* build base images for torch 2.7.1
* fix: update base docker to use torch 2.7.1
* fix: update doc for main base to use 2.7.1
* make sure to install fa2 in base uv too
* use no build isolation for uv+flashattn
* install psutil also for fa2
* longer timeout for flash attn build
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* add uv tooling for e2e gpu tests
* fixes from PR feedback
* simplify check
* fix env var
* make sure to use uv for other install
* use raw_dockerfile_image
* Fix import
* fix args to experimental dockerfile image call
* use updated modal versions
* bump hf deps
* upgrade liger-kernel too
* install cce from fork for transformers fix
* fix reference to vocab size in gemma3 patch
* use padding_idx instead of pad_token_id
* remove fixed gemma3 patch
* use updated cce fork
* fix local mllama cce patches w docstring
* add test for multipack with trainer setup and fix trainer for trainer refactor upstream
* bump modal version
* guard for iterable datasetS
* mllama model arch layout changed in latest transformers
* fix batch sampler with drop_last
* fix: address upstream vlm changes for lora
* fix: update references to old lora target path
* fix: remove mllama fa2 patch due to upstream fix
* fix: lora kernel patch path for multimodal models
* fix: removed mllama from quarto
* run test for came optim on 2.6.0+
* fix fsdp2 patch and remove deprecated patch
* make sure to set sequence_parallel_degree for grpo
* Add SP test for GRPO
* add sp to grpo config for trainer
* use reward_funcs as kwarg to grpo trainer
* fix the comprehension for reward funcs
* reward funcs already passed in as args
* init sp_group right before training
* fix check for adding models to SP context
* make sure to pass args to super
* upgrade deepspeed
* use updated trl and add reasoning flags for vllm
* patch the worker
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* chore: update pre-commit hooks
* trigger linter when pre commit hooks are updated
* fix type checks from upgraded pre-commit
---------
Co-authored-by: djsaunde <1245942+djsaunde@users.noreply.github.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* update doc and skip brittle grpo test
* fix the path to run the multigpu tests
* increase timeout, use LOC instead of NVL
* typo
* use hf cache from s3 backed cloudfront
* mark grpo as flaky test dues to vllm start
* lean mistral ft tests, remove e2e torch 2.4.1 test
* make sure to pass save_only_model for RL
* more tests to make ci leaner, add cleanup to modal ci
* fix module for import in e2e tests
* use mp spawn to prevent deadlocks with packing
* make sure cleanup shell script is executable when cloned out
* repop cache
* pre-cache as a step
* fix the name
* add reason for pytest skipif
* restore pytorch matrix
* remove max-parallel now that we've optimized this a bit
* Add: SFTPlugin with llmcompressor
* Update: review comments!
* Add:llmcompressor instalable
* pre commit hooks
* Use: warning over warn
* Revert: TODO's
* Update llmcompressor version to latest
* Apply suggestions from @markurtz
Co-authored-by: Mark Kurtz <mark.j.kurtz@gmail.com>
* Address review comments from @markurtz
* Add: llcompressor installable
* Rename: sft.yaml to sparse-finetuning.yaml
* Use: absolute import
* Update model config
* Move: LLMCompressorPlugin into it's own submodule
* Add: `llm_compressor` integration documentation
* Rebase and updates!
