Unable to use FLCE with FSDP+PEFT+embeddings layers

[winglian]

🐛 Describe the bug

when trying to train both LoRA layers on the base model and also set modules_to_save on the lora config which makes the embeddings layers trainable (my assumption is it also applies a LoRA linear adapter to the nn.Embeddings), results in the error:

[rank0]:   File "/home/ubuntu/miniconda3/envs/pytorch/lib/python3.11/site-packages/liger_kernel/transformers/fused_linear_cross_entropy.py", line 13, in forward
[rank0]:     return LigerFusedLinearCrossEntropyFunction.apply(
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/ubuntu/miniconda3/envs/pytorch/lib/python3.11/site-packages/torch/autograd/function.py", line 598, in apply
[rank0]:     return super().apply(*args, **kwargs)  # type: ignore[misc]
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: RuntimeError: Output 0 of ViewBackward0 is a view and its base or another view of its base has been modified inplace. This view is the output of a function that returns multiple views. Such functions do not allow the output views to be modified inplace. You should replace the inplace operation by an out-of-place one.

Reproduce

base_model: NousResearch/Meta-Llama-3.1-8B
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
    # liger_rope: true
    # liger_rms_norm: true
    # liger_swiglu: true
liger_fused_linear_cross_entropy: true

datasets:
  - path: tatsu-lab/alpaca
    type: alpaca
dataset_prepared_path:
val_set_size: 0.005
output_dir: ./outputs/lora-out

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: l8b-peft-dora-rslora
wandb_entity: 
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
special_tokens:
   pad_token: <|end_of_text|>
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD

Versions

No response

[ByronHsu]

Thank you @winglian for raising the issue! Looks like FLCE fails at torch autograd when we make embedding + peft layer trainable together. We are happy to take a look! Would be nice if you can provide complete reproducible script. Thanks!

[kostum123]

Thank you @winglian for raising the issue! Looks like FLCE fails at torch autograd when we make embedding + peft layer trainable together. We are happy to take a look! Would be nice if you can provide complete reproducible script. Thanks!

Can we create a list of what is working and what is not, with the corresponding dates, and keep it updated? For example, the DPO training labels issue, the PEFT+ embedding training issue, etc. Also, indicate which ones are going to be fixed and which ones are not.