Doc styling (#8067)
* Important files * Styling them all * Revert "Styling them all" This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e. * Syling them for realsies * Fix syntax error * Fix benchmark_utils * More fixes * Fix modeling auto and script * Remove new line * Fixes * More fixes * Fix more files * Style * Add FSMT * More fixes * More fixes * More fixes * More fixes * Fixes * More fixes * More fixes * Last fixes * Make sphinx happy
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Sylvain Gugger
@@ -287,8 +287,8 @@ class ModuleUtilsMixin:
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Whether or not the attentions scores are computed by chunks or not.
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Returns:
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:obj:`torch.Tensor` with shape :obj:`[num_hidden_layers x batch x num_heads x seq_length x seq_length]`
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or list with :obj:`[None]` for each layer.
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:obj:`torch.Tensor` with shape :obj:`[num_hidden_layers x batch x num_heads x seq_length x seq_length]` or
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list with :obj:`[None]` for each layer.
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"""
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if head_mask is not None:
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head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
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@@ -358,9 +358,9 @@ class ModuleUtilsMixin:
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"""
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Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a
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batch with this transformer model. Default approximation neglects the quadratic dependency on the number of
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tokens (valid if :obj:`12 * d_model << sequence_length`) as laid out in `this paper <https://arxiv.org/pdf/2001.08361.pdf>`__ section
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2.1. Should be overriden for transformers with parameter re-use e.g. Albert or Universal Transformers, or
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if doing long-range modeling with very high sequence lengths.
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tokens (valid if :obj:`12 * d_model << sequence_length`) as laid out in `this paper
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<https://arxiv.org/pdf/2001.08361.pdf>`__ section 2.1. Should be overriden for transformers with parameter
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re-use e.g. Albert or Universal Transformers, or if doing long-range modeling with very high sequence lengths.
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Args:
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batch_size (:obj:`int`):
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@@ -390,23 +390,24 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
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* prune heads in the self-attention heads.
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Class attributes (overridden by derived classes):
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- **config_class** (:class:`~transformers.PretrainedConfig`) -- A subclass of
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:class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
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- **load_tf_weights** (:obj:`Callable`) -- A python `method` for loading a TensorFlow checkpoint in a
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PyTorch model, taking as arguments:
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- **load_tf_weights** (:obj:`Callable`) -- A python `method` for loading a TensorFlow checkpoint in a PyTorch
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model, taking as arguments:
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- **model** (:class:`~transformers.PreTrainedModel`) -- An instance of the model on which to load the
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TensorFlow checkpoint.
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- **config** (:class:`~transformers.PreTrainedConfig`) -- An instance of the configuration associated
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to the model.
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- **config** (:class:`~transformers.PreTrainedConfig`) -- An instance of the configuration associated to
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the model.
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- **path** (:obj:`str`) -- A path to the TensorFlow checkpoint.
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- **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
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derived classes of the same architecture adding modules on top of the base model.
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- **authorized_missing_keys** (:obj:`Optional[List[str]]`) -- A list of re pattern of tensor names to ignore
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when loading the model (and avoid unnecessary warnings).
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- **keys_to_never_save** (:obj:`Optional[List[str]]`) -- A list of of tensor names to ignore
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when saving the model (useful for keys that aren't trained, but which are deterministic)
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- **keys_to_never_save** (:obj:`Optional[List[str]]`) -- A list of of tensor names to ignore when saving the
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model (useful for keys that aren't trained, but which are deterministic)
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"""
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config_class = None
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@@ -684,9 +685,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
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Arguments:
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heads_to_prune (:obj:`Dict[int, List[int]]`):
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Dictionary with keys being selected layer indices (:obj:`int`) and associated values being the list
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of heads to prune in said layer (list of :obj:`int`). For instance {1: [0, 2], 2: [2, 3]} will
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prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
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Dictionary with keys being selected layer indices (:obj:`int`) and associated values being the list of
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heads to prune in said layer (list of :obj:`int`). For instance {1: [0, 2], 2: [2, 3]} will prune heads
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0 and 2 on layer 1 and heads 2 and 3 on layer 2.
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"""
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# save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads
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for layer, heads in heads_to_prune.items():
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@@ -743,8 +744,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
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r"""
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Instantiate a pretrained pytorch model from a pre-trained model configuration.
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The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated).
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To train the model, you should first set it back in training mode with ``model.train()``.
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The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated). To
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train the model, you should first set it back in training mode with ``model.train()``.
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The warning `Weights from XXX not initialized from pretrained model` means that the weights of XXX do not come
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pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
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@@ -806,21 +807,19 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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file exists.
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proxies (:obj:`Dict[str, str], `optional`):
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A dictionary of proxy servers to use by protocol or endpoint, e.g.,
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:obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each
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request.
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A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
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'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
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output_loading_info(:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether ot not to also return a dictionary containing missing keys, unexpected keys and error
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messages.
