modeling#

class T5Model(config: T5Config)[源代码]#

基类：T5PretrainedModel

The bare T5 Model transformer outputting raw hidden-states without any specific head on top.

This model inherits from PretrainedModel. Refer to the superclass documentation for the generic methods.

This model is also a Paddle paddle.nn.Layer subclass. Use it as a regular Paddle Layer and refer to the Paddle documentation for all matter related to general usage and behavior.

参数:: (class (config) -- T5Config): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained] method to load the model weights.

get_input_embeddings()[源代码]#

get input embedding of model

返回:: embedding of model
返回类型:: nn.Embedding

set_input_embeddings(new_embeddings)[源代码]#

set new input embedding for model

参数:: value (Embedding) -- the new embedding of model
抛出:: NotImplementedError -- Model has not implement set_input_embeddings method

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_output=None, cache=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[源代码]#

The T5Model forward method, overrides the __call__() special method.

参数:

input_ids (Tensor) -- Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. Its data type should be int64 and it has a shape of [batch_size, sequence_length].
attention_mask (Tensor, optional) -- Mask used in multi-head attention to avoid performing attention on to some unwanted positions, usually the paddings or the subsequent positions. Its data type can be int, float. When the data type is int, the masked tokens have 0 values and the others have 1 values. When the data type is float, the masked tokens have 0.0 values and the others have 1.0 values. It is a tensor with shape broadcasted to [batch_size, num_attention_heads, sequence_length, sequence_length]. Defaults to None, which means nothing needed to be prevented attention to.
decoder_input_ids (Tensor, optional) -- Indices of decoder input sequence tokens in the vocabulary. Its data type should be int64 and it has a shape of [batch_size, sequence_length]. Defaults to None, which means no decoder_input_ids is provided, the model will create the tensor by shifting the input_ids to the right.
decoder_attention_mask (Tensor, optional) -- Mask used in multi-head attention to avoid performing attention to some unwanted positions in decoder_input_ids. Its data type and shape is the same as attention_mask. Defaults to None.
encoder_output (tuple, optional) -- The output of the encoder, a tuple consists last_hidden_state, hidden_states`(optional), `attentions`(optional). The data type of `last_hidden_state is float32 and its shape is [batch_size, sequence_length, hidden_size]. hidden_states is hidden_states of all layers in the Transformer encoder. The length of hidden_states is num_hidden_layers + 1. For all element in the tuple, its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size]. attentions is attentions of all layers of in the Transformer encoder. The length of attentions is num_hidden_layers. For all element in the tuple, its data type should be float32 and its shape is [batch_size, num_attention_heads, sequence_length, sequence_length].
cache (Tuple[Tuple[Tensor]], optional) -- Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model. Can be used to speed up sequential decoding. The input_ids which have their past given to this model should not be passed as input ids as they have already been computed. Defaults to None.
inputs_embeds (Tensor, optional) -- Optionally, instead of passing input_ids you can choose to directly pass an embedded representation of shape (batch_size, sequence_length, hidden_size). This is useful if you want more control over how to convert input_ids indices into associated vectors than the model's internal embedding lookup matrix. Default to None.
decoder_inputs_embeds (Tensor, optional) --
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation of shape (batch_size, target_sequence_length, hidden_size). If cache is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model's internal embedding lookup matrix. Default to None.

If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.
use_cache (bool, optional) -- Whether or not to use cache. If set to True, past_buckets_states states are returned and can be used to speed up decoding. Defaults to False.
output_attentions (bool, optional) -- Whether or not to return the attentions tensors of all attention layers. Defaults to False.
output_hidden_states (bool, optional) -- Whether or not to return the output of all hidden layers. Defaults to False.
return_dict (bool, optional) -- Whether or not to return a class:Seq2SeqModelOutput. If False, the output will be a tuple of tensors. Defaults to False.

返回:

An instance of Seq2SeqModelOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of Seq2SeqModelOutput.

tuple: Returns tuple (last_hidden_state, cache, decoder_hidden_states, decoder_attentions, cross_attentions, encoder_last_hidden_state, encoder_hidden_states, encoder_attentions)

With the fields:

last_hidden_state (Tensor):
Sequence of hidden-states at the last layer of the decoder of the model. It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
cache (List[tuple(Tensor, Tensor)], optional):
returned when use_cache=True is passed. List of tuple(Tensor, Tensor) of length config["num_layers"], with the first element being the previous buckets of shape [batch_size, num_heads, num_hashes, sequence_length] and the second being the previous hidden_states of shape [batch_size, sequence_length, hidden_size].
decoder_hidden_states (tuple(Tensor), optional)
returned when output_hidden_states=True is passed. Tuple of Tensor (one for the output of the embeddings + one for the output of decoder each layer) of shape (batch_size, sequence_length, hidden_size).
decoder_attentions (tuple(Tensor), optional):
returned when output_attentions=True is passed. tuple of Tensor (one for each layer) of shape. Each Tensor has a data type of float32 and its shape is [batch_size, num_heads, sequence_length, sequence_length].
cross_attentions (tuple(Tensor), optional):
returned when output_attentions=True is passed. tuple of Tensor (one for each layer) of shape. Each Tensor has a data type of float32 and its shape is [batch_size, num_heads, sequence_length, sequence_length].
encoder_last_hidden_state (Tensor):
Sequence of hidden-states at the last layer of the encoder of the model. It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
encoder_hidden_states (tuple(Tensor), optional):
returned when output_hidden_states=True is passed. tuple of Tensor (one for the output of the embeddings + one for the output of encoder each layer). Each Tensor has a data type of float32 and its shape is [batch_size, sequence_length, hidden_size].
encoder_attentions (tuple(Tensor), optional):
returned when output_attentions=True is passed. tuple of Tensor (one for each layer) of shape. Each Tensor has a data type of float32 and its shape is [batch_size, num_heads, sequence_length, sequence_length].

