transformer_rankers.models.pairwise_bert.BertForPairwiseLearning

class transformer_rankers.models.pairwise_bert.BertForPairwiseLearning(config, loss_function='cross-entropy', smoothing=0.1)

Bases: transformers.modeling_bert.BertPreTrainedModel

BERT based model for pairwise learning. It expects both the <q, positive_doc> and the <q, negative_doc> for doing the forward pass. The loss is cross-entropy for the difference between positive_doc and negative_doc scores (labels are 1 if score positive_neg > score negative_doc otherwise 0) based on “Learning to Rank using Gradient Descent” 2005 ICML.

__init__(config, loss_function='cross-entropy', smoothing=0.1)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(config[, loss_function, smoothing])	Initializes internal Module state, shared by both nn.Module and ScriptModule.
add_memory_hooks()	Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.
add_module(name, module)	Adds a child module to the current module.
adjust_logits_during_generation(logits, **kwargs)	Implement in subclasses of PreTrainedModel for custom behavior to adjust the logits in the generate method.
apply(fn)	Applies fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
enforce_repetition_penalty_(lprobs, …)	Enforce the repetition penalty (from the CTRL paper).
eval()	Sets the module in evaluation mode.
extra_repr()	Set the extra representation of the module
float()	Casts all floating point parameters and buffers to float datatype.
forward([input_ids_pos, attention_mask_pos, …])	Defines the computation performed at every call.
from_pretrained(…)	Instantiate a pretrained pytorch model from a pre-trained model configuration.
generate([input_ids, max_length, …])	Generates sequences for models with a language modeling head.
get_extended_attention_mask(attention_mask, …)	Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
get_head_mask(head_mask, num_hidden_layers)	Prepare the head mask if needed.
get_input_embeddings()	Returns the model’s input embeddings.
get_output_embeddings()	Returns the model’s output embeddings.
half()	Casts all floating point parameters and buffers to half datatype.
init_weights()	Initializes and prunes weights if needed.
invert_attention_mask(encoder_attention_mask)	Invert an attention mask (e.g., switches 0.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
load_tf_weights(config, tf_checkpoint_path)	Load tf checkpoints in a pytorch model.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
num_parameters([only_trainable])	Get the number of (optionally, trainable) parameters in the model.
parameters([recurse])	Returns an iterator over module parameters.
postprocess_next_token_scores(scores, …)
prepare_inputs_for_generation(input_ids, …)	Implement in subclasses of PreTrainedModel for custom behavior to prepare inputs in the generate method.
prune_heads(heads_to_prune)	Prunes heads of the base model.
register_backward_hook(hook)	Registers a backward hook on the module.
register_buffer(name, tensor)	Adds a persistent buffer to the module.
register_forward_hook(hook)	Registers a forward hook on the module.
register_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_parameter(name, param)	Adds a parameter to the module.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
reset_memory_hooks_state()	Reset the mem_rss_diff attribute of each module (see add_memory_hooks()).
resize_token_embeddings([new_num_tokens])	Resizes input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
save_pretrained(save_directory)	Save a model and its configuration file to a directory, so that it can be re-loaded using the :func:`~transformers.PreTrainedModel.from_pretrained` class method.
set_input_embeddings(value)	Set model’s input embeddings
share_memory()
state_dict([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
tie_weights()	Tie the weights between the input embeddings and the output embeddings.
to(*args, **kwargs)	Moves and/or casts the parameters and buffers.
train([mode])	Sets the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
zero_grad()	Sets gradients of all model parameters to zero.

Attributes

authorized_missing_keys
base_model	The main body of the model.
base_model_prefix
device	The device on which the module is (assuming that all the module parameters are on the same device).
dtype	The dtype of the module (assuming that all the module parameters have the same dtype).
dummy_inputs	Dummy inputs to do a forward pass in the network.
dump_patches

add_memory_hooks()

Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.

Increase in memory consumption is stored in a mem_rss_diff attribute for each module and can be reset to zero with model.reset_memory_hooks_state().

add_module(name, module)

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters

• name (string) – name of the child module. The child module can be accessed from this module using the given name

• module (Module) – child module to be added to the module.

adjust_logits_during_generation(logits, **kwargs)

Implement in subclasses of PreTrainedModel for custom behavior to adjust the logits in the generate method.

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also nn-init-doc).

Parameters

fn (Module -> None) – function to be applied to each submodule

Returns

self

Return type

Module

Example:

>>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)

property base_model

The main body of the model.

