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Clean the Trainer state (#7490)

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* Trainer should not modify its TrainingArguments

* Trainer should not modify its TrainingArguments

* Trainer should not modify its TrainingArguments

* Add test of resumed training

* Fixes

* Non multiGPU test

* Clean Trainer state

* Add more to the state

* Documentation

* One last test

* Make resume training test more complete

* Unwanted changes
This commit is contained in:
Sylvain Gugger
2020-10-01 13:07:04 -04:00
committed by GitHub
parent 2a358f45ef
commit 29baa8fabe
4 changed files with 161 additions and 88 deletions
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2
src/transformers/__init__.py
View File

@@ -201,7 +201,7 @@ from .tokenization_xlm_roberta import XLMRobertaTokenizer
from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
# Trainer # Trainer
from .trainer_utils import EvalPrediction, set_seed from .trainer_utils import EvalPrediction, TrainerState, set_seed
from .training_args import TrainingArguments from .training_args import TrainingArguments
from .training_args_tf import TFTrainingArguments from .training_args_tf import TFTrainingArguments
from .utils import logging from .utils import logging

150
src/transformers/trainer.py
View File

@@ -1,5 +1,4 @@
import inspect import inspect
import json
import math import math
import os import os
import re import re
@@ -260,10 +259,11 @@ class Trainer:
"You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method." "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
) )
self.tb_writer = tb_writer self.tb_writer = tb_writer
self.log_history = []
if "prediction_loss_only" in kwargs: if "prediction_loss_only" in kwargs:
warnings.warn( 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.", "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. Setting "
+ f"`args.prediction_loss_only={kwargs['prediction_loss_only']}",
FutureWarning, FutureWarning,
) )
self.args.prediction_loss_only = kwargs.pop("prediction_loss_only") self.args.prediction_loss_only = kwargs.pop("prediction_loss_only")
@@ -302,19 +302,20 @@ class Trainer:
if isinstance(eval_dataset, datasets.Dataset): if isinstance(eval_dataset, datasets.Dataset):
self._remove_unused_columns(self.eval_dataset, description="evaluation") self._remove_unused_columns(self.eval_dataset, description="evaluation")
self.global_step = None self.state = TrainerState()
self.epoch = None # Internal variable for total_flos used to count as tensors (for distributed + TPU), will be sent in the
self.total_flos = None # state at each call to self.log.
self._total_flos = None
if self.args.fp16 and _use_native_amp: if self.args.fp16 and _use_native_amp:
self.scaler = torch.cuda.amp.GradScaler() self.scaler = torch.cuda.amp.GradScaler()
self.hp_search_backend = None self.hp_search_backend = None
self.use_tune_checkpoints = False self.use_tune_checkpoints = False
if self.args.label_names is None: default_label_names = (
self.args.label_names = ( ["start_positions, end_positions"]
["start_positions, end_positions"] if type(self.model) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values()
if type(self.model) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values() else ["labels"]
else ["labels"] )
) self.label_names = default_label_names if self.args.label_names is None else self.args.label_names
def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None): def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None):
if not self.args.remove_unused_columns: if not self.args.remove_unused_columns:
@@ -588,16 +589,16 @@ class Trainer:
if trial.should_prune(): if trial.should_prune():
raise optuna.TrialPruned() raise optuna.TrialPruned()
elif self.hp_search_backend == HPSearchBackend.RAY: elif self.hp_search_backend == HPSearchBackend.RAY:
if self.global_step % self.args.save_steps == 0: if self.state.global_step % self.args.save_steps == 0:
self._tune_save_checkpoint() self._tune_save_checkpoint()
tune.report(objective=self.objective, **metrics) tune.report(objective=self.objective, **metrics)
def _tune_save_checkpoint(self): def _tune_save_checkpoint(self):
if not self.use_tune_checkpoints: if not self.use_tune_checkpoints:
return return
with tune.checkpoint_dir(step=self.global_step) as checkpoint_dir: with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:
self.args.output_dir = checkpoint_dir self.args.output_dir = checkpoint_dir
output_dir = os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.global_step}") output_dir = os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
