checkpoint_utils.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import ast
import collections
import contextlib
import logging
import os
import re
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union

import torch
from fairseq.dataclass.configs import CheckpointConfig, FairseqConfig
from fairseq.dataclass.utils import (
    convert_namespace_to_omegaconf,
    overwrite_args_by_name,
)
from fairseq.file_io import PathManager
from fairseq.models import FairseqDecoder, FairseqEncoder
from omegaconf import DictConfig, open_dict


logger = logging.getLogger(__name__)


def save_checkpoint(cfg: CheckpointConfig, trainer, epoch_itr, val_loss):
    from fairseq import meters

    # only one worker should attempt to create the required dir
    if cfg.distributed_rank == 0:
        os.makedirs(cfg.save_dir, exist_ok=True)

    prev_best = getattr(save_checkpoint, "best", val_loss)
    if val_loss is not None:
        best_function = max if cfg.maximize_best_checkpoint_metric else min
        save_checkpoint.best = best_function(val_loss, prev_best)

    if cfg.no_save:
        return

    trainer.consolidate_optimizer()

    if not trainer.is_data_parallel_master:
        return

    write_timer = meters.StopwatchMeter()
    write_timer.start()

    epoch = epoch_itr.epoch
    end_of_epoch = epoch_itr.end_of_epoch()
    updates = trainer.get_num_updates()

    logger.info(f"Preparing to save checkpoint for epoch {epoch} @ {updates} updates")

    def is_better(a, b):
        return a >= b if cfg.maximize_best_checkpoint_metric else a <= b

    suffix = cfg.checkpoint_suffix or ""
    checkpoint_conds = collections.OrderedDict()
    checkpoint_conds["checkpoint{}{}.pt".format(epoch, suffix)] = (
        end_of_epoch and not cfg.no_epoch_checkpoints and epoch % cfg.save_interval == 0
    )
    checkpoint_conds["checkpoint_{}_{}{}.pt".format(epoch, updates, suffix)] = (
        not end_of_epoch
        and cfg.save_interval_updates > 0
        and updates % cfg.save_interval_updates == 0
    )
    checkpoint_conds["checkpoint_best{}.pt".format(suffix)] = val_loss is not None and (
        not hasattr(save_checkpoint, "best")
        or is_better(val_loss, save_checkpoint.best)
    )
    if val_loss is not None and cfg.keep_best_checkpoints > 0:
        checkpoint_conds[
            "checkpoint.best_{}_{:.2f}.pt".format(cfg.best_checkpoint_metric, val_loss)
        ] = not hasattr(save_checkpoint, "best") or is_better(
            val_loss, save_checkpoint.best
        )
    checkpoint_conds[
        "checkpoint_last{}.pt".format(suffix)
    ] = not cfg.no_last_checkpoints

    extra_state = {"train_iterator": epoch_itr.state_dict(), "val_loss": val_loss}
    if hasattr(save_checkpoint, "best"):
        extra_state.update({"best": save_checkpoint.best})

    checkpoints = [
        os.path.join(cfg.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond
    ]
    if len(checkpoints) > 0:
        trainer.save_checkpoint(checkpoints[0], extra_state)
        for cp in checkpoints[1:]:
            assert PathManager.copy(
                checkpoints[0], cp, overwrite=True
            ), f"Failed to copy {checkpoints[0]} to {cp}"

        write_timer.stop()
        logger.info(
            "Saved checkpoint {} (epoch {} @ {} updates, score {}) (writing took {} seconds)".format(
                checkpoints[0], epoch, updates, val_loss, write_timer.sum
            )
        )

    if not end_of_epoch and cfg.keep_interval_updates > 0:
        # remove old checkpoints; checkpoints are sorted in descending order
        checkpoints = checkpoint_paths(
            cfg.save_dir, pattern=r"checkpoint_\d+_(\d+)\.pt"
        )
        for old_chk in checkpoints[cfg.keep_interval_updates :]:
            if os.path.lexists(old_chk):
                os.remove(old_chk)

