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import numpy as np
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
from colabdesign.shared.utils import copy_dict, update_dict, Key, dict_to_str
from colabdesign.shared.prep import prep_pos
from colabdesign.shared.protein import _np_get_6D_binned
from colabdesign.shared.model import design_model, soft_seq
from .trrosetta import TrRosetta, get_model_params
# borrow some stuff from AfDesign
from colabdesign.af.prep import prep_pdb
from colabdesign.af.alphafold.common import protein
class mk_tr_model(design_model):
def __init__(self, protocol="fixbb", num_models=1,
sample_models=True, data_dir="params/tr",
optimizer="sgd", learning_rate=0.1,
loss_callback=None):
assert protocol in ["fixbb","hallucination","partial"]
self.protocol = protocol
self._data_dir = "." if os.path.isfile(os.path.join("models",f"model_xaa.npy")) else data_dir
self._loss_callback = loss_callback
self._num = 1
# set default options
self.opt = {"temp":1.0, "soft":1.0, "hard":1.0, "dropout":False,
"num_models":num_models,"sample_models":sample_models,
"weights":{}, "lr":1.0, "alpha":1.0,
"learning_rate":learning_rate, "use_pssm":False,
"norm_seq_grad":True}
self._args = {"optimizer":optimizer}
self._params = {}
self._inputs = {}
# setup model
self._model = self._get_model()
self._model_params = []
for k in list("abcde"):
p = os.path.join(self._data_dir,os.path.join("models",f"model_xa{k}.npy"))
self._model_params.append(get_model_params(p))
if protocol in ["hallucination","partial"]:
self._bkg_model = TrRosetta(bkg_model=True)
def _get_model(self):
runner = TrRosetta()
def _get_loss(inputs, outputs):
opt = inputs["opt"]
aux = {"outputs":outputs, "losses":{}}
log_p = jax.tree_util.tree_map(jax.nn.log_softmax, outputs)
# bkg loss
if self.protocol in ["hallucination","partial"]:
p = jax.tree_util.tree_map(jax.nn.softmax, outputs)
log_q = jax.tree_util.tree_map(jax.nn.log_softmax, inputs["6D_bkg"])
aux["losses"]["bkg"] = {}
for k in ["dist","omega","theta","phi"]:
aux["losses"]["bkg"][k] = -(p[k]*(log_p[k]-log_q[k])).sum(-1).mean()
# cce loss
if self.protocol in ["fixbb","partial"]:
if "pos" in opt:
pos = opt["pos"]
log_p = jax.tree_util.tree_map(lambda x:x[:,pos][pos,:], log_p)
q = inputs["6D"]
aux["losses"]["cce"] = {}
for k in ["dist","omega","theta","phi"]:
aux["losses"]["cce"][k] = -(q[k]*log_p[k]).sum(-1).mean()
if self._loss_callback is not None:
aux["losses"].update(self._loss_callback(outputs))
# weighted loss
w = opt["weights"]
tree_multi = lambda x,y: jax.tree_util.tree_map(lambda a,b:a*b, x,y)
losses = {k:(tree_multi(v,w[k]) if k in w else v) for k,v in aux["losses"].items()}
loss = sum(jax.tree_util.tree_leaves(losses))
return loss, aux
def _model(params, model_params, inputs, key):
inputs["params"] = params
opt = inputs["opt"]
seq = soft_seq(params["seq"], inputs["bias"], opt)
if "fix_pos" in opt:
if "pos" in self.opt:
seq_ref = jax.nn.one_hot(inputs["batch"]["aatype_sub"],20)
p = opt["pos"][opt["fix_pos"]]
fix_seq = lambda x:x.at[...,p,:].set(seq_ref)
else:
seq_ref = jax.nn.one_hot(inputs["batch"]["aatype"],20)
p = opt["fix_pos"]
fix_seq = lambda x:x.at[...,p,:].set(seq_ref[...,p,:])
seq = jax.tree_util.tree_map(fix_seq, seq)
inputs.update({"seq":seq["pseudo"][0],
"prf":jnp.where(opt["use_pssm"],seq["pssm"],seq["pseudo"])[0]})
rate = jnp.where(opt["dropout"],0.15,0.0)
outputs = runner(inputs, model_params, key, rate)
loss, aux = _get_loss(inputs, outputs)
