* 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
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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>
* split sdp attn into its own patch
* sync xformers patch to follow shared format and be diffable
* update flash-attn patch for 70B/GQA and inference using helper from flash-attn tests
* speed up flash-attn inference
* fix patch to check position ids and don't use multipack for evals
* copy LlamaModel.forward and LlamaDecoderLayer.forward into monkeypatch
* update forwards so we only calculate cu_seqlens once
* enable eval dataloader using multipack again
* fix the patch to work properly and work with FSDP
---------
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>
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
* fix attetion mask with packing
* set position ids and use block diagonal attn mask
* fix expand mask for multiple batch items, make sure we pad position_ids
* don't move masks to cpu
* use multi pack dataloader w random sampler
* add position_ids back
* more fixes for dataloader integration
* est total tokens, fix field loop
* more fixes, position_ids seems broken
* more fixes for sample packing
* use distributed sampler, avoid accelerate prepare
* use accelerator prepare for dataloader
* fix for position_ids w packing
* Update src/axolotl/utils/dataloader.py
* validation for sample packing and doc
* more fixes for 4k and optimizations
* optimized expand mask fn
* better handling of variance in multipack dataloader length and trainer hanging when it runs out of data
* fix rounding of len of batches to int
* better handling so that all devices have the same dataloader len
* fix step calc for packing
* pass sample packing efficiency to training args
* add a test for the mask expansion for sequence packing
* only process eval dataset for packing if not None
* don't split batches when packing
* weighted CE losses
* weighted CEL fixes
* limit packing to sequences of max seq len
* seq_len_multiple for packing
* make sure the chunk size is an int
* sample_packing_seq_len_multiplier config
* use cumulative seq len with var len flash attn v2 w packing
* properly calculate max len
* fix flash-attn, xformers, packing, support chatml
* fix chatml system prompt for openorca, legacy tokenizer opts
* add chatml
* add unit tests for cum seq lens, add ability to build cu_seq_lens from positional ids, fix prompt test
* fix test and pylint checks
* more packing and dataset optimizations and fixes
* filter w multiple cpus
* more fixes and optimizations
* fixes and go back to distributed sampler since batch sampler won't work
* fix counts by accounting for num devices
* fix steps calculation
* previous accelerate is still most performant
* add numba to requirements.
* use custom distributed checks
* fix sampler to prevent overfit w new epochs
* let's not cleanup the cached datasets
* calculate cum seq lens with pos_ids instead of mask, simplify packing params, fix distributed barrier
* speed optimizations and set accelerate fsdp env vars
* optimize dataset concatenation?
* more optimizations for dataset handling
* fix import for annotation
* manual pre-commit fixes
* another sum optimization and bug fix for calc steps
* fix packing estimations
* fix formatting
* pylint problems
* add back flash attention branch for handling unpacked sequences seperately
* Address PR feedback
* add optional sample packing config params to readme
