Open manishiitg opened 5 months ago
@manishiitg is that the correct config/yaml you submitted? It says mistral, but the title of this says qwen moe.
sorry this is the correct config
base_model: Qwen/Qwen1.5-MoE-A2.7B
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: manishiitg/aditi-syn-train-small-v3
type: completion
# 25 has only sythentic data, and has judge removed data
hub_model_id: manishiitg/open-aditi-chat-hi-1.25-moe
hf_use_auth_token: true
wandb_project: open-aditi-chat-hi-1.25--moe
dataset_prepared_path: manishiitg
push_dataset_to_hub: manishiitg
val_set_size: .1
output_dir: /sky-notebook/manishiitg/open-aditi-chat-hi-1.25--moe
sequence_len: 2048 # supports up to 32k
sample_packing: false
pad_to_sequence_len: false
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 3
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: true ## manage check point resume from here
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 20 ## increase based on your dataset
save_strategy: steps
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
Would you be able to update the original issue with the correct config? Secondly, have you tried the docs/nccl
to see if it helps?
the issue I'm seeing is during the backwards step in accelerator
+1 keeps happening for Mixtral-8x22B
@NanoCode012 updated the original yaml posted
Please check that this issue hasn't been reported before.
Expected Behavior
should work
Current behaviour
getting error
Steps to reproduce
docker run --gpus all \ --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \ -v ~/sky_workdir:/sky_workdir \ -v /root/.cache:/root/.cache \ -v /sky-notebook:/sky-notebook \ -e "WANDB_API_KEY=1d2a6c1df7576a38308685e2d1a26dbb5cdb53ac" \ winglian/axolotl:main-20240408-py3.10-cu118-2.1.2 \ accelerate launch -m axolotl.cli.train /sky_workdir/hi-qwen-moe.yaml --deepspeed /sky_workdir/zero2.json
Config yaml
Possible solution
No response
Which Operating Systems are you using?
Python Version
main
axolotl branch-commit
main
Acknowledgements