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run.py
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import os
import sys
import socket
import logging
from timeit import default_timer as timer
from tqdm import tqdm
import pickle
import jax
from jax.experimental.checkify import checkify, index_checks
from jax import numpy as jnp
import optax
import haiku as hk
import matplotlib.pyplot as plt
import torch
import einops
from omegaconf import OmegaConf
from hydra.utils import instantiate, get_class, call
from torch.utils.tensorboard import SummaryWriter
import numpy as np
from functools import partial
from score_sde.models.flow import SDEPushForward
from score_sde.losses import get_ema_loss_step_fn
from score_sde.utils import TrainState, save, restore
from score_sde.utils.loggers_pl import LoggerCollection
from score_sde.datasets import random_split, DataLoader, TensorDataset
from riemannian_score_sde.utils.normalization import compute_normalization
from riemannian_score_sde.utils.vis import plot, plot_ref
from context_generator.utils.misc import load_config, inf_iterator
from context_generator.datasets.pdbredo_chain import get_pdbredo_chain_dataset
from context_generator.utils.data import PaddingCollate
from context_generator.utils.protein.constants import chi_pi_periodic, AA
from context_generator.models.da_encoder import RDEEncoder
from context_generator.modules.encoders.single import PerResidueEncoder
from context_generator.modules.encoders.pair import ResiduePairEncoder
from context_generator.modules.encoders.attn import GAEncoder
log = logging.getLogger(__name__)
writer = SummaryWriter('train')
context_gen_cfg = f"./context_generator/configs/train/diff.yml"
# jax.config.update('jax_array', True)
def aggregate_sidechain_accuracy(aa, chi_pred, chi_native, chi_mask):
aa = aa.reshape(-1)
chi_mask = chi_mask.reshape(-1, 4)
diff = torch.min(
(chi_pred - chi_native) % (2 * np.pi),
(chi_native - chi_pred) % (2 * np.pi),
) # (N, L, 4)
diff = torch.rad2deg(diff)
diff = diff.reshape(-1, 4)
diff_flip = torch.min(
( (chi_pred + np.pi) - chi_native) % (2 * np.pi),
(chi_native - (chi_pred + np.pi) ) % (2 * np.pi),
)
diff_flip = torch.rad2deg(diff_flip)
diff_flip = diff_flip.reshape(-1, 4)
acc = [{j:[] for j in range(1, 4+1)} for i in range(20)]
for i in range(aa.size(0)):
for j in range(4):
chi_number = j+1
if not chi_mask[i, j].item(): continue
if chi_pi_periodic[AA(aa[i].item())][chi_number-1]:
diff_this = min(diff[i, j].item(), diff_flip[i, j].item())
else:
diff_this = diff[i, j].item()
acc[aa[i].item()][chi_number].append(diff_this)
table = np.full((20, 4), np.nan)
for i in range(20):
for j in range(1, 4+1):
if len(acc[i][j]) > 0:
table[i, j-1] = np.mean(acc[i][j])
return table
def make_sidechain_accuracy_table_image(tag: str, diff: np.ndarray):
from Bio.PDB.Polypeptide import index_to_three
columns = ['chi1', 'chi2', 'chi3', 'chi4']
rows = [index_to_three(i) for i in range(20)]
cell_text = diff.tolist()
fig, ax = plt.subplots(dpi=200)
ax.axis('tight')
ax.axis('off')
ax.set_title(tag)
ax.table(
cellText=cell_text,
colLabels=columns,
rowLabels=rows,
loc='center'
)
return fig
def run(cfg):
def train(train_state):
loss = instantiate(
cfg.loss, pushforward=pushforward, model=model, eps=cfg.eps, train=True
)
train_step_fn = get_ema_loss_step_fn(loss, optimizer=optimiser, train=True)
train_step_fn = jax.jit(train_step_fn)
rng = train_state.rng
t = tqdm(
range(train_state.step, cfg.steps),
total=cfg.steps - train_state.step,
bar_format="{desc}{bar}{r_bar}",
mininterval=1,
)
train_time = timer()
total_train_time = 0
for step in t:
# data, context = next(train_ds)
batch = next(train_it)
