modeling#

class BlenderbotSmallModel(config: BlenderbotSmallConfig)[source]#

Bases: BlenderbotSmallPretrainedModel

Construct a bare BlenderbotSmall Model.

This model inherits from PretrainedModel. Check the superclass documentation for the generic methods and the library implements for all its model.

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.

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_output=None, use_cache=False, cache=None, **kwargs)[source]#
Parameters:

  • input_ids (Tensor) – Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. It’s data type should be int64 and has a shape of [batch_size, sequence_length].

  • attention_mask (Tensor, optional) –

    Mask to indicate whether to perform attention on each input token or not. The values should be either 0 or 1. The attention scores will be set to -infinity for any positions in the mask that are 0, and will be unchanged for positions that are 1.

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

    It’s data type should be float32 and has a shape of [batch_size, sequence_length]. Defaults to None.

  • decoder_input_ids (Tensor, optional) – If not provided, decoder_input_ids will be automatically generated based on decoder_start_token_id and input_ids.

  • decoder_attention_mask (Tensor, optional) – If not provided, the default decoder_attention_mask will be a tensor with upper triangular part being -np.inf. the shape will be (decoder_length, decoder_length)

  • encoder_output (Tensor, optional) – The output of encoder. If not provided, a new encoder_output will be generated from BlenderbotEncoder. Defaults to None.

  • use_cache (bool, optional) – Indicates whether to use cache to speed up decoding. Defaults to False

  • cache (list, optional) – It is a list, and each element in the list is a tuple( (incremental_cache, static_cache) ). See TransformerDecoder.gen_cache for more details. It is only used for inference and should be None for training. Default None.

Returns:

If use_cache=False, the return will be the last hidden state of decoder with shape of [batch_size, seq_lens, hidden_size]. seq_lens corresponds to the length of input sequence. Otherwise, the return will be a tuple of (decoder_output, cache). Please refer to class paddle.nn.TransformerDecoder for more information regarding cache.

Return type:

Tensor|tuple

Example

import paddle from paddlenlp.transformers import BlenderbotSmallTokenizer, BlenderbotSmallModel

# “blenderbot_small-90M” is pretrained weight of BlenderbotSmallForConditionalGeneration, # Therefore some weight of additional layers in BlenderbotSmallForConditionalGeneration # might not be loaded and used. pretrained_model_name = “blenderbot_small-90M” tokenizer = BlenderbotSmallTokenizer.from_pretrained(pretrained_model_name) model = BlenderbotSmallModel.from_pretrained(pretrained_model_name)

sample_text = “My friends are cool but they eat too many carbs.” inputs = tokenizer(sample_text, return_attention_mask=True, return_token_type_ids=False) inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} decoder_output = model(**inputs)

get_input_embeddings()[source]#

get input embedding of model

Returns:

embedding of model

Return type:

nn.Embedding

set_input_embeddings(value)[source]#

set new input embedding for model

Parameters:

value (Embedding) – the new embedding of model

Raises:

NotImplementedError – Model has not implement set_input_embeddings method

get_encoder()[source]#

This method is required for model with encoder-decoder architecture.

class BlenderbotSmallPretrainedModel(*args, **kwargs)[source]#

Bases: PretrainedModel

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

config_class#

alias of BlenderbotSmallConfig

base_model_class#

alias of BlenderbotSmallModel

class BlenderbotSmallEncoder(config: BlenderbotSmallConfig, embed_tokens=None)[source]#

Bases: BlenderbotSmallPretrainedModel

The encoder of BlenderbotSmall Model. Please refer to PretrainedModel or BlenderbotSmallModel for more details regarding methods and arguments.

forward(input_ids=None, attention_mask=None)[source]#
Returns:

The last hidden-states at the last layer of the encoder. It’s data type should be float and has a shape of (batch_size, seq_lens, hidden_size). seq_lens corresponds to the length of input sequence.

