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import jax
import jax.numpy as jnp
import numpy as np
from inspect import signature
from colabdesign.af.alphafold.model import data, config, model, all_atom
from colabdesign.shared.model import design_model
from colabdesign.shared.utils import Key
from colabdesign.af.prep import _af_prep
from colabdesign.af.loss import _af_loss, get_plddt, get_pae, get_ptm
from colabdesign.af.loss import get_contact_map, get_seq_ent_loss, get_mlm_loss
from colabdesign.af.utils import _af_utils
from colabdesign.af.design import _af_design
from colabdesign.af.inputs import _af_inputs, update_seq, update_aatype
################################################################
# MK_DESIGN_MODEL - initialize model, and put it all together
################################################################
class mk_af_model(design_model, _af_inputs, _af_loss, _af_prep, _af_design, _af_utils):
def __init__(self,
protocol="fixbb",
use_multimer=False,
use_templates=False,
debug=False,
data_dir=".",
**kwargs):
assert protocol in ["fixbb","hallucination","binder","partial"]
self.protocol = protocol
self._num = kwargs.pop("num_seq",1)
self._args = {"use_templates":use_templates, "use_multimer":use_multimer, "use_bfloat16":True,
"recycle_mode":"last", "use_mlm": False, "realign": True,
"debug":debug, "repeat":False, "homooligomer":False, "copies":1,
"optimizer":"sgd", "best_metric":"loss",
"traj_iter":1, "traj_max":10000,
"clear_prev": True, "use_dgram":False,
"shuffle_first":True, "use_remat":True,
"alphabet_size":20,
"use_initial_guess":False, "use_initial_atom_pos":False}
if self.protocol == "binder": self._args["use_templates"] = True
self.opt = {"dropout":True, "pssm_hard":False, "learning_rate":0.1, "norm_seq_grad":True,
"num_recycles":0, "num_models":1, "sample_models":True,
"temp":1.0, "soft":0.0, "hard":0.0, "alpha":2.0,
"con": {"num":2, "cutoff":14.0, "binary":False, "seqsep":9, "num_pos":float("inf")},
"i_con": {"num":1, "cutoff":21.6875, "binary":False, "num_pos":float("inf")},
"template": {"rm_ic":False},
"weights": {"seq_ent":0.0, "plddt":0.0, "pae":0.0, "exp_res":0.0, "helix":0.0},
"fape_cutoff":10.0}
self._params = {}
self._inputs = {}
self._tmp = {"traj":{"seq":[],"xyz":[],"plddt":[],"pae":[]},
"log":[],"best":{}}
# set arguments/options
if "initial_guess" in kwargs: kwargs["use_initial_guess"] = kwargs.pop("initial_guess")
model_names = kwargs.pop("model_names",None)
keys = list(kwargs.keys())
for k in keys:
if k in self._args: self._args[k] = kwargs.pop(k)
if k in self.opt: self.opt[k] = kwargs.pop(k)
# collect callbacks
self._callbacks = {"model": {"pre": kwargs.pop("pre_callback",None),
"post":kwargs.pop("post_callback",None),
"loss":kwargs.pop("loss_callback",None)},
"design":{"pre": kwargs.pop("pre_design_callback",None),
"post":kwargs.pop("post_design_callback",None)}}
for m,n in self._callbacks.items():
for k,v in n.items():
if v is None: v = []
if not isinstance(v,list): v = [v]
self._callbacks[m][k] = v
if self._args["use_mlm"]:
self.opt["mlm_dropout"] = 0.15
self.opt["weights"]["mlm"] = 0.1
assert len(kwargs) == 0, f"ERROR: the following inputs were not set: {kwargs}"
#############################
# configure AlphaFold
#############################
if self._args["use_multimer"]:
self._cfg = config.model_config("model_1_multimer")
# TODO
self.opt["pssm_hard"] = True
else:
self._cfg = config.model_config("model_1_ptm" if self._args["use_templates"] else "model_3_ptm")
if self._args["recycle_mode"] in ["average","first","last","sample"]:
num_recycles = 0
else:
num_recycles = self.opt["num_recycles"]
self._cfg.model.num_recycle = num_recycles
self._cfg.model.global_config.use_remat = self._args["use_remat"]
self._cfg.model.global_config.use_dgram = self._args["use_dgram"]
self._cfg.model.global_config.bfloat16 = self._args["use_bfloat16"]
# load model_params
if model_names is None:
model_names = []
if self._args["use_multimer"]:
model_names += [f"model_{k}_multimer_v3" for k in [1,2,3,4,5]]
else:
if self._args["use_templates"]:
model_names += [f"model_{k}_ptm" for k in [1,2]]
else:
model_names += [f"model_{k}_ptm" for k in [1,2,3,4,5]]
self._model_params, self._model_names = [],[]
for model_name in model_names:
