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Unverified Commit 27b3031d authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
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Mass conversion of documentation from rst to Markdown (#14866) 

* Convert docstrings of all configurations and tokenizers

* Processors and fixes

* Last modeling files and fixes to models

* Pipeline modules

* Utils files

* Data submodule

* All the other files

* Style

* Missing examples

* Style again

* Fix copies

* Say bye bye to rst docstrings forever
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src/transformers/training_args_tf.py
View file @ 27b3031d
......@@ -33,134 +33,135 @@ class TFTrainingArguments(TrainingArguments):
TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using :class:`~transformers.HfArgumentParser` we can turn this class into `argparse
<https://docs.python.org/3/library/argparse.html#module-argparse>`__ arguments that can be specified on the command
Using [`HfArgumentParser`] we can turn this class into [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the command
line.
Parameters:
output_dir (:obj:`str`):
output_dir (`str`):
The output directory where the model predictions and checkpoints will be written.
overwrite_output_dir (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`True`, overwrite the content of the output directory. Use this to continue training if
:obj:`output_dir` points to a checkpoint directory.
do_train (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to run training or not. This argument is not directly used by :class:`~transformers.Trainer`, it's
intended to be used by your training/evaluation scripts instead. See the `example scripts
<https://github.com/huggingface/transformers/tree/master/examples>`__ for more details.
do_eval (:obj:`bool`, `optional`):
Whether to run evaluation on the validation set or not. Will be set to :obj:`True` if
:obj:`evaluation_strategy` is different from :obj:`"no"`. This argument is not directly used by
:class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
overwrite_output_dir (`bool`, *optional*, defaults to `False`):
If `True`, overwrite the content of the output directory. Use this to continue training if
`output_dir` points to a checkpoint directory.
do_train (`bool`, *optional*, defaults to `False`):
Whether to run training or not. This argument is not directly used by [`Trainer`], it's
intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/master/examples) for more details.
do_eval (`bool`, *optional*):
Whether to run evaluation on the validation set or not. Will be set to `True` if
`evaluation_strategy` is different from `"no"`. This argument is not directly used by
[`Trainer`], it's intended to be used by your training/evaluation scripts instead. See
the [example scripts](https://github.com/huggingface/transformers/tree/master/examples) for more
details.
do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`):
do_predict (`bool`, *optional*, defaults to `False`):
Whether to run predictions on the test set or not. This argument is not directly used by
:class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
[`Trainer`], it's intended to be used by your training/evaluation scripts instead. See
the [example scripts](https://github.com/huggingface/transformers/tree/master/examples) for more
details.
evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.IntervalStrategy`, `optional`, defaults to :obj:`"no"`):
evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
The evaluation strategy to adopt during training. Possible values are:
* :obj:`"no"`: No evaluation is done during training.
* :obj:`"steps"`: Evaluation is done (and logged) every :obj:`eval_steps`.
* :obj:`"epoch"`: Evaluation is done at the end of each epoch.
- `"no"`: No evaluation is done during training.
- `"steps"`: Evaluation is done (and logged) every `eval_steps`.
- `"epoch"`: Evaluation is done at the end of each epoch.
per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8):
per_device_train_batch_size (`int`, *optional*, defaults to 8):
The batch size per GPU/TPU core/CPU for training.
per_device_eval_batch_size (:obj:`int`, `optional`, defaults to 8):
per_device_eval_batch_size (`int`, *optional*, defaults to 8):
The batch size per GPU/TPU core/CPU for evaluation.
gradient_accumulation_steps: (:obj:`int`, `optional`, defaults to 1):
gradient_accumulation_steps: (`int`, *optional*, defaults to 1):
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.
