train_openfold.py 21.7 KB
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import argparse
import logging
import os
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import sys
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
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from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
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from pytorch_lightning.loggers import WandbLogger
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from pytorch_lightning.plugins.training_type import DeepSpeedPlugin, DDPPlugin
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import torch

from openfold.config import model_config
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from openfold.data.data_modules import OpenFoldDataModule, OpenFoldMultimerDataModule
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from openfold.model.model import AlphaFold
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from openfold.model.torchscript import script_preset_
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from openfold.np import residue_constants
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from openfold.utils.argparse_utils import remove_arguments
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from openfold.utils.callbacks import (
    EarlyStoppingVerbose,
)
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from openfold.utils.exponential_moving_average import ExponentialMovingAverage
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from openfold.utils.loss import AlphaFoldLoss, lddt_ca
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from openfold.utils.lr_schedulers import AlphaFoldLRScheduler
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from openfold.utils.multi_chain_permutation import multi_chain_permutation_align
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from openfold.utils.seed import seed_everything
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from openfold.utils.superimposition import superimpose
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from openfold.utils.tensor_utils import tensor_tree_map
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from openfold.utils.validation_metrics import (
    drmsd,
    gdt_ts,
    gdt_ha,
)
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from openfold.utils.import_weights import (
    import_jax_weights_,
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    import_openfold_weights_
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)
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from scripts.zero_to_fp32 import (
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    get_fp32_state_dict_from_zero_checkpoint,
    get_global_step_from_zero_checkpoint
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)
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from scripts.zero_to_fp32 import get_optim_files, parse_optim_states, get_model_state_file
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from openfold.utils.logger import PerformanceLoggingCallback

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class OpenFoldWrapper(pl.LightningModule):
    def __init__(self, config):
        super(OpenFoldWrapper, self).__init__()
        self.config = config
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        self.model = AlphaFold(config)
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        self.is_multimer = self.config.globals.is_multimer
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        self.loss = AlphaFoldLoss(config.loss)
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        self.ema = ExponentialMovingAverage(
            model=self.model, decay=config.ema.decay
        )
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        self.cached_weights = None
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        self.last_lr_step = -1
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    def forward(self, batch):
        return self.model(batch)

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    def _log(self, loss_breakdown, batch, outputs, train=True):
        phase = "train" if train else "val"
        for loss_name, indiv_loss in loss_breakdown.items():
            self.log(
                f"{phase}/{loss_name}", 
                indiv_loss, 
                on_step=train, on_epoch=(not train), logger=True,
            )

            if(train):
                self.log(
                    f"{phase}/{loss_name}_epoch",
                    indiv_loss,
                    on_step=False, on_epoch=True, logger=True,
                )

        with torch.no_grad():
            other_metrics = self._compute_validation_metrics(
                batch, 
                outputs,
                superimposition_metrics=(not train)
            )

        for k,v in other_metrics.items():
            self.log(
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                f"{phase}/{k}",
                torch.mean(v),
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                on_step=False, on_epoch=True, logger=True
            )

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    def training_step(self, batch, batch_idx):
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        if(self.ema.device != batch["aatype"].device):
            self.ema.to(batch["aatype"].device)

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        ground_truth = batch.pop('gt_features', None)

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        # Run the model
        outputs = self(batch)
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        # Remove the recycling dimension
        batch = tensor_tree_map(lambda t: t[..., -1], batch)

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        if self.is_multimer:
            batch = multi_chain_permutation_align(out=outputs,
                                                  features=batch,
                                                  ground_truth=ground_truth)

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        # Compute loss
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        loss, loss_breakdown = self.loss(
            outputs, batch, _return_breakdown=True
        )
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        # Log it
        self._log(loss_breakdown, batch, outputs)
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        return loss
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    def on_before_zero_grad(self, *args, **kwargs):
        self.ema.update(self.model)
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    def validation_step(self, batch, batch_idx):
        # At the start of validation, load the EMA weights
        if(self.cached_weights is None):
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            # model.state_dict() contains references to model weights rather
            # than copies. Therefore, we need to clone them before calling 
            # load_state_dict().
            clone_param = lambda t: t.detach().clone()
            self.cached_weights = tensor_tree_map(clone_param, self.model.state_dict())
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            self.model.load_state_dict(self.ema.state_dict()["params"])
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        ground_truth = batch.pop('gt_features', None)

