[example] Add fastfold tutorial (#2528)

* add fastfold example

* pre-commit polish

* pre-commit polish readme and add empty test ci

* Add test_ci and reduce the default sequence length

examples/tutorial/fastfold/README.md

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+# FastFold Inference
+
+## Table of contents
+
+- [Overview](#📚-overview)
+- [Quick Start](#🚀-quick-start)
+- [Dive into FastFold](#🔍-dive-into-fastfold)
+
+## 📚 Overview
+
+This example lets you to quickly try out the inference of FastFold.
+
+**NOTE: We use random data and random parameters in this example.**
+
+
+## 🚀 Quick Start
+
+1. Install FastFold
+
+We highly recommend installing an Anaconda or Miniconda environment and install PyTorch with conda.
+
+```
+git clone https://github.com/hpcaitech/FastFold
+cd FastFold
+conda env create --name=fastfold -f environment.yml
+conda activate fastfold
+python setup.py install
+```
+
+2. Run the inference scripts.
+
+```bash
+python inference.py --gpus=1 --n_res=256 --chunk_size=None --inplace
+```
++ `gpus` means the DAP size
++ `n_res` means the length of residue sequence
++ `chunk_size` introduces a memory-saving technology at the cost of speed, None means not using, 16 may be a good trade off for long sequences.
++ `inplace` introduces another memory-saving technology with zero cost, drop `--inplace` if you do not want it.
+
+## 🔍 Dive into FastFold
+
+There are another features of FastFold, such as:
++ more excellent kernel based on triton
++ much faster data processing based on ray
++ training supported
+
+More detailed information can be seen [here](https://github.com/hpcaitech/FastFold/).

examples/tutorial/fastfold/inference.py

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

examples/tutorial/fastfold/test_ci.sh

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+set -euxo pipefail
+
+git clone https://github.com/hpcaitech/FastFold
+cd FastFold
+pip install -r requirements/requirements.txt
+python setup.py install
+pip install -r requirements/test_requirements.txt
+cd ..
+
+python inference.py