,118 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst,
}
}
static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) {
static void concat_f32_cuda(const float * x, const float * y, float * dst, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, int dim, cudaStream_t stream) {
int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
dim3 gridDim(num_blocks, ne1, ne2);
concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
if (dim == 0) {
concat_f32_dim0<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne00);
return;
}
if (dim == 1) {
concat_f32_dim1<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne01);
return;
}
concat_f32_dim2<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
}
// non-contiguous kernel (slow)
static __global__ void concat_f32_non_cont(
const char * src0,
const char * src1,
char * dst,
int64_t ne00,
int64_t ne01,
int64_t ne02,
int64_t ne03,
uint64_t nb00,
uint64_t nb01,
uint64_t nb02,
uint64_t nb03,
int64_t /*ne10*/,
int64_t /*ne11*/,
int64_t /*ne12*/,
int64_t /*ne13*/,
uint64_t nb10,
uint64_t nb11,
uint64_t nb12,
uint64_t nb13,
int64_t ne0,
int64_t /*ne1*/,
int64_t /*ne2*/,
int64_t /*ne3*/,
uint64_t nb0,
uint64_t nb1,
uint64_t nb2,
uint64_t nb3,
int32_t dim) {
const int64_t i3 = blockIdx.z;
const int64_t i2 = blockIdx.y;
const int64_t i1 = blockIdx.x;
int64_t o[4] = {0, 0, 0, 0};
o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
const float * x;
for (int i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00);
} else {
x = (const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10);
}
float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
*y = *x;
}
}
void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
const float * src0_d = (const float *)src0->data;
const float * src1_d = (const float *)src1->data;
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
const int32_t dim = ((int32_t *) dst->op_params)[0];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
for (int i3 = 0; i3 < dst->ne[3]; i3++) {
concat_f32_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream);
if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
const float * src0_d = (const float *)src0->data;
const float * src1_d = (const float *)src1->data;
float * dst_d = (float *)dst->data;
if (dim != 3) {
for (int i3 = 0; i3 < dst->ne[3]; i3++) {
concat_f32_cuda(
src0_d + i3 * (src0->nb[3] / 4),
src1_d + i3 * (src1->nb[3] / 4),
dst_d + i3 * ( dst->nb[3] / 4),
src0->ne[0], src0->ne[1], src0->ne[2],
dst->ne[0], dst->ne[1], dst->ne[2], dim, stream);
}
} else {
const size_t size0 = ggml_nbytes(src0);
const size_t size1 = ggml_nbytes(src1);
CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream));
CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream));
}
} else {
dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]);
concat_f32_non_cont<<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
(const char *)src0->data,
(const char *)src1->data,
( char *)dst->data,
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3],
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
}
}

138
ggml-cuda/convert.cu
View file

@ -131,7 +131,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
const block_q2_K * x = (const block_q2_K *) vx;
const int64_t tid = threadIdx.x;
#if QK_K == 256
const int64_t n = tid/32;
const int64_t l = tid - 32*n;
const int64_t is = 8*n + l/16;
@ -145,17 +144,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
#else
const int64_t is = tid/16; // 0 or 1
const int64_t il = tid%16; // 0...15
const uint8_t q = x[i].qs[il] >> (2*is);
dst_t * y = yy + i*QK_K + 16*is + il;
float dall = __low2half(x[i].dm);
float dmin = __high2half(x[i].dm);
y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
#endif
}
template<typename dst_t>
@ -164,7 +152,6 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t
const int64_t i = blockIdx.x;
const block_q3_K * x = (const block_q3_K *) vx;
#if QK_K == 256
const int64_t r = threadIdx.x/4;
const int64_t tid = r/2;
const int64_t is0 = r%2;
@ -188,31 +175,8 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t
const uint8_t * hm = x[i].hmask;
for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
#else
const int64_t tid = threadIdx.x;
const int64_t is = tid/16; // 0 or 1
const int64_t il = tid%16; // 0...15
const int64_t im = il/8; // 0...1
const int64_t in = il%8; // 0...7
dst_t * y = yy + i*QK_K + 16*is + il;
const uint8_t q = x[i].qs[il] >> (2*is);
const uint8_t h = x[i].hmask[in] >> (2*is + im);
const float d = (float)x[i].d;
if (is == 0) {
y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
} else {
y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
}
#endif
}
#if QK_K == 256
static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
if (j < 4) {
d = q[j] & 63; m = q[j + 4] & 63;
@ -221,7 +185,6 @@ static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t
m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4);
}
}
#endif
template<typename dst_t>
static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
@ -229,7 +192,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t
const int64_t i = blockIdx.x;
#if QK_K == 256
// assume 32 threads
const int64_t tid = threadIdx.x;
const int64_t il = tid/8;
@ -253,15 +215,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t
y[l + 0] = d1 * (q[l] & 0xF) - m1;
y[l +32] = d2 * (q[l] >> 4) - m2;
}
#else
const int64_t tid = threadIdx.x;
const uint8_t * q = x[i].qs;
dst_t * y = yy + i*QK_K;
const float d = (float)x[i].dm[0];
const float m = (float)x[i].dm[1];
y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4);
#endif
}
template<typename dst_t>
@ -270,7 +223,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t
const int64_t i = blockIdx.x;
#if QK_K == 256
// assume 64 threads - this is very slightly better than the one below
const int64_t tid = threadIdx.x;
const int64_t il = tid/16; // il is in 0...3
@ -297,18 +249,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t
hm <<= 1;
y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2;
y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2;
#else
const int64_t tid = threadIdx.x;
const uint8_t q = x[i].qs[tid];
const int64_t im = tid/8; // 0...3
const int64_t in = tid%8; // 0...7