.. warning::
<Tip warning={true}>
When using gradient accumulation, one step is counted as one step with backward pass. Therefore,
logging, evaluation, save will be conducted every ``gradient_accumulation_steps * xxx_step`` training
logging, evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training
examples.
learning_rate (:obj:`float`, `optional`, defaults to 5e-5):
</Tip>
learning_rate (`float`, *optional*, defaults to 5e-5):
The initial learning rate for Adam.
weight_decay (:obj:`float`, `optional`, defaults to 0):
weight_decay (`float`, *optional*, defaults to 0):
The weight decay to apply (if not zero).
adam_beta1 (:obj:`float`, `optional`, defaults to 0.9):
adam_beta1 (`float`, *optional*, defaults to 0.9):
The beta1 hyperparameter for the Adam optimizer.
adam_beta2 (:obj:`float`, `optional`, defaults to 0.999):
adam_beta2 (`float`, *optional*, defaults to 0.999):
The beta2 hyperparameter for the Adam optimizer.
adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8):
adam_epsilon (`float`, *optional*, defaults to 1e-8):
The epsilon hyperparameter for the Adam optimizer.
max_grad_norm (:obj:`float`, `optional`, defaults to 1.0):
max_grad_norm (`float`, *optional*, defaults to 1.0):
Maximum gradient norm (for gradient clipping).
num_train_epochs(:obj:`float`, `optional`, defaults to 3.0):
num_train_epochs(`float`, *optional*, defaults to 3.0):
Total number of training epochs to perform.
max_steps (:obj:`int`, `optional`, defaults to -1):
max_steps (`int`, *optional*, defaults to -1):
If set to a positive number, the total number of training steps to perform. Overrides
:obj:`num_train_epochs`.
warmup_ratio (:obj:`float`, `optional`, defaults to 0.0):
Ratio of total training steps used for a linear warmup from 0 to :obj:`learning_rate`.
warmup_steps (:obj:`int`, `optional`, defaults to 0):
Number of steps used for a linear warmup from 0 to :obj:`learning_rate`. Overrides any effect of
:obj:`warmup_ratio`.
logging_dir (:obj:`str`, `optional`):
`TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
`runs/**CURRENT_DATETIME_HOSTNAME**`.
logging_strategy (:obj:`str` or :class:`~transformers.trainer_utils.IntervalStrategy`, `optional`, defaults to :obj:`"steps"`):
`num_train_epochs`.
warmup_ratio (`float`, *optional*, defaults to 0.0):
Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
warmup_steps (`int`, *optional*, defaults to 0):
Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of
`warmup_ratio`.
logging_dir (`str`, *optional*):
[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to
*runs/**CURRENT_DATETIME_HOSTNAME***.
logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
The logging strategy to adopt during training. Possible values are:
* :obj:`"no"`: No logging is done during training.
* :obj:`"epoch"`: Logging is done at the end of each epoch.
* :obj:`"steps"`: Logging is done every :obj:`logging_steps`.
- `"no"`: No logging is done during training.
- `"epoch"`: Logging is done at the end of each epoch.
- `"steps"`: Logging is done every `logging_steps`.
logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to log and evaluate the first :obj:`global_step` or not.
logging_steps (:obj:`int`, `optional`, defaults to 500):
Number of update steps between two logs if :obj:`logging_strategy="steps"`.
save_strategy (:obj:`str` or :class:`~transformers.trainer_utils.IntervalStrategy`, `optional`, defaults to :obj:`"steps"`):
logging_first_step (`bool`, *optional*, defaults to `False`):
Whether to log and evaluate the first `global_step` or not.
logging_steps (`int`, *optional*, defaults to 500):
Number of update steps between two logs if `logging_strategy="steps"`.
save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
The checkpoint save strategy to adopt during training. Possible values are:
* :obj:`"no"`: No save is done during training.
* :obj:`"epoch"`: Save is done at the end of each epoch.
* :obj:`"steps"`: Save is done every :obj:`save_steps`.
- `"no"`: No save is done during training.
- `"epoch"`: Save is done at the end of each epoch.
