* server: tests: add models endpoint scenario
* server: /v1/models add some metadata
* server: tests: add debug field in context before scenario
* server: tests: download model from HF, add batch size
* server: tests: add passkey test
* server: tests: add group attention params
* server: do not truncate prompt tokens if self-extend through group attention is enabled
* server: logs: do not truncate log values
* server: tests - passkey - first good working value of nga
* server: tests: fix server timeout
* server: tests: fix passkey, add doc, fix regex content matching, fix timeout
* server: tests: fix regex content matching
* server: tests: schedule slow tests on master
* server: metrics: fix when no prompt processed
* server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1
* server: tests: increase timeout for completion
* server: tests: keep only the PHI-2 test
* server: tests: passkey add a negative test
Reduces peak tmpfs usage and should prevent the check from failing from
running out of space.
Fixes the 'No space left on device' issue mentioned in #5703.
* [ggml-quants] Provide ggml_vqtbl1q_u8 for 64bit compatibility
vqtbl1q_u8 is not part of arm v7 neon library
* [android-example] Remove abi filter after arm v7a fix
* [github-workflows] Do not skip Android armeabi-v7a build
* server: tests: init scenarios
- health and slots endpoints
- completion endpoint
- OAI compatible chat completion requests w/ and without streaming
- completion multi users scenario
- multi users scenario on OAI compatible endpoint with streaming
- multi users with total number of tokens to predict exceeds the KV Cache size
- server wrong usage scenario, like in Infinite loop of "context shift" #3969
- slots shifting
- continuous batching
- embeddings endpoint
- multi users embedding endpoint: Segmentation fault #5655
- OpenAI-compatible embeddings API
- tokenize endpoint
- CORS and api key scenario
* server: CI GitHub workflow
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
GitHub does not expose environment and repository variables to PRs coming from forks implies that we've been disabling the Nix CI actions for most PRs.
The `if:` also didn't make much sense, because we can always pull from cachix, and there's no point (albeit no risk either) in pushing cache for the untrusted code.
* llava: remove prog parameter from ArgumentParser
This commit removes the `prog` parameter from `ArgumentParser`
so that it uses the default value which is the name of the script.
The motivation for this change is that currently the usage output looks
like this:
```console
$ python examples/llava/convert-image-encoder-to-gguf.py --help
usage: convert_hf_to_gguf.py [-h] ...
```
And with this change it will look like this:
```console
$ python examples/llava/convert-image-encoder-to-gguf.py --help
usage: convert-image-encoder-to-gguf.py [-h] ...
```
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* ci: add W503 to flake8 ignore list
This commit adds W503 to the ignore list for flake8. This is done to
avoid the following error:
W503 line break before binary operator
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---------
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* support SYCL backend windows build
* add windows build in CI
* add for win build CI
* correct install oneMKL
* fix install issue
* fix ci
* fix install cmd
* fix install cmd
* fix install cmd
* fix install cmd
* fix install cmd
* fix win build
* fix win build
* fix win build
* restore other CI part
* restore as base
* rm no new line
* fix no new line issue, add -j
* fix grammer issue
* allow to trigger manually, fix format issue
* fix format
* add newline
* fix format
* fix format
* fix format issuse
---------
Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
* first update for migration
* update init_cublas
* add debug functio, commit all help code
* step 1
* step 2
* step3 add fp16, slower 31->28
* add GGML_LIST_DEVICE function
* step 5 format device and print
* step6, enhance error check, remove CUDA macro, enhance device id to fix none-zero id issue
* support main device is non-zero
* step7 add debug for code path, rm log
* step 8, rename all macro & func from cuda by sycl
* fix error of select non-zero device, format device list
* ren ggml-sycl.hpp -> ggml-sycl.h
* clear CMAKE to rm unused lib and options
* correct queue: rm dtct:get_queue
* add print tensor function to debug
* fix error: wrong result in 658746bb26702e50f2c59c0e4ada8e9da6010481
* summary dpct definition in one header file to replace folder:dpct
* refactor device log
* mv dpct definition from folder dpct to ggml-sycl.h