* Tests, Style, Updates
* Add: .qmd file
* Address Review Comments:
* deleted redundant docs/llm_compressor.qmd
* incorporated feedback in integration README.md
* added llmcompressor integration to docs/custom_integrations.qmd
Signed-off-by: Rahul Tuli <rtuli@redhat.com>
* Add: line about further optimizations using llmcompressor
Signed-off-by: Rahul Tuli <rtuli@redhat.com>
* Apply patch from @winglian
Signed-off-by: Rahul Tuli <rtuli@redhat.com>
* Fix: Test
Signed-off-by: Rahul Tuli <rtuli@redhat.com>
* additional fixes for docker and saving compressed
* split llmcompressor from vllm checks
* Reset session between tests
Signed-off-by: Rahul Tuli <rtuli@redhat.com>
* move decorator to test method instead of class
* make sure to reset the session after each test
* move import of llmcompressor to reset session inside test
---------
Signed-off-by: Rahul Tuli <rtuli@redhat.com>
Co-authored-by: Mark Kurtz <mark.j.kurtz@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* update trl to 0.17.0
* grpo + vllm no longer supported with 2.5.1 due to vllm constraints
* disable VLLM_USE_V1 for ci
* imporve handle killing off of multiprocessing vllm service
* debug why this doesn't run in CI
* increase vllm wait time
* increase timeout to 5min
* upgrade to vllm 0.8.4
* dump out the vllm log for debugging
* use debug logging
* increase vllm start timeout
* use NVL instead
* disable torch compile cache
* revert some commented checks now that grpo tests are fixed
* increase vllm timeoout back to 5min
* builds for torch==2.7.0
* use xformers==0.0.29.post3
* no vllm support with torch 2.7
* update default, fix conditional
* no xformers for 270
* no vllm on 2.7.0 for multigpu test too
* remove deprecated verbose arg from scheduler
* 2.7.0 tests on cpu
* fixes for delinearization, and make qlora work with fsdp2
* Add back mistakenly removed lm_eval
* typo [skip ci]
* patch evals for torch.compile + fsdp2
* also check torch_compile w fsdp2
* lots of fixes for flex attn with llama4
* fix patch check and patch llama4 too
* attempt to make the patches stick
* use transformers 4.51.2
* update configs and README for llama4
* remove torch.compile for CI test
* cleanup any existing singletons
* set singleton cache to None instead of deleting
* use importlib reload with monkeypatch
* don't worry about transformers version, mark inputs with grads, fix regex
* make sure embeds aren't on cpu
* logging and mem improvements
* vllm version and add to docker, make sure to save processor on conversion
* fix ambiguous tensor bool check
* fix vllm to not use v1, upgrade hf transformers
* fix tests
* make flex_attn_compile_kwargs configurable, since this depends on model params
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
* llama4 support
* add xet support [skip ci]
* be flexible on transformers version and skip test on version
* don't use deepspeed for the fix_untrained_tokens test
* reordering to trigger torch 2.6.0 tests first
* slightly smaller train set
* use 4.51.0 for now
* remove stray print, add llama4 chat template to schema, bump peft to 0.15.1
* patches to make llama4 performant
* add preliminary fp8 support
* make torch 2.6.0 the default image
* fix tests against upstream main
* fix attribute access
* use fixture dataset
* fix dataset load
* correct the fixtures + tests
* more fixtures
* add accidentally removed shakespeare fixture
* fix conversion from unittest to pytest class
* nightly main ci caches
* build 12.6.3 cuda base image
* override for fix from huggingface/transformers#37162
* address PR feedback
* add grpo scale_rewards config for trl#3135
* options to connect to vllm server directly w grpo trl#3094
* temperature support trl#3029
* sampling/generation kwargs for grpo trl#2989
* make vllm_enable_prefix_caching a config param trl#2900
* grpo multi-step optimizeations trl#2899
* remove overrides for grpo trainer
* bump trl to 0.16.0
* add cli to start vllm-serve via trl
* call the python module directly
* update to use vllm with 2.6.0 too now and call trl vllm serve from module
* vllm 0.8.1
* use python3
* use sys.executable
* remove context and wait for start
* fixes to make it actually work
* fixes so the grpo tests pass with new vllm paradigm
* explicit host/port and check in start vllm
* make sure that vllm doesn't hang by setting quiet so outouts go to dev null
* also bump bnb to latest release
* add option for wait from cli and nccl debugging for ci
* grpo + vllm test on separate devices for now
* make sure grpo + vllm tests runs single worker since pynccl comms would conflict
* fix cli
* remove wait and add caching for argilla dataset
* refactoring configs
* chore: lint
* add vllm config
* fixup vllm grpo args
* fix one more incorrect schema/config path
* fix another vlllm reference and increase timeout
* make the tests run a bit faster
* change mbsz back so it is correct for grpo
* another change mbsz back so it is correct for grpo
* fixing cli args
* nits
* adding docs
* docs
* include tensor parallel size for vllm in pydantic schema
* moving start_vllm, more docs
* limit output len for grpo vllm
* vllm enable_prefix_caching isn't a bool cli arg
* fix env ordering in tests and also use pid check when looking for vllm
---------
Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
* fix: update chat_template