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        # Run the model
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        outputs = self(batch)
        batch = tensor_tree_map(lambda t: t[..., -1], batch)
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        batch["use_clamped_fape"] = 0.
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        if self.is_multimer:
            batch = multi_chain_permutation_align(out=outputs,
                                                  features=batch,
                                                  ground_truth=ground_truth)

        # Compute loss and other metrics
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        _, loss_breakdown = self.loss(
            outputs, batch, _return_breakdown=True
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        )
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        self._log(loss_breakdown, batch, outputs, train=False)
        
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    def validation_epoch_end(self, _):
        # Restore the model weights to normal
        self.model.load_state_dict(self.cached_weights)
        self.cached_weights = None
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    def _compute_validation_metrics(self, 
        batch, 
        outputs, 
        superimposition_metrics=False
    ):
        metrics = {}
        
        gt_coords = batch["all_atom_positions"]
        pred_coords = outputs["final_atom_positions"]
        all_atom_mask = batch["all_atom_mask"]
    
        # This is super janky for superimposition. Fix later
        gt_coords_masked = gt_coords * all_atom_mask[..., None]
        pred_coords_masked = pred_coords * all_atom_mask[..., None]
        ca_pos = residue_constants.atom_order["CA"]
        gt_coords_masked_ca = gt_coords_masked[..., ca_pos, :]
        pred_coords_masked_ca = pred_coords_masked[..., ca_pos, :]
        all_atom_mask_ca = all_atom_mask[..., ca_pos]
    
        lddt_ca_score = lddt_ca(
            pred_coords,
            gt_coords,
            all_atom_mask,
            eps=self.config.globals.eps,
            per_residue=False,
        )
   
        metrics["lddt_ca"] = lddt_ca_score
   
        drmsd_ca_score = drmsd(
            pred_coords_masked_ca,
            gt_coords_masked_ca,
            mask=all_atom_mask_ca, # still required here to compute n
        )
   
        metrics["drmsd_ca"] = drmsd_ca_score
    
        if(superimposition_metrics):
            superimposed_pred, alignment_rmsd = superimpose(
                gt_coords_masked_ca, pred_coords_masked_ca, all_atom_mask_ca,
            )
            gdt_ts_score = gdt_ts(
                superimposed_pred, gt_coords_masked_ca, all_atom_mask_ca
            )
            gdt_ha_score = gdt_ha(
                superimposed_pred, gt_coords_masked_ca, all_atom_mask_ca
            )

            metrics["alignment_rmsd"] = alignment_rmsd
            metrics["gdt_ts"] = gdt_ts_score
            metrics["gdt_ha"] = gdt_ha_score
    
        return metrics

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    def configure_optimizers(self, 
        learning_rate: float = 1e-3,
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        eps: float = 1e-5,
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    ) -> torch.optim.Adam:
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#        return torch.optim.Adam(
#            self.model.parameters(),
#            lr=learning_rate,
#            eps=eps
#        )
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        # Ignored as long as a DeepSpeed optimizer is configured
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        optimizer = torch.optim.Adam(
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            self.model.parameters(), 
            lr=learning_rate, 
            eps=eps
        )
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        if self.last_lr_step != -1:
            for group in optimizer.param_groups:
                if 'initial_lr' not in group:
                    group['initial_lr'] = learning_rate

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        lr_scheduler = AlphaFoldLRScheduler(
            optimizer,
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            last_epoch=self.last_lr_step
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        )
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        return {
            "optimizer": optimizer,
            "lr_scheduler": {
                "scheduler": lr_scheduler,
                "interval": "step",
                "name": "AlphaFoldLRScheduler",
            }
        }
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    def on_load_checkpoint(self, checkpoint):
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        ema = checkpoint["ema"]
        if(not self.model.template_config.enabled):
            ema["params"] = {k:v for k,v in ema["params"].items() if not "template" in k}
        self.ema.load_state_dict(ema)
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    def on_save_checkpoint(self, checkpoint):
        checkpoint["ema"] = self.ema.state_dict()

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    def resume_last_lr_step(self, lr_step):
        self.last_lr_step = lr_step