data = batch.pop('chi_native')
for k, v in list(batch.items()):
if isinstance(v, list) or isinstance(v, int):
_ = batch.pop(k)
# print(v_del)
data = einops.rearrange(jnp.stack([jnp.sin(data), jnp.cos(data)], axis=-1), 'b l m n -> b l (m n)')
xbatch = {"data": data, "context": batch}
rng, next_rng = jax.random.split(rng)
(rng, train_state), loss = train_step_fn((next_rng, train_state), xbatch)
if jnp.isnan(loss).any():
log.warning("Loss is nan")
return train_state, False
if step % 50 == 0:
logger.log_metrics({"train/loss": loss}, step)
t.set_description(f"Loss: {loss:.3f}")
writer.add_scalar("train/loss", np.array(loss), step)
if step > 0 and step % cfg.val_freq == 0:
logger.log_metrics(
{"train/time_per_it": (timer() - train_time) / cfg.val_freq}, step
)
total_train_time += timer() - train_time
ckpt_path_by_step = os.path.join(ckpt_path, f'step_{step}')
os.makedirs(ckpt_path_by_step, exist_ok=True)
save(ckpt_path_by_step, train_state)
save(ckpt_path, train_state)
if cfg.train_plot:
generate_plots(train_state, "val", step=step)
train_time = timer()
logger.log_metrics({"train/total_time": total_train_time}, step)
return train_state, True
def generate_plots(train_state, stage, step=None):
log.info("Generating plots")
rng = jax.random.PRNGKey(cfg.seed)
model_w_dicts = (model, train_state.params_ema, train_state.model_state)
sampler_kwargs = dict(N=100, eps=cfg.eps, predictor="GRW")
sampler = pushforward.get_sampler(model_w_dicts, train=False, **sampler_kwargs)
chi_pred, chi_native, chi_masked_flag, chi_corrupt_flag, aa_all = [], [], [], [], []
for i, vbatch in enumerate(tqdm(val_dl, desc='Validate', dynamic_ncols=True)):
data = vbatch.pop('chi_native')
chi_native.append(data)
for k, v in list(vbatch.items()):
if isinstance(v, list) or isinstance(v, int):
_ = vbatch.pop(k)
chi_masked_flag.append(
vbatch['chi_masked_flag'][..., None] * vbatch['chi_mask']
)
chi_corrupt_flag.append(
vbatch['chi_corrupt_flag'][..., None] * vbatch['chi_mask']
)
aa_all.append(vbatch['aa'])
shape = (int(data.shape[0]*data.shape[1]),)
rng, next_rng = jax.random.split(rng)
#HACK
xs = sampler(next_rng, shape, vbatch)
prop_in_M = data_manifold.belongs(xs, atol=1e-4).mean()
log.info(f"Prop samples in M = {100 * prop_in_M.item():.1f}%")
xs = jnp.stack(
[jnp.arctan2(xs[..., 0], xs[..., 1]), jnp.arctan2(xs[..., 2], xs[..., 3]), jnp.arctan2(xs[..., 4], xs[..., 5]), jnp.arctan2(xs[..., 6], xs[..., 7])],
axis=-1,
)
chi_pred.append(np.asarray(xs).reshape(data.shape[0], data.shape[1], -1))
chi_pred, chi_native = np.concatenate(chi_pred, axis=0), np.concatenate(chi_native, axis=0)
chi_masked_flag = np.concatenate(chi_masked_flag, axis=0)
chi_corrupt_flag = np.concatenate(chi_corrupt_flag, axis=0)
aa_all = np.concatenate(aa_all, axis=0)
chi_pred, chi_native = torch.tensor(chi_pred), torch.tensor(chi_native)
chi_masked_flag = torch.tensor(chi_masked_flag)
chi_corrupt_flag = torch.tensor(chi_corrupt_flag)
aa_all = torch.tensor(aa_all)
log.info(f"Data prepared")
acc_table_masked = aggregate_sidechain_accuracy(aa_all, chi_pred, chi_native, chi_masked_flag)
acc_table_corrupt = aggregate_sidechain_accuracy(aa_all, chi_pred, chi_native, chi_corrupt_flag)
print(acc_table_masked)
print(acc_table_corrupt)
writer.add_figure(
'val/acc_table_masked',
make_sidechain_accuracy_table_image('masked', acc_table_masked),
global_step=step
)
writer.add_figure(
'val/acc_table_corrupt',
make_sidechain_accuracy_table_image('corrupt', acc_table_corrupt),
global_step=step
)
def generate_samples(train_state, stage, step=None):
log.info("Generating samples")
rng = jax.random.PRNGKey(cfg.seed)
model_w_dicts = (model, train_state.params_ema, train_state.model_state)