    if cfg.keep_last_epochs > 0:
        # remove old epoch checkpoints; checkpoints are sorted in descending order
        checkpoints = checkpoint_paths(cfg.save_dir, pattern=r"checkpoint(\d+)\.pt")
        for old_chk in checkpoints[cfg.keep_last_epochs :]:
            if os.path.lexists(old_chk):
                os.remove(old_chk)

    if cfg.keep_best_checkpoints > 0:
        # only keep the best N checkpoints according to validation metric
        checkpoints = checkpoint_paths(
            cfg.save_dir,
            pattern=r"checkpoint\.best_{}_(\d+\.?\d*)\.pt".format(
                cfg.best_checkpoint_metric
            ),
        )
        if not cfg.maximize_best_checkpoint_metric:
            checkpoints = checkpoints[::-1]
        for old_chk in checkpoints[cfg.keep_best_checkpoints :]:
            if os.path.lexists(old_chk):
                os.remove(old_chk)


def load_checkpoint(cfg: CheckpointConfig, trainer, **passthrough_args):
    """
    Load a checkpoint and restore the training iterator.

    *passthrough_args* will be passed through to
    ``trainer.get_train_iterator``.
    """

    reset_optimizer = cfg.reset_optimizer
    reset_lr_scheduler = cfg.reset_lr_scheduler
    optimizer_overrides = ast.literal_eval(cfg.optimizer_overrides)
    reset_meters = cfg.reset_meters
    reset_dataloader = cfg.reset_dataloader

    if cfg.finetune_from_model is not None and (
        reset_optimizer or reset_lr_scheduler or reset_meters or reset_dataloader
    ):
        raise ValueError(
            "--finetune-from-model can not be set together with either --reset-optimizer"
            " or reset_lr_scheduler or reset_meters or reset_dataloader"
        )

    suffix = cfg.checkpoint_suffix
    if (
        cfg.restore_file == "checkpoint_last.pt"
    ):  # default value of restore_file is 'checkpoint_last.pt'
        checkpoint_path = os.path.join(
            cfg.save_dir, "checkpoint_last{}.pt".format(suffix)
        )
        first_launch = not PathManager.exists(checkpoint_path)
        if cfg.finetune_from_model is not None and first_launch:
            # if there is no last checkpoint to restore, start the finetune from pretrained model
            # else just use usual logic to load checkpoint, e.g. restart from last checkpoint and etc.
            if PathManager.exists(cfg.finetune_from_model):
                checkpoint_path = cfg.finetune_from_model
                reset_optimizer = True
                reset_lr_scheduler = True
                reset_meters = True
                reset_dataloader = True
                logger.info(
                    f"loading pretrained model from {checkpoint_path}: "
                    "optimizer, lr scheduler, meters, dataloader will be reset"
                )
            else:
                raise ValueError(
                    f"--funetune-from-model {cfg.finetune_from_model} does not exist"
                )
    elif cfg.model_parallel_size > 1:
        checkpoint_path = cfg.restore_file.replace(".pt", suffix + ".pt")
    else:
        checkpoint_path = cfg.restore_file

    if cfg.restore_file != "checkpoint_last.pt" and cfg.finetune_from_model:
        raise ValueError(
            "--finetune-from-model and --restore-file (non-default value) "
            "can not be specified together: " + str(cfg)
        )

    extra_state = trainer.load_checkpoint(
        checkpoint_path,
        reset_optimizer,
        reset_lr_scheduler,
        optimizer_overrides,
        reset_meters=reset_meters,
    )

    if (
        extra_state is not None
        and "best" in extra_state
        and not reset_optimizer
        and not reset_meters
    ):
        save_checkpoint.best = extra_state["best"]

    if extra_state is not None and not reset_dataloader:
        # restore iterator from checkpoint
        itr_state = extra_state["train_iterator"]
        epoch_itr = trainer.get_train_iterator(
            epoch=itr_state["epoch"], load_dataset=True, **passthrough_args
        )
        epoch_itr.load_state_dict(itr_state)
    else:
        epoch_itr = trainer.get_train_iterator(
            epoch=1, load_dataset=True, **passthrough_args
        )

    trainer.lr_step(epoch_itr.epoch)

    return extra_state, epoch_itr


def load_checkpoint_to_cpu(path, arg_overrides=None, load_on_all_ranks=False):
    """Loads a checkpoint to CPU (with upgrading for backward compatibility).