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    def load_from_jax(self, jax_path):
        model_basename = os.path.splitext(
                os.path.basename(
                    os.path.normpath(jax_path)
                )
        )[0]
        model_version = "_".join(model_basename.split("_")[1:])
        import_jax_weights_(
                self.model, jax_path, version=model_version
        )

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def main(args):
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    if(args.seed is not None):
        seed_everything(args.seed) 

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    config = model_config(
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        args.config_preset, 
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        train=True, 
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        low_prec=(str(args.precision) == "16")
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    ) 
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    model_module = OpenFoldWrapper(config)

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    if(args.resume_from_ckpt):
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        if(os.path.isdir(args.resume_from_ckpt)):  
            last_global_step = get_global_step_from_zero_checkpoint(args.resume_from_ckpt)
        else:
            sd = torch.load(args.resume_from_ckpt)
            last_global_step = int(sd['global_step'])
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        model_module.resume_last_lr_step(last_global_step)
        logging.info("Successfully loaded last lr step...")
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    if(args.resume_from_ckpt and args.resume_model_weights_only):
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        if(os.path.isdir(args.resume_from_ckpt)):
            sd = get_fp32_state_dict_from_zero_checkpoint(args.resume_from_ckpt)
        else:
            sd = torch.load(args.resume_from_ckpt)
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        if 'module' in sd:
            module_sd = {k[len("module."):]:v for k,v in sd['module'].items()}
            import_openfold_weights_(model=model_module, state_dict=module_sd)
        elif 'state_dict' in sd:
            import_openfold_weights_(model=model_module, state_dict=sd['state_dict'])
        else:
            import_openfold_weights_(model=model_module, state_dict=sd)
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        logging.info("Successfully loaded model weights...")
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    if(args.resume_from_jax_params):
        model_module.load_from_jax(args.resume_from_jax_params)
        logging.info(f"Successfully loaded JAX parameters at {args.resume_from_jax_params}...")
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    # TorchScript components of the model
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    if(args.script_modules):
        script_preset_(model_module)
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    if "multimer" in args.config_preset:
        data_module = OpenFoldMultimerDataModule(
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        config=config.data, 
        batch_seed=args.seed,
        **vars(args)
    )
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    else:
        data_module = OpenFoldDataModule(
            config=config.data, 
            batch_seed=args.seed,
            **vars(args)
        )
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    data_module.prepare_data()
    data_module.setup()
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    callbacks = []
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    if(args.checkpoint_every_epoch):
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        mc = ModelCheckpoint(
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            every_n_epochs=1,
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            auto_insert_metric_name=False,
            save_top_k=-1,
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        )
        callbacks.append(mc)

    if(args.early_stopping):
        es = EarlyStoppingVerbose(
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            monitor="val/lddt_ca",
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            min_delta=args.min_delta,
            patience=args.patience,
            verbose=False,
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            mode="max",
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            check_finite=True,
            strict=True,
        )
        callbacks.append(es)
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    if(args.log_performance):
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        global_batch_size = args.num_nodes * args.gpus
        perf = PerformanceLoggingCallback(
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            log_file=os.path.join(args.output_dir, "performance_log.json"),
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            global_batch_size=global_batch_size,
        )
        callbacks.append(perf)
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    if(args.log_lr):
        lr_monitor = LearningRateMonitor(logging_interval="step")
        callbacks.append(lr_monitor)

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    loggers = []
    if(args.wandb):
        wdb_logger = WandbLogger(
            name=args.experiment_name,
            save_dir=args.output_dir,
            id=args.wandb_id,
            project=args.wandb_project,
            **{"entity": args.wandb_entity}
        )
        loggers.append(wdb_logger)

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    if(args.deepspeed_config_path is not None):
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        strategy = DeepSpeedPlugin(
            config=args.deepspeed_config_path,
        )
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        if(args.wandb):
            wdb_logger.experiment.save(args.deepspeed_config_path)
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            wdb_logger.experiment.save("openfold/config.py")
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    elif (args.gpus is not None and args.gpus > 1) or args.num_nodes > 1:
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        strategy = DDPPlugin(find_unused_parameters=False)
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    else:
        strategy = None
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    if(args.wandb):
        freeze_path = f"{wdb_logger.experiment.dir}/package_versions.txt"
        os.system(f"{sys.executable} -m pip freeze > {freeze_path}")
        wdb_logger.experiment.save(f"{freeze_path}")

