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chao1224 / MoleculeSTM

Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)
https://chao1224.github.io/MoleculeSTM
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RuntimeError: RNG state is wrong size #18

Closed Lzcstan closed 3 months ago

Lzcstan commented 6 months ago

Hello! Thank you for your excellent work! I hope to try the scripts you provided and have downloaded the relevant checkpoints following your tutorial. But when I used python pretrain.py --verbose --batch_size=32 --molecule_type=SMILES --epochs=2 to run the pre-trained script, the following error occurred:

arguments        Namespace(seed=42, device=0, dataspace_path='../data', dataset='PubChemSTM',        
text_type='SciBERT', molecule_type='SMILES', representation_frozen=False, batch_size=32,             
text_lr=0.0001, mol_lr=1e-05, text_lr_scale=1, mol_lr_scale=1, num_workers=8, epochs=2, decay=0,     
verbose=True, output_model_dir=None, max_seq_len=512,                                                
megamolbart_input_dir='../data/pretrained_MegaMolBART/checkpoints',                                  
vocab_path='../MoleculeSTM/bart_vocab.txt', pretrain_gnn_mode='GraphMVP_G', gnn_emb_dim=300,         
num_layer=5, JK='last', dropout_ratio=0.5, gnn_type='gin', graph_pooling='mean', SSL_loss='EBM_NCE', 
SSL_emb_dim=256, CL_neg_samples=1, T=0.1, normalize=True)                                            
len of CID2text: 324292                                                                              
len of CID2SMILES: 324270                                                                            
CID 28145 missing
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missing 22
len of text_list: 361885
using world size: 1 and model-parallel size: 1 
using torch.float32 for parameters ...
-------------------- arguments --------------------
  adam_beta1 ...................... 0.9
  adam_beta2 ...................... 0.999
  adam_eps ........................ 1e-08
  adlr_autoresume ................. False
  adlr_autoresume_interval ........ 1000
  apply_query_key_layer_scaling ... False
  apply_residual_connection_post_layernorm  False
  attention_dropout ............... 0.1
  attention_softmax_in_fp32 ....... False
  batch_size ...................... None
  bert_load ....................... None
  bias_dropout_fusion ............. False
  bias_gelu_fusion ................ False
  block_data_path ................. None
  checkpoint_activations .......... False
  checkpoint_in_cpu ............... False
  checkpoint_num_layers ........... 1
  clip_grad ....................... 1.0
  contigious_checkpointing ........ False
  cpu_optimizer ................... False
  cpu_torch_adam .................. False
  data_impl ....................... infer
  data_path ....................... None
  dataset_path .................... None
  DDP_impl ........................ local
  deepscale ....................... False
  deepscale_config ................ None
  deepspeed ....................... False
  deepspeed_activation_checkpointing  False
  deepspeed_config ................ None
  deepspeed_mpi ................... False
  distribute_checkpointed_activations  False                                                         
  distributed_backend ............. nccl                                                             
  dynamic_loss_scale .............. True                                                             
  eod_mask_loss ................... False
  eval_interval ................... 1000
  eval_iters ...................... 100
  exit_interval ................... None
  faiss_use_gpu ................... False
  finetune ........................ False
  fp16 ............................ False
  fp16_lm_cross_entropy ........... False
  fp32_allreduce .................. False
  gas ............................. 1
  hidden_dropout .................. 0.1
  hidden_size ..................... 256
  hysteresis ...................... 2
  ict_head_size ................... None
  ict_load ........................ None
  indexer_batch_size .............. 128
  indexer_log_interval ............ 1000
  init_method_std ................. 0.02
  load ............................ ../data/pretrained_MegaMolBART/checkpoints                       
  local_rank ...................... None
  log_interval .................... 100
  loss_scale ...................... None
  loss_scale_window ............... 1000
  lr .............................. None
  lr_decay_iters .................. None
  lr_decay_style .................. linear
  make_vocab_size_divisible_by .... 128
  mask_prob ....................... 0.15
  max_position_embeddings ......... 512
  merge_file ...................... None
  min_lr .......................... 0.0
  min_scale ....................... 1
  mmap_warmup ..................... False
  model_parallel_size ............. 1
  no_load_optim ................... False
  no_load_rng ..................... False
  no_save_optim ................... False
  no_save_rng ..................... False
  num_attention_heads ............. 8
  num_layers ...................... 4
  num_unique_layers ............... None
  num_workers ..................... 2
  onnx_safe ....................... None
  openai_gelu ..................... False
  override_lr_scheduler ........... False
  param_sharing_style ............. grouped
  params_dtype .................... torch.float32
  partition_activations ........... False
  pipe_parallel_size .............. 0
  profile_backward ................ False
  query_in_block_prob ............. 0.1
  rank ............................ 0
  report_topk_accuracies .......... []
  reset_attention_mask ............ False
  reset_position_ids .............. False
  save ............................ None
  save_interval ................... None
  scaled_masked_softmax_fusion .... False
  scaled_upper_triang_masked_softmax_fusion  False 
  seed ............................ 1234
  seq_length ...................... None
  short_seq_prob .................. 0.1
  split ........................... 969, 30, 1
  synchronize_each_layer .......... False
  tensorboard_dir ................. None
  titles_data_path ................ None
  tokenizer_type .................. GPT2BPETokenizer
  train_iters ..................... None
  use_checkpoint_lr_scheduler ..... False
  use_cpu_initialization .......... False
  use_one_sent_docs ............... False
  vocab_file ...................... ../MoleculeSTM/bart_vocab.txt
  warmup .......................... 0.01
  weight_decay .................... 0.01
  world_size ...................... 1
  zero_allgather_bucket_size ...... 0.0
  zero_contigious_gradients ....... False
  zero_reduce_bucket_size ......... 0.0
  zero_reduce_scatter ............. False
  zero_stage ...................... 1.0
---------------- end of arguments ---------------- 
> initializing torch distributed ...
> initializing model parallel with size 1                                                            
> setting random seeds to 1234 ...                                                                   
> initializing model parallel cuda seeds on global rank 0, model parallel rank 0, and data parallel r
ank 0 with model parallel seed: 3952 and data parallel seed: 1234                                    
Loading vocab from ../MoleculeSTM/bart_vocab.txt.
Loading from ../data/pretrained_MegaMolBART/checkpoints
global rank 0 is loading checkpoint ../data/pretrained_MegaMolBART/checkpoints/iter_0134000/mp_rank_0
0/model_optim_rng.pt
could not find arguments in the checkpoint ...
Traceback (most recent call last):
  File "/data02/luozc/chemgpt/MoleculeSTM/scripts/pretrain.py", line 270, in <module>
    MegaMolBART_wrapper = MegaMolBART(
  File "/data02/luozc/chemgpt/MoleculeSTM/scripts/../MoleculeSTM/models/mega_molbart/mega_mol_bart.py
", line 98, in __init__
    self.model = self.load_model(args, self.tokenizer, decoder_max_seq_len)
  File "/data02/luozc/chemgpt/MoleculeSTM/scripts/../MoleculeSTM/models/mega_molbart/mega_mol_bart.py
", line 157, in load_model
    self.iteration = load_checkpoint(model, None, None)
  File "/home/oem/anaconda3/envs/chemgpt/lib/python3.10/site-packages/megatron/checkpointing.py", lin
e 287, in load_checkpoint
    torch.cuda.set_rng_state(state_dict['cuda_rng_state'])
  File "/home/oem/anaconda3/envs/chemgpt/lib/python3.10/site-packages/torch/cuda/random.py", line 75,
 in set_rng_state
    _lazy_call(cb)
  File "/home/oem/anaconda3/envs/chemgpt/lib/python3.10/site-packages/torch/cuda/__init__.py", line 2
29, in _lazy_call
    callable()
  File "/home/oem/anaconda3/envs/chemgpt/lib/python3.10/site-packages/torch/cuda/random.py", line 73,
 in cb
    default_generator.set_state(new_state_copy)
RuntimeError: RNG state is wrong size