sampler_kwargs = dict(N=100, eps=cfg.eps, predictor="GRW")
sampler = pushforward.get_sampler(model_w_dicts, train=False, **sampler_kwargs)
likelihood_fn = pushforward.get_log_prob(model_w_dicts, train=False)
likelihood_fn = jax.jit(likelihood_fn)
chi_pred, chi_native, chi_masked_flag, chi_corrupt_flag, aa_all = [], [], [], [], []
for i, vbatch in enumerate(tqdm(val_dl, desc='Validate', dynamic_ncols=True)):
output_dir = os.path.join("sample_results", vbatch['id'][0])
os.makedirs(output_dir, exist_ok=True)
with open(os.path.join(output_dir, 'data.pickle'), 'wb') as f:
pickle.dump(vbatch, f)
data = vbatch.pop('chi_native')
chi_native.append(data)
for k, v in list(vbatch.items()):
if isinstance(v, list) or isinstance(v, int):
_ = vbatch.pop(k)
chi_masked_flag.append(
vbatch['chi_masked_flag'][..., None] * vbatch['chi_mask']
)
with open(os.path.join(output_dir, 'masked_flag.pickle'), 'wb') as f:
pickle.dump(vbatch['chi_masked_flag'][..., None] * vbatch['chi_mask'], f)
chi_corrupt_flag.append(
vbatch['chi_corrupt_flag'][..., None] * vbatch['chi_mask']
)
aa_all.append(vbatch['aa'])
shape = (int(data.shape[0]*data.shape[1]),)
xs_all = []
logp_step_all = []
for _ in range(5):
rng, next_rng = jax.random.split(rng)
xs = sampler(next_rng, shape, vbatch)
prop_in_M = data_manifold.belongs(xs, atol=1e-4).mean()
log.info(f"Prop samples in M = {100 * prop_in_M.item():.1f}%")
logp_step, nfe = likelihood_fn(xs.reshape(data.shape[0], data.shape[1], -1), vbatch)
xs_all.append(xs)
logp_step_all.append(logp_step)
xs_all = np.stack(xs_all, axis=0)
xs_all = np.stack(
[np.arctan2(xs_all[..., 0], xs_all[..., 1]), np.arctan2(xs_all[..., 2], xs_all[..., 3]), np.arctan2(xs_all[..., 4], xs_all[..., 5]), np.arctan2(xs_all[..., 6], xs_all[..., 7])],
axis=-1,
)
xs_all = xs_all.reshape(xs_all.shape[0], data.shape[0], data.shape[1], -1)
logp_step_all = np.stack(logp_step_all, axis=0)
logp_step_all = logp_step_all.reshape(logp_step_all.shape[0], data.shape[0], data.shape[1], )
xs = torch.tensor(xs_all)
logprobs = torch.tensor(logp_step_all)
logprobs_max, smp_idx = logprobs.max(dim=0) # (N, L)
smp_idx = smp_idx[None, :, :, None].repeat(1, 1, 1, 4) # (1, N, L, 4)
xs = torch.gather(xs, dim=0, index=smp_idx).squeeze(0)
with open(os.path.join(output_dir, 'xs.pickle'), 'wb') as f:
pickle.dump(xs.numpy(), f)
chi_pred.append(xs.numpy())
chi_pred, chi_native = np.concatenate(chi_pred, axis=0), np.concatenate(chi_native, axis=0)
chi_masked_flag = np.concatenate(chi_masked_flag, axis=0)
chi_corrupt_flag = np.concatenate(chi_corrupt_flag, axis=0)
aa_all = np.concatenate(aa_all, axis=0)
chi_pred, chi_native = torch.tensor(chi_pred), torch.tensor(chi_native)
chi_masked_flag = torch.tensor(chi_masked_flag)
chi_corrupt_flag = torch.tensor(chi_corrupt_flag)
aa_all = torch.tensor(aa_all)
log.info(f"Data prepared")
acc_table_masked = aggregate_sidechain_accuracy(aa_all, chi_pred, chi_native, chi_masked_flag)
acc_table_corrupt = aggregate_sidechain_accuracy(aa_all, chi_pred, chi_native, chi_corrupt_flag)
print(acc_table_masked)
print(acc_table_corrupt)
writer.add_figure(
'val/acc_table_masked',
make_sidechain_accuracy_table_image('masked', acc_table_masked),
global_step=step
)
writer.add_figure(
'val/acc_table_corrupt',
make_sidechain_accuracy_table_image('corrupt', acc_table_corrupt),
global_step=step
)
### Main
log.info("Stage : Startup")
log.info(f"Jax devices: {jax.devices()}")
run_path = os.getcwd()
log.info(f"run_path: {run_path}")
log.info(f"hostname: {socket.gethostname()}")
ckpt_path = os.path.join(run_path, cfg.ckpt_dir)
os.makedirs(ckpt_path, exist_ok=True)
if cfg.mode == "test":
ckpt_path = "./tmp/checkpoint/SidechainDiff_ckpt"