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    trainer = pl.Trainer.from_argparse_args(
        args,
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        default_root_dir=args.output_dir,
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        strategy=strategy,
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        callbacks=callbacks,
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        logger=loggers,
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    )

    if(args.resume_model_weights_only):
        ckpt_path = None
    else:
        ckpt_path = args.resume_from_ckpt

    trainer.fit(
        model_module, 
        datamodule=data_module,
        ckpt_path=ckpt_path,
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    )


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def bool_type(bool_str: str):
    bool_str_lower = bool_str.lower()
    if bool_str_lower in ('false', 'f', 'no', 'n', '0'):
        return False
    elif bool_str_lower in ('true', 't', 'yes', 'y', '1'):
        return True
    else:
        raise ValueError(f'Cannot interpret {bool_str} as bool')


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if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "train_data_dir", type=str,
        help="Directory containing training mmCIF files"
    )
    parser.add_argument(
        "train_alignment_dir", type=str,
        help="Directory containing precomputed training alignments"
    )
    parser.add_argument(
        "template_mmcif_dir", type=str,
        help="Directory containing mmCIF files to search for templates"
    )
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    parser.add_argument(
        "output_dir", type=str,
        help='''Directory in which to output checkpoints, logs, etc. Ignored
                if not on rank 0'''
    )
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    parser.add_argument(
        "max_template_date", type=str,
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        help='''Cutoff for all templates. In training mode, templates are also 
                filtered by the release date of the target'''
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    )
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    parser.add_argument(
        "--train_mmcif_data_cache_path", type=str, default=None,
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        help="Path to the json file which records all the information of mmcif structures used during training"
    )
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    parser.add_argument(
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        "--use_single_seq_mode", type=str, default=False,
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        help="Use single sequence embeddings instead of MSAs."
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    )
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    parser.add_argument(
        "--distillation_data_dir", type=str, default=None,
        help="Directory containing training PDB files"
    )
    parser.add_argument(
        "--distillation_alignment_dir", type=str, default=None,
        help="Directory containing precomputed distillation alignments"
    )
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    parser.add_argument(
        "--val_data_dir", type=str, default=None,
        help="Directory containing validation mmCIF files"
    )
    parser.add_argument(
        "--val_alignment_dir", type=str, default=None,
        help="Directory containing precomputed validation alignments"
    )
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    parser.add_argument(
        "--val_mmcif_data_cache_path", type=str, default=None,
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        help="path to the json file which records all the information of mmcif structures used during validation"
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    )
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    parser.add_argument(
        "--kalign_binary_path", type=str, default='/usr/bin/kalign',
        help="Path to the kalign binary"
    )
    parser.add_argument(
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        "--train_filter_path", type=str, default=None,
        help='''Optional path to a text file containing names of training
                examples to include, one per line. Used to filter the training 
                set'''
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    )
    parser.add_argument(
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        "--distillation_filter_path", type=str, default=None,
        help="""See --train_filter_path"""
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    )
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    parser.add_argument(
        "--obsolete_pdbs_file_path", type=str, default=None,
        help="""Path to obsolete.dat file containing list of obsolete PDBs and 
             their replacements."""
    )
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    parser.add_argument(
        "--template_release_dates_cache_path", type=str, default=None,
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        help="""Output of scripts/generate_mmcif_cache.py run on template mmCIF
                files."""
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    )
    parser.add_argument(
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        "--use_small_bfd", type=bool_type, default=False,
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        help="Whether to use a reduced version of the BFD database"
    )
    parser.add_argument(
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        "--seed", type=int, default=None,
        help="Random seed"
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    )
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    parser.add_argument(
        "--deepspeed_config_path", type=str, default=None,
        help="Path to DeepSpeed config. If not provided, DeepSpeed is disabled"
    )
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    parser.add_argument(
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        "--checkpoint_every_epoch", action="store_true", default=False,
        help="""Whether to checkpoint at the end of every training epoch"""
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    )
    parser.add_argument(
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        "--early_stopping", type=bool_type, default=False,
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        help="Whether to stop training when validation loss fails to decrease"
    )
    parser.add_argument(
        "--min_delta", type=float, default=0,
        help="""The smallest decrease in validation loss that counts as an 
                improvement for the purposes of early stopping"""
    )
    parser.add_argument(