aux.update({"seq":seq,"opt":opt})
return loss, aux
return {"grad_fn":jax.jit(jax.value_and_grad(_model, has_aux=True, argnums=0)),
"fn":jax.jit(_model)}
def prep_inputs(self, pdb_filename=None, chain=None, length=None,
pos=None, fix_pos=None, atoms_to_exclude=None, ignore_missing=True,
**kwargs):
'''
prep inputs for TrDesign
'''
if self.protocol in ["fixbb", "partial"]:
# parse PDB file and return features compatible with TrRosetta
pdb = prep_pdb(pdb_filename, chain, ignore_missing=ignore_missing)
self._inputs["batch"] = pdb["batch"]
if fix_pos is not None:
self.opt["fix_pos"] = prep_pos(fix_pos, **pdb["idx"])["pos"]
if self.protocol == "partial" and pos is not None:
self._pos_info = prep_pos(pos, **pdb["idx"])
p = self._pos_info["pos"]
aatype = self._inputs["batch"]["aatype"]
self._inputs["batch"] = jax.tree_util.tree_map(lambda x:x[p], self._inputs["batch"])
self.opt["pos"] = p
if "fix_pos" in self.opt:
sub_i,sub_p = [],[]
p = p.tolist()
for i in self.opt["fix_pos"].tolist():
if i in p:
sub_i.append(i)
sub_p.append(p.index(i))
self.opt["fix_pos"] = np.array(sub_p)
self._inputs["batch"]["aatype_sub"] = aatype[sub_i]
self._inputs["6D"] = _np_get_6D_binned(self._inputs["batch"]["all_atom_positions"],
self._inputs["batch"]["all_atom_mask"])
self._len = len(self._inputs["batch"]["aatype"])
self.opt["weights"]["cce"] = {"dist":1/6,"omega":1/6,"theta":2/6,"phi":2/6}
if atoms_to_exclude is not None:
if "N" in atoms_to_exclude:
# theta = [N]-CA-CB-CB
self.opt["weights"]["cce"] = dict(dist=1/4,omega=1/4,phi=1/2,theta=0)
if "CA" in atoms_to_exclude:
# theta = N-[CA]-CB-CB
# omega = [CA]-CB-CB-[CA]
# phi = [CA]-CB-CB
self.opt["weights"]["cce"] = dict(dist=1,omega=0,phi=0,theta=0)
if self.protocol in ["hallucination", "partial"]:
# compute background distribution
if length is not None: self._len = length
self._inputs["6D_bkg"] = []
key = jax.random.PRNGKey(0)
for n in range(1,6):
p = os.path.join(self._data_dir,os.path.join("bkgr_models",f"bkgr0{n}.npy"))
self._inputs["6D_bkg"].append(self._bkg_model(get_model_params(p), key, self._len))
self._inputs["6D_bkg"] = jax.tree_util.tree_map(lambda *x:np.stack(x).mean(0), *self._inputs["6D_bkg"])
# reweight the background
self.opt["weights"]["bkg"] = dict(dist=1/6,omega=1/6,phi=2/6,theta=2/6)
self._opt = copy_dict(self.opt)
self.restart(**kwargs)
def set_opt(self, *args, **kwargs):
'''
set [opt]ions
-------------------
note: model.restart() resets the [opt]ions to their defaults
use model.set_opt(..., set_defaults=True)
or model.restart(..., reset_opt=False) to avoid this
-------------------
model.set_opt(num_models=1)
model.set_opt(con=dict(num=1)) or set_opt({"con":{"num":1}})
model.set_opt(lr=1, set_defaults=True)
'''
if kwargs.pop("set_defaults", False):
update_dict(self._opt, *args, **kwargs)
update_dict(self.opt, *args, **kwargs)
def restart(self, seed=None, opt=None, weights=None,
seq=None, reset_opt=True, **kwargs):
if reset_opt:
self.opt = copy_dict(self._opt)
self.set_opt(opt)
self.set_weights(weights)
self.set_seed(seed)
# set sequence
self.set_seq(seq, **kwargs)
# setup optimizer
self._k = 0
self.set_optimizer()
# clear previous best
self._tmp = {"best":{}}
def run(self, backprop=True):
'''run model to get outputs, losses and gradients'''
# decide which model params to use
ns = np.arange(5)
m = min(self.opt["num_models"],len(ns))
if self.opt["sample_models"] and m != len(ns):
model_num = np.random.choice(ns,(m,),replace=False)
else:
model_num = ns[:m]