    If doing single-GPU training or if the checkpoint is only being loaded by at
    most one process on each node (current default behavior is for only rank 0
    to read the checkpoint from disk), load_on_all_ranks should be False to
    avoid errors from torch.distributed not having been initialized or
    torch.distributed.barrier() hanging.

    If all processes on each node may be loading the checkpoint
    simultaneously, load_on_all_ranks should be set to True to avoid I/O
    conflicts.

    There's currently no support for > 1 but < all processes loading the
    checkpoint on each node.
    """
    local_path = PathManager.get_local_path(path)
    # The locally cached file returned by get_local_path() may be stale for
    # remote files that are periodically updated/overwritten (ex:
    # checkpoint_last.pt) - so we remove the local copy, sync across processes
    # (if needed), and then download a fresh copy.
    if local_path != path and PathManager.path_requires_pathmanager(path):
        try:
            os.remove(local_path)
        except FileNotFoundError:
            # With potentially multiple processes removing the same file, the
            # file being missing is benign (missing_ok isn't available until
            # Python 3.8).
            pass
        if load_on_all_ranks:
            torch.distributed.barrier()
        local_path = PathManager.get_local_path(path)

    with open(local_path, "rb") as f:
        state = torch.load(f, map_location=torch.device("cpu"))

    if "args" in state and state["args"] is not None and arg_overrides is not None:
        args = state["args"]
        for arg_name, arg_val in arg_overrides.items():
            setattr(args, arg_name, arg_val)

    if "cfg" in state and state["cfg"] is not None and arg_overrides is not None:
        overwrite_args_by_name(state["cfg"], arg_overrides)

    state = _upgrade_state_dict(state)
    return state


def load_model_ensemble(
    filenames,
    arg_overrides: Optional[Dict[str, Any]] = None,
    task=None,
    strict=True,
    suffix="",
    num_shards=1,
    state=None,
):
    """Loads an ensemble of models.

    Args:
        filenames (List[str]): checkpoint files to load
        arg_overrides (Dict[str,Any], optional): override model args that
            were used during model training
        task (fairseq.tasks.FairseqTask, optional): task to use for loading
    """
    assert not (
        strict and num_shards > 1
    ), "Cannot load state dict with strict=True and checkpoint shards > 1"
    ensemble, args, _task = load_model_ensemble_and_task(
        filenames,
        arg_overrides,
        task,
        strict,
        suffix,
        num_shards,
        state,
    )
    return ensemble, args


def load_model_ensemble_and_task(
    filenames,
    arg_overrides: Optional[Dict[str, Any]] = None,
    task=None,
    strict=True,
    suffix="",
    num_shards=1,
    state=None,
):
    assert state is None or len(filenames) == 1

    from fairseq import tasks

    assert not (
        strict and num_shards > 1
    ), "Cannot load state dict with strict=True and checkpoint shards > 1"
    ensemble = []
    cfg = None
    for filename in filenames:
        orig_filename = filename
        assert num_shards > 0
        for shard_idx in range(num_shards):
            if num_shards == 1:
                filename = filename.replace(".pt", suffix + ".pt")
            else:
                filename = orig_filename[:-3] + f"_part{shard_idx}.pt"

            if not PathManager.exists(filename):
                raise IOError("Model file not found: {}".format(filename))
            if state is None:
                state = load_checkpoint_to_cpu(filename, arg_overrides)
            if "args" in state and state["args"] is not None:
                cfg = convert_namespace_to_omegaconf(state["args"])
            elif "cfg" in state and state["cfg"] is not None:
                cfg = state["cfg"]
            else:
                raise RuntimeError(
                    f"Neither args nor cfg exist in state keys = {state.keys()}"
                )

            if task is None:
                task = tasks.setup_task(cfg.task)

            # build model for ensemble
            model = task.build_model(cfg.model)

            model.load_state_dict(state["model"], strict=strict, model_cfg=cfg.model)

            # reset state so it gets loaded for the next model in ensemble
            state = None

        ensemble.append(model)
    return ensemble, cfg, task


def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt"):
    """Retrieves all checkpoints found in `path` directory.