* fix: handle gemma3 showing a lot of no content for turn 0
* fix: remove unknown config from examples
* fix: test
* fix: temporary disable gemma2 test
* fix: stop overwriting config.text_config unnecessarily
* fix: handling of set cache to the text_config section
* feat: add liger gemma support and bump liger to 0.5.5
* fix: add double use_cache setting
* fix: add support for final_logit_softcap in CCE for gemma2/3
* fix: set use_cache before model load
* feat: add missing layernorm override
* fix: handle gemma3 rmsnorm
* fix: use wrapper to pass dim as hidden_size
* fix: change dim to positional
* fix: patch with wrong mlp
* chore: refactor use_cache handling
* fix import issues
* fix tests.e2e.utils import
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* hf offline decorator for tests to workaround rate limits
* fail quicker so we can see logs
* try new cache name
* limit files downloaded
* phi mini predownload
* offline decorator for phi tokenizer
* handle meta llama 8b offline too
* make sure to return fixtures if they are wrapped too
* more fixes
* more things offline
* more offline things
* fix the env var
* fix the model name
* handle gemma also
* force reload of modules to recheck offline status
* prefetch mistral too
* use reset_sessions so hub picks up offline mode
* more fixes
* rename so it doesn't seem like a context manager
* fix backoff
* switch out tinyshakespeare dataset since it runs a py script to fetch data and doesn't work offline
* include additional dataset
* more fixes
* more fixes
* replace tiny shakespeaere dataset
* skip some tests for now
* use more robust check using snapshot download to determine if a dataset name is on the hub
* typo for skip reason
* use local_files_only
* more fixtures
* remove local only
* use tiny shakespeare as pretrain dataset and streaming can't be offline even if precached
* make sure fixtures aren't offline
improve the offline reset
try bumping version of datasets
reorder reloading and setting
prime a new cache
run the tests now with fresh cache
try with a static cache
* now run all the ci again with hopefully a correct cache
* skip wonky tests for now
* skip wonky tests for now
* handle offline mode for model card creation
* add 12.8.1 cuda to the base matrix
* use nightly
* bump deepspeed and set no binary
* deepspeed binary fixes hopefully
* install deepspeed by itself
* multiline fix
* make sure ninja is installed
* try with reversion of packaging/setuptools/wheel install
* use license instead of license-file
* try rolling back packaging and setuptools versions
* comment out license for validation for now
* make sure packaging version is consistent
* more parity across tests and docker images for packaging/setuptools
* refactor trainer to prevent circular dependencies later
fix loader default
KD dataset loading and KD with logprobs
filter bad rows
make batch smaller
handle padding/collation for KD datasets
make it work
flipped the slice
cross entropy loss coefficient during KD
make sure to multiply against the correct loss
chore: lint
triton wip
no where support
v2 trial
no torch.exp inside triton kernel
no log etc
no torch.tensor
v3
fix kwarg
don't use triton for now
better rescaling for temperatures
hash for temperature too
use kd_alpha in the correct loss method
fix kd loss so it's causal (fixes repeating tokens)
var naming and add todo
chore: lint
refactor so we can easily add new loss functions
add license block
remove references to triton kd for now
handle token/logprob shifting
support for custom trainer classes from plugins
refactor kd chat template loader
move more things to kd plugin
remove moved class from import
make plugin setup concise
increase logging around loading plugins
add copyrights
remove duplicate code
more info on preprocess for kd and fix import
be a bit pickier about loading dynamic prompt strategies
kd sample packing
make loss torch script compat
support streaming for processing sft datasts?
improve iterable support
ensure that batch vs single is done properly
tweak check for batched prompt data
reward can use same batch check
fix reward trainer calls for tokenization
improve check for batched
reward model doesn't work well with batched
add kd trainer e2e test
linting
rename test files so it gets picked up
make the kd e2e fit in vram for ci and add lora version
set lora_dropout explicitly
lower lr
make sure to set tokenizer from l3 70b and save safetensors
make sure to use the correct tokenizer
fix adapter model check
make sure to use tensorboard to capture loss for checks
chore: lint
chore: lint
improve logprob masking and shift in trainer
more fixes
try tests for kd on l40s
don't shift student logits for kd
no batching for kd chat templates
make sure to truncate logprobs if there are more than top_k
change up logic so we always truncate to top_k
use iter instead of tuple
fix finding the top-k rather than assuming first position has the correct val
apply z-score scaling to kd
kd loss needs to be calculated in full precision
Always re-normalize teacher distribution
various fixes
* support for configurable top-k/softmax ordering
* add attribute check for filter rows and lint
* fix logic
* handle none case for conversion to int
* fix student logit off by one
* set kd_temp to 1.0 for test loss
* address PR feedback