- `"steps"`: Save is done every `save_steps`.
save_steps (:obj:`int`, `optional`, defaults to 500):
Number of updates steps before two checkpoint saves if :obj:`save_strategy="steps"`.
save_total_limit (:obj:`int`, `optional`):
save_steps (`int`, *optional*, defaults to 500):
Number of updates steps before two checkpoint saves if `save_strategy="steps"`.
save_total_limit (`int`, *optional*):
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
:obj:`output_dir`.
no_cuda (:obj:`bool`, `optional`, defaults to :obj:`False`):
`output_dir`.
no_cuda (`bool`, *optional*, defaults to `False`):
Whether to not use CUDA even when it is available or not.
seed (:obj:`int`, `optional`, defaults to 42):
seed (`int`, *optional*, defaults to 42):
Random seed that will be set at the beginning of training.
fp16 (:obj:`bool`, `optional`, defaults to :obj:`False`):
fp16 (`bool`, *optional*, defaults to `False`):
Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.
fp16_opt_level (:obj:`str`, `optional`, defaults to 'O1'):
For :obj:`fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
on the `Apex documentation <https://nvidia.github.io/apex/amp.html>`__.
local_rank (:obj:`int`, `optional`, defaults to -1):
fp16_opt_level (`str`, *optional*, defaults to 'O1'):
For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
on the [Apex documentation](https://nvidia.github.io/apex/amp).
local_rank (`int`, *optional*, defaults to -1):
During distributed training, the rank of the process.
tpu_num_cores (:obj:`int`, `optional`):
tpu_num_cores (`int`, *optional*):
When training on TPU, the number of TPU cores (automatically passed by launcher script).
debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
debug (`bool`, *optional*, defaults to `False`):
Whether to activate the trace to record computation graphs and profiling information or not.
dataloader_drop_last (:obj:`bool`, `optional`, defaults to :obj:`False`):
dataloader_drop_last (`bool`, *optional*, defaults to `False`):
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
or not.
eval_steps (:obj:`int`, `optional`, defaults to 1000):
eval_steps (`int`, *optional*, defaults to 1000):
Number of update steps before two evaluations.
past_index (:obj:`int`, `optional`, defaults to -1):
Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can
past_index (`int`, *optional*, defaults to -1):
Some models like [TransformerXL](../model_doc/transformerxl) or :doc*XLNet <../model_doc/xlnet>* can
make use of the past hidden states for their predictions. If this argument is set to a positive int, the
``Trainer`` will use the corresponding output (usually index 2) as the past state and feed it to the model
at the next training step under the keyword argument ``mems``.
tpu_name (:obj:`str`, `optional`):
`Trainer` will use the corresponding output (usually index 2) as the past state and feed it to the model
at the next training step under the keyword argument `mems`.
tpu_name (`str`, *optional*):
The name of the TPU the process is running on.
tpu_zone (:obj:`str`, `optional`):
tpu_zone (`str`, *optional*):
The zone of the TPU the process is running on. If not specified, we will attempt to automatically detect
from metadata.
gcp_project (:obj:`str`, `optional`):
gcp_project (`str`, *optional*):
Google Cloud Project name for the Cloud TPU-enabled project. If not specified, we will attempt to
automatically detect from metadata.
run_name (:obj:`str`, `optional`):
run_name (`str`, *optional*):
A descriptor for the run. Notably used for wandb logging.
xla (:obj:`bool`, `optional`):
xla (`bool`, *optional*):
Whether to activate the XLA compilation or not.
"""
......@@ -259,7 +260,7 @@ class TFTrainingArguments(TrainingArguments):
@property
def train_batch_size(self) -> int:
"""
The actual batch size for training (may differ from :obj:`per_gpu_train_batch_size` in distributed training).
The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training).
"""
if self.per_gpu_train_batch_size:
logger.warning(
......@@ -272,7 +273,7 @@ class TFTrainingArguments(TrainingArguments):
@property
def eval_batch_size(self) -> int:
"""
The actual batch size for evaluation (may differ from :obj:`per_gpu_eval_batch_size` in distributed training).
The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training).
"""
if self.per_gpu_eval_batch_size:
logger.warning(
......
src/transformers/utils/fx.py
View file @ 27b3031d
......@@ -404,12 +404,12 @@ class HFTracer(Tracer):
def path_of_module(self, mod: nn.Module) -> str:
"""
Helper method to find the qualified name of ``mod`` in the Module hierarchy of ``root``. For example, if
``root`` has a submodule named ``foo``, which has a submodule named ``bar``, passing ``bar`` into this function
Helper method to find the qualified name of `mod` in the Module hierarchy of `root`. For example, if
`root` has a submodule named `foo`, which has a submodule named `bar`, passing `bar` into this function
will return the string "foo.bar".