* update readme, refactor build script
* fix build with sycl
* set nthread=1 when sycl, increase performance
* add run script, comment debug code
* add ls-sycl-device tool
* add ls-sycl-device, rm unused files
* rm rear space
* dos2unix
* Update README_sycl.md
* fix return type
* remove sycl version from include path
* restore rm code to fix hang issue
* add syc and link for sycl readme
* rm original sycl code before refactor
* fix code err
* add know issue for pvc hang issue
* enable SYCL_F16 support
* align pr4766
* check for sycl blas, better performance
* cleanup 1
* remove extra endif
* add build&run script, clean CMakefile, update guide by review comments
* rename macro to intel hardware
* editor config format
* format fixes
* format fixes
* editor format fix
* Remove unused headers
* skip build sycl tool for other code path
* replace tab by space
* fix blas matmul function
* fix mac build
* restore hip dependency
* fix conflict
* ren as review comments
* mv internal function to .cpp file
* export funciton print_sycl_devices(), mv class dpct definition to source file
* update CI/action for sycl code, fix CI error of repeat/dup
* fix action ID format issue
* rm unused strategy
* enable llama_f16 in ci
* fix conflict
* fix build break on MacOS, due to CI of MacOS depend on external ggml, instead of internal ggml
* fix ci cases for unsupported data type
* revert unrelated changed in cuda cmake
remove useless nommq
fix typo of GGML_USE_CLBLAS_SYCL
* revert hip cmake changes
* fix indent
* add prefix in func name
* revert no mmq
* rm cpu blas duplicate
* fix no_new_line
* fix src1->type==F16 bug.
* pass batch offset for F16 src1
* fix batch error
* fix wrong code
* revert sycl checking in test-sampling
* pass void as arguments of ggml_backend_sycl_print_sycl_devices
* remove extra blank line in test-sampling
* revert setting n_threads in sycl
* implement std::isinf for icpx with fast math.
* Update ci/run.sh
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update examples/sycl/run-llama2.sh
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update examples/sycl/run-llama2.sh
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update CMakeLists.txt
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update CMakeLists.txt
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update CMakeLists.txt
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update CMakeLists.txt
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add copyright and MIT license declare
* update the cmd example
---------
Co-authored-by: jianyuzh <jianyu.zhang@intel.com>
Co-authored-by: luoyu-intel <yu.luo@intel.com>
Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: add Dockerfiles for each platform that user ./server instead of ./main
* feat: update .github/workflows/docker.yml to build server-first docker containers
* doc: add information about running the server with Docker to README.md
* doc: add information about running with docker to the server README
* doc: update n-gpu-layers to show correct GPU usage
* fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA
* scripts : add lib.sh and lib_test.sh
* scripts : stub out new ci-run.sh script
* scripts : switch to PascalCase for functions
This looks a little odd at first, but I find it very useful as a
convention to know if a command is part of our code vs a builtin.
* scripts : add some fancy conversion from snake_case to PascalCase
* Add venv to ci/run.sh
* Revert scripts work
* scripts : add wrapper script for local use of ci/run.sh
* Simplify .gitignore for tests, clang-tidy fixes
* Label all ctest tests
* ci : ctest uses -L main
* Attempt at writing ctest_with_model
* Update test-model-load-cancel
* ci : add ctest_with_model for debug and release
ggml-ci
* Fix gg_get_model function
ggml-ci
* got stuck on CMake
* Add get_model.cpp to tests/CMakeLists.txt
ggml-ci
* Fix README.md output for ctest_with_model
ggml-ci
* workflows : use `-L main` for all ctest
ggml-ci
* Fixes
* GG_RUN_CTEST_MODELFILE => LLAMACPP_TESTMODELFILE
* Always show warning rather than failing if model file variable is not
set
* scripts : update usage text for ci-run.sh
* Introduce starter project for Android
Based on examples/llama.swiftui.
* Add github workflow
* Set NDK version
* Only build arm64-v8a in CI
* Sync bench code
* Rename CI prop to skip-armeabi-v7a
* Remove unused tests
* python: add check-requirements.sh and GitHub workflow
This script and workflow forces package versions to remain compatible
across all convert*.py scripts, while allowing secondary convert scripts
to import dependencies not wanted in convert.py.
