Recent RunPod Axolotl error

[drummerv]

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

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

Expected Behavior

I ran Axolotl around two days ago and it worked fine. 8xH100 SXM using RunPod's Axolotl Jupyter template.

Current behaviour

When I ran the same config today, it gave me this error:

RuntimeErrorRuntimeErrorRuntimeError: : : CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling cublasCreate(handle)CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling cublasCreate(handle)CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling cublasCreate(handle)

Steps to reproduce

1. Use RunPod's Axolotl Jupyter template
2. Use 8xH100 SXM in Secure or Community
3. Run training
4. Wait for it to load the model
5. It doesn't

Config yaml

base_model: ChaoticNeutrals/Poppy_Porpoise-0.72-L3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false
sequence_len: 8192
bf16: auto
fp16:
tf32: false
flash_attention: true
special_tokens:
  bos_token: <|begin_of_text|>
  pad_token: <|end_of_text|>
  eos_token: <|end_of_text|>

# Data
datasets:
  - path: TheDrummer/siayn-v6
    type: customllama3 # src/axolotl/prompt_strategies
warmup_steps: 30

# save_safetensors: true

# WandB
wandb_project: llama-3some
wandb_entity: 

# Iterations
num_epochs: 2

# Evaluation
val_set_size: 0.0125
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
eval_sample_packing: false
eval_batch_size: 1

# LoRA
output_dir: ./Llama-3some-8B-v2-Workspace
adapter: lora
lora_model_dir:
lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:

# Sampling
sample_packing: true
pad_to_sequence_len: true

# Batching
gradient_accumulation_steps: 1
micro_batch_size: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
   use_reentrant: true

# Optimizer
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

# Misc
train_on_inputs: false
group_by_length: false
early_stopping_patience:
local_rank:
logging_steps: 1
xformers_attention:
debug:
weight_decay: 0
fsdp:
fsdp_config:

# Checkpoints
resume_from_checkpoint:
saves_per_epoch: 2

Possible solution

Does the runpod / docker template use the latest commit? We can narrow it down to the last 1 to 2 days.

Which Operating Systems are you using?

• Linux
• macOS
• Windows

Python Version

main-latest

axolotl branch-commit

main-latest

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.

[drummerv]

Tried it again today on a 8x H100 SXM

Traceback (most recent call last):
  File "/root/miniconda3/envs/py3.10/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/root/miniconda3/envs/py3.10/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/workspace/axolotl/src/axolotl/cli/train.py", line 59, in <module>
    fire.Fire(do_cli)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 143, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 477, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 693, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/workspace/axolotl/src/axolotl/cli/train.py", line 35, in do_cli
    return do_train(parsed_cfg, parsed_cli_args)
  File "/workspace/axolotl/src/axolotl/cli/train.py", line 53, in do_train
    dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
  File "/workspace/axolotl/src/axolotl/cli/__init__.py", line 403, in load_datasets
    train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
  File "/workspace/axolotl/src/axolotl/utils/data/sft.py", line 57, in prepare_dataset
    with zero_first(is_main_process()):
  File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 135, in __enter__
    return next(self.gen)
  File "/workspace/axolotl/src/axolotl/utils/distributed.py", line 68, in zero_first
    barrier()
  File "/workspace/axolotl/src/axolotl/utils/distributed.py", line 34, in barrier
    dist.barrier()
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 47, in wrapper
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 3696, in barrier
    work = default_pg.barrier(opts=opts)
RuntimeError: CUDA error: uncorrectable ECC error encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

[drummerv]

Tried it on 8x H100 PCIE

Found it unusual that it got stuck on this part:

[2024-05-08 14:11:28,264] [INFO] [axolotl.load_tokenized_prepared_datasets:410] [PID:3641] [RANK:0] merging datasets

Been waiting for 10 minutes. I'm killing it.