Args:
mod (str): The ``Module`` to retrieve the qualified name for.
mod (str): The `Module` to retrieve the qualified name for.
"""
# Prefer the O(1) algorithm
if hasattr(self, "submodule_paths") and self.submodule_paths:
......@@ -506,24 +506,24 @@ def symbolic_trace(
Performs symbolic tracing on the model.
Args:
model (:obj:`PretrainedModel`):
model ([`PretrainedModel`]):
The model to trace.
input_names (:obj:`List[str]`, `optional`):
input_names (`List[str]`, *optional*):
The names of the inputs of the traced model. If unset, model.dummy_inputs().keys() are used instead.
batch_size (:obj:`int`, `optional`, defaults to 1):
batch_size (`int`, *optional*, defaults to 1):
The batch size of the traced model inputs.
sequence_length (:obj:`int` or :obj:`List[int]]`):
sequence_length (`int` or `List[int]]`):
The sequence length of the traced model inputs. For sequence-to-sequence models with different sequence
lengths between the encoder and the decoder inputs, this must be :obj:`[encoder_sequence_length,
decoder_sequence_length]`.
num_choices (:obj:`int`, `optional`, defaults to -1):
lengths between the encoder and the decoder inputs, this must be `[encoder_sequence_length, decoder_sequence_length]`.
num_choices (`int`, *optional*, defaults to -1):
The number of possible choices for a multiple choice task.
Returns:
:obj:`torch.fx.GraphModule`: A GraphModule constructed by recording operations seen while tracing the model.
`torch.fx.GraphModule`: A GraphModule constructed by recording operations seen while tracing the model.
Example::
Example:
```python
from transformers.utils.fx import symbolic_trace
traced_model = symbolic_trace(
model,
......@@ -531,7 +531,7 @@ def symbolic_trace(
batch_size=1,
sequence_length=128,
)
"""
```"""
if input_names is None:
input_names = model.dummy_inputs.keys()
......
src/transformers/utils/logging.py
View file @ 27b3031d
......@@ -46,7 +46,7 @@ _default_log_level = logging.WARNING
def _get_default_logging_level():
"""
If TRANSFORMERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
not - fall back to ``_default_log_level``
not - fall back to `_default_log_level`
"""
env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None)
if env_level_str:
......@@ -125,18 +125,19 @@ def get_verbosity() -> int:
Return the current level for the 🤗 Transformers's root logger as an int.
Returns:
:obj:`int`: The logging level.
`int`: The logging level.
.. note::
<Tip>
🤗 Transformers has following logging levels:
- 50: ``transformers.logging.CRITICAL`` or ``transformers.logging.FATAL``
- 40: ``transformers.logging.ERROR``
- 30: ``transformers.logging.WARNING`` or ``transformers.logging.WARN``
- 20: ``transformers.logging.INFO``
- 10: ``transformers.logging.DEBUG``
"""
- 50: `transformers.logging.CRITICAL` or `transformers.logging.FATAL`
- 40: `transformers.logging.ERROR`
- 30: `transformers.logging.WARNING` or `transformers.logging.WARN`
- 20: `transformers.logging.INFO`
- 10: `transformers.logging.DEBUG`
</Tip>"""
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
......@@ -147,14 +148,14 @@ def set_verbosity(verbosity: int) -> None:
Set the verbosity level for the 🤗 Transformers's root logger.
Args:
verbosity (:obj:`int`):
verbosity (`int`):
Logging level, e.g., one of:
- ``transformers.logging.CRITICAL`` or ``transformers.logging.FATAL``
- ``transformers.logging.ERROR``
- ``transformers.logging.WARNING`` or ``transformers.logging.WARN``
- ``transformers.logging.INFO``
- ``transformers.logging.DEBUG``
- `transformers.logging.CRITICAL` or `transformers.logging.FATAL`
- `transformers.logging.ERROR`
- `transformers.logging.WARNING` or `transformers.logging.WARN`
- `transformers.logging.INFO`
- `transformers.logging.DEBUG`
"""
_configure_library_root_logger()
......@@ -162,22 +163,22 @@ def set_verbosity(verbosity: int) -> None:
def set_verbosity_info():
"""Set the verbosity to the :obj:`INFO` level."""