const int64_t is = tid/16; // 0 or 1
const uint8_t h = x[i].qh[in] >> im;
const float d = x[i].d;
dst_t * y = yy + i*QK_K + tid;
y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16));
#endif
}
template<typename dst_t>
@ -316,7 +256,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t
const block_q6_K * x = (const block_q6_K *) vx;
const int64_t i = blockIdx.x;
#if QK_K == 256
// assume 64 threads - this is very slightly better than the one below
const int64_t tid = threadIdx.x;
@ -336,24 +275,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t
y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32);
y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
#else
// assume 32 threads
const int64_t tid = threadIdx.x;
const int64_t ip = tid/16; // 0 or 1
const int64_t il = tid - 16*ip; // 0...15
dst_t * y = yy + i*QK_K + 16*ip + il;
const float d = x[i].d;
const uint8_t ql = x[i].ql[16*ip + il];
const uint8_t qh = x[i].qh[il] >> (2*ip);
const int8_t * sc = x[i].scales;
y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32);
#endif
}
template<typename dst_t>
@ -363,7 +284,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
const block_iq2_xxs * x = (const block_iq2_xxs *) vx;
const int64_t tid = threadIdx.x;
#if QK_K == 256
const int64_t il = tid/8; // 0...3
const int64_t ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@ -374,10 +294,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
@ -387,7 +303,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
const block_iq2_xs * x = (const block_iq2_xs *) vx;
const int64_t tid = threadIdx.x;
#if QK_K == 256
const int64_t il = tid/8; // 0...3
const int64_t ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@ -396,10 +311,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
@ -409,7 +320,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
const block_iq2_s * x = (const block_iq2_s *) vx;
const int64_t tid = threadIdx.x;
#if QK_K == 256
const int64_t il = tid/8; // 0...3
const int64_t ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@ -417,10 +327,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
@ -430,7 +336,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
const block_iq3_xxs * x = (const block_iq3_xxs *) vx;
const int64_t tid = threadIdx.x;
#if QK_K == 256
const int64_t il = tid/8; // 0...3
const int64_t ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@ -445,10 +350,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
}
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
@ -458,7 +359,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
const block_iq3_s * x = (const block_iq3_s *) vx;
const int64_t tid = threadIdx.x;
#if QK_K == 256
const int64_t il = tid/8; // 0...3
const int64_t ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@ -471,10 +371,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
}
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
@ -484,7 +380,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
const block_iq1_s * x = (const block_iq1_s *) vx;
const int64_t tid = threadIdx.x;
#if QK_K == 256
const int64_t il = tid/8; // 0...3
const int64_t ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@ -497,10 +392,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
for (int j = 0; j < 8; ++j) {
y[j] = d * (q[j] + delta);
}
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
@ -510,7 +401,6 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_
const block_iq1_m * x = (const block_iq1_m *) vx;
const int64_t tid = threadIdx.x;
#if QK_K == 256
const int64_t il = tid/8; // 0...3
const int64_t ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
@ -527,13 +417,8 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_
for (int j = 0; j < 8; ++j) {
y[j] = d * (q[j] + delta);
}
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {
@ -550,10 +435,8 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst
y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
y[j+16] = d * kvalues_iq4nl[q4[j] >> 4];
}
}
#if QK_K != 64
template<typename dst_t>
static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
const int64_t i = blockIdx.x;
@ -570,7 +453,6 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst
y[j+16] = d * kvalues_iq4nl[q4[j] >> 4];
}
}
#endif
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) {
@ -592,21 +474,13 @@ static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half *
template<typename dst_t>
static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
const int nb = k / QK_K;
#if QK_K == 256
dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
#else
dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
template<typename dst_t>
static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
const int nb = k / QK_K;
#if QK_K == 256
dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
#else
dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
template<typename dst_t>
@ -632,21 +506,13 @@ static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int64_t k
template<typename dst_t>
static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
const int nb = k / QK_K;
#if QK_K == 256
dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
#else
dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
template<typename dst_t>
static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
const int nb = k / QK_K;
#if QK_K == 256
dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
#else
dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
template<typename dst_t>
@ -700,11 +566,7 @@ static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int64_t
template<typename dst_t>
static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
const int nb = (k + QK_K - 1) / QK_K;
#if QK_K == 64
dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
#else
dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
template <typename src_t, typename dst_t>

151
ggml-cuda/dmmv.cu
View file

@ -22,7 +22,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1
@ -71,37 +70,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
tmp += dall * sum1 - dmin * sum2;
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3
const int offset = tid * K_QUANTS_PER_ITERATION;
uint32_t uaux[2];
