import inspect import json import math import os import re import shutil import warnings from contextlib import contextmanager from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Tuple, Union from nltk import word_tokenize import numpy as np import torch from packaging import version from torch import nn from torch.utils.data.dataloader import DataLoader from torch.utils.data.dataset import Dataset from torch.utils.data.distributed import DistributedSampler from torch.utils.data.sampler import RandomSampler, Sampler, SequentialSampler from tqdm.auto import tqdm, trange from torch.nn.utils.rnn import pad_sequence import random from transformers.data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator from transformers.file_utils import is_datasets_available, is_torch_tpu_available from transformers.integrations import ( default_hp_search_backend, is_comet_available, is_optuna_available, is_ray_available, is_tensorboard_available, is_wandb_available, run_hp_search_optuna, run_hp_search_ray, ) from transformers.modeling_utils import PreTrainedModel from transformers.optimization import AdamW, get_linear_schedule_with_warmup, get_constant_schedule_with_warmup from transformers.tokenization_utils_base import PreTrainedTokenizerBase from transformers.trainer_utils import ( PREFIX_CHECKPOINT_DIR, BestRun, EvalPrediction, EvaluationStrategy, HPSearchBackend, PredictionOutput, TrainOutput, default_compute_objective, default_hp_space, set_seed, ) from transformers.training_args import TrainingArguments from transformers.utils import logging _use_native_amp = False _use_apex = False EPS = 1e-12 INIT_GUMBEL_TEMP = 5.0 control_lst = ['positive', 'negative', 'neutral'] Control_Temp = {'positive': 3967, 'negative':4633, 'neutral':8500} control_Map = [torch.LongTensor([3967]), torch.LongTensor([4633]), torch.LongTensor([8500])] sst_lst = [(0, 2), (1, 3), (4,)] sst_standard = ["positive", "negative", "very positive", "very negative", "neutral"] # Control_?Map = {j:i for i, j in enumerate(control_lst)} # Check if Pytorch version >= 1.6 to switch between Native AMP and Apex if version.parse(torch.__version__) < version.parse("1.6"): from transformers.file_utils import is_apex_available if is_apex_available(): from apex import amp _use_apex = True else: _use_native_amp = True from torch.cuda.amp import autocast if is_datasets_available(): import datasets if is_torch_tpu_available(): import torch_xla.core.xla_model as xm import torch_xla.debug.metrics as met import torch_xla.distributed.parallel_loader as pl if is_tensorboard_available(): try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter if is_wandb_available(): import wandb if is_comet_available(): import comet_ml if is_optuna_available(): import optuna if is_ray_available(): from ray import tune logger = logging.get_logger(__name__) @contextmanager def torch_distributed_zero_first(local_rank: int): """ Decorator to make all processes in distributed training wait for each local_master to do something. Args: local_rank (:obj:`int`): The rank of the local process. """ if local_rank not in [-1, 0]: torch.distributed.barrier() yield if local_rank == 0: torch.distributed.barrier() def helper_token2bpe(offsets): full_lst = [] for example_offset in offsets: bpe2token = [] token2bpe = [] token_idx = -1 # print(example_offset) for bpe_idx, (a,b) in enumerate(example_offset): # print(token2bpe, a, b, bpe_idx) if b - a > 0: if a == 0: # new token token_idx += 1 bpe2token.append(token_idx) token2bpe.append([]) token2bpe[-1].append(bpe_idx) else: # prev token. bpe2token.append(token_idx) token2bpe[-1].append(bpe_idx) else: bpe2token.append(None) full_lst.append((bpe2token, token2bpe)) return full_lst class SequentialDistributedSampler(Sampler): """ Distributed Sampler that subsamples indicies sequentially, making it easier to collate all results at the end. Even though we only use this sampler for eval and predict (no training), which means that the model params won't have to be synced (i.e. will not hang for synchronization even if varied number of forward passes), we still add extra samples to the sampler to make it evenly divisible (like in `DistributedSampler`) to make it easy to `gather` or `reduce` resulting tensors at the end of the loop. """ def __init__(self, dataset, num_replicas=None, rank=None): if num_replicas is None: if not torch.distributed.is_available(): raise RuntimeError("Requires distributed package to be available") num_replicas = torch.distributed.get_world_size() if rank is None: if not torch.distributed.is_available(): raise RuntimeError("Requires distributed package to be available") rank = torch.distributed.get_rank() self.dataset = dataset self.num_replicas = num_replicas self.rank = rank self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) self.total_size = self.num_samples * self.num_replicas def __iter__(self): indices = list(range(len(self.dataset))) # add extra samples to make it evenly divisible indices += indices[: (self.total_size - len(indices))] assert ( len(indices) == self.total_size ), f"Indices length {len(indices)} and total size {self.total_size} mismatched" # subsample indices = indices[self.rank * self.num_samples : (self.rank + 1) * self.num_samples] assert ( len(indices) == self.num_samples ), f"Indices length {len(indices)} and sample number {self.num_samples} mismatched" return iter(indices) def __len__(self): return self.num_samples def get_tpu_sampler(dataset: Dataset): if xm.xrt_world_size() <= 1: return RandomSampler(dataset) return DistributedSampler(dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()) class Trainer_Prefix: """ Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. Args: model (:class:`~transformers.PreTrainedModel`, `optional`): The model to train, evaluate or use for predictions. If not provided, a ``model_init`` must be passed. args (:class:`~transformers.TrainingArguments`, `optional`): The arguments to tweak for training. Will default to a basic instance of :class:`~transformers.TrainingArguments` with the ``output_dir`` set to a directory named `tmp_trainer` in the current directory if not provided. data_collator (:obj:`DataCollator`, `optional`): The function to use to form a batch from a list of elements of :obj:`train_dataset` or :obj:`eval_dataset`. Will default to :func:`~transformers.default_data_collator` if no ``tokenizer`` is provided, an instance of :func:`~transformers.DataCollatorWithPadding` otherwise. train_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`): The dataset to use for training. If it is an :obj:`datasets.Dataset`, columns not accepted by the ``model.forward()`` method are automatically removed. eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`): The dataset to use for evaluation. If it is an :obj:`datasets.Dataset`, columns not accepted by the ``model.forward()`` method are automatically removed. tokenizer (:class:`PreTrainedTokenizerBase`, `optional`): The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs the maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model. model_init (:obj:`Callable[[], PreTrainedModel]`, `optional`): A function that instantiates the model to be used. If provided, each call to :meth:`~transformers.Trainer.train` will start from a new instance of the model as given by this function. compute_metrics (:obj:`Callable[[EvalPrediction], Dict]`, `optional`): The function that will be used to compute metrics at evaluation. Must take a :class:`~transformers.EvalPrediction` and return a dictionary string to metric values. tb_writer (:obj:`SummaryWriter`, `optional`): Object to write to TensorBoard. optimizers (:obj:`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR`, `optional`): A tuple containing the optimizer and the scheduler to use. Will default to an instance of :class:`~transformers.AdamW` on your model and a scheduler given by :func:`~transformers.get_linear_schedule_with_warmup` controlled by :obj:`args`. kwargs: Deprecated keyword arguments. """ def __init__( self, model: Optional[PreTrainedModel] = None, model_gpt2 : Optional[PreTrainedModel] = None, args: TrainingArguments = None, data_collator: Optional[DataCollator] = None, train_dataset: Optional[Dataset] = None, eval_dataset: Optional[Dataset] = None, tokenizer: Optional["PreTrainedTokenizerBase"] = None, model_init: Callable[[], PreTrainedModel] = None, compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None, tb_writer: Optional["SummaryWriter"] = None, optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), task_mode: Optional[str] = None, use_dropout: Optional[bool] = False, distill: Optional[bool] = False, matching_objective:Optional[str]= None, finetuned_gpt2: Optional[PreTrainedModel] = None, **kwargs, ): if args is None: logger.info("No `TrainingArguments` passed, using the current path as `output_dir`.") args = TrainingArguments("tmp_trainer") self.args = args # Seed must be set before instantiating the model when using model set_seed(self.args.seed) assert ( model is not None or model_init is not None ), "You must provide a model to use `Trainer`, either by using the `model` argument or the `model_init` argument." assert model_init is None self.model = model.to(args.device) if model is not None else None self.gpt2 = model_gpt2.to(args.device) if model_gpt2 is not None else None default_collator = default_data_collator if tokenizer is None else DataCollatorWithPadding(tokenizer) self.data_collator = data_collator if data_collator is not None else default_collator self.train_dataset = train_dataset self.eval_dataset = eval_dataset self.tokenizer = tokenizer self.model_init = model_init self.compute_metrics = compute_metrics self.optimizer, self.lr_scheduler = optimizers self.task_mode = task_mode self.use_dropout = use_dropout self.curr_best_eval = 10000000. self.distill = distill if self.distill: self.matching_objective = matching_objective self.finetuned_gpt2 = finetuned_gpt2 if model_init is not None and (self.optimizer is not None or self.lr_scheduler is not None): raise RuntimeError( "Passing a `model_init` is incompatible with providing the `optimizers` argument." "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method." ) self.tb_writer = tb_writer self.log_history = [] if "prediction_loss_only" in kwargs: warnings.warn( "Passing `prediction_loss_only` as a keyword argument is deprecated and won't be possible in a future version. Use `args.prediction_loss_only` instead.", FutureWarning, ) self.args.prediction_loss_only = kwargs.pop("prediction_loss_only") assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}." if tb_writer is None and is_tensorboard_available() and self.is_world_process_zero(): self.tb_writer = SummaryWriter(log_dir=self.args.logging_dir) if not is_tensorboard_available(): logger.warning( "You are instantiating a Trainer but Tensorboard is not installed. You should consider installing it." ) # Will be set to True by `self._setup_loggers()` on first call to `self.log()`. self._loggers_initialized = False # Create output directory if needed if self.is_world_process_zero(): os.makedirs(self.args.output_dir, exist_ok=True) if is_torch_tpu_available(): # Set an xla_device flag on the model's config. # We'll find a more elegant and not need to do this in the future. self.model.config.xla_device = True if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)): self.data_collator = self.data_collator.collate_batch warnings.warn( ( "The `data_collator` should now be a simple callable (function, class with `__call__`), classes " + "with a `collate_batch` are deprecated and won't be supported in a future version." ), FutureWarning, ) if is_datasets_available(): if isinstance(train_dataset, datasets.Dataset): self._remove_unused_columns(self.train_dataset, description="training") if isinstance(eval_dataset, datasets.Dataset): self._remove_unused_columns(self.eval_dataset, description="evaluation") self.global_step = None self.epoch = None self.total_flos = None if self.args.fp16 and _use_native_amp: self.scaler = torch.cuda.amp.GradScaler() self.hp_search_backend = None self.use_tune_checkpoints = False if self.args.label_names is None: self.args.label_names = (["labels"] ) def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None): if not self.args.remove_unused_columns: return # Inspect model forward signature to keep only the arguments it accepts. signature = inspect.signature(self.model.forward) signature_columns = list(signature.parameters.keys()) # Labels may be named label or label_ids, the default data collator handles that. signature_columns += ["label", "label_ids"] columns = [k for k in signature_columns if k in dataset.column_names] ignored_columns = list(set(dataset.column_names) - set(signature_columns)) dset_description = "" if description is None else f"in the {description} set " logger.info( f"The following columns {dset_description}don't have a corresponding argument in `{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}." ) dataset.set_format(type=dataset.format["type"], columns=columns) def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]: if isinstance(self.train_dataset, torch.utils.data.IterableDataset): return None elif is_torch_tpu_available(): return get_tpu_sampler(self.train_dataset) else: return ( RandomSampler(self.train_dataset) if self.args.local_rank == -1 else DistributedSampler(self.train_dataset) ) def get_train_dataloader(self) -> DataLoader: """ Returns the training :class:`~torch.utils.data.DataLoader`. Will use no sampler if :obj:`self.train_dataset` is a :obj:`torch.utils.data.IterableDataset`, a random sampler (adapted to distributed training if necessary) otherwise. Subclass and override this method if you want to inject some custom behavior. """ if self.train_dataset is None: raise ValueError("Trainer: training requires a train_dataset.") train_sampler = self._get_train_sampler() return