self.save_model(output_dir) self.save_model(output_dir)
if self.is_world_master(): if self.is_world_master():
torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
@@ -632,16 +633,16 @@ class Trainer:
num_update_steps_per_epoch = len(train_dataloader) // self.args.gradient_accumulation_steps 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) num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
if self.args.max_steps > 0: if self.args.max_steps > 0:
t_total = self.args.max_steps max_steps = self.args.max_steps
num_train_epochs = self.args.max_steps // num_update_steps_per_epoch + int( num_train_epochs = self.args.max_steps // num_update_steps_per_epoch + int(
self.args.max_steps % num_update_steps_per_epoch > 0 self.args.max_steps % num_update_steps_per_epoch > 0
) )
else: else:
t_total = int(num_update_steps_per_epoch * self.args.num_train_epochs) max_steps = int(num_update_steps_per_epoch * self.args.num_train_epochs)
num_train_epochs = self.args.num_train_epochs num_train_epochs = self.args.num_train_epochs
self.args.max_steps = t_total num_train_epochs = int(np.ceil(num_train_epochs))
self.create_optimizer_and_scheduler(num_training_steps=t_total) self.create_optimizer_and_scheduler(num_training_steps=max_steps)
self.state = TrainerState() self.state = TrainerState()
# Check if saved optimizer or scheduler states exist # Check if saved optimizer or scheduler states exist
@@ -658,17 +659,14 @@ class Trainer:
self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt"))) self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt")))
reissue_pt_warnings(caught_warnings) reissue_pt_warnings(caught_warnings)
# Check if a saved Trainer state exist # Moxed precision training with apex (torch < 1.6)
if model_path is not None and os.path.isfile(os.path.join(model_path, "trainer_state.json")):
self.state = TrainerState.load_from_json(os.path.join(model_path, "trainer_state.json"))
model = self.model model = self.model
if self.args.fp16 and _use_apex: if self.args.fp16 and _use_apex:
if not is_apex_available(): if not is_apex_available():
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") 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) model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization) # Multi-gpu training (should be after apex fp16 initialization)
if self.args.n_gpu > 1: if self.args.n_gpu > 1:
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)
@@ -706,37 +704,35 @@ class Trainer:
logger.info(" Instantaneous batch size per device = %d", self.args.per_device_train_batch_size) 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(" 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(" Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total) logger.info(" Total optimization steps = %d", max_steps)
self.global_step = 0 self.state.epoch = 0
self.epoch = 0
epochs_trained = 0 epochs_trained = 0
steps_trained_in_current_epoch = 0 steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint # Check if continuing training from a checkpoint
if model_path is not None: if model_path and os.path.isfile(os.path.join(model_path, "trainer_state.json")):
# set global_step to global_step of last saved checkpoint from model path self.state = TrainerState.load_from_json(os.path.join(model_path, "trainer_state.json"))
try: epochs_trained = self.state.global_step // num_update_steps_per_epoch
self.global_step = int(model_path.split("-")[-1].split(os.path.sep)[0]) steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
epochs_trained = self.global_step // num_update_steps_per_epoch logger.info(" Continuing training from checkpoint, will skip to saved global_step")
steps_trained_in_current_epoch = self.global_step % (num_update_steps_per_epoch) logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", self.state.global_step)
logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
logger.info(" Continuing training from checkpoint, will skip to saved global_step") # This should be the same if the state has been saved but in case the training arguments changed, it's safer
logger.info(" Continuing training from epoch %d", epochs_trained) # to set this after the load.
logger.info(" Continuing training from global step %d", self.global_step) self.state.max_steps = max_steps
logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) self.state.num_train_epochs = num_train_epochs
except ValueError:
self.global_step = 0
logger.info(" Starting fine-tuning.")
tr_loss = torch.tensor(0.0).to(self.args.device) tr_loss = torch.tensor(0.0).to(self.args.device)