[drummerv]

A different error for 1xH100 SXM

  warnings.warn(
/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization
  warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
cuBLAS API failed with status 15
A: torch.Size([49152, 4096]), B: torch.Size([4096, 4096]), C: (49152, 4096); (lda, ldb, ldc): (c_int(1572864), c_int(131072), c_int(1572864)); (m, n, k): (c_int(49152), c_int(4096), c_int(4096))
Traceback (most recent call last):
  File "/root/miniconda3/envs/py3.10/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/root/miniconda3/envs/py3.10/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/workspace/axolotl/src/axolotl/cli/train.py", line 59, in <module>
    fire.Fire(do_cli)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 143, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 477, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/fire/core.py", line 693, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/workspace/axolotl/src/axolotl/cli/train.py", line 35, in do_cli
    return do_train(parsed_cfg, parsed_cli_args)
  File "/workspace/axolotl/src/axolotl/cli/train.py", line 55, in do_train
    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
  File "/workspace/axolotl/src/axolotl/train.py", line 170, in train
    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 1780, in train
    return inner_training_loop(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 2118, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 3036, in training_step
    loss = self.compute_loss(model, inputs)
  File "/workspace/axolotl/src/axolotl/core/trainer_builder.py", line 493, in compute_loss
    return super().compute_loss(model, inputs, return_outputs=return_outputs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/trainer.py", line 3059, in compute_loss
    outputs = model(**inputs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/utils/operations.py", line 822, in forward
    return model_forward(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/utils/operations.py", line 810, in __call__
    return convert_to_fp32(self.model_forward(*args, **kwargs))
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 16, in decorate_autocast
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/peft/peft_model.py", line 1395, in forward
    return self.base_model(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/peft/tuners/tuners_utils.py", line 179, in forward
    return self.model.forward(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/hooks.py", line 166, in new_forward
    output = module._old_forward(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 1196, in forward
    outputs = self.model(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/hooks.py", line 166, in new_forward
    output = module._old_forward(*args, **kwargs)
  File "/workspace/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py", line 809, in llama_model_forward
    layer_outputs = torch.utils.checkpoint.checkpoint(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/_compile.py", line 24, in inner
    return torch._dynamo.disable(fn, recursive)(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 328, in _fn
    return fn(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 17, in inner
    return fn(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/utils/checkpoint.py", line 451, in checkpoint
    return CheckpointFunction.apply(function, preserve, *args)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/autograd/function.py", line 539, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/utils/checkpoint.py", line 230, in forward
    outputs = run_function(*args)
  File "/workspace/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py", line 803, in custom_forward
    return module(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/hooks.py", line 166, in new_forward
    output = module._old_forward(*args, **kwargs)
  File "/workspace/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py", line 902, in forward
    hidden_states, self_attn_weights, present_key_value = self.self_attn(
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/hooks.py", line 166, in new_forward
    output = module._old_forward(*args, **kwargs)
  File "/workspace/axolotl/src/axolotl/monkeypatch/llama_attn_hijack_flash.py", line 417, in flashattn_forward
    query_states = self.q_proj(hidden_states)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/peft/tuners/lora/bnb.py", line 217, in forward
    result = self.base_layer(x, *args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/hooks.py", line 166, in new_forward
    output = module._old_forward(*args, **kwargs)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/bitsandbytes/nn/modules.py", line 687, in forward
    out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py", line 562, in matmul
    return MatMul8bitLt.apply(A, B, out, bias, state)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/autograd/function.py", line 539, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py", line 401, in forward
    out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
  File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/bitsandbytes/functional.py", line 1967, in igemmlt
    raise Exception('cublasLt ran into an error!')
Exception: cublasLt ran into an error!

[winglian]

@drummerv I don't know that the " RunPod's Axolotl Jupyter template" is the "official" correct template. This direct link should get you the correct image (https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz), although there is a bug w runpod using that link, so for now you can use this link: https://www.runpod.io/console/explore/v2ickqhz9s?ref=6i7fkpdz

[drummerv]

@winglian Fixed it. I just had to rollback to an older commit:

git checkout 132eb740f036eff0fa8b239ddaf0b7a359ed1732

[winglian]

thanks, there are quite a few changes since then! Just going to drop this here so I can remember to look through the changeset later: 132eb74...main

[zaporter]

@drummerv If it helps, I also just ran into this cuBLAS API failed with status 15 on a h100, and while going back to an old commit did not fix this (I tried probably 15 containers from around that time period), loading in 16 bit instead of 4 or 8 bit solved this issue (on main@HEAD).

This issue on alpaca-lora seems related: tloen/alpaca-lora#174