Type

torch.nn.Module

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

Returns

self

Return type

Module

buffers(recurse=True)

Returns an iterator over module buffers.

Parameters

recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.

Yields

torch.Tensor – module buffer

Example:

>>> for buf in model.buffers():
>>>     print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

children()

Returns an iterator over immediate children modules.

Yields

Module – a child module

config_class

alias of transformers.configuration_bert.BertConfig

cpu()

Moves all model parameters and buffers to the CPU.

Returns

self

Return type

Module

cuda(device=None)

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Parameters

device (int, optional) – if specified, all parameters will be copied to that device

Returns

self

Return type

Module

property device

The device on which the module is (assuming that all the module parameters are on the same device).

Type

torch.device

double()

Casts all floating point parameters and buffers to double datatype.

Returns

self

Return type

Module

property dtype

The dtype of the module (assuming that all the module parameters have the same dtype).

Type

torch.dtype

property dummy_inputs

Dummy inputs to do a forward pass in the network.

Type

Dict[str, torch.Tensor]

enforce_repetition_penalty_(lprobs, batch_size, num_beams, prev_output_tokens, repetition_penalty)

Enforce the repetition penalty (from the CTRL paper).

eval()

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

This is equivalent with self.train(False).

Returns

self

Return type

Module

extra_repr()

Set the extra representation of the module

To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable.

float()

Casts all floating point parameters and buffers to float datatype.

Returns

self

Return type

Module

forward(input_ids_pos=None, attention_mask_pos=None, token_type_ids_pos=None, input_ids_neg=None, attention_mask_neg=None, token_type_ids_neg=None, labels=None)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

classmethod from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

Instantiate a pretrained pytorch model from a pre-trained model configuration.

The model is set in evaluation mode by default using model.eval() (Dropout modules are deactivated). To train the model, you should first set it back in training mode with model.train().

The warning Weights from XXX not initialized from pretrained model means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task.

The warning Weights from XXX not used in YYY means that the layer XXX is not used by YYY, therefore those weights are discarded.

Parameters

• pretrained_model_name_or_path (str, optional) –

  Can be either:

  • A string with the shortcut name of a pretrained model to load from cache or download, e.g., bert-base-uncased.

  • A string with the identifier name of a pretrained model that was user-uploaded to our S3, e.g., dbmdz/bert-base-german-cased.

  • A path to a directory containing model weights saved using save_pretrained(), e.g., ./my_model_directory/.

  • A path or url to a tensorflow index checkpoint file (e.g, ./tf_model/model.ckpt.index). In this case, from_tf should be set to True and a configuration object should be provided as config argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

  • None if you are both providing the configuration and state dictionary (resp. with keyword arguments config and state_dict).

• model_args (sequence of positional arguments, optional) – All remaning positional arguments will be passed to the underlying model’s __init__ method.

• config (Union[PretrainedConfig, str], optional) –

  Can be either:

  • an instance of a class derived from PretrainedConfig,

  • a string valid as input to from_pretrained().

  Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:

  • The model is a model provided by the library (loaded with the shortcut name string of a pretrained model).

  • The model was saved using save_pretrained() and is reloaded by suppling the save directory.

  • The model is loaded by suppling a local directory as pretrained_model_name_or_path and a configuration JSON file named config.json is found in the directory.

• state_dict (Dict[str, torch.Tensor], optional) –

  A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

• cache_dir (str, optional) – Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.

• from_tf (bool, optional, defaults to False) – Load the model weights from a TensorFlow checkpoint save file (see docstring of pretrained_model_name_or_path argument).

• force_download (bool, optional, defaults to False) – Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.

• resume_download (bool, optional, defaults to False) – Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.

• proxies (Dict[str, str], `optional) – A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.

• output_loading_info (bool, optional, defaults to False) – Whether ot not to also return a dictionnary containing missing keys, unexpected keys and error messages.

• local_files_only (bool, optional, defaults to False) – Whether or not to only look at local files (e.g., not try doanloading the model).

• use_cdn (bool, optional, defaults to True) – Whether or not to use Cloudfront (a Content Delivery Network, or CDN) when searching for the model on our S3 (faster). Should be set to False for checkpoints larger than 20GB.

• kwargs (remaining dictionary of keyword arguments, optional) –

  Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., output_attention=True). Behaves differently depending on whether a config is provided or automatically loaded:

  • If a configuration is provided with config, **kwargs will be directly passed to the underlying model’s __init__ method (we assume all relevant updates to the configuration have already been done)

  • If a configuration is not provided, kwargs will be first passed to the configuration class initialization function (from_pretrained()). Each key of kwargs that corresponds to a configuration attribute will be used to override said attribute with the supplied kwargs value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s __init__ function.