self.total_flos = self.state.total_flos self._total_flos = self.state.total_flos
logging_loss_scalar = 0.0 logging_loss_scalar = 0.0
model.zero_grad() model.zero_grad()
disable_tqdm = self.args.disable_tqdm or not self.is_local_process_zero() 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) train_pbar = trange(epochs_trained, num_train_epochs, desc="Epoch", disable=disable_tqdm)
for epoch in range(epochs_trained, int(np.ceil(num_train_epochs))): for epoch in range(epochs_trained, num_train_epochs):
if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler): if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
train_dataloader.sampler.set_epoch(epoch) train_dataloader.sampler.set_epoch(epoch)
@@ -762,7 +758,7 @@ class Trainer:
continue continue
tr_loss += self.training_step(model, inputs) tr_loss += self.training_step(model, inputs)
self.total_flos += self.floating_point_ops(inputs) self._total_flos += self.floating_point_ops(inputs)
if (step + 1) % self.args.gradient_accumulation_steps == 0 or ( if (step + 1) % self.args.gradient_accumulation_steps == 0 or (
# last step in epoch but step is always smaller than gradient_accumulation_steps # last step in epoch but step is always smaller than gradient_accumulation_steps
@@ -787,11 +783,11 @@ class Trainer:
self.lr_scheduler.step() self.lr_scheduler.step()
model.zero_grad() model.zero_grad()
self.global_step += 1 self.state.global_step += 1
self.epoch = epoch + (step + 1) / len(epoch_iterator) self.state.epoch = epoch + (step + 1) / len(epoch_iterator)
if (self.args.logging_steps > 0 and self.global_step % self.args.logging_steps == 0) or ( if (self.args.logging_steps > 0 and self.state.global_step % self.args.logging_steps == 0) or (
self.global_step == 1 and self.args.logging_first_step self.state.global_step == 1 and self.args.logging_first_step
): ):
logs: Dict[str, float] = {} logs: Dict[str, float] = {}
tr_loss_scalar = tr_loss.item() tr_loss_scalar = tr_loss.item()
@@ -808,7 +804,7 @@ class Trainer:
if ( if (
self.args.evaluation_strategy == EvaluationStrategy.STEPS self.args.evaluation_strategy == EvaluationStrategy.STEPS
and self.global_step % self.args.eval_steps == 0 and self.state.global_step % self.args.eval_steps == 0
): ):
metrics = self.evaluate() metrics = self.evaluate()
self._report_to_hp_search(trial, epoch, metrics) self._report_to_hp_search(trial, epoch, metrics)
@@ -818,12 +814,12 @@ class Trainer:
if ( if (
not self.args.load_best_model_at_end not self.args.load_best_model_at_end
and self.args.save_steps > 0 and self.args.save_steps > 0
and self.global_step % self.args.save_steps == 0 and self.state.global_step % self.args.save_steps == 0
): ):
self._save_training(model, trial) self._save_training(model, trial)
epoch_pbar.update(1) epoch_pbar.update(1)
if self.args.max_steps > 0 and self.global_step >= self.args.max_steps: if self.state.global_step >= max_steps:
break break
epoch_pbar.close() epoch_pbar.close()
train_pbar.update(1) train_pbar.update(1)
@@ -843,7 +839,7 @@ class Trainer:
"You enabled PyTorch/XLA debug metrics but you don't have a TPU " "You enabled PyTorch/XLA debug metrics but you don't have a TPU "
"configured. Check your training configuration if this is unexpected." "configured. Check your training configuration if this is unexpected."
) )
if self.args.max_steps > 0 and self.global_step >= self.args.max_steps: if self.state.global_step >= max_steps:
break break
train_pbar.close() train_pbar.close()
@@ -865,7 +861,7 @@ class Trainer:
state_dict = torch.load(os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME)) state_dict = torch.load(os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME))
self.model.load_state_dict(state_dict) self.model.load_state_dict(state_dict)
return TrainOutput(self.global_step, tr_loss.item() / self.global_step) return TrainOutput(self.state.global_step, tr_loss.item() / self.state.global_step)
def _save_training(self, model, trial, metrics=None): def _save_training(self, model, trial, metrics=None):
# In all cases (even distributed/parallel), self.model is always a reference # In all cases (even distributed/parallel), self.model is always a reference
@@ -875,7 +871,7 @@ class Trainer:
else: else:
assert model is self.model, f"Model {model} should be a reference to self.model" assert model is self.model, f"Model {model} should be a reference to self.model"
# Save model checkpoint # Save model checkpoint
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.global_step}" checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