params = data.get_model_haiku_params(model_name=model_name, data_dir=data_dir, fuse=True)
if params is not None:
if not self._args["use_multimer"] and not self._args["use_templates"]:
params = {k:v for k,v in params.items() if "template" not in k}
self._model_params.append(params)
self._model_names.append(model_name)
else:
print(f"WARNING: '{model_name}' not found")
#####################################
# set protocol specific functions
#####################################
idx = ["fixbb","hallucination","binder","partial"].index(self.protocol)
self.prep_inputs = [self._prep_fixbb, self._prep_hallucination, self._prep_binder, self._prep_partial][idx]
self._get_loss = [self._loss_fixbb, self._loss_hallucination, self._loss_binder, self._loss_partial][idx]
def _get_model(self, cfg, callback=None):
a = self._args
runner = model.RunModel(cfg,
recycle_mode=a["recycle_mode"],
use_multimer=a["use_multimer"])
# setup function to get gradients
def _model(params, model_params, inputs, key):
inputs["params"] = params
opt = inputs["opt"]
aux = {}
key = Key(key=key).get
#######################################################################
# INPUTS
#######################################################################
# get sequence
seq = self._get_seq(inputs, aux, key())
# update sequence features
pssm = jnp.where(opt["pssm_hard"], seq["hard"], seq["pseudo"])
if a["use_mlm"]:
shape = seq["pseudo"].shape[:2]
mlm = jax.random.bernoulli(key(),opt["mlm_dropout"],shape)
update_seq(seq["pseudo"], inputs, seq_pssm=pssm, mlm=mlm)
else:
update_seq(seq["pseudo"], inputs, seq_pssm=pssm)
# update amino acid sidechain identity
update_aatype(seq["pseudo"][0].argmax(-1), inputs)
# define masks
inputs["msa_mask"] = jnp.where(inputs["seq_mask"],inputs["msa_mask"],0)
inputs["seq"] = aux["seq"]
# update template features
inputs["mask_template_interchain"] = opt["template"]["rm_ic"]
if a["use_templates"]:
self._update_template(inputs, key())
# set dropout
inputs["use_dropout"] = opt["dropout"]
if "batch" not in inputs:
inputs["batch"] = None
# pre callback
for fn in self._callbacks["model"]["pre"]:
fn_args = {"inputs":inputs, "opt":opt, "aux":aux,
"seq":seq, "key":key(), "params":params}
sub_args = {k:fn_args.get(k,None) for k in signature(fn).parameters}
fn(**sub_args)
#######################################################################
# OUTPUTS
#######################################################################
outputs = runner.apply(model_params, key(), inputs)
# add aux outputs
aux.update({"atom_positions": outputs["structure_module"]["final_atom_positions"],
"atom_mask": outputs["structure_module"]["final_atom_mask"],
"residue_index": inputs["residue_index"],
"aatype": inputs["aatype"],
"plddt": get_plddt(outputs),
"pae": get_pae(outputs),
"ptm": get_ptm(inputs, outputs),
"i_ptm": get_ptm(inputs, outputs, interface=True),
"cmap": get_contact_map(outputs, opt["con"]["cutoff"]),
"i_cmap": get_contact_map(outputs, opt["i_con"]["cutoff"]),
"prev": outputs["prev"]})
#######################################################################
# LOSS
#######################################################################
aux["losses"] = {}
# add protocol specific losses
self._get_loss(inputs=inputs, outputs=outputs, aux=aux)
# sequence entropy loss
aux["losses"].update(get_seq_ent_loss(inputs))
# experimental masked-language-modeling
if a["use_mlm"]:
aux["mlm"] = outputs["masked_msa"]["logits"]
mask = jnp.where(inputs["seq_mask"],mlm,0)
aux["losses"].update(get_mlm_loss(outputs, mask=mask, truth=seq["pssm"]))
# run user defined callbacks
for c in ["loss","post"]:
for fn in self._callbacks["model"][c]:
fn_args = {"inputs":inputs, "outputs":outputs, "opt":opt,
"aux":aux, "seq":seq, "key":key(), "params":params}
sub_args = {k:fn_args.get(k,None) for k in signature(fn).parameters}
if c == "loss": aux["losses"].update(fn(**sub_args))
if c == "post": fn(**sub_args)
# save for debugging
if a["debug"]: aux["debug"] = {"inputs":inputs,"outputs":outputs}
# weighted loss
w = opt["weights"]
loss = sum([v * w[k] if k in w else v for k,v in aux["losses"].items()])
return loss, aux
return {"grad_fn":jax.jit(jax.value_and_grad(_model, has_aux=True, argnums=0)),
"fn":jax.jit(_model), "runner":runner}
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