    Checkpoints are identified by matching filename to the specified pattern. If
    the pattern contains groups, the result will be sorted by the first group in
    descending order.
    """
    pt_regexp = re.compile(pattern)
    files = os.listdir(path)

    entries = []
    for i, f in enumerate(files):
        m = pt_regexp.fullmatch(f)
        if m is not None:
            idx = float(m.group(1)) if len(m.groups()) > 0 else i
            entries.append((idx, m.group(0)))
    return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)]


def torch_persistent_save(obj, f):
    if isinstance(f, str):
        with PathManager.open(f, "wb") as h:
            torch_persistent_save(obj, h)
        return
    for i in range(3):
        try:
            return torch.save(obj, f)
        except Exception:
            if i == 2:
                logger.error(traceback.format_exc())


def save_state(
    filename,
    cfg: FairseqConfig,
    model_state_dict,
    criterion,
    optimizer,
    lr_scheduler,
    num_updates,
    optim_history=None,
    extra_state=None,
    **kwargs,
):
    from fairseq import utils

    if optim_history is None:
        optim_history = []
    if extra_state is None:
        extra_state = {}
    state_dict = {
        "cfg": cfg,
        "args": kwargs.get("args", None),
        "model": model_state_dict or {},
        "optimizer_history": optim_history
        + [
            {
                "criterion_name": criterion.__class__.__name__,
                "optimizer_name": optimizer.__class__.__name__,
                "lr_scheduler_state": lr_scheduler.state_dict(),
                "num_updates": num_updates,
            }
        ],
        "extra_state": extra_state,
    }
    if utils.has_parameters(criterion):
        state_dict["criterion"] = criterion.state_dict()

    if cfg is None:
        cfg = state_dict["args"]
        assert cfg is not None, "must provide cfg or args"

    if isinstance(cfg, DictConfig):
        no_save_optimizer_state = cfg.checkpoint.no_save_optimizer_state
    else:
        no_save_optimizer_state = cfg.no_save_optimizer_state
    if not no_save_optimizer_state:
        state_dict["last_optimizer_state"] = optimizer.state_dict()

    # keep everything on CPU
    state_dict = utils.move_to_cpu(state_dict)

    if PathManager.supports_rename(filename):
        # do atomic save
        with PathManager.open(filename + ".tmp", "wb") as f:
            torch_persistent_save(state_dict, f)
        PathManager.rename(filename + ".tmp", filename)
    else:
        # fallback to non-atomic save
        with PathManager.open(filename, "wb") as f:
            torch_persistent_save(state_dict, f)


def _upgrade_state_dict(state):
    """Helper for upgrading old model checkpoints."""
    from fairseq import models, registry, tasks

    # add optimizer_history
    if "optimizer_history" not in state:
        state["optimizer_history"] = [
            {"criterion_name": "CrossEntropyCriterion", "best_loss": state["best_loss"]}
        ]
        state["last_optimizer_state"] = state["optimizer"]
        del state["optimizer"]
        del state["best_loss"]
    # move extra_state into sub-dictionary
    if "epoch" in state and "extra_state" not in state:
        state["extra_state"] = {
            "epoch": state["epoch"],
            "batch_offset": state["batch_offset"],
            "val_loss": state["val_loss"],
        }
        del state["epoch"]
        del state["batch_offset"]
        del state["val_loss"]
    # reduce optimizer history's memory usage (only keep the last state)
    if "optimizer" in state["optimizer_history"][-1]:
        state["last_optimizer_state"] = state["optimizer_history"][-1]["optimizer"]
        for optim_hist in state["optimizer_history"]:
            del optim_hist["optimizer"]
    # record the optimizer class name
    if "optimizer_name" not in state["optimizer_history"][-1]:
        state["optimizer_history"][-1]["optimizer_name"] = "FairseqNAG"
    # move best_loss into lr_scheduler_state
    if "lr_scheduler_state" not in state["optimizer_history"][-1]:
        state["optimizer_history"][-1]["lr_scheduler_state"] = {
            "best": state["optimizer_history"][-1]["best_loss"]
        }
        del state["optimizer_history"][-1]["best_loss"]
    # keep track of number of updates
    if "num_updates" not in state["optimizer_history"][-1]:
        state["optimizer_history"][-1]["num_updates"] = 0
    # old model checkpoints may not have separate source/target positions
    if hasattr(state["args"], "max_positions") and not hasattr(
        state["args"], "max_source_positions"
    ):
        state["args"].max_source_positions = state["args"].max_positions
        state["args"].max_target_positions = state["args"].max_positions
    # use stateful training data iterator
    if "train_iterator" not in state["extra_state"]:
        state["extra_state"]["train_iterator"] = {
            "epoch": state["extra_state"]["epoch"],
            "iterations_in_epoch": state["extra_state"].get("batch_offset", 0),
        }