model_num = np.array(model_num).tolist()
# run in serial
aux_all = []
for n in model_num:
model_params = self._model_params[n]
self._inputs["opt"] = self.opt
flags = [self._params, model_params, self._inputs, self.key()]
if backprop:
(loss,aux),grad = self._model["grad_fn"](*flags)
else:
loss,aux = self._model["fn"](*flags)
grad = jax.tree_util.tree_map(np.zeros_like, self._params)
aux.update({"loss":loss, "grad":grad})
aux_all.append(aux)
# average results
self.aux = jax.tree_util.tree_map(lambda *x:np.stack(x).mean(0), *aux_all)
self.aux["model_num"] = model_num
def step(self, backprop=True, callback=None, save_best=True, verbose=1):
self.run(backprop=backprop)
if callback is not None: callback(self)
# modify gradients
if self.opt["norm_seq_grad"]: self._norm_seq_grad()
self._state, self.aux["grad"] = self._optimizer(self._state, self.aux["grad"], self._params)
# apply gradients
lr = self.opt["learning_rate"]
self._params = jax.tree_util.tree_map(lambda x,g:x-lr*g, self._params, self.aux["grad"])
# increment
self._k += 1
# save results
if save_best:
if "aux" not in self._tmp["best"] or self.aux["loss"] < self._tmp["best"]["aux"]["loss"]:
self._tmp["best"]["aux"] = self.aux
# print
if verbose and (self._k % verbose) == 0:
x = self.get_loss(get_best=False)
x["models"] = self.aux["model_num"]
print(dict_to_str(x, print_str=f"{self._k}", keys=["models"]))
def predict(self, seq=None, models=0):
self.set_opt(dropout=False)
if seq is not None:
self.set_seq(seq=seq, set_state=False)
self.run(backprop=False)
def design(self, iters=100, opt=None, weights=None, save_best=True, verbose=1):
self.set_opt(opt)
self.set_weights(weights)
for _ in range(iters):
self.step(save_best=save_best, verbose=verbose)
def plot(self, mode="preds", dpi=100, get_best=True):
'''plot predictions'''
assert mode in ["preds","feats","bkg_feats"]
if mode == "preds":
aux = self._tmp["best"]["aux"] if (get_best and "aux" in self._tmp["best"]) else self.aux
x = aux["outputs"]
elif mode == "feats":
x = self._inputs["6D"]
elif mode == "bkg_feats":
x = self._inputs["6D_bkg"]
x = jax.tree_util.tree_map(np.asarray, x)
plt.figure(figsize=(4*4,4), dpi=dpi)
for n,k in enumerate(["theta","phi","dist","omega"]):
v = x[k]
plt.subplot(1,4,n+1)
plt.title(k)
plt.imshow(v.argmax(-1),cmap="binary")
plt.show()
def get_loss(self, k=None, get_best=True):
aux = self._tmp["best"]["aux"] if (get_best and "aux" in self._tmp["best"]) else self.aux
if k is None:
return {k:self.get_loss(k, get_best=get_best) for k in aux["losses"].keys()}
losses = aux["losses"][k]
weights = aux["opt"]["weights"][k]
weighted_losses = jax.tree_util.tree_map(lambda l,w:l*w, losses, weights)
return float(sum(jax.tree_util.tree_leaves(weighted_losses)))
def af_callback(self, weight=1.0, seed=None):
def callback(af_model):
# copy [opt]ions from afdesign
for k,v in af_model.opt.items():
if k in self.opt and k not in ["weights"]:
self.opt[k] = af_model.opt[k]
# update sequence input
self._params["seq"] = af_model._params["seq"]
# run trdesign
self.run(backprop = weight > 0)
# add gradients
af_model.aux["grad"]["seq"] += weight * self.aux["grad"]["seq"]
# add loss
af_model.aux["loss"] += weight * self.aux["loss"]
# for verbose printout
if self.protocol in ["hallucination","partial"]:
af_model.aux["losses"]["TrD_bkg"] = self.get_loss("bkg", get_best=False)
if self.protocol in ["fixbb","partial"]:
af_model.aux["losses"]["TrD_cce"] = self.get_loss("cce", get_best=False)
self.restart(seed=seed)
return callback |