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[tutorial] update fastfold tutorial (#2565) 

* update readme

* update

* update
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.gitmodules
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......@@ -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
FastFold @ 19ce8406
Subproject commit 19ce840650fd865bd3684684dac051ec3a7bc762
examples/tutorial/fastfold/README.md
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## Table of contents
- [Overview](#📚-overview)
- [Quick Start](#🚀-quick-start)
- [Dive into FastFold](#🔍-dive-into-fastfold)
- [FastFold Inference](#fastfold-inference)
- [Table of contents](#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.**
This example lets you to try out the inference of FastFold.
## 🚀 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.
It may take ~900GB space to keep datasets.
```
./scripts/download_all_data.sh data/
```
3. 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.
bash inference.sh
```
You can find predictions under the `outputs` dir.
## 🔍 Dive into FastFold
......
examples/tutorial/fastfold/inference.py deleted 100644 → 0
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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 deleted 100644 → 0
View file @ 79079a9d
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
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