Return type:

Tensor

class BlenderbotSmallDecoder(config: BlenderbotSmallConfig, embed_tokens=None)[source]#

Bases: BlenderbotSmallPretrainedModel

The decoder of BlenderbotSmall Model. Please refer to PretrainedModel and BlenderbotModel for more information regarding methods and arguments.

forward(decoder_input_ids=None, decoder_attention_mask=None, encoder_output=None, memory_mask=None, use_cache=False, cache=None)[source]#

Please refer to BlenderbotModel for more information regarding the arguments.

class BlenderbotSmallForConditionalGeneration(config: BlenderbotSmallConfig)[source]#

Bases: BlenderbotSmallPretrainedModel

Please refer to BlenderbotModel for more information regarding arguments. :returns:

If use_cache=False, the return will be a tensor with shape of

[batch_size, seq_lens, hidden_size]. Otherwise, the return will be a tuple of (decoder_output, cache).

Return type:

Tensor|tuple

Example

import paddle
from paddlenlp.transformers import BlenderbotSmallTokenizer, BlenderbotSmallForConditionalGeneration

pretrained_model_name = "blenderbot_small-90M"
tokenizer = BlenderbotSmallTokenizer.from_pretrained(pretrained_model_name)
model = BlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name)

sample_text = "My friends are cool but they eat too many carbs."
inputs = tokenizer(sample_text, return_attention_mask=True, return_token_type_ids=False)
inputs = {k: paddle.to_tensor([v]) for (k, v) in inputs.items()}
result_ids, score = model.generate(input_ids=inputs['input_ids'],
                                   max_length=60,
                                   min_length=20,
                                   decode_strategy='beam_search',
                                   num_beams=10,
                                   length_penalty=0.65
                                   )
for sequence_ids in result_ids.numpy().tolist():
    print("User:    ", sample_text)
    print("bot:     ", tokenizer.convert_ids_to_string(sequence_ids))
forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_output=None, use_cache=False, cache=None)[source]#

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

Parameters:

  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments

get_encoder()[source]#

This method is required for model with encoder-decoder architecture.

class BlenderbotSmallForCausalLM(config: BlenderbotSmallConfig)[source]#

Bases: BlenderbotSmallPretrainedModel

Constructs BLenderbotSmall For Causal Language Model. This model is equivalent to the blenderbotSmall decoder without cross-attention.

forward(input_ids=None, attention_mask=None, use_cache=False, cache=None, **kwargs)[source]#
Parameters:

  • input_ids (Tensor) – Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. It’s data type should be int64 and has a shape of [batch_size, sequence_length].

  • attention_mask (Tensor, optional) –

    Mask to indicate whether to perform attention on each input token or not. The values should be either 0 or 1. The attention scores will be set to -infinity for any positions in the mask that are 0, and will be unchanged for positions that are 1.

    • 1 for tokens that are not masked,

    • 0 for tokens that are masked.

    It’s data type should be float32 and has a shape of [batch_size, sequence_length]. Defaults to None.

  • use_cache (bool, optional) – Indicates whether to use cache to speed up decoding. Defaults to False

  • cache (list, optional) – It is a list, and each element in the list is a tuple( (incremental_cache, static_cache) ). See paddle.nn.TransformerDecoder.gen_cache for more details. It is only used for inference and should be None for training. Default None.

Returns:

If use_cache=False, the return will be a tensor with shape of

[batch_size, seq_lens, hidden_size]. Otherwise, the return will be a tuple of (lm_logits, cache).

Return type:

Tensor|tuple

Example

import paddle from paddlenlp.transformers import BlenderbotSmallTokenizer, BlenderbotSmallForCausalLM use_cache = False text = “My friends are cool but they eat too many carbs.” model_name = “blenderbot_small-90M” tokenizer = BlenderbotSmallTokenizer.from_pretrained(model_name) model = BlenderbotSmallForCausalLM.from_pretrained(model_name) model.eval() inputs = tokenizer(text, return_attention_mask=True, return_token_type_ids=False) inputs = {k: paddle.to_tensor([v]) for (k, v) in inputs.items()}

with paddle.no_grad():

outputs = model(**inputs, use_cache=use_cache) # outputs is a tuple of (lm_logits, cache) if use_cache=True.

prepare_inputs_for_generation(input_ids, attention_mask=None, use_cache=True, cache=None, **kwargs)[source]#

Prepare inputs for decoder to generate sentences. :returns: A dictionary containing necessary inputs for generating next token. :rtype: dict