if self.hp_search_backend is not None and trial is not None: 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() run_id = trial.number if self.hp_search_backend == HPSearchBackend.OPTUNA else tune.get_trial_id()
checkpoint_folder += f"-run-{run_id}" checkpoint_folder += f"-run-{run_id}"
@@ -1022,22 +1018,15 @@ class Trainer:
) )
return self._log(logs, iterator=iterator) return self._log(logs, iterator=iterator)
if self.epoch is not None: if self.state.epoch is not None:
logs["epoch"] = self.epoch logs["epoch"] = self.state.epoch
if self.total_flos is not None: if self._total_flos is not None:
if self.args.local_rank != -1: self.store_flos()
total_flos = distributed_broadcast_scalars([self.total_flos]).sum().item() logs["total_flos"] = self.state.total_flos
else:
total_flos = self.total_flos
if total_flos > 0:
logs["total_flos"] = total_flos
if self.global_step is None:
# when logging evaluation metrics without training
self.global_step = 0
if self.tb_writer: if self.tb_writer:
for k, v in logs.items(): for k, v in logs.items():
if isinstance(v, (int, float)): if isinstance(v, (int, float)):
self.tb_writer.add_scalar(k, v, self.global_step) self.tb_writer.add_scalar(k, v, self.state.global_step)
else: else:
logger.warning( logger.warning(
"Trainer is attempting to log a value of " "Trainer is attempting to log a value of "
@@ -1051,15 +1040,16 @@ class Trainer:
self.tb_writer.flush() self.tb_writer.flush()
if is_wandb_available(): if is_wandb_available():
if self.is_world_process_zero(): if self.is_world_process_zero():
wandb.log(logs, step=self.global_step) wandb.log(logs, step=self.state.global_step)
if is_comet_available(): if is_comet_available():
if self.is_world_process_zero(): if self.is_world_process_zero():
experiment = comet_ml.config.get_global_experiment() experiment = comet_ml.config.get_global_experiment()
if experiment is not None: if experiment is not None:
experiment._log_metrics(logs, step=self.global_step, epoch=self.epoch, framework="transformers") experiment._log_metrics(
output = {**logs, **{"step": self.global_step}} logs, step=self.state.global_step, epoch=self.state.epoch, framework="transformers"
if self.is_world_process_zero(): )
self.log_history.append(output) output = {**logs, **{"step": self.state.global_step}}
self.state.log_history.append(output)
if iterator is not None: if iterator is not None:
iterator.write(output) iterator.write(output)
else: else:
@@ -1205,9 +1195,6 @@ class Trainer:
if xm.is_master_ordinal(): if xm.is_master_ordinal():
os.makedirs(output_dir, exist_ok=True) os.makedirs(output_dir, exist_ok=True)
torch.save(self.args, os.path.join(output_dir, "training_args.bin")) 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()`. # Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()` # They can then be reloaded using `from_pretrained()`
@@ -1238,17 +1225,14 @@ class Trainer:
# Good practice: save your training arguments together with the trained model # Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, "training_args.bin")) 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): def store_flos(self):
# Storing the number of floating-point operations that went into the model # Storing the number of floating-point operations that went into the model
if self.total_flos is not None: if self._total_flos is not None:
if self.args.local_rank != -1: if self.args.local_rank != -1:
self.state.total_flos = distributed_broadcast_scalars([self.total_flos]).sum().item() self.state.total_flos = distributed_broadcast_scalars([self._total_flos]).sum().item()
else: else:
self.state.total_flos = self.total_flos self.state.total_flos = self._total_flos
def _sorted_checkpoints(self, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False) -> List[str]: def _sorted_checkpoints(self, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False) -> List[str]:
ordering_and_checkpoint_path = [] ordering_and_checkpoint_path = []
@@ -1466,7 +1450,7 @@ class Trainer:
Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
A tuple with the loss, logits and labels (each being optional). 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) has_labels = all(inputs.get(k) is not None for k in self.label_names)
inputs = self._prepare_inputs(inputs) inputs = self._prepare_inputs(inputs)
with torch.no_grad(): with torch.no_grad():
@@ -1490,7 +1474,7 @@ class Trainer:
logits = logits[0] logits = logits[0]
if has_labels: if has_labels:
labels = tuple(inputs.get(name).detach() for name in self.args.label_names) labels = tuple(inputs.get(name).detach() for name in self.label_names)
if len(labels) == 1: if len(labels) == 1:
labels = labels[0] labels = labels[0]
else: else:

35
src/transformers/trainer_utils.py
View File

@@ -221,13 +221,46 @@ def distributed_broadcast_scalars(
@dataclass @dataclass
class TrainerState: class TrainerState:
""" """
A class containing the `Trainer` fields that will be saved along the model and optimizer. A class containing the `Trainer` inner state that will be saved along the model and optimizer.
.. note::
In all this class, one step is to be understood as one update step. When using gradient accumulation, one
update step may require several forward and backward passes: if you use :obj:`gradient_accumulation_steps=n`,
then one update step requires going throuch `n` batches.
Args:
epoch (:obj:`float`, `optional`):
Only set during training, will represent the epoch the training is at (the decimal part being the
percentage of the current epoch completed).
global_step (:obj:`int`, `optional`, defaults to 0):
During training, represents the number of update steps completed.
max_steps (:obj:`int`, `optional`, defaults to 0):
The number of update steps to do during the current training.
total_flos (:obj:`int`, `optional`, defaults to 0):
The total number of floating operations done by the model since the beginning of training.
log_history (:obj:`List[Dict[str, float]]`, `optional`):
The list of logs done since the beginning of training.
best_metric (:obj:`float`, `optional`):
When tracking the best model, the value of the best metric encountered so far.
best_model_checkpoint (:obj:`str`, `optional`):
When tracking the best model, the value of the name of the checkpoint for the best model encountered so
far.
""" """
epoch: Optional[float] = None
global_step: int = 0
max_steps: int = 0
num_train_epochs: int = 0
total_flos: int = 0 total_flos: int = 0
log_history: List[Dict[str, float]] = None
best_metric: Optional[float] = None best_metric: Optional[float] = None
best_model_checkpoint: Optional[str] = None best_model_checkpoint: Optional[str] = None
def __post_init__(self):
if self.log_history is None:
self.log_history = []
def save_to_json(self, json_path: str): def save_to_json(self, json_path: str):
""" Save the content of this instance in JSON format inside :obj:`json_path`.""" """ Save the content of this instance in JSON format inside :obj:`json_path`."""
json_string = json.dumps(dataclasses.asdict(self), indent=2, sort_keys=True) + "\n" json_string = json.dumps(dataclasses.asdict(self), indent=2, sort_keys=True) + "\n"