"""Set the verbosity to the `INFO` level."""
return set_verbosity(INFO)
def set_verbosity_warning():
"""Set the verbosity to the :obj:`WARNING` level."""
"""Set the verbosity to the `WARNING` level."""
return set_verbosity(WARNING)
def set_verbosity_debug():
"""Set the verbosity to the :obj:`DEBUG` level."""
"""Set the verbosity to the `DEBUG` level."""
return set_verbosity(DEBUG)
def set_verbosity_error():
"""Set the verbosity to the :obj:`ERROR` level."""
"""Set the verbosity to the `ERROR` level."""
return set_verbosity(ERROR)
......
src/transformers/utils/notebook.py
View file @ 27b3031d
......@@ -65,28 +65,29 @@ class NotebookProgressBar:
Class attributes (overridden by derived classes)
- **warmup** (:obj:`int`) -- The number of iterations to do at the beginning while ignoring
:obj:`update_every`.
- **update_every** (:obj:`float`) -- Since calling the time takes some time, we only do it every presumed
:obj:`update_every` seconds. The progress bar uses the average time passed up until now to guess the next
- **warmup** (`int`) -- The number of iterations to do at the beginning while ignoring
`update_every`.
- **update_every** (`float`) -- Since calling the time takes some time, we only do it every presumed
`update_every` seconds. The progress bar uses the average time passed up until now to guess the next
value for which it will call the update.
Args:
total (:obj:`int`):
total (`int`):
The total number of iterations to reach.
prefix (:obj:`str`, `optional`):
prefix (`str`, *optional*):
A prefix to add before the progress bar.
leave (:obj:`bool`, `optional`, defaults to :obj:`True`):
leave (`bool`, *optional*, defaults to `True`):
Whether or not to leave the progress bar once it's completed. You can always call the
:meth:`~transformers.utils.notebook.NotebookProgressBar.close` method to make the bar disappear.
parent (:class:`~transformers.notebook.NotebookTrainingTracker`, `optional`):
A parent object (like :class:`~transformers.utils.notebook.NotebookTrainingTracker`) that spawns progress
bars and handle their display. If set, the object passed must have a :obj:`display()` method.
width (:obj:`int`, `optional`, defaults to 300):
[`~utils.notebook.NotebookProgressBar.close`] method to make the bar disappear.
parent ([`~notebook.NotebookTrainingTracker`], *optional*):
A parent object (like [`~utils.notebook.NotebookTrainingTracker`]) that spawns progress
bars and handle their display. If set, the object passed must have a `display()` method.
width (`int`, *optional*, defaults to 300):
The width (in pixels) that the bar will take.
Example::
Example:
```python
import time
pbar = NotebookProgressBar(100)
......@@ -94,7 +95,7 @@ class NotebookProgressBar:
pbar.update(val)
time.sleep(0.07)
pbar.update(100)
"""
```"""
warmup = 5
update_every = 0.2
......@@ -118,17 +119,17 @@ class NotebookProgressBar:
def update(self, value: int, force_update: bool = False, comment: str = None):
"""
The main method to update the progress bar to :obj:`value`.
The main method to update the progress bar to `value`.
Args:
value (:obj:`int`):
The value to use. Must be between 0 and :obj:`total`.
force_update (:obj:`bool`, `optional`, defaults to :obj:`False`):
value (`int`):
The value to use. Must be between 0 and `total`.
force_update (`bool`, *optional*, defaults to `False`):
Whether or not to force and update of the internal state and display (by default, the bar will wait for
:obj:`value` to reach the value it predicted corresponds to a time of more than the :obj:`update_every`
`value` to reach the value it predicted corresponds to a time of more than the `update_every`
attribute since the last update to avoid adding boilerplate).
comment (:obj:`str`, `optional`):
comment (`str`, *optional*):
A comment to add on the left of the progress bar.
