* attempt to also run e2e tests that needs gpus
* fix stray quote
* checkout specific github ref
* dockerfile for tests with proper checkout
ensure wandb is dissabled for docker pytests
clear wandb env after testing
clear wandb env after testing
make sure to provide a default val for pop
tryin skipping wandb validation tests
explicitly disable wandb in the e2e tests
explicitly report_to None to see if that fixes the docker e2e tests
split gpu from non-gpu unit tests
skip bf16 check in test for now
build docker w/o cache since it uses branch name ref
revert some changes now that caching is fixed
skip bf16 check if on gpu w support
* pytest skip for auto-gptq requirements
* skip mamba tests for now, split multipack and non packed lora llama tests
* split tests that use monkeypatches
* fix relative import for prev commit
* move other tests using monkeypatches to the correct run
* [Feat] streaming multipack
* WIP make continued pretraining work w multipack
* fix up hadrcoding, lint
* fix dict check
* update test for updated pretraining multipack code
* fix hardcoded data collator fix for multipack pretraining
* fix the collator to be the max length for multipack pretraining
* don't bother with latest tag for test
* cleanup docker build/test
---------
Co-authored-by: jinwonkim93@github.com <jinwonkim>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* bump transformers and update attention class map name
* also run the tests in docker
* add mixtral e2e smoke test
* fix base name for docker image in test
* mixtral lora doesn't seem to work, at least check qlora
* add testcase for mixtral w sample packing
* check monkeypatch for flash attn multipack
* also run the e2e tests in docker
* use all gpus to run tests in docker ci
* use privileged mode too for docker w gpus
* rename the docker e2e actions for gh ci
* set privileged mode for docker and update mixtral model self attn check
* use fp16/bf16 for mixtral w fa2
* skip e2e tests on docker w gpus for now
* tests to validate mistral and mixtral patches
* fix rel import
* add torch to requirements.txt at build time to force version to stick
* fix xformers check
* better handling of xformers based on installed torch version
* fix for ci w/o torch
* support for mamba
* more mamba fixes
* use fork for mamba kwargs fix
* grad checkpointing doesn't work
* fix extras for mamaba
* mamba loss fix
* use fp32 and remove verbose logging
* mamba fixes
* fix collator for mamba
* set model_type on training_args
* don't save safetensors for mamba
* update mamba config to disable safetensor checkpooints, install for tests
* no evals for mamba tests
* handle save_pretrained
* handle unused safetensors arg
* isolate torch from the requirements.txt
* fix typo for removed line ending
* pin transformers and accelerate to latest releases
* try w auto-gptq==0.5.1
* update README to remove manual peft install
* pin xformers to 0.0.22
* bump flash-attn to 2.3.3
* pin flash attn to exact version
* add mistral monkeypatch
* add arg for decoder attention masl
* fix lint for duplicate code
* make sure to update transformers too
* tweak install for e2e
* move mistral patch to conditional
* run e2e tests after all other checks have passed
* tweak tests so they get run on PRs or push to main
* change dependent action for chcecking
* one test workflow to rule them all
* no need for custom action, just use needs
* whoops, python version should be a string
* e2e tests can run on any available gpu
* update readme to point to direct link to runpod template, cleanup install instrucitons
* default install flash-attn and auto-gptq now too
* update readme w flash-attn extra
* fix version in setup
* auto gptq support
* more tweaks and add yml
* remove old gptq docker
* don't need explicit peft install for tests
* fix setup.py to use extra index url
install torch for tests
fix cuda version for autogptq index
set torch in requirements so that it installs properly
move gptq install around to work with github cicd
* gptq doesn't play well with sample packing
* address pr feedback
* remove torch install for now
* set quantization_config from model config
* Fix the implementation for getting quant config from model config
* recast loralayer, norm, lmhead + embed token weights per original qlora
* try again for the fix
* refactor torch dtype picking
* linter fixes
* missing import for LoraLayer
* fix install for tests now that peft is involved
* flash attn pip
* add packaging
* add packaging to apt get
* install flash attn in dockerfile
* remove unused whls
* add wheel
* clean up pr
fix packaging requirement for ci
upgrade pip for ci
skip build isolation for requiremnents to get flash-attn working
install flash-attn seperately
* install wheel for ci
* no flash-attn for basic cicd
* install flash-attn as pip extras
---------
Co-authored-by: Ubuntu <mgh@mgh-vm.wsyvwcia0jxedeyrchqg425tpb.ax.internal.cloudapp.net>
Co-authored-by: mhenrichsen <some_email@hey.com>
Co-authored-by: Mads Henrichsen <mads@BrbartiendeMads.lan>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Fix(template): Inform to place stack trace to Issue
* Update following suggestions
Co-authored-by: Wing Lian <wing.lian@gmail.com>
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
