* support for auto_find_batch_size when packing
* make sure to return data from validation
* make sure to return data from validation
* actually expose multipack_real_batches in the config
* calculate gathered efficiency in sampler
* tweak to fix auto find and use actual sampler len for multipack
* uncomment
* use args for bsz when not available from auto find
* Update supported models for Liger Kernel
Add Mistral LCE, Gemma LCE, Gemma 2 without LCE (softcapping is not yet implemented for Gemma in Liger Kernel LCE forward), Phi3 without LCE
* move import to their appropriate conditions
* Integrate Phi3 LCE support
https://github.com/linkedin/Liger-Kernel/pull/103/
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
* efficiently save very large llms when using FSDP
* fix parsing and index of sharded chunks
* only save fsdp on main process
* debugging for rename
* save sharded state dict
* remove unused new param
* get state dict directly
* tweak acc merge fsdp to shard the weight files
* sharded_state_dict alongside save_safetensors seems to hang on checkpoint save
* update sklearn versrion, torch compile env vars, don't worry about failure on preprocess load model
* There is already a condition check within the function. This outer one is not necessary
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
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Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* refactor one_cycle lr scheduler so it's reusable in more situations
* fix validation for lr_scheduler
* default to cosine anneal strategy
* one cycle lr exepects cos
* fix 405b with lower cpu ram requirements
* make sure to use doouble quant and only skip output embeddings
* set model attributes
* more fixes for sharded fsdp loading
* update the base model in example to use pre-quantized nf4-bf16 weights
* upstream fixes for qlora+fsdp
* Add flexible configuration options for chat dataset training
- Introduce roles_to_train parameter to set training labels by role
- Add train_on_eos option to configure training on end-of-sequence tokens
- Implement per-message training configuration in dataset
- Allow fine-grained control over training specific portions of messages
- Add message_field_training and message_field_training_detail settings
- Implement mapping between dataset character offsets and tokenized prompt
- Enhance test suite to cover new functionality
* Fix missing field inits, things weren't working from yaml.
* Add flexible configuration options for chat dataset training
- Introduce roles_to_train parameter to set training labels by role
- Add train_on_eos option to configure training on end-of-sequence tokens
- Implement per-message training configuration in dataset
- Allow fine-grained control over training specific portions of messages
- Add message_field_training and message_field_training_detail settings
- Implement mapping between dataset character offsets and tokenized prompt
- Enhance test suite to cover new functionality
* Fix missing field inits, things weren't working from yaml.
* chore: lint
* Revert test repo back to NousResearch after opening PR to fix the tokenizer_config.json.
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
* various batch of fixes
* more tweaks
* fix autoawq requirement for torch flexibility
* simplify conditionals
* multi-node fixes wip
* bump transformers and include 405b qlora+fsdp yaml
* swaps to use newer sample packing for mistral
* fix multipack patch test
* patch the common fa utils
* update for refactor of flash attn unpad
* remove un-needed drop attn mask for mistral
* bump transformers to main to pick up latest mistral fix for 12b and refactor of fa2
* update test
* Implementing a basic chat_template strategy for DPO datasets
This mimics the sft chat_template strategy such that users can:
* Specify the messages field
* Specify the per message role and content fields
* speicfy the chosen and rejected fields
* Let the tokenizer construct the raw prompt
* Ensure the chosen and rejected fields don't have any prefix tokens
* Adding additional dpo chat template unittests
* Rename test class