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Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
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local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to only look at local files (e.g., not try doanloading the model).
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use_cdn(:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether or not to use Cloudfront (a Content Delivery Network, or CDN) when searching for the model on
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our S3 (faster). Should be set to :obj:`False` for checkpoints larger than 20GB.
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mirror(:obj:`str`, `optional`, defaults to :obj:`None`):
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Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem,
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you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please
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refer to the mirror site for more information.
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Mirror source to accelerate downloads in China. If you are from China and have an accessibility
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problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
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Please refer to the mirror site for more information.
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kwargs (remaining dictionary of keyword arguments, `optional`):
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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:obj:`output_attentions=True`). Behaves differently depending on whether a ``config`` is provided or
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@@ -1142,8 +1141,8 @@ class PoolerStartLogits(nn.Module):
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hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, seq_len, hidden_size)`):
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The final hidden states of the model.
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p_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, seq_len)`, `optional`):
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS).
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1.0 means token should be masked.
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
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should be masked.
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Returns:
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:obj:`torch.FloatTensor`: The start logits for SQuAD.
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@@ -1192,8 +1191,8 @@ class PoolerEndLogits(nn.Module):
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start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
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The position of the first token for the labeled span.
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p_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, seq_len)`, `optional`):
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS).
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1.0 means token should be masked.
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
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should be masked.
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.. note::
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@@ -1296,13 +1295,15 @@ class SquadHeadOutput(ModelOutput):
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Args:
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loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned if both :obj:`start_positions` and :obj:`end_positions` are provided):
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Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
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Classification loss as the sum of start token, end token (and is_impossible if provided) classification
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losses.
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start_top_log_probs (``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
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Log probabilities for the top config.start_n_top start token possibilities (beam-search).
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start_top_index (``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
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Indices for the top config.start_n_top start token possibilities (beam-search).
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end_top_log_probs (``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
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Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
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Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities
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(beam-search).
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end_top_index (``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
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Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
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cls_logits (``torch.FloatTensor`` of shape ``(batch_size,)``, `optional`, returned if ``start_positions`` or ``end_positions`` is not provided):
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@@ -1361,8 +1362,8 @@ class SQuADHead(nn.Module):
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is_impossible (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
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Whether the question has a possible answer in the paragraph or not.
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p_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, seq_len)`, `optional`):
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS).
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1.0 means token should be masked.
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Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
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should be masked.
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return_dict (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to return a :class:`~transformers.file_utils.ModelOuput` instead of a plain tuple.
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@@ -1441,8 +1442,8 @@ class SequenceSummary(nn.Module):
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Args:
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config (:class:`~transformers.PretrainedConfig`):
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The config used by the model. Relevant arguments in the config class of the model are (refer to the
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actual config class of your model for the default values it uses):
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The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
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config class of your model for the default values it uses):
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- **summary_type** (:obj:`str`) -- The method to use to make this summary. Accepted values are:
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@@ -1455,7 +1456,7 @@ class SequenceSummary(nn.Module):
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- **summary_use_proj** (:obj:`bool`) -- Add a projection after the vector extraction.
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- **summary_proj_to_labels** (:obj:`bool`) -- If :obj:`True`, the projection outputs to
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:obj:`config.num_labels` classes (otherwise to :obj:`config.hidden_size`).
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- **summary_activation** (:obj:`Optional[str]`) -- Set to :obj:`"tanh"` to add a tanh activation to the
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- **summary_activation** (:obj:`Optional[str]`) -- Set to :obj:`"tanh"` to add a tanh activation to the
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output, another string or :obj:`None` will add no activation.
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- **summary_first_dropout** (:obj:`float`) -- Optional dropout probability before the projection and
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activation.
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@@ -1618,8 +1619,8 @@ def prune_layer(
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dim (:obj:`int`, `optional`): The dimension on which to keep the indices.
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Returns:
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:obj:`torch.nn.Linear` or :class:`~transformers.modeling_utils.Conv1D`:
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The pruned layer as a new layer with :obj:`requires_grad=True`.
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:obj:`torch.nn.Linear` or :class:`~transformers.modeling_utils.Conv1D`: The pruned layer as a new layer with
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:obj:`requires_grad=True`.
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"""
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if isinstance(layer, nn.Linear):
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return prune_linear_layer(layer, index, dim=0 if dim is None else dim)
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@@ -1647,7 +1648,8 @@ def apply_chunking_to_forward(
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chunk_dim (:obj:`int`):
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The dimension over which the :obj:`input_tensors` should be chunked.
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input_tensors (:obj:`Tuple[torch.Tensor]`):
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The input tensors of ``forward_fn`` which will be chunked.
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The input tensors of ``forward_fn`` which will be chunked
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Returns:
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:obj:`torch.Tensor`: A tensor with the same shape as the :obj:`foward_fn` would have given if applied`.
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