    # backward compatibility, cfg updates
    if "args" in state and state["args"] is not None:
        # default to translation task
        if not hasattr(state["args"], "task"):
            state["args"].task = "translation"
        # --raw-text and --lazy-load are deprecated
        if getattr(state["args"], "raw_text", False):
            state["args"].dataset_impl = "raw"
        elif getattr(state["args"], "lazy_load", False):
            state["args"].dataset_impl = "lazy"
        # epochs start at 1
        if state["extra_state"]["train_iterator"] is not None:
            state["extra_state"]["train_iterator"]["epoch"] = max(
                state["extra_state"]["train_iterator"].get("epoch", 1), 1
            )
        # --remove-bpe ==> --postprocess
        if hasattr(state["args"], "remove_bpe"):
            state["args"].post_process = state["args"].remove_bpe
        # --min-lr ==> --stop-min-lr
        if hasattr(state["args"], "min_lr"):
            state["args"].stop_min_lr = state["args"].min_lr
            del state["args"].min_lr
        # binary_cross_entropy => wav2vec criterion
        if (
            hasattr(state["args"], "criterion")
            and state["args"].criterion == "binary_cross_entropy"
        ):
            state["args"].criterion = "wav2vec"
        # speech_pretraining => audio pretraining
        if (
            hasattr(state["args"], "task")
            and state["args"].task == "speech_pretraining"
        ):
            state["args"].task = "audio_pretraining"
        # audio_cpc => wav2vec
        if hasattr(state["args"], "arch") and state["args"].arch == "audio_cpc":
            state["args"].arch = "wav2vec"
        # convert legacy float learning rate to List[float]
        if hasattr(state["args"], "lr") and isinstance(state["args"].lr, float):
            state["args"].lr = [state["args"].lr]

        state["cfg"] = convert_namespace_to_omegaconf(state["args"])

    if "cfg" in state and state["cfg"] is not None:
        with open_dict(state["cfg"]):
            # any upgrades for Hydra-based configs
            pass

    return state


def prune_state_dict(state_dict, model_cfg: Optional[DictConfig]):
    """Prune the given state_dict if desired for LayerDrop
    (https://arxiv.org/abs/1909.11556).

    Training with LayerDrop allows models to be robust to pruning at inference
    time. This function prunes state_dict to allow smaller models to be loaded
    from a larger model and re-maps the existing state_dict for this to occur.

    It's called by functions that load models from checkpoints and does not
    need to be called directly.
    """
    arch = None
    if model_cfg is not None:
        arch = (
            model_cfg._name
            if isinstance(model_cfg, DictConfig)
            else getattr(model_cfg, "arch", None)
        )

    if not model_cfg or arch is None or arch == "ptt_transformer":
        # args should not be none, but don't crash if it is.
        return state_dict

    encoder_layers_to_keep = getattr(model_cfg, "encoder_layers_to_keep", None)
    decoder_layers_to_keep = getattr(model_cfg, "decoder_layers_to_keep", None)

    if not encoder_layers_to_keep and not decoder_layers_to_keep:
        return state_dict

    # apply pruning
    logger.info(
        "Pruning model to specified layer configuration - this works best if the model was trained with LayerDrop"
    )

    def create_pruning_pass(layers_to_keep, layer_name):
        keep_layers = sorted(
            int(layer_string) for layer_string in layers_to_keep.split(",")
        )
        mapping_dict = {}
        for i in range(len(keep_layers)):
            mapping_dict[str(keep_layers[i])] = str(i)

        regex = re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name))
        return {"substitution_regex": regex, "mapping_dict": mapping_dict}

    pruning_passes = []
    if encoder_layers_to_keep:
        pruning_passes.append(create_pruning_pass(encoder_layers_to_keep, "encoder"))
    if decoder_layers_to_keep:
        pruning_passes.append(create_pruning_pass(decoder_layers_to_keep, "decoder"))

    new_state_dict = {}
    for layer_name in state_dict.keys():
        match = re.search(r"\.layers\.(\d+)\.", layer_name)
        # if layer has no number in it, it is a supporting layer, such as an
        # embedding
        if not match:
            new_state_dict[layer_name] = state_dict[layer_name]
            continue

        # otherwise, layer should be pruned.
        original_layer_number = match.group(1)
        # figure out which mapping dict to replace from
        for pruning_pass in pruning_passes:
            if original_layer_number in pruning_pass["mapping_dict"] and pruning_pass[
                "substitution_regex"
            ].search(layer_name):
                new_layer_number = pruning_pass["mapping_dict"][original_layer_number]
                substitution_match = pruning_pass["substitution_regex"].search(
                    layer_name
                )
                new_state_key = (
                    layer_name[: substitution_match.start(1)]
                    + new_layer_number
                    + layer_name[substitution_match.end(1) :]
                )
                new_state_dict[new_state_key] = state_dict[layer_name]

    # Since layers are now pruned, *_layers_to_keep are no longer needed.
    # This is more of "It would make it work fix" rather than a proper fix.
    if isinstance(model_cfg, DictConfig):
        context = open_dict(model_cfg)
    else:
        context = contextlib.ExitStack()
    with context:
        if hasattr(model_cfg, "encoder_layers_to_keep"):
            model_cfg.encoder_layers_to_keep = None
        if hasattr(model_cfg, "decoder_layers_to_keep"):
            model_cfg.decoder_layers_to_keep = None

    return new_state_dict


def load_pretrained_component_from_model(
    component: Union[FairseqEncoder, FairseqDecoder], checkpoint: str
):
    """
    Load a pretrained FairseqEncoder or FairseqDecoder from checkpoint into the
    provided `component` object. If state_dict fails to load, there may be a
    mismatch in the architecture of the corresponding `component` found in the
    `checkpoint` file.
    """
    if not PathManager.exists(checkpoint):
        raise IOError("Model file not found: {}".format(checkpoint))
    state = load_checkpoint_to_cpu(checkpoint)
    if isinstance(component, FairseqEncoder):
        component_type = "encoder"
    elif isinstance(component, FairseqDecoder):
        component_type = "decoder"
    else:
        raise ValueError(
            "component to load must be either a FairseqEncoder or "
            "FairseqDecoder. Loading other component types are not supported."
        )
    component_state_dict = OrderedDict()
    for key in state["model"].keys():
        if key.startswith(component_type):
            # encoder.input_layers.0.0.weight --> input_layers.0.0.weight
            component_subkey = key[len(component_type) + 1 :]
            component_state_dict[component_subkey] = state["model"][key]
    component.load_state_dict(component_state_dict, strict=True)
    return component


def verify_checkpoint_directory(save_dir: str) -> None:
    if not os.path.exists(save_dir):
        os.makedirs(save_dir, exist_ok=True)
    temp_file_path = os.path.join(save_dir, "dummy")
    try:
        with open(temp_file_path, "w"):
            pass
    except OSError as e:
        logger.warning(
            "Unable to access checkpoint save directory: {}".format(save_dir)
        )
        raise e
    else:
        os.remove(temp_file_path)