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RosettaCommons / RFdiffusion

Code for running RFdiffusion
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Code is not working for CPU-only device (2015 intel macbook pro) #179

Closed danielguion closed 9 months ago

danielguion commented 9 months ago

Hello, I don't have access to GPUs - I'm installing this on my intel macbookpro. However, that is proving to be difficult. I made it as far as installing the software on my intel macbook pro and editing the cuda and nvtx-dependent code throughout the codebase to rely on CPUs. However I still get the following error:


(SE3nv) % ./scripts/run_inference.py 'contigmap.contigs=[150-150]' inference.output_prefix=test_outputs/test inference.num_designs=10

[2023-12-21 01:25:31,029][__main__][INFO] - Setting device to CPU.
Reading models from 
/RFdiffusion/rfdiffusion/inference/../../models
[2023-12-21 01:25:31,030][rfdiffusion.inference.model_runners][INFO] - Reading checkpoint from /RFdiffusion/rfdiffusion/inference/../../models/Base_ckpt.pt
This is inf_conf.ckpt_path
/RFdiffusion/rfdiffusion/inference/../../models/Base_ckpt.pt
Assembling -model, -diffuser and -preprocess configs from checkpoint
USING MODEL CONFIG: self._conf[model][n_extra_block] = 4
USING MODEL CONFIG: self._conf[model][n_main_block] = 32
USING MODEL CONFIG: self._conf[model][n_ref_block] = 4
USING MODEL CONFIG: self._conf[model][d_msa] = 256
USING MODEL CONFIG: self._conf[model][d_msa_full] = 64
USING MODEL CONFIG: self._conf[model][d_pair] = 128
USING MODEL CONFIG: self._conf[model][d_templ] = 64
USING MODEL CONFIG: self._conf[model][n_head_msa] = 8
USING MODEL CONFIG: self._conf[model][n_head_pair] = 4
USING MODEL CONFIG: self._conf[model][n_head_templ] = 4
USING MODEL CONFIG: self._conf[model][d_hidden] = 32
USING MODEL CONFIG: self._conf[model][d_hidden_templ] = 32
USING MODEL CONFIG: self._conf[model][p_drop] = 0.15
USING MODEL CONFIG: self._conf[model][SE3_param_full] = {'num_layers': 1, 'num_channels': 32, 'num_degrees': 2, 'n_heads': 4, 'div': 4, 'l0_in_features': 8, 'l0_out_features': 8, 'l1_in_features': 3, 'l1_out_features': 2, 'num_edge_features': 32}
USING MODEL CONFIG: self._conf[model][SE3_param_topk] = {'num_layers': 1, 'num_channels': 32, 'num_degrees': 2, 'n_heads': 4, 'div': 4, 'l0_in_features': 64, 'l0_out_features': 64, 'l1_in_features': 3, 'l1_out_features': 2, 'num_edge_features': 64}
USING MODEL CONFIG: self._conf[model][freeze_track_motif] = False
USING MODEL CONFIG: self._conf[model][use_motif_timestep] = True
USING MODEL CONFIG: self._conf[diffuser][T] = 50
USING MODEL CONFIG: self._conf[diffuser][b_0] = 0.01
USING MODEL CONFIG: self._conf[diffuser][b_T] = 0.07
USING MODEL CONFIG: self._conf[diffuser][schedule_type] = linear
USING MODEL CONFIG: self._conf[diffuser][so3_type] = igso3
USING MODEL CONFIG: self._conf[diffuser][crd_scale] = 0.25
USING MODEL CONFIG: self._conf[diffuser][so3_schedule_type] = linear
USING MODEL CONFIG: self._conf[diffuser][min_b] = 1.5
USING MODEL CONFIG: self._conf[diffuser][max_b] = 2.5
USING MODEL CONFIG: self._conf[diffuser][min_sigma] = 0.02
USING MODEL CONFIG: self._conf[diffuser][max_sigma] = 1.5
USING MODEL CONFIG: self._conf[preprocess][sidechain_input] = False
USING MODEL CONFIG: self._conf[preprocess][motif_sidechain_input] = True
USING MODEL CONFIG: self._conf[preprocess][d_t1d] = 22
USING MODEL CONFIG: self._conf[preprocess][d_t2d] = 44
USING MODEL CONFIG: self._conf[preprocess][prob_self_cond] = 0.5
USING MODEL CONFIG: self._conf[preprocess][str_self_cond] = True
USING MODEL CONFIG: self._conf[preprocess][predict_previous] = False
[2023-12-21 01:25:36,369][rfdiffusion.inference.model_runners][INFO] - Loading checkpoint.
[2023-12-21 01:25:36,805][rfdiffusion.diffusion][INFO] - Using cached IGSO3.
Successful diffuser __init__
[2023-12-21 01:25:36,827][__main__][INFO] - Making design test_outputs/test_0
[2023-12-21 01:25:36,833][rfdiffusion.inference.model_runners][INFO] - Using contig: ['150-150']
With this beta schedule (linear schedule, beta_0 = 0.04, beta_T = 0.28), alpha_bar_T = 0.00013696050154976547
[2023-12-21 01:25:36,892][rfdiffusion.inference.model_runners][INFO] - Sequence init: ------------------------------------------------------------------------------------------------------------------------------------------------------
Error executing job with overrides: ['contigmap.contigs=[150-150]', 'inference.output_prefix=test_outputs/test', 'inference.num_designs=10']
Traceback (most recent call last):
  File "/RFdiffusion/./scripts/run_inference.py", line 87, in main
    px0, x_t, seq_t, plddt = sampler.sample_step(
  File "/RFdiffusion/rfdiffusion/inference/model_runners.py", line 664, in sample_step
    msa_prev, pair_prev, px0, state_prev, alpha, logits, plddt = self.model(msa_masked,
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/RFdiffusion/rfdiffusion/RoseTTAFoldModel.py", line 102, in forward
    msa, pair, R, T, alpha_s, state = self.simulator(seq, msa_latent, msa_full, pair, xyz[:,:,:3],
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/RFdiffusion/rfdiffusion/Track_module.py", line 420, in forward
    msa_full, pair, R_in, T_in, state, alpha = self.extra_block[i_m](msa_full,
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "RFdiffusion/rfdiffusion/Track_module.py", line 332, in forward
    R, T, state, alpha = self.str2str(msa, pair, R_in, T_in, xyz, state, idx, motif_mask=motif_mask, top_k=0)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/RFdiffusion/rfdiffusion/Track_module.py", line 266, in forward
    shift = self.se3(G, node.reshape(B*L, -1, 1), l1_feats, edge_feats)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/RFdiffusion/rfdiffusion/SE3_network.py", line 83, in forward
    return self.se3(G, node_features, edge_features)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/se3_transformer-1.0.0-py3.9.egg/se3_transformer/model/transformer.py", line 140, in forward
    basis = basis or get_basis(graph.edata['rel_pos'], max_degree=self.max_degree, compute_gradients=False,
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/se3_transformer-1.0.0-py3.9.egg/se3_transformer/model/basis.py", line 166, in get_basis
    with nvtx_range('spherical harmonics'):
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/contextlib.py", line 119, in __enter__
    return next(self.gen)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/cuda/nvtx.py", line 89, in range
    range_push(msg.format(*args, **kwargs))
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/cuda/nvtx.py", line 31, in range_push
    return _nvtx.rangePushA(msg)
  File "/Users/Daniel/opt/anaconda3/envs/SE3nv/lib/python3.9/site-packages/torch/cuda/nvtx.py", line 10, in _fail
    raise RuntimeError(
RuntimeError: NVTX functions not installed. Are you sure you have a CUDA build?

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
(SE3nv) Daniel@Daniels-MacBook-Pro RFdiffusion %

This is my yml: name: SE3nv channels:

For my project, I need to use the active site model and a custom PDB file because I reference residues to model from multiple chains to construct and active site. And the only way I think the program allows for this is if I edit the lines of the said residues to be on the same chain (i.e. not off-the-shelf pdb file). Is it possible to enable this functionality into the google collab notebook?

danielguion commented 9 months ago

Resolved using fork mentioned by @YaoYinYing in this comment.