const uint8_t * d = (const uint8_t *)uaux;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + offset;
const uint8_t * q = x[i].qs + offset;
const uint32_t * s = (const uint32_t *)x[i].scales;
uaux[0] = s[0] & 0x0f0f0f0f;
uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
const float2 dall = __half22float2(x[i].dm);
float sum1 = 0, sum2 = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
const uint8_t ql = q[l];
sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
+ y[l+16] * d[1] * ((ql >> 2) & 3)
+ y[l+32] * d[2] * ((ql >> 4) & 3)
+ y[l+48] * d[3] * ((ql >> 6) & 3);
sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
}
tmp += dall.x * sum1 - dall.y * sum2;
}
#endif
// sum up partial sums and write back result
tmp = warp_reduce_sum(tmp);
@ -123,8 +91,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx,
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const uint16_t kmask1 = 0x0303;
const uint16_t kmask2 = 0x0f0f;
@ -175,34 +141,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx,
tmp += d * sum;
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3
const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14
const int in = offset/8; // 0 or 1
const int im = offset%8; // 0...7
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + offset;
const uint8_t * q = x[i].qs + offset;
const uint8_t * s = x[i].scales;
const float dall = (float)x[i].d;
float sum = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
const uint8_t hl = x[i].hmask[im+l] >> in;
const uint8_t ql = q[l];
sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
+ y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
+ y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
+ y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
}
tmp += sum;
}
#endif
// sum up partial sums and write back result
tmp = warp_reduce_sum(tmp);
@ -221,7 +159,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
const block_q4_K * x = (const block_q4_K *)vx + ib0;
#if QK_K == 256
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
@ -306,36 +243,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
#endif
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
const int step = tid * K_QUANTS_PER_ITERATION;
uint16_t aux16[2];
const uint8_t * s = (const uint8_t *)aux16;
float tmp = 0;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const uint8_t * q = x[i].qs + step;
const float * y = yy + i*QK_K + step;
const uint16_t * a = (const uint16_t *)x[i].scales;
aux16[0] = a[0] & 0x0f0f;
aux16[1] = (a[0] >> 4) & 0x0f0f;
const float d = (float)x[i].dm[0];
const float m = (float)x[i].dm[1];
float sum = 0.f;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+ y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+ y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3])
+ y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]);
}
tmp += sum;
}
#endif
// sum up partial sums and write back result
tmp = warp_reduce_sum(tmp);
@ -355,7 +262,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
@ -426,30 +332,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
const int step = tid * K_QUANTS_PER_ITERATION;
const int im = step/8;
const int in = step%8;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const uint8_t * q = x[i].qs + step;
const int8_t * s = x[i].scales;
const float * y = yy + i*QK_K + step;
const float d = x[i].d;
float sum = 0.f;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
const uint8_t h = x[i].qh[in+j] >> im;
sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
+ y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
+ y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16))
+ y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16));
}
tmp += sum;
}
#endif
// sum up partial sums and write back result
tmp = warp_reduce_sum(tmp);
@ -470,8 +352,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx,
const block_q6_K * x = (const block_q6_K *)vx + ib0;
#if QK_K == 256
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1
@ -526,37 +406,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx,
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3
const int step = tid * K_QUANTS_PER_ITERATION;
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + step;
const uint8_t * ql = x[i].ql + step;
const uint8_t * qh = x[i].qh + step;
const int8_t * s = x[i].scales;
const float d = x[i+0].d;
float sum = 0;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+ y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+ y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32)
+ y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32);
}
tmp += sum;
}
#endif
// sum up partial sums and write back result
tmp = warp_reduce_sum(tmp);

591
ggml-cuda/fattn-common.cuh
View file

@ -1,4 +1,8 @@
#pragma once
#include "common.cuh"
#include "convert.cuh"
#include "vecdotq.cuh"
#include <cstdint>
@ -34,11 +38,523 @@ typedef void (* fattn_kernel_t)(
const int nb11,
const int nb12,
const int nb13,
const int nb21,
const int nb22,
const int nb23,
const int ne0,
const int ne1,
const int ne2,
const int ne3);
typedef half (*vec_dot_KQ_f16_t)(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
typedef float (*vec_dot_KQ_f32_t)(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
template<typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c;
GGML_UNUSED(Q_v);
half sum = 0.0f;
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const int ib = k_KQ / QI8_1;
const int iqs4 = k_KQ % QI4_0;
const int shift = k_KQ & (QI8_1/2);
const int v = (get_int_from_uint8(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
const int u = Q_q8[k_KQ_0/WARP_SIZE];
const int sumi = __dp4a(v, u, 0);
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
const half2 * Q_ds = (const half2 *) Q_ds_v;
const half2 sum2 = __half2half2(K_q4_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE];
sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2) /* *8/QI8_1 == 1 */);
} else
#endif // FP16_AVAILABLE
{
const float2 * Q_ds = (const float2 *) Q_ds_v;
sum += (T) (__half2float(K_q4_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (8/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y));
}
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template<typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c;