* Move requirements into ./requirements
* Fail on "==" being used for package requirements (but can be suppressed)
* Enforce "compatible release" syntax instead of ==
* Update workflow
* Add upper version bound for transformers and protobuf
* improve check-requirements.sh
* small syntax change
* don't remove venvs if nocleanup is passed
* See if this fixes docker workflow
* Move check-requirements.sh into ./scripts/
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
* flake.lock: update to hotfix CUDA::cuda_driver
Required to support https://github.com/ggerganov/llama.cpp/pull/4606
* flake.nix: rewrite
1. Split into separate files per output.
2. Added overlays, so that this flake can be integrated into others.
The names in the overlay are `llama-cpp`, `llama-cpp-opencl`,
`llama-cpp-cuda`, and `llama-cpp-rocm` so that they fit into the
broader set of Nix packages from [nixpkgs](https://github.com/nixos/nixpkgs).
3. Use [callPackage](https://summer.nixos.org/blog/callpackage-a-tool-for-the-lazy/)
rather than `with pkgs;` so that there's dependency injection rather
than dependency lookup.
4. Add a description and meta information for each package.
The description includes a bit about what's trying to accelerate each one.
5. Use specific CUDA packages instead of cudatoolkit on the advice of SomeoneSerge.
6. Format with `serokell/nixfmt` for a consistent style.
7. Update `flake.lock` with the latest goods.
* flake.nix: use finalPackage instead of passing it manually
* nix: unclutter darwin support
* nix: pass most darwin frameworks unconditionally
...for simplicity
* *.nix: nixfmt
nix shell github:piegamesde/nixfmt/rfc101-style --command \
nixfmt flake.nix .devops/nix/*.nix
* flake.nix: add maintainers
* nix: move meta down to follow Nixpkgs style more closely
* nix: add missing meta attributes
nix: clarify the interpretation of meta.maintainers
nix: clarify the meaning of "broken" and "badPlatforms"
nix: passthru: expose the use* flags for inspection
E.g.:
```
❯ nix eval .#cuda.useCuda
true
```
* flake.nix: avoid re-evaluating nixpkgs too many times
* flake.nix: use flake-parts
* nix: migrate to pname+version
* flake.nix: overlay: expose both the namespace and the default attribute
* ci: add the (Nix) flakestry workflow
* nix: cmakeFlags: explicit OFF bools
* nix: cuda: reduce runtime closure
* nix: fewer rebuilds
* nix: respect config.cudaCapabilities
* nix: add the impure driver's location to the DT_RUNPATHs
* nix: clean sources more thoroughly
...this way outPaths change less frequently,
and so there are fewer rebuilds
* nix: explicit mpi support
* nix: explicit jetson support
* flake.nix: darwin: only expose the default
---------
Co-authored-by: Someone Serge <sergei.kozlukov@aalto.fi>
* Downgrade CUDA to 11.4
This helps the binary be smaller and adds K80 support, the manual compiles we did already had this.
* Update kcpp-build-release-win-cuda.yaml
* Update kcpp-build-release-win-cuda.yaml
* Update kcpp-build-release-win-cuda.yaml
* Update kcpp-build-release-win-cuda.yaml
* Update kcpp-build-release-win-cuda.yaml
* Update kcpp-build-release-win-cuda.yaml
* Restore concedo_experimental
* .sh script V1
* koboldcpp.sh polish
* koboldcpp.sh dist generator
* Include html's in dist
* RWKV in Linux Dist
* Lower dependency requirements
* Eliminate wget dependency
* More distinct binary name
I know its technically amd64, but I don't want to cause confusion among nvidia users.
* Use System OpenCL
Unsure how this will behave in the pyinstaller build, but pocl ended up CPU only. With a bit of luck the pyinstaller uses the one from the actual system if compiled in a system without opencl, while conda now includes it for that specific system.
* Add cblas dependency
Missing this causes compile failures on some system's
* ICD workaround
Ideally we find a better solution, but conda forces ICD and needs this for the successful compile. However, pyinstaller then embeds the ICD causing it to be limited to the system it was compiled for. By temporarily removing the ICD pyinstaller can't find it and everything remains functional. Ideally we do this on a pyinstaller level, but I could not find any good options to do so yet.
* Fix & Nocuda
* Automatically build Linux Binary
* Auto build on v tag
* Better on release
* Fix missing jobs:
* More distinct name
* I am to retro...
* Fix release upload
* Another upload attempt
* Another upload attempt
* Also rebuild on release edit
* Placebo commit to maybe fix CI
---------
Co-authored-by: root <root@DESKTOP-DQ1QRAG>
