mirror of https://github.com/hpcaitech/ColossalAI
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Subproject commit 19ce840650fd865bd3684684dac051ec3a7bc762
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# Copyright 2023 HPC-AI Tech Inc.
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# Copyright 2021 AlQuraishi Laboratory
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# Copyright 2021 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import time
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import fastfold
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import numpy as np
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import torch
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import torch.multiprocessing as mp
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from fastfold.config import model_config
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from fastfold.data import data_transforms
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from fastfold.model.fastnn import set_chunk_size
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from fastfold.model.hub import AlphaFold
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from fastfold.utils.inject_fastnn import inject_fastnn
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from fastfold.utils.tensor_utils import tensor_tree_map
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if int(torch.__version__.split(".")[0]) >= 1 and int(torch.__version__.split(".")[1]) > 11:
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torch.backends.cuda.matmul.allow_tf32 = True
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def random_template_feats(n_templ, n):
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b = []
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batch = {
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"template_mask": np.random.randint(0, 2, (*b, n_templ)),
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"template_pseudo_beta_mask": np.random.randint(0, 2, (*b, n_templ, n)),
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"template_pseudo_beta": np.random.rand(*b, n_templ, n, 3),
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"template_aatype": np.random.randint(0, 22, (*b, n_templ, n)),
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"template_all_atom_mask": np.random.randint(0, 2, (*b, n_templ, n, 37)),
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"template_all_atom_positions": np.random.rand(*b, n_templ, n, 37, 3) * 10,
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"template_torsion_angles_sin_cos": np.random.rand(*b, n_templ, n, 7, 2),
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"template_alt_torsion_angles_sin_cos": np.random.rand(*b, n_templ, n, 7, 2),
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"template_torsion_angles_mask": np.random.rand(*b, n_templ, n, 7),
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}
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batch = {k: v.astype(np.float32) for k, v in batch.items()}
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batch["template_aatype"] = batch["template_aatype"].astype(np.int64)
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return batch
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def random_extra_msa_feats(n_extra, n):
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b = []
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batch = {
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"extra_msa": np.random.randint(0, 22, (*b, n_extra, n)).astype(np.int64),
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"extra_has_deletion": np.random.randint(0, 2, (*b, n_extra, n)).astype(np.float32),
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"extra_deletion_value": np.random.rand(*b, n_extra, n).astype(np.float32),
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"extra_msa_mask": np.random.randint(0, 2, (*b, n_extra, n)).astype(np.float32),
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}
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return batch
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def generate_batch(n_res):
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batch = {}
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tf = torch.randint(21, size=(n_res,))
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batch["target_feat"] = torch.nn.functional.one_hot(tf, 22).float()
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batch["aatype"] = torch.argmax(batch["target_feat"], dim=-1)
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batch["residue_index"] = torch.arange(n_res)
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batch["msa_feat"] = torch.rand((128, n_res, 49))
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t_feats = random_template_feats(4, n_res)
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batch.update({k: torch.tensor(v) for k, v in t_feats.items()})
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extra_feats = random_extra_msa_feats(5120, n_res)
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batch.update({k: torch.tensor(v) for k, v in extra_feats.items()})
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batch["msa_mask"] = torch.randint(low=0, high=2, size=(128, n_res)).float()
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batch["seq_mask"] = torch.randint(low=0, high=2, size=(n_res,)).float()
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batch.update(data_transforms.make_atom14_masks(batch))
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batch["no_recycling_iters"] = torch.tensor(2.)
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add_recycling_dims = lambda t: (t.unsqueeze(-1).expand(*t.shape, 3))
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batch = tensor_tree_map(add_recycling_dims, batch)
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return batch
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def inference_model(rank, world_size, result_q, batch, args):
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os.environ['RANK'] = str(rank)
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os.environ['LOCAL_RANK'] = str(rank)
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os.environ['WORLD_SIZE'] = str(world_size)
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# init distributed for Dynamic Axial Parallelism
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fastfold.distributed.init_dap()
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torch.cuda.set_device(rank)
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config = model_config(args.model_name)
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if args.chunk_size:
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config.globals.chunk_size = args.chunk_size
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config.globals.inplace = args.inplace
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config.globals.is_multimer = False
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model = AlphaFold(config)
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model = inject_fastnn(model)
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model = model.eval()
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model = model.cuda()
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set_chunk_size(model.globals.chunk_size)
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with torch.no_grad():
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batch = {k: torch.as_tensor(v).cuda() for k, v in batch.items()}
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t = time.perf_counter()
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out = model(batch)
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print(f"Inference time: {time.perf_counter() - t}")
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out = tensor_tree_map(lambda x: np.array(x.cpu()), out)
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result_q.put(out)
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torch.distributed.barrier()
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torch.cuda.synchronize()
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def inference_monomer_model(args):
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batch = generate_batch(args.n_res)
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manager = mp.Manager()
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result_q = manager.Queue()
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torch.multiprocessing.spawn(inference_model, nprocs=args.gpus, args=(args.gpus, result_q, batch, args))
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out = result_q.get()
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# get unrelexed pdb and save
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# batch = tensor_tree_map(lambda x: np.array(x[..., -1].cpu()), batch)
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# plddt = out["plddt"]
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# plddt_b_factors = np.repeat(plddt[..., None], residue_constants.atom_type_num, axis=-1)
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# unrelaxed_protein = protein.from_prediction(features=batch,
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# result=out,
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# b_factors=plddt_b_factors)
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# with open('demo_unrelex.pdb', 'w+') as fp:
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# fp.write(unrelaxed_protein)
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def main(args):
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inference_monomer_model(args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--gpus", type=int, default=1, help="""Number of GPUs with which to run inference""")
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parser.add_argument("--n_res", type=int, default=50, help="virtual residue number of random data")
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parser.add_argument("--model_name", type=str, default="model_1", help="model name of alphafold")
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parser.add_argument('--chunk_size', type=int, default=None)
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parser.add_argument('--inplace', default=False, action='store_true')
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args = parser.parse_args()
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main(args)
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@ -1,10 +0,0 @@
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set -euxo pipefail
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git clone https://github.com/hpcaitech/FastFold
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cd FastFold
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pip install -r requirements/requirements.txt
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python setup.py install
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pip install -r requirements/test_requirements.txt
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cd ..
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python inference.py
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