示例

import paddle
from paddlenlp.transformers import T5Model, T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained('t5-base')
model = T5Model.from_pretrained('t5-base')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
input_ids = paddle.to_tensor([inputs["input_ids"]], dtype="int64")
decoder_inputs = tokenizer("It means you can")
decoder_input_ids = paddle.to_tensor([decoder_inputs["input_ids"]], dtype="int64")

outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
last_hidden_state = outputs[0]
print(last_hidden_state.shape)
# [1, 5, 768]

class T5PretrainedModel(*args, **kwargs)[源代码]#

基类：PretrainedModel

An abstract class for pretrained T5 models. It provides T5 related model_config_file, resource_files_names, pretrained_resource_files_map, pretrained_init_configuration, base_model_prefix for downloading and loading pretrained models. See PretrainedModel for more details.

config_class#: T5Config 的别名

base_model_class#: T5Model 的别名

class T5ForConditionalGeneration(config: T5Config)[源代码]#

基类：T5PretrainedModel

The T5 Model transformer with a language modeling head on top.

参数:: config (T5Config) -- An instance of T5Config used to construct T5ForConditionalGeneration.

get_input_embeddings()[源代码]#

get input embedding of model

返回:: embedding of model
返回类型:: nn.Embedding

set_input_embeddings(new_embeddings)[源代码]#

set new input embedding for model

参数:: value (Embedding) -- the new embedding of model
抛出:: NotImplementedError -- Model has not implement set_input_embeddings method

get_output_embeddings()[源代码]#

To be overwrited for models with output embeddings

返回:: the otuput embedding of model
返回类型:: Optional[Embedding]

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_output=None, cache=None, labels=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)[源代码]#

参数:

input_ids (Tensor, optional) -- See T5Model.
attention_mask (Tensor, optional) -- See T5Model.
decoder_input_ids (Tensor, optional) -- See T5Model.
decoder_attention_mask (Tensor, optional) -- See T5Model.
encoder_output (tuple(Tensor), optional) -- See T5Model.
cache (List[tuple(Tensor, Tensor)], optional) -- See T5Model.
labels (Tensor, optional) -- Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set labels = input_ids Indices are selected in [-100, 0, ..., vocab_size] All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., vocab_size]. Shape is [batch_size, sequence_length] and dtype is int64.
inputs_embeds (Tensor, optional) -- Optionally, instead of passing input_ids you can choose to directly pass an embedded representation of shape (batch_size, sequence_length, hidden_size). This is useful if you want more control over how to convert input_ids indices into associated vectors than the model's internal embedding lookup matrix. Default to None.
decoder_inputs_embeds (Tensor , optional) --
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation of shape (batch_size, target_sequence_length, hidden_size). If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model's internal embedding lookup matrix. Default to None.

If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.
use_cache (bool, optional) -- See T5Model.
output_attentions (bool, optional) -- See T5Model.
output_hidden_states (bool, optional) -- See T5Model.
return_dict (bool, optional) -- Whether or not to return a class:Seq2SeqLMOutput. If False, the output will be a tuple of tensors. Defaults to False.

返回:

An instance of Seq2SeqLMOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of Seq2SeqLMOutput.

tuple: Returns tuple (loss, logits, cache, decoder_hidden_states, decoder_attentions, cross_attentions, encoder_last_hidden_state, encoder_hidden_states, encoder_attentions)

With the fields:

loss (Tensor):
returned when labels is provided. Language modeling loss. It's data type should be float32 and its shape is [1,].
logits (Tensor):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). It's data type should be float32 and its shape is [batch_size, sequence_length, vocab_size].
cache (List[tuple(Tensor, Tensor)], optional):
See T5Model.
decoder_hidden_states (tuple(Tensor), optional)
See T5Model.
decoder_attentions (tuple(Tensor), optional):
See T5Model.
cross_attentions (tuple(Tensor), optional):
See T5Model.
encoder_last_hidden_state (Tensor):
See T5Model.
encoder_hidden_states (tuple(Tensor), optional):
See T5Model.
encoder_attentions (tuple(Tensor), optional):
See T5Model.

示例

import paddle
from paddlenlp.transformers import T5ForConditionalGeneration, T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained('t5-base')
model = T5ForConditionalGeneration.from_pretrained('t5-base')

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs, labels=inputs["input_ids"])

loss = output[0]
logits = output[1]

class T5EncoderModel(config: T5Config)[源代码]#

基类：T5PretrainedModel

base_model_class#: T5EncoderModel 的别名

get_input_embeddings() → Embedding[源代码]#

get input embedding of model

返回:: embedding of model
返回类型:: nn.Embedding

set_input_embeddings(new_embeddings: Embedding) → None[源代码]#

set new input embedding for model

参数:: value (Embedding) -- the new embedding of model
抛出:: NotImplementedError -- Model has not implement set_input_embeddings method

Defines the computation performed at every call. Should be overridden by all subclasses.

参数:

*inputs (tuple) -- unpacked tuple arguments
**kwargs (dict) -- unpacked dict arguments

modeling

目录

modeling#