Support for GLM Models

[ashmalvayani]

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🔖 Feature description

Can you add the support for the recent GLM models that are released like GLM-4-9b-chat models in axolotl?

✔️ Solution

I have tried with the following script but this doesn't run sucessfully:

base_model: THUDM/glm-4-9b-chat
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

trust_remote_code: true

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
    - path: Datasets2.json
      ds_type: json
      type: alpaca
dataset_prepared_path: last_run_prepared/glm
val_set_size: 0
output_dir: ./outputs/glm-lora

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: ArabicGLM-8B
wandb_entity:
wandb_watch:
wandb_name: ArabicGLM-8B
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_bnb_8bit
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: 

warmup_steps: 95
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
save_total_limit: 5
save_steps: 200

debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<|endoftext|>"
  eos_token: "<|endoftext|>"

Error Trace:

  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__
    data = self._next_data()
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 674, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/mnt/beegfs/fahad.khan/axolotl/src/axolotl/monkeypatch/data/batch_dataset_fetcher.py", line 32, in fetch
    return self.collate_fn(data)
  File "/mnt/beegfs/fahad.khan/axolotl/src/axolotl/utils/collators.py", line 106, in __call__
    features = self.tokenizer.pad(
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 3355, in pad
    return BatchEncoding(batch_outputs, tensor_type=return_tensors)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 224, in __init__
    self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 775, in convert_to_tensors
    raise ValueError(
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`position_ids` in this case) have excessive nesting (inputs type `list` where ty
pe `int` is expected).
Traceback (most recent call last):
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 759, in convert_to_tensors
    tensor = as_tensor(value)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 721, in as_tensor
    return torch.tensor(value)
ValueError: expected sequence of length 773 at dim 1 (got 438) 

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/mnt/beegfs/fahad.khan/axolotl/src/axolotl/cli/train.py", line 59, in <module>
    fire.Fire(do_cli)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/fire/core.py", line 143, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/fire/core.py", line 477, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/fire/core.py", line 693, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/mnt/beegfs/fahad.khan/axolotl/src/axolotl/cli/train.py", line 35, in do_cli
    return do_train(parsed_cfg, parsed_cli_args)
  File "/mnt/beegfs/fahad.khan/axolotl/src/axolotl/cli/train.py", line 55, in do_train
    return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
  File "/mnt/beegfs/fahad.khan/axolotl/src/axolotl/train.py", line 163, in train
    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/trainer.py", line 1859, in train
    return inner_training_loop(
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/trainer.py", line 2165, in _inner_training_loop
    for step, inputs in enumerate(epoch_iterator):
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/accelerate/data_loader.py", line 452, in __iter__
    current_batch = next(dataloader_iter)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__
    data = self._next_data()
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 674, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/mnt/beegfs/fahad.khan/axolotl/src/axolotl/monkeypatch/data/batch_dataset_fetcher.py", line 32, in fetch
    return self.collate_fn(data)
  File "/mnt/beegfs/fahad.khan/axolotl/src/axolotl/utils/collators.py", line 106, in __call__
    features = self.tokenizer.pad(
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 3355, in pad
    return BatchEncoding(batch_outputs, tensor_type=return_tensors)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 224, in __init__
    self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
  File "/home/ashmal.vayani/anaconda3/envs/axolotl/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 775, in convert_to_tensors
    raise ValueError(
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`position_ids` in this case) have excessive nesting (inputs type `list` where type `int` is expected).

I think the main error lies in this line because I've set the padding to True by default:

ValueError: expected sequence of length 773 at dim 1 (got 438)

❓ Alternatives

📝 Additional Context

Acknowledgements

• My issue title is concise, descriptive, and in title casing.
• I have searched the existing issues to make sure this feature has not been requested yet.
• I have provided enough information for the maintainers to understand and evaluate this request.

[NanoCode012]

We do support GLM #2546 , for GLM4.5 see in progress #3030