"""
self.value = value
......@@ -203,10 +204,10 @@ class NotebookTrainingTracker(NotebookProgressBar):
Args:
num_steps (:obj:`int`): The number of steps during training.
column_names (:obj:`List[str]`, `optional`):
num_steps (`int`): The number of steps during training.
column_names (`List[str]`, *optional*):
The list of column names for the metrics table (will be inferred from the first call to
:meth:`~transformers.utils.notebook.NotebookTrainingTracker.write_line` if not set).
[`~utils.notebook.NotebookTrainingTracker.write_line`] if not set).
"""
def __init__(self, num_steps, column_names=None):
......@@ -230,7 +231,7 @@ class NotebookTrainingTracker(NotebookProgressBar):
Write the values in the inner table.
Args:
values (:obj:`Dict[str, float]`): The values to display.
values (`Dict[str, float]`): The values to display.
"""
if self.inner_table is None:
self.inner_table = [list(values.keys()), list(values.values())]
......@@ -250,9 +251,9 @@ class NotebookTrainingTracker(NotebookProgressBar):
easily updated).
Args:
total (:obj:`int`): The number of iterations for the child progress bar.
prefix (:obj:`str`, `optional`): A prefix to write on the left of the progress bar.
width (:obj:`int`, `optional`, defaults to 300): The width (in pixels) of the progress bar.
total (`int`): The number of iterations for the child progress bar.
prefix (`str`, *optional*): A prefix to write on the left of the progress bar.
width (`int`, *optional*, defaults to 300): The width (in pixels) of the progress bar.
"""
self.child_bar = NotebookProgressBar(total, prefix=prefix, parent=self, width=width)
return self.child_bar
......@@ -267,7 +268,7 @@ class NotebookTrainingTracker(NotebookProgressBar):
class NotebookProgressCallback(TrainerCallback):
"""
A :class:`~transformers.TrainerCallback` that displays the progress of training or evaluation, optimized for
A [`TrainerCallback`] that displays the progress of training or evaluation, optimized for
Jupyter Notebooks or Google colab.
"""
......
src/transformers/utils/versions.py
View file @ 27b3031d
......@@ -55,18 +55,18 @@ def require_version(requirement: str, hint: Optional[str] = None) -> None:
"""
Perform a runtime check of the dependency versions, using the exact same syntax used by pip.
The installed module version comes from the `site-packages` dir via `importlib_metadata`.
The installed module version comes from the *site-packages* dir via *importlib_metadata*.
Args:
requirement (:obj:`str`): pip style definition, e.g., "tokenizers==0.9.4", "tqdm>=4.27", "numpy"
hint (:obj:`str`, `optional`): what suggestion to print in case of requirements not being met
requirement (`str`): pip style definition, e.g., "tokenizers==0.9.4", "tqdm>=4.27", "numpy"
hint (`str`, *optional*): what suggestion to print in case of requirements not being met
Example::
Example:
```python
require_version("pandas>1.1.2")
require_version("numpy>1.18.5", "this is important to have for whatever reason")
"""
```"""
hint = f"\n{hint}" if hint is not None else ""
......
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/configuration_{{cookiecutter.lowercase_modelname}}.py
View file @ 27b3031d
......@@ -28,97 +28,98 @@ logger = logging.get_logger(__name__)
class {{cookiecutter.camelcase_modelname}}Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.{{cookiecutter.camelcase_modelname}}Model`.
This is the configuration class to store the configuration of a [`~{{cookiecutter.camelcase_modelname}}Model`].
It is used to instantiate an {{cookiecutter.modelname}} model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the {{cookiecutter.modelname}} `{{cookiecutter.checkpoint_identifier}} <https://huggingface.co/{{cookiecutter.checkpoint_identifier}}>`__ architecture.
the {{cookiecutter.modelname}} [{{cookiecutter.checkpoint_identifier}}](https://huggingface.co/{{cookiecutter.checkpoint_identifier}}) architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
Configuration objects inherit from [`PretrainedConfig`] and can be used
to control the model outputs. Read the documentation from [`PretrainedConfig`]
for more information.