Examples:

from transformers import BertConfig, BertModel
# Download model and configuration from S3 and cache.
model = BertModel.from_pretrained('bert-base-uncased')
# Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable).
model = BertModel.from_pretrained('./test/saved_model/')
# Update configuration during loading.
model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)

generate(input_ids: Optional[torch.LongTensor] = None, max_length: Optional[int] = None, min_length: Optional[int] = None, do_sample: Optional[bool] = None, early_stopping: Optional[bool] = None, num_beams: Optional[int] = None, temperature: Optional[float] = None, top_k: Optional[int] = None, top_p: Optional[float] = None, repetition_penalty: Optional[float] = None, bad_words_ids: Optional[Iterable[int]] = None, bos_token_id: Optional[int] = None, pad_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, length_penalty: Optional[float] = None, no_repeat_ngram_size: Optional[int] = None, num_return_sequences: Optional[int] = None, attention_mask: Optional[torch.LongTensor] = None, decoder_start_token_id: Optional[int] = None, use_cache: Optional[bool] = None, **model_kwargs) → torch.LongTensor

Generates sequences for models with a language modeling head. The method currently supports greedy decoding, beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.

Adapted in part from Facebook’s XLM beam search code.

Apart from input_ids and attention_mask, all the arguments below will default to the value of the attribute of the same name inside the PretrainedConfig of the model. The default values indicated are the default values of those config.

Most of these parameters are explained in more detail in this blog post.

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) – The sequence used as a prompt for the generation. If None the method initializes it as an empty torch.LongTensor of shape (1,).

• max_length (int, optional, defaults to 20) – The maximum length of the sequence to be generated.

• min_length (int, optional, defaults to 10) – The minimum length of the sequence to be generated.

• do_sample (bool, optional, defaults to False) – Whether or not to use sampling ; use greedy decoding otherwise.

• early_stopping (bool, optional, defaults to False) – Whether to stop the beam search when at least num_beams sentences are finished per batch or not.

• num_beams (int, optional, defaults to 1) – Number of beams for beam search. 1 means no beam search.

• temperature (float, optional, defaults tp 1.0) – The value used to module the next token probabilities.

• top_k (int, optional, defaults to 50) – The number of highest probability vocabulary tokens to keep for top-k-filtering.

• top_p (float, optional, defaults to 1.0) – If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.

• repetition_penalty (float, optional, defaults to 1.0) – The parameter for repetition penalty. 1.0 means no penalty. See this paper for more details.

• pad_token_id (int, optional) – The id of the padding token.

• bos_token_id (int, optional) – The id of the beginning-of-sequence token.

• eos_token_id (int, optional) – The id of the end-of-sequence token.

• length_penalty (float, optional, defaults to 1.0) –

  Exponential penalty to the length. 1.0 means no penalty.

  Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer sequences.

• no_repeat_ngram_size (int, optional, defaults to 0) – If set to int > 0, all ngrams of that size can only occur once.

• bad_words_ids (List[int], optional) – List of token ids that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use tokenizer.encode(bad_word, add_prefix_space=True).

• num_return_sequences (int, optional, defaults to 1) – The number of independently computed returned sequences for each element in the batch.

• attention_mask (torch.LongTensor of shape (batch_size, sequence_length), optional) –

  Mask to avoid performing attention on padding token indices. Mask values are in [0, 1], 1 for tokens that are not masked, and 0 for masked tokens.

  If not provided, will default to a tensor the same shape as input_ids that masks the pad token.

  What are attention masks?

• decoder_start_token_id (int, optional) – If an encoder-decoder model starts decoding with a different token than bos, the id of that token.

• use_cache – (bool, optional, defaults to True): Whether or not the model should use the past last key/values attentions (if applicable to the model) to speed up decoding.

• model_kwargs – Additional model specific kwargs will be forwarded to the forward function of the model.

Returns

The generated sequences. The second dimension (sequence_length) is either equal to max_length or shorter if all batches finished early due to the eos_token_id.

