# 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 numpy as np import os import re import time import traceback import math from collections import OrderedDict from typing import Any, Dict, Optional, Union import torch from fairseq.dataclass.configs import CheckpointConfig from fairseq.dataclass.utils import ( convert_namespace_to_omegaconf, overwrite_args_by_name, ) from fairseq.distributed.fully_sharded_data_parallel import FSDP, has_FSDP from fairseq.file_io import PathManager from fairseq.models import FairseqDecoder, FairseqEncoder from omegaconf import DictConfig, open_dict, OmegaConf from data import data_utils 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 trainer.data_parallel_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() # TODO(SS): do we need this if no_save_optimizer_state if not trainer.should_save_checkpoint_on_current_rank: if trainer.always_call_state_dict_during_save_checkpoint: trainer.state_dict() 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 = trainer.checkpoint_suffix 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: worst_best = getattr(save_checkpoint, "best", None) chkpts = checkpoint_paths( cfg.save_dir, pattern=r"checkpoint\.best_{}_(\d+\.?\d*){}\.pt".format( cfg.best_checkpoint_metric, suffix ), ) if len(chkpts) > 0: p = chkpts[-1] if cfg.maximize_best_checkpoint_metric else chkpts[0] worst_best = float(p.rsplit("_")[-1].replace("{}.pt".format(suffix), "")) # add random digits to resolve ties with data_utils.numpy_seed(epoch, updates, val_loss): rand_sfx = np.random.randint(0, cfg.keep_best_checkpoints) checkpoint_conds[ "checkpoint.best_{}_{:.3f}{}{}.pt".format( cfg.best_checkpoint_metric, val_loss, rand_sfx, suffix ) ] = worst_best is None or is_better(val_loss, worst_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:]: if cfg.write_checkpoints_asynchronously: # TODO[ioPath]: Need to implement a delayed asynchronous # file copying/moving feature. logger.warning( f"ioPath is not copying {checkpoints[0]} to {cp} " "since async write mode is on." ) else: 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 if cfg.keep_interval_updates_pattern == -1: checkpoints = checkpoint_paths( cfg.save_dir, pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix) ) else: checkpoints = checkpoint_paths( cfg.save_dir, pattern=r"checkpoint_\d+_(\d+){}\.pt".format(suffix), keep_match=True, ) checkpoints = [ x[0] for x in checkpoints if x[1] % cfg.keep_interval_updates_pattern != 0 ] for old_chk in checkpoints[cfg.keep_interval_updates :]: if os.path.lexists(old_chk): os.remove(old_chk) elif PathManager.exists(old_chk): PathManager.rm(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".format(suffix) ) for old_chk in checkpoints[cfg.keep_last_epochs :]: if os.path.lexists(old_chk): os.remove(old_chk) elif PathManager.exists(old_chk): PathManager.rm(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, suffix ), ) 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) elif PathManager.exists(old_chk): PathManager.rm(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 = trainer.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 suffix is not None: 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) _n = itr_state['iterations_in_epoch'] offset = sum(len(_) for _ in epoch_itr.batch_sampler[:_n]) epoch_itr.dataset.dataset._seek(offset=offset) true_num = int(math.ceil(len(epoch_itr.dataset) / 8)) * 8 another_offset = ((epoch_itr.epoch - 1) * true_num + offset) // 8 if hasattr(epoch_itr.dataset, 'pure_text_dataset'): text_offset = (2 * another_offset) % len(epoch_itr.dataset.pure_text_dataset) epoch_itr.dataset.pure_text_dataset._seek(offset=text_offset) if hasattr(epoch_itr.dataset, 'pure_image_dataset'): image_offset = another_offset % len(epoch_itr.dataset.pure_image_dataset) epoch_itr.dataset.pure_image_dataset._seek(offset=image_offset) if hasattr(epoch_itr.dataset, 'detection_dataset'): detection_offset = another_offset % len(epoch_itr.dataset.detection_dataset) epoch_itr.dataset.detection_dataset._seek(offset=detection_offset) 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: # hack to be able to set Namespace in dict config. this should be removed when we update to newer # omegaconf version that supports object flags, or when we migrate all existing models from omegaconf import _utils old_primitive = _utils.is_primitive_type _utils.is_primitive_type = lambda _: True state["cfg"] = OmegaConf.create(state["cfg"]) _utils.is_primitive_type = old_primitive OmegaConf.set_struct(state["cfg"], True) if 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 get_maybe_sharded_checkpoint_filename( filename: str, suffix: str, shard_idx: int, num_shards: int ) -> str: orig_filename = filename filename = filename.replace(".pt", suffix + ".pt") fsdp_filename = filename[:-3] + f"-shard{shard_idx}.pt" model_parallel_filename = orig_filename[:-3] + f"_part{shard_idx}.pt" if PathManager.exists(fsdp_filename): return fsdp_filename elif num_shards > 1: return model_parallel_filename else: return filename 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 model_shard_state = {"shard_weights": [], "shard_metadata": []} assert num_shards > 0 st = time.time() for shard_idx in range(num_shards): filename = get_maybe_sharded_checkpoint_filename( orig_filename, suffix, shard_idx, num_shards ) 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) if "task_state" in state: task.load_state_dict(state["task_state"]) if "fsdp_metadata" in state and num_shards > 1: model_shard_state["shard_weights"].append(state["model"]) model_shard_state["shard_metadata"].append(state["fsdp_metadata"]) # check FSDP import before the code goes too far if not has_FSDP: raise ImportError( "Cannot find FullyShardedDataParallel. " "Please install fairscale with: pip install fairscale" ) if shard_idx == num_shards - 1: consolidated_model_state = FSDP.consolidate_shard_weights( shard_weights=model_shard_state["shard_weights"], shard_metadata=model_shard_state["shard_metadata"], ) model = task.build_model(cfg.model) model.load_state_dict( consolidated_model_state, strict=strict, model_cfg=cfg.model ) else: # model parallel checkpoint or unsharded checkpoint model = task.build_model(cfg.model) model.load_state_dict( state["model"], strict=False, model_cfg=cfg.model ) # reset state so it gets loaded for the next model in ensemble state = None if shard_idx % 10 == 0 and shard_idx > 0: elapsed = time.time() - st logger.info( f"Loaded {shard_idx} shards in {elapsed:.2f}s, {elapsed / (shard_idx+1):.2f}s/shard" ) # build model for ensemble ensemble.append(model) return ensemble, cfg, task def checkpoint_paths(path, pattern=r"checkpoint(\d+)\.pt", keep_match=False): """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 = PathManager.ls(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))) if keep_match: return [(os.path.join(path, x[1]), x[0]) for x in sorted(entries, reverse=True)] else: return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)] def torch_persistent_save(obj, filename, async_write: bool = False): if async_write: with PathManager.opena(filename, "wb") as f: _torch_persistent_save(obj, f) else: with PathManager.open(filename, "wb") as f: _torch_persistent_save(obj, f) # if PathManager.supports_rename(filename): # # do atomic save # with PathManager.open(filename + ".tmp", "wb") as f: # _torch_persistent_save(obj, f) # PathManager.rename(filename + ".tmp", filename) # else: # # fallback to non-atomic save # with PathManager.open(filename, "wb") as f: # _torch_persistent_save(obj, f) 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()) raise def _upgrade_state_dict(state): """Helper for upgrading old model checkpoints.""" # 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 ( "args" in state and 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 / kd_binary_cross_entropy => wav2vec criterion if ( hasattr(state["args"], "criterion") and state["args"].criterion in [ "binary_cross_entropy", "kd_binary_cross_entropy", ] ): state["args"].criterion = "wav2vec" # remove log_keys if it's None (criteria will supply a default value of []) if hasattr(state["args"], "log_keys") and state["args"].log_keys is None: delattr(state["args"], "log_keys") # 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] # convert task data arg to a string instead of List[string] if ( hasattr(state["args"], "data") and isinstance(state["args"].data, list) and len(state["args"].data) > 0 ): state["args"].data = state["args"].data[0] # remove keys in state["args"] related to teacher-student learning for key in [ "static_teachers", "static_teacher_weights", "dynamic_teachers", "dynamic_teacher_weights", ]: if key in state["args"]: delattr(state["args"], key) state["cfg"] = convert_namespace_to_omegaconf(state["args"]) if "cfg" in state and state["cfg"] is not None: cfg = state["cfg"] with open_dict(cfg): # any upgrades for Hydra-based configs if ( "task" in cfg and "eval_wer_config" in cfg.task and isinstance(cfg.task.eval_wer_config.print_alignment, bool) ): cfg.task.eval_wer_config.print_alignment = "hard" if "generation" in cfg and isinstance(cfg.generation.print_alignment, bool): cfg.generation.print_alignment = "hard" if cfg.generation.print_alignment else None if ( "model" in cfg and "w2v_args" in cfg.model and cfg.model.w2v_args is not None and ( hasattr(cfg.model.w2v_args, "task") or "task" in cfg.model.w2v_args ) and hasattr(cfg.model.w2v_args.task, "eval_wer_config") and cfg.model.w2v_args.task.eval_wer_config is not None and isinstance( cfg.model.w2v_args.task.eval_wer_config.print_alignment, bool ) ): cfg.model.w2v_args.task.eval_wer_config.print_alignment = "hard" 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) def load_ema_from_checkpoint(fpath): """Loads exponential moving averaged (EMA) checkpoint from input and returns a model with ema weights. Args: fpath: A string path of checkpoint to load from. Returns: A dict of string keys mapping to various values. The 'model' key from the returned dict should correspond to an OrderedDict mapping string parameter names to torch Tensors. """ params_dict = collections.OrderedDict() new_state = None with PathManager.open(fpath, 'rb') as f: new_state = torch.load( f, map_location=( lambda s, _: torch.serialization.default_restore_location(s, 'cpu') ), ) # EMA model is stored in a separate "extra state" model_params = new_state['extra_state']['ema'] for key in list(model_params.keys()): p = model_params[key] if isinstance(p, torch.HalfTensor): p = p.float() if key not in params_dict: params_dict[key] = p.clone() # NOTE: clone() is needed in case of p is a shared parameter else: raise ValueError("Key {} is repeated in EMA model params.".format(key)) if len(params_dict) == 0: raise ValueError( f"Input checkpoint path '{fpath}' does not contain " "ema model weights, is this model trained with EMA?" ) new_state['model'] = params_dict return new_state