* fix: drop long seq even if not sample packing
* fix: logging import
* fix: cfg passed being none
* fix: try to fix logging
* fix: refactor call to not use accelerate log
* fix: try to fix circular import issue
* fix: don't drop when skip prepare
* chore: remove duplicate line
* fix: update warning to mention that sequences will be trimmed
* fix: do not drop seq if input_ids don't exist
* fix: increase RM unittest sequence length to reduce trim warnings
* fix: solve conflicts
* fix: default min_seq_len in case of None
* 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
* misc fixes for garbage collection and L40S w NCCL P2P
* patch bnb fix for triton check
* chore: lint
* change up import
* try patching differently
* remove patch for bnb fix for now
* more verbose checks and tweak train loss threshold
* native support for modal cloud from CLI
* do lm_eval in cloud too
* Fix the sub call to lm-eval
* lm_eval option to not post eval, and append not extend
* cache bust when using branch, grab sha of latest image tag, update lm-eval dep
* allow minimal yaml for lm eval
* include modal in requirements
* update link in README to include utm
* pr feedback
* use chat template
* revision support
* apply chat template as arg
* add wandb name support, allow explicit a100-40gb
* cloud is optional
* handle accidental setting of tasks with a single task str
* document the modal cloud yaml for clarity [skip ci]
* cli docs
* support spawn vs remote for lm-eval
* Add support for additional docker commands in modal image build
* cloud config shouldn't be a dir
* Update README.md
Co-authored-by: Charles Frye <cfrye59@gmail.com>
* fix annotation args
---------
Co-authored-by: Charles Frye <cfrye59@gmail.com>
* current
not clean working version
move torch trainer to do_cli
update code with config changes and clean up
edit config
cleanup
add run name to trainer
* address comments
* use axolotl train in multigpu tests and add ray tests for multi-gpu
* accelerate uses underscores for main_process_port arg
* chore: lint
* fix order of accelerate args
* include ray train in docker images
* current
not clean working version
move torch trainer to do_cli
update code with config changes and clean up
edit config
cleanup
add run name to trainer
* address comments
* use axolotl train in multigpu tests and add ray tests for multi-gpu
* accelerate uses underscores for main_process_port arg
* chore: lint
* fix order of accelerate args
* include ray train in docker images
* fix bf16 resolution behavior
* move dtype logic
* x
Signed-off-by: SumanthRH <sumanthrh@anyscale.com>
* rename
Signed-off-by: SumanthRH <sumanthrh@anyscale.com>
* add to sidebar
Signed-off-by: SumanthRH <sumanthrh@anyscale.com>
* Apply suggestions from code review
Co-authored-by: Eric Tang <46737979+erictang000@users.noreply.github.com>
* Update docs/ray-integration.qmd
Co-authored-by: Eric Tang <46737979+erictang000@users.noreply.github.com>
* pre-commit fixes
Signed-off-by: SumanthRH <sumanthrh@anyscale.com>
* use output_dir instead of hardcoded saves path
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* bugfix storage dir
* change type\ for resources_per_worker
---------
Signed-off-by: SumanthRH <sumanthrh@anyscale.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: SumanthRH <sumanthrh@anyscale.com>
Co-authored-by: Sumanth R Hegde <39546518+SumanthRH@users.noreply.github.com>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* support for custom lr groups for non-embedding modules
invert name check for group modules
include lr_groups in training args
additional conditional for creating optimizer
fix regular params as w weight decay
fix lookup and add docs
* address pr feedback
* fix for pretrain with packing
* fix model name and loss expected
* make sure to check with micro batch size for pretraining
* change loss threshholds based on parametrization
* make tests smaller for CI
* fix pretrain packing
* fix pretrain packing test
* address pr feedback