GGML_UNUSED(Q_v);
T sum = 0.0f;
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const int ib = k_KQ / QI8_1;
const int iqs4 = k_KQ % QI4_1;
const int shift = k_KQ & (QI8_1/2);
const int v = (get_int_from_uint8_aligned(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
const int u = Q_q8[k_KQ_0/WARP_SIZE];
const int sumi = __dp4a(v, u, 0);
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
const half2 * Q_ds = (const half2 *) Q_ds_v;
const half2 d4d8_m4s8 = K_q4_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE];
const half2 sumid4d8_m4s8scaled = d4d8_m4s8 * make_half2(sumi, 1.0f/QI8_1);
sum += (T) (__low2half(sumid4d8_m4s8scaled) + __high2half(sumid4d8_m4s8scaled));
} else
#endif // FP16_AVAILABLE
{
const float2 * Q_ds = (const float2 *) Q_ds_v;
const float sumid4d8 = __low2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi;
const float m4s8scaled = __high2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1;
sum += (T) (sumid4d8 + m4s8scaled);
}
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template<typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c;
GGML_UNUSED(Q_v);
T sum = 0.0f;
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const int ib = k_KQ / QI8_1;
const int iqs4 = k_KQ % QI5_0;
const int iqs8 = k_KQ % QI8_1;
const int shift = k_KQ & (QI8_1/2);
int v = (get_int_from_uint8(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
const int vh = get_int_from_uint8(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0);
v |= (vh << 4) & 0x00000010; // 0 -> 4
v |= (vh << 11) & 0x00001000; // 1 -> 12
v |= (vh << 18) & 0x00100000; // 2 -> 20
v |= (vh << 25) & 0x10000000; // 3 -> 28
const int u = Q_q8[k_KQ_0/WARP_SIZE];
const int sumi = __dp4a(v, u, 0);
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
const half2 * Q_ds = (const half2 *) Q_ds_v;
const half2 sum2 = __half2half2(K_q5_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE];
sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2)*__float2half(2.0f)) /* *16/QI8_1 == 2 */;
} else
#endif // FP16_AVAILABLE
{
const float2 * Q_ds = (const float2 *) Q_ds_v;
sum += (T) (__half2float(K_q5_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (16/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y));
}
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template<typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c;
GGML_UNUSED(Q_v);
T sum = 0.0f;
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const int ib = k_KQ / QI8_1;
const int iqs4 = k_KQ % QI5_1;
const int iqs8 = k_KQ % QI8_1;
const int shift = k_KQ & (QI8_1/2);
int v = (get_int_from_uint8(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
const int vh = get_int_from_uint8(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1);
v |= (vh << 4) & 0x00000010; // 0 -> 4
v |= (vh << 11) & 0x00001000; // 1 -> 12
v |= (vh << 18) & 0x00100000; // 2 -> 20
v |= (vh << 25) & 0x10000000; // 3 -> 28
const int u = Q_q8[k_KQ_0/WARP_SIZE];
const int sumi = __dp4a(v, u, 0);
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
const half2 * Q_ds = (const half2 *) Q_ds_v;
const half2 d5d8_m5s8 = K_q5_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE];
const half2 sumid5d8_m5s8scaled = d5d8_m5s8 * make_half2(sumi, 1.0f/QI8_1);
sum += (T) (__low2half(sumid5d8_m5s8scaled) + __high2half(sumid5d8_m5s8scaled));
} else
#endif // FP16_AVAILABLE
{
const float2 * Q_ds = (const float2 *) Q_ds_v;
const float sumid5d8 = __low2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi;
const float m5s8scaled = __high2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1;
sum += (T) (sumid5d8 + m5s8scaled);
}
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template <typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c;
GGML_UNUSED(Q_v);
T sum = 0.0f;
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const int ib = k_KQ / QI8_0;
const int iqs = k_KQ % QI8_0;
const int v = get_int_from_int8(K_q8_0[ib].qs, iqs);
T Q_d;
if (std::is_same<T, half>::value) {
const half2 * Q_ds = (const half2 *) Q_ds_v;
Q_d = __low2half(Q_ds[k_KQ_0/WARP_SIZE]);
} else {
const float2 * Q_ds = (const float2 *) Q_ds_v;
Q_d = Q_ds[k_KQ_0/WARP_SIZE].x;
}
sum += vec_dot_q8_0_q8_1_impl<T, 1>(&v, &Q_q8[k_KQ_0/WARP_SIZE], K_q8_0[ib].d, Q_d);
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template <typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds_v) {
const half2 * K_h2 = (const half2 *) K_c;
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
const half2 * Q_h2 = (const half2 *) Q_v;
half2 sum2 = make_half2(0.0f, 0.0f);
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const half2 K_ik = K_h2[k_KQ];
sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE];
}
return __low2half(sum2) + __high2half(sum2);
}
#endif // FP16_AVAILABLE
const float2 * Q_f2 = (const float2 *) Q_v;
float sum = 0.0f;
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const half2 K_ik = K_h2[k_KQ];
sum += __low2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].x;
sum += __high2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].y;
}
return sum;
}
template <typename Tds>
static __device__ __forceinline__ void quantize_q8_1_to_shared(
const float * __restrict__ x, const float scale, int * __restrict__ yq32, void * __restrict__ yds) {
float vals[sizeof(int)] = {0.0f};
#pragma unroll
for (int l = 0; l < sizeof(int); ++l) {
vals[l] = scale * x[4*threadIdx.x + l];
}
float amax = fabsf(vals[0]);
float sum = vals[0];
#pragma unroll
for (int l = 1; l < sizeof(int); ++l) {
amax = fmaxf(amax, fabsf(vals[l]));
sum += vals[l];
}
#pragma unroll
for (int mask = QI8_1/2; mask > 0; mask >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, 32));
sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, 32);
}
const float d = amax / 127;
int q32 = 0;
int8_t * q8 = (int8_t *) &q32;
if (d != 0.0f) {
#pragma unroll
for (int l = 0; l < sizeof(int); ++l) {
q8[l] = roundf(vals[l] / d);
}
}
yq32[threadIdx.x] = q32;
if (threadIdx.x % QI8_1 == 0) {
if (std::is_same<Tds, half2>::value) {
((half2 *) yds)[threadIdx.x/QI8_1] = make_half2(d, sum);
} else {
((float2 *) yds)[threadIdx.x/QI8_1] = make_float2(d, sum);
}
}
}