        "--patience", type=int, default=3,
        help="Early stopping patience"
    )
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    parser.add_argument(
        "--resume_from_ckpt", type=str, default=None,
        help="Path to a model checkpoint from which to restore training state"
    )
    parser.add_argument(
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        "--resume_model_weights_only", type=bool_type, default=False,
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        help="Whether to load just model weights as opposed to training state"
    )
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    parser.add_argument(
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        "--resume_from_jax_params", type=str, default=None,
        help="""Path to an .npz JAX parameter file with which to initialize the model"""
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    )
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    parser.add_argument(
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        "--log_performance", type=bool_type, default=False,
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        help="Measure performance"
    )
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    parser.add_argument(
        "--wandb", action="store_true", default=False,
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        help="Whether to log metrics to Weights & Biases"
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    )
    parser.add_argument(
        "--experiment_name", type=str, default=None,
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        help="Name of the current experiment. Used for wandb logging"
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    )
    parser.add_argument(
        "--wandb_id", type=str, default=None,
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        help="ID of a previous run to be resumed"
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    )
    parser.add_argument(
        "--wandb_project", type=str, default=None,
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        help="Name of the wandb project to which this run will belong"
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    )
    parser.add_argument(
        "--wandb_entity", type=str, default=None,
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        help="wandb username or team name to which runs are attributed"
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    )
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    parser.add_argument(
        "--script_modules", type=bool_type, default=False,
        help="Whether to TorchScript eligible components of them model"
    )
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    parser.add_argument(
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        "--train_chain_data_cache_path", type=str, default=None,
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    )
    parser.add_argument(
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        "--distillation_chain_data_cache_path", type=str, default=None,
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    )
    parser.add_argument(
        "--train_epoch_len", type=int, default=10000,
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        help=(
            "The virtual length of each training epoch. Stochastic filtering "
            "of training data means that training datasets have no "
            "well-defined length. This virtual length affects frequency of "
            "validation & checkpointing (by default, one of each per epoch)."
        )
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    )
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    parser.add_argument(
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        "--log_lr", action="store_true", default=False,
        help="Whether to log the actual learning rate"
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    )
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    parser.add_argument(
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        "--config_preset", type=str, default="initial_training",
        help=(
            'Config setting. Choose e.g. "initial_training", "finetuning", '
            '"model_1", etc. By default, the actual values in the config are '
            'used.'
        )
    )
    parser.add_argument(
        "--_distillation_structure_index_path", type=str, default=None,
    )
    parser.add_argument(
        "--alignment_index_path", type=str, default=None,
        help="Training alignment index. See the README for instructions."
    )
    parser.add_argument(
        "--distillation_alignment_index_path", type=str, default=None,
        help="Distillation alignment index. See the README for instructions."
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    )
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    parser = pl.Trainer.add_argparse_args(parser)
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    # Disable the initial validation pass
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    parser.set_defaults(
        num_sanity_val_steps=0,
    )

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    # Remove some buggy/redundant arguments introduced by the Trainer
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    remove_arguments(
        parser, 
        [
            "--accelerator", 
            "--resume_from_checkpoint",
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            "--reload_dataloaders_every_epoch",
            "--reload_dataloaders_every_n_epochs",
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        ]
    ) 
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    args = parser.parse_args()

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    if(args.seed is None and 
        ((args.gpus is not None and args.gpus > 1) or 
         (args.num_nodes is not None and args.num_nodes > 1))):
        raise ValueError("For distributed training, --seed must be specified")

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    if(str(args.precision) == "16" and args.deepspeed_config_path is not None):
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        raise ValueError("DeepSpeed and FP16 training are not compatible")

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    if(args.resume_from_jax_params is not None and args.resume_from_ckpt is not None):
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        raise ValueError("Choose between loading pretrained Jax-weights and a checkpoint-path")

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    # This re-applies the training-time filters at the beginning of every epoch
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    args.reload_dataloaders_every_n_epochs = 1
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    main(args)