How should I fix it? I'm using a server with NVIDIA H800, which has cuda==12.1 and pytorch==2.1.2 Thanks again 🙏

chao1224 commented 6 months ago

Hi @Lzcstan,

I guess this might be due to the cuda and pytorch version. My cuda version is 11 (and pytorch-1.9). Can you try to downgrade them?

Lzcstan commented 6 months ago

Hi @Lzcstan,

I guess this might be due to the cuda and pytorch version. My cuda version is 11 (and pytorch-1.9). Can you try to downgrade them?

But I guess NVIDIA H800 could not use cuda<12, I tried cuda==11.3 and pytorch==1.10.1 but get the follow error:

[2024-01-10 03:20:35,380] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
/home/oem/anaconda3/envs/mol_stm/lib/python3.7/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022
  warnings.warn("pyprof will be removed by the end of June, 2022", FutureWarning)
arguments        Namespace(CL_neg_samples=1, JK='last', SSL_emb_dim=256, SSL_loss='EBM_NCE', T=0.1, batch_size=32, dataset='PubChemSTM', dataspace_path='../data', decay=0, device=0, dropout_ratio=0.5, epochs=2, gnn_emb_dim=300, 
gnn_type='gin', graph_pooling='mean', max_seq_len=512, megamolbart_input_dir='../data/pretrained_MegaMolBART/checkpoints', mol_lr=1e-05, mol_lr_scale=1, molecule_type='SMILES', normalize=True, num_layer=5, num_workers=8, 
output_model_dir=None, pretrain_gnn_mode='GraphMVP_G', representation_frozen=False, seed=42, text_lr=0.0001, text_lr_scale=1, text_type='SciBERT', verbose=True, vocab_path='../MoleculeSTM/bart_vocab.txt')
Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
/home/oem/anaconda3/envs/mol_stm/lib/python3.7/site-packages/torch/cuda/__init__.py:143: UserWarning: 
NVIDIA H800 with CUDA capability sm_90 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_61 sm_70 sm_75 sm_80 sm_86 compute_37.
If you want to use the NVIDIA H800 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

  warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))

Here is the support for my guess.

chao1224 commented 6 months ago

Hi @Lzcstan,

I am afraid that I don't have H800 to check the code, and according to the exception messages you listed above, it is the incompatibility between H800 (cuda and pytorch) and megatron.

Lzcstan commented 3 months ago

Hi @Lzcstan,

I am afraid that I don't have H800 to check the code, and according to the exception messages you listed above, it is the incompatibility between H800 (cuda and pytorch) and megatron.

  • I checked the megatron github repo, and according to this link, running megatron w/ H800 should work. (which is good to know)

  • What I am not sure is how compatible is MEgatron-LM-v1.1.5 with H800. Can you try to use the following CMD?


cd MolBART/megatron_molbart/Megatron-LM-v1.1.5-3D_parallelism

pip install .
  • Also, now you are using pip install megatron-lm, and another information that might be helpful is to print out the state_dict['cuda_rng_state'] before the line torch.cuda.set_rng_state(state_dict['cuda_rng_state']) in the source code.

Hi, I checked the shape of RNG state of CUDA and found that H800 cannot fit with the checkpoint. Switching the GPU solves my problem, I will close this issue. Thank you for your kind reply:-)