typedef half (*dequantize_1_f16_t)(const void *, const int64_t);
typedef float (*dequantize_1_f32_t)(const void *, const int64_t);
template <typename T>
static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__ vx, const int64_t i) {
const block_q4_0 * x = (const block_q4_0 *) vx;
const int64_t ib = i / QK4_0;
const int iqs = i % (QK4_0/2);
const int shift = (i % QK4_0) / (QK4_0/2);
const T d = x[ib].d;
const int q0 = x[ib].qs[iqs];
const int q = ((q0 >> (4*shift)) & 0x0F) - 8;
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
return ((half) d)*((half) q);
}
#endif // FP16_AVAILABLE
return ((float) d)*((float) q);
}
template <typename T>
static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__ vx, const int64_t i) {
const block_q4_1 * x = (const block_q4_1 *) vx;
const int64_t ib = i / QK4_1;
const int iqs = i % (QK4_1/2);
const int shift = (i % QK4_1) / (QK4_1/2);
const half2 dm = x[ib].dm;
const int q0 = x[ib].qs[iqs];
const int q = ((q0 >> (4*shift)) & 0x0F);
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
return __low2half(dm)*((half) q) + __high2half(dm);
}
#endif // FP16_AVAILABLE
return __low2float(dm)*((float) q) + __high2float(dm);
}
template <typename T>
static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ vx, const int64_t i) {
const block_q5_0 * x = (const block_q5_0 *) vx;
const int64_t ib = i / QK5_0;
const int idq = i % QK5_0;
const int iqs = i % (QK5_0/2);
const int shift = (i % QK5_0) / (QK5_0/2);
const T d = x[ib].d;
const int ql0 = x[ib].qs[iqs];
const int qh0 = get_int_from_uint8(x[ib].qh, 0);
const int ql = ((ql0 >> (4*shift)) & 0x0F);
const int qh = ((qh0 >> idq) << 4) & 0x10;
const int q = (ql | qh) - 16;
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
return ((half) d)*((half) q);
}
#endif // FP16_AVAILABLE
return ((float) d)*((float) q);
}
template <typename T>
static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ vx, const int64_t i) {
const block_q5_1 * x = (const block_q5_1 *) vx;
const int64_t ib = i / QK5_1;
const int idq = i % QK5_1;
const int iqs = i % (QK5_1/2);
const int shift = (i % QK5_1) / (QK5_1/2);
const half2 dm = x[ib].dm;
const int ql0 = x[ib].qs[iqs];
const int qh0 = get_int_from_uint8_aligned(x[ib].qh, 0);
const int ql = ((ql0 >> (4*shift)) & 0x0F);
const int qh = ((qh0 >> idq) << 4) & 0x10;
const int q = (ql | qh);
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
return __low2half(dm)*((half) q) + __high2half(dm);
}
#endif // FP16_AVAILABLE
return __low2float(dm)*((float) q) + __high2float(dm);
}
template <typename T>
static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__ vx, const int64_t i) {
const block_q8_0 * x = (const block_q8_0 *) vx;
const int64_t ib = i / QK8_0;
const int iqs = i % QK8_0;
const T d = x[ib].d;
const int q = x[ib].qs[iqs];
#if FP16_AVAILABLE
if (std::is_same<T, half>::value) {
return ((half) d)*((half) q);
}
#endif // FP16_AVAILABLE
return ((float) d)*((float) q);
}
template <typename T>
static __device__ __forceinline__ T dequantize_1_f16(const void * __restrict__ vx, const int64_t i) {
const half * x = (const half *) vx;
return x[i];
}
template <int D>
constexpr __device__ vec_dot_KQ_f16_t get_vec_dot_KQ_f16(ggml_type type_K) {
return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<half, D> :
type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<half, D> :
type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<half, D> :
type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<half, D> :
type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<half, D> :
type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<half, D> :
nullptr;
}
template <int D>
constexpr __device__ vec_dot_KQ_f32_t get_vec_dot_KQ_f32(ggml_type type_K) {
return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<float, D> :
type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<float, D> :
type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<float, D> :
type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<float, D> :
type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<float, D> :
type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<float, D> :
nullptr;
}
constexpr __device__ dequantize_1_f16_t get_dequantize_1_f16(ggml_type type_V) {
return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0<half> :
type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1<half> :
type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0<half> :
type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1<half> :
type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0<half> :
type_V == GGML_TYPE_F16 ? dequantize_1_f16<half> :
nullptr;
}
constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0<float> :
type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1<float> :
type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0<float> :
type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1<float> :
type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0<float> :
type_V == GGML_TYPE_F16 ? dequantize_1_f16<float> :
nullptr;
}
template<int D, int parallel_blocks> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
@ -83,8 +599,32 @@ static __global__ void flash_attn_combine_results(
dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
}
static void on_no_fattn_vec_case(const int D) {
if (D == 64) {
fprintf(stderr, "Unsupported KV type combination for head_size 64.\n");
fprintf(stderr, "By default only f16 KV cache is supported.\n");
fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for V cache quantization support.\n");
GGML_ASSERT(false);
} else if (D == 128) {
fprintf(stderr, "Unsupported KV type combination for head_size 128.\n");
fprintf(stderr, "Supported combinations:\n");
fprintf(stderr, " - K == q4_0, V == q4_0, 4.50 BPV\n");
fprintf(stderr, " - K == q8_0, V == q8_0, 8.50 BPV\n");
fprintf(stderr, " - K == f16, V == f16, 16.00 BPV\n");
fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n");
GGML_ASSERT(false);
} else {
fprintf(stderr, "Unsupported KV type combination for head_size 256.\n");
fprintf(stderr, "Only f16 is supported.\n");
GGML_ASSERT(false);
}
}
template <int D, int parallel_blocks>
void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) {