62
tests/test_trainer.py
View File

@@ -1,4 +1,4 @@
import json import dataclasses
import os import os
import tempfile import tempfile
import unittest import unittest
@@ -22,6 +22,7 @@ if is_torch_available():
LineByLineTextDataset, LineByLineTextDataset,
PreTrainedModel, PreTrainedModel,
Trainer, Trainer,
TrainerState,
) )
@@ -155,7 +156,7 @@ class TrainerIntegrationTest(unittest.TestCase):
self.assertTrue(torch.allclose(model.b, b)) self.assertTrue(torch.allclose(model.b, b))
def check_saved_checkpoints(self, output_dir, freq, total, is_pretrained=True): def check_saved_checkpoints(self, output_dir, freq, total, is_pretrained=True):
file_list = [WEIGHTS_NAME, "training_args.bin", "log_history.json", "optimizer.pt", "scheduler.pt"] file_list = [WEIGHTS_NAME, "training_args.bin", "optimizer.pt", "scheduler.pt", "trainer_state.json"]
if is_pretrained: if is_pretrained:
file_list.append("config.json") file_list.append("config.json")
for step in range(freq, total, freq): for step in range(freq, total, freq):
@@ -168,7 +169,7 @@ class TrainerIntegrationTest(unittest.TestCase):
self, output_dir, freq, total, trainer, metric, greater_is_better=False, is_pretrained=True self, output_dir, freq, total, trainer, metric, greater_is_better=False, is_pretrained=True
): ):
checkpoint = os.path.join(output_dir, f"checkpoint-{(total // freq) * freq}") checkpoint = os.path.join(output_dir, f"checkpoint-{(total // freq) * freq}")
log_history = json.load(open(os.path.join(checkpoint, "log_history.json"))) log_history = TrainerState.load_from_json(os.path.join(checkpoint, "trainer_state.json")).log_history
values = [d[metric] for d in log_history] values = [d[metric] for d in log_history]
best_value = max(values) if greater_is_better else min(values) best_value = max(values) if greater_is_better else min(values)
@@ -188,6 +189,12 @@ class TrainerIntegrationTest(unittest.TestCase):
metrics = trainer.evaluate() metrics = trainer.evaluate()
self.assertEqual(metrics[metric], best_value) self.assertEqual(metrics[metric], best_value)
def test_training_arguments_are_left_untouched(self):
trainer = get_regression_trainer()
trainer.train()
args = TrainingArguments("./regression")
self.assertEqual(args.to_dict(), trainer.args.to_dict())
def test_reproducible_training(self): def test_reproducible_training(self):
# Checks that training worked, model trained and seed made a reproducible training. # Checks that training worked, model trained and seed made a reproducible training.
trainer = get_regression_trainer(learning_rate=0.1) trainer = get_regression_trainer(learning_rate=0.1)
@@ -368,6 +375,55 @@ class TrainerIntegrationTest(unittest.TestCase):
trainer.train() trainer.train()
self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False) self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False)
def test_can_resume_training(self):
if torch.cuda.device_count() > 2:
# This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
# save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
# won't be the same since the training dataloader is shuffled).
return
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(tmpdir, "checkpoint-5")
# Reinitialize trainer and load model
model = RegressionPreTrainedModel.from_pretrained(checkpoint)
trainer = Trainer(model, trainer.args, train_dataset=trainer.train_dataset)
trainer.train(model_path=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.assertEqual(state, state1)
# With a regular model that is not a PreTrainedModel
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1, pretrained=False
)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(tmpdir, "checkpoint-5")
# Reinitialize trainer and load model
model = RegressionModel()
state_dict = torch.load(os.path.join(checkpoint, WEIGHTS_NAME))
model.load_state_dict(state_dict)
trainer = Trainer(model, trainer.args, train_dataset=trainer.train_dataset)
trainer.train(model_path=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.assertEqual(state, state1)
def test_load_best_model_at_end(self): def test_load_best_model_at_end(self):
total = int(self.n_epochs * 64 / self.batch_size) total = int(self.n_epochs * 64 / self.batch_size)
with tempfile.TemporaryDirectory() as tmpdir: with tempfile.TemporaryDirectory() as tmpdir: