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Seq2seq trainer (#9241)

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* Add label smoothing in Trainer

* Add options for scheduler and Adafactor in Trainer

* Put Seq2SeqTrainer in the main lib

* Apply suggestions from code review

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Address review comments and adapt scripts

* Documentation

* Move test not using script to tests folder

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Sylvain Gugger
2020-12-22 11:33:44 -05:00
committed by GitHub
parent 1fc7119181
commit 490b39e614
20 changed files with 655 additions and 166 deletions
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5
src/transformers/__init__.py
View File

@@ -287,8 +287,9 @@ from .trainer_callback import (
TrainerControl,
TrainerState,
)
from .trainer_utils import EvalPrediction, EvaluationStrategy, set_seed
from .trainer_utils import EvalPrediction, EvaluationStrategy, SchedulerType, set_seed
from .training_args import TrainingArguments
from .training_args_seq2seq import Seq2SeqTrainingArguments
from .training_args_tf import TFTrainingArguments
from .utils import logging
@@ -682,11 +683,13 @@ if is_torch_available():
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
# Trainer
from .trainer import Trainer
from .trainer_pt_utils import torch_distributed_zero_first
from .trainer_seq2seq import Seq2SeqTrainer
else:
from .utils.dummy_pt_objects import *

53
src/transformers/optimization.py
View File

@@ -15,12 +15,13 @@
"""PyTorch optimization for BERT model."""
import math
from typing import Callable, Iterable, Tuple
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
@@ -215,6 +216,56 @@ def get_polynomial_decay_schedule_with_warmup(
return LambdaLR(optimizer, lr_lambda, last_epoch)
TYPE_TO_SCHEDULER_FUNCTION = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
}
def get_scheduler(
name: Union[str, SchedulerType],
optimizer: Optimizer,
num_warmup_steps: Optional[int] = None,
num_training_steps: Optional[int] = None,
):
"""
Unified API to get any scheduler from its name.
Args:
name (:obj:`str` or `:obj:`SchedulerType`):
The name of the scheduler to use.
optimizer (:obj:`torch.optim.Optimizer`):
The optimizer that will be used during training.
num_warmup_steps (:obj:`int`, `optional`):
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
num_training_steps (:obj:`int`, `optional`):
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it's unset and the scheduler type requires it.
"""
name = SchedulerType(name)
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(optimizer)
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)
class AdamW(Optimizer):
"""
Implements Adam algorithm with weight decay fix as introduced in `Decoupled Weight Decay Regularization

55
src/transformers/trainer.py
View File

@@ -57,7 +57,7 @@ from .data.data_collator import DataCollator, DataCollatorWithPadding, default_d
from .file_utils import WEIGHTS_NAME, is_apex_available, is_datasets_available, is_in_notebook, is_torch_tpu_available
from .modeling_utils import PreTrainedModel
from .models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from .optimization import AdamW, get_linear_schedule_with_warmup
from .optimization import Adafactor, AdamW, get_scheduler
from .tokenization_utils_base import PreTrainedTokenizerBase
from .trainer_callback import (
CallbackHandler,
@@ -70,6 +70,7 @@ from .trainer_callback import (
)
from .trainer_pt_utils import (
DistributedTensorGatherer,
LabelSmoother,
SequentialDistributedSampler,
distributed_broadcast_scalars,
distributed_concat,
@@ -320,6 +321,12 @@ class Trainer:
)
self.use_apex = True
# Label smoothing
if self.args.label_smoothing_factor != 0:
self.label_smoother = LabelSmoother(epsilon=self.args.label_smoothing_factor)
else:
self.label_smoother = None
self.state = TrainerState()
self.control = TrainerControl()
# Internal variable for total_flos used to count as tensors (for distributed + TPU), will be sent in the
@@ -507,24 +514,32 @@ class Trainer:
"weight_decay": 0.0,
},
]
optimizer_cls = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
optimizer_cls = Adafactor
optimizer_kwargs = {"scale_parameter": False, "relative_step": False}
else:
optimizer_cls = AdamW
optimizer_kwargs = {
"betas": (self.args.adam_beta1, self.args.adam_beta2),
"eps": self.args.adam_epsilon,
}
optimizer_kwargs["lr"] = self.args.learning_rate
if self.sharded_dpp:
self.optimizer = OSS(
params=optimizer_grouped_parameters,
optim=AdamW,
lr=self.args.learning_rate,
betas=(self.args.adam_beta1, self.args.adam_beta2),
eps=self.args.adam_epsilon,
optim=optimizer_cls,
**optimizer_kwargs,
)
else:
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,
)
self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
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
self.lr_scheduler = get_scheduler(
self.args.lr_scheduler_type,
self.optimizer,
num_warmup_steps=self.args.warmup_steps,
num_training_steps=num_training_steps,
)
def num_examples(self, dataloader: DataLoader) -> int:
@@ -1168,8 +1183,12 @@ class Trainer:
# TODO: this needs to be fixed and made cleaner later.
if self.args.past_index >= 0:
self._past = outputs[self.args.past_index]
# We don't use .loss here since the model may return tuples instead of ModelOutput.
return outputs["loss"] if isinstance(outputs, dict) else outputs[0]
if self.label_smoother is not None and "labels" in inputs:
return self.label_smoother(outputs, inputs["labels"])
else:
# We don't use .loss here since the model may return tuples instead of ModelOutput.
return outputs["loss"] if isinstance(outputs, dict) else outputs[0]
def is_local_process_zero(self) -> bool:
"""
@@ -1556,11 +1575,13 @@ class Trainer:
else:
outputs = model(**inputs)
if has_labels:
if self.label_smoother is not None and "labels" in inputs:
loss = self.label_smoother(outputs, inputs["labels"]).mean().detach()
else:
loss = (outputs["loss"] if isinstance(outputs, dict) else outputs[0]).mean().detach()
if isinstance(outputs, dict):
loss = outputs["loss"].mean().detach()
logits = tuple(v for k, v in outputs.items() if k not in ignore_keys + ["loss"])
else:
loss = outputs[0].mean().detach()
logits = outputs[1:]
else:
loss = None

30
src/transformers/trainer_pt_utils.py
View File

@@ -19,6 +19,7 @@ Torch utilities for the Trainer class.
import math
import warnings
from contextlib import contextmanager
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
@@ -360,3 +361,32 @@ class DistributedTensorGatherer:
if self._offsets[0] != self.process_length:
logger.warn("Not all data has been set. Are you sure you passed all values?")
return nested_truncate(self._storage, self.num_samples)
@dataclass
class LabelSmoother:
"""
Adds label-smoothing on a pre-computed output from a Transformers model.
Args:
epsilon (:obj:`float`, `optional`, defaults to 0.1):
The label smoothing factor.
ignore_index (:obj:`int`, `optional`, defaults to -100):
The index in the labels to ignore when computing the loss.
"""
epsilon: float = 0.1
ignore_index: int = -100
def __call__(self, model_output, labels):
model_loss = model_output["loss"] if isinstance(model_output, dict) else model_output[0]
logits = model_output["logits"] if isinstance(model_output, dict) else model_output[1]
log_probs = -torch.nn.functional.log_softmax(logits, dim=-1)
# Look at the ignored index and mask the corresponding log_probs.
padding_mask = labels.unsqueeze(-1).eq(self.ignore_index)
log_probs.masked_fill_(padding_mask, 0.0)
# Take the mean over the label dimensions, then divide by the number of active elements (i.e. not-padded):
smoothed_loss = log_probs.mean(dim=-1).sum() / (padding_mask.numel() - padding_mask.long().sum())
return (1 - self.epsilon) * model_loss + self.epsilon * smoothed_loss

231
src/transformers/trainer_seq2seq.py Normal file
View File

@@ -0,0 +1,231 @@
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from packaging import version
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from torch.utils.data.dataset import Dataset
from .file_utils import is_torch_tpu_available
from .trainer import Trainer
from .trainer_pt_utils import get_tpu_sampler
from .trainer_utils import PredictionOutput
from .training_args import ParallelMode
from .utils import logging
if version.parse(torch.__version__) >= version.parse("1.6"):
from torch.cuda.amp import autocast
logger = logging.get_logger(__name__)
class Seq2SeqTrainer(Trainer):
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:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size,
distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
)
return (
RandomSampler(self.train_dataset)
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset)
)
def evaluate(
self,
eval_dataset: Optional[Dataset] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
max_length: Optional[int] = None,
num_beams: Optional[int] = 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. It must implement the
:obj:`__len__` method.
ignore_keys (:obj:`List[str]`, `optional`):
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.
metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"eval"`):
An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
"eval_bleu" if the prefix is ``"eval"`` (default)
max_length (:obj:`int`, `optional`):
The maximum target length to use when predicting with the generate method.
num_beams (:obj:`int`, `optional`):
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.
Returns:
A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
dictionary also contains the epoch number which comes from the training state.
"""
self._max_length = max_length
self._num_beams = num_beams
return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
def predict(
self,
test_dataset: Dataset,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
max_length: Optional[int] = None,
num_beams: Optional[int] = None,
) -> 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. Has to implement the method :obj:`__len__`
ignore_keys (:obj:`List[str]`, `optional`):
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.
metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"eval"`):
An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
"eval_bleu" if the prefix is ``"eval"`` (default)
max_length (:obj:`int`, `optional`):
The maximum target length to use when predicting with the generate method.
num_beams (:obj:`int`, `optional`):
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.
.. note::
If your predictions or labels have different sequence lengths (for instance because you're doing dynamic
padding in a token classification task) the predictions will be padded (on the right) to allow for
concatenation into one array. The padding index is -100.
Returns: `NamedTuple` A namedtuple with the following keys:
- 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).
"""
self._max_length = max_length
self._num_beams = num_beams
return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
def prediction_step(
self,
model: nn.Module,
inputs: Dict[str, Union[torch.Tensor, Any]],
prediction_loss_only: bool,
ignore_keys: Optional[List[str]] = None,
) -> 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).
"""
if not self.args.predict_with_generate or prediction_loss_only:
return super()(self, model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys)
has_labels = "labels" in inputs
inputs = self._prepare_inputs(inputs)
gen_kwargs = {
"max_length": self._max_length if self._max_length is not None else self.model.config.max_length,
"num_beams": self._num_beams if self._num_beams is not None else self.model.config.num_beams,
}
generated_tokens = self.model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
**gen_kwargs,
)
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])
with torch.no_grad():
if self.use_amp:
with autocast():
outputs = model(**inputs)
else:
outputs = model(**inputs)
if has_labels:
if self.label_smoother is not None:
loss = self.label_smoother(outputs, inputs["labels"]).mean().detach()
else:
loss = (outputs["loss"] if isinstance(outputs, dict) else outputs[0]).mean().detach()
else:
loss = None
if self.args.prediction_loss_only:
return (loss, None, None)
labels = inputs["labels"]
if labels.shape[-1] < gen_kwargs["max_length"]:
labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])
return (loss, generated_tokens, labels)
def _pad_tensors_to_max_len(self, tensor, max_length):
if self.tokenizer is None:
raise ValueError(
f"Tensor need to be padded to `max_length={max_length}` but no tokenzier was passed when creating "
"this `Trainer`. Make sure to create your `Trainer` with the appropriate tokenizer."
)
# If PAD token is not defined at least EOS token has to be defined
pad_token_id = (
self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
)
padded_tensor = pad_token_id * torch.ones(
(tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
)
padded_tensor[:, : tensor.shape[-1]] = tensor
return padded_tensor

9
src/transformers/trainer_utils.py
View File

@@ -201,3 +201,12 @@ def speed_metrics(split, start_time, num_samples=None):
samples_per_second = 1 / (runtime / num_samples)
result[f"{split}_samples_per_second"] = round(samples_per_second, 3)
return result
class SchedulerType(ExplicitEnum):
LINEAR = "linear"
COSINE = "cosine"
COSINE_WITH_RESTARTS = "cosine_with_restarts"
POLYNOMIAL = "polynomial"
CONSTANT = "constant"
CONSTANT_WITH_WARMUP = "constant_with_warmup"

23
src/transformers/training_args.py
View File

@@ -19,7 +19,7 @@ from enum import Enum
from typing import Any, Dict, List, Optional, Tuple
from .file_utils import cached_property, is_torch_available, is_torch_tpu_available, torch_required
from .trainer_utils import EvaluationStrategy
from .trainer_utils import EvaluationStrategy, SchedulerType
from .utils import logging
@@ -121,6 +121,9 @@ class TrainingArguments:
max_steps (:obj:`int`, `optional`, defaults to -1):
If set to a positive number, the total number of training steps to perform. Overrides
:obj:`num_train_epochs`.
lr_scheduler_type (:obj:`str` or :class:`~transformers.SchedulerType`, `optional`, defaults to :obj:`"linear"`):
The scheduler type to use. See the documentation of :class:`~transformers.SchedulerType` for all possible
values.
warmup_steps (:obj:`int`, `optional`, defaults to 0):
Number of steps used for a linear warmup from 0 to :obj:`learning_rate`.
logging_dir (:obj:`str`, `optional`):
@@ -217,6 +220,13 @@ class TrainingArguments:
sharded_ddp (:obj:`bool`, `optional`, defaults to :obj:`False`):
Use Sharded DDP training from `FairScale <https://github.com/facebookresearch/fairscale>`__ (in distributed
training only). This is an experimental feature.
label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0):
The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
labels are changed from 0s and 1s to :obj:`label_smoothing_factor/num_labels` and :obj:`1 -
label_smoothing_factor + label_smoothing_factor/num_labels` respectively.
adafactor (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the :class:`~transformers.Adafactor` optimizer instead of
:class:`~transformers.AdamW`.
"""
output_dir: str = field(
@@ -246,7 +256,7 @@ class TrainingArguments:
)
evaluation_strategy: EvaluationStrategy = field(
default="no",
metadata={"help": "Run evaluation during training at each logging step."},
metadata={"help": "The evaluation strategy to use."},
)
prediction_loss_only: bool = field(
default=False,
@@ -296,6 +306,10 @@ class TrainingArguments:
default=-1,
metadata={"help": "If > 0: set total number of training steps to perform. Override num_train_epochs."},
)
lr_scheduler_type: SchedulerType = field(
default="linear",
metadata={"help": "The scheduler type to use."},
)
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
logging_dir: Optional[str] = field(default_factory=default_logdir, metadata={"help": "Tensorboard log dir."})
@@ -392,11 +406,16 @@ class TrainingArguments:
default=False,
metadata={"help": "Whether or not to use sharded DDP training (in distributed training only)."},
)
label_smoothing_factor: float = field(
default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
)
adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace Adam by Adafactor."})
def __post_init__(self):
if self.disable_tqdm is None:
self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
self.evaluation_strategy = EvaluationStrategy(self.evaluation_strategy)
self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
if self.do_eval is False and self.evaluation_strategy != EvaluationStrategy.NO:
self.do_eval = True
if self.eval_steps is None:

42
src/transformers/training_args_seq2seq.py Normal file
View File

@@ -0,0 +1,42 @@
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from dataclasses import dataclass, field
from .file_utils import add_start_docstrings
from .training_args import TrainingArguments
logger = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class Seq2SeqTrainingArguments(TrainingArguments):
"""
sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use a `sortish sampler` or not. Only possible if the underlying datasets are `Seq2SeqDataset` for
now but will become generally available in the near future.
It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for
the training set.
predict_with_generate (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
"""
sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
predict_with_generate: bool = field(
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
)

9
src/transformers/utils/dummy_pt_objects.py
View File

@@ -2279,6 +2279,10 @@ def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
requires_pytorch(get_polynomial_decay_schedule_with_warmup)
def get_scheduler(*args, **kwargs):
requires_pytorch(get_scheduler)
class Trainer:
def __init__(self, *args, **kwargs):
requires_pytorch(self)
@@ -2286,3 +2290,8 @@ class Trainer:
def torch_distributed_zero_first(*args, **kwargs):
requires_pytorch(torch_distributed_zero_first)
class Seq2SeqTrainer:
def __init__(self, *args, **kwargs):
requires_pytorch(self)