DataLoader( self.train_dataset, batch_size=self.args.train_batch_size, sampler=train_sampler, collate_fn=self.data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, worker_init_fn=np.random.seed(self.args.seed) ) def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.sampler.Sampler]: if isinstance(eval_dataset, torch.utils.data.IterableDataset): return None elif is_torch_tpu_available(): return SequentialDistributedSampler(eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()) elif self.args.local_rank != -1: return SequentialDistributedSampler(eval_dataset) else: return SequentialSampler(eval_dataset) def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader: """ Returns the evaluation :class:`~torch.utils.data.DataLoader`. Will use no sampler if :obj:`self.eval_dataset` is a :obj:`torch.utils.data.IterableDataset`, a sequential sampler (adapted to distributed training if necessary) otherwise. Subclass and override this method if you want to inject some custom behavior. Args: eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`): If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not accepted by the ``model.forward()`` method are automatically removed. """ if eval_dataset is None and self.eval_dataset is None: raise ValueError("Trainer: evaluation requires an eval_dataset.") elif eval_dataset is not None and is_datasets_available() and isinstance(eval_dataset, datasets.Dataset): self._remove_unused_columns(eval_dataset, description="evaluation") eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset eval_sampler = self._get_eval_sampler(eval_dataset) return DataLoader( eval_dataset, sampler=eval_sampler, batch_size=self.args.eval_batch_size, collate_fn=self.data_collator, drop_last=self.args.dataloader_drop_last, num_workers=self.args.dataloader_num_workers, worker_init_fn=np.random.seed(self.args.seed) ) def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader: """ Returns the test :class:`~torch.utils.data.DataLoader`. Will use no sampler if :obj:`test_dataset` is a :obj:`torch.utils.data.IterableDataset`, a sequential sampler (adapted to distributed training if necessary) otherwise. Subclass and override this method if you want to inject some custom behavior. Args: eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`): The test dataset to use. If it is an :obj:`datasets.Dataset`, columns not accepted by the ``model.forward()`` method are automatically removed. """ if is_datasets_available() and isinstance(test_dataset, datasets.Dataset): self._remove_unused_columns(test_dataset, description="test") test_sampler = self._get_eval_sampler(test_dataset) # We use the same batch_size as for eval. return DataLoader( test_dataset, sampler=test_sampler, batch_size=self.args.eval_batch_size, collate_fn=self.data_collator, drop_last=self.args.dataloader_drop_last, worker_init_fn=np.random.seed(self.args.seed) ) def create_optimizer_and_scheduler(self, num_training_steps: int): """ Setup the optimizer and the learning rate scheduler. We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass. """ if self.optimizer is None: no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in self.model.named_parameters() if (not any(nd in n for nd in no_decay)) and p.requires_grad], "weight_decay": self.args.weight_decay, }, { "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay) and p.requires_grad], "weight_decay": 0.0, }, ] self.optimizer = AdamW( optimizer_grouped_parameters, lr=self.args.learning_rate, betas=(self.args.adam_beta1, self.args.adam_beta2), eps=self.args.adam_epsilon, ) # for n, p in self.model.named_parameters(): # print(n,p.requires_grad) print(self.optimizer.state_dict()) if self.lr_scheduler is None: self.lr_scheduler = get_linear_schedule_with_warmup( self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps ) def setup_wandb(self): """ Setup the optional Weights & Biases (`wandb`) integration. One can subclass and override this method to customize the setup if needed. Find more information `here `__. You can also override the following environment variables: Environment: WANDB_WATCH: (Optional, ["gradients", "all", "false"]) "gradients" by default, set to "false" to disable gradient logging or "all" to log gradients and parameters WANDB_PROJECT: (Optional): str - "huggingface" by default, set this to a custom string to store results in a different project WANDB_DISABLED: (Optional): boolean - defaults to false, set to "true" to disable wandb entirely """ if hasattr(self, "_setup_wandb"): warnings.warn( "The `_setup_wandb` method is deprecated and won't be called in a future version, define `setup_wandb` in your subclass.", FutureWarning, ) return self._setup_wandb() if self.is_world_process_zero(): logger.info( 'Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"' ) try: combined_dict = {**self.model.config.to_dict(), **self.args.to_sanitized_dict()} except AttributeError: # in case the model has no config combined_dict = {**self.args.to_sanitized_dict()} wandb.init( project=os.getenv("WANDB_PROJECT", "huggingface"), config=combined_dict, name=self.args.run_name ) # keep track of model topology and gradients, unsupported on TPU if not is_torch_tpu_available() and os.getenv("WANDB_WATCH") != "false": wandb.watch( self.model, log=os.getenv("WANDB_WATCH", "gradients"), log_freq=max(100, self.args.logging_steps) ) def setup_comet(self): """ Setup the optional Comet.ml integration. Environment: COMET_MODE: (Optional): str - "OFFLINE", "ONLINE", or "DISABLED" COMET_PROJECT_NAME: (Optional): str - Comet.ml project name for experiments COMET_OFFLINE_DIRECTORY: (Optional): str - folder to use for saving offline experiments when `COMET_MODE` is "OFFLINE" For a number of configurable items in the environment, see `here `__ """ if self.is_world_master(): comet_mode = os.getenv("COMET_MODE", "ONLINE").upper() args = {"project_name": os.getenv("COMET_PROJECT_NAME", "huggingface")} experiment = None if comet_mode == "ONLINE": experiment = comet_ml.Experiment(**args) logger.info("Automatic Comet.ml online logging enabled") elif comet_mode == "OFFLINE": args["offline_directory"] = os.getenv("COMET_OFFLINE_DIRECTORY", "./") experiment = comet_ml.OfflineExperiment(**args) logger.info("Automatic Comet.ml offline logging enabled; use `comet upload` when finished") if experiment is not None: experiment._set_model_graph(self.model, framework="transformers") experiment._log_parameters(self.args, prefix="args/", framework="transformers") experiment._log_parameters(self.model.config, prefix="config/", framework="transformers") def num_examples(self, dataloader: DataLoader) -> int: """ Helper to get number of samples in a :class:`~torch.utils.data.DataLoader` by accessing its dataset. """ return len(dataloader.dataset) def _setup_loggers(self): if self._loggers_initialized: return if is_wandb_available(): self.setup_wandb() elif os.environ.get("WANDB_DISABLED") != "true": logger.info( "You are instantiating a Trainer but W&B is not installed. To use wandb logging, " "run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface." ) if