loggers = [instantiate(logger_cfg) for logger_cfg in cfg.logger.values()]
logger = LoggerCollection(loggers)
logger.log_hyperparams(OmegaConf.to_container(cfg, resolve=True))
log.info("Stage : Instantiate model")
rng = jax.random.PRNGKey(cfg.seed)
data_manifold = instantiate(cfg.manifold)
transform = instantiate(cfg.transform, data_manifold)
model_manifold = transform.domain
beta_schedule = instantiate(cfg.beta_schedule)
flow = instantiate(cfg.flow, manifold=model_manifold, beta_schedule=beta_schedule)
base = instantiate(cfg.base, model_manifold, flow)
pushforward = instantiate(cfg.pushf, flow, base, transform=transform)
log.info("Stage : Instantiate dataset")
rng, next_rng = jax.random.split(rng)
cg_config, _ = load_config(context_gen_cfg)
train_ds = get_pdbredo_chain_dataset(cg_config.data.train)
val_ds = get_pdbredo_chain_dataset(cg_config.data.val)
train_dl = torch.utils.data.DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, collate_fn=PaddingCollate())
train_it = inf_iterator(train_dl)
val_dl = torch.utils.data.DataLoader(val_ds, batch_size=1, shuffle=False, collate_fn=PaddingCollate())
log.info('Train %d | Val %d' % (len(train_ds), len(val_ds)))
log.info("Stage : Instantiate vector field model")
# @partial(checkify, errors=index_checks)
def model(y, t, context):
"""Vector field s_\theta: y, t, context -> T_y M"""
output_shape = get_class(cfg.generator._target_).output_shape(model_manifold)
score = instantiate(
cfg.generator,
cfg.architecture,
cfg.embedding,
output_shape,
manifold=model_manifold,
)
encoder = RDEEncoder(
single_encoder=PerResidueEncoder(
feat_dim=cg_config.model.encoder.node_feat_dim,
max_num_atoms=5
),
masked_bias=hk.Embed(
vocab_size=2,
embed_dim=cg_config.model.encoder.node_feat_dim,
),
pair_encoder=ResiduePairEncoder(
feat_dim=cg_config.model.encoder.pair_feat_dim,
max_num_atoms=5, # N, CA, C, O, CB,
),
attn_encoder=GAEncoder(
**cg_config.model.encoder
)
)
enc_context = encoder(context)
enc_context = einops.rearrange(enc_context, 'b l d -> (b l) d')
t_expanded = jnp.expand_dims(t.reshape(-1), -1)
enc_context = jnp.concatenate([t_expanded, enc_context], axis=-1)
if len(y.shape) == 3:
y = einops.rearrange(y, 'b l d -> (b l) d')
return score(y, enc_context)
model = hk.transform_with_state(model)
rng, next_rng = jax.random.split(rng)
ibatch = next(train_it)
data = ibatch.pop('chi_native')
for k, v in list(ibatch.items()):
if isinstance(v, list) or isinstance(v, int):
_ = ibatch.pop(k)
data = einops.rearrange(jnp.stack([jnp.sin(data), jnp.cos(data)], axis=-1), 'b l m n -> b l (m n)')
t = jnp.zeros((data.shape[0], data.shape[1], 1))
params, state = model.init(rng=next_rng, y=transform.inv(data), t=t, context=ibatch)
log.info("Stage : Instantiate optimiser")
schedule_fn = instantiate(cfg.scheduler)
optimiser = optax.chain(instantiate(cfg.optim), optax.scale_by_schedule(schedule_fn))
opt_state = optimiser.init(params)
if cfg.resume or cfg.mode == "test": # if resume or evaluate
train_state = restore(ckpt_path)
else:
rng, next_rng = jax.random.split(rng)
train_state = TrainState(
opt_state=opt_state,
model_state=state,
step=0,
params=params,
ema_rate=cfg.ema_rate,
params_ema=params,
rng=next_rng, # TODO: we should actually use this for reproducibility
)
save(ckpt_path, train_state)
if cfg.mode == "train" or cfg.mode == "all":
if train_state.step == 0 and cfg.test_plot:
generate_plots(train_state, "test", step=-1)
log.info("Stage : Training")
train_state, success = train(train_state)
if cfg.mode == "test" or (cfg.mode == "all" and success):
if cfg.test_plot:
generate_samples(train_state, "test", step=-1)
success = True
logger.save()
logger.finalize("success" if success else "failure")