Numpy 2.0

[ajfriedman18]

It appears the numpy 2.0 release (Jun 16, 2024) on pypi breaks the Docker image. From looking at the Dockerfile, it appears that the numpy version is not set, which likely results in 2.0 getting pulled now.

Running some tests now and will look at PR for fix

Full stack trace:

+ python3.9 /app/RFdiffusion/scripts/run_inference.py inference.output_prefix=output inference.model_directory_path=model_parameters inference.input_pdb=5TPN.pdb inference.num_designs=3 'contigmap.contigs=[10-40/A163-181/10-40]'
--
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
Traceback (most recent call last):  File "/app/RFdiffusion/scripts/run_inference.py", line 24, in <module>
from rfdiffusion.util import writepdb_multi, writepdb
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/util.py", line 2, in <module>
from rfdiffusion.chemical import *
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/chemical.py", line 184, in <module>
init_N = torch.tensor([-0.5272, 1.3593, 0.000]).float()
/usr/local/lib/python3.9/dist-packages/rfdiffusion/chemical.py:184: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at  ../torch/csrc/utils/tensor_numpy.cpp:68.)
init_N = torch.tensor([-0.5272, 1.3593, 0.000]).float()
[2024-06-25 13:52:11,440][__main__][INFO] - Found GPU with device_name NVIDIA A10G. Will run RFdiffusion on NVIDIA A10G
Reading models from model_parameters
[2024-06-25 13:52:11,441][rfdiffusion.inference.model_runners][INFO] - Reading checkpoint from model_parameters/Base_ckpt.pt
This is inf_conf.ckpt_path
model_parameters/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
[2024-06-25 13:52:17,826][rfdiffusion.inference.model_runners][INFO] - Loading checkpoint.
[2024-06-25 13:52:21,623][rfdiffusion.diffusion][INFO] - Calculating IGSO3.
Error executing job with overrides: ['inference.output_prefix=output', 'inference.model_directory_path=model_parameters', 'inference.input_pdb=5TPN.pdb', 'inference.num_designs=3', 'contigmap.contigs=[10-40/A163-181/10-40]']
Traceback (most recent call last):
File "/app/RFdiffusion/scripts/run_inference.py", line 194, in <module>
main()
File "/usr/local/lib/python3.9/dist-packages/hydra/main.py", line 94, in decorated_main
_run_hydra(
File "/usr/local/lib/python3.9/dist-packages/hydra/_internal/utils.py", line 394, in _run_hydra
_run_app(
File "/usr/local/lib/python3.9/dist-packages/hydra/_internal/utils.py", line 457, in _run_app
run_and_report(
File "/usr/local/lib/python3.9/dist-packages/hydra/_internal/utils.py", line 223, in run_and_report
raise ex
File "/usr/local/lib/python3.9/dist-packages/hydra/_internal/utils.py", line 220, in run_and_report
return func()
File "/usr/local/lib/python3.9/dist-packages/hydra/_internal/utils.py", line 458, in <lambda>
lambda: hydra.run(
File "/usr/local/lib/python3.9/dist-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/usr/local/lib/python3.9/dist-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/usr/local/lib/python3.9/dist-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/app/RFdiffusion/scripts/run_inference.py", line 54, in main
sampler = iu.sampler_selector(conf)
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/inference/utils.py", line 511, in sampler_selector
sampler = model_runners.SelfConditioning(conf)
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/inference/model_runners.py", line 37, in __init__
self.initialize(conf)
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/inference/model_runners.py", line 130, in initialize
self.diffuser = Diffuser(**self._conf.diffuser, cache_dir=schedule_directory)
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/diffusion.py", line 582, in __init__
self.so3_diffuser = IGSO3(
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/diffusion.py", line 198, in __init__
self.igso3_vals = self._calc_igso3_vals(L=L)
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/diffusion.py", line 236, in _calc_igso3_vals
igso3_vals = igso3.calculate_igso3(
File "/usr/local/lib/python3.9/dist-packages/rfdiffusion/igso3.py", line 93, in calculate_igso3
discrete_sigma = 10 ** np.linspace(np.log10(min_sigma), np.log10(max_sigma), num_sigma + 1)[1:]
File "/usr/local/lib/python3.9/dist-packages/torch/_tensor.py", line 757, in __array__
return self.numpy()
RuntimeError: Numpy is not available
Task failed