An error occurred while converting the training model file：KeyError: 'hidden_irreps'

[why626726-cell]

Dear researchers,
I encountered an error. After completing the training of the MACE model, I wanted to convert the trained model file into a .pt file for use in LAMMPS. When I tried to perform the conversion using the ML-IAP version with the command:python -m mace.cli.create_lammps_model elec_compiled.model --format=mliap
An error occurred:
_/home/bingxing2/ailab/wanghaoyuan/.conda/envs/para-mace/lib/python3.11/site-packages/e3nn/o3/_wigner.py:10: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
_Jd, _W3j_flat, _W3j_indices = torch.load(os.path.join(os.path.dirname(file), 'constants.pt'))
/home/bingxing2/ailab/wanghaoyuan/.conda/envs/para-mace/lib/python3.11/site-packages/mace/cli/create_lammps_model.py:79: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
model = torch.load(
/home/bingxing2/ailab/wanghaoyuan/.conda/envs/para-mace/lib/python3.11/site-packages/torch/serialization.py:1328: UserWarning: 'torch.load' received a zip file that looks like a TorchScript archive dispatching to 'torch.jit.load' (call 'torch.jit.load' directly to silence this warning)
warnings.warn(
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in run_code
File "/home/bingxing2/ailab/wanghaoyuan/.conda/envs/para-mace/lib/python3.11/site-packages/mace/cli/create_lammps_model.py", line 114, in
main()
File "/home/bingxing2/ailab/wanghaoyuan/.conda/envs/para-mace/lib/python3.11/site-packages/mace/cli/create_lammps_model.py", line 91, in main
model = run_e3nn_to_cueq(copy.deepcopy(model))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/bingxing2/ailab/wanghaoyuan/.conda/envs/para-mace/lib/python3.11/site-packages/mace/cli/convert_e3nn_cueq.py", line 192, in run
num_product_irreps = len(config["hidden_irreps"].slices()) - 1
~~~~~~^^^^^^^^^^^^^^^^^
KeyError: 'hidden_irreps'

However, my training command already included the hidden_irreps parameter. Could anyone please help me figure out the cause of this error? Thank you very much.

[why626726-cell]

my training code is here:
mace_run_train
--name="${MODEL_NAME}"
--train_file="${TRAIN_FILE}"
--valid_fraction=0.1
--E0s="${MY_E0S}"
--energy_key="${ENERGY_KEY}"
--forces_key="${FORCES_KEY}"
--model="MACE"
--num_interactions=2
--num_channels=128
--max_L=1
--hidden_irreps='128x0e + 128x1o'
--r_max=5.0
--batch_size=8
--max_num_epochs=8
--swa
--start_swa=150
--scheduler_patience=10
--patience=20
--eval_interval=2
--error_table="PerAtomRMSE"
--device=cuda
--default_dtype="float32"
--seed=127

[ilyes319]

hey @why626726-cell , you should not use the model with _compiled in the name, use the file without it.

[why626726-cell]

Thank you very much for your reply. I will try your suggestion.