[tutorial] update fastfold tutorial (#2565)

* update readme

* update

* update

.gitmodules

@@ -2,3 +2,6 @@
 	path = inference
 	url = https://github.com/hpcaitech/EnergonAI.git
 	branch = main
+[submodule "examples/tutorial/fastfold/FastFold"]
+	path = examples/tutorial/fastfold/FastFold
+	url = https://github.com/hpcaitech/FastFold

examples/tutorial/fastfold/FastFold

@@ -0,0 +1 @@
+Subproject commit 19ce840650fd865bd3684684dac051ec3a7bc762

examples/tutorial/fastfold/README.md

@@ -2,23 +2,21 @@
 
 ## Table of contents
 
-- [Overview](#📚-overview)
+- [FastFold Inference](#fastfold-inference)
-- [Quick Start](#🚀-quick-start)
+  - [Table of contents](#table-of-contents)
-- [Dive into FastFold](#🔍-dive-into-fastfold)
+  - [📚 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.
+This example lets you to 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.
+We highly recommend you to install FastFold with conda.
-
 ```
 git clone https://github.com/hpcaitech/FastFold
 cd FastFold
@@ -27,15 +25,19 @@ conda activate fastfold
 python setup.py install
 ```
 
-2. Run the inference scripts.
+2. Download datasets.
 
-```bash
+It may take ~900GB space to keep datasets.
-python inference.py --gpus=1 --n_res=256 --chunk_size=None --inplace
 ```
-+ `gpus` means the DAP size
+./scripts/download_all_data.sh data/
-+ `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.
+3. Run the inference scripts.
+
+```
+bash inference.sh
+```
+You can find predictions under the `outputs` dir.
 
 ## 🔍 Dive into FastFold
 

examples/tutorial/fastfold/inference.py

@@ -1,153 +0,0 @@
-# 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

@@ -1,10 +0,0 @@
-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