Args:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
vocab_size (:obj:`int`, `optional`, defaults to 30522):
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the {{cookiecutter.modelname}} model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.{{cookiecutter.camelcase_modelname}}Model` or
:class:`~transformers.TF{{cookiecutter.camelcase_modelname}}Model`.
hidden_size (:obj:`int`, `optional`, defaults to 768):
`inputs_ids` passed when calling [`~{{cookiecutter.camelcase_modelname}}Model`] or
[`~TF{{cookiecutter.camelcase_modelname}}Model`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 12):
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler.
If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.{{cookiecutter.camelcase_modelname}}Model` or
:class:`~transformers.TF{{cookiecutter.camelcase_modelname}}Model`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`~{{cookiecutter.camelcase_modelname}}Model`] or
[`~TF{{cookiecutter.camelcase_modelname}}Model`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if ``config.is_decoder=True``.
relevant if `config.is_decoder=True`.
{% else -%}
vocab_size (:obj:`int`, `optional`, defaults to 50265):
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the {{cookiecutter.modelname}} model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.{{cookiecutter.camelcase_modelname}}Model` or
:class:`~transformers.TF{{cookiecutter.camelcase_modelname}}Model`.
d_model (:obj:`int`, `optional`, defaults to 1024):
`inputs_ids` passed when calling [`~{{cookiecutter.camelcase_modelname}}Model`] or
[`~TF{{cookiecutter.camelcase_modelname}}Model`].
d_model (`int`, *optional*, defaults to 1024):
Dimension of the layers and the pooler layer.
encoder_layers (:obj:`int`, `optional`, defaults to 12):
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (:obj:`int`, `optional`, defaults to 12):
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (:obj:`int`, `optional`, defaults to 16):
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096):
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
dropout (:obj:`float`, `optional`, defaults to 0.1):
`"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (:obj:`float`, `optional`, defaults to 0.0):
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (:obj:`float`, `optional`, defaults to 0.0):
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (:obj:`int`, `optional`, defaults to 1024):
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (:obj:`float`, `optional`, defaults to 0.02):
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the encoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0):
The LayerDrop probability for the decoder. See the `LayerDrop paper <see
https://arxiv.org/abs/1909.11556>`__ for more details.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
encoder_layerdrop: (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see
https://arxiv.org/abs/1909.11556) for more details.
decoder_layerdrop: (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see
https://arxiv.org/abs/1909.11556) for more details.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
{% endif -%}
Example::
Example:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Model, {{cookiecutter.camelcase_modelname}}Config
>>> # Initializing a {{cookiecutter.modelname}} {{cookiecutter.checkpoint_identifier}} style configuration
......@@ -129,7 +130,8 @@ class {{cookiecutter.camelcase_modelname}}Config(PretrainedConfig):
>>> # Accessing the model configuration
>>> configuration = model.config
"""
```
"""
model_type = "{{cookiecutter.lowercase_modelname}}"
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else -%}
......
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_fast_{{cookiecutter.lowercase_modelname}}.py
View file @ 27b3031d
......@@ -42,12 +42,12 @@ PRETRAINED_INIT_CONFIGURATION = {
class {{cookiecutter.camelcase_modelname}}TokenizerFast(BertTokenizerFast):
r"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
:class:`~transformers.{{cookiecutter.camelcase_modelname}}TokenizerFast` is identical to :class:`~transformers.BertTokenizerFast` and runs
[`~{{cookiecutter.camelcase_modelname}}TokenizerFast`] is identical to [`BertTokenizerFast`] and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass :class:`~transformers.BertTokenizerFast` for usage examples and documentation concerning
Refer to superclass [`BertTokenizerFast`] for usage examples and documentation concerning
parameters.
"""
......@@ -86,12 +86,12 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
class {{cookiecutter.camelcase_modelname}}TokenizerFast(BartTokenizerFast):
r"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
:class:`~transformers.{{cookiecutter.camelcase_modelname}}TokenizerFast` is identical to :class:`~transformers.BartTokenizerFast` and runs
[`~{{cookiecutter.camelcase_modelname}}TokenizerFast`] is identical to [`BartTokenizerFast`] and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass :class:`~transformers.BartTokenizerFast` for usage examples and documentation concerning
Refer to superclass [`BartTokenizerFast`] for usage examples and documentation concerning
parameters.
"""
......@@ -129,10 +129,10 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (:obj:`str`):
vocab_file (`str`):
Path to the vocabulary file.
"""
......@@ -182,13 +182,13 @@ class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast)
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (:obj:`List[int]`):
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of zeros.
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
......
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py
View file @ 27b3031d
......@@ -43,10 +43,10 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(BertTokenizer):
r"""
Construct a {{cookiecutter.modelname}} tokenizer.
:class:`~transformers.{{cookiecutter.camelcase_modelname}}Tokenizer` is identical to :class:`~transformers.BertTokenizer` and runs end-to-end
[`~{{cookiecutter.camelcase_modelname}}Tokenizer`] is identical to [`BertTokenizer`] and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning
parameters.
"""
......@@ -85,10 +85,10 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(BartTokenizer):
"""
Construct a {{cookiecutter.modelname}} tokenizer.
:class:`~transformers.{{cookiecutter.camelcase_modelname}}Tokenizer` is identical to :class:`~transformers.BartTokenizer` and runs end-to-end
[`~{{cookiecutter.camelcase_modelname}}Tokenizer`] is identical to [`BartTokenizer`] and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass :class:`~transformers.BartTokenizer` for usage examples and documentation concerning
Refer to superclass [`BartTokenizer`] for usage examples and documentation concerning
parameters.
"""
......@@ -125,7 +125,7 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
Construct a {{cookiecutter.modelname}} tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (:obj:`str`):
vocab_file (`str`):
Path to the vocabulary file.
"""
......@@ -173,11 +173,11 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (:obj:`str`):
save_directory (`str`):
The directory in which to save the vocabulary.
Returns:
:obj:`Tuple(str)`: Paths to the files saved.
`Tuple(str)`: Paths to the files saved.
"""
def build_inputs_with_special_tokens(
......@@ -188,17 +188,17 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
by concatenating and adding special tokens.
A {{cookiecutter.modelname}} sequence has the following format:
- single sequence: ``<s> X </s>``
- pair of sequences: ``<s> A </s></s> B </s>``
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (:obj:`List[int]`):
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
......@@ -211,18 +211,18 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` method.
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (:obj:`List[int]`):
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
......@@ -241,13 +241,13 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (:obj:`List[int]`):
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of zeros.
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
......@@ -264,10 +264,10 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (:obj:`str`):
vocab_file (`str`):
Path to the vocabulary file.
"""
......@@ -317,13 +317,13 @@ class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast)
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (:obj:`List[int]`):
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of zeros.
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
......
utils/check_repo.py
View file @ 27b3031d
......@@ -593,6 +593,51 @@ def check_all_objects_are_documented():
"The following objects are in the public init so should be documented:\n - "
+ "\n - ".join(undocumented_objs)
)
check_docstrings_are_in_md()
# Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`.
_re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`")
# Re pattern to catch things between double backquotes.
_re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)")
# Re pattern to catch example introduction.
_re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE)
def is_rst_docstring(docstring):
"""
Returns `True` if `docstring` is written in rst.
"""
if _re_rst_special_words.search(docstring) is not None:
return True
if _re_double_backquotes.search(docstring) is not None:
return True
if _re_rst_example.search(docstring) is not None:
return True
return False
def check_docstrings_are_in_md():
"""Check all docstrings are in md"""
files_with_rst = []
for file in Path(PATH_TO_TRANSFORMERS).glob("**/*.py"):
with open(file, "r") as f:
code = f.read()
docstrings = code.split('"""')
for idx, docstring in enumerate(docstrings):
if idx % 2 == 0 or not is_rst_docstring(docstring):
continue
files_with_rst.append(file)
break
if len(files_with_rst) > 0:
raise ValueError(
"The following files have docstrings written in rst:\n"
+ "\n".join([f"- {f}" for f in files_with_rst])
+ "To fix this run `doc_builder convert path_to_py_file` after installing `doc_builder`\n"
"(`pip install git+https://github.com/huggingface/doc-builder`)"
)
def check_repo_quality():
......
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