modeling¶

class BlenderbotSmallModel(vocab_size, bos_token_id=1, pad_token_id=0, eos_token_id=2, decoder_start_token_id=1, d_model=512, num_encoder_layers=8, num_decoder_layers=8, encoder_attention_heads=16, decoder_attention_heads=16, encoder_ffn_dim=2048, decoder_ffn_dim=2048, dropout=0.1, activation_function='gelu', attention_dropout=0.0, activation_dropout=0.0, max_position_embeddings=512, init_std=0.02, scale_embedding=True, normalize_before=False)[source]¶

Bases: paddlenlp.transformers.blenderbot_small.modeling.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.

Parameters

vocab_size (int) – Vocabulary size of the BlenderbotSmall model.
bos_token_id (int, optional) – The id for begging of sentences token. Defaults to 1.
pad_token_id (int, optional) – The id for padding token. Defaults to 0.
eos_token_id (int, optional) – The id for end of sentence token. Defaults to 2.
decoder_start_token_id (int, optional) – The id indicating the start of decoding sentence. Defaults to 1.
d_model (int, optional) – Dimensionality of the layers and the pooler layer. Defaults to 512.
num_encoder_layers (int, optional) – Number of Transformer encoder layers for BlenderbotSmallEncoder. Defaults to 8.
num_decoder_layers (int, optional) – Number of Transformer decoder layers for BlenderbotSmallDecoder. Defaults to 8.
encoder_attention_heads (int, optional) – Number of attention heads for each Transformer encoder layer in BlenderbotSmallEncoder. Defaults to 16.
decoder_attention_heads (int, optional) – Number of attention heads for each Transformer decoder layer in BlenderbotSmallDecoder. Defaults to 16.
encoder_ffn_dim (int, optional) – Dimensionality of the feed-forward layer for each Transformer encoder layer in BlenderbotSmallEncoder. Defaults to 2048.
decoder_ffn_dim (int, optional) – Dimensionality of the feed-forward layer for each Transformer dncoder layer in BlenderbotSmallDncoder. Defaults to 2048.
dropout (float, optional) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. Defaults to 0.1.
activation_function (str, optional) – The non-linear activation function (function or string) in the encoder and pooler. "gelu", "relu" and any other paddle supported activation functions are supported. Defaults to "gelu".
attention_dropout (float, optional) – The dropout ratio for the attention probabilities. Defaults to 0.0.
activation_dropout (float, optional) – The dropout ratio for activations inside the fully connected layer.
max_position_embeddings (int, optional) –
, The max position index of an input sequence. Defaults to 512.
init_std (float, optional) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Defaults to 0.02.
scale_embedding (bool, optional) – Indicate whether to scale embeddings by diving by sqrt(d_model). Defaults to True.
normalize_before (bool, optional) – Indicate whether to put layer normalization into preprocessing of MHA and FFN sub-layers. If True, pre-process is layer normalization and post-precess includes dropout, residual connection. Otherwise, no pre-process and post-precess includes dropout, residual connection, layer normalization. Defaults to False.

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_encoder()[source]¶: This method is required for model with encoder-decoder architecture.

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

Bases: paddlenlp.transformers.model_utils.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.

init_weights(layer)[source]¶: Initialization hook

base_model_class¶: alias of paddlenlp.transformers.blenderbot_small.modeling.BlenderbotSmallModel

class BlenderbotSmallEncoder(vocab_size, embed_tokens=None, pad_token_id=0, d_model=512, num_encoder_layers=6, encoder_attention_heads=12, encoder_ffn_dim=2048, dropout=0.1, activation_function='gelu', attention_dropout=0.0, activation_dropout=0.0, max_position_embeddings=1024, init_std=0.02, scale_embedding=True, normalize_before=False)[source]¶

Bases: paddlenlp.transformers.blenderbot_small.modeling.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(vocab_size, embed_tokens=None, pad_token_id=1, d_model=768, num_decoder_layers=6, decoder_attention_heads=12, decoder_ffn_dim=3072, dropout=0.1, activation_function='gelu', attention_dropout=0.1, activation_dropout=0.1, max_position_embeddings=1024, init_std=0.02, scale_embedding=True, normalize_before=False)[source]¶

Bases: paddlenlp.transformers.blenderbot_small.modeling.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(blenderbot_small)[source]¶

Bases: paddlenlp.transformers.blenderbot_small.modeling.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(blenderbot_small)[source]¶

Bases: paddlenlp.transformers.blenderbot_small.modeling.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