Return type

torch.LongTensor of shape (batch_size * num_return_sequences, sequence_length)

Examples:

tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
outputs = model.generate(max_length=40)  # do greedy decoding
print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))

tokenizer = AutoTokenizer.from_pretrained('openai-gpt')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from S3 and cache.
input_context = 'The dog'
input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)  # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
for i in range(3): #  3 output sequences were generated
    print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
input_context = 'The dog'
input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True)  # generate 3 candidates using sampling
for i in range(3): #  3 output sequences were generated
    print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

tokenizer = AutoTokenizer.from_pretrained('ctrl')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('ctrl')    # Download model and configuration from S3 and cache.
input_context = 'Legal My neighbor is'  # "Legal" is one of the control codes for ctrl
input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)  # generate sequences
print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))

tokenizer = AutoTokenizer.from_pretrained('gpt2')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('gpt2')    # Download model and configuration from S3 and cache.
input_context = 'My cute dog'  # "Legal" is one of the control codes for ctrl
bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)  # generate sequences without allowing bad_words to be generated

get_extended_attention_mask(attention_mask: torch.Tensor, input_shape: Tuple[int], device: <property object at 0x7ff2538d2c78>) → torch.Tensor

Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

Parameters

• attention_mask (torch.Tensor) – Mask with ones indicating tokens to attend to, zeros for tokens to ignore.

• input_shape (Tuple[int]) – The shape of the input to the model.

• device – (torch.device): The device of the input to the model.

Returns

torch.Tensor The extended attention mask, with a the same dtype as attention_mask.dtype.

get_head_mask(head_mask: Optional[torch.Tensor], num_hidden_layers: int, is_attention_chunked: bool = False) → torch.Tensor

Prepare the head mask if needed.

Parameters

• head_mask (torch.Tensor with shape [num_heads] or [num_hidden_layers x num_heads], optional) – The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).

• num_hidden_layers (int) – The number of hidden layers in the model.

• is_attention_chunked – (bool, optional, defaults to :obj:`False): Whether or not the attentions scores are computed by chunks or not.

Returns

torch.Tensor with shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] or list with [None] for each layer.

get_input_embeddings() → torch.nn.modules.module.Module

Returns the model’s input embeddings.

Returns

A torch module mapping vocabulary to hidden states.

Return type

nn.Module

get_output_embeddings() → torch.nn.modules.module.Module

Returns the model’s output embeddings.

Returns

A torch module mapping hidden states to vocabulary.

Return type

nn.Module

half()

Casts all floating point parameters and buffers to half datatype.

Returns

self

Return type

Module

init_weights()

Initializes and prunes weights if needed.

invert_attention_mask(encoder_attention_mask: torch.Tensor) → torch.Tensor

Invert an attention mask (e.g., switches 0. and 1.).

Parameters

encoder_attention_mask (torch.Tensor) – An attention mask.

Returns

The inverted attention mask.

Return type

torch.Tensor

load_state_dict(state_dict, strict=True)

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters

• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns

• missing_keys is a list of str containing the missing keys

• unexpected_keys is a list of str containing the unexpected keys

Return type

NamedTuple with missing_keys and unexpected_keys fields

load_tf_weights(config, tf_checkpoint_path)

Load tf checkpoints in a pytorch model.

modules()

Returns an iterator over all modules in the network.

Yields

Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
        print(idx, '->', m)

0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)

named_buffers(prefix='', recurse=True)

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters

• prefix (str) – prefix to prepend to all buffer names.

• recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.

Yields

(string, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> for name, buf in self.named_buffers():
>>>    if name in ['running_var']:
>>>        print(buf.size())

named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields

(string, Module) – Tuple containing a name and child module

Example:

>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)

named_modules(memo=None, prefix='')

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Yields

(string, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
        print(idx, '->', m)

0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

named_parameters(prefix='', recurse=True)

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters

• prefix (str) – prefix to prepend to all parameter names.

• recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

Yields

(string, Parameter) – Tuple containing the name and parameter

Example:

>>> for name, param in self.named_parameters():
>>>    if name in ['bias']:
>>>        print(param.size())

num_parameters(only_trainable: bool = False) → int

Get the number of (optionally, trainable) parameters in the model.

Parameters

only_trainable (bool, optional, defaults to False) – Whether or not to return only the number of trainable parameters

Returns

The number of parameters.

Return type

int

parameters(recurse=True)

Returns an iterator over module parameters.

This is typically passed to an optimizer.

Parameters

recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

Yields

Parameter – module parameter

Example:

>>> for param in model.parameters():
>>>     print(type(param), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

prepare_inputs_for_generation(input_ids, **kwargs)

Implement in subclasses of PreTrainedModel for custom behavior to prepare inputs in the generate method.

prune_heads(heads_to_prune: Dict[int, List[int]])

Prunes heads of the base model.