void launch_fattn(
ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel,
const int nwarps, const int cols_per_block, const bool need_f16_K, const bool need_f16_V
) {
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
@ -94,8 +634,6 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern
ggml_tensor * KQV = dst;
GGML_ASSERT(Q->type == GGML_TYPE_F32);
GGML_ASSERT(K->type == GGML_TYPE_F16);
GGML_ASSERT(V->type == GGML_TYPE_F16);
GGML_ASSERT(KQV->type == GGML_TYPE_F32);
GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
@ -107,9 +645,49 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern
ggml_cuda_pool & pool = ctx.pool();
cudaStream_t main_stream = ctx.stream();
ggml_cuda_pool_alloc<half> K_f16(pool);
ggml_cuda_pool_alloc<half> V_f16(pool);
ggml_cuda_pool_alloc<float> dst_tmp(pool);
ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
char * K_data = (char *) K->data;
size_t nb11 = K->nb[1];
size_t nb12 = K->nb[2];
size_t nb13 = K->nb[3];
char * V_data = (char *) V->data;
size_t nb21 = V->nb[1];
size_t nb22 = V->nb[2];
size_t nb23 = V->nb[3];
if (need_f16_K && K->type != GGML_TYPE_F16) {
K_f16.alloc(ggml_nelements(K));
to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type);
to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream);
K_data = (char *) K_f16.ptr;
const size_t bs = ggml_blck_size(K->type);
const size_t ts = ggml_type_size(K->type);
nb11 = nb11*bs*sizeof(half)/ts;
nb12 = nb12*bs*sizeof(half)/ts;
nb13 = nb13*bs*sizeof(half)/ts;
}
if (need_f16_V && V->type != GGML_TYPE_F16) {
V_f16.alloc(ggml_nelements(V));
to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type);
to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream);
V_data = (char *) V_f16.ptr;
const size_t bs = ggml_blck_size(V->type);
const size_t ts = ggml_type_size(V->type);
nb21 = nb21*bs*sizeof(half)/ts;
nb22 = nb22*bs*sizeof(half)/ts;
nb23 = nb23*bs*sizeof(half)/ts;
}
if (parallel_blocks > 1) {
dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
@ -133,8 +711,8 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern
fattn_kernel<<<blocks_num, block_dim, shmem, main_stream>>>(
(const char *) Q->data,
(const char *) K->data,
(const char *) V->data,
K_data,
V_data,
mask ? ((const char *) mask->data) : nullptr,
(parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
scale, max_bias, m0, m1, n_head_log2,
@ -142,7 +720,8 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0,
Q->nb[1], Q->nb[2], Q->nb[3],
K->nb[1], K->nb[2], K->nb[3],
nb11, nb12, nb13,
nb21, nb22, nb23,
KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
);
CUDA_CHECK(cudaGetLastError());

7
ggml-cuda/fattn-tile-f16.cu
View file

@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f16(
const int nb11,
const int nb12,
const int nb13,
const int nb21,
const int nb22,
const int nb23,
const int ne0,
const int ne1,
const int ne2,
@ -275,13 +278,13 @@ void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
constexpr int D = 64;
constexpr int nwarps = 8;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
} break;
case 128: {
constexpr int D = 128;
constexpr int nwarps = 8;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
} break;
default: {
GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");

7
ggml-cuda/fattn-tile-f32.cu
View file

@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f32(
const int nb11,
const int nb12,
const int nb13,
const int nb21,
const int nb22,
const int nb23,
const int ne0,
const int ne1,
const int ne2,
@ -272,13 +275,13 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
constexpr int D = 64;
constexpr int nwarps = 8;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
} break;
case 128: {
constexpr int D = 128;
constexpr int nwarps = 8;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
} break;
default: {
GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");

330
ggml-cuda/fattn-vec-f16.cu
View file

@ -1,330 +0,0 @@
#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-vec-f16.cuh"
template<int D, int ncols, int parallel_blocks> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_vec_ext_f16(
const char * __restrict__ Q,
const char * __restrict__ K,
const char * __restrict__ V,
const char * __restrict__ mask,
float * __restrict__ dst,
float2 * __restrict__ dst_meta,
const float scale,
const float max_bias,
const float m0,
const float m1,
const uint32_t n_head_log2,
const int ne00,
const int ne01,
const int ne02,
const int ne03,
const int ne10,
const int ne11,
const int ne12,
const int ne13,
const int ne31,
const int nb31,
const int nb01,
const int nb02,
const int nb03,
const int nb11,
const int nb12,
const int nb13,
const int ne0,
const int ne1,
const int ne2,
const int ne3) {
#if FP16_AVAILABLE
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
const half * maskh = (const half *) mask + ne11*ic0;
const int stride_KV = nb11 / sizeof(half);
const int stride_KV2 = nb11 / sizeof(half2);
const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
const half slopeh = __float2half(slopef);
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
constexpr int nwarps = D / WARP_SIZE;
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
__builtin_assume(tid < D);
__shared__ half KQ[ncols*D];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
KQ[j*D + tid] = -HALF_MAX_HALF;
}
half2 * KQ2 = (half2 *) KQ;
half kqmax[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax[j] = -HALF_MAX_HALF;
}
half kqsum[ncols] = {0.0f};
__shared__ half kqmax_shared[ncols][WARP_SIZE];
__shared__ half kqsum_shared[ncols][WARP_SIZE];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
if (threadIdx.y == 0) {
kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
kqsum_shared[j][threadIdx.x] = 0.0f;
}
}
__syncthreads();
// Convert Q to half2 and store in registers:
half2 Q_h2[ncols][D/(2*WARP_SIZE)];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f);
Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
}
}
half2 VKQ[ncols] = {{0.0f, 0.0f}};
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
// Calculate KQ tile and keep track of new maximum KQ values:
// For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
// see https://github.com/ggerganov/llama.cpp/pull/7061 .
// Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
half kqmax_new = kqmax[0];
half kqmax_new_arr[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax_new_arr[j] = kqmax[j];
}
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
const int i_KQ = i_KQ_0 + threadIdx.y;
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
break;
}
half2 sum2[ncols] = {{0.0f, 0.0f}};
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE];
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum2[j] = warp_reduce_sum(sum2[j]);
half sum = __low2half(sum2[j]) + __high2half(sum2[j]);
sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
if (ncols == 1) {
kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
} else {
kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
}
if (threadIdx.x == 0) {
KQ[j*D + i_KQ] = sum;
}
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
if (threadIdx.x == 0) {
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
}
}
__syncthreads();
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half kqmax_new_j = kqmax_shared[j][threadIdx.x];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
kqmax[j] = kqmax_new_j;
const half val = hexp(KQ[j*D + tid] - kqmax[j]);
kqsum[j] = kqsum[j]*KQ_max_scale + val;
KQ[j*D + tid] = val;
VKQ[j] *= __half2half2(KQ_max_scale);
}
__syncthreads();
#pragma unroll
for (int k0 = 0; k0 < D; k0 += 2) {
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
break;
}
half2 V_k;
reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
}
}
__syncthreads();
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqsum[j] = warp_reduce_sum(kqsum[j]);
if (threadIdx.x == 0) {
kqsum_shared[j][threadIdx.y] = kqsum[j];
}
}
__syncthreads();
#pragma unroll
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
if (ncols > 2 && ic0 + j_VKQ >= ne01) {
break;
}
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
if (parallel_blocks == 1) {
dst_val /= kqsum[j_VKQ];
}
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
}
if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
}
#else
NO_DEVICE_CODE;
#endif // FP16_AVAILABLE
}
void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_tensor * KQV = dst;
ggml_tensor * Q = dst->src[0];
const int32_t precision = KQV->op_params[2];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64: {
constexpr int D = 64;
constexpr int nwarps = D/WARP_SIZE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
} break;
case 128: {
constexpr int D = 128;
constexpr int nwarps = D/WARP_SIZE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
} break;
case 256: {
constexpr int D = 256;
constexpr int nwarps = D/WARP_SIZE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
} break;
default:
GGML_ASSERT(false);
break;
}
}
template <int cols_per_block, int parallel_blocks>
void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
switch (Q->ne[0]) {
case 64: {
constexpr int D = 64;
constexpr int nwarps = D/WARP_SIZE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
} break;
case 128: {
constexpr int D = 128;
constexpr int nwarps = D/WARP_SIZE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
} break;
default: {
GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
} break;
}
}
void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];
const int32_t precision = KQV->op_params[2];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
if (Q->ne[1] == 1) {
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
}

396
ggml-cuda/fattn-vec-f16.cuh
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@ -1,5 +1,397 @@
#include "common.cuh"
#include "fattn-common.cuh"
void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_vec_ext_f16(
const char * __restrict__ Q,
const char * __restrict__ K,
const char * __restrict__ V,
const char * __restrict__ mask,
float * __restrict__ dst,
float2 * __restrict__ dst_meta,
const float scale,
const float max_bias,
const float m0,
const float m1,
const uint32_t n_head_log2,
const int ne00,
const int ne01,
const int ne02,
const int ne03,
const int ne10,
const int ne11,
const int ne12,
const int ne13,
const int ne31,
const int nb31,
const int nb01,
const int nb02,
const int nb03,
const int nb11,
const int nb12,
const int nb13,
const int nb21,
const int nb22,
const int nb23,
const int ne0,
const int ne1,
const int ne2,
const int ne3) {
#if FP16_AVAILABLE
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16<D>(type_K);
constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16;
constexpr dequantize_1_f16_t dequantize_1_v = get_dequantize_1_f16(type_V);
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
Q += nb02* blockIdx.y + nb01*ic0;
K += nb12*(blockIdx.y / gqa_ratio);
V += nb22*(blockIdx.y / gqa_ratio);
const half * maskh = (const half *) mask + ne11*ic0;
const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
const half slopeh = __float2half(slopef);
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
constexpr int nwarps = D / WARP_SIZE;
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
__builtin_assume(tid < D);
__shared__ half KQ[ncols*D];
half2 * KQ2 = (half2 *) KQ;
half kqmax[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax[j] = -HALF_MAX_HALF;
}
half kqsum[ncols] = {0.0f};
__shared__ half kqmax_shared[ncols][WARP_SIZE];
__shared__ half kqsum_shared[ncols][WARP_SIZE];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
if (threadIdx.y == 0) {
kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
kqsum_shared[j][threadIdx.x] = 0.0f;
}
}
__syncthreads();
// Convert Q to half2 (f16 K) or q8_1 (quantized K) and store in registers:
half2 Q_h2[ncols][D/(2*WARP_SIZE)];
int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D/(sizeof(int)*QK8_1)];
half2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1];
if (Q_q8_1) {
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
const int j = j0 + threadIdx.y;
if (j0 + nwarps > ncols && j >= ncols) {
break;
}
// Reuse KQ as temporary storage for converting Q to q8_1:
int * tmp_q_i32 = (int *) &KQ[j*D];
half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int));
// Set memory to zero if out of bounds:
if (ncols > 2 && ic0 + j >= ne01) {
#pragma unroll
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
tmp_q_i32[i] = 0;
}
if (threadIdx.x < D/QK8_1) {
tmp_q_ds[threadIdx.x] = make_half2(0.0f, 0.0f);
}
continue;
}
const float * Q_f = (const float *) (Q + j*nb01);
#pragma unroll
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
quantize_q8_1_to_shared<half2>(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds);
}
}
__syncthreads();
#pragma unroll
for (int j = 0; j < ncols; ++j) {
int * tmp_q_i32 = (int *) &KQ[j*D];
half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int));
#pragma unroll
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i];
Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1];
}
}
__syncthreads();
} else {
#pragma unroll
for (int j = 0; j < ncols; ++j) {
const float2 * Q_f2_j = (const float2 *) (Q + j*nb01);
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2_j[i] : make_float2(0.0f, 0.0f);
Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
}
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
KQ[j*D + tid] = -HALF_MAX_HALF;
}
half2 VKQ[ncols] = {{0.0f, 0.0f}};
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
// Calculate KQ tile and keep track of new maximum KQ values:
// For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
// see https://github.com/ggerganov/llama.cpp/pull/7061 .
// Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
half kqmax_new = kqmax[0];
half kqmax_new_arr[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax_new_arr[j] = kqmax[j];
}
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
const int i_KQ = i_KQ_0 + threadIdx.y;
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
break;
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]);
sum = warp_reduce_sum(sum);
sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
if (ncols == 1) {
kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
} else {
kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
}
if (threadIdx.x == 0) {
KQ[j*D + i_KQ] = sum;
}
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
if (threadIdx.x == 0) {
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
}
}
__syncthreads();
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half kqmax_new_j = kqmax_shared[j][threadIdx.x];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
kqmax[j] = kqmax_new_j;
const half val = hexp(KQ[j*D + tid] - kqmax[j]);
kqsum[j] = kqsum[j]*KQ_max_scale + val;
KQ[j*D + tid] = val;
VKQ[j] *= __half2half2(KQ_max_scale);
}
__syncthreads();
#pragma unroll
for (int k0 = 0; k0 < D; k0 += 2) {
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
break;
}
half2 V_k;
reinterpret_cast<half&>(V_k.x) = dequantize_1_v(V + (k_VKQ_0 + k0 + 0)*nb21, tid);
reinterpret_cast<half&>(V_k.y) = dequantize_1_v(V + (k_VKQ_0 + k0 + 1)*nb21, tid);
#pragma unroll
for (int j = 0; j < ncols; ++j) {
VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
}
}
__syncthreads();
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqsum[j] = warp_reduce_sum(kqsum[j]);
if (threadIdx.x == 0) {
kqsum_shared[j][threadIdx.y] = kqsum[j];
}
}
__syncthreads();
#pragma unroll
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
if (ncols > 2 && ic0 + j_VKQ >= ne01) {
break;
}
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
if (parallel_blocks == 1) {
dst_val /= kqsum[j_VKQ];
}
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
}
if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
}
#else
NO_DEVICE_CODE;
#endif // FP16_AVAILABLE
}
template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
constexpr int nwarps = D/WARP_SIZE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V>;
constexpr bool need_f16_K = D != 128;
constexpr bool need_f16_V = D != 128 && D != 64;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
}
template <int D, ggml_type type_K, ggml_type type_V>
void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_tensor * KQV = dst;
ggml_tensor * Q = dst->src[0];
ggml_tensor * K = dst->src[1];
ggml_tensor * V = dst->src[2];
const int32_t precision = KQV->op_params[2];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
GGML_ASSERT(K->type == type_K);
GGML_ASSERT(V->type == type_V);
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
}
#define DECL_FATTN_VEC_F16_CASE(D, type_K, type_V) \
template void ggml_cuda_flash_attn_ext_vec_f16_case \
<D, type_K, type_V>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
extern DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);

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