is_comet_available(): self.setup_comet() elif os.environ.get("COMET_MODE") != "DISABLED": logger.info( "To use comet_ml logging, run `pip/conda install comet_ml` " "see https://www.comet.ml/docs/python-sdk/huggingface/" ) self._loggers_initialized = True def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]): """ HP search setup code """ if self.hp_search_backend is None or trial is None: return params = self.hp_space(trial) if self.hp_search_backend == HPSearchBackend.OPTUNA else trial for key, value in params.items(): if not hasattr(self.args, key): raise AttributeError( f"Trying to set {key} in the hyperparameter search but there is no corresponding field in `TrainingArguments`." ) old_attr = getattr(self.args, key, None) # Casting value to the proper type if old_attr is not None: value = type(old_attr)(value) setattr(self.args, key, value) if self.hp_search_backend == HPSearchBackend.OPTUNA: logger.info("Trial:", trial.params) def _report_to_hp_search( self, trial: Union["optuna.Trial", Dict[str, Any]], epoch: int, metrics: Dict[str, float] ): if self.hp_search_backend is None or trial is None: return self.objective = self.compute_objective(metrics) if self.hp_search_backend == HPSearchBackend.OPTUNA: trial.report(self.objective, epoch) if trial.should_prune(): raise optuna.TrialPruned() elif self.hp_search_backend == HPSearchBackend.RAY: if self.global_step % self.args.save_steps == 0: self._tune_save_checkpoint() tune.report(objective=self.objective, **metrics) def _tune_save_checkpoint(self): if not self.use_tune_checkpoints: return with tune.checkpoint_dir(step=self.global_step) as checkpoint_dir: self.args.output_dir = checkpoint_dir output_dir = os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.global_step}") self.save_model(output_dir) if self.is_world_master(): torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", Dict[str, Any]] = None): """ Main training entry point. Args: model_path (:obj:`str`, `optional`): Local path to the model if the model to train has been instantiated from a local path. If present, training will resume from the optimizer/scheduler states loaded here. trial (:obj:`optuna.Trial` or :obj:`Dict[str, Any]`, `optional`): The trial run or the hyperparameter dictionary for hyperparameter search. """ # This might change the seed so needs to run first. self._hp_search_setup(trial) # Model re-init if self.model_init is not None: # Seed must be set before instantiating the model when using model_init. set_seed(self.args.seed) model = self.model_init() self.model = model.to(self.args.device) # Reinitializes optimizer and scheduler self.optimizer, self.lr_scheduler = None, None # Data loader and number of training steps train_dataloader = self.get_train_dataloader() num_update_steps_per_epoch = len(train_dataloader) // self.args.gradient_accumulation_steps num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1) if self.args.max_steps > 0: t_total = self.args.max_steps num_train_epochs = self.args.max_steps // num_update_steps_per_epoch + int( self.args.max_steps % num_update_steps_per_epoch > 0 ) else: t_total = int(num_update_steps_per_epoch * self.args.num_train_epochs) num_train_epochs = self.args.num_train_epochs self.args.max_steps = t_total self.create_optimizer_and_scheduler(num_training_steps=t_total) # Check if saved optimizer or scheduler states exist if ( model_path is not None and os.path.isfile(os.path.join(model_path, "optimizer.pt")) and os.path.isfile(os.path.join(model_path, "scheduler.pt")) ): # Load in optimizer and scheduler states self.optimizer.load_state_dict( torch.load(os.path.join(model_path, "optimizer.pt"), map_location=self.args.device) ) self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt"))) model = self.model if self.args.fp16 and _use_apex: if not is_apex_available(): raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if self.args.n_gpu > 1: model = torch.nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if self.args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[self.args.local_rank], output_device=self.args.local_rank, find_unused_parameters=True, ) if self.tb_writer is not None: self.tb_writer.add_text("args", self.args.to_json_string()) self.tb_writer.add_hparams(self.args.to_sanitized_dict(), metric_dict={}) # Train! if is_torch_tpu_available(): total_train_batch_size = self.args.train_batch_size * xm.xrt_world_size() else: total_train_batch_size = ( self.args.train_batch_size * self.args.gradient_accumulation_steps * (torch.distributed.get_world_size() if self.args.local_rank != -1 else 1) ) logger.info("***** Running training *****") logger.info(" Num examples = %d", self.num_examples(train_dataloader)) logger.info(" Num Epochs = %d", num_train_epochs) logger.info(" Instantaneous batch size per device = %d", self.args.per_device_train_batch_size) logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", total_train_batch_size) logger.info(" Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) self.global_step = 0 self.epoch = 0 self.total_flos = 0 epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if model_path is not None: # set global_step to global_step of last saved checkpoint from model path try: self.global_step = int(model_path.split("-")[-1].split(os.path.sep)[0]) # print(model, model.module) if self.args.n_gpu > 1: self.total_flos = getattr(model.module.config, "total_flos", 0) else: self.total_flos = getattr(model.config, "total_flos", 0) epochs_trained = self.global_step // num_update_steps_per_epoch steps_trained_in_current_epoch = self.global_step % (num_update_steps_per_epoch) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", self.global_step) logger.info(" Continuing training from %d non-embedding floating-point operations", self.total_flos) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) except ValueError: self.global_step = 0 self.total_flos = 0 logger.info(" Starting fine-tuning.") tr_loss = torch.tensor(0.0).to(self.args.device) logging_loss_scalar = 0.0 model.zero_grad() disable_tqdm = self.args.disable_tqdm or not self.is_local_process_zero() train_pbar = trange(epochs_trained, int(np.ceil(num_train_epochs)), desc="Epoch", disable=disable_tqdm) for epoch in range(epochs_trained, int(np.ceil(num_train_epochs))): if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler): train_dataloader.sampler.set_epoch(epoch) if is_torch_tpu_available(): parallel_loader = pl.ParallelLoader(train_dataloader, [self.args.device]).per_device_loader( self.args.device ) epoch_iterator = parallel_loader else: epoch_iterator = train_dataloader # Reset the past mems state at the beginning of each epoch if necessary. if self.args.past_index >= 0: self._past = None epoch_pbar = tqdm(epoch_iterator, desc="Iteration", disable=disable_tqdm) for step, inputs in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 epoch_pbar.update(1) continue tr_loss += self.training_step(model, inputs) self.total_flos += self.floating_point_ops(inputs) if (step + 1) % self.args.gradient_accumulation_steps == 0 or ( # last step in epoch but step is always smaller than gradient_accumulation_steps len(epoch_iterator) <= self.args.gradient_accumulation_steps and (step + 1) == len(epoch_iterator) ): if self.args.fp16 and _use_native_amp: self.scaler.unscale_(self.optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm) elif self.args.fp16 and _use_apex: torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.args.max_grad_norm) else: torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm) if is_torch_tpu_available(): xm.optimizer_step(self.optimizer) elif self.args.fp16 and _use_native_amp: self.scaler.step(self.optimizer) self.scaler.update() else: self.optimizer.step() # URGENT self.lr_scheduler.step() model.zero_grad() self.global_step += 1 self.epoch = epoch + (step + 1) / len(epoch_iterator) if (self.args.logging_steps > 0 and self.global_step % self.args.logging_steps == 0) or ( self.global_step == 1 and self.args.logging_first_step ): logs: Dict[str, float] = {} tr_loss_scalar = tr_loss.item() logs["loss"] = (tr_loss_scalar - logging_loss_scalar) / self.args.logging_steps # backward compatibility for pytorch schedulers logs["learning_rate"] = ( self.lr_scheduler.get_last_lr()[0] if version.parse(torch.__version__) >= version.parse("1.4") else self.lr_scheduler.get_lr()[0] ) logging_loss_scalar = tr_loss_scalar self.log(logs) # print(self.args.evaluation_strategy == EvaluationStrategy.STEPS ) # print(self.global_step % self.args.eval_steps == 0) # print() if ( self.args.evaluation_strategy == EvaluationStrategy.STEPS and self.global_step % self.args.eval_steps == 0 ): metrics = self.evaluate() self._report_to_hp_search(trial, epoch, metrics) #############################EARLY STOPPING######################## if 'lowdata' in self.args.output_dir or 'earlystop' in self.args.output_dir: self.save_based_on_eval = True else: self.save_based_on_eval = False print('if not see a line lowdata: below, then did not go into low data. ') if self.save_based_on_eval and metrics["eval_loss"] < self.curr_best_eval: print('lowdata:', self.global_step, self.curr_best_eval, metrics["eval_loss"], 'perplexity={}'.format(math.exp(metrics["eval_loss"]))) self.curr_best_eval = metrics["eval_loss"] if hasattr(model, "module"): assert ( model.module is self.model ), f"Module {model.module} should be a reference to self.model" else: assert model is self.model, f"Model {model} should be a reference to self.model" # Save model checkpoint output_dir_name = os.path.basename(self.args.output_dir) checkpoint_folder = f"{output_dir_name}-{PREFIX_CHECKPOINT_DIR}-{self.global_step}" if self.hp_search_backend is not None and trial is not None: run_id = ( trial.number if self.hp_search_backend == HPSearchBackend.OPTUNA else tune.get_trial_id() ) checkpoint_folder += f"-run-{run_id}" output_dir = os.path.join(self.args.output_dir, checkpoint_folder) self.store_flos() print('saving to output_dir', output_dir) self.save_model(output_dir) if self.is_world_process_zero(): self._rotate_checkpoints(use_mtime=True) ##################################################### if self.args.save_steps > 0 and self.global_step % self.args.save_steps == 0: print('saving model at a checkpoint!!') # In all cases (even distributed/parallel), self.model is always a reference # to the model we want to save. if hasattr(model, "module"): assert ( model.module is self.model ), f"Module {model.module} should be a reference to self.model" else: assert model is self.model, f"Model {model} should be a reference to self.model" # Save model checkpoint checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.global_step}" if self.hp_search_backend is not None and trial is not None: run_id = ( trial.number if self.hp_search_backend == HPSearchBackend.OPTUNA else tune.get_trial_id() ) checkpoint_folder += f"-run-{run_id}" output_dir = os.path.join(self.args.output_dir, checkpoint_folder) self.store_flos() self.save_model(output_dir) if self.is_world_process_zero(): self._rotate_checkpoints(use_mtime=True) if is_torch_tpu_available(): xm.rendezvous("saving_optimizer_states") xm.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) elif self.is_world_process_zero(): torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) epoch_pbar.update(1) if self.args.max_steps > 0 and self.global_step >= self.args.max_steps: break epoch_pbar.close() train_pbar.update(1) if self.args.evaluation_strategy == EvaluationStrategy.EPOCH: metrics = self.evaluate() self._report_to_hp_search(trial, epoch, metrics) if self.args.tpu_metrics_debug or self.args.debug: if is_torch_tpu_available(): # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.) xm.master_print(met.metrics_report()) else: logger.warning( "You enabled PyTorch/XLA debug metrics but you don't have a TPU " "configured. Check your training configuration if this is unexpected." ) if self.args.max_steps > 0 and self.global_step >= self.args.max_steps: break train_pbar.close() if self.tb_writer: self.tb_writer.close() if self.args.past_index and hasattr(self, "_past"): # Clean the state at the end of training delattr(self, "_past") logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n") return TrainOutput(self.global_step, tr_loss.item() / self.global_step) def hyperparameter_search( self, hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None, compute_objective: Optional[Callable[[Dict[str, float]], float]] = None, n_trials: int = 20, direction: str = "minimize", backend: Optional[Union["str", HPSearchBackend]] = None, **kwargs ) -> BestRun: """ Launch an hyperparameter search using ``optuna`` or ``Ray Tune``. The optimized quantity is determined by :obj:`compute_objectie`, which defaults to a function returning the evaluation loss when no metric is provided, the sum of all metrics otherwise. .. warning:: To use this method, you need to have provided a ``model_init`` when initializing your :class:`~transformers.Trainer`: we need to reinitialize the model at each new run. This is incompatible with the ``optimizers`` argument, so you need to subclass :class:`~transformers.Trainer` and override the method :meth:`~transformers.Trainer.create_optimizer_and_scheduler` for custom optimizer/scheduler. Args: hp_space (:obj:`Callable[["optuna.Trial"], Dict[str, float]]`, `optional`): A function that defines the hyperparameter search space. Will default to :func:`~transformers.trainer_utils.default_hp_space_optuna` or :func:`~transformers.trainer_utils.default_hp_space_ray` depending on your backend. compute_objective (:obj:`Callable[[Dict[str, float]], float]`, `optional`): A function computing the objective to minimize or maximize from the metrics returned by the :obj:`evaluate` method. Will default to :func:`~transformers.trainer_utils.default_compute_objective`. n_trials (:obj:`int`, `optional`, defaults to 100): The number of trial runs to test. direction(:obj:`str`, `optional`, defaults to :obj:`"minimize"`): Whether to optimize greater or lower objects. Can be :obj:`"minimize"` or :obj:`"maximize"`, you should pick :obj:`"minimize"` when optimizing the validation loss, :obj:`"maximize"` when optimizing one or several metrics. backend(:obj:`str` or :class:`~transformers.training_utils.HPSearchBackend`, `optional`): The backend to use for hyperparameter search. Will default to optuna or Ray Tune, depending on which one is installed. If both are installed, will default to optuna. kwargs: Additional keyword arguments passed along to :obj:`optuna.create_study` or :obj:`ray.tune.run`. For more information see: - the documentation of `optuna.create_study `__ - the documentation of `tune.run `__ Returns: :class:`transformers.trainer_utils.BestRun`: All the informations about the best run. """ if backend is None: backend = default_hp_search_backend() if backend is None: raise RuntimeError( "At least one of optuna or ray should be installed. " "To install optuna run `pip install optuna`." "To install ray run `pip install ray[tune]`." ) backend = HPSearchBackend(backend) if backend == HPSearchBackend.OPTUNA and not is_optuna_available(): raise RuntimeError("You picked the optuna backend, but it is not installed. Use `pip install optuna`.") if backend == HPSearchBackend.RAY and not is_ray_available(): raise RuntimeError( "You picked the Ray Tune backend, but it is not installed. Use `pip install 'ray[tune]'`." ) self.hp_search_backend = backend if self.model_init is None: raise RuntimeError( "To use hyperparameter search, you need to pass your model through a model_init function." ) self.hp_space = default_hp_space[backend] if hp_space is None else hp_space self.compute_objective = default_compute_objective if compute_objective is None else compute_objective run_hp_search = run_hp_search_optuna if backend == HPSearchBackend.OPTUNA else run_hp_search_ray best_run = run_hp_search(self, n_trials, direction, **kwargs) self.hp_search_backend = None return best_run def log(self, logs: Dict[str, float], iterator: Optional[tqdm] = None) -> None: """ Log :obj:`logs` on the various objects watching training. Subclass and override this method to inject custom behavior. Args: logs (:obj:`Dict[str, float]`): The values to log. iterator (:obj:`tqdm`, `optional`): A potential tqdm progress bar to write the logs on. """ # Set up loggers like W&B or Comet ML self._setup_loggers() if hasattr(self, "_log"): warnings.warn( "The `_log` method is deprecated and won't be called in a future version, define `log` in your subclass.", FutureWarning, ) return self._log(logs, iterator=iterator) if self.epoch is not None: logs["epoch"] = self.epoch if self.total_flos is not None: if self.args.local_rank != -1: total_flos = distributed_broadcast_scalars([self.total_flos]).sum().item() else: total_flos = self.total_flos if total_flos > 0: logs["total_flos"] = self.total_flos if self.global_step is None: # when logging evaluation metrics without training self.global_step = 0 if self.tb_writer: for k, v in logs.items(): if isinstance(v, (int, float)): self.tb_writer.add_scalar(k, v, self.global_step) else: logger.warning( "Trainer is attempting to log a value of " '"%s" of type %s for key "%s" as a scalar. ' "This invocation of Tensorboard's writer.add_scalar() " "is incorrect so we dropped this attribute.", v, type(v), k, ) self.tb_writer.flush() if is_wandb_available(): if self.is_world_process_zero(): wandb.log(logs, step=self.global_step) if is_comet_available(): if self.is_world_process_zero(): experiment = comet_ml.config.get_global_experiment() if experiment is not None: experiment._log_metrics(logs, step=self.global_step, epoch=self.epoch, framework="transformers") output = {**logs, **{"step": self.global_step}} if self.is_world_process_zero(): self.log_history.append(output) if iterator is not None: iterator.write(output) else: print(output) def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]: """ Prepare :obj:`inputs` before feeding them to the model, converting them to tensors if they are not already and handling potential state. """ for k, v in inputs.items(): if isinstance(v, torch.Tensor): inputs[k] = v.to(self.args.device) if self.args.past_index >= 0 and self._past is not None: assert False inputs["mems"] = self._past return inputs def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor: """ Perform a training step on a batch of inputs. Subclass and override to inject custom behavior. Args: model (:obj:`nn.Module`): The model to train. inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`): The inputs and targets of the model. The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument :obj:`labels`. Check your model's documentation for all accepted arguments. Return: :obj:`torch.Tensor`: The tensor with training loss on this batch. """ if hasattr(self, "_training_step"): warnings.warn( "The `_training_step` method is deprecated and won't be called in a future version, define `training_step` in your subclass.", FutureWarning, ) return self._training_step(model, inputs, self.optimizer) model.train() if self.use_dropout: if self.gpt2 is not None: self.gpt2.train() inputs = self._prepare_inputs(inputs) if self.args.fp16 and _use_native_amp: with autocast(): if self.distill: loss = self.compute_loss_distill(model, inputs, gpt2_model=self.gpt2, ) else: loss = self.compute_loss(model, inputs, gpt2_model=self.gpt2) else: if self.distill: loss = self.compute_loss_distill(model, inputs, gpt2_model=self.gpt2) else: loss = self.compute_loss(model, inputs, gpt2_model=self.gpt2) if self.args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if self.args.gradient_accumulation_steps > 1: loss = loss / self.args.gradient_accumulation_steps if self.args.fp16 and _use_native_amp: self.scaler.scale(loss).backward() elif self.args.fp16 and _use_apex: with amp.scale_loss(loss, self.optimizer) as scaled_loss: scaled_loss.backward() else: # print(loss) loss.backward() # print('max allocated_memory:', torch.cuda.max_memory_allocated(0), 'total_memory:', torch.cuda.get_device_properties(0).total_memory, # 'percentage', torch.cuda.max_memory_allocated(0)/torch.cuda.get_device_properties(0).total_memory) return loss.detach() def compute_loss(self, model, inputs, gpt2_model=None): """ How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior. """ # outputs = model.forward_weighted(**inputs) if 'prompt_lab' in inputs: prompt_lab_ = inputs['prompt_lab'] k = torch.cat(self.discri_labels_code, dim=0) inputs['control_code'] = torch.index_select(k, 0, prompt_lab_) del inputs['prompt_lab'] outputs = model(**inputs, gpt2_model=gpt2_model) # Save past state if it exists if self.args.past_index >= 0: self._past = outputs[self.args.past_index] # print(outputs[0]) # We don't use .loss here since the model may return tuples instead of ModelOutput. # print(outputs[0], outputs.loss) # URGENT # print('compute_loss', outputs[0]) return outputs[0].mean() def compute_loss_distill(self, model, inputs, gpt2_model=None): """ How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior. """ # outputs = model.forward_weighted(**inputs) with torch.no_grad(): output_finetuned = self.finetuned_gpt2(**inputs) outputs = model(**inputs, gpt2_model=gpt2_model) # Save past state if it exists if self.args.past_index >= 0: self._past = outputs[self.args.past_index] if self.matching_objective == 'kl': # distrib_finetuned=torch.log_softmax(output_finetuned.logits[:,:,:-2], dim=-1) #bsz, seqlen, vocab distrib_finetuned=torch.log_softmax(output_finetuned.logits, dim=-1) #bsz, seqlen, vocab distrib_prefix = torch.log_softmax(outputs.logits, dim=-1) # bsz, seqlen, vocab loss = torch.sum(distrib_finetuned.exp() * (distrib_finetuned - distrib_prefix), dim=-1) #bsz, seqlen elif self.matching_objective == 'logits': loss = torch.norm(output_finetuned.logits - outputs.logits, dim=-1) #bsz, seqlen # loss = torch.norm(output_finetuned.logits[:,:,:-2] - outputs.logits, dim=-1) #bsz, seqlen elif self.matching_objective == 'last_layer': activation_diff = output_finetuned.last_hidden_state - outputs.last_hidden_state loss = torch.norm(activation_diff, dim=-1) # bsz, seqlen else: assert False, "invalid matching_objective" return loss.sum(dim=-1).mean() def is_local_master(self) -> bool: """ Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several machines) main process. .. warning:: This method is deprecated, use :meth:`~transformers.Trainer.is_local_process_zero` instead. """ warnings.warn("This method is deprecated, use `Trainer.is_local_process_zero()` instead.", FutureWarning) return self.is_local_process_zero() def is_local_process_zero(self) -> bool: """ Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several machines) main process. """ if is_torch_tpu_available(): return xm.is_master_ordinal(local=True) else: return self.args.local_rank in [-1, 0] def is_world_master(self) -> bool: """ Whether or not this process is the global main process (when training in a distributed fashion on several machines, this is only going to be :obj:`True` for one process). .. warning:: This method is deprecated, use :meth:`~transformers.Trainer.is_world_process_zero` instead. """ warnings.warn("This method is deprecated, use `Trainer.is_world_process_zero()` instead.", FutureWarning) return self.is_world_process_zero() def is_world_process_zero(self) -> bool: """ Whether or not this process is the global main process (when training in a distributed fashion on several machines, this is only going to be :obj:`True` for one process). """ if is_torch_tpu_available(): return xm.is_master_ordinal(local=False) else: return self.args.local_rank == -1 or torch.distributed.get_rank() == 0 def save_model(self, output_dir: Optional[str] = None): """ Will save the model, so you can reload it using :obj:`from_pretrained()`. Will only save from the world_master process (unless in TPUs). """ if is_torch_tpu_available(): self._save_tpu(output_dir) elif self.is_world_process_zero(): self._save(output_dir) def _save_tpu(self, output_dir: Optional[str] = None): output_dir = output_dir if output_dir is not None else self.args.output_dir logger.info("Saving model checkpoint to %s", output_dir) if xm.is_master_ordinal(): os.makedirs(output_dir, exist_ok=True) torch.save(self.args, os.path.join(output_dir, "training_args.bin")) json.dump( self.log_history, open(os.path.join(output_dir, "log_history.json"), "w"), indent=2, ensure_ascii=False ) # Save a trained model and configuration using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` if not isinstance(self.model, PreTrainedModel): raise ValueError("Trainer.model appears to not be a PreTrainedModel") xm.rendezvous("saving_checkpoint") self.model.save_pretrained(output_dir) if self.tokenizer is not None: self.tokenizer.save_pretrained(output_dir) def _save(self, output_dir: Optional[str] = None): output_dir = output_dir if output_dir is not None else self.args.output_dir os.makedirs(output_dir, exist_ok=True) logger.info("Saving model checkpoint to %s", output_dir) # Save a trained model and configuration using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` if not isinstance(self.model, PreTrainedModel): raise ValueError("Trainer.model appears to not be a PreTrainedModel") self.model.save_pretrained(output_dir) if self.tokenizer is not None: self.tokenizer.save_pretrained(output_dir) # Good practice: save your training arguments together with the trained model torch.save(self.args, os.path.join(output_dir, "training_args.bin")) json.dump( self.log_history, open(os.path.join(output_dir, "log_history.json"), "w"), indent=2, ensure_ascii=False ) def store_flos(self): # Storing the number of floating-point operations that went into the model if self.total_flos is not None: if self.args.local_rank != -1: total_flos = distributed_broadcast_scalars([self.total_flos]).sum().item() else: total_flos = self.total_flos if total_flos > 0: self.model.config.total_flos = total_flos def _sorted_checkpoints(self, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False) -> List[str]: output_dir_name = os.path.basename(self.args.output_dir) checkpoint_prefix = f"{output_dir_name}-{PREFIX_CHECKPOINT_DIR}" ordering_and_checkpoint_path = [] glob_checkpoints = [str(x) for x in Path(self.args.output_dir).glob(f"{checkpoint_prefix}-*")] for path in glob_checkpoints: if use_mtime: ordering_and_checkpoint_path.append((os.path.getmtime(path), path)) else: regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path) if regex_match and regex_match.groups(): ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path)) checkpoints_sorted = sorted(ordering_and_checkpoint_path) checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted] return checkpoints_sorted def _rotate_checkpoints(self, use_mtime=False) -> None: if self.args.save_total_limit is None or self.args.save_total_limit <= 0: return # Check if we should delete older checkpoint(s) checkpoints_sorted = self._sorted_checkpoints(use_mtime=use_mtime) if len(checkpoints_sorted) <= self.args.save_total_limit: return number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - self.args.save_total_limit) checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete] for checkpoint in checkpoints_to_be_deleted: logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint)) shutil.rmtree(checkpoint) def evaluate(self, eval_dataset: Optional[Dataset] = None) -> Dict[str, float]: """ Run evaluation and returns metrics. The calling script will be responsible for providing a method to compute metrics, as they are task-dependent (pass it to the init :obj:`compute_metrics` argument). You can also subclass and override this method to inject custom behavior. Args: eval_dataset (:obj:`Dataset`, `optional`): Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not accepted by the ``model.forward()`` method are automatically removed. Returns: A dictionary containing the evaluation loss and the potential metrics computed from the predictions. """ eval_dataloader = self.get_eval_dataloader(eval_dataset) output = self.prediction_loop(eval_dataloader, description="Evaluation") self.log(output.metrics) if self.args.tpu_metrics_debug or self.args.debug: # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.) xm.master_print(met.metrics_report()) return output.metrics def predict(self, test_dataset: Dataset) -> PredictionOutput: """ Run prediction and returns predictions and potential metrics. Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method will also return metrics, like in :obj:`evaluate()`. Args: test_dataset (:obj:`Dataset`): Dataset to run the predictions on. If it is an :obj:`datasets.Dataset`, columns not accepted by the ``model.forward()`` method are automatically removed. Returns: `NamedTuple`: predictions (:obj:`np.ndarray`): The predictions on :obj:`test_dataset`. label_ids (:obj:`np.ndarray`, `optional`): The labels (if the dataset contained some). metrics (:obj:`Dict[str, float]`, `optional`): The potential dictionary of metrics (if the dataset contained labels). """ test_dataloader = self.get_test_dataloader(test_dataset) return self.prediction_loop(test_dataloader, description="Prediction") def prediction_loop( self, dataloader: DataLoader, description: str, prediction_loss_only: Optional[bool] = None ) -> PredictionOutput: """ Prediction/evaluation loop, shared by :obj:`Trainer.evaluate()` and :obj:`Trainer.predict()`. Works both with or without labels. """ if hasattr(self, "_prediction_loop"): warnings.warn( "The `_prediction_loop` method is deprecated and won't be called in a future version, define `prediction_loop` in your subclass.", FutureWarning, ) return self._prediction_loop(dataloader, description, prediction_loss_only=prediction_loss_only) prediction_loss_only = ( prediction_loss_only if prediction_loss_only is not None else self.args.prediction_loss_only ) assert not getattr( self.model.config, "output_attentions", False ), "The prediction loop does not work with `output_attentions=True`." assert not getattr( self.model.config, "output_hidden_states", False ), "The prediction loop does not work with `output_hidden_states=True`." model = self.model # multi-gpu eval if self.args.n_gpu > 1: model = torch.nn.DataParallel(model) else: model = self.model # Note: in torch.distributed mode, there's no point in wrapping the model # inside a DistributedDataParallel as we'll be under `no_grad` anyways. batch_size = dataloader.batch_size logger.info("***** Running %s *****", description) logger.info(" Num examples = %d", self.num_examples(dataloader)) logger.info(" Batch size = %d", batch_size) eval_losses: List[float] = [] preds: torch.Tensor = None label_ids: torch.Tensor = None entropy_losses: List[float] = [] model.eval() if self.gpt2 is not None: self.gpt2.eval() print(model.training) print(self.gpt2.training) if is_torch_tpu_available(): dataloader = pl.ParallelLoader(dataloader, [self.args.device]).per_device_loader(self.args.device) if self.args.past_index >= 0: self._past = None disable_tqdm = not self.is_local_process_zero() or self.args.disable_tqdm for inputs in tqdm(dataloader, desc=description, disable=disable_tqdm): loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only) batch_size = inputs[list(inputs.keys())[0]].shape[0] if loss is not None: eval_losses.extend([loss] * batch_size) if logits is not None: preds = logits if preds is None else nested_concat(preds, logits, dim=0) temp_logits = [torch.log_softmax(x) for x in logits] entropy_losses.extend([(x.exp() * x).sum() for x in temp_logits]) if labels is not None: label_ids = labels if label_ids is None else nested_concat(label_ids, labels, dim=0) if self.args.past_index and hasattr(self, "_past"): # Clean the state at the end of the evaluation loop delattr(self, "_past") if self.compute_metrics is not None and preds is not None and label_ids is not None: metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids)) else: metrics = {} # Prefix all keys with eval_ for key in list(metrics.keys()): if not key.startswith("eval_"): metrics[f"eval_{key}"] = metrics.pop(key) if len(entropy_losses) > 0: metrics['entropy'] = np.mean(entropy_losses) print('entropy', metrics['entropy'] ) return PredictionOutput(predictions=preds, label_ids=label_ids, metrics=metrics) def prediction_step( self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]], prediction_loss_only: bool ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: """ Perform an evaluation step on :obj:`model` using obj:`inputs`. Subclass and override to inject custom behavior. Args: model (:obj:`nn.Module`): The model to evaluate. inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`): The inputs and targets of the model. The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument :obj:`labels`. Check your model's documentation for all accepted arguments. prediction_loss_only (:obj:`bool`): Whether or not to return the loss only. Return: Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and labels (each being optional). """ has_labels = all(inputs.get(k) is not None for k in self.args.label_names) inputs = self._prepare_inputs(inputs) # At eval time, set the weights to 1/bsz. and see the results.. # if 'weights' in inputs: # weights = inputs['weights'] # bsz = weights.view(-1).shape[0] # weights = (torch.ones(weights.shape)/bsz).to(weights.device) # inputs['weights'] = weights with torch.no_grad(): # outputs = model.forward_weighted(**inputs) outputs = model(**inputs, gpt2_model=self.gpt2) if has_labels: # The .mean() is to reduce in case of distributed training loss = outputs[0].mean().item() logits = outputs[1:] else: loss = None # Slicing so we get a tuple even if `outputs` is a `ModelOutput`. logits = outputs[:] if self.args.past_index >= 0: self._past = outputs[self.args.past_index if has_labels else self.args.past_index - 1] if prediction_loss_only: return (loss, None, None) logits = tuple(logit.detach() for logit in logits) if len(logits) == 1: logits = logits[0] if has_labels: labels = tuple(inputs.get(name).detach() for name in self.args.label_names) if len(labels) == 1: labels = labels[0] else: labels = None return (loss, logits, labels) def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]): """ For models that inherit from :class:`~transformers.PretrainedModel`, uses that method to compute the number of floating point operations for every backward + forward pass. If using another model, either implement such a method in the model or subclass and override this method. Args: model (:obj:`nn.Module`): The model to evaluate. inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`): The inputs and targets of the model. Returns: :obj:`int`: The number of floating-point operations. """ if isinstance(self.model, torch.nn.DataParallel) or isinstance( self.model, torch.nn.parallel.DistributedDataParallel ): model = self.model.module else: model = self.model if hasattr(model, "floating_point_ops"): return model.floating_point_ops(inputs) else: return 0