Parameters

heads_to_prune (Dict[int, List[int]]) – Dictionary with keys being selected layer indices (int) and associated values being the list of heads to prune in said layer (list of int). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.

register_backward_hook(hook)

Registers a backward hook on the module.

The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:

hook(module, grad_input, grad_output) -> Tensor or None

The grad_input and grad_output may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of grad_input in subsequent computations.

Returns

a handle that can be used to remove the added hook by calling handle.remove()

Return type

torch.utils.hooks.RemovableHandle

Warning

The current implementation will not have the presented behavior for complex Module that perform many operations. In some failure cases, grad_input and grad_output will only contain the gradients for a subset of the inputs and outputs. For such Module, you should use torch.Tensor.register_hook() directly on a specific input or output to get the required gradients.

register_buffer(name, tensor)

Adds a persistent buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the persistent state.

Buffers can be accessed as attributes using given names.

Parameters

• name (string) – name of the buffer. The buffer can be accessed from this module using the given name

• tensor (Tensor) – buffer to be registered.

Example:

>>> self.register_buffer('running_mean', torch.zeros(num_features))

register_forward_hook(hook)

Registers a forward hook on the module.

The hook will be called every time after forward() has computed an output. It should have the following signature:

hook(module, input, output) -> None or modified output

The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called.

Returns

a handle that can be used to remove the added hook by calling handle.remove()

Return type

torch.utils.hooks.RemovableHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

The hook will be called every time before forward() is invoked. It should have the following signature:

hook(module, input) -> None or modified input

The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple).

Returns

a handle that can be used to remove the added hook by calling handle.remove()

Return type

torch.utils.hooks.RemovableHandle

register_parameter(name, param)

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters

• name (string) – name of the parameter. The parameter can be accessed from this module using the given name

• param (Parameter) – parameter to be added to the module.

requires_grad_(requires_grad=True)

Change if autograd should record operations on parameters in this module.

This method sets the parameters’ requires_grad attributes in-place.

This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).

Parameters

requires_grad (bool) – whether autograd should record operations on parameters in this module. Default: True.

Returns

self

Return type

Module

reset_memory_hooks_state()

Reset the mem_rss_diff attribute of each module (see add_memory_hooks()).

resize_token_embeddings(new_num_tokens: Optional[int] = None) → torch.nn.modules.sparse.Embedding

Resizes input token embeddings matrix of the model if new_num_tokens != config.vocab_size.

Takes care of tying weights embeddings afterwards if the model class has a tie_weights() method.

Parameters

new_num_tokens (int, optional) – The number of new tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or None, just returns a pointer to the input tokens torch.nn.Embedding module of the model wihtout doing anything.

Returns

Pointer to the input tokens Embeddings Module of the model.

Return type

torch.nn.Embedding

save_pretrained(save_directory)

Save a model and its configuration file to a directory, so that it can be re-loaded using the :func:`~transformers.PreTrainedModel.from_pretrained` class method.

Parameters

save_directory (str) – Directory to which to save. Will be created if it doesn’t exist.

set_input_embeddings(value: torch.nn.modules.module.Module)

Set model’s input embeddings

Parameters

value (nn.Module) – A module mapping vocabulary to hidden states.

state_dict(destination=None, prefix='', keep_vars=False)

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns

a dictionary containing a whole state of the module

Return type

dict

Example:

>>> module.state_dict().keys()
['bias', 'weight']

tie_weights()

Tie the weights between the input embeddings and the output embeddings.

If the torchscript flag is set in the configuration, can’t handle parameter sharing so we are cloning the weights instead.

to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

This can be called as

to(device=None, dtype=None, non_blocking=False)

to(dtype, non_blocking=False)

to(tensor, non_blocking=False)

to(memory_format=torch.channels_last)

Its signature is similar to torch.Tensor.to(), but only accepts floating point desired dtype s. In addition, this method will only cast the floating point parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters

• device (torch.device) – the desired device of the parameters and buffers in this module

• dtype (torch.dtype) – the desired floating point type of the floating point parameters and buffers in this module

• tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module

• memory_format (torch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)

Returns

self

Return type

Module

Example:

>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)

train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters

mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.

Returns

self

Return type

Module

type(dst_type)

Casts all parameters and buffers to dst_type.

Parameters

dst_type (type or string) – the desired type

Returns

self

Return type

Module

zero_grad()

Sets gradients of all model parameters to zero.