Using two 8xH100 nodes to train. encounter error bf16 requested, but AMP is not supported on this GPU. Requires Ampere series or above.

[michaellin99999]

Please check that this issue hasn't been reported before.

• I searched previous Bug Reports didn't find any similar reports.

Expected Behavior

This issue should not occur, as H100 definitely supports bf16.

Current behaviour

outputs error: Value error, bf16 requested, but AMP is not supported on this GPU. Requires Ampere series or above.

Steps to reproduce

run the script https://github.com/axolotl-ai-cloud/axolotl/blob/main/docs/multi-node.qmd

Config yaml

base_model: openlm-research/open_llama_3b_v2 [0/3]model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
push_dataset_to_hub:
datasets:
- path: teknium/GPT4-LLM-Cleaned
type: alpaca
dataset_prepared_path:
val_set_size: 0.02
adapter: lora
lora_model_dir:
sequence_len: 1024
sample_packing: true
lora_r: 8
lora_alpha: 16
lora_dropout: 0.0
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/lora-out
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint::

lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/lora-out
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: false
fp16: true
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
gptq_groupsize:
s2_attention:
gptq_model_v1:
warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_offload_params: true
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"

Possible solution

no idea what is causing this issue.

Which Operating Systems are you using?

• Linux
• macOS
• Windows

Python Version

3.11.9

axolotl branch-commit

none

Acknowledgements

• My issue title is concise, descriptive, and in title casing.
• I have searched the existing issues to make sure this bug has not been reported yet.
• I am using the latest version of axolotl.
• I have provided enough information for the maintainers to reproduce and diagnose the issue.

[michaellin99999]

the same settings used in Regular training, works.

[michaellin99999]

settings in accelerate:
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: MULTI_GPU
downcast_bf16: 'no'
enable_cpu_affinity: false
gpu_ids: all
machine_rank: 0
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

[michaellin99999]

this is the snippet for multinode slave settings:
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: MULTI_GPU
downcast_bf16: 'no'
enable_cpu_affinity: false
gpu_ids: all
machine_rank: 1
main_process_ip: 192.168.108.22
main_process_port: 5000
main_training_function: main
mixed_precision: fp16
num_machines: 2
num_processes: 16
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

[winglian]

I recommend not using the accelerate config and removing that file. axolotl handles much of that automatically. See https://axolotlai.substack.com/p/fine-tuning-llama-31b-waxolotl-on

[michaellin99999]

ok, is it the accelerate config causing the issue?

[ehartford]

Often, it is

[michaellin99999]

we tried that still same issue, also went through https://axolotlai.substack.com/p/fine-tuning-llama-31b-waxolotl-on this requires axolot cloud, Im using my own two 8xh100 clusters. any scripts that work?