Max Sequence Length

[ujjawalmadan]

Had a question about the max_seq_length hyper parameter.

I just started training and set the config for SFT to be the below:

# Model arguments
model_name_or_path: mistralai/Mistral-7B-v0.1
model_revision: main
torch_dtype: bfloat16
use_flash_attention_2: true

# Data training arguments
dataset_mixer:
  HuggingFaceH4/ultrachat_200k: 1.0
dataset_splits:
- train_sft
- test_sft
preprocessing_num_workers: 12

# SFT trainer config
bf16: true
do_eval: true
evaluation_strategy: epoch
gradient_accumulation_steps: 32
gradient_checkpointing: true
hub_model_id: custom_1_0
hub_strategy: every_save
learning_rate: 2.0e-05
log_level: info
logging_steps: 5  
logging_strategy: steps
lr_scheduler_type: cosine
max_seq_length: 8192
max_steps: -1
num_train_epochs: 1
output_dir: data/scout_version_1_0
overwrite_output_dir: true
per_device_eval_batch_size: 8
per_device_train_batch_size: 2
push_to_hub: true
remove_unused_columns: true
report_to:
- tensorboard
save_strategy: "no"
save_total_limit: null
seed: 42
tf32: true

However, I got this warning below:

[WARNING|tokenization_utils_base.py:3831] 2023-11-15 19:02:06,747 >> Token indices sequence length is longer than the specified maximum sequence length for this model (2576 > 2048). Running this sequence through the model will result in indexingerrors

This was also from the logs:

2023-11-15 19:01:23 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1 distributed training: True, 16-bits training: False
2023-11-15 19:01:23 - INFO - __main__ - Model parameters ModelArguments(base_model_revision=None, model_name_or_path='mistralai/Mistral-7B-v0.1', model_revision='main', model_code_revision=None, torch_dtype='bfloat16', trust_remote_code=False, use_flash_attention_2=True, use_peft=False, lora_r=16, lora_alpha=32, lora_dropout=0.05, lora_target_modules=None, lora_modules_to_save=None, load_in_8bit=False, load_in_4bit=False, bnb_4bit_quant_type='nf4', use_bnb_nested_quant=False)
2023-11-15 19:01:23 - INFO - __main__ - Data parameters DataArguments(chat_template=None, dataset_mixer={'HuggingFaceH4/ultrachat_200k': 1.0}, dataset_splits=['train_sft', 'test_sft'], max_train_samples=None, max_eval_samples=None, preprocessing_num_workers=12, truncation_side=None)
2023-11-15 19:01:23 - INFO - __main__ - Training/evaluation parameters SFTConfig(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=True,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=True,
do_predict=False,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=epoch,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=32,
gradient_checkpointing=True,
gradient_checkpointing_kwargs=None,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=scout_version_1_0,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=info,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=data/scout_version_1_0/runs/Nov15_19-01-23_ip-10-10-9-47.ec2.internal,
logging_first_step=True,
logging_nan_inf_filter=True,
logging_steps=5,
logging_strategy=steps,
lr_scheduler_type=cosine,
max_grad_norm=1.0,
max_seq_length=8192,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=1,
optim=adamw_torch,
optim_args=None,
output_dir=data/scout_version_1_0,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=2,
prediction_loss_only=False,
push_to_hub=True,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard'],
resume_from_checkpoint=None,
run_name=data/scout_version_1_0,
save_on_each_node=False,
save_safetensors=True,
save_steps=500,
save_strategy=no,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
split_batches=False,
tf32=True,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)

Any thoughts as to what might be happening? Ran into the same thing when trying Llama